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+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Data Preprocessing-Shadman.ipynb","provenance":[{"file_id":"1YKQ3ItCmgfpn66WhGy9ey9jpWQ5o2nus","timestamp":1605399621858}],"collapsed_sections":[],"authorship_tag":"ABX9TyMPOzx26A46vj6bEt6AgME4"},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"Qjc-UWAOCify"},"source":["# Data Preprocessing\n","\n","Tasks\n","1.   Load Kaggle traffic sign data from drive\n","2.   Unpickle files\n","3.   Filter out classes not related to speed\n","4.   Normalize number of samples per class\n","5.   Split into train / val / test sets\n","6.   Shuffle datasets\n","7.   Resize\n","8.   Normalize pixel values\n","9.   Make pytorch dataloaders"]},{"cell_type":"code","metadata":{"id":"cPKNBe0NUG71","executionInfo":{"status":"ok","timestamp":1605399972962,"user_tz":300,"elapsed":3867,"user":{"displayName":"Shadman Siddiqui","photoUrl":"","userId":"01981550049686806435"}}},"source":["import pickle\n","import numpy as np\n","import torch\n","import torchvision.transforms as transforms\n","from torch.utils.data import TensorDataset, DataLoader\n","import matplotlib.pyplot as plt"],"execution_count":1,"outputs":[]},{"cell_type":"code","metadata":{"id":"ljaGNZa8HUcq","executionInfo":{"status":"ok","timestamp":1605400005466,"user_tz":300,"elapsed":21237,"user":{"displayName":"Shadman Siddiqui","photoUrl":"","userId":"01981550049686806435"}},"outputId":"1e0bcd8b-09f2-4b18-9c64-c1c4be2dfcc5","colab":{"base_uri":"https://localhost:8080/"}},"source":["# mount drive\n","from google.colab import drive\n","drive.mount('/content/gdrive')"],"execution_count":2,"outputs":[{"output_type":"stream","text":["Mounted at /content/gdrive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Dy0lMjtXasiZ","executionInfo":{"status":"ok","timestamp":1605400533927,"user_tz":300,"elapsed":820,"user":{"displayName":"Shadman Siddiqui","photoUrl":"","userId":"01981550049686806435"}}},"source":["# Data directory\n","# Change this as needed\n","data_dir = '/content/gdrive/My Drive/APS360 Project/'"],"execution_count":12,"outputs":[]},{"cell_type":"code","metadata":{"id":"Si9g90e0Ud0i","executionInfo":{"status":"ok","timestamp":1605400535768,"user_tz":300,"elapsed":917,"user":{"displayName":"Shadman Siddiqui","photoUrl":"","userId":"01981550049686806435"}}},"source":["# Data loaders\n","\n","def get_data_loader(batch_size):\n","    ''' The Kaggle dataset is split into three pickle files: train.pickle,\n","    valid.pickle, and test.pickle.\n","    The function will combine the three datasets and resplit them such that the\n","    resulting split is approximately 70% training, 15% validation, and 15% testing.\n","    The function filters classes 0-8 only, as they are related to speed.\n","    The splitting ratio will be applied to each class, to avoid imbalance of \n","    classes in the training/validation/testing samples.'''\n","\n","    classes = ('Speed limit (20km/h)',\n","            'Speed limit (30km/h)',\n","            'Speed limit (50km/h)',\n","            'Speed limit (60km/h)',\n","            'Speed limit (70km/h)',\n","            'Speed limit (80km/h)',\n","            'End of speed limit (80km/h)',\n","            'Speed limit (100km/h)',\n","            'Speed limit (120km/h)')\n","\n","    # load pickle files\n","    # will combine datasets from three seperate pikcle files\n","    \n","    with open(data_dir+'train.pickle', 'rb') as file:\n","        data1 = pickle.load(file)\n","\n","    with open(data_dir+'valid.pickle', 'rb') as file:\n","        data2 = pickle.load(file)\n","\n","    with open (data_dir+'test.pickle', 'rb') as file:\n","        data3 = pickle.load(file)\n","\n","    images = np.concatenate((data1['features'], data2['features'], data3['features']))\n","    labels = np.concatenate((data1['labels'], data2['labels'], data3['labels']))\n","    \n","    # sort into classes\n","    class_images = []\n","    class_labels = []\n","    for i in range(9):\n","        class_indices = np.where(labels==i)\n","        #print(i, 'has', len(class_indices[0]), 'elements') # check number of samples for each class\n","        class_images.append(images[class_indices])\n","        class_labels.append(labels[class_indices])\n","\n","    # normalize number of samples in each class\n","    desired_size=3000\n","    extra_samples = []\n","    extra_labels = []\n","    for i in range(9):\n","        # Randomly sample from the original class images to duplicate to extra\n","        # Duplicate enough samples to make the total for the class 3000\n","        extra_samples.append(\n","            class_images[i][np.random.randint(\n","                low=0,\n","                high=class_images[i].shape[0],\n","                size=desired_size-class_images[i].shape[0])])\n","        # Add random noise to create variation from originals\n","        noise = np.random.normal(0,1, extra_samples[i].size)\n","        noise = noise.reshape(extra_samples[i].shape[0],extra_samples[i].shape[1],extra_samples[i].shape[2],extra_samples[i].shape[3]).astype('uint8')\n","        extra_samples[i] = extra_samples[i]+noise\n","\n","        # add labels for extra samples\n","        extra_labels.append(np.full(extra_samples[i].shape[0], i))\n","\n","        # append to original\n","        class_images[i] = np.concatenate((class_images[i],extra_samples[i]))\n","        class_labels[i] = np.concatenate((class_labels[i],extra_labels[i]))\n","\n","    # split into train /  val / test\n","    train_split = 0.7\n","    val_split = 0.85\n","\n","    train_image_arrays = [class_images[i][0:int(train_split*class_images[i].shape[0])] for i in range(9)]\n","    train_label_arrays = [class_labels[i][0:int(train_split*class_images[i].shape[0])] for i in range(9)]\n","    train_images = np.concatenate(train_image_arrays)\n","    train_labels = np.concatenate(train_label_arrays)\n","\n","    val_image_arrays = [class_images[i][int(train_split*class_images[i].shape[0]):int(val_split*class_images[i].shape[0])] for i in range(9)]\n","    val_label_arrays = [class_labels[i][int(train_split*class_images[i].shape[0]):int(val_split*class_images[i].shape[0])] for i in range(9)]\n","    val_images = np.concatenate(val_image_arrays)\n","    val_labels = np.concatenate(val_label_arrays)\n","\n","    test_image_arrays = [class_images[i][int(val_split*class_images[i].shape[0]):] for i in range(9)]\n","    test_label_arrays = [class_labels[i][int(val_split*class_images[i].shape[0]):] for i in range(9)]\n","    test_images = np.concatenate(test_image_arrays)\n","    test_labels = np.concatenate(test_label_arrays)\n","\n","    # shuffle\n","    np.random.seed(9001)\n","    indices = list(range(train_images.shape[0]))\n","    np.random.shuffle(indices)\n","    train_images = train_images[indices]\n","    train_labels = train_labels[indices]\n","    \n","    indices = list(range(val_images.shape[0]))\n","    np.random.shuffle(indices)\n","    val_images = val_images[indices]\n","    val_labels = val_labels[indices]\n","   \n","    indices = list(range(test_images.shape[0]))\n","    np.random.shuffle(indices)\n","    test_images = test_images[indices]\n","    test_labels = test_labels[indices]\n","\n","    # make into torch datasets\n","    train_image_tensor = torch.Tensor(train_images.transpose(0,3,1,2))\n","    train_label_tensor = torch.Tensor(train_labels)\n","    \n","    val_image_tensor = torch.Tensor(val_images.transpose(0,3,1,2))\n","    val_label_tensor = torch.Tensor(val_labels)\n","    \n","    test_image_tensor = torch.Tensor(test_images.transpose(0,3,1,2))\n","    test_label_tensor = torch.Tensor(test_labels)\n","   \n","    trainset = TensorDataset(train_image_tensor, train_label_tensor)\n","    valset = TensorDataset(val_image_tensor, val_label_tensor)\n","    testset = TensorDataset(test_image_tensor, test_label_tensor)\n","\n","    # resize and normalization\n","    transform = transforms.Compose(\n","    [transforms.Resize((32,32)),\n","      transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n","    \n","    #trainset.transform = transform\n","    #valset.transform = transform\n","    #testset.transform = transform\n","\n","    # make data loaders\n","    train_loader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,\n","                                        num_workers=1)\n","    val_loader = torch.utils.data.DataLoader(valset, batch_size=batch_size,\n","                                        num_workers=1)\n","    test_loader = torch.utils.data.DataLoader(testset, batch_size=batch_size,\n","                                        num_workers=1)\n","    \n","    return train_loader, val_loader, test_loader, classes "],"execution_count":13,"outputs":[]},{"cell_type":"code","metadata":{"id":"W_HTih2z9ZUo","executionInfo":{"status":"ok","timestamp":1605400592689,"user_tz":300,"elapsed":10238,"user":{"displayName":"Shadman Siddiqui","photoUrl":"","userId":"01981550049686806435"}},"outputId":"0af8d84d-fdfe-4793-bbff-e9b4abaef58f","colab":{"base_uri":"https://localhost:8080/"}},"source":["batch_size = 32\n","train_loader, val_loader, test_loader, classes = get_data_loader (batch_size)\n","print (classes)\n","print (len(train_loader))\n","print (len(val_loader))\n","print (len(test_loader))"],"execution_count":15,"outputs":[{"output_type":"stream","text":["('Speed limit (20km/h)', 'Speed limit (30km/h)', 'Speed limit (50km/h)', 'Speed limit (60km/h)', 'Speed limit (70km/h)', 'Speed limit (80km/h)', 'End of speed limit (80km/h)', 'Speed limit (100km/h)', 'Speed limit (120km/h)')\n","591\n","127\n","127\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"1J6vbY4YETHN","executionInfo":{"status":"ok","timestamp":1605400600586,"user_tz":300,"elapsed":2058,"user":{"displayName":"Shadman Siddiqui","photoUrl":"","userId":"01981550049686806435"}},"outputId":"2017e60b-b423-4f0f-b292-7cc4868485ed","colab":{"base_uri":"https://localhost:8080/","height":236}},"source":["# Check one batch\n","dataiter = iter(test_loader)\n","images, labels = dataiter.next()\n","images = images.numpy().astype(int) # convert images to numpy for display\n","labels = labels.int()\n","\n","# plot the images in the batch, along with the corresponding labels\n","fig = plt.figure(figsize=(25, 4))\n","for idx in np.arange(20):\n","    ax = fig.add_subplot(2, 20/2, idx+1, xticks=[], yticks=[])\n","    plt.imshow(np.transpose(images[idx], (1, 2, 0)))\n","    ax.set_title(classes[labels[idx]])"],"execution_count":16,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1800x288 with 20 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"k5rbyYhrHTAJ"},"source":["# close all figures to prevent memory leak\n","plt.close('all')"],"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/002 Baseline_Testing.ipynb b/002 Baseline_Testing.ipynb
new file mode 100644
index 0000000..0733cfc
--- /dev/null
+++ b/002 Baseline_Testing.ipynb	
@@ -0,0 +1 @@
+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"002 Baseline_Testing.ipynb","provenance":[{"file_id":"1YKQ3ItCmgfpn66WhGy9ey9jpWQ5o2nus","timestamp":1605399621858}],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"markdown","metadata":{"id":"Qjc-UWAOCify"},"source":["# Data Preprocessing\n","\n","Tasks\n","1.   Load Kaggle traffic sign data from drive\n","2.   Unpickle files\n","3.   Filter out classes not related to speed\n","4.   Normalize number of samples per class\n","5.   Split into train / val / test sets\n","6.   Shuffle datasets\n","7.   Resize\n","8.   Normalize pixel values\n","9.   Make pytorch dataloaders"]},{"cell_type":"code","metadata":{"id":"cPKNBe0NUG71"},"source":["import pickle\n","import numpy as np\n","import torch\n","import torchvision.transforms as transforms\n","from torch.utils.data import TensorDataset, DataLoader\n","import matplotlib.pyplot as plt"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"ljaGNZa8HUcq","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1605491495884,"user_tz":300,"elapsed":47150,"user":{"displayName":"Avelyn Wong","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gg0Gx_4UO4Canl0bxQX9l2YCVEf9TrzDq31I9-c9A=s64","userId":"04927963856256154784"}},"outputId":"97a04934-9762-4927-bd5b-4f74f0ffe357"},"source":["# mount drive\n","from google.colab import drive\n","drive.mount('/content/gdrive')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Mounted at /content/gdrive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Dy0lMjtXasiZ"},"source":["# Data directory\n","# Change this as needed\n","data_dir = '/content/gdrive/My Drive/2020-21 School Year/APS360/APS360 Project/'"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Si9g90e0Ud0i"},"source":["# Data loaders\n","\n","def get_data_loader(batch_size):\n","    ''' The Kaggle dataset is split into three pickle files: train.pickle,\n","    valid.pickle, and test.pickle.\n","    The function will combine the three datasets and resplit them such that the\n","    resulting split is approximately 70% training, 15% validation, and 15% testing.\n","    The function filters classes 0-8 only, as they are related to speed.\n","    The splitting ratio will be applied to each class, to avoid imbalance of \n","    classes in the training/validation/testing samples.'''\n","\n","    classes = ('Speed limit (20km/h)',\n","            'Speed limit (30km/h)',\n","            'Speed limit (50km/h)',\n","            'Speed limit (60km/h)',\n","            'Speed limit (70km/h)',\n","            'Speed limit (80km/h)',\n","            'End of speed limit (80km/h)',\n","            'Speed limit (100km/h)',\n","            'Speed limit (120km/h)')\n","\n","    # load pickle files\n","    # will combine datasets from three seperate pikcle files\n","    \n","    with open(data_dir+'train.pickle', 'rb') as file:\n","        data1 = pickle.load(file)\n","\n","    with open(data_dir+'valid.pickle', 'rb') as file:\n","        data2 = pickle.load(file)\n","\n","    with open (data_dir+'test.pickle', 'rb') as file:\n","        data3 = pickle.load(file)\n","\n","    images = np.concatenate((data1['features'], data2['features'], data3['features']))\n","    labels = np.concatenate((data1['labels'], data2['labels'], data3['labels']))\n","    \n","    # sort into classes\n","    class_images = []\n","    class_labels = []\n","    for i in range(9):\n","        class_indices = np.where(labels==i)\n","        #print(i, 'has', len(class_indices[0]), 'elements') # check number of samples for each class\n","        class_images.append(images[class_indices])\n","        class_labels.append(labels[class_indices])\n","\n","    # normalize number of samples in each class\n","    desired_size=3000\n","    extra_samples = []\n","    extra_labels = []\n","    for i in range(9):\n","        # Randomly sample from the original class images to duplicate to extra\n","        # Duplicate enough samples to make the total for the class 3000\n","        extra_samples.append(\n","            class_images[i][np.random.randint(\n","                low=0,\n","                high=class_images[i].shape[0],\n","                size=desired_size-class_images[i].shape[0])])\n","        # Add random noise to create variation from originals\n","        noise = np.random.normal(0,1, extra_samples[i].size)\n","        noise = noise.reshape(extra_samples[i].shape[0],extra_samples[i].shape[1],extra_samples[i].shape[2],extra_samples[i].shape[3]).astype('uint8')\n","        extra_samples[i] = extra_samples[i]+noise\n","\n","        # add labels for extra samples\n","        extra_labels.append(np.full(extra_samples[i].shape[0], i))\n","\n","        # append to original\n","        class_images[i] = np.concatenate((class_images[i],extra_samples[i]))\n","        class_labels[i] = np.concatenate((class_labels[i],extra_labels[i]))\n","\n","    # split into train /  val / test\n","    train_split = 0.7\n","    val_split = 0.85\n","\n","    train_image_arrays = [class_images[i][0:int(train_split*class_images[i].shape[0])] for i in range(9)]\n","    train_label_arrays = [class_labels[i][0:int(train_split*class_images[i].shape[0])] for i in range(9)]\n","    train_images = np.concatenate(train_image_arrays)\n","    train_labels = np.concatenate(train_label_arrays)\n","\n","    val_image_arrays = [class_images[i][int(train_split*class_images[i].shape[0]):int(val_split*class_images[i].shape[0])] for i in range(9)]\n","    val_label_arrays = [class_labels[i][int(train_split*class_images[i].shape[0]):int(val_split*class_images[i].shape[0])] for i in range(9)]\n","    val_images = np.concatenate(val_image_arrays)\n","    val_labels = np.concatenate(val_label_arrays)\n","\n","    test_image_arrays = [class_images[i][int(val_split*class_images[i].shape[0]):] for i in range(9)]\n","    test_label_arrays = [class_labels[i][int(val_split*class_images[i].shape[0]):] for i in range(9)]\n","    test_images = np.concatenate(test_image_arrays)\n","    test_labels = np.concatenate(test_label_arrays)\n","\n","    # shuffle\n","    np.random.seed(9001)\n","    indices = list(range(train_images.shape[0]))\n","    np.random.shuffle(indices)\n","    train_images = train_images[indices]\n","    train_labels = train_labels[indices]\n","    \n","    indices = list(range(val_images.shape[0]))\n","    np.random.shuffle(indices)\n","    val_images = val_images[indices]\n","    val_labels = val_labels[indices]\n","   \n","    indices = list(range(test_images.shape[0]))\n","    np.random.shuffle(indices)\n","    test_images = test_images[indices]\n","    test_labels = test_labels[indices]\n","\n","    # make into torch datasets\n","    train_image_tensor = torch.Tensor(train_images.transpose(0,3,1,2))\n","    train_label_tensor = torch.Tensor(train_labels)\n","    \n","    val_image_tensor = torch.Tensor(val_images.transpose(0,3,1,2))\n","    val_label_tensor = torch.Tensor(val_labels)\n","    \n","    test_image_tensor = torch.Tensor(test_images.transpose(0,3,1,2))\n","    test_label_tensor = torch.Tensor(test_labels)\n","   \n","    trainset = TensorDataset(train_image_tensor, train_label_tensor)\n","    valset = TensorDataset(val_image_tensor, val_label_tensor)\n","    testset = TensorDataset(test_image_tensor, test_label_tensor)\n","\n","    # resize and normalization\n","    transform = transforms.Compose(\n","    [transforms.Resize((32,32)),\n","      transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n","    \n","    #trainset.transform = transform\n","    #valset.transform = transform\n","    #testset.transform = transform\n","\n","    # make data loaders\n","    train_loader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,\n","                                        num_workers=1)\n","    val_loader = torch.utils.data.DataLoader(valset, batch_size=batch_size,\n","                                        num_workers=1)\n","    test_loader = torch.utils.data.DataLoader(testset, batch_size=batch_size,\n","                                        num_workers=1)\n","    \n","    return train_loader, val_loader, test_loader, classes "],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"W_HTih2z9ZUo","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1605491507291,"user_tz":300,"elapsed":58541,"user":{"displayName":"Avelyn Wong","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gg0Gx_4UO4Canl0bxQX9l2YCVEf9TrzDq31I9-c9A=s64","userId":"04927963856256154784"}},"outputId":"c70834d2-9fad-4e1e-c291-bfd42ecacb6d"},"source":["batch_size = 32\n","train_loader, val_loader, test_loader, classes = get_data_loader (batch_size)\n","print (classes)\n","print (len(train_loader))\n","print (len(val_loader))\n","print (len(test_loader))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["('Speed limit (20km/h)', 'Speed limit (30km/h)', 'Speed limit (50km/h)', 'Speed limit (60km/h)', 'Speed limit (70km/h)', 'Speed limit (80km/h)', 'End of speed limit (80km/h)', 'Speed limit (100km/h)', 'Speed limit (120km/h)')\n","591\n","127\n","127\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"1J6vbY4YETHN","colab":{"base_uri":"https://localhost:8080/","height":266},"executionInfo":{"status":"ok","timestamp":1605491508150,"user_tz":300,"elapsed":59393,"user":{"displayName":"Avelyn Wong","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gg0Gx_4UO4Canl0bxQX9l2YCVEf9TrzDq31I9-c9A=s64","userId":"04927963856256154784"}},"outputId":"6fba313f-609e-4096-fa6c-98167532dfb5"},"source":["# Check one batch\n","dataiter = iter(test_loader)\n","images, labels = dataiter.next()\n","images = images.numpy().astype(int) # convert images to numpy for display\n","labels = labels.int()\n","\n","# plot the images in the batch, along with the corresponding labels\n","fig = plt.figure(figsize=(25, 4))\n","for idx in np.arange(20):\n","    ax = fig.add_subplot(2, 20/2, idx+1, xticks=[], yticks=[])\n","    plt.imshow(np.transpose(images[idx], (1, 2, 0)))\n","    ax.set_title(classes[labels[idx]])"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1800x288 with 20 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"k5rbyYhrHTAJ"},"source":["# close all figures to prevent memory leak\n","plt.close('all')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"d6UmCUytrUdf"},"source":["def get_model_name(name, batch_size, learning_rate, epoch):\n","    \"\"\" Generate a name for the model consisting of all the hyperparameter values\n","\n","    Args:\n","        config: Configuration object containing the hyperparameters\n","    Returns:\n","        path: A string with the hyperparameter name and value concatenated\n","    \"\"\"\n","    path = \"model_{0}_bs{1}_lr{2}_epoch{3}\".format(name,\n","                                                   batch_size,\n","                                                   learning_rate,\n","                                                   epoch)\n","    return path\n","\n","def normalize_label(labels):\n","    \"\"\"\n","    Given a tensor containing 2 possible values, normalize this to 0/1\n","\n","    Args:\n","        labels: a 1D tensor containing two possible scalar values\n","    Returns:\n","        A tensor normalize to 0/1 value\n","    \"\"\"\n","    max_val = torch.max(labels)\n","    min_val = torch.min(labels)\n","    norm_labels = (labels - min_val)//(max_val - min_val)             #this is kinda brilliant\n","    return norm_labels\n","\n","\n"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Yy0uWVQws3ig"},"source":["#TRAIN\n","\n","Tasks:\n"," - Calculate training accuracy\n"," - Calculate training loss\n"," - Calculate validation accuracy\n"," - Calculate validation loss\n"," - Store weights in epoch files (we may have to be smart about WHERE we're saving this so that we don't clutter up the drive)\n"," - Get accuracy of model after training is complete\n"," - Plot everything\n"]},{"cell_type":"code","metadata":{"id":"rPjuItwogPhe"},"source":["def train(model, train_loader, val_loader, batch_size=27, num_epochs=21, learning_rate = 0.001):\n","\n","    torch.manual_seed(500)\n","    criterion = nn.CrossEntropyLoss()\n","    optimizer = optim.Adam(model.parameters(), lr=learning_rate)\n","\n","    train_acc, val_acc, train_loss, val_loss = [], [], [], []\n","\n","    # training\n","    print (\"Training Started...\")\n","    if torch.cuda.is_available():\n","        print(\"U S I N G   C U D A \")\n","\n","    for epoch in range(num_epochs):                                     # the number of iterations\n","        sum_train_loss = 0.0\n","        sum_val_loss = 0.0\n","\n","        n = 0                                                           # Number of training iterations in this epoch\n","        m = 0                                                           # Number of validation iterations in this epoch\n","\n","        for imgs, labels in iter(train_loader):\n","                \n","            if use_cuda and torch.cuda.is_available():\n","              imgs = imgs.cuda()\n","              labels = labels.cuda()\n","\n","            out = model(imgs)                                           # forward pass\n","            loss = criterion(out, labels.long())                               # compute the total loss\n","            loss.backward()                                             # backward pass (compute parameter updates)\n","            optimizer.step()                                            # make the updates for each parameter\n","            optimizer.zero_grad()                                       # a clean up step for PyTorch\n","            sum_train_loss += loss.item() \n","            n += 1\n","        \n","        for imgs, labels in iter(val_loader):\n","            \n","            if use_cuda and torch.cuda.is_available():\n","              imgs = imgs.cuda()\n","              labels = labels.cuda()                                    # cudafication for speeeeed\n","\n","            out = model(imgs)                                            \n","            loss = criterion(out, labels.long())                               # compute loss with Cross Entropy\n","            sum_val_loss += loss.item()\n","            m += 1\n","\n","        # track accuracy and loss\n","        train_acc.append(get_accuracy(model, train_loader))\n","        val_acc.append(get_accuracy(model, val_loader))\n","        train_loss.append(sum_train_loss/n)\n","        val_loss.append(sum_val_loss/m)\n","\n","        ################################################################################################################\n","        model_path = get_model_name(model.name, batch_size, learning_rate, epoch+1) \n","        torch.save(model.state_dict(), model_path)\n","        print('epoch: ', epoch,'training acc: ', train_acc[-1],'val acc: ', val_acc[-1],'training loss: ', train_loss[-1],'val loss: ', val_loss[-1])\n","\n","    return train_acc, val_acc, train_loss, val_loss\n","\n","def get_accuracy(model, data_loader):\n","\n","  correct = 0\n","  total = 0\n","  for imgs, labels in data_loader:\n","      \n","      if use_cuda and torch.cuda.is_available():\n","        imgs = imgs.cuda()\n","        labels = labels.cuda()\n","\n","      output = model(imgs)\n","      #select index with maximum prediction score\n","      pred = output.max(1, keepdim=True)[1]\n","      correct += pred.eq(labels.view_as(pred)).sum().item()\n","      total += imgs.shape[0]\n","\n","  return correct / total\n","\n","\n","\n","\n","def plot_training_curve(train_acc, val_acc, train_loss, val_loss):\n","    \"\"\" Plots the training curve for a model run, given the csv files\n","    containing the train/validation error/loss.\n","\n","    Args:\n","        path: The base path of the csv files produced during training\n","    \"\"\"\n","    import matplotlib.pyplot as plt\n","\n","    plt.title(\"Train vs Validation Accuracy\")\n","    n = len(train_acc) # number of epochs\n","    plt.plot(range(1,n+1), train_acc, label=\"Train\")\n","    plt.plot(range(1,n+1), val_acc, label=\"Validation\")\n","    plt.xlabel(\"Epoch\")\n","    plt.ylabel(\"Accuracy\")\n","    plt.legend(loc='best')\n","    plt.show()\n","    plt.title(\"Train vs Validation Loss\")\n","    plt.plot(range(1,n+1), train_loss, label=\"Train\")\n","    plt.plot(range(1,n+1), val_loss, label=\"Validation\")\n","    plt.xlabel(\"Epoch\")\n","    plt.ylabel(\"Loss\")\n","    plt.legend(loc='best')\n","    plt.show()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"EUAk7MuTpGNJ"},"source":["#BASELINE TRAINING"]},{"cell_type":"code","metadata":{"id":"AZQrHw5UAm0R"},"source":["import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","\n","import torch.optim as optim                                                     # For gradient descent\n","import matplotlib.pyplot as plt\n","import numpy as np"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"26nStRssBcJA"},"source":["class BaseANN(nn.Module):\n","  \n","  def __init__(self):\n","    super(BaseANN, self).__init__()\n","\n","    self.name = \"BaseANN\"\n","    self.num_classes = 9\n","\n","    #model layers\n","    self.fc1 = nn.Linear(32*32*3, 32)\n","    self.fc2 = nn.Linear(32, self.num_classes)\n","  \n","  def forward(self, x):\n","    #flatten image for input into fc layers\n","    x = x.view(-1, 32*32*3)\n","    x = F.relu(self.fc1(x))\n","    x = self.fc2(x)\n","    x = x.squeeze(1)\n","\n","    return x"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"02v0CZV_EOcW"},"source":["#Hyperparameter Tuning:"]},{"cell_type":"code","metadata":{"id":"m-iyie7sCvlR","colab":{"base_uri":"https://localhost:8080/","height":858},"executionInfo":{"status":"ok","timestamp":1605483082342,"user_tz":300,"elapsed":25417,"user":{"displayName":"Avelyn Wong","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gg0Gx_4UO4Canl0bxQX9l2YCVEf9TrzDq31I9-c9A=s64","userId":"04927963856256154784"}},"outputId":"07345888-6032-48fc-b275-c1236c94ed06"},"source":["model1 = BaseANN()\n","use_cuda = False\n","\n","batch_size = 128\n","train_loader, val_loader, test_loader, classes = get_data_loader(batch_size)\n","\n","train_acc, val_acc, train_loss, val_loss = train(model1, train_loader, val_loader, batch_size=128, num_epochs=15, learning_rate = 0.00001)\n","plot_training_curve(train_acc, val_acc, train_loss, val_loss)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Training Started...\n","U S I N G   C U D A \n","epoch:  0 training acc:  0.15925925925925927 val acc:  0.17037037037037037 training loss:  8.096337094500258 val loss:  3.3408625200390816\n","epoch:  1 training acc:  0.15084656084656084 val acc:  0.1417283950617284 training loss:  2.387978985502913 val loss:  2.2587592229247093\n","epoch:  2 training acc:  0.1547089947089947 val acc:  0.14716049382716048 training loss:  2.184168069749265 val loss:  2.2235593646764755\n","epoch:  3 training acc:  0.16433862433862434 val acc:  0.1523456790123457 training loss:  2.155098575192529 val loss:  2.215673729777336\n","epoch:  4 training acc:  0.17624338624338623 val acc:  0.1582716049382716 training loss:  2.1241066826356425 val loss:  2.188247613608837\n","epoch:  5 training acc:  0.1939153439153439 val acc:  0.17135802469135802 training loss:  2.0905723305972845 val loss:  2.1570746675133705\n","epoch:  6 training acc:  0.20476190476190476 val acc:  0.18518518518518517 training loss:  2.0614272734603367 val loss:  2.1344232708215714\n","epoch:  7 training acc:  0.21714285714285714 val acc:  0.19604938271604938 training loss:  2.0346894570299097 val loss:  2.1155725978314877\n","epoch:  8 training acc:  0.22756613756613756 val acc:  0.205679012345679 training loss:  2.0068333889987016 val loss:  2.094408866018057\n","epoch:  9 training acc:  0.23634920634920634 val acc:  0.21481481481481482 training loss:  1.9719411400524345 val loss:  2.0685490369796753\n","epoch:  10 training acc:  0.2602116402116402 val acc:  0.23358024691358026 training loss:  1.9319500399602425 val loss:  2.032306369394064\n","epoch:  11 training acc:  0.2752910052910053 val acc:  0.24074074074074073 training loss:  1.8880516959203255 val loss:  2.003454953432083\n","epoch:  12 training acc:  0.3419047619047619 val acc:  0.3106172839506173 training loss:  1.8307152350206632 val loss:  1.9306582771241665\n","epoch:  13 training acc:  0.38952380952380955 val acc:  0.3523456790123457 training loss:  1.732436021437516 val loss:  1.841020755469799\n","epoch:  14 training acc:  0.41264550264550265 val acc:  0.37407407407407406 training loss:  1.656552096476426 val loss:  1.7910428568720818\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"74zFFxuvEFkn"},"source":["Model is underfitting, both train and val loss are low."]},{"cell_type":"code","metadata":{"id":"r3EPxNN4EWn9","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1605483144473,"user_tz":300,"elapsed":47625,"user":{"displayName":"Avelyn Wong","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gg0Gx_4UO4Canl0bxQX9l2YCVEf9TrzDq31I9-c9A=s64","userId":"04927963856256154784"}},"outputId":"84f85ead-74fc-4ea5-cbb7-371386a1546a"},"source":["#more epochs to increase accuracy/reduce loss\n","\n","model2 = BaseANN()\n","use_cuda = False\n","train_acc, val_acc, train_loss, val_loss = train(model2, train_loader, val_loader, batch_size=128, num_epochs=30, learning_rate = 0.00001)\n","plot_training_curve(train_acc, val_acc, train_loss, val_loss)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Training Started...\n","U S I N G   C U D A \n","epoch:  0 training acc:  0.14264550264550266 val acc:  0.14296296296296296 training loss:  7.131617668512705 val loss:  2.3019504249095917\n","epoch:  1 training acc:  0.15306878306878308 val acc:  0.14938271604938272 training loss:  2.220419527711095 val loss:  2.209401585161686\n","epoch:  2 training acc:  0.16111111111111112 val acc:  0.15555555555555556 training loss:  2.1802436306669906 val loss:  2.1834071427583694\n","epoch:  3 training acc:  0.164021164021164 val acc:  0.15530864197530864 training loss:  2.1566891186946147 val loss:  2.166357807815075\n","epoch:  4 training acc:  0.17132275132275132 val acc:  0.16617283950617284 training loss:  2.1338969646273434 val loss:  2.147640347480774\n","epoch:  5 training acc:  0.17783068783068784 val acc:  0.17382716049382715 training loss:  2.1107289911927403 val loss:  2.1267651319503784\n","epoch:  6 training acc:  0.18677248677248678 val acc:  0.1814814814814815 training loss:  2.0859210620055326 val loss:  2.119963362812996\n","epoch:  7 training acc:  0.201005291005291 val acc:  0.1962962962962963 training loss:  2.059635378218986 val loss:  2.104951836168766\n","epoch:  8 training acc:  0.2338095238095238 val acc:  0.2237037037037037 training loss:  2.030808379521241 val loss:  2.0828521884977818\n","epoch:  9 training acc:  0.2758201058201058 val acc:  0.2696296296296296 training loss:  1.9767565501702797 val loss:  2.0292747244238853\n","epoch:  10 training acc:  0.292962962962963 val acc:  0.28271604938271605 training loss:  1.9266050385462272 val loss:  1.9956772401928902\n","epoch:  11 training acc:  0.3126984126984127 val acc:  0.30592592592592593 training loss:  1.8855908746654924 val loss:  1.962392657995224\n","epoch:  12 training acc:  0.33031746031746034 val acc:  0.31876543209876546 training loss:  1.8447795985518276 val loss:  1.9276212565600872\n","epoch:  13 training acc:  0.35015873015873017 val acc:  0.3402469135802469 training loss:  1.8040932408861212 val loss:  1.8985066786408424\n","epoch:  14 training acc:  0.3683068783068783 val acc:  0.35703703703703704 training loss:  1.7620409018284566 val loss:  1.8641093261539936\n","epoch:  15 training acc:  0.3887830687830688 val acc:  0.37753086419753085 training loss:  1.7225321048014872 val loss:  1.8257265090942383\n","epoch:  16 training acc:  0.4066666666666667 val acc:  0.39037037037037037 training loss:  1.6839536138482996 val loss:  1.78616588935256\n","epoch:  17 training acc:  0.42148148148148146 val acc:  0.4032098765432099 training loss:  1.6470328980201 val loss:  1.754949625581503\n","epoch:  18 training acc:  0.43555555555555553 val acc:  0.4175308641975309 training loss:  1.6129359772076477 val loss:  1.7325779907405376\n","epoch:  19 training acc:  0.4452380952380952 val acc:  0.4288888888888889 training loss:  1.5808011067880166 val loss:  1.7088555507361889\n","epoch:  20 training acc:  0.45634920634920634 val acc:  0.4380246913580247 training loss:  1.5508376376048938 val loss:  1.6732471287250519\n","epoch:  21 training acc:  0.4678835978835979 val acc:  0.4444444444444444 training loss:  1.5216161987266026 val loss:  1.6410844810307026\n","epoch:  22 training acc:  0.47714285714285715 val acc:  0.457037037037037 training loss:  1.4927852032957851 val loss:  1.615603245794773\n","epoch:  23 training acc:  0.4884126984126984 val acc:  0.4679012345679012 training loss:  1.464875583713119 val loss:  1.5882743261754513\n","epoch:  24 training acc:  0.4998941798941799 val acc:  0.4767901234567901 training loss:  1.4361274894830343 val loss:  1.5630310662090778\n","epoch:  25 training acc:  0.5105291005291005 val acc:  0.485679012345679 training loss:  1.4068181071732495 val loss:  1.5273751243948936\n","epoch:  26 training acc:  0.522962962962963 val acc:  0.49506172839506174 training loss:  1.3754861749507286 val loss:  1.4951452165842056\n","epoch:  27 training acc:  0.533968253968254 val acc:  0.5091358024691358 training loss:  1.3426937831414711 val loss:  1.4691828899085522\n","epoch:  28 training acc:  0.5440211640211641 val acc:  0.5106172839506172 training loss:  1.3115963670047555 val loss:  1.4354440979659557\n","epoch:  29 training acc:  0.5546560846560846 val acc:  0.522962962962963 training loss:  1.2820833494534363 val loss:  1.4150762520730495\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"lp1q7Cr6Bpyw","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1605491569476,"user_tz":300,"elapsed":40854,"user":{"displayName":"Avelyn Wong","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gg0Gx_4UO4Canl0bxQX9l2YCVEf9TrzDq31I9-c9A=s64","userId":"04927963856256154784"}},"outputId":"d80faab6-13fb-4f97-b616-6c2cfb12dd01"},"source":["#increasing batch size and learning rate\n","\n","batch_size = 256\n","train_loader, val_loader, test_loader, classes = get_data_loader(batch_size)\n","\n","model3 = BaseANN()\n","use_cuda = False\n","train_acc, val_acc, train_loss, val_loss = train(model3, train_loader, val_loader, batch_size=256, num_epochs=30, learning_rate = 0.0001)\n","plot_training_curve(train_acc, val_acc, train_loss, val_loss)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Training Started...\n","U S I N G   C U D A \n","epoch:  0 training acc:  0.11095238095238096 val acc:  0.1108641975308642 training loss:  5.388014300449474 val loss:  2.2025095969438553\n","epoch:  1 training acc:  0.1111111111111111 val acc:  0.1108641975308642 training loss:  2.202562232275267 val loss:  2.202214077115059\n","epoch:  2 training acc:  0.1111111111111111 val acc:  0.1108641975308642 training loss:  2.2021990176793693 val loss:  2.201960653066635\n","epoch:  3 training acc:  0.1111111111111111 val acc:  0.1108641975308642 training loss:  2.2018661917866886 val loss:  2.2016886323690414\n","epoch:  4 training acc:  0.1111111111111111 val acc:  0.1108641975308642 training loss:  2.201544445914191 val loss:  2.2014330327510834\n","epoch:  5 training acc:  0.11105820105820106 val acc:  0.1108641975308642 training loss:  2.201235191242115 val loss:  2.201170101761818\n","epoch:  6 training acc:  0.11105820105820106 val acc:  0.1108641975308642 training loss:  2.200953693003268 val loss:  2.200952932238579\n","epoch:  7 training acc:  0.11105820105820106 val acc:  0.1108641975308642 training loss:  2.200659658457782 val loss:  2.200754538178444\n","epoch:  8 training acc:  0.1111111111111111 val acc:  0.1108641975308642 training loss:  2.2002857697976603 val loss:  2.2006291151046753\n","epoch:  9 training acc:  0.11015873015873016 val acc:  0.11061728395061729 training loss:  2.1991540451307556 val loss:  2.2013571113348007\n","epoch:  10 training acc:  0.11074074074074074 val acc:  0.1108641975308642 training loss:  2.198726051562541 val loss:  2.2005486339330673\n","epoch:  11 training acc:  0.10989417989417989 val acc:  0.11012345679012346 training loss:  2.1981893552316203 val loss:  2.201670452952385\n","epoch:  12 training acc:  0.1108994708994709 val acc:  0.1108641975308642 training loss:  2.1974820575198613 val loss:  2.2000370770692825\n","epoch:  13 training acc:  0.11365079365079366 val acc:  0.1111111111111111 training loss:  2.1969223570179297 val loss:  2.1998472213745117\n","epoch:  14 training acc:  0.11365079365079366 val acc:  0.1111111111111111 training loss:  2.196446866602511 val loss:  2.1998757272958755\n","epoch:  15 training acc:  0.11386243386243386 val acc:  0.1111111111111111 training loss:  2.196100170547898 val loss:  2.199775353074074\n","epoch:  16 training acc:  0.11375661375661375 val acc:  0.1111111111111111 training loss:  2.1956437826156616 val loss:  2.19958758354187\n","epoch:  17 training acc:  0.11391534391534391 val acc:  0.1111111111111111 training loss:  2.195366804664199 val loss:  2.1988993734121323\n","epoch:  18 training acc:  0.11402116402116402 val acc:  0.1111111111111111 training loss:  2.1948791162387744 val loss:  2.198948085308075\n","epoch:  19 training acc:  0.11417989417989417 val acc:  0.11135802469135803 training loss:  2.1944357027878634 val loss:  2.1990780383348465\n","epoch:  20 training acc:  0.11402116402116402 val acc:  0.11135802469135803 training loss:  2.1940339127102413 val loss:  2.1992177069187164\n","epoch:  21 training acc:  0.11391534391534391 val acc:  0.11135802469135803 training loss:  2.193774220105764 val loss:  2.199108436703682\n","epoch:  22 training acc:  0.11391534391534391 val acc:  0.11135802469135803 training loss:  2.193431113217328 val loss:  2.1990324407815933\n","epoch:  23 training acc:  0.11396825396825397 val acc:  0.11135802469135803 training loss:  2.1931470761428007 val loss:  2.1987902969121933\n","epoch:  24 training acc:  0.11402116402116402 val acc:  0.11160493827160493 training loss:  2.192813077488461 val loss:  2.198611319065094\n","epoch:  25 training acc:  0.11412698412698413 val acc:  0.11160493827160493 training loss:  2.1926038136353365 val loss:  2.1985400170087814\n","epoch:  26 training acc:  0.11417989417989417 val acc:  0.11160493827160493 training loss:  2.1924066414704195 val loss:  2.1985634863376617\n","epoch:  27 training acc:  0.11417989417989417 val acc:  0.11160493827160493 training loss:  2.192102789878845 val loss:  2.1983627378940582\n","epoch:  28 training acc:  0.1144973544973545 val acc:  0.11259259259259259 training loss:  2.1918040771742127 val loss:  2.1976445764303207\n","epoch:  29 training acc:  0.11417989417989417 val acc:  0.11160493827160493 training loss:  2.1920150679510995 val loss:  2.19824281334877\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAY4AAAEWCAYAAABxMXBSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3deXyU1b348c83O9l3lqzsiAIJhF0QpF61IrihoGWprUtvXdr+Wq3eVq239va2tlftYmttQS2IioobLpW6oKDs+yJbSMKahCQkhCyTnN8fzxMMIctMMpOZZL7v1yuvzHOeZb7PTDLfOec8zzlijEEppZRyVoC3A1BKKdW1aOJQSinlEk0cSimlXKKJQymllEs0cSillHKJJg6llFIu0cShvEZE3hWR+d6Ooz1EZJGI/NJ+PElE9jizbTufq0JE+rV3f6XcTROHcon9IdbwUy8iZxot3+LKsYwxVxpjnvNUrK0Rkdkikisi0qQ8SEROiMh0Z49ljFlljBnsprg+FpHvNjl+pDHmgDuO38pzlohIqKeeQ3UvmjiUS+wPsUhjTCSQB1zdqGxxw3YiEuS9KJ2yHIgFLmlSfgVggPc6PSIvEJFMYBLWOc/o5Of29b8R1QJNHMotRGSKiBSIyP0icgxYKCJxIvK2iBTa32jfFpHURvuc/XYtIgtE5DMRedze9qCIXNnCc90vIsualD0pIk81OtYBESm3j3NeTcgYUwW8DMxrsmoesMQY4xCRV0TkmIiUicinInJha+feaDlbRDbaz/8SENZoXYuviYg8hvUh/ke7BvdHu9yIyAD7cYyIPG/vf0hEfiYiAa6+hk3O9wtgEXBOs6GIpInIa/ZzFTfEY6+7TUR22ee4U0RGNo3VXm7cpNeev5F4EVkoIkfs9cvt8u0icnWj7YJFpEhEsts4X+UGmjiUO/UC4oEM4Hasv6+F9nI6cAb4Y4t7w1hgD5AI/Ab4e9OmJNtS4JsiEgUgIoHAjcASEYkAngKuNMZEAROAzS0833PADSLSwz5ODHC1XQ7wLjAQSAY2AoubO0hjIhKCVZt5Aeu1eAW4vtEmLb4mxpj/AlYBd9k1uLuaeYo/ADFAP6za0jzg243WO/saNphnn9di4HIR6WmfRyDwNnAIyARSsF53RGQW8Ii9bzRWTaW4tdelEVf/Rl4AwoELsd6H/7PLnwe+1Wi7bwJHjTGbnIxDdYQxRn/0p10/QC7wDfvxFKAGCGtl+yygpNHyx8B37ccLgH2N1oVjNZ/0auFYnwHz7MeXAfvtxxFAKdaHdQ8nzmEvcLP9+DZgSwvbxdrxxNjLi4BfNjr3AvvxZOAIII32Xd2wrSuvSaMyAwwAAu3XeGijdXcAH7fzNbwYqAUS7eXdwA/tx+OBQiComf3eB+5t4ZgGGNBouenr5PTfCNAbqAfimtmuD1AORNvLy4D7vP0/4S8/WuNQ7lRorCYgAEQkXET+ajepnAI+BWLtb7PNOdbwwBhTaT+MbGHbJcAc+/HN9jLGmNPATcCdwFEReUdEhrQS8/N83Vw1115GRAJF5Ncist+OPdfeJrGVY4H1gXbY2J9mtkMND9rxmjSWCAQ3Pp79OKXRsiuv4XzgA2NMkb28hK+bq9KAQ8YYRzP7pQH7nYi3Oa78jaQBJ40xJU0PYow5AnwOXC8iscCVOFEjVO6hiUO5U9Ohlv8fMBgYa4yJxvo2DtBa04mzXgGm2O3h12InDgBjzPvGmMuwvrHuBv7WynFeAKaJyHhgHF9/+NwMzAS+gdU0lOlk7EeBlCbNQ+mNHrf1mrQ2XHURVg0ho8mxD7cR03ns5rkbgUvsfpxjwA+BESIyAsgH0qX5Dux8oH8Lh67Equk06NVkvSt/I/lAvJ0YmvMcVnPVLGCNMcbl10G1jyYO5UlRWG3WpSISDzzsrgMbYwqxmnUWAgeNMbsARKSniMy0+zqqgQqs5o6WjpOL1ez1IvAvY0zDN/Yoe/9irA/CXzkZ2hrAAdxjd9heB4xptL6t1+Q4Vv9Fc7HWYXXoPyYiUSKSAfwI+KeTsTV2DVAHDMVqHsoCLsDqY5kHrMVKgr8WkQgRCRORifa+zwI/FpFRYhlgxwJWf9LNdo3tCs6/aq2pFl8PY8xRrH6mP9ud6MEiMrnRvsuBkcC92DVF1Tk0cShPegLogfVN+Qvcf4nrEqwawZJGZQFYH6ZHgJNYH1zfa+M4z2F9i2/84fM8VjPQYWAnVvxtMsbUANdh9TecxGo2e63RJm29Jk9iddiXiH2VWBN3A6eBA1gJbwnwD2dia2I+sNAYk2eMOdbwg9UxfQvWN/6rsfpW8oAC+1wwxrwCPGY/dznWB3i8fdx77f1K7eMsbyOOtl6PuVi1rN3ACeAHDSuMMWeAV4G+nPsaKw+Tc5tilVKq6xCRh4BBxphvtbmxchu9AUcp1SXZTVvfwaqVqE6kTVVKqS5HRG7D6jx/1xjzqbfj8TfaVKWUUsolWuNQSinlEr/o40hMTDSZmZneDkMppbqUDRs2FBljkpqW+0XiyMzMZP369d4OQymluhQROdRcuTZVKaWUcokmDqWUUi7RxKGUUsolftHH0Zza2loKCgqoqqpqe2PVprCwMFJTUwkODvZ2KEopD/PbxFFQUEBUVBSZmZm0Ps+NaosxhuLiYgoKCujbt6+3w1FKeZjfNlVVVVWRkJCgScMNRISEhAStvSnlJ/w2cQCaNNxIX0ul/IdfJw6llOpqPvmqkO2Hy7wagyYOLykuLiYrK4usrCx69epFSkrK2eWamppW912/fj333HNPJ0WqlPIVq/cX8e2Fa7nl2S/JP1nZ9g4e4red496WkJDA5s2bAXjkkUeIjIzkxz/+8dn1DoeDoKDm356cnBxycnI6JU6llG84fqqKe17cREZCBEUV1fzn4o28cud4woKdma7evbTG4UMWLFjAnXfeydixY7nvvvtYu3Yt48ePJzs7mwkTJrBnzx4APv74Y6ZPnw5YSefWW29lypQp9OvXj6eeam7SOKVUV1ZbV89dSzZyurqOv84dxe9mjWDb4TIefXunV+LRGgfwi7d2sPPIKbcec2ifaB6++kKX9ysoKGD16tUEBgZy6tQpVq1aRVBQEB9++CEPPvggr7766nn77N69m48++ojy8nIGDx7M9773Pb2fQqlu5Dfv7WZdbglPzs5iUM8oBvWM4s5L+vOXT/aTkxHHdSNTOzUeTRw+ZtasWQQGWlXPsrIy5s+fz969exERamtrm93nqquuIjQ0lNDQUJKTkzl+/DipqZ37h6SU8oz3th/lb6sOMm98BjOzUs6W//g/BrE5v4QHX9/G0D7RDOkV3WkxaeKAdtUMPCUiIuLs45///OdMnTqV119/ndzcXKZMmdLsPqGhoWcfBwYG4nA4PB2mUqoTHCw6zU9e2cqItFj+66oLzlkXFBjAU3Oymf7UZ3zvnxt5866JRIV1TkuD9nH4sLKyMlJSrG8YixYt8m4wSqlOdaamju/9cwNBgcKfbxlJaND5neDJUWH88eaR5J2s5L5lW+msGV01cfiw++67jwceeIDs7GytRSjlR4wx/Gz5dvYcL+eJ2dmkxPZocdsxfeO5/4rBvLv9GH//7GCnxOcXc47n5OSYphM57dq1iwsuuKCFPVR76GuqlHu8uDaPB17bxr3TBvLDywa1ub0xhjv/uYEPd51g6e3jGJ0Z75Y4RGSDMea8a/+1xqGUUj5kW0EZD7+xg0kDE7ln2kCn9hERfjtrBGlxPfj+4o0Ulld7NEZNHEop5SNKK2v43uINJEaG8OTsbAIDnB8DLjosmD/fMoqyM7Xc8+ImHHX1HotTE4dSSvmA+nrDj17ewvFTVfzplpHER4S4fIyhfaL55TUXseZAMb//11ceiNKiiUMppXzA05/s59+7T/Czq4aSnR7X7uPMyklj9ug0/vzxfj7cedyNEX5N7+NQSrmkvt7w4ro8lq7N57bJ/bh6eG8dVt9WX284WHyaTXmlbM4vYUt+GSdPtz5oaYOjZWe4ekQf5o3P6HAcj8y4kG2Hy/jRy5t5++5JpCeEd/iYjWniUEo57WDRaX766la+PHiSuPBg7nlxE29uPsIvr7mIXjFh3g6v05WcrmFzfimb8kvZlFfClvxSTlVZl85HhgYxIi2GQT2jnDpWQmQI904b6JYkHBYcyNO3jOJXK3YRHur+QRA1cXjJ1KlT+elPf8rll19+tuyJJ55gz549PP300+dtP2XKFB5//HFycnL45je/yZIlS4iNjT1nm+ZG2W1q+fLlDBo0iKFDhwLw0EMPMXnyZL7xjW+46cxUd+Soq+cfnx/kdx98RUhQAP97/TCuH5nKws9zefyDPVz2+0948KoLmD06rVvXPowxfLaviNc3HmZjXgm5xdbQ5gECg3pGcdXwPmSnxZKdHkv/pEgCXOjcdrf0hHD+MneUR46ticNL5syZw9KlS89JHEuXLuU3v/lNm/uuWLGi3c+7fPlypk+ffjZxPProo+0+lvIPu46e4v5Xt7K1oIzLhvbkl9dcRM9oq3Zx2+R+XDa0Jz99bSsPvLaNNzcf4dfXDyMjIaKNo3YtlTUOXtt4mEWrc9l3ooK48GBGZ8Zz0+h0stJiGZ4aQ0So/3ycaue4l9xwww288847Zydtys3N5ciRI7z44ovk5ORw4YUX8vDDDze7b2ZmJkVFRQA89thjDBo0iIsvvvjssOsAf/vb3xg9ejQjRozg+uuvp7KyktWrV/Pmm2/yk5/8hKysLPbv38+CBQtYtmwZACtXriQ7O5thw4Zx6623Ul1dffb5Hn74YUaOHMmwYcPYvXu3J18a5SOqHXX8/oM9XP2HzzhSeoY/3TySZ+aOOps0GmQmRrDku+P41bXD2H64jMuf+JRnVx2grr7r31xcUFLJ/6zYxbhfreRny7cTFhzA72aN4IsHp/HMvBy+N6U/4/sn+FXSAA/XOETkCuBJIBB41hjz6ybrJwNPAMOB2caYZY3WvQeMAz4zxkxv5thPAbcaYyI7HOi7P4Vj2zp8mHP0GgZX/rrF1fHx8YwZM4Z3332XmTNnsnTpUm688UYefPBB4uPjqaurY9q0aWzdupXhw4c3e4wNGzawdOlSNm/ejMPhYOTIkYwaZVVNr7vuOm677TYAfvazn/H3v/+du+++mxkzZjB9+nRuuOGGc45VVVXFggULWLlyJYMGDWLevHk8/fTT/OAHPwAgMTGRjRs38uc//5nHH3+cZ5991h2vkvJRG/NKuH/ZVvaeqOC67BR+Pn0oca1cHhoQINw8Np2pQ5L42evb+eU7u3hr61F+c/1wBvdyro3fVxhjWHvwJItW5/L+jmOICFdc2ItvT8xkVEZct26Kc5bHahwiEgj8CbgSGArMEZGhTTbLAxYAS5o5xG+BuS0cOwdo//VqPqKhuQqsZqo5c+bw8ssvM3LkSLKzs9mxYwc7d7Y8UcuqVau49tprCQ8PJzo6mhkzZpxdt337diZNmsSwYcNYvHgxO3bsaDWWPXv20LdvXwYNsoY3mD9/Pp9++unZ9ddddx0Ao0aNIjc3t72nrHxcZY2DR9/ayfVPr+Z0tYOF3x7N72/KajVpNNY7pgfPzs/hydlZ5J+sZPofVvHEh19R4/DczWjuUlVbxyvr85n+h8+46ZkvWHOgmNsn92fVfVP50y0jycmM16Rh82SNYwywzxhzAEBElgIzgbOfhMaYXHvdeX9VxpiVIjKlabmdkH4L3Axc65ZIW6kZeNLMmTP54Q9/yMaNG6msrCQ+Pp7HH3+cdevWERcXx4IFC6iqqmrXsRcsWMDy5csZMWIEixYt4uOPP+5QrA1Dt+uw7d3TifIqFn+Rx+Iv8yiqqGbuuAzuu2Jwu4bpFhFmZqVw8YBEHn17J098uJdX1hcwb3wGs0enExPu/UnGjDEcKatiU14Jm/NK2ZxfyrbDZVQ76hnUM5L/uW4Y12Sl0COk86dl7Qo8mThSgPxGywXAWDcc9y7gTWPM0a6e/SMjI5k6dSq33norc+bM4dSpU0RERBATE8Px48d59913W5yDA2Dy5MksWLCABx54AIfDwVtvvcUdd9wBQHl5Ob1796a2tpbFixefHZ49KiqK8vLy8441ePBgcnNz2bdvHwMGDOCFF17gkksu8ch5K9+xtaCUhZ/n8vbWI9TWGaYOTuL7UweQ44ZB8hIiQ3lydjbXjUzl6Y/38T/v7uaJD/dy3cgUvj0xkwHJndeEVVHtYGuBlSA251mXzzaM5xQaFMBFKTHMHZfBpUOSGd8/QWsWbehSPToi0geYBUxxYtvbgdsB0tPTPRtYB8yZM4drr72WpUuXMmTIELKzsxkyZAhpaWlMnDix1X1HjhzJTTfdxIgRI0hOTmb06NFn1/33f/83Y8eOJSkpibFjx55NFrNnz+a2227jqaeeOtspDhAWFsbChQuZNWsWDoeD0aNHc+edd3rmpJVXOerqeW/HMRZ+nsuGQyVEhARyy9gM5k/IpG+i+6+GumRQEpcMSmLnkVMsWn2QVzYUsPjLPCYNTOTbEzOZMijZrZet1tUb9p2oYHN+iXWPRV4pXx0vp6Gvvm9iBJMGJJKVHktWWixDekUTEqTXCbnCY8Oqi8h44BFjzOX28gMAxpj/aWbbRcDbjTvH7fIpwI8bOsdF5Crg70BD+006cMAYM6C1WHRY9c6hr6lvKzldw4vr8nhhzSGOllWRHh/O/AmZzMpJJbqTZo4DKK6o5sW1ebzwxSGOn6qmb2IE88dncENOGpHtuDqpsLzaqknkl7Apr5StBWVUVFvNqTE9ghmRFkt2WqyVKFJjne6vUS0Pq+7JGsc6YKCI9AUOA7Ox+iXazRjzDtCrYVlEKtpKGkr5u+Onqnjiw728vqmAqtp6JvRP4NGZF3HpkGSXRl91l4TIUO66dCB3XNKfFduOsvDzXB55aye/++Arrs7qQ4ITH+zGwKGTlWzKK6Gg5AwAQQHCkN5RXJudQpZ9E17fxAhtdvIAjyUOY4xDRO4C3se6HPcfxpgdIvIosN4Y86aIjAZex7pC6moR+YUx5kIAEVkFDAEiRaQA+I4x5n1PxatUd2OM4eX1+fzynV1UO+q5LjuFBRMzGdIr2tuhARAcGMDMrBRmZqWwKa+ERatzWbahwOnhwHtFh5GdHsf88Zlkp8dyUUoMYcHamd0ZPNrHYYxZAaxoUvZQo8frgNQW9p3kxPE7dA+HMUa/jbiJP8wk2RmMMXx1vIKByR0briKvuJIHXt/K5/uKGds3nv+9fjiZHui/cJfs9Diy0+N40tuBKKd0qc5xdwoLC6O4uJiEBL2CoqOMMRQXFxMW5n+D3LlTtaOO+5Zt5Y3NR8hICGf+eKv/wZVLYuvqDYtW5/L4+3sIDBAeu/Yi5oxO9+qYSar78ds5x2traykoKGj3fRLqXGFhYaSmphIc7P1r9Lui0soabn9hA2sPnmT++Ay2HznFhkMlRIYGccOoVKeueNp7vJz7Xt3KprxSLh2SzGPXXkTvmB6ddAaqO/JG57hPCw4Opm/fvt4OQynyiitZsGgtBSfP8NScbGaM6APAlvxSFq3OZfGXh3huTS5TByfz7YmZXDwg8Zxaco2jnr98sp8//HsvkaFBPDk7ixkj+mhNWnmM39Y4lPIFG/NKuO259dQZwzNzcxjT9/wb706cquKfX+ax5MtDFFXUMCA5kgUTMrluZAp7j1dw/6tb2X2snKtH9OGRq4eSEBnqhTNR3VFLNQ5NHEp5ybvbjvKDlzbTKyaMhQtG0y+p9Ws9qh11vL3lKAtXH2T74VNEhQVxutpBUlQov7xmGJcN7dlJkSt/oU1VSvkIYwzPrjrIr97dRXZaLH+bl+NULSE0KJDrR6Vy3cgU1h8qYfEXh4iLCOGHlw3q1Bv4lNLEoVQnctTV84u3dvLCF4e4alhvfnfjCJfvPRARRmfGM9oN40kp1R6aOJTqJKerHdz94ib+vfsEd1zSj/svH6KXyaouSROHUh1wtOwMlTV1bW53pqbubCf2Y9dexC1jMzohOqU8QxOHUu20Jb+UmX/63OntI0ICeXZ+DlMHJ3swKqU8TxOHUu10sOg0AD+76gKSotru3M5OiyM9IdzTYSnlcZo4lGqnogprIqBZo9J8YlY7pTqLzl6iVDsVVdQQEhhAdA/9/qX8iyYOpdqpqKKahMgQHdpD+R1NHEq1U1FFNYk6vIfyQ5o4lGqnhhqHUv5GE4dS7VRcUaM1DuWXNHEo1Q7GGE0cym9p4lCqHU6dcVBTV0+iNlUpP6SJQ6l2KLTv4dAah/JHmjiUaodiTRzKj2niUKodiipqAEiM0qYq5X80cSjVDg3DjSREaI1D+R9NHEq1Q3FFNQEC8RFa41D+RxOHUu1QWFFDfEQIgToRk/JDmjiUagcdbkT5M00cSrWDDjei/JkmDqXaQe8aV/5ME4dS7aBNVcqfaeJQykWVNQ4qa+o0cSi/pYlDKRcVlVs3/2kfh/JXmjiUclHRaevmvyStcSg/pYlDKRcVles4Vcq/aeJQykU6TpXydx5NHCJyhYjsEZF9IvLTZtZPFpGNIuIQkRuarHtPREpF5O0m5X8XkS0islVElolIpCfPQammGsap0uFGlL/yWOIQkUDgT8CVwFBgjogMbbJZHrAAWNLMIX4LzG2m/IfGmBHGmOH2/ne5LWilnFBcUU10WBChQYHeDkUpr/BkjWMMsM8Yc8AYUwMsBWY23sAYk2uM2QrUN93ZGLMSKG+m/BSAiAjQAzAeiF2pFhVV1JAYpf0byn95MnGkAPmNlgvssg4TkYXAMWAI8Ad3HFMpZxXqzX/Kz3XJznFjzLeBPsAu4KbmthGR20VkvYisLyws7NT4VPdm3TWu/RvKf3kycRwG0hotp9plbmGMqcNq/rq+hfXPGGNyjDE5SUlJ7npapXScKuX3PJk41gEDRaSviIQAs4E3O3JAsQxoeAzMAHZ3OFKlnFTjqKfsTK0mDuXXPJY4jDEOrCue3sdqUnrZGLNDRB4VkRkAIjJaRAqAWcBfRWRHw/4isgp4BZgmIgUicjkgwHMisg3YBvQGHvXUOSjVVLF917gON6L8WZAnD26MWQGsaFL2UKPH67CasJrbd1ILh53otgCVclFxw81/WuNQfqxLdo4r5S2FFTrciFKaOJRyQcM4VTrAofJnmjiUckHDOFXax6H8mSYOpVxQXFFNj+BAIkI92j2olE/TxKGUC4oqqnVUXOX3NHEo5YIivflPKU0cSrmiqKKahAhNHMq/aeJQygVFFTUkaVOV8nOaOJRyUl294eRpHRlXqTYTh4hcLSKaYJTfK6msod7ozX9KOZMQbgL2ishvRGSIpwNSylc1TBmr93Aof9dm4jDGfAvIBvYDi0RkjT3XRZTHo1PKh+g4VconfPkM7HrbqyE41QRlT9e6DGv+i97AtcBGEbnbg7Ep5VOKdJwq5W2OGvjwYfj0t14Nw5k+jhki8jrwMRAMjDHGXAmMAP6fZ8NTyncU6jhVytuObILaSji2FarKvBaGM+MmXA/8nzHm08aFxphKEfmOZ8JSyvcUn64hOFCI7qHDjSgvOfSZ9dvUQ94XMOhyr4ThTFPVI8DahgUR6SEimQDGmJUeiUopH1RUbt38Z00+qZQX5H4O8f0gMARyP/NaGM4kjleA+kbLdXaZUn5Fx6lSXlVXa9Uy+k+DlFE+nziCjDE1DQv2Y/3vUX5Hx6lSXnV0C9SehsyJkDHRWq465ZVQnEkchQ1zhAOIyEygyHMhKeWbinWcKuVNuaus3xkXQ+bFYOog/0uvhOJM4rgTeFBE8kQkH7gfuMOzYSnlW4wxVo1Dm6qUt+R+DomDITIJ0sZAQJDXmqvavDzEGLMfGCcikfZyhcejUsrHnKpyUFNXr5fiKu+oc1j9G8NnWcshEVY/x6HPvRKOU9cVishVwIVAWMMVJcaYRz0Yl1I+RYcbUV51bAvUlFtNVA0yJsLnT0J1BYRGdmo4ztwA+Bes8aruBgSYBWR4OC6lfIoON6K8KteuWWQ0ShyZE73Wz+FMH8cEY8w8oMQY8wtgPDDIs2Ep5Vt0uBHlVYc+h4QBENXz67K0cSCBXmmuciZxVNm/K0WkD1CLNV6VUn5DE4fymvo6OLT63GYqsJqn+mR7pYPcmcTxlojEAr8FNgK5wBJPBqWUrykqr0YE4sKDvR2K8jfHtkH1qXObqRpkToTDG6GmslNDajVx2BM4rTTGlBpjXsXq2xhijHmoU6JTykcUna4hPjyEoECd00x1soamqMyJ56/LnAT1tVCw9vx1HtTqf4Exph74U6PlamOM94ZkVMpLisp1yljlJbmfWeNTRfc5f13aWJCATm+ucubr00oRuV50ZDflx3ScKuUV9fVW/0ZGM7UNgLBo6D3i66uuOokzieMOrEENq0XklIiUi4h3BkhRykuKKmp0uBHV+U7sgKrS8zvGG8u8GA6vh9oznRaWM1PHRhljAowxIcaYaHs5ujOCU8pXFFdoU5XygoYmqJZqHGB1mtfVQMG6zokJJ+4cF5HJzZU3ndhJqe7qTE0dp2vqtKlKdb7czyA2A2LTWt4mfRwgVnNV32Y/rt3OmSFHftLocRgwBtgAXOqRiJTyMXoPh/KK+nrriqrB32x9ux6x0Ht4p94I6Mwgh1c3XhaRNOAJj0WklI/5OnFojUN1osJdcKak9f6NBhkXw7pnobYKgsM8Hlp7LkovAC5wZkMRuUJE9ojIPhH5aTPrJ4vIRhFxiMgNTda9JyKlIvJ2k/LF9jG3i8g/RETvyFIeVaTjVClvODs+VSv9Gw0yL4a6aji8wbMx2ZwZ5PAPIvKU/fNHYBXWHeRt7ReIdQ/IlcBQYI6IDG2yWR6wgObvRP8tMLeZ8sXAEGAY0AP4bluxKNUR2lSlvCJ3FcSkQZwTY8pmjAek05qrnKlxrMfq09gArAHuN8Z8y4n9xgD7jDEH7OlmlwIzG29gjMk1xmzl3DnNG9atBMqbKV9hbMBaINWJWJRqt6JyHVJddTJjmh+fqiU94qDnRV/PEuhhznSOLwOqjDF1YNUkRCTcGNPW4CgpQH6j5QJgbPvCPJ/dRDUXuLeF9bcDtwOkp6e762mVHyo+XUNUWBChQYHeDgyrIEUAABsMSURBVEX5i8I9UFnkXDNVg8yLYcMicFRDkGdrx07dOY7VJNSgB/ChZ8JxyZ+BT40xzaZYY8wzxpgcY0xOUlJSJ4emupPCimqd+U91roaaQ3PjU7UkcyI4zliDHnqYM4kjrPF0sfbjcCf2Oww0vvg41S7rMBF5GEgCfuSO4ynVGh2nSnW6Q59DdArE9XV+n/QJ9r6eH7fKmcRxWkRGNiyIyCjAmXvb1wEDRaSviIQAs4E32xfm10Tku8DlwBx7EEalPErHqVKdyhjriqqMieDKEIERCZB8YaeMW+VM4vgB8IqIrBKRz4CXgLva2skY47C3ex/YBbxsjNkhIo+KyAwAERktIgVY09H+VUR2NOwvIquwxsiaJiIFInK5veovQE9gjYhsFhEd4l15VPFpHadKdaKivXD6hGvNVA0yJ1pTydbVuj+uRpy5AXCdiAwBBttFe4wxTkVljFkBrGhS9lCjx+to4aooY8ykFsqd6dBXyi1q6+oprazVpirVeRqamjKb/QhsXcZEWPsMHNkEaWPcG1cjztzH8X0gwhiz3RizHYgUkf/0WERK+ZDihpv/tKlKdZbczyGylzUHh6sarsLy8PwczjRV3WaMKW1YMMaUALd5LiSlfEfDzX/aVKU6hTHWh36mi/0bDSKTIGmITySOwMaTONl3hOvXL+UXGhJHktY4VGc4eQAqjjl/419zMhr6ORzui6sJZxLHe8BLIjJNRKYBLwLveiwipXyIjlOlOtXZ+Tc6kDgyL4aaCji6xT0xNcOZxHE/8G/gTvtnG+feEKhUt6XjVKlOlfsZRCRD4sD2H+NsP4fnhh9xZgbAeuBLIBdr/KlLsS6vVarbKyqvJiw4gPAQHW5EeZgx1o1/7e3faBDVExIHeXTAwxYvbRWRQcAc+6cI6/4NjDFTPRaNUj6m+HQNiZGhSEf+kZVyRslBOHXYtfGpWpIxEba/avVzBLr/DobWahy7sWoX040xFxtj/gDUuT0CpXxYkc41rjpLwx3fHekYb5B5MVSfgmNbO36sZrSWOK4DjgIficjf7I5x/dql/EqhjlOlOsuhzyE8wbqctqMaai0eaq5qMXEYY5YbY2ZjTZr0EdbQI8ki8rSI/IdHolHKx1hNVXopruoEuZ+5Pj5VS6J7Q3x/j41b5cyQI6exZuhbIiJxWONK3Q984JGIlPIR9fWGk3Yfh/IDJYfghJeu+6kqg7J8mHC3+46ZORF2vAH1dRDg3os7XOo1se8af8b+UapbK6msoa7eaI3DH+R9Cf+8zrr/wZv6TXHfsTInwcbn4fh26D3CfcfFxcShlD85e/NflNY4urX8dfDP6yGyJ8xc5vHZ81oUFgMJ/d13vIGXwe0fW0Otu5kmDqVaUKzjVHV/BeutmkZkEix4G6L7eDsi9+kRZ/14gDN3jivllwp1nKru7fAGeOFa60qm+d0saXiYJg6lWqDjVHVjhzfC89dCeLxV04hJ8XZEXYomDqVaUFRRTVCAENMj2NuhKHc6shleuAZ6xFg1jZhm55JTrdDEoVQLiiuqSYgM0eFGupOjW+D5mRAabSWN2DRvR9QlaeJQqgVFFXoPR7dybJuVNEIireapuAxvR9RlaeJQqgU6TlU3cmw7PDcDgsPtpJHp7Yi6NE0cSrWgSMep6h6O74TnZ0BQGMx/C+L7ejuiLk/v41CqGcYYinScqpYV7YWPfgW1Z7wdSdsK1kJgiFXTcOcNdn5ME4dSzSivdlDjqNcaR3OK9sGi6VbS6Ar9BD0vhKt+r0nDjTRxKNWMonJ7yli9+e9cxfvhuelQ74DvfADJbhgCXHU5mjiUakbDzX863EgjxfutmkZdjXUpqyYNv6Wd40o1o2GcKm2qsp08CM9dDY4qq4O551BvR6S8SGscSjWjqEKbqs4qybWSRm2lnTTcP9qq6lq0xqFUMworahCB+HA/Txwlh2DR1VBdDvPegF7DvB2R8gFa41CqGcUV1cSFhxAU6MffrUrzrY7w6jIrabh5MiDVdfnxf4VSLbPuGvfj2kZZASy6Cs6Uwdzl0Cfb2xEpH6I1DqWa4dfjVJUdtq6eOlNiJY2Ukd6OSPkYTRxucqqqlqraOq89f3RYMGHB7p2Q3t1qHPUEBQgBAb4/2mxRRTUjUmPb3vBMKTiqPR9QZ6kqhRdnw+kimPs6pI7ydkTKB2ni6KDT1Q5++/4enluTizHeiyM4ULigdzTZabFkpceSlRZHZkK414YEN8aQd7KSTXmlbM4vZVN+KTuPlDF1cDLPzMvxSkyuKK6oIaGtpqovn4H37gdT3zlBdZaQSPjWa5A22tuRKB+liaMDPv2qkAde28bh0jPcPDadob2jvRKHAY6UnmFzXimvbCjguTWHAIgLD2ZEWixZabFkp8eRlRpLTLhnJiUqO1PLlnwrSTT8nDxt3UQXHhLIsJQYhvaO5pOvCqmqrfPp2lFVbR0V1Y7Wm6rW/g3e/QkM/A8YdEXnBdcZMi+GpMHejkL5MI8mDhG5AngSCASeNcb8usn6ycATwHBgtjFmWaN17wHjgM+MMdMbld8F/ADoDyQZY4o8eQ7NKaus5b/f2cmyDQX0S4rglTvHMzozvrPDaFZdvWHviXLrm35eKZvyS/jkq8KztaH0+HDCQ9z7oX2mto5DxZUAiMCApEimDUm2klVaLIN6RhIUGMCHO4/z3efXsymvlPH9E9wagzsV2sONJLWUONb9HVb8GAZ/E2Y9B0F+3Imu/JLHEoeIBAJ/Ai4DCoB1IvKmMWZno83ygAXAj5s5xG+BcOCOJuWfA28DH7s5ZKe8t/0oP39jBydP1/CfU/pzz7SBPvXtOTBAGNIrmiG9opkzJh2A8qpathaUsTm/lJ1HT+Goc2/TSlBAADeMTCU7PY7haTFEhzVfqxnTL54AgTX7i3w6cbR689/6hfDOj6xaxqxFmjSUX/JkjWMMsM8YcwBARJYCM4GzicMYk2uvO++TzBizUkSmNFO+yd7HI0G35ER5FQ+/sYN3tx9jaO9oFi4YzUUpMZ0aQ3tFhQUzcUAiEwckejWO6LBghqXGsuZAsVfjaEtxS+NUbXwe3v6B1Tx14/MQ5KdXXSm/58nEkQLkN1ouAMZ68PnOISK3A7cDpKent/s4xhhe3XiY/357J2dq6/jJ5YO5fXI/gv35xrAOGN8vgWdXHaCyxkF4iG92sX1d42iUGDb9E968BwZ8A258QZOG8mvd9tPPGPOMMSbHGJOTlJTUrmMUlFQyf+E6fvzKFgYmR7Linkl8f+oATRodMKF/Ao56w7rcEm+H0qKGxJEQYTdDbV4Cb9wF/afCTYshOMyL0SnlfZ78yncYSGu0nGqXdRn3Lt3MrqOn+MWMC5k7LqNL3H/g63Iy4wgOFNbsL+aSQe1L6J5WVFFDVFiQ1Xe15SVY/p/Q7xKYvUSThlJ4NnGsAwaKSF+shDEbuNmDz+d2/3PdMMJDAkmNC/d2KN1GeEgQWWmxrNnf6RfDOc0abiQUtr4Cy++EvpNg9osQ3MPboSnlEzzW5mKMcQB3Ae8Du4CXjTE7RORREZkBICKjRaQAmAX8VUR2NOwvIquAV4BpIlIgIpfb5ffY+6QCW0XkWU+dw6CeUZo0PGB8/0S2HS7jVFWtt0Np1olT1cwIXA2v3w4ZE2HOSxCifwdKNfBo76QxZgWwoknZQ40er8NKAM3tO6mF8qeAp9wYZsu2LYOTBzrlqfxGxkQm9B/CUyv3svbASb4xtGfHjpe/FgJDoE+WW8Krraun15EPuCfgScgYDzdr0lCqKd+8rMVXbH0J9n7g7Si6l4AgRl33D0KDQli9v7hjiWPXW/DKAohJhXs2W3cfdtCOw2XcL89RETuYmJtfhpCIDh9Tqe5GE0dr5iz1dgTdS3U5LJ5F8Gu3ckfP/+KD/R34UN79jpU0QqOsGeqObXXLfBEHN39ClhRzatwjEBrZ4eMp1R3pdaWtCQjUH3f+9IiFby2D3lnce/IxUk58fHZub5fseRdeng+9s+D2j0ECYcdyt7zl4fveppYgokfMcMvxlOqONHGozhUWA3NfoyrhQp4OfoIDq19zbf8978FLc60pTOe+BnGZ0Hcy7FxOR4cnrqur56JTH7M/KsdKckqpZmniUJ0vLIaQb7/BV2SQveZu2Psv5/b76gN4eS70usiaKyIshtPVDqoHXW1dxHB8e4fCOrT9c1IopLL/9LY3VsqPaeJQXhEcEcfTaY9zQNJh6S2w78PWd9j7Ibz0LUi+wEoaPWIxxjDvH2v54dZUkADY+UaHYjq9aRm1JpDe467v0HGU6u40cSivyRrUl1mV91MbPxBevBn2rWx+w30rYenNkDTImsq0RxwAm/NL2XCohPdz63CkT7T6OdrbXGUMvQreZ2PgcHr36tPOM1LKP2jiUF4zvn8CZUTyQc4zkDjISg77Pzp3o/0fWeWJg2DemxD+9bwnL6w5RIBYc5DsirsUivfCiV3tisUc3UyS4yi5PS/ryCkp5Rc0cSivuaB3NDE9gvkk3wHz3oD4/vDiHDjwibXBgU+s+a/j+1vrGyWNoopq3t56lJvHphPTI5hXz2QDYnWSt0PJ+mU4TAChF13thjNTqnvTxKG8JjBAGNcv3pqfIyIB5r8J8X1hyU3w+ZPW7/h+VnnEuRM/vbQun5q6ehZM6MvkQUm8vb8OkzGhff0cxhC0+01W119I1uD+bjo7pbovTRzKq8b3SyD/5BnyT1ZCRKLVHBWXAf96yPo9702rvBFHXT2LvzjExAEJDEiOZOrgJIoqqjmScgUU7oYTu10L4vh2oivz+CxkAhkJOryIUm3RxKG8aoI9K+Ga/fasgJFJMP8tmHyf9Tvy/KHXV+4+wZGyKuaNzwRg8qAkRODdutFYzVWu1TrMjuXUEcCpjCs6fWZJpboiTRzKqwYmR5IYGXLudLKRyXDpf1m/m/H8mlz6xIQxbYi1PjEylOGpsbxzsB7Sx7vWz2EMjm2v80XdBVw4sF8HzkQp/6GJQ3mViDC+fyKr9xdhnLiUdt+Jcj7fV8wt4zIIajQT49TBSWzOL+X0gKvgxE4o/Mq5AE7sJLh0PyvqxzK2X0Lb2yulNHEo7xvfL4Hjp6o5UHS6zW1fWHOIkMAAZo9OO6d86uBkjIFVQROsAmebq3a+QT0BfBEyngFJOqihUs7QxKG8bkJ/65v+6v3FrW5XUe3g1Y2HmT68NwmRoeesG5YSQ0JECO/mCaSNdSlxbAkYSv++/XRqYKWcpIlDeV1GQjh9YsL4oo3E8frGAiqqHcwdn3HeuoAA4ZLBSXz6VSH1F8yE49ugeH/rT3xiNxTu5rXqHMb0jW99W6XUWZo4lNeJCOP6J7DmQDH19c33cxhjeH7NIYanxpCV1vzItVMGJ1NSWcuO2EusgrY6yXe+gUF4r24047R/QymnaeJQPmFC/0ROnq5hz/HyZtevOVDM3hMVzB2X0eIls5MHJhIg8K+CYEjJaXuOjp3LyY0YzpnQJC7oHd3RU1DKb2jiUD5hvN3PsaaF5qoX1hwiLjyYq0e0PABhbHgII9Pj+GhPIVx4jTUrYEtzxhd+BSd28k7dWHIy4wjU/g2lnKaJQ/mElNgeZCSEN9tBfrTsDB/sPM6No9MICw5s9ThThySz7XAZRemXWwU732x+Q7vz/IXS4dq/oZSLNHEonzGhfwJfHiymrkk/x5Iv86g3hm+NPb9TvKkpg607zT861gP6jGy5n2PnG5QkZHOceMb21f4NpVyhiUP5jPH9EymvcrDjSNnZsmpHHS+uzWPakGTS4tseR2po72iSo0L5eE8hDJ0JRzZByaFzNyreD8e38WWPyYQFBzAsJcbdp6JUt6aJQ/mM8f3Ov5/jve3HKKqoYa49LlVbRISpg5P5dG8htUNmWIVN7+mwayEvlmcxMj2OkCD9N1DKFfofo3xGUlQoA5Mjz0kcz63OpW9iBJMGJLay57mmDkmivMrBxlMx0HvE+Yljx3IcfXL49ESo9m8o1Q6aOJRPmdA/gXUHT1LjqGf74TI25pXyrXEZLt3VPXFAIkEBYl1dNfQaOLweSvOtlScPwLGtHEyehjFo/4ZS7aCJQ/mU8f0TOVNbx9aCUp5fk0uP4EBuGJXq0jGiwoIZnRnPx3tOWP0cALvsq6vs2scHjCM4UMhOb/5mQqVUyzRxKJ8yrl88IrBi2zHe2HyEa7JTiOkR7PJxpg5JYvexco4E9oFew76+GXDnG9BnJB8eCWVEamybl/cqpc6niUP5lNjwEIb2jmbR6oNUO+qZ18y4VM6YMtiaq+Ps1VUFa+HQGjiyiZohM9hWUKb9G0q1kyYO5XMm9E+g3sCYzPh2DwUyMDmSlNgefLTnhNXPAfDG9wHYEjUFR73R+TeUaidNHMrnTBpo3cQ3f0Jmu48hIkwZnMTqfUVUx/aD5Avh5H7oncWqwnACBEZlxLkpYqX8iyYO5XMmDUzkrbsu5pvDenXoOFMHJ3O6po71uSVfd5IPncmXB09yUUoMkaFBbohWKf+jiUP5HBFhWGpMi6PgOmvCgARCAgP4aPcJyL4F+k+j+qIb2ZRfyphM7d9Qqr00cahuKzwkiLH94q1+jphUmPsaW0rDqXHUa/+GUh3g0cQhIleIyB4R2SciP21m/WQR2SgiDhG5ocm690SkVETeblLeV0S+tI/5koiEePIcVNc2dXAy+wtPk1dcCcDag9Zd6aMztX9DqfbyWOIQkUDgT8CVwFBgjogMbbJZHrAAWNLMIX4LzG2m/H+B/zPGDABKgO+4K2bV/UwdYl+W+9UJAL48eJIhvaKIDdfvG0q1lydrHGOAfcaYA8aYGmApMLPxBsaYXGPMVqC+6c7GmJXAOdPBidXofSmwzC56DrjGA7GrbqJvYgSZCeF8tPsEjrp6Nhwq0fs3lOogTyaOFCC/0XKBXdYRCUCpMcbR1jFF5HYRWS8i6wsLCzv4tKormzI4mdX7i9lwqITKmjpNHEp1ULftHDfGPGOMyTHG5CQlJXk7HOVFU4ckU+2o58mVewE0cSjVQZ5MHIeBtEbLqXZZRxQDsSLScAG+O46purmxfeMJCw5g9f5i+iVGkBwV5u2QlOrSPJk41gED7augQoDZQAsTQDvHGGOAj4CGK7DmA2+0vIdSEBYcyIT+1nweWttQquM8ljjsfoi7gPeBXcDLxpgdIvKoiMwAEJHRIlIAzAL+KiI7GvYXkVXAK8A0ESkQkcvtVfcDPxKRfVh9Hn/31Dmo7mOqPRe5Jg6lOs6jYy4YY1YAK5qUPdTo8Tqs5qbm9p3UQvkBrCu2lHLajKwUDhZVctnQnt4ORakuTwfrUX4hpkcwD13d9DYipVR7dNurqpRSSnmGJg6llFIu0cShlFLKJZo4lFJKuUQTh1JKKZdo4lBKKeUSTRxKKaVcoolDKaWUS8Qa/ql7E5FC4FCT4kSgyAvheEp3Ox/ofuek5+P7uts5dfR8Mowx5w0v7heJozkist4Yk+PtONylu50PdL9z0vPxfd3tnDx1PtpUpZRSyiWaOJRSSrnEnxPHM94OwM262/lA9zsnPR/f193OySPn47d9HEoppdrHn2scSiml2kETh1JKKZf4XeIQkStEZI+I7BORn3o7HncQkVwR2SYim0VkvbfjcZWI/ENETojI9kZl8SLyLxHZa/+O82aMrmrhnB4RkcP2+7RZRL7pzRhdISJpIvKRiOwUkR0icq9d3iXfp1bOpyu/R2EislZEttjn9Au7vK+IfGl/5r0kIiEdfi5/6uMQkUDgK+AyoABYB8wxxuz0amAdJCK5QI4xpkveuCQik4EK4HljzEV22W+Ak8aYX9sJPs4Yc78343RFC+f0CFBhjHncm7G1h4j0BnobYzaKSBSwAbgGWEAXfJ9aOZ8b6brvkQARxpgKEQkGPgPuBX4EvGaMWSoifwG2GGOe7shz+VuNYwywzxhzwBhTAywFZno5Jr9njPkUONmkeCbwnP34Oax/6i6jhXPqsowxR40xG+3H5cAuIIUu+j61cj5dlrFU2IvB9o8BLgWW2eVueY/8LXGkAPmNlgvo4n8sNgN8ICIbROR2bwfjJj2NMUftx8eAnt4Mxo3uEpGtdlNWl2jWaUpEMoFs4Eu6wfvU5HygC79HIhIoIpuBE8C/gP1AqTHGYW/ils88f0sc3dXFxpiRwJXA9+1mkm7DWO2p3aFN9WmgP5AFHAV+591wXCcikcCrwA+MMacar+uK71Mz59Ol3yNjTJ0xJgtIxWphGeKJ5/G3xHEYSGu0nGqXdWnGmMP27xPA61h/MF3dcbsduqE9+oSX4+kwY8xx+x+7HvgbXex9stvNXwUWG2Nes4u77PvU3Pl09feogTGmFPgIGA/EikiQvcotn3n+ljjWAQPtqwxCgNnAm16OqUNEJMLu3ENEIoD/ALa3vleX8CYw3348H3jDi7G4RcMHrO1autD7ZHe8/h3YZYz5faNVXfJ9aul8uvh7lCQisfbjHlgXAe3CSiA32Ju55T3yq6uqAOzL654AAoF/GGMe83JIHSIi/bBqGQBBwJKudk4i8iIwBWsI6OPAw8By4GUgHWtI/BuNMV2ms7mFc5qC1QRigFzgjkb9Az5NRC4GVgHbgHq7+EGsfoEu9z61cj5z6Lrv0XCszu9ArErBy8aYR+3PiKVAPLAJ+JYxprpDz+VviUMppVTH+FtTlVJKqQ7SxKGUUsolmjiUUkq5RBOHUkopl2jiUEop5RJNHEq5gYjUNRpRdbM7R14WkczGo+wq5W1BbW+ilHLCGXuoB6W6Pa1xKOVB9lwpv7HnS1krIgPs8kwR+bc9mN5KEUm3y3uKyOv2nApbRGSCfahAEfmbPc/CB/adwUp5hSYOpdyjR5OmqpsarSszxgwD/og1agHAH4DnjDHDgcXAU3b5U8AnxpgRwEhgh10+EPiTMeZCoBS43sPno1SL9M5xpdxARCqMMZHNlOcClxpjDtiD6h0zxiSISBHWREK1dvlRY0yiiBQCqY2HhLCH/f6XMWagvXw/EGyM+aXnz0yp82mNQynPMy08dkXjsYXq0P5J5UWaOJTyvJsa/V5jP16NNTozwC1YA+4BrAS+B2cn5YnprCCVcpZ+a1HKPXrYM681eM8Y03BJbpyIbMWqNcyxy+4GForIT4BC4Nt2+b3AMyLyHayaxfewJhRSymdoH4dSHmT3ceQYY4q8HYtS7qJNVUoppVyiNQ6llFIu0RqHUkopl2jiUEop5RJNHEoppVyiiUMppZRLNHEopZRyyf8HZW1ru6Jwg4wAAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 1 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"E574HhopNtPB","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1605483453315,"user_tz":300,"elapsed":97780,"user":{"displayName":"Avelyn Wong","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gg0Gx_4UO4Canl0bxQX9l2YCVEf9TrzDq31I9-c9A=s64","userId":"04927963856256154784"}},"outputId":"dcdb4a38-2d62-48b3-bcf2-f692671aba4a"},"source":["#keeping original learning rate and batch size, increasing epochs\n","\n","model4 = BaseANN()\n","use_cuda = False\n","\n","batch_size = 128\n","train_loader, val_loader, test_loader, classes = get_data_loader(batch_size)\n","\n","train_acc, val_acc, train_loss, val_loss = train(model4, train_loader, val_loader, batch_size=128, num_epochs=60, learning_rate = 0.00001)\n","plot_training_curve(train_acc, val_acc, train_loss, val_loss)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Training Started...\n","U S I N G   C U D A \n","epoch:  0 training acc:  0.15957671957671957 val acc:  0.15259259259259259 training loss:  9.425501942634583 val loss:  4.760327190160751\n","epoch:  1 training acc:  0.18851851851851853 val acc:  0.1782716049382716 training loss:  2.83140367269516 val loss:  2.254886083304882\n","epoch:  2 training acc:  0.20645502645502645 val acc:  0.2017283950617284 training loss:  2.167328053229564 val loss:  2.1697077602148056\n","epoch:  3 training acc:  0.22291005291005292 val acc:  0.21555555555555556 training loss:  2.111359416633039 val loss:  2.1262101866304874\n","epoch:  4 training acc:  0.23084656084656086 val acc:  0.2254320987654321 training loss:  2.073980724489367 val loss:  2.090551517903805\n","epoch:  5 training acc:  0.23814814814814814 val acc:  0.23679012345679012 training loss:  2.0466554027956887 val loss:  2.0574803575873375\n","epoch:  6 training acc:  0.24312169312169313 val acc:  0.24345679012345678 training loss:  2.024282693057447 val loss:  2.032763935625553\n","epoch:  7 training acc:  0.24661375661375662 val acc:  0.24592592592592594 training loss:  2.004108111600618 val loss:  2.015140797942877\n","epoch:  8 training acc:  0.252010582010582 val acc:  0.24518518518518517 training loss:  1.983876603680688 val loss:  1.9962318576872349\n","epoch:  9 training acc:  0.25502645502645505 val acc:  0.248641975308642 training loss:  1.9626124557611104 val loss:  1.978999450802803\n","epoch:  10 training acc:  0.2598941798941799 val acc:  0.25135802469135804 training loss:  1.9461175066393774 val loss:  1.9675225913524628\n","epoch:  11 training acc:  0.2642328042328042 val acc:  0.25851851851851854 training loss:  1.9293062719138894 val loss:  1.9586624763906002\n","epoch:  12 training acc:  0.26915343915343914 val acc:  0.27209876543209877 training loss:  1.912473313711785 val loss:  1.9476887471973896\n","epoch:  13 training acc:  0.277989417989418 val acc:  0.2750617283950617 training loss:  1.886703125528387 val loss:  1.9144948534667492\n","epoch:  14 training acc:  0.29068783068783066 val acc:  0.2883950617283951 training loss:  1.8565063210758004 val loss:  1.8938690088689327\n","epoch:  15 training acc:  0.3003174603174603 val acc:  0.29555555555555557 training loss:  1.8235323364670213 val loss:  1.8623922169208527\n","epoch:  16 training acc:  0.31343915343915346 val acc:  0.30864197530864196 training loss:  1.7895664242473808 val loss:  1.8403360806405544\n","epoch:  17 training acc:  0.3286772486772487 val acc:  0.32024691358024693 training loss:  1.753355874403103 val loss:  1.8091782815754414\n","epoch:  18 training acc:  0.34634920634920635 val acc:  0.3325925925925926 training loss:  1.7192164933359302 val loss:  1.7854016982018948\n","epoch:  19 training acc:  0.3584656084656085 val acc:  0.3459259259259259 training loss:  1.6808299390045371 val loss:  1.7585896216332912\n","epoch:  20 training acc:  0.37592592592592594 val acc:  0.35703703703703704 training loss:  1.645401361826304 val loss:  1.7320341765880585\n","epoch:  21 training acc:  0.3901058201058201 val acc:  0.37209876543209874 training loss:  1.6114559310513574 val loss:  1.7043054699897766\n","epoch:  22 training acc:  0.4041269841269841 val acc:  0.3916049382716049 training loss:  1.575510305327338 val loss:  1.6772838421165943\n","epoch:  23 training acc:  0.42338624338624337 val acc:  0.408641975308642 training loss:  1.5416553036586658 val loss:  1.6540869139134884\n","epoch:  24 training acc:  0.43656084656084654 val acc:  0.42 training loss:  1.5033680409998507 val loss:  1.6211358197033405\n","epoch:  25 training acc:  0.4497354497354497 val acc:  0.43061728395061727 training loss:  1.4671908842550743 val loss:  1.581595316529274\n","epoch:  26 training acc:  0.4613756613756614 val acc:  0.4397530864197531 training loss:  1.4287334390588708 val loss:  1.555823139846325\n","epoch:  27 training acc:  0.47444444444444445 val acc:  0.45135802469135805 training loss:  1.3894323158908535 val loss:  1.52989212423563\n","epoch:  28 training acc:  0.48417989417989415 val acc:  0.4617283950617284 training loss:  1.3478132204429523 val loss:  1.4929806403815746\n","epoch:  29 training acc:  0.5067724867724868 val acc:  0.4817283950617284 training loss:  1.3044897970315572 val loss:  1.4694616720080376\n","epoch:  30 training acc:  0.5226984126984127 val acc:  0.4948148148148148 training loss:  1.2610365599393845 val loss:  1.4315998069941998\n","epoch:  31 training acc:  0.5463492063492064 val acc:  0.5165432098765432 training loss:  1.2211398659525692 val loss:  1.3980287723243237\n","epoch:  32 training acc:  0.5645502645502646 val acc:  0.5261728395061729 training loss:  1.1796725299712774 val loss:  1.3729909025132656\n","epoch:  33 training acc:  0.5867195767195768 val acc:  0.5469135802469136 training loss:  1.1395538115018122 val loss:  1.3313722796738148\n","epoch:  34 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0.7805771976709366\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYIAAAEWCAYAAABrDZDcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3dd3hUVfrA8e9LQggQCITQAyR0QakBFFiKFSsWkKICNhTLWndFZV1F9Ocqu7ZFdgGlKEgVRIqoCIpKCYRICT0ECIQWIJSQ/v7+uIM7xgQSyGSSzPt5nnlyy5k77xmGeeeec+85oqoYY4zxXWW8HYAxxhjvskRgjDE+zhKBMcb4OEsExhjj4ywRGGOMj7NEYIwxPs4SgSkUIrJYRAZ7O46LISKTRGSUa/lPIrItP2Uv8rVOi0jDi32+MZ5gicCHub6Uzj2yReSs2/o9BTmWqt6oqpM9Fev5iEh/EYkXEcmx3V9EDovILfk9lqquUNVmhRTXchF5KMfxg1Q1rjCOf57XPC4i5Tz1Gqb0sUTgw1xfSkGqGgTsBW512zb1XDkR8fdelPkyD6gCdM+xvRegwNdFHpEXiEg48CecOt9WxK9d3D8j5jwsEZg/EJEeIpIgIi+IyEFgoohUFZEFInLE9YtzgYiEuT3nt1+/IjJERH4SkdGusrtF5MY8XusFEZmdY9v7IvKB27HiROSU6zh/OFNR1VRgJjAox65BwDRVzRSRWSJyUESSReRHEWl5vrq7rbcVkWjX688AAt325fmeiMgbOF/K/3adYf3btV1FpLFrOVhEpriev0dERohImYK+hznquwqYBPyumU5E6onIF67XSjoXj2vfwyKyxVXHWBFplzNW17p7E9rFfEZCRGSiiBxw7Z/n2r5JRG51K1dWRI6KSNsL1NcUEksEJi+1gBCgATAU57My0bVeHzgL/DvPZ0MnYBsQCrwNfJyz6cZlOnCTiFQCEBE/4G5gmohUBD4AblTVSkBnICaP15sM9BGR8q7jBAO3urYDLAaaADWAaGBqbgdxJyIBOGcbn+K8F7OAu9yK5PmeqOrLwArgCdcZ1hO5vMSHQDDQEOdsZhBwv9v+/L6H5wxy1WsqcIOI1HTVww9YAOwBwoG6OO87ItIXeNX13Mo4ZxJJ53tf3BT0M/IpUAFoifPv8K5r+xTgXrdyNwGJqro+n3GYS6Wq9rAHQDxwrWu5B5AOBJ6nfBvguNv6cuAh1/IQYKfbvgo4zRW18jjWT8Ag1/J1wC7XckXgBM6Xb/l81GEHMNC1/DDwax7lqrjiCXatTwJGudU9wbXcDTgAiNtzfzlXtiDvids2BRoDfq73uIXbvkeA5Rf5HnYFMoBQ1/pW4BnX8lXAEcA/l+ctAZ7K45gKNHZbz/k+5fszAtQGsoGquZSrA5wCKrvWZwN/9fb/CV962BmBycsRdZpcABCRCiLyX1cTxkngR6CK69dmbg6eW1DVFNdiUB5lpwEDXMsDXeuo6hmgH/AokCgiC0Wk+XlinsL/mofuc60jIn4i8paI7HLFHu8qE3qeY4HzBbVfXd9OLnvOLVzEe+IuFCjrfjzXcl239YK8h4OBb1T1qGt9Gv9rHqoH7FHVzFyeVw/YlY94c1OQz0g94JiqHs95EFU9APwM3CUiVYAbyccZmyk8lghMXnIOS/sc0AzopKqVcX4tA5yvqSK/ZgE9XO3Jd+BKBACqukRVr8P5RbkVGH+e43wKXCMiVwFX8r8vk4FAb+BanKaY8HzGngjUzdEcU99t+ULvyfmG9j2K8wu+QY5j779ATH/gag67G+ju6gc5CDwDtBaR1sA+oL7k3qG7D2iUx6FTcM5EzqmVY39BPiP7gBDXF31uJuM0D/UFVqpqgd8Hc/EsEZj8qoTT5ntCREKAvxfWgVX1CE4zykRgt6puARCRmiLS29VXkAacxmleyOs48TjNTJ8D36rquV/UlVzPT8L5Ynszn6GtBDKBP7s6MO8EOrrtv9B7cgin/T+3WLNwOrjfEJFKItIAeBb4LJ+xubsdyAJa4DTHtAEuw+mjGASswUlqb4lIRREJFJEurudOAJ4XkfbiaOyKBZz+mIGuM6pe/PGqrJzyfD9UNRGnn+YjV6dyWRHp5vbceUA74ClcZ3Km6FgiMPn1HlAe55fsKgr/ksxpOL/Yp7ltK4Pz5XgAOIbzRTTsAseZjPMr2/3LZApOs8t+IBYn/gtS1XTgTpz2+mM4zVRfuBW50HvyPk4H9nFxXQWVw5PAGSAOJ4FNAz7JT2w5DAYmqupeVT147oHTUXsPzi/yW3H6JvYCCa66oKqzgDdcr30K5ws5xHXcp1zPO+E6zrwLxHGh9+M+nLOgrcBh4OlzO1T1LDAHiOD377EpAvL75k9jjPEOEXkFaKqq916wsClUdhOIMcbrXE1JD+KcNZgiZk1DxhivEpGHcTqTF6vqj96OxxdZ05Axxvg4OyMwxhgfV+L6CEJDQzU8PNzbYRhjTImybt26o6paPbd9JS4RhIeHs3btWm+HYYwxJYqI7MlrnzUNGWOMj7NEYIwxPs4SgTHG+LgS10eQm4yMDBISEkhNTb1wYZMvgYGBhIWFUbZsWW+HYozxsFKRCBISEqhUqRLh4eGcf94Okx+qSlJSEgkJCURERHg7HGOMh5WKpqHU1FSqVatmSaCQiAjVqlWzMyxjfESpSASAJYFCZu+nMb6jVDQNGWNMaXUqNYOYfSeI3nOCa1vUoGWd4EJ/DUsEhSApKYlrrrkGgIMHD+Ln50f16s4NfGvWrCEgICDP565du5YpU6bwwQe5DVdvjPElWdlK3JHT/JqQTPTe40TvOc62Q6dQBREICQqwRFBcVatWjZiYGABeffVVgoKCeP7553/bn5mZib9/7m91ZGQkkZGRRRKnMaZ4OXQylVVxSWxMSGZDQjKbDiSTkp4FQKVy/rSpX4Vel9eiXf2qtKlfhcqBnrmKzxKBhwwZMoTAwEDWr19Ply5d6N+/P0899RSpqamUL1+eiRMn0qxZM5YvX87o0aNZsGABr776Knv37iUuLo69e/fy9NNP8+c//9nbVTHGFJKz6Vms3p3Eih1HWbHjCNsPnQagnH8ZWtSpTN/2YVwRVoVWYcE0rh5EmTJF01dX6hLBa19tJvbAyUI9Zos6lfn7rS0L/LyEhAR++eUX/Pz8OHnyJCtWrMDf35/vvvuOl156iTlz5vzhOVu3bmXZsmWcOnWKZs2aMWzYMLuW35gS6mRqBuv2HGdt/DGidh8nZt8J0rOyCfAvQ8fwEO5qF0aXxqE0q1WJsn7eu3an1CWC4qRv3774+fkBkJyczODBg9mxYwciQkZGRq7PufnmmylXrhzlypWjRo0aHDp0iLCwsKIM2xhzkc6mZ7FqdxI/bj/CqrhjbD14ElXwLyO0rBvMkC7hdG0cSseIEALL+nk73N+UukRwMb/cPaVixYq/Lf/tb3+jZ8+ezJ07l/j4eHr06JHrc8qVK/fbsp+fH5mZmZ4O0xhzCXYePsX3Ww/z4/ajrIk/RnpmNuX8yxAZXpWnrmlCx/AQ2tSvQoWA4vt1W3wjK2WSk5OpW7cuAJMmTfJuMMaYS7Lj0CkWbkxk4YZEdhx22vmb1Ajivisb0L1p9WL3i/9CLBEUkb/+9a8MHjyYUaNGcfPNN3s7HGNMPqVnZhOfdIYdh04Tm5jMt7GH2H7oNCLQITyE125ryXUtalKnSnlvh3rRPDpnsYj0At4H/IAJqvpWjv31gclAFVeZ4aq66HzHjIyM1JwT02zZsoXLLrusMEM32PtqfFN6ZjZLtxxi4cZEth48RfzRM2RmO9+TZQQiw0O4+Yra3Hh5LWpUDvRytPknIutUNddr1T12RiAifsAY4DogAYgSkfmqGutWbAQwU1XHikgLYBEQ7qmYjDEmL7EHTjJr3T6+jDnAsTPpVK9Ujjb1qnBDy5o0qVGJJjWDaFQ9qEQ1+eSXJ5uGOgI7VTUOQESmA70B90SgQGXXcjBwwIPxGGPM7xxMTmXBhgPMi9nPpv0nCfArw3UtatInMoxuTarjV0TX8XubJxNBXWCf23oC0ClHmVeBb0TkSaAicG1uBxKRocBQgPr16xd6oMYY33H0dBqLNyby1YZEouKPoQqX163Mq7e2oHebulStmPeQMKWVtzuLBwCTVPWfInIV8KmIXK6q2e6FVHUcMA6cPgIvxGmMKcGys5Ufdxzhs1V7+X7rIbLVucrn2WubckvrOkSEVrzwQbwtMw1UoWzh90t4MhHsB+q5rYe5trl7EOgFoKorRSQQCAUOezAuY4yPOH4mnVnr9jF19V72JKUQGhTAI90bcXubujSrVcnb4V1Y8n7Y+S3s+BbilsPN/4LW/Qr9ZTyZCKKAJiISgZMA+gMDc5TZC1wDTBKRy4BA4IgHYzLG+IB9x1L4aPlO5kTvJz0zm44RITx/fTNuaFmLAP9iPA3LyUQ4EA371sDO7+DQJmd7cD1o1Q+qN/XIy3osEahqpog8ASzBuTT0E1XdLCIjgbWqOh94DhgvIs/gdBwPUU9ez+ohPXv2ZPjw4dxwww2/bXvvvffYtm0bY8eO/UP5Hj16MHr0aCIjI7npppuYNm0aVapU+V2Z3EYxzWnevHk0bdqUFi1aAPDKK6/QrVs3rr02164WY0q9vUkp/HvZDr6I3k+ZMkKf9mEMuqoBzWtVvvCTi4oqpCTBiT1wYi8k7YT9650EcCrRKVPGH+pfBde9Dk2uh+rNnHGoPcSjfQSuewIW5dj2ittyLNDFkzEUhQEDBjB9+vTfJYLp06fz9ttvX/C5ixad97aJ85o3bx633HLLb4lg5MiRF30sY0qy+KNn+Peyncxdvx+/MsK9Vzbg0e6NqBVcDK7zP3sC4pbB9m9g/zpI3gcZKb8vE9IIwrtC3fZQpx3UugICKhRZiN7uLC4V+vTpw4gRI0hPTycgIID4+HgOHDjA559/zrPPPsvZs2fp06cPr7322h+eGx4eztq1awkNDeWNN95g8uTJ1KhRg3r16tG+fXsAxo8fz7hx40hPT6dx48Z8+umnxMTEMH/+fH744QdGjRrFnDlzeP3117nlllvo06cPS5cu5fnnnyczM5MOHTowduxYypUrR3h4OIMHD+arr74iIyODWbNm0bx586J+y4wpFPFHz/Dh9zuZF7Mf/zLC4KvCebR7Q+/e6JV+Bg5vgfgVTtv+3lWgWRBYBRp0gcbXQpX6UKWe628DCPTuGUvpSwSLh8PBjYV7zFpXwI1v5bk7JCSEjh07snjxYnr37s306dO5++67eemllwgJCSErK4trrrmGDRs20KpVq1yPsW7dOqZPn05MTAyZmZm0a9fut0Rw55138vDDDwMwYsQIPv74Y5588kluu+2237743aWmpjJkyBCWLl1K06ZNGTRoEGPHjuXpp58GIDQ0lOjoaD766CNGjx7NhAkTCuNdMqbI5EwAQzqH80g3LySAtNOw52c4sN5pzz+0GY7txmnpxvnu6Pq007xTNxL8iudXbvGMqgQ61zx0LhF8/PHHzJw5k3HjxpGZmUliYiKxsbF5JoIVK1Zwxx13UKGCczp42223/bZv06ZNjBgxghMnTnD69OnfNUHlZtu2bURERNC0qdOxNHjwYMaMGfNbIrjzzjsBaN++PV988cUl192YorIxIZmJP+/my18PUNZPuL9zOEO7N6RGpUJMAKpwLA4SY6BsBagQChVdj7IV4eAG2LUUdi1zfu1nZwAC1Ro5X/ytB0CNFlC3HVSuU3hxeVDpSwTn+eXuSb179+aZZ54hOjqalJQUQkJCGD16NFFRUVStWpUhQ4aQmpp6UcceMmQI8+bNo3Xr1kyaNInly5dfUqznhrq2Ya5NSZCemc3iTYlM/iWe6L0nqBDg55wBFGYCSNrlNOXE/wTxP8OpPAY5ED+nmQecL/2rHoNG10BYJASUgHsR8lD6EoGXBAUF0bNnTx544AEGDBjAyZMnqVixIsHBwRw6dIjFixfnOQcBQLdu3RgyZAgvvvgimZmZfPXVVzzyyCMAnDp1itq1a5ORkcHUqVN/G866UqVKnDp16g/HatasGfHx8ezcufO3PoXu3bt7pN7GeMrxM+lMWbmHz1bv4cipNMKrVeCVW1rQJzKs8Obu3R8N378Ou7531ivWcDptw7tAWAfIyoSUo3DmqPP37HGo3hwa9oRKNQsnhmLAEkEhGjBgAHfccQfTp0+nefPmtG3blubNm1OvXj26dDn/xVHt2rWjX79+tG7dmho1atChQ4ff9r3++ut06tSJ6tWr06lTp9++/Pv378/DDz/MBx98wOzZs38rHxgYyMSJE+nbt+9vncWPPvqoZyptTCE7ejqN8Svi+GzlHs6kZ9GjWXUGdw6ne5PqhTeH7+GtsGwUbPkKyofAta9C81ugWmOPXqZZXHl0GGpPsGGoi469r6YoHTqZyn9/iGPamj2kZ2ZzS6s6PHF1Y5rWLKQ7gDPTncs3oyfDr9MhIAg6PwlXDvP6VTtFwSvDUBtjTH5kZSvjV8Txr2+3k5Wt3N6mLo/3bETD6kGXduDMdNi/1tXu/5Nzt27mWfAPhM5PQNdnoUJI4VSihLNEYIzxmgMnzvLszBhWxR2jV8tavHTTZdSvdok3Uqk6TT7fvOzcuYtArcuh/RCn7b9BF0sAOZSaRKCqiA+27XlKSWsyNCXPgg0HeOmLjWRmK2/3aUXf9mGX/n/40Gb4ejjs/tG5hLPvZGjYHcpXLZygS6lSkQgCAwNJSkqiWrVqlgwKgaqSlJREYGAxuD3flDrJKRmMXBDLnOgE2tSrwnv92hB+qcNApxyDZW/A2k8gMBhuGg3t7y+2N3AVN6XiXQoLCyMhIYEjR2zg0sISGBhIWFiYt8MwpciRU2l8/NNuPlu1h5T0TP58TROevLoxZf0uYTTQ7Gyn83fpa5B6Ejo8DD2GW9NPAZWKRFC2bFkiIiK8HYYxJhcJx1MY92McM6L2kZGVzc2t6vBYj0ZcVvsSr9Q5EAMLn3M6hBt0cc4CarYonKB9TKlIBMaY4udESjrvLNnGjKh9iMBd7cJ4pHujS58N7OwJpxkoaoIz/MMd46DV3T55/X9hsURgjClU2dnK7HUJvPX1VpLPZnBvp/o82qMRtYPLX9qBj2x3moFipkJqMnR4CHq+DOWrXPi55rwsERhjCs3mA8n8bd4moveeoEN4VV6//fJLmxQmI9W5FHTdJNjzkzNhS/ObnXsA6rQptLh9nSUCY8wlO52WyT+/2cbkX+KpWiGA0X1bc1e7uhd3Fd+ZJGd0zx3fOOP5p56AquHOMBBt7oGgGoUcvbFEYIy5JEu3HOJv8zaReDKVezrV5y/XNye4QgEHhUtNhtXjYMcSSFgLqNP+3+xGZ67eiO5QphjPNVzCWSIwxlyUw6dSee2rWBZuSKRpzSBmD+xM+wYXcePW/nUw637nLuC67ZzLP5tcB7Xb2pd/EbFEYIwpEFVl1toERi2MJTUjm+eua8oj3RsR4F/AL21VWDUWvn0FKtWGB7+Feh0u/DxT6DyaCESkF/A+4AdMUNW3cux/F+jpWq0A1FBVuwTAmGLqREo6w+ds5OvNB+kYEcL/3XkFjS5mcLiUY/Dl47BtETS7GW4fY8NAeJHHEoGI+AFjgOuABCBKROarauy5Mqr6jFv5J4G2norHGHNpVu5K4pkZMSSdSeOlm5rzUNeGFzc/QNwPThI4dRB6vQWdHrV7ALzMk2cEHYGdqhoHICLTgd5AbB7lBwB/92A8xpiLkJGVzfvf7WDM8p2EV6vIF4O6cEVYcMEPdOogLHkZNs2GqhHw4BKo277wAzYF5slEUBfY57aeAHTKraCINAAigO/z2D8UGApQv379wo3SGJOng8mpDJu6jvV7T3B3ZBh/v7UlFcsV8GsjKxPWjINlb0JWOnQfDl2fhrKXeIOZKTTFpbO4PzBb9dys0L+nquOAceDMUFaUgRnjq2L2nWDolLWcScvkwwFtubV1nYIfZF8ULHgaDm1yJnm/6R2o1qjwgzWXxJOJYD9Qz209zLUtN/2Bxz0YizGmAOauT+CFORupUakcnz7YhWa1CjhdZEYqLH8TfvnQuSLo7ilw2W3WF1BMeTIRRAFNRCQCJwH0BwbmLCQizYGqwEoPxmKMyYesbOWdJdv4zw+76BQRwth72xNSMaBgBzmwHuY+Cke2Qtv74IY3fWJO4JLMY4lAVTNF5AlgCc7lo5+o6mYRGQmsVdX5rqL9gelqU2IZ41UnUtJ5buavLN16mIGd6vPqrS0Ldm9AZjqsGA0/jnaGgRg4C5pe77mATaHxaB+Bqi4CFuXY9kqO9Vc9GYMx5sK+jT3ES3M3cvxMOiN7t+S+KxsUbJwg976AVv3gxn/YfQElSHHpLDbGeMGJlHRenb+ZeTEHaF6rEhOHdODyugW4NPTsCWd2sLUTnb6AflPhsls8F7DxCEsExviobzYf5KW5mziRks5T1zTh8Z6N898UpAqb5sDXL0LKUbhyGPR8CcoVsFPZFAuWCIzxMckpGfx9/ibmxRzgstqVmfxAB1rWKeBZwBcPO8NE12kL98yyuQFKOEsExviQZVsP88KcDRw7cxFnAQDJCTC1LxzdDr3+AR0fhjJ+ngvYFAlLBMb4gFOpGYxasIUZa/fRrGYlPiloXwDAwU1OEkg/DffOgYY9PBGq8QJLBMaUcuv2HOPPn8eQmHyWYT0a8fS1TSjnX8Bf8XHLYcZ9EBAE9y+GWpd7JFbjHZYIjCnFft55lAcnR1GrciCzh3WmXf2LuKRzw0yY9xhUawz3zobgsMIP1HiVJQJjSqkfth9h6JS1RIRW5LOHOhEaVK5gB1B1bhD7fhSE/wn6fQblbbqQ0sgSgTGl0NIthxj2WTSNawTx2UOdCj5MRFaGc4PY+s/giruh97/Bv4CJxJQYlgiMKWW+3nSQJz+PpkXtykx5oNNFTCR/EmYOgrhl0O2vzv0BNlhcqWaJwJhSZMGGAzw1PYbWYcFMeqAjlQMLmASSE2Dq3XB0G/QeA23v9UygplixRGBMKfFlzH6emRFDZIMQPrm/A0EFnUAmYR3MuAfSz8A9s6FRzws/x5QKBbiTxBhTXH0RncAzM2LoGBHCpAcKmARUYc14+OQG8CsLD3xtScDH2BmBMSXcrLX7+OucDVzVsBofD+5A+YAC3COQfga+eho2zoQmN8Ad/4EKIZ4L1hRLlgiMKcFmRO1l+Bcb6dIolPGDIguWBI7ucG4SO7IVrh4BXZ+DMtZI4IssERhTQn2+Zi8vfrGRbk2rM+6+9gSWzWcSyDjrDBu97A3nktD75lpTkI+zRGBMCfSdayKZHs2q859785kEMtMgegqs+CecSnTGCur9EQTX9XS4ppizRGBMCbNpfzJ/nr6eK+oGM/aefCSBrAyImepMIZm8D+pfBXeOh4g/FU3AptizRGBMCXIwOZWHJq8luHxZJuSnT+DMUZg+EPathrqRcNsH0LCn3SBmfscSgTElREp6Jg9NieJUagazHu1MjcqB53/CkW3OsNGnDzlnAFf0tQRgcuXRSwREpJeIbBORnSIyPI8yd4tIrIhsFpFpnozHmJIqO1t5enoMsQdO8uHAtrSoU/n8T4j7ASZcBxkpMGQhtLrbkoDJk8fOCETEDxgDXAckAFEiMl9VY93KNAFeBLqo6nERqeGpeIwpqVSVt77eyjexh/j7rS24unnN8z8h+lNnwLhqTWDgDKjaoGgCNSWWJ5uGOgI7VTUOQESmA72BWLcyDwNjVPU4gKoe9mA8xpQ4mVnZjFwQy5SVe7jvygYM6Ryed+GMs7B0JKz6CBpdDX0nQWABZyEzPsmTiaAusM9tPQHolKNMUwAR+RnwA15V1a9zHkhEhgJDAerXr++RYI0pbs6kZfLnz9ezdOthhnZryPBezZG8mnf2roYvH4ekHdBxKNzwpjNchDH54O3OYn+gCdADCAN+FJErVPWEeyFVHQeMA4iMjNSiDtKYonboZCoPTIpiS+JJXr/9cu67Mo/mnfQU58awlWOcmcPum+ucDRhTAJ5MBPuBem7rYa5t7hKA1aqaAewWke04iSHKg3EZU6xtPXiSByZGceJsBh8P7kDP5nl0ne1dBfOGwbE4iHwArhsJ5SoVbbCmVPDkVUNRQBMRiRCRAKA/MD9HmXk4ZwOISChOU1GcB2MypljbfCCZvmNXkqXKzEeuyjsJ7PkFJt8G2VkwaD7c8q4lAXPRPHZGoKqZIvIEsASn/f8TVd0sIiOBtao637XvehGJBbKAv6hqkqdiMqY4SzqdxtAp6wgK9GfOsM7UqVI+94KHt8Ln/aFKfXjwGxst1Fwyj/YRqOoiYFGOba+4LSvwrOthjM/KyMrmsanRHD2dxuxHz5METibC1D7gVw7unW1JwBQKb3cWG2OAUQtiWb37GO/2a80VYXlc8pl60rlTOOUY3L8IqoYXaYym9LJEYIyXzYzax+SVe3ioawR3tA3LvVBmujOh/OFYGDgT6rQp2iBNqWaJwBgvit57nBHzNtG1cSjDb2yeeyFV+OopiFvmTCjf5NqiDdKUejYdkTFecjA5lUc/XUfN4HJ8OKAt/n55/Hf84W34dRr0eBHa3lu0QRqfcMFEICK3ioglDGMK0eFTqQycsIozaZmMHxRJ1YoBuRfcMAuWvwmtB0D3F4o2SOMz8vMF3w/YISJvi0ge567GmPw6ejqNgeNXczA5lUkPdKR5rTxGEt27Cr58DBp0gVvft9FDjcdcMBGo6r1AW2AXMElEVorIUBGxu1eMKaCk02ncM341+4+fZeKQDnQIz+Pyz2NxzoQywfWg32fO3MLGeEi+mnxU9SQwG5gO1AbuAKJF5EkPxmZMqXL8TDr3TFhNfNIZPh4cSaeG1XIvePY4TOsHmg33zLJ7BYzH5aeP4DYRmQssB8oCHVX1RqA18JxnwzOmdEhOyeDej1cTd/QMEwZH0rlxaO4FszKcy0SP7YZ+U6Fao6IN1Pik/Fw+ehfwrqr+6L5RVVNE5EHPhGVM6ZGVrTw2bR07Dp1m3KD2/KlJ9dwLqsJXT8PuH+H2/0B4l6IN1Pis/CSCV4HEcysiUh6oqarxqrrUU4EZU1p8sHQHP+9M4u27Wg8RPtUAAB2zSURBVNGj2Xkm4VsxGmI+c64OajOg6AI0Pi8/fQSzgGy39SzXNmPMBazYcYQPvt/BXe3C6BuZx13DABtnw/ejoFU/534BY4pQfhKBv6qmn1txLedx0bMx5pxDJ1N5enoMTWoE8frtLfOeXWzPL868Ag26wG0f2mWipsjlJxEcEZHbzq2ISG/gqOdCMqbky8zK5slp6zmbkcVH97SjQkAerbBHdzqXiVZpYJeJGq/JTx/Bo8BUEfk3IDjzEA/yaFTGlHD//HY7a+Kd0UQb18jjlpsTe2FaX5AycM9Mu0zUeM0FE4Gq7gKuFJEg1/ppj0dlTAn2Xewhxi7fxYCO9XMfTTTlGKz4J6wZB2X8YdCXENKw6AM1xiVfo4+KyM1ASyDwXDunqo70YFzGlEgLNhzg2Rm/0rJOZf5+a4vf70w/A6vGws/vQ/ppaDPQ6RgOPk8nsjFF4IKJQET+A1QAegITgD7AGg/HZUyJ8/FPuxm1MJb29asyYXAkgWX9/rdz1/cwdxicPgjNboJrXoEal3kvWGPc5OeMoLOqthKRDar6moj8E1js6cCMKSmys5X/W7yF8St206tlLd7r3+b3SeBQLMy4zxk3qO8kaHCV12I1Jjf5SQSprr8pIlIHSMIZb8gYn5eWmcXzszbw1a8HGHxVA165tSV+Zdwu/zyT5Ew0H1AR7p0DwXW9F6wxecjP5aNfiUgV4B0gGogHpuXn4CLSS0S2ichOERmey/4hInJERGJcj4cKErwx3pSZlc3DU9bx1a8HGH5jc169LUcSyMqAWYPh1EHoP82SgCm2zntG4JqQZqmqngDmiMgCIFBVky90YBHxA8YA1wEJQJSIzFfV2BxFZ6jqExcXvjHe89birfy4/Qhv3XkF/TvW/2OBxX+F+BVwxzgIiyz6AI3Jp/OeEahqNs6X+bn1tPwkAZeOwE5VjXPdjTwd6H3RkRpTjHwZs58JP+1mSOfw3JPAmvGw9hPo8hS07lf0ARpTAPlpGloqIndJnvfH56kuzs1n5yS4tuV0l4hsEJHZIlIvtwO5JsJZKyJrjxw5UsAwjClcWxJP8sKcDXQMD+Hlm3O58mfX97D4BWhyA1zz96IP0JgCyk8ieARnkLk0ETkpIqdE5GQhvf5XQLiqtgK+BSbnVkhVx6lqpKpGVq+exxC+xhSB5JQMHvl0HcHly/Lve9pS1n3C+exs+OldmNoXQpvCXROgjF/eBzOmmMjPncUXOyXlfsD9F36Ya5v7sZPcVicAb1/kaxnjcVnZylMz1pOYfJbpQ6+iRqXA/+08dQjmDoW45dDidmeO4cA85iI2ppjJzw1l3XLbnnOimlxEAU1EJAInAfQHBuY4dm1VPTfXwW3AlgtGbIwXqCqjv9nG8m1HeOOOy2nfoOr/du74DuY+4tw5fOv70G6wjSBqSpT83EfwF7flQJxO4HXA1ed7kqpmisgTwBLAD/hEVTeLyEhgrarOB/7sGtk0EzgGDCl4FYzxrJW7knhr8RZ+TUimf4d6DDzXOawKy96AH9+BGi2gz0So0dy7wRpzEURVC/YEp0P3PVW9yzMhnV9kZKSuXbvWGy9tfMy2g6f4x9db+X7rYWoHB/LMdU25q13Y/+4VWPFPWDoS2twLN4+GsuW9G7Ax5yEi61Q11+uY8zXoXA4JgA2SYkqt5JQM3lgUy+x1CVQs588LvZpzf5fw3w8bETPNSQJX9HUmkymTn+sujCme8tNH8CFw7rShDNAG5w5jY0qdqPhjPPX5eg6fSuOBLhE83rMxVSvmmJBvx3fw5RPQsAf0/siSgCnx8nNG4N4Okwl8rqo/eygeY7wiK1sZs2wn7323nbCqFZgzrDOt61X5Y8H962DmIKjZAu7+FPxt1lZT8uUnEcwGUlU1C5yhI0SkgqqmeDY0Y4rGweRUnp6xnlVxx+jdpg6jbr+cSoFl/1gwaRdMvRsqVoN75tjloabUyE8iWApcC5ybmaw88A3Q2VNBGVMUVJWFGxP527xNpGVmM7pva+5qVzf3Sea3fwMLnwXNhnvnQqWaRR+wMR6Sn0QQ6D49paqeFpEKHozJGI87dDKVEfM28W3sIVqFBfNuvzY0qh70x4JHtsOSl2Dnt1CtCdw9BUIbF33AxnhQfhLBGRFpp6rRACLSHjjr2bCM8QxVZebafYxauIX0zGxeuqk5D3SJwN8vR4fv2RPO/QGr/wNlK8ANb0KHh61PwJRK+UkETwOzROQAIEAtwIZTNCXOvmMpDP9iAz/vTKJTRAj/uKsV4aEVf19IFTbNcQaNS0mCdoPg6r9BkI1xZUqv/Iw1FCUizYFmrk3bVDXDs2EZU7gWb0zkr3M2oApv3HE5AzrUp0yZHH0ByfudfoDtX0Pd9nDfF1C7tXcCNqYI5ec+gseBqaq6ybVeVUQGqOpHHo/OmEuUmpHF6wtimbp6L63DgvlwQDvqV8vRxZWdDesmwrd/B82CG/4POj1iI4can5GfpqGHVdV9cprjIvIwYInAFGs7D5/iiWnr2XrwFEO7NeT565sR4J+jLyBxA3z9Iuz5CSK6O4PGhUR4J2BjvCQ/icBPRERdgxK5pqC0HjNTbKkqs9Yl8PcvN1M+wI+J93egZ7Mavy90KBaWvwlbvoLAYGeYiLb32aihxiflJxF8DcwQkf+61h8BFnsuJGMu3qnUDEbM28SXMQe4smEI7/dvS83KbvMGHNkOP7wFm76AgCDo/gJc+RiUz+UuYmN8RH4SwQvAUOBR1/oGnCuHjClWNiYk8+Tn0ew9lsJz1zXlsZ6N/zdS6Jkk+H4kRE8B//Lwp2fhqiegQoh3gzamGMjPVUPZIrIaaATcDYQCczwdmDH5pap88nM8by3eQmhQOaYPvYqOEa4v+OwsZxL570dB2ino9Cj86TmoGOrdoI0pRvJMBCLSFBjgehwFZgCoas+iCc2YC8vMyuapGTEs3JDItZfV5J0+rf43WujeVbDoeTi4ESK6wY1vQw0bQd2YnM53RrAVWAHcoqo7AUTkmSKJyph8yM5W/jpnAws3JPJCr+Y82r2hM05QyjH4ZgTETIXKYdB3MrTobR3BxuThfIngTpx5hpeJyNfAdJw7i43xOlVl5IJYvojez3PXNWVYj0bOXcEbZsLXwyE1Gbo+A93+AgEVL3xAY3xYnolAVecB80SkItAbZ6iJGiIyFpirqt8UUYzG/MEHS3cy6Zd4HuwawRNXN4bj8bDgWdi1FOpGwm0fQM2W3g7TmBLhglMrqeoZVZ2mqrcCYcB6nCuJLkhEeonINhHZKSLDz1PuLhFREcl1Pk1j3E36eTfvfredPu3DePmmy5CoCTDmSti3Bm4aDQ9+Y0nAmAIo0JzFqnocGOd6nJfrxrMxwHU48xxHich8VY3NUa4S8BSwuiCxGN80d30Cr34Vy/UtavLWHZdTZvkbziihTa6HW96D4LreDtGYEseTk612BHaqapyqpuP0MfTOpdzrwD+AVA/GYkq47Gzl/e928OzMX+ncqBof9G+D/3cjnCTQbhAMmG5JwJiL5MlEUBfY57ae4Nr2GxFpB9RT1YUejMOUcMkpGTw4OYp3v9vOHW3q8vF97Qj8+hlY9RF0Gga3fmADxBlzCQrUNFSYRKQM8C9gSD7KDsW5u5n69et7NjBTrGw+kMywz6JJTD7L67dfzr2RtZEvH4ONs5wrgnq+bJeFGnOJPHlGsB+o57Ye5tp2TiXgcmC5iMQDVwLzc+swVtVxqhqpqpHVq9sEIb7ii+gE7vzoF9Iys5g+9Cru61AHmX2/kwSufRWuHmFJwJhC4MkzgiigiYhE4CSA/sDAcztVNRlnuAoARGQ58LyqrvVgTKYESM/MZtTCWKas3EOniBD+PbAd1SuWhXnDYOsC6PUPuPLRCx/IGJMvHksEqpopIk8ASwA/4BNV3SwiI4G1qjrfU69tSq7Dp1J5fGo0UfHHeahrBMNvbI5/GYElL8OG6dBzhCUBYwqZR/sIVHURsCjHtlfyKNvDk7GY4m/dnuMM+2wdJ1MzeL9/G3q3cV1bsOKfsGqMM2Bct+e9G6QxpZDXOouNOUdVmbp6L699tZnaweWZ/EBHLqtd2dm5bhIsHQlX3O1MIWl9AsYUOksExqtUlbeXbGPs8l30aFad9/u1JbhCWWdn7HxY8Aw0vg5u/wjKePLaBmN8lyUC4zWqyhsLtzDhp90M7FSf13tf/r+JZDbMhC8fd8YNunsy+JX1brDGlGKWCIxXZGcrr321mckr9zCkczh/v7WFM4R0drYzl/CP70CDrtD/Mxs91BgPs0Rgilx2tvLyvI18vmYfD/8pgpduusxJAukpMO9RiP3SmUj+5n+Bf4C3wzWm1LNEYIpUVrbywpwNzF6XwOM9G/H89c2cJHAyEaYPgAMxcP0oZz5h6xg2pkhYIjBFZt+xFP4y+1dWxR3jmWub8udrGjtJYNcymPcYpJ2EAZ9Dsxu9HaoxPsUSgfE4VeXzNft4Y2EsIsLbfVpxd2Q9OH3YuVFs40wIaQj3zIJal3s7XGN8jiUC41GJyWd5Yc5Gftx+hC6Nq/GPu1oRFhwIaz+B716FjLPQ/QXo+iyUDfR2uMb4JEsExmOWbD7I87N+JTNLeb13S+7p1IAySTvgk8cgIQrC/wS3vAuhTbwdqjE+zRKB8Yj5vx7gmRkxXF43mPf7tSE8tCLETIOFz4F/INz+H2jd3zqEjSkGLBGYQjdv/X6enRlDZHgIE4d0oKKkwdxh8Os05yzgzvFQuba3wzTGuFgiMIVqzroE/jL7VzpGhPDJkA5UOL4NZg2BozucvoDuL9hsYsYUM5YITKGZuXYfL8zZQOdG1ZhwXyTlf50MS16CwGAY9CU07O7tEI0xubBEYArFtNV7eXneRro2DmX8HWEEzuoPO7+FRlfDHf+FoBreDtEYkwdLBOaSZGc7o4f+5wdn9NBx7fYSML4fZKTCTaMh8kEbNdSYYs4SgbloZ9OzeHZmDIs3HeSB9lUYwX8oM3cO1G0Pd4yD0MbeDtEYkw+WCMxFOXwylYenrGXD/mTGdzzItXHPIilHnakkuz4DfvbRMqaksP+tpsC2JJ7kwUlR+J09wspG86i1YTHUvAIGzoA6bbwdnjGmgCwRmHxTVWavS+CVLzfRP+BnXi4/Bf/Es3D136DLUzZ5jDEllEcTgYj0At4H/IAJqvpWjv2PAo8DWcBpYKiqxnoyJnNxTqVmMGLeJn6O2cK04E9omxYFdTrBbf+G6k29HZ4x5hJ4LBGIiB8wBrgOSACiRGR+ji/6aar6H1f524B/Ab08FZO5OBsSTvDk5+tpcHwVP1QaT4WsM9DrH9BxqF0RZEwp4Mkzgo7ATlWNAxCR6UBv4LdEoKon3cpXBNSD8ZgCyszKZuLP8by7ZBMvBc7h3oB5UKU59FkANVt4OzxjTCHxZCKoC+xzW08AOuUsJCKPA88CAcDVuR1IRIYCQwHq169f6IGa31NVvt50kHeWbCMjKY6Flf9LRNpWaD8Ebvg/CKjg7RCNMYXI653FqjoGGCMiA4ERwOBcyowDxgFERkbaWYMHrdyVxFuLt8D+dfwlaAXXV/iJMpSFvpOg5R3eDs8Y4wGeTAT7gXpu62GubXmZDoz1YDzmPPYdS+HNuaupFvcl7wQsp2m53SgVkFZ3Qbe/QtUG3g7RGOMhnkwEUUATEYnASQD9gYHuBUSkiarucK3eDOzAFLl50QlEf/kho5lExbKpZFe/HDr8E7nibgis7O3wjDEe5rFEoKqZIvIEsATn8tFPVHWziIwE1qrqfOAJEbkWyACOk0uzkPGc5LMZjPpiDZ23vsFIv59JDesKN46kTJ12NmGMMT7Eo30EqroIWJRj2ytuy0958vVN3lbHJTF2+jxeSXuHcL9DZPd4mcBuz9lcAcb4IK93FpuitfvoGcZ+v5WAXz9lXNnPoGIIZe7+CsK7ejs0Y4yXWCIo7dJOwa7vSYrfwJ6t0VQ4sYNRkkhA2UwyG16D/13joGKot6M0xniRJYJSLGNfNFkzBhF4eh/VgBStQWrVJmQ27U1AeCT+l/W2O4ONMZYIShNVJe7oGVZsO0yZ6In0T/qIY1TmFX2Jyzpez+AeLagXVM7bYRpjihlLBCWYqrLryGlW7z7GGtfjZPJx3iz7Mb39fmF75Ss5cPV7vNeiCUHl7J/aGJM7+3YoYfYdS2HFjqOs2HGE1buPcexMOgA1KpWjb62DDPV/h8ope+HqV2ja5RmaWtOPMeYCLBEUY1nZyp6kM2w9eIqVu5JYseMI8UkpANQJDqRHs+p0rRdI9/QfCdk2Ddm3HoJqwqD5EPEnL0dvjCkpLBEUE6rK5gMnWbHjKFsPnmT7odPsOnKa9MxsACoE+HFVw2oMuaoB3cOE8Oy9yOZJsGwWpJ+GGi3gxneg1d1Qvop3K2OMKVEsEXhRakYWK3cl8d2WQ3y/9TCJyakA1K1SniY1g+jVQGgXsJfG2bupkbYHv2M7YcVOSEt2DuBfHi6/0xkVNKyD3Q1sjLkolgiKSOaZ4+yLi+Vg/FbOJG5Hju/GP+Uwadn+dJJAbqhSmZqXVaNetSAqJO+ExA2w5+D/DlCpDoQ2hiv6QGgTqNYEwiLt178x5pJZIvAQVWXb7r0cWTWN2rvn0jhjGxFAhGv/iTJVSKlQk8oBSgU5TJmMs7A/BfakOV/0DXtA7VZQuzXUugICg71XGWNMqWaJoBBlZmWzfEsi8au/JDxhPn/KjqK5ZLK7TDjL6gylYt0W1Iq4jLoRLahSvjL2W94YUxxYIigEh06mMiNqH5tXLeH5tI+4tsx+TvkFsye8P9W6DCGiUSQR1n5vjCmmLBFcJFVl5a4kPl21h19id/OXMtP4r/9SzgbVJevGiVRqcSuV/Mp6O0xjjLkgSwQFlJaZxfyYA3z80262HjzFXeWj+aniJIIyj8GVT1C+x4tQLsjbYRpjTL5ZIsinpNNpTF29lykr93D0dBrXhp7g4/ozqXt4OVS7Am6dBXXbeTtMY4wpMEsE+RAVf4wHJ0VxMjWTWxv581LEAmrtnIGkV4DrXocrHwM/eyuNMSWTfXtdwPdbDzHss2gaBpdhaYf1VP91LGSmQocHofsLNpa/MabEs0RwHl9EJ/CX2Ru4I/QA/9B38YtKgOa3wLWvOTd3GWNMKWCJIA8TVsQxamEsr9b8mcGnxiOVa8P9i6FBZ2+HZowxhcqjYxSLSC8R2SYiO0VkeC77nxWRWBHZICJLRaSBJ+PJD1XlnSVb+dfC9cwK/ZghyR8hja+BR360JGCMKZU8dkYgIn7AGOA6IAGIEpH5qhrrVmw9EKmqKSIyDHgb6OepmC5EVXlj4RaW/fwTy4LHUOPMXrj6b9D1WZvS0RhTanmyaagjsFNV4wBEZDrQG/gtEajqMrfyq4B7PRjPeZ1LAit+/oFF5UcS4FceufcLaNTTWyEZY0yR8GQiqAvsc1tPADqdp/yDwOLcdojIUGAoQP369Qsrvt+oKm8u2sL0nzbzQ/AYAspWQh5aClXqFfprGWNMcVMs2jtE5F4gEngnt/2qOk5VI1U1snr16oX62ueSwPgVccyoNY2Q9ANIn4mWBIwxPsOTiWA/4P5tGuba9jsici3wMnCbqqZ5MJ4/UFX+b/FWxq/YzUeNo2h54nvkmr9BeJeiDMMYY7zKk4kgCmgiIhEiEgD0B+a7FxCRtsB/cZLAYQ/GkqsZUfsY92McL7U6zY0HxkDTG6HzU0UdhjHGeJXHEoGqZgJPAEuALcBMVd0sIiNF5DZXsXeAIGCWiMSIyPw8Dlfojp5O4/8Wb+XqBv48fHCkc5/AHWPt6iBjjM/x6A1lqroIWJRj2ytuy9d68vXP581FWzibns6H5SYgRw/DA0ugfFVvhWOMMV7jk3cW/7LrKF9E72dSszVU3LMMbv6njRxqjPFZPtcOkpaZxYh5m+hR5TDdE8Y6YwdFPujtsIwxxmt87oxg3A9xJBw5wfyaYxG/KnDr+2DTSBpjfJhPJYL4o2f4cNlOPqq9iKDj22DgTBtG2hjj83ymaUhV+duXm+jit4Vrjs+EyAeg6Q3eDssYY7zOZ84IFmxIJGbHXlZW+S9SoSFcP8rbIRljTLHgM2cEweXLMj50OhXTjsCd4yCgordDMsaYYsFnzgi6pa+A099B9+EQFuntcIwxptjwmTMCyleBZjdDt+e9HYkxxhQrPnNGQKOrnYcxxpjf8Z0zAmOMMbmyRGCMMT7OEoExxvg4SwTGGOPjLBEYY4yPs0RgjDE+zhKBMcb4OEsExhjj40RVvR1DgYjIEWBPPoqGAkc9HE5RKk31KU11gdJVn9JUF7D6uGugqtVz21HiEkF+ichaVS01gwqVpvqUprpA6apPaaoLWH3yy5qGjDHGx1kiMMYYH1eaE8E4bwdQyEpTfUpTXaB01ac01QWsPvlSavsIjDHG5E9pPiMwxhiTD5YIjDHGx5XKRCAivURkm4jsFJHh3o6noETkExE5LCKb3LaFiMi3IrLD9beqN2PMLxGpJyLLRCRWRDaLyFOu7SWuPiISKCJrRORXV11ec22PEJHVrs/bDBEJ8HasBSEifiKyXkQWuNZLbH1EJF5ENopIjIisdW0rcZ81ABGpIiKzRWSriGwRkas8VZdSlwhExA8YA9wItAAGiEgL70ZVYJOAXjm2DQeWqmoTYKlrvSTIBJ5T1RbAlcDjrn+PklifNOBqVW0NtAF6iciVwD+Ad1W1MXAceNCLMV6Mp4AtbuslvT49VbWN2/X2JfGzBvA+8LWqNgda4/wbeaYuqlqqHsBVwBK39ReBF70d10XUIxzY5La+DajtWq4NbPN2jBdZry+B60p6fYAKQDTQCedOT3/X9t99/or7AwhzfaFcDSwApITXJx4IzbGtxH3WgGBgN64Lejxdl1J3RgDUBfa5rSe4tpV0NVU10bV8EKjpzWAuhoiEA22B1ZTQ+riaUWKAw8C3wC7ghKpmuoqUtM/be8BfgWzXejVKdn0U+EZE1onIUNe2kvhZiwCOABNdzXYTRKQiHqpLaUwEpZ46PwdK1HW/IhIEzAGeVtWT7vtKUn1UNUtV2+D8ku4INPdySBdNRG4BDqvqOm/HUoi6qmo7nKbhx0Wkm/vOEvRZ8wfaAWNVtS1whhzNQIVZl9KYCPYD9dzWw1zbSrpDIlIbwPX3sJfjyTcRKYuTBKaq6heuzSW2PgCqegJYhtN0UkVE/F27StLnrQtwm4jEA9Nxmofep+TWB1Xd7/p7GJiLk6xL4mctAUhQ1dWu9dk4icEjdSmNiSAKaOK68iEA6A/M93JMhWE+MNi1PBinrb3YExEBPga2qOq/3HaVuPqISHURqeJaLo/T17EFJyH0cRUrEXUBUNUXVTVMVcNx/p98r6r3UELrIyIVRaTSuWXgemATJfCzpqoHgX0i0sy16RogFk/VxdudIh7qaLkJ2I7Tfvuyt+O5iPg/BxKBDJxfBg/itN0uBXYA3wEh3o4zn3XpinP6ugGIcT1uKon1AVoB61112QS84treEFgD7ARmAeW8HetF1K0HsKAk18cV96+ux+Zz//dL4mfNFXcbYK3r8zYPqOqputgQE8YY4+NKY9OQMcaYArBEYIwxPs4SgTHG+DhLBMYY4+MsERhjjI+zRGBMDiKS5Rq98tyj0AYpE5Fw91FljSkO/C9cxBifc1adYSSM8Ql2RmBMPrnGun/bNd79GhFp7NoeLiLfi8gGEVkqIvVd22uKyFzX/AW/ikhn16H8RGS8a06Db1x3KRvjNZYIjPmj8jmahvq57UtW1SuAf+OM3AnwITBZVVsBU4EPXNs/AH5QZ/6Cdjh3uwI0AcaoakvgBHCXh+tjzHnZncXG5CAip1U1KJft8TgT08S5BtI7qKrVROQozhjxGa7tiaoaKiJHgDBVTXM7RjjwrToTiyAiLwBlVXWU52tmTO7sjMCYgtE8lgsizW05C+urM15micCYgunn9nela/kXnNE7Ae4BVriWlwLD4LcJbYKLKkhjCsJ+iRjzR+Vds5Cd87WqnruEtKqIbMD5VT/Ate1JnJmk/oIzq9T9ru1PAeNE5EGcX/7DcEaVNaZYsT4CY/LJ1UcQqapHvR2LMYXJmoaMMcbH2RmBMcb4ODsjMMYYH2eJwBhjfJwlAmOM8XGWCIwxxsdZIjDGGB/3/zEb6gX1glNvAAAAAElFTkSuQmCC\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"CADXYOKcRZZa","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1605483625554,"user_tz":300,"elapsed":96043,"user":{"displayName":"Avelyn Wong","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gg0Gx_4UO4Canl0bxQX9l2YCVEf9TrzDq31I9-c9A=s64","userId":"04927963856256154784"}},"outputId":"b72eaf4c-a3c7-4d19-9927-b3ce83a62400"},"source":["#increasing learning rate\n","\n","model4 = BaseANN()\n","use_cuda = False\n","\n","batch_size = 128\n","train_loader, val_loader, test_loader, classes = get_data_loader(batch_size)\n","\n","train_acc, val_acc, train_loss, val_loss = train(model4, train_loader, val_loader, batch_size=128, num_epochs=60, learning_rate = 0.00002)\n","plot_training_curve(train_acc, val_acc, train_loss, val_loss)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Training Started...\n","U S I N G   C U D A \n","epoch:  0 training acc:  0.23333333333333334 val acc:  0.21777777777777776 training loss:  6.60860979718131 val loss:  4.2134082689881325\n","epoch:  1 training acc:  0.29947089947089944 val acc:  0.28271604938271605 training loss:  3.2781028618683687 val loss:  3.253260873258114\n","epoch:  2 training acc:  0.20851851851851852 val acc:  0.19012345679012346 training loss:  2.385628477947132 val loss:  2.2266306579113007\n","epoch:  3 training acc:  0.27174603174603174 val acc:  0.24074074074074073 training loss:  2.0625038308066292 val loss:  2.130404584109783\n","epoch:  4 training acc:  0.32915343915343914 val acc:  0.3037037037037037 training loss:  1.9632926599399463 val loss:  2.0355150252580643\n","epoch:  5 training acc:  0.36756613756613754 val acc:  0.3362962962962963 training loss:  1.8699643281666007 val loss:  1.9429389461874962\n","epoch:  6 training acc:  0.4 val acc:  0.3671604938271605 training loss:  1.7821109931211214 val loss:  1.8628149405121803\n","epoch:  7 training acc:  0.42846560846560844 val acc:  0.38814814814814813 training loss:  1.6987933121822976 val loss:  1.8075582459568977\n","epoch:  8 training acc:  0.45285714285714285 val acc:  0.42148148148148146 training loss:  1.625614886348312 val loss:  1.7529821023344994\n","epoch:  9 training acc:  0.4801058201058201 val acc:  0.44296296296296295 training loss:  1.5492029447813291 val loss:  1.6796405911445618\n","epoch:  10 training acc:  0.5088888888888888 val acc:  0.46987654320987654 training loss:  1.4762603270040977 val loss:  1.6122341081500053\n","epoch:  11 training acc:  0.5407936507936508 val acc:  0.4928395061728395 training loss:  1.4052803741919029 val loss:  1.5684168860316277\n","epoch:  12 training acc:  0.5646031746031746 val acc:  0.5192592592592593 training loss:  1.3402051385995504 val loss:  1.5211875438690186\n","epoch:  13 training acc:  0.5841798941798941 val acc:  0.5320987654320988 training loss:  1.2776393125186096 val loss:  1.476207535713911\n","epoch:  14 training acc:  0.6063492063492063 val acc:  0.5498765432098766 training loss:  1.2132877087270892 val loss:  1.423488724976778\n","epoch:  15 training acc:  0.638994708994709 val acc:  0.5874074074074074 training loss:  1.139531107367696 val loss:  1.374552182853222\n","epoch:  16 training acc:  0.6667195767195767 val acc:  0.6187654320987654 training loss:  1.061650042195578 val loss:  1.2914814613759518\n","epoch:  17 training acc:  0.6921164021164021 val acc:  0.6308641975308642 training loss:  0.9943706207984203 val loss:  1.2673887498676777\n","epoch:  18 training acc:  0.7060846560846561 val acc:  0.6469135802469136 training loss:  0.9376035989136309 val loss:  1.2197767160832882\n","epoch:  19 training acc:  0.7257671957671957 val acc:  0.6622222222222223 training loss:  0.8913874291890377 val loss:  1.1722194198518991\n","epoch:  20 training acc:  0.7423809523809524 val acc:  0.6767901234567901 training loss:  0.8478858333987158 val loss:  1.1462133638560772\n","epoch:  21 training acc:  0.7541269841269841 val acc:  0.6928395061728395 training loss:  0.8072925250272494 val loss:  1.1151029355823994\n","epoch:  22 training acc:  0.764973544973545 val acc:  0.7069135802469135 training loss:  0.771658051255587 val loss:  1.0754075981676579\n","epoch:  23 training acc:  0.7767724867724868 val acc:  0.7185185185185186 training loss:  0.7373565934799813 val loss:  1.0359656680375338\n","epoch:  24 training acc:  0.7838095238095238 val acc:  0.7298765432098765 training loss:  0.7066323934374629 val loss:  1.0334147233515978\n","epoch:  25 training acc:  0.7942857142857143 val acc:  0.7390123456790123 training loss:  0.6805798270412393 val loss:  0.997663764283061\n","epoch:  26 training acc:  0.7961375661375661 val acc:  0.7437037037037038 training loss:  0.6548691063716605 val loss:  0.9984368495643139\n","epoch:  27 training acc:  0.8067195767195767 val acc:  0.7503703703703704 training loss:  0.6318584974553134 val loss:  0.9752444345504045\n","epoch:  28 training acc:  0.8108465608465608 val acc:  0.7565432098765432 training loss:  0.6084943015027691 val loss:  0.9535565935075283\n","epoch:  29 training acc:  0.8164550264550264 val acc:  0.7627160493827161 training loss:  0.5873324875090573 val loss:  0.9358003363013268\n","epoch:  30 training acc:  0.8241269841269842 val acc:  0.768641975308642 training loss:  0.5677353190006437 val loss:  0.907821711152792\n","epoch:  31 training acc:  0.8250793650793651 val acc:  0.7760493827160494 training loss:  0.546152413092755 val loss:  0.8997602928429842\n","epoch:  32 training acc:  0.8328571428571429 val acc:  0.7846913580246914 training loss:  0.5265653242130537 val loss:  0.8777827825397253\n","epoch:  33 training acc:  0.8421693121693121 val acc:  0.7920987654320988 training loss:  0.5118222693736488 val loss:  0.8528352659195662\n","epoch:  34 training acc:  0.8391005291005291 val acc:  0.7913580246913581 training loss:  0.4974522351010426 val loss:  0.8504848629236221\n","epoch:  35 training acc:  0.8458201058201058 val acc:  0.797037037037037 training loss:  0.4862804600113147 val loss:  0.8350963424891233\n","epoch:  36 training acc:  0.8526455026455027 val acc:  0.8034567901234568 training loss:  0.4711482510776133 val loss:  0.8136768825352192\n","epoch:  37 training acc:  0.8603703703703703 val acc:  0.8076543209876543 training loss:  0.4593099081838453 val loss:  0.7967769429087639\n","epoch:  38 training acc:  0.864021164021164 val acc:  0.8135802469135802 training loss:  0.443555702832905 val loss:  0.7771802973002195\n","epoch:  39 training acc:  0.8702645502645503 val acc:  0.8153086419753086 training loss:  0.4323608454417538 val loss:  0.759801683947444\n","epoch:  40 training acc:  0.8782539682539683 val acc:  0.8234567901234567 training loss:  0.4195187798625714 val loss:  0.7386875385418534\n","epoch:  41 training acc:  0.8792063492063492 val acc:  0.8219753086419753 training loss:  0.41004492241788554 val loss:  0.7372196046635509\n","epoch:  42 training acc:  0.8832804232804233 val acc:  0.8269135802469135 training loss:  0.39937752142951294 val loss:  0.7183807166293263\n","epoch:  43 training acc:  0.8833862433862434 val acc:  0.8296296296296296 training loss:  0.3878940922787061 val loss:  0.7260130327194929\n","epoch:  44 training acc:  0.8913756613756614 val acc:  0.8301234567901234 training loss:  0.3812846508179162 val loss:  0.7081085834652185\n","epoch:  45 training acc:  0.8958201058201058 val acc:  0.8333333333333334 training loss:  0.3688103838546856 val loss:  0.6988796899095178\n","epoch:  46 training acc:  0.8938095238095238 val acc:  0.8365432098765432 training loss:  0.3652808469896381 val loss:  0.6957030082121491\n","epoch:  47 training acc:  0.9024867724867724 val acc:  0.8404938271604938 training loss:  0.35096441158974495 val loss:  0.6830334113910794\n","epoch:  48 training acc:  0.9055026455026455 val acc:  0.8429629629629629 training loss:  0.34432557879670245 val loss:  0.6728543266654015\n","epoch:  49 training acc:  0.9113756613756614 val acc:  0.8508641975308642 training loss:  0.3383611623902579 val loss:  0.6600607186555862\n","epoch:  50 training acc:  0.9129100529100529 val acc:  0.8491358024691358 training loss:  0.32478713707343954 val loss:  0.6486301068216562\n","epoch:  51 training acc:  0.916984126984127 val acc:  0.851358024691358 training loss:  0.3185625201141512 val loss:  0.6400376195088029\n","epoch:  52 training acc:  0.9183068783068783 val acc:  0.8538271604938271 training loss:  0.31138685224829493 val loss:  0.6380558423697948\n","epoch:  53 training acc:  0.9217989417989418 val acc:  0.8530864197530864 training loss:  0.3031457492427246 val loss:  0.6341553339734674\n","epoch:  54 training acc:  0.9224338624338624 val acc:  0.8575308641975309 training loss:  0.2935448097618851 val loss:  0.6112292073667049\n","epoch:  55 training acc:  0.9252910052910053 val acc:  0.860246913580247 training loss:  0.2876405418925994 val loss:  0.6201129099354148\n","epoch:  56 training acc:  0.9261375661375661 val acc:  0.8644444444444445 training loss:  0.2797359204976945 val loss:  0.6052301125600934\n","epoch:  57 training acc:  0.9251322751322751 val acc:  0.8634567901234568 training loss:  0.27020080307045496 val loss:  0.6008574245497584\n","epoch:  58 training acc:  0.9307936507936508 val acc:  0.8679012345679012 training loss:  0.26162393854276556 val loss:  0.5946442876011133\n","epoch:  59 training acc:  0.9319047619047619 val acc:  0.8688888888888889 training loss:  0.255886241894316 val loss:  0.5792885422706604\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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GdSK+CD+7KmpVmyiyMy5qWnpzNjxoxkF2NMS5kmmpxgRMmWTrXDi4hACgV8VqR7THg104iIQAoFvIYMFhHZX8oE/L4hg9VEIyICKRXwvg1eNXgRES91Ar67DV5Xs4qIAKkU8GqDFxHZT8oEfI7a4EVE9pMyAd/dBq+rWUVEvJQJ+O5+8BpRUkTES5mAD6UZmekacExEpFvKBDz4E626bZ+IiJdSAZ8TVQ1eRKRbSgV8ViSsfvAiIoGUCvicqO7LKiLSLaUCPjsaUj94EZFAigW87uokItItoXd0MrNyoBGIAzHn3NJEri87ElY/eBGRwHDcsu8c51z1MKwnqMGriUZEBFKsiSYnGqK5I6a7uIuIkPiAd8CTZvaGmV3X1wJmdp2ZrTSzlUd69/XsaBjnoKVDtXgRkUQH/OnOuSXA+cBnzezMAxdwzt3unFvqnFtaWlp6RCvLimo8GhGRbgkNeOfcjuC5EngIODGR68vpGVFSNXgRkYQFvJllm1lu9zTwIeCdRK0P9t3VSV0lRUQS24tmPPCQmXWv517n3J8TuL6em37oalYRkQQGvHNuC7AwUd/fl+42eI1HIyKSgt0kAQ0ZLCJCigV8dlRt8CIi3VIq4LN0klVEpEdKBXx2RN0kRUS6pVTAh0NpZKSn6UInERFSLOBBN/0QEemWcgGfFdGY8CIikIIBryGDRUS8lAv4nGhINXgREVIw4LOjuquTiAikYsBHdJJVRARSMeCjIVrUBi8ikooBr140IiKQggGfE7TB676sIjLWpVzAZ0XCdDlo7VQzjYiMbSkX8Lptn4iIl3IBryGDRUS81Ah45yDWDuwLeHWVFJGxbvQHfLwT/nMGvHgzoBtvi4h0G/0BH0qHrBKoXAv4fvAALR1qgxeRsW30BzzAuHlQuQ7w3SRBTTQiIikS8POhdgt0tuokq4hIIOEBb2YhM/ubmT2asJWMmwc4qFq/rw1eTTQiMsYNRw3+i8C6hK5h3Hz/XLmupw1eNXgRGesSGvBmVgZ8FPhFItdD0UwIRaFyLeFQGtFwmgJeRMa8RNfgbwG+BnQdbAEzu87MVprZyqqqqsGtJRSG0tn7nWjVSVYRGesSFvBmdgFQ6Zx741DLOedud84tdc4tLS0tHfwKx83vCfisaEjdJEVkzEtkDf404EIzKwfuB841s98kbG3j5kHDDmit000/RERIYMA75250zpU556YDVwHPOuf+PlHr632iNUdjwouIpEg/eOgV8Gt10w8REYYp4J1zzzvnLkjoSvLLIJLb01VS/eBFZKxLnRq8Wc+QBdkR1eBFRFIn4CEI+LVkR0I6ySoiY16KBfx8aK1lXFodze26L6uIjG2pFfDj/YnWss5yuhy0xw56fZWISMrrV8CbWbaZpQXTs83sQjNLT2zRBiHoSTOxfSugIYNFZGzrbw3+RSDDzCYDTwKfBO5OVKEGLbsEskspadkCaMAxERnb+hvw5pxrAS4Bfuqcuxw4JnHFOgLj5lHYtAlQDV5ExrZ+B7yZnQJcDTwWzAslpkhHaNx8chs3YXRpPBoRGdP6G/BfAm4EHnLOrTGzmcBziSvWERg3n1CshTKrUg1eRMa0cH8Wcs69ALwAEJxsrXbOfSGRBRu04ETrHKtQG7yIjGn97UVzr5nlmVk28A6w1sy+mtiiDVLpHABm23YFvIiMaf1topnvnGsAPg48DszA96QZeTLy6Mqbwpy0Cprb1QYvImNXfwM+Pej3/nHgEedcJzByLxMdP081eBEZ8/ob8LcB5UA28KKZTQMaElWoI5U2/hhm2U5a2tqSXRQRkaTpV8A75251zk12zn3EeduAcxJctsEbN5+Ixcls2JLskoiIJE1/T7Lmm9l/d98c28z+C1+bH5nGzQOgoGlzkgsiIpI8/W2iuRNoBK4IHg3AXYkq1BErPpo4aRQ1K+BFZOzqVz94YJZz7tJer79rZqsSUaAhkZ5BZXoZhU0bk10SEZGk6W8NvtXMTu9+YWanAa2JKdLQaMydxcTO92jrVFdJERmb+luDvx74lZnlB6/3AtckpkhDI23cXKbWvMC6ndUsmDY+2cURERl2/e1F85ZzbiFwHHCcc24xcG5CS3aE8qccQ9i62LH5nWQXRUQkKQZ0RyfnXENwRSvAlxNQniFTPH0BAPXvrUlySUREkuNIbtlnh3zTLMPMXjOzt8xsjZl99wjWNWBppbPpwqD63eFcrYjIiNHfNvi+HG6ognbgXOdcUzDMwV/M7HHn3CtHsM7+i2RRF5lIXuNmnHOYHXJ/JCKScg4Z8GbWSN9BbkDmoT7rnHNAU/AyPXgM6/g1rfmzmL5nGxV7W5lSlDWcqxYRSbpDNtE453Kdc3l9PHKdc4et/ZtZKOgvXwk85Zx7dagK3h/pE+Yx03axZsfe4VytiMiIcCRt8IflnIs75xYBZcCJZnbsgcuY2XXdQyBUVVUN6foLph5L1DrZuXX9kH6viMhokNCA7+acq8Pf4u/Dfbx3u3NuqXNuaWlp6ZCuNzLBj0nTvEM9aURk7ElYwJtZqZkVBNOZwAeB4a1Kl8wGIK1GQxaIyNhzJL1oDmci8EszC+F3JA845x5N4PreL7OA5kgJ41vLqW/tJD8zfVhXLyKSTAkLeOfcamBxor6/vzoKZ3NU2w7W7Wrg5JnFyS6OiMiwGZY2+GSKTpzLLNvJup31yS6KiMiwSvmAz5w0n1xrZcd7GhteRMaWlA94K50LQNuudUkuiYjI8Er5gKd0DgDRuk10xruSXBgRkeGT+gGfXUpHeh4zXQVbqpqTXRoRkWGT+gFvRrx4Dkel7WDtLp1oFZGxI/UDHohOnMdRtpO1OxsOv7CISIoYEwGfVjqHYmtge0VFsosiIjJsxkTAE/Sk6dyzDj+KsYhI6hsjAe/HpBnXvo3KxvYkF0ZEZHiMjYDPKyMezuRo26F2eBEZM8ZGwKelQclsjrIdrN2lgBeRsWFsBDwQGjeXOeFdCngRGTPGTMBTOofxrpr15Tt1RauIjAljJ+BL/JAF2Y1b+P0b6i4pIqlv7AR8MCbNuSV1/PjZTbTH4kkukIhIYo2dgC+cAWnpXDKlkR11rfz29e3JLpGISEKNnYAPhaH4KKbEt3Pi9CJ+8uwm2jpVixeR1DV2Ah5gwrHY5ue5eeIz1DU28ZtXtiW7RCIiCTO2Av6D34NZ5zL1bz/kxexvsPa5+2hu60x2qUREEmJsBXzeJFh2L3zyIfJzc/jvrv+k9raPQqXu9iQiqWdsBXy3WeeS+fmXuafwn8jf+w7uZ6fBn74KLbXJLpmIyJBJWMCb2RQze87M1prZGjP7YqLWNSihdI679Buc1fZfrB7/cXj9F3DrYnjlfyGuZhsRGf0SWYOPAV9xzs0HTgY+a2bzE7i+AVtQls+JxxzNlTsu5w8nPUDXxEXw56/Dz06FjU8lu3giIkckYQHvnNvlnHszmG4E1gGTE7W+wfreRcdy0oxivvx8B+fXfJkN59wOXTG45zL49cWwZ02yiygiMijD0gZvZtOBxcCrw7G+gRifl8Hdnz6B2z55PE0dcf7u8Ry+UnobjWd9F3a8Cf97OjzyeWjck+yiiogMiCX6DkdmlgO8APzAOfeHPt6/DrgOYOrUqcdv25a8vumtHXF+9vwm/veFLUTCafzz6SVc0/kA4ZV3QCgCp38JTv4niOYkrYwiIr2Z2RvOuaV9vpfIgDezdOBR4Ann3H8fbvmlS5e6lStXJqw8/VVe3cz3H1vH0+v2MLkgk++dkcm5FT/F1j0CmYU+5E+8DjILkl1UERnjkhLwZmbAL4Fa59yX+vOZkRLw3V7aVM33H1vH2l0NLJ5awP85sYN5794G7z4O0Tw44TNwymchuyTZRRWRMSpZAX86sAJ4G+gegP2bzrk/HewzIy3gAeJdjt+/WcHNT2ygsrGd84+dwI1LYkxd8zNY8zCkZ8L8j8MxH4eZ50A4kuwii8gYkrQmmoEaiQHfrbk9xs9XbOHnL26htTPOZceX8ZUlxvi3b4e1j0B7PUTzYe5H4JiLFfYiMiwU8EOopqmdnz6/mV+/7E8G//3J0/inM6dQsudlWPMQrH9sX9jPOX9fzT49I8klF5FUpIBPgB11rdzy1Lv8/s0KouEQnzp1GtedMZPiDIMtz8Ha/wfrH4W2eojkwpwPw4Ir4KgP+JuAi4gMAQV8Am2qbOLHz27kkbd2kpke4lOnTOe6M2dSlB2BWAeUv+jb6tc/Cq17ofgoOPkGWLgMItnJLr6IjHIK+GGwqbKRW5/ZxB9X7yQrPcTVJ09j+anTmVSQ6ReId/pa/cv/AzvfhIwCWPpp390yb1JyCy8io5YCfhh1B/1jb+8C4PxjJ/APp89g8dRCv4BzsP1VH/TrHwXMn5hdei3MOFvNNyIyIAr4JNhR18ovXyrnvtfeo7EtxpKpBXzmjJn83TETCKWZX2hvOay8E/72G2ip8feNXfppWHS1+taLSL8o4JOoqT3Ggyu3c9dL5WyraWFacRafOWMmlx9fRkZ6yC8Ua4d1f/Rhv+2vYCEoWwozz4YZZ0HZCepyKSJ9UsCPAPEux5NrdvO/L27hre11FGdHuObU6Xzy5GkUZvcK78r18PYDsOUF31bvuiA9C6adBkedB0d9EIpngVnyNkZERgwF/AjinOO1rbXc9uIWnl1fSWZ6iEuPn8ynT5vBrNIDBjFrrYPyv8DWF2Dzc1Cz0c8vnO6D/ugPwvTT1RtHZAxTwI9QG3Y3csdftvDwqp10xLo4Z04p154+g9OPKsH6qqHvLfc3Itn0jA/9zhY/yuWUk2DWOTDrXJiwUCdqRcYQBfwIV93Uzm9e2cZvXtlGdVMHc8bncs2p07l48WQyI6G+PxRrh20vweZn/YVVu9/28zOL/MVU8z7mm3RUuxdJaQr4UaKtM84f39rJnX8tZ92uBvIz07nqhCn8/cnTmFKUdegPN1X6dvvNz8C7T0BrLYQzfa1+3gUw+8OQVTQ8GyIiw0YBP8o453i9fC93v7SVJ9bswTnHefPGc/VJUznj6NJ93SwPJh6D916CdY/6vvYNO8DSfFPO0R/yYT9unk7UiqQABfwotrOulXte3cZ9r22ntrmDyQWZXL60jCuWTtl3leyhOOd742z4M7z7Z9i92s/PnwLzL4JTPgd5ExO7ESKSMAr4FNAei/P02kruf/09VmysxgzOml3KlUuncO68cUTDB2mrP1DDTtj4pA/8jU9CWthfXHXalxT0IqOQAj7FbK9t4Xcrt/PAygp2N7RRkJXOxxdN5rLjyzhmUl7fPXD6UrsVVvwXrLoXQulw/KfhtC8q6EVGEQV8iop3OVZsrOLBNyp4cu0eOmJdzJ2QyxVLp3Dx4sn7X0B1KLVbYcXNsOo+cHHImeAvpiqa6Z9LZsO0U/39aEVkRFHAjwH1LZ088tYOfvdGBasr6omE0vjgMeO56oQpnDarhLTDnZgFqN3ihzau2Qw1m6B2MzRX+fcsBFNO9F0wj/4QTFigk7QiI4ACfoxZt6uB376+nYdX7aCupZPJBZksO3EKV580rf+1+m5t9bBnre9+ufEp2LXKz8+ZEIT9B/1FVhn5Q78hInJYCvgxqq0zzlNr9/Db17fzl03VZKSncemSMq49vY9hEfqrcc++vvabn/O3J7RQ0AXzA1B2om/SyRmnGr7IMFDAC+/uaeTOv2zlD3/bQUesi3PnjuMzZ8zglJnF/T8pe6B4DCpe971xNj2172pa8PekLTkaSudA6VzfpDPhOMguHpoNEhFAAS+9VDe1c88r7/HrV8qpbupg4ZQCbjhrFh+aP75/7fSH0rgHKtdA9Uao2gDV7/pH0559y+RO8mE/aRFMPcW362s4BZFBU8DL+7R1xvnDmzu47cXNbKtpYVZpNtefNYuLFk0mEh7iwcqaa2DP276Gv/tt2LUaqjf4oZDTwjBxoQ/7aafB1JM1pILIACQl4M3sTuACoNI5d2x/PqOAH36xeBePv7Obnz6/mXW7GpiQl8GyE6dy5QlTmJCfkbgVtzXA9tf8kArbXoYdKyHe4d8rneeDftqpMPl4iOT4HUFayD+H0iEcTVzZREaRZAX8mUAT8CsF/MjnnOOFd6u44y9bWbGxmlCacd7ccVx98jTOOKqf3SyPRGebH1Jh20vw3iv+vrXtDQdfvmgmTF7q73ZVdjyMX6C7XsmYlLQmGjObDjyqgB9dttU0c99r2/ndyu3UNHcwpSiTjy+azMcWTmL2+Bw1LUMAABC9SURBVNzhKURXHPasgV1vQbzdv+6K+Udnq2/qqVgJTbv98qGIr+nj/Pg7OHBAZgEUzfA7hO5H4QwonKa2f0kJIzrgzew64DqAqVOnHr9t27aElUcGpj0W54k1e/jt6+/x8uYauhzMGZ/LBcdN5IKFk5hRkuSAdM6PlFmx0tf+O5oBC7pnBs/N1f4CrtrNvk9/b9mlUDDN3yGrYCrkTfKP3ImQN9m/r5unyAg3ogO+N9XgR66qxnYef2cXf3xrJ6+X7wVgVmk2Z80ex5mzSzh5ZvG+m4iPVC21fliGvVv93bHqtvnnvdugvsIP09BbKALFR/uhlcfN9ecGSuf4m6pEsv15APX1lyRTwMuQ2lnXyuPv7OaFd6t4dUsN7bEuIuE0TppRxBlHl3DqrBLmT8xLfLv9UOqK+2EZGnb6R+MuvwOo2uBvhF7/3vs/kxb2QR/J9TX/wun7P4pmQu4E7QQkoRTwkjBtnXFe3VrLi+9W8cK7VWyqbAKgMCudU2YVc+qsEk6dVcyMkuzBX1A1ErQ3Bn37N/qTvx1N0N7km4XaG/wRwN5t0FDhu392i+T4AduKj4bioyB3vL/yt3evoHAG5Iz3V//mjIP0fozzLxJIVi+a+4CzgRJgD3CTc+6OQ31GAT/67a5v46XN1fx1Uw0vba5mV30bAKW5UU6cUcTJM4o4aWYxR5XmjK4afn/FOqB+u28Gqt3qB22r3uif697Dn/k9jGieD/yCqcHRwLTgXME0f6FYdonfOYigC50kSZxzbK1u5pUttby6tYZXt9Syu8EHfn5mOgunFLBoSgGLg+cBD4Q22nS2QVvd/j2CXJc/Gmiq8lf8Nlf6++s27AzOEWzzn+nNQn4HkDvBnxDOnwz5Zf4uXflT/HTOOO0ExggFvIwIzjm217byytYa3ty2l1Xb63h3TyNdwZ/gtOIsjisrYGFZPgunFHDMpDyyIuHkFnokaKv3QV/3nj830Lg7eOzyj/odftC33iy0bweQN9HX/PMn+yOB7iODzEKdH0gBCngZsZraY7xdUc+q7XWs2r6X1RX1Pc06aQZHj8vlmEl5zJvoH/Mn5VGU6jX9wWir9+cB6iv27QgadkHjzuB51/svHIvk7mvusTS/U7A03zsob5LvKpo/2T/njPfXI3S0+PMOHU0Qa/M9irqXzZsI0WG6TkJ6KOBlVKlsbGP19npWV9Sxekc963Y1sKehvef98XlRjh6Xy1HjcphVms2s0hxmjcthXG50dJ/ITbS2eh/+3UcDddv8dQKua/9HZ2vQm2jHoa8m7ks0zx8ZRPN82GcEz+EMP8REWhjS0iEUhvQsyCr2Yw9lFvnpzEJ/b4Foro4u+kkBL6NebXMH63Y1sHZnA+t2NbC5qonNVc00tcd6lsmJhplVms3M0pye5xkl2UwrzlJTz2C11fsmoOYqH9KR7OCR44eGaKnxRwjdO4SGnf6cQXujf7TV+51ErB3indDV6c9BxDv9EcHBWJrfSWTk+0dW8b6dQfd0ZiFkFPirlbunM/L8jmQMUcBLSnLOUdnYzqbKJjZVNrGlqokt1c1srmxiZ9DM021cbpRpxVlMK85mes9zNtNKssjLGFuBMGLEO6F1r99JtNT659ZaPxBd946hrR5a6/z8lhr/OPCK5AOFov4IIJrjn0PRoBkqFDxbcBQR8TuDUMQ/orn7rl8omuHPV6QHA+51xf0Oq6PJH+FkFPidzQg4ka2AlzGnpSPGlqpmymua2VbTQnl1M9tqW9hW07xfcw9AUXaEqUVZTCnKYkphJmWFWUwp8s8T8zNG/hW6Y0085gO/tc7vINqC59a9+44ceq5TaPJHDy7uQ9p1Bb2YOoOjiOBIIt7pv6+jsdeKzId4Zyt0try/HJYGWSW+x1J2CaRn+6OaULTXc9Q3RaVn+Odwhj8iyRm/79qHI2yOOlTA67hVUlJWJMyxk/M5dvL77xXb0hHjvdoWyqt94JfXtPBebTOrK+p4/O1dxLr2r/SU5ESZXJjJ5IIMJuVn+h1BUSZTCrMoK8wiM6IdwLAKhfddFDaUnPNHCLVb/fhFe7f63kqR7OCIIHff+YTWvb47a3Ol7+LaXOWf4x1+hxHrfm7ve+fQWzjDXwR3w1+HdntQwMsYlBUJM3dCHnMn5L3vvXiXY3dDG9trW6jY28rOOv/YUdfK+t2NPLu+krbOrv0+U5obZXJBZrATyGRSfgaTCjIZn5dBSW6U4uyIjgJGAzNfE88ugSknDN33Orcv6Dtb/RFHU3C9Q/e1DwlqSVHAi/QSSjMf1gV9DxfgnKOqqZ3tta1U7G1he20L79W2sLOujXU7G3h67R7aY13v+1xuNExJbpSSnAjF2VFKcoPnnAhF2VHyM9P3e+RmhFPzSt+xyCxoogna8/Mnw/hjhmXVCniRATAzxuVmMC43g+OnFb7vfeccNc0d7KxrpbKhneqm7kdHz/TmqiZeK+9gb0vHQStuaQbFOVFKcvxOoDQnSklulIKsdAqzIhRkplOQFaEgK52SnChF2RFC2iHIARTwIkPIzIJQPvwtBWPxLmpbOtjb3El9ayd1LR3Ut3ZPd/bsEKoa29lS1UxVUzsdfRwdgD/yKM6OUJobpTQ3SlFWhLzgaKAgyz8XZkUozI5QlBWhMDudnGhY1w2kOAW8SJKEQ2k9RwP94ZyjrbOLvS0d1LV0Utfqdw41ze1UNvgdQVVTO5WNbWzc00RDayeNva4TOFB6yMjPjJCbESY3I0xONBxMp5OX4XcKeZlh/5zhdxQFWek9O45oWOcVRjoFvMgoYWZkRkJkRjKZdJBzBAeKxbtobItRFxwh7G3poLa5k73NHdQGO4qm9hiNbZ00tcUor26hsa2ThrbYfheR9SUzPUR2NERGeoisSIjMdD+dl5lOYVY6hdkRf9SQlU5WJEx6KI30kBEOnrMjYYpzIpTkRHUSOkEU8CIpLBxK80GbHQEGdovF7p1Dd7NRfWsnda2d1AdNSXUtnbR0xmnriNPa6R8tHXG217awusIfXXTE+25SOlB2JBScY4iQlR7yO7Jgh5EZSSMj3D0dIhpOIzMSojArQlH2vkdhls5DHEgBLyJ92n/nMHDOOVo64uxt6aC1I05n3NEZ7yLW1UVHzNHcHqOm2Z+ArmnqoKa5nb0tnbR1xKlqbPc7jWDn0RY8ug7Rm9DMH1V07xgy0tP2HV1EwmQF01nRENmRMFmRMNnRENnRMNnRMDnRELkZ6T3NVLkZYXIio7s3kwJeRBLCzHrCcyg45+iMu57A39vSQW1TBzXNHdQ2++eW9hhtsTitHV20BUcVrR1x6ls72V3fSkuHf93cEXvf9QwHEwmnkRFOC3YafgeybycRIicaJisaIiea3nM+IzcjTG40PdhxhMnJ8DuTnGiYzPTQsJ3cVsCLyKhgZkTCRiScRn5mOuPz+ndy+mBi8S6aO+I0t8do6YjR2OYf3eckul+3xeK0d3b1HEV0N0W1dMSpbmqnuSNGc3ucprZYv5ukoj07jDSi4RDj86L87vpTj2h7+qKAF5ExKRxKIz/T7yyGSnss3rNjaGzzJ7Cb2mI0d8RoCnYCrR0x2mPdO4wu2mJxMhN0klkBLyIyRKLhENGcUL+ugxgOackugIiIJIYCXkQkRSngRURSVEID3sw+bGYbzGyTmX0jkesSEZH9JSzgzSwE/A9wPjAfWGZm8xO1PhER2V8ia/AnApucc1uccx3A/cBFCVyfiIj0ksiAnwxs7/W6Ipi3HzO7zsxWmtnKqqqqBBZHRGRsSfpJVufc7c65pc65paWlpckujohIykjkhU47gCm9XpcF8w7qjTfeqDazbf347hKg+gjKNpKk0rZAam1PKm0LaHtGsiPZlmkHe8Ncgm72amZh4F3gPHywvw58wjm3Zgi+e6VzbumRfs9IkErbAqm1Pam0LaDtGckStS0Jq8E752Jm9jngCSAE3DkU4S4iIv2T0LFonHN/Av6UyHWIiEjfkn6SdZBuT3YBhlAqbQuk1vak0raAtmckS8i2JKwNXkREkmu01uBFROQwFPAiIilqVAX8aB+8zMzuNLNKM3un17wiM3vKzDYGz4XJLGN/mdkUM3vOzNaa2Roz+2Iwf7RuT4aZvWZmbwXb891g/gwzezX4m/utmQ3uDtRJYGYhM/ubmT0avB7N21JuZm+b2SozWxnMG5V/awBmVmBmD5rZejNbZ2anJGJ7Rk3Ap8jgZXcDHz5g3jeAZ5xzRwPPBK9HgxjwFefcfOBk4LPBv8do3Z524Fzn3EJgEfBhMzsZ+L/A/+ecOwrYC/xDEss4UF8E1vV6PZq3BeAc59yiXv3FR+vfGsCPgD875+YCC/H/TkO/Pc65UfEATgGe6PX6RuDGZJdrENsxHXin1+sNwMRgeiKwIdllHOR2/T/gg6mwPUAW8CZwEv7qwnAwf7+/wZH8wF85/gxwLvAoYKN1W4LylgMlB8wblX9rQD6wlaCTSyK3Z9TU4Onn4GWj0Hjn3K5gejcwPpmFGQwzmw4sBl5lFG9P0KSxCqgEngI2A3XOuViwyGj6m7sF+BrQFbwuZvRuC4ADnjSzN8zsumDeaP1bmwFUAXcFTWi/MLNsErA9oyngU57zu+5R1W/VzHKA3wNfcs419H5vtG2Pcy7unFuEr/2eCMxNcpEGxcwuACqdc28kuyxD6HTn3BJ8E+1nzezM3m+Osr+1MLAE+JlzbjHQzAHNMUO1PaMp4Ac8eNkoscfMJgIEz5VJLk+/mVk6Ptzvcc79IZg9arenm3OuDngO34xREIyrBKPnb+404EIzK8ffh+FcfJvvaNwWAJxzO4LnSuAh/A54tP6tVQAVzrlXg9cP4gN/yLdnNAX868DRQU+ACHAV8EiSyzQUHgGuCaavwbdlj3hmZsAdwDrn3H/3emu0bk+pmRUE05n48wnr8EF/WbDYqNge59yNzrky59x0/P+TZ51zVzMKtwXAzLLNLLd7GvgQ8A6j9G/NObcb2G5mc4JZ5wFrScT2JPuEwwBPTnwEP0LlZuBfk12eQZT/PmAX0Infi/8Dvm30GWAj8DRQlOxy9nNbTscfQq4GVgWPj4zi7TkO+FuwPe8A3w7mzwReAzYBvwOiyS7rALfrbODR0bwtQbnfCh5ruv/vj9a/taDsi4CVwd/bw0BhIrZHQxWIiKSo0dREIyIiA6CAFxFJUQp4EZEUpYAXEUlRCngRkRSlgJcxxcziwYiE3Y8hG6DKzKb3HilUJNkSek9WkRGo1fnhCERSnmrwIvSMN/6fwZjjr5nZUcH86Wb2rJmtNrNnzGxqMH+8mT0UjB//lpmdGnxVyMx+Howp/2RwVaxIUijgZazJPKCJ5spe79U75xYAP8GPxgjwY+CXzrnjgHuAW4P5twIvOD9+/BL8FZYARwP/45w7BqgDLk3w9ogclK5klTHFzJqcczl9zC/H3/BjSzCI2m7nXLGZVePH6O4M5u9yzpWYWRVQ5pxr7/Ud04GnnL9hA2b2dSDdOff9xG+ZyPupBi+yjzvI9EC095qOo/NckkQKeJF9ruz1/HIw/RJ+REaAq4EVwfQzwA3Qc6OQ/OEqpEh/qXYhY01mcNembn92znV3lSw0s9X4WviyYN7n8Xfe+Sr+LjyfDuZ/EbjdzP4BX1O/AT9SqMiIoTZ4EXra4Jc656qTXRaRoaImGhGRFKUavIhIilINXkQkRSngRURSlAJeRCRFKeBFRFKUAl5EJEX9/yQYuqJsQ9F5AAAAAElFTkSuQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"GoSpjFQcO91u"},"source":["#Determining Accuracy for Different Classes"]},{"cell_type":"code","metadata":{"id":"w7R9uze8ToFW"},"source":["#modified dataloader for testing accuracy of class subsets\n","\n","def get_class_dataloader(batch_size, ts_class):\n","    ''' The Kaggle dataset is split into three pickle files: train.pickle,\n","    valid.pickle, and test.pickle.\n","    The function will combine the three datasets and resplit them such that the\n","    resulting split is approximately 70% training, 15% validation, and 15% testing.\n","    The function filters classes 0-8 only, as they are related to speed.\n","    The splitting ratio will be applied to each class, to avoid imbalance of \n","    classes in the training/validation/testing samples.'''\n","\n","    classes = ('Speed limit (20km/h)',\n","            'Speed limit (30km/h)',\n","            'Speed limit (50km/h)',\n","            'Speed limit (60km/h)',\n","            'Speed limit (70km/h)',\n","            'Speed limit (80km/h)',\n","            'End of speed limit (80km/h)',\n","            'Speed limit (100km/h)',\n","            'Speed limit (120km/h)')\n","\n","    # load pickle files\n","    # will combine datasets from three seperate pikcle files\n","    \n","    with open(data_dir+'train.pickle', 'rb') as file:\n","        data1 = pickle.load(file)\n","\n","    with open(data_dir+'valid.pickle', 'rb') as file:\n","        data2 = pickle.load(file)\n","\n","    with open (data_dir+'test.pickle', 'rb') as file:\n","        data3 = pickle.load(file)\n","\n","    images = np.concatenate((data1['features'], data2['features'], data3['features']))\n","    labels = np.concatenate((data1['labels'], data2['labels'], data3['labels']))\n","    \n","    # sort into classes\n","    class_images = []\n","    class_labels = []\n","    for i in range(9):\n","        class_indices = np.where(labels==i)\n","        #print(i, 'has', len(class_indices[0]), 'elements') # check number of samples for each class\n","        class_images.append(images[class_indices])\n","        class_labels.append(labels[class_indices])\n","\n","    # normalize number of samples in each class\n","    desired_size=3000\n","    extra_samples = []\n","    extra_labels = []\n","    for i in range(9):\n","        # Randomly sample from the original class images to duplicate to extra\n","        # Duplicate enough samples to make the total for the class 3000\n","        extra_samples.append(\n","            class_images[i][np.random.randint(\n","                low=0,\n","                high=class_images[i].shape[0],\n","                size=desired_size-class_images[i].shape[0])])\n","        # Add random noise to create variation from originals\n","        noise = np.random.normal(0,1, extra_samples[i].size)\n","        noise = noise.reshape(extra_samples[i].shape[0],extra_samples[i].shape[1],extra_samples[i].shape[2],extra_samples[i].shape[3]).astype('uint8')\n","        extra_samples[i] = extra_samples[i]+noise\n","\n","        # add labels for extra samples\n","        extra_labels.append(np.full(extra_samples[i].shape[0], i))\n","\n","        # append to original\n","        class_images[i] = np.concatenate((class_images[i],extra_samples[i]))\n","        class_labels[i] = np.concatenate((class_labels[i],extra_labels[i]))\n","\n","    train_split=0.7\n","\n","    train_image_arrays = [class_images[ts_class][0:int(train_split*class_images[ts_class].shape[0])]]\n","    train_label_arrays = [class_labels[ts_class][0:int(train_split*class_images[ts_class].shape[0])]]\n","    train_images = np.concatenate(train_image_arrays)\n","    train_labels = np.concatenate(train_label_arrays)\n","\n","    # make into torch datasets\n","    train_image_tensor = torch.Tensor(train_images.transpose(0,3,1,2))\n","    train_label_tensor = torch.Tensor(train_labels)\n","   \n","    trainset = TensorDataset(train_image_tensor, train_label_tensor)\n","\n","    # resize and normalization\n","    transform = transforms.Compose(\n","    [transforms.Resize((32,32)),\n","      transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n","    \n","    #trainset.transform = transform\n","    #valset.transform = transform\n","    #testset.transform = transform\n","\n","    # make dataloader\n","    train_loader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,\n","                                        num_workers=1)\n","    \n","    return train_loader"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"17fVeCwOPEFH","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1605484269224,"user_tz":300,"elapsed":14147,"user":{"displayName":"Avelyn Wong","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gg0Gx_4UO4Canl0bxQX9l2YCVEf9TrzDq31I9-c9A=s64","userId":"04927963856256154784"}},"outputId":"25bf617c-5bbc-4cf6-8b1c-7e28fc593703"},"source":["#determine accuracy of best model on each of the 9 classes\n","\n","classes = ('Speed limit (20km/h)',\n","            'Speed limit (30km/h)',\n","            'Speed limit (50km/h)',\n","            'Speed limit (60km/h)',\n","            'Speed limit (70km/h)',\n","            'Speed limit (80km/h)',\n","            'End of speed limit (80km/h)',\n","            'Speed limit (100km/h)',\n","            'Speed limit (120km/h)')\n","\n","for ts_class in range(9):\n","  data_loader = get_class_dataloader(128, ts_class)\n","  print(f\"class: {classes[ts_class]}\")\n","  print(f\"accuracy: {get_accuracy(model4, data_loader)}\")\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["class: Speed limit (20km/h)\n","accuracy: 0.9738095238095238\n","class: Speed limit (30km/h)\n","accuracy: 0.8942857142857142\n","class: Speed limit (50km/h)\n","accuracy: 0.9333333333333333\n","class: Speed limit (60km/h)\n","accuracy: 0.91\n","class: Speed limit (70km/h)\n","accuracy: 0.9452380952380952\n","class: Speed limit (80km/h)\n","accuracy: 0.9085714285714286\n","class: End of speed limit (80km/h)\n","accuracy: 0.9923809523809524\n","class: Speed limit (100km/h)\n","accuracy: 0.8828571428571429\n","class: Speed limit (120km/h)\n","accuracy: 0.9080952380952381\n"],"name":"stdout"}]}]}
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diff --git a/003 Primary Model Testing.ipynb b/003 Primary Model Testing.ipynb
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+++ b/003 Primary Model Testing.ipynb	
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+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Primary Model.ipynb","provenance":[],"collapsed_sections":[],"authorship_tag":"ABX9TyPAUIfeMgqdwiEhD4nXWlnL"},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"code","metadata":{"id":"WQmXvERQ9Gwd"},"source":["import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","\n","import torch.optim as optim                                                     # For gradient descent\n","import matplotlib.pyplot as plt\n","import numpy as np\n","\n","# Creating a CNN\n","class SignClassifier(nn.Module):\n","\n","    def __init__(self):\n","        super(SignClassifier, self).__init__()\n","        self.name = \"Net\"                                                       \n","        self.conv1 = nn.Conv2d(3,5,5)                                           # First kernel is a 5 by 5, 3 color channels, it has output of 5    -> given 32x32x3, you are left with 28x28x5\n","        self.pool = nn.MaxPool2d(2,2)                                           # Max pooling layer with kernel size 2 and stride 2                 -> you are left with 14x14x5\n","        self.conv2 = nn.Conv2d(5,10,3)                                          # Second kernel is 5 by 5, it changes input depth from 5 to 10      -> you are left with 10x10x10\n","        self.conv3 = nn.Conv2d(10,12,5)                                         # Third kernel is 5 by 5, it changes input depth from 10 to 12      -> you are left with 6x6x12\n","                                                                                \n","        self.fc1 = nn.Linear(8*8*12, 200)                                       # Fully Connected Layers\n","        self.fc2 = nn.Linear(200, 100)\n","        self.fc3 = nn.Linear(100,9)                                             # 9 possible outputs\n","\n","    def forward(self, x):\n","        x = self.pool(F.relu(self.conv1(x)))                                    # Apply first kernel, then activation function, then max pooling \n","        x = F.relu(self.conv2(x))                                               # Apply second kernel, then activation function\n","        x = F.relu(self.conv3(x))                                               # Apply second kernel, then activation function\n","        x = x.view(-1, 8*8*12)                                                  # flatten tensor for ANN portion\n","        x = F.relu(self.fc1(x))                                                 # Apply activation function on first fully connected layer\n","        x = F.relu(self.fc2(x))                                                 # Apply activation function on second fully connected layer\n","        x = self.fc3(x)                                                         # final activation function is included with criterion\n","        return x\n"],"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/004 Training and Plotting code.ipynb b/004 Training and Plotting code.ipynb
new file mode 100644
index 0000000..98daf61
--- /dev/null
+++ b/004 Training and Plotting code.ipynb	
@@ -0,0 +1 @@
+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"004 Training and Plotting code.ipynb","provenance":[{"file_id":"1YKQ3ItCmgfpn66WhGy9ey9jpWQ5o2nus","timestamp":1605399621858}],"collapsed_sections":["Qjc-UWAOCify"]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"Qjc-UWAOCify"},"source":["# Data Preprocessing\n","\n","Tasks\n","1.   Load Kaggle traffic sign data from drive\n","2.   Unpickle files\n","3.   Filter out classes not related to speed\n","4.   Normalize number of samples per class\n","5.   Split into train / val / test sets\n","6.   Shuffle datasets\n","7.   Resize\n","8.   Normalize pixel values\n","9.   Make pytorch dataloaders"]},{"cell_type":"code","metadata":{"id":"cPKNBe0NUG71"},"source":["import pickle\n","import numpy as np\n","import torch\n","import torchvision.transforms as transforms\n","from torch.utils.data import TensorDataset, DataLoader\n","import matplotlib.pyplot as plt"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"ljaGNZa8HUcq","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1607203494610,"user_tz":300,"elapsed":23041,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}},"outputId":"51eb4ba6-316b-4458-9c8a-496003baff83"},"source":["# mount drive\n","from google.colab import drive\n","drive.mount('/content/gdrive')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Mounted at /content/gdrive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Dy0lMjtXasiZ"},"source":["# Data directory\n","# Change this as needed\n","data_dir = '/content/gdrive/My Drive/APS360 Project/'"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Si9g90e0Ud0i"},"source":["# Data loaders\n","\n","def get_data_loader(batch_size):\n","    ''' The Kaggle dataset is split into three pickle files: train.pickle,\n","    valid.pickle, and test.pickle.\n","    The function will combine the three datasets and resplit them such that the\n","    resulting split is approximately 70% training, 15% validation, and 15% testing.\n","    The function filters classes 0-8 only, as they are related to speed.\n","    The splitting ratio will be applied to each class, to avoid imbalance of \n","    classes in the training/validation/testing samples.'''\n","\n","    classes = ('Speed limit (20km/h)',\n","            'Speed limit (30km/h)',\n","            'Speed limit (50km/h)',\n","            'Speed limit (60km/h)',\n","            'Speed limit (70km/h)',\n","            'Speed limit (80km/h)',\n","            'End of speed limit (80km/h)',\n","            'Speed limit (100km/h)',\n","            'Speed limit (120km/h)')\n","\n","    # load pickle files\n","    # will combine datasets from three seperate pikcle files\n","    \n","    with open(data_dir+'train.pickle', 'rb') as file:\n","        data1 = pickle.load(file)\n","\n","    with open(data_dir+'valid.pickle', 'rb') as file:\n","        data2 = pickle.load(file)\n","\n","    with open (data_dir+'test.pickle', 'rb') as file:\n","        data3 = pickle.load(file)\n","\n","    images = np.concatenate((data1['features'], data2['features'], data3['features']))\n","    labels = np.concatenate((data1['labels'], data2['labels'], data3['labels']))\n","    \n","    # sort into classes\n","    class_images = []\n","    class_labels = []\n","    for i in range(9):\n","        class_indices = np.where(labels==i)\n","        #print(i, 'has', len(class_indices[0]), 'elements') # check number of samples for each class\n","        class_images.append(images[class_indices])\n","        class_labels.append(labels[class_indices])\n","\n","    # normalize number of samples in each class\n","    desired_size=3000\n","    extra_samples = []\n","    extra_labels = []\n","    for i in range(9):\n","        # Randomly sample from the original class images to duplicate to extra\n","        # Duplicate enough samples to make the total for the class 3000\n","        extra_samples.append(\n","            class_images[i][np.random.randint(\n","                low=0,\n","                high=class_images[i].shape[0],\n","                size=desired_size-class_images[i].shape[0])])\n","        # Add random noise to create variation from originals\n","        noise_intensity = 0.05\n","\n","        extra_samples[i] = (1-noise_intensity)*extra_samples[i]+noise_intensity*(255*torch.rand(*extra_samples[i].shape).detach().numpy())\n","        extra_samples[i] = np.clip(extra_samples[i], 0, 255)\n","\n","        # add labels for extra samples\n","        extra_labels.append(np.full(extra_samples[i].shape[0], i))\n","\n","        # append to original\n","        class_images[i] = np.concatenate((class_images[i],extra_samples[i]))\n","        class_labels[i] = np.concatenate((class_labels[i],extra_labels[i]))\n","\n","    # split into train /  val / test\n","    train_split = 0.7\n","    val_split = 0.85\n","\n","    train_image_arrays = [class_images[i][0:int(train_split*class_images[i].shape[0])] for i in range(9)]\n","    train_label_arrays = [class_labels[i][0:int(train_split*class_images[i].shape[0])] for i in range(9)]\n","    train_images = np.concatenate(train_image_arrays)\n","    train_labels = np.concatenate(train_label_arrays)\n","\n","    val_image_arrays = [class_images[i][int(train_split*class_images[i].shape[0]):int(val_split*class_images[i].shape[0])] for i in range(9)]\n","    val_label_arrays = [class_labels[i][int(train_split*class_images[i].shape[0]):int(val_split*class_images[i].shape[0])] for i in range(9)]\n","    val_images = np.concatenate(val_image_arrays)\n","    val_labels = np.concatenate(val_label_arrays)\n","\n","    test_image_arrays = [class_images[i][int(val_split*class_images[i].shape[0]):] for i in range(9)]\n","    test_label_arrays = [class_labels[i][int(val_split*class_images[i].shape[0]):] for i in range(9)]\n","    test_images = np.concatenate(test_image_arrays)\n","    test_labels = np.concatenate(test_label_arrays)\n","\n","    # shuffle\n","    np.random.seed(9001)\n","    indices = list(range(train_images.shape[0]))\n","    np.random.shuffle(indices)\n","    train_images = train_images[indices]\n","    train_labels = train_labels[indices]\n","    \n","    indices = list(range(val_images.shape[0]))\n","    np.random.shuffle(indices)\n","    val_images = val_images[indices]\n","    val_labels = val_labels[indices]\n","   \n","    indices = list(range(test_images.shape[0]))\n","    np.random.shuffle(indices)\n","    test_images = test_images[indices]\n","    test_labels = test_labels[indices]\n","\n","    # make into torch datasets\n","    train_image_tensor = torch.Tensor(train_images.transpose(0,3,1,2))\n","    train_label_tensor = torch.Tensor(train_labels)\n","    \n","    val_image_tensor = torch.Tensor(val_images.transpose(0,3,1,2))\n","    val_label_tensor = torch.Tensor(val_labels)\n","    \n","    test_image_tensor = torch.Tensor(test_images.transpose(0,3,1,2))\n","    test_label_tensor = torch.Tensor(test_labels)\n","   \n","    trainset = TensorDataset(train_image_tensor, train_label_tensor)\n","    valset = TensorDataset(val_image_tensor, val_label_tensor)\n","    testset = TensorDataset(test_image_tensor, test_label_tensor)\n","\n","    # resize and normalization\n","    transform = transforms.Compose(\n","    [transforms.Resize((32,32)),\n","      transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n","    \n","    #trainset.transform = transform\n","    #valset.transform = transform\n","    #testset.transform = transform\n","\n","    # make data loaders\n","    train_loader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,\n","                                        num_workers=1)\n","    val_loader = torch.utils.data.DataLoader(valset, batch_size=batch_size,\n","                                        num_workers=1)\n","    test_loader = torch.utils.data.DataLoader(testset, batch_size=batch_size,\n","                                        num_workers=1)\n","    \n","    return train_loader, val_loader, test_loader, classes "],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"1kOmRch0ZG-5"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"W_HTih2z9ZUo","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1607203508082,"user_tz":300,"elapsed":5805,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}},"outputId":"2bfa4cea-d9a1-4701-8b15-ab1e3f430a51"},"source":["batch_size = 32\n","train_loader, val_loader, test_loader, classes = get_data_loader (batch_size)\n","print (classes)\n","print (len(train_loader))\n","print (len(val_loader))\n","print (len(test_loader))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["('Speed limit (20km/h)', 'Speed limit (30km/h)', 'Speed limit (50km/h)', 'Speed limit (60km/h)', 'Speed limit (70km/h)', 'Speed limit (80km/h)', 'End of speed limit (80km/h)', 'Speed limit (100km/h)', 'Speed limit (120km/h)')\n","591\n","127\n","127\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"1J6vbY4YETHN","colab":{"base_uri":"https://localhost:8080/","height":268},"executionInfo":{"status":"ok","timestamp":1607203512131,"user_tz":300,"elapsed":1231,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}},"outputId":"b009b37f-837f-4f11-9900-95bafb57aa5f"},"source":["# Check one batch\n","dataiter = iter(test_loader)\n","images, labels = dataiter.next()\n","images = images.numpy().astype(int) # convert images to numpy for display\n","labels = labels.int()\n","\n","# plot the images in the batch, along with the corresponding labels\n","fig = plt.figure(figsize=(25, 4))\n","for idx in np.arange(18):\n","    ax = fig.add_subplot(2, 18/2, idx+1, xticks=[], yticks=[])\n","    plt.imshow(np.transpose(images[idx], (1, 2, 0)))\n","    ax.set_title(classes[labels[idx]])"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1800x288 with 18 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"k5rbyYhrHTAJ"},"source":["# close all figures to prevent memory leak\n","plt.close('all')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"d6UmCUytrUdf"},"source":["def get_model_name(name, batch_size, learning_rate, epoch):\n","    \"\"\" Generate a name for the model consisting of all the hyperparameter values\n","\n","    Args:\n","        config: Configuration object containing the hyperparameters\n","    Returns:\n","        path: A string with the hyperparameter name and value concatenated\n","    \"\"\"\n","    path = \"model_{0}_bs{1}_lr{2}_epoch{3}\".format(name,\n","                                                   batch_size,\n","                                                   learning_rate,\n","                                                   epoch)\n","    return path\n","\n","def normalize_label(labels):\n","    \"\"\"\n","    Given a tensor containing 2 possible values, normalize this to 0/1\n","\n","    Args:\n","        labels: a 1D tensor containing two possible scalar values\n","    Returns:\n","        A tensor normalize to 0/1 value\n","    \"\"\"\n","    max_val = torch.max(labels)\n","    min_val = torch.min(labels)\n","    norm_labels = (labels - min_val)//(max_val - min_val)             #this is kinda brilliant\n","    return norm_labels\n","\n","\n"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Yy0uWVQws3ig"},"source":["#TRAIN\n","\n","Tasks:\n"," - Calculate training accuracy\n"," - Calculate training loss\n"," - Calculate validation accuracy\n"," - Calculate validation loss\n"," - Store weights in epoch files (we may have to be smart about WHERE we're saving this so that we don't clutter up the drive)\n"," - Get accuracy of model after training is complete\n"," - Plot everything\n"]},{"cell_type":"code","metadata":{"id":"rPjuItwogPhe"},"source":["def train(model, train_loader, val_loader, batch_size=27, num_epochs=21, learn_rate = 0.001):\n","\n","    torch.manual_seed(1000)\n","    criterion = nn.CrossEntropyLoss()\n","    optimizer = optim.Adam(model.parameters(), lr=learn_rate)\n","\n","    train_acc, val_acc, train_loss, val_loss = [], [], [], []\n","\n","    # training\n","    print (\"Training Started...\")\n","    if torch.cuda.is_available():\n","        print(\"U S I N G   C U D A \")\n","\n","    for epoch in range(num_epochs):                                     # the number of iterations\n","        sum_train_loss = 0.0\n","        sum_val_loss = 0.0\n","\n","        n = 0                                                           # Number of training iterations in this epoch\n","        m = 0                                                           # Number of validation iterations in this epoch\n","\n","        for imgs, labels in iter(train_loader):\n","                \n","            if use_cuda and torch.cuda.is_available():\n","              imgs = imgs.cuda()\n","              labels = labels.cuda()\n","\n","            out = model(imgs)                                           # forward pass\n","            loss = criterion(out, labels)                               # compute the total loss\n","            loss.backward()                                             # backward pass (compute parameter updates)\n","            optimizer.step()                                            # make the updates for each parameter\n","            optimizer.zero_grad()                                       # a clean up step for PyTorch\n","            sum_train_loss += loss.item() \n","            n += 1\n","        \n","        for imgs, labels in iter(val_loader):\n","            if use_cuda and torch.cuda.is_available():\n","              imgs = imgs.cuda()\n","              labels = labels.cuda()                                    # cudafication for speeeeed\n","\n","            out = model(img)                                            \n","            loss = criterion(out, labels)                               # compute loss with Cross Entropy\n","            sum_val_loss += loss.item()\n","            m += 1\n","\n","        # track accuracy and loss\n","        train_acc.append(get_accuracy(model, train_loader))\n","        val_acc.append(get_accuracy(model, val_loader))\n","        train_loss.append(sum_train_loss/n)\n","        val_loss.append(sum_val_loss/m)\n","\n","        ################################################################################################################\n","        model_path = get_model_name(model.name, batch_size, learning_rate, epoch+1) \n","        torch.save(net.state_dict(), model_path)\n","        print('epoch', epoch,'training acc', train_acc[-1],'val acc', val_acc[-1])\n","\n","    return train_acc, val_acc, train_loss, val_loss\n","\n","\n","\n","def get_accuracy(model, data_loader):\n","  correct = 0\n","  total = 0\n","  for imgs, labels in data_loader:\n","      \n","      if use_cuda and torch.cuda.is_available():\n","        imgs = imgs.cuda()\n","        labels = labels.cuda()\n","\n","      output = model(imgs)\n","      #select index with maximum prediction score\n","      pred = output.max(1, keepdim=True)[1]\n","      correct += pred.eq(labels.view_as(pred)).sum().item()\n","      total += imgs.shape[0]\n","\n","  return correct / total\n","\n","\n","\n","\n","def plot_training_curve(train_acc, val_acc, train_loss, val_loss):\n","    \"\"\" Plots the training curve for a model run, given the csv files\n","    containing the train/validation error/loss.\n","\n","    Args:\n","        path: The base path of the csv files produced during training\n","    \"\"\"\n","    import matplotlib.pyplot as plt\n","\n","    plt.title(\"Train vs Validation Accuracy\")\n","    n = len(train_acc) # number of epochs\n","    plt.plot(range(1,n+1), train_acc, label=\"Train\")\n","    plt.plot(range(1,n+1), val_acc, label=\"Validation\")\n","    plt.xlabel(\"Epoch\")\n","    plt.ylabel(\"Accuracy\")\n","    plt.legend(loc='best')\n","    plt.show()\n","    plt.title(\"Train vs Validation Loss\")\n","    plt.plot(range(1,n+1), train_loss, label=\"Train\")\n","    plt.plot(range(1,n+1), val_loss, label=\"Validation\")\n","    plt.xlabel(\"Epoch\")\n","    plt.ylabel(\"Loss\")\n","    plt.legend(loc='best')\n","    plt.show()"],"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/005 Training and Hyperparameter Tuning for Primary Model.ipynb b/005 Training and Hyperparameter Tuning for Primary Model.ipynb
new file mode 100644
index 0000000..db15131
--- /dev/null
+++ b/005 Training and Hyperparameter Tuning for Primary Model.ipynb	
@@ -0,0 +1 @@
+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"005 Training and Hyperparameter Tuning for Primary Model.ipynb","provenance":[{"file_id":"1YKQ3ItCmgfpn66WhGy9ey9jpWQ5o2nus","timestamp":1605399621858}],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"markdown","metadata":{"id":"Qjc-UWAOCify"},"source":["# Data Preprocessing\n","\n","Tasks\n","1.   Load Kaggle traffic sign data from drive\n","2.   Unpickle files\n","3.   Filter out classes not related to speed\n","4.   Normalize number of samples per class\n","5.   Split into train / val / test sets\n","6.   Shuffle datasets\n","7.   Resize\n","8.   Normalize pixel values\n","9.   Make pytorch dataloaders"]},{"cell_type":"code","metadata":{"id":"cPKNBe0NUG71","executionInfo":{"status":"ok","timestamp":1605635476349,"user_tz":300,"elapsed":3855,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}}},"source":["import pickle\n","import numpy as np\n","import torch\n","import torchvision.transforms as transforms\n","from torch.utils.data import TensorDataset, DataLoader\n","import matplotlib.pyplot as plt"],"execution_count":2,"outputs":[]},{"cell_type":"code","metadata":{"id":"ljaGNZa8HUcq","executionInfo":{"status":"ok","timestamp":1605635610284,"user_tz":300,"elapsed":136835,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}},"outputId":"b190d90f-1362-4959-afb5-1bbf14aa3102","colab":{"base_uri":"https://localhost:8080/"}},"source":["# mount drive\n","from google.colab import drive\n","drive.mount('/content/gdrive')"],"execution_count":3,"outputs":[{"output_type":"stream","text":["Mounted at /content/gdrive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Dy0lMjtXasiZ","executionInfo":{"status":"ok","timestamp":1605635624549,"user_tz":300,"elapsed":302,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}}},"source":["# Data directory\n","# Change this as needed\n","data_dir = '/content/gdrive/My Drive/APS360 Project/'"],"execution_count":8,"outputs":[]},{"cell_type":"code","metadata":{"id":"Si9g90e0Ud0i","executionInfo":{"status":"ok","timestamp":1605635625666,"user_tz":300,"elapsed":402,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}}},"source":["# Data loaders\n","\n","def get_data_loader(batch_size):\n","    ''' The Kaggle dataset is split into three pickle files: train.pickle,\n","    valid.pickle, and test.pickle.\n","    The function will combine the three datasets and resplit them such that the\n","    resulting split is approximately 70% training, 15% validation, and 15% testing.\n","    The function filters classes 0-8 only, as they are related to speed.\n","    The splitting ratio will be applied to each class, to avoid imbalance of \n","    classes in the training/validation/testing samples.'''\n","\n","    classes = ('Speed limit (20km/h)',\n","            'Speed limit (30km/h)',\n","            'Speed limit (50km/h)',\n","            'Speed limit (60km/h)',\n","            'Speed limit (70km/h)',\n","            'Speed limit (80km/h)',\n","            'End of speed limit (80km/h)',\n","            'Speed limit (100km/h)',\n","            'Speed limit (120km/h)')\n","\n","    # load pickle files\n","    # will combine datasets from three seperate pikcle files\n","    \n","    with open(data_dir+'train.pickle', 'rb') as file:\n","        data1 = pickle.load(file)\n","\n","    with open(data_dir+'valid.pickle', 'rb') as file:\n","        data2 = pickle.load(file)\n","\n","    with open (data_dir+'test.pickle', 'rb') as file:\n","        data3 = pickle.load(file)\n","\n","    images = np.concatenate((data1['features'], data2['features'], data3['features']))\n","    labels = np.concatenate((data1['labels'], data2['labels'], data3['labels']))\n","    \n","    # sort into classes\n","    class_images = []\n","    class_labels = []\n","    for i in range(9):\n","        class_indices = np.where(labels==i)\n","        #print(i, 'has', len(class_indices[0]), 'elements') # check number of samples for each class\n","        class_images.append(images[class_indices])\n","        class_labels.append(labels[class_indices])\n","\n","    # normalize number of samples in each class\n","    desired_size=3000\n","    extra_samples = []\n","    extra_labels = []\n","    for i in range(9):\n","        # Randomly sample from the original class images to duplicate to extra\n","        # Duplicate enough samples to make the total for the class 3000\n","        extra_samples.append(\n","            class_images[i][np.random.randint(\n","                low=0,\n","                high=class_images[i].shape[0],\n","                size=desired_size-class_images[i].shape[0])])\n","        # Add random noise to create variation from originals\n","        noise = np.random.normal(0,1, extra_samples[i].size)\n","        noise = noise.reshape(extra_samples[i].shape[0],extra_samples[i].shape[1],extra_samples[i].shape[2],extra_samples[i].shape[3]).astype('uint8')\n","        extra_samples[i] = extra_samples[i]+noise\n","\n","        # add labels for extra samples\n","        extra_labels.append(np.full(extra_samples[i].shape[0], i))\n","\n","        # append to original\n","        class_images[i] = np.concatenate((class_images[i],extra_samples[i]))\n","        class_labels[i] = np.concatenate((class_labels[i],extra_labels[i]))\n","\n","    # split into train /  val / test\n","    train_split = 0.7\n","    val_split = 0.85\n","\n","    train_image_arrays = [class_images[i][0:int(train_split*class_images[i].shape[0])] for i in range(9)]\n","    train_label_arrays = [class_labels[i][0:int(train_split*class_images[i].shape[0])] for i in range(9)]\n","    train_images = np.concatenate(train_image_arrays)\n","    train_labels = np.concatenate(train_label_arrays)\n","\n","    val_image_arrays = [class_images[i][int(train_split*class_images[i].shape[0]):int(val_split*class_images[i].shape[0])] for i in range(9)]\n","    val_label_arrays = [class_labels[i][int(train_split*class_images[i].shape[0]):int(val_split*class_images[i].shape[0])] for i in range(9)]\n","    val_images = np.concatenate(val_image_arrays)\n","    val_labels = np.concatenate(val_label_arrays)\n","\n","    test_image_arrays = [class_images[i][int(val_split*class_images[i].shape[0]):] for i in range(9)]\n","    test_label_arrays = [class_labels[i][int(val_split*class_images[i].shape[0]):] for i in range(9)]\n","    test_images = np.concatenate(test_image_arrays)\n","    test_labels = np.concatenate(test_label_arrays)\n","\n","    # shuffle\n","    np.random.seed(9001)\n","    indices = list(range(train_images.shape[0]))\n","    np.random.shuffle(indices)\n","    train_images = train_images[indices]\n","    train_labels = train_labels[indices]\n","    \n","    indices = list(range(val_images.shape[0]))\n","    np.random.shuffle(indices)\n","    val_images = val_images[indices]\n","    val_labels = val_labels[indices]\n","   \n","    indices = list(range(test_images.shape[0]))\n","    np.random.shuffle(indices)\n","    test_images = test_images[indices]\n","    test_labels = test_labels[indices]\n","\n","    # make into torch datasets\n","    train_image_tensor = torch.Tensor(train_images.transpose(0,3,1,2))\n","    train_label_tensor = torch.Tensor(train_labels)\n","    \n","    val_image_tensor = torch.Tensor(val_images.transpose(0,3,1,2))\n","    val_label_tensor = torch.Tensor(val_labels)\n","    \n","    test_image_tensor = torch.Tensor(test_images.transpose(0,3,1,2))\n","    test_label_tensor = torch.Tensor(test_labels)\n","   \n","    trainset = TensorDataset(train_image_tensor, train_label_tensor)\n","    valset = TensorDataset(val_image_tensor, val_label_tensor)\n","    testset = TensorDataset(test_image_tensor, test_label_tensor)\n","\n","    # resize and normalization\n","    transform = transforms.Compose(\n","    [transforms.Resize((32,32)),\n","      transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n","    \n","    #trainset.transform = transform\n","    #valset.transform = transform\n","    #testset.transform = transform\n","\n","    # make data loaders\n","    train_loader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,\n","                                        num_workers=1)\n","    val_loader = torch.utils.data.DataLoader(valset, batch_size=batch_size,\n","                                        num_workers=1)\n","    test_loader = torch.utils.data.DataLoader(testset, batch_size=batch_size,\n","                                        num_workers=1)\n","    \n","    return train_loader, val_loader, test_loader, classes "],"execution_count":9,"outputs":[]},{"cell_type":"code","metadata":{"id":"W_HTih2z9ZUo","executionInfo":{"status":"ok","timestamp":1605635637027,"user_tz":300,"elapsed":6779,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}},"outputId":"0e800b3e-f3ba-4bab-dbca-7008549f723e","colab":{"base_uri":"https://localhost:8080/"}},"source":["batch_size = 32\n","train_loader, val_loader, test_loader, classes = get_data_loader (batch_size)\n","print (classes)\n","print (len(train_loader))\n","print (len(val_loader))\n","print (len(test_loader))"],"execution_count":10,"outputs":[{"output_type":"stream","text":["('Speed limit (20km/h)', 'Speed limit (30km/h)', 'Speed limit (50km/h)', 'Speed limit (60km/h)', 'Speed limit (70km/h)', 'Speed limit (80km/h)', 'End of speed limit (80km/h)', 'Speed limit (100km/h)', 'Speed limit (120km/h)')\n","591\n","127\n","127\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"1J6vbY4YETHN","executionInfo":{"status":"ok","timestamp":1605635639155,"user_tz":300,"elapsed":1238,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}},"outputId":"c697abf0-80e3-4647-bdd2-982f48b2509d","colab":{"base_uri":"https://localhost:8080/","height":268}},"source":["# Check one batch\n","dataiter = iter(test_loader)\n","images, labels = dataiter.next()\n","images = images.numpy().astype(int) # convert images to numpy for display\n","labels = labels.int()\n","\n","# plot the images in the batch, along with the corresponding labels\n","fig = plt.figure(figsize=(25, 4))\n","for idx in np.arange(20):\n","    ax = fig.add_subplot(2, 20/2, idx+1, xticks=[], yticks=[])\n","    plt.imshow(np.transpose(images[idx], (1, 2, 0)))\n","    ax.set_title(classes[labels[idx]])"],"execution_count":11,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1800x288 with 20 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"k5rbyYhrHTAJ","executionInfo":{"status":"ok","timestamp":1605635668747,"user_tz":300,"elapsed":254,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}}},"source":["# close all figures to prevent memory leak\n","plt.close('all')"],"execution_count":14,"outputs":[]},{"cell_type":"code","metadata":{"id":"d6UmCUytrUdf","executionInfo":{"status":"ok","timestamp":1605635669968,"user_tz":300,"elapsed":270,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}}},"source":["def get_model_name(name, batch_size, learning_rate, epoch):\n","    \"\"\" Generate a name for the model consisting of all the hyperparameter values\n","\n","    Args:\n","        config: Configuration object containing the hyperparameters\n","    Returns:\n","        path: A string with the hyperparameter name and value concatenated\n","    \"\"\"\n","    path = \"model_{0}_bs{1}_lr{2}_epoch{3}\".format(name,\n","                                                   batch_size,\n","                                                   learning_rate,\n","                                                   epoch)\n","    return path\n","\n","def normalize_label(labels):\n","    \"\"\"\n","    Given a tensor containing 2 possible values, normalize this to 0/1\n","\n","    Args:\n","        labels: a 1D tensor containing two possible scalar values\n","    Returns:\n","        A tensor normalize to 0/1 value\n","    \"\"\"\n","    max_val = torch.max(labels)\n","    min_val = torch.min(labels)\n","    norm_labels = (labels - min_val)//(max_val - min_val)             #this is kinda brilliant\n","    return norm_labels\n","\n","\n"],"execution_count":15,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Yy0uWVQws3ig"},"source":["#TRAIN\n","\n","Tasks:\n"," - Calculate training accuracy\n"," - Calculate training loss\n"," - Calculate validation accuracy\n"," - Calculate validation loss\n"," - Store weights in epoch files (we may have to be smart about WHERE we're saving this so that we don't clutter up the drive)\n"," - Get accuracy of model after training is complete\n"," - Plot everything\n"]},{"cell_type":"code","metadata":{"id":"rPjuItwogPhe","executionInfo":{"status":"ok","timestamp":1605636136057,"user_tz":300,"elapsed":307,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}}},"source":["def train(model, train_loader, val_loader, batch_size=27, num_epochs=21, learning_rate = 0.001):\n","\n","    torch.manual_seed(1000)\n","    criterion = nn.CrossEntropyLoss()\n","    optimizer = optim.Adam(model.parameters(), lr=learning_rate)\n","\n","    train_acc, val_acc, train_loss, val_loss = [], [], [], []\n","\n","    # training\n","    print (\"Training Started...\\n\")\n","    if torch.cuda.is_available():\n","        print(\"U S I N G   C U D A \\n\\n\")\n","\n","    for epoch in range(num_epochs):                                     # the number of iterations\n","        sum_train_loss = 0.0\n","        sum_val_loss = 0.0\n","\n","        n = 0                                                           # Number of training iterations in this epoch\n","        m = 0                                                           # Number of validation iterations in this epoch\n","\n","        for imgs, labels in iter(train_loader):\n","                \n","            if use_cuda and torch.cuda.is_available():\n","              imgs = imgs.cuda()\n","              labels = labels.cuda()\n","\n","            out = model(imgs)                                           # forward pass\n","            loss = criterion(out, labels.long())                               # compute the total loss\n","            loss.backward()                                             # backward pass (compute parameter updates)\n","            optimizer.step()                                            # make the updates for each parameter\n","            optimizer.zero_grad()                                       # a clean up step for PyTorch\n","            sum_train_loss += loss.item() \n","            n += 1\n","        \n","        for imgs, labels in iter(val_loader):\n","            \n","            if use_cuda and torch.cuda.is_available():\n","              imgs = imgs.cuda()\n","              labels = labels.cuda()                                    # cudafication for speeeeed\n","\n","            out = model(imgs)                                            \n","            loss = criterion(out, labels.long())                               # compute loss with Cross Entropy\n","            sum_val_loss += loss.item()\n","            m += 1\n","\n","        # track accuracy and loss\n","        train_acc.append(get_accuracy(model, train_loader))\n","        val_acc.append(get_accuracy(model, val_loader))\n","        train_loss.append(sum_train_loss/n)\n","        val_loss.append(sum_val_loss/m)\n","\n","        ################################################################################################################\n","        model_path = get_model_name(model.name, batch_size, learning_rate, epoch+1) \n","        torch.save(model.state_dict(), model_path)\n","\n","        print('Epoch: ', epoch + 1,                                                 #A little text formatting goes a long way\n","              '\\t Training acc:', round(train_acc[-1],4),\n","              '\\t Val acc:%.4f' % val_acc[-1],\n","              '\\t Training loss:%.4f' % train_loss[-1],\n","              '\\t Val loss:%.4f' % val_loss[-1])\n","\n","    return train_acc, val_acc, train_loss, val_loss\n","\n","def get_accuracy(model, data_loader):\n","\n","  correct = 0\n","  total = 0\n","  for imgs, labels in data_loader:\n","      \n","      if use_cuda and torch.cuda.is_available():\n","        imgs = imgs.cuda()\n","        labels = labels.cuda()\n","\n","      output = model(imgs)\n","      #select index with maximum prediction score\n","      pred = output.max(1, keepdim=True)[1]\n","      correct += pred.eq(labels.view_as(pred)).sum().item()\n","      total += imgs.shape[0]\n","\n","  return correct / total\n","\n","\n","\n","\n","def plot_training_curve(train_acc, val_acc, train_loss, val_loss):\n","    \"\"\" Plots the training curve for a model run, given the csv files\n","    containing the train/validation error/loss.\n","\n","    Args:\n","        path: The base path of the csv files produced during training\n","    \"\"\"\n","    import matplotlib.pyplot as plt\n","\n","    plt.title(\"Train vs Validation Accuracy\")\n","    n = len(train_acc) # number of epochs\n","    plt.plot(range(1,n+1), train_acc, label=\"Train\")\n","    plt.plot(range(1,n+1), val_acc, label=\"Validation\")\n","    plt.xlabel(\"Epoch\")\n","    plt.ylabel(\"Accuracy\")\n","    plt.legend(loc='best')\n","    plt.show()\n","    plt.title(\"Train vs Validation Loss\")\n","    plt.plot(range(1,n+1), train_loss, label=\"Train\")\n","    plt.plot(range(1,n+1), val_loss, label=\"Validation\")\n","    plt.xlabel(\"Epoch\")\n","    plt.ylabel(\"Loss\")\n","    plt.legend(loc='best')\n","    plt.show()"],"execution_count":20,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"0Q5T62z8XhGh"},"source":["#Primary Model Architecture and Testing\n","\n","Tasks:\n"," - Build a CNN model with 3 convolutional layers and 3 fully connected layers\n"," - Perform a sanity check to test if model is capable of overfitting\n"," - Use training code to test on model and tune hyperparameters to obtain best results\n"," - Print and Plot Results\n"," - Try 10 different hyperparameter settings and compare\n"]},{"cell_type":"code","metadata":{"id":"GqVmU6tztUUL","executionInfo":{"status":"ok","timestamp":1605635678186,"user_tz":300,"elapsed":273,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}}},"source":["import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","\n","import torch.optim as optim                                                     # For gradient descent\n","import matplotlib.pyplot as plt\n","import numpy as np\n","\n","# Creating a CNN\n","class SignClassifier(nn.Module):\n","\n","    def __init__(self):\n","        super(SignClassifier, self).__init__()\n","        self.name = \"Net\"                                                       \n","        self.conv1 = nn.Conv2d(3,5,5)                                           # First kernel is a 5 by 5, 3 color channels, it has output of 5    -> given 32x32x3, you are left with 28x28x5\n","        self.pool = nn.MaxPool2d(2,2)                                           # Max pooling layer with kernel size 2 and stride 2                 -> you are left with 14x14x5\n","        self.conv2 = nn.Conv2d(5,10,3)                                          # Second kernel is 5 by 5, it changes input depth from 5 to 10      -> you are left with 10x10x10\n","        self.conv3 = nn.Conv2d(10,12,5)                                         # Third kernel is 5 by 5, it changes input depth from 10 to 12      -> you are left with 6x6x12\n","                                                                                \n","        self.fc1 = nn.Linear(8*8*12, 200)                                       # Fully Connected Layers\n","        self.fc2 = nn.Linear(200, 100)\n","        self.fc3 = nn.Linear(100,9)                                             # 9 possible outputs\n","\n","    def forward(self, x):\n","        x = self.pool(F.relu(self.conv1(x)))                                    # Apply first kernel, then activation function, then max pooling \n","        x = F.relu(self.conv2(x))                                               # Apply second kernel, then activation function\n","        x = F.relu(self.conv3(x))                                               # Apply second kernel, then activation function\n","        x = x.view(-1, 8*8*12)                                                  # flatten tensor for ANN portion\n","        x = F.relu(self.fc1(x))                                                 # Apply activation function on first fully connected layer\n","        x = F.relu(self.fc2(x))                                                 # Apply activation function on second fully connected layer\n","        x = self.fc3(x)                                                         # final activation function is included with criterion\n","        x = x.squeeze(1) # Flatten to [batch_size]\n","        return x\n"],"execution_count":17,"outputs":[]},{"cell_type":"code","metadata":{"id":"DjFo214RVNS1","executionInfo":{"status":"ok","timestamp":1605635682427,"user_tz":300,"elapsed":2419,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}},"outputId":"9ba0355d-04ae-4617-d6b1-173ed8e0e9b4","colab":{"base_uri":"https://localhost:8080/"}},"source":["# Modified Dataloader's so that trainset and valset have only 72 images each to check if model is capable of overfitting\n","\n","def get_data_loader(batch_size):\n","    ''' The Kaggle dataset is split into three pickle files: train.pickle,\n","    valid.pickle, and test.pickle.\n","    The function will combine the three datasets and resplit them such that the\n","    resulting split is approximately 70% training, 15% validation, and 15% testing.\n","    The function filters classes 0-8 only, as they are related to speed.\n","    The splitting ratio will be applied to each class, to avoid imbalance of \n","    classes in the training/validation/testing samples.'''\n","\n","    classes = ('Speed limit (20km/h)',\n","            'Speed limit (30km/h)',\n","            'Speed limit (50km/h)',\n","            'Speed limit (60km/h)',\n","            'Speed limit (70km/h)',\n","            'Speed limit (80km/h)',\n","            'End of speed limit (80km/h)',\n","            'Speed limit (100km/h)',\n","            'Speed limit (120km/h)')\n","\n","    # load pickle files\n","    # will combine datasets from three seperate pikcle files\n","    \n","    with open(data_dir+'train.pickle', 'rb') as file:\n","        data1 = pickle.load(file)\n","\n","    with open(data_dir+'valid.pickle', 'rb') as file:\n","        data2 = pickle.load(file)\n","\n","    with open (data_dir+'test.pickle', 'rb') as file:\n","        data3 = pickle.load(file)\n","\n","    images = np.concatenate((data1['features'], data2['features'], data3['features']))\n","    labels = np.concatenate((data1['labels'], data2['labels'], data3['labels']))\n","    \n","    # sort into classes\n","    class_images = []\n","    class_labels = []\n","    for i in range(9):\n","        class_indices = np.where(labels==i)\n","        #print(i, 'has', len(class_indices[0]), 'elements') # check number of samples for each class\n","        class_images.append(images[class_indices])\n","        class_labels.append(labels[class_indices])\n","\n","    # normalize number of samples in each class\n","    desired_size=3000\n","    extra_samples = []\n","    extra_labels = []\n","    for i in range(9):\n","        # Randomly sample from the original class images to duplicate to extra\n","        # Duplicate enough samples to make the total for the class 3000\n","        extra_samples.append(\n","            class_images[i][np.random.randint(\n","                low=0,\n","                high=class_images[i].shape[0],\n","                size=desired_size-class_images[i].shape[0])])\n","        # Add random noise to create variation from originals\n","        noise = np.random.normal(0,1, extra_samples[i].size)\n","        noise = noise.reshape(extra_samples[i].shape[0],extra_samples[i].shape[1],extra_samples[i].shape[2],extra_samples[i].shape[3]).astype('uint8')\n","        extra_samples[i] = extra_samples[i]+noise\n","\n","        # add labels for extra samples\n","        extra_labels.append(np.full(extra_samples[i].shape[0], i))\n","\n","        # append to original\n","        class_images[i] = np.concatenate((class_images[i],extra_samples[i]))\n","        class_labels[i] = np.concatenate((class_labels[i],extra_labels[i]))\n","\n","    # split into train /  val / test\n","    train_split = float(1/360)\n","    val_split = float(2/360) ###################################################################################################################################################################\n","\n","    train_image_arrays = [class_images[i][0:int(train_split*class_images[i].shape[0])] for i in range(9)]\n","    train_label_arrays = [class_labels[i][0:int(train_split*class_images[i].shape[0])] for i in range(9)]\n","    train_images = np.concatenate(train_image_arrays)\n","    train_labels = np.concatenate(train_label_arrays)\n","\n","    val_image_arrays = [class_images[i][int(train_split*class_images[i].shape[0]):int(val_split*class_images[i].shape[0])] for i in range(9)]\n","    val_label_arrays = [class_labels[i][int(train_split*class_images[i].shape[0]):int(val_split*class_images[i].shape[0])] for i in range(9)]\n","    val_images = np.concatenate(val_image_arrays)\n","    val_labels = np.concatenate(val_label_arrays)\n","\n","    test_image_arrays = [class_images[i][int(val_split*class_images[i].shape[0]):] for i in range(9)]\n","    test_label_arrays = [class_labels[i][int(val_split*class_images[i].shape[0]):] for i in range(9)]\n","    test_images = np.concatenate(test_image_arrays)\n","    test_labels = np.concatenate(test_label_arrays)\n","\n","    # shuffle\n","    np.random.seed(9001)\n","    indices = list(range(train_images.shape[0]))\n","    np.random.shuffle(indices)\n","    train_images = train_images[indices]\n","    train_labels = train_labels[indices]\n","    \n","    indices = list(range(val_images.shape[0]))\n","    np.random.shuffle(indices)\n","    val_images = val_images[indices]\n","    val_labels = val_labels[indices]\n","   \n","    indices = list(range(test_images.shape[0]))\n","    np.random.shuffle(indices)\n","    test_images = test_images[indices]\n","    test_labels = test_labels[indices]\n","\n","    # make into torch datasets\n","    train_image_tensor = torch.Tensor(train_images.transpose(0,3,1,2))\n","    train_label_tensor = torch.Tensor(train_labels)\n","    \n","    val_image_tensor = torch.Tensor(val_images.transpose(0,3,1,2))\n","    val_label_tensor = torch.Tensor(val_labels)\n","    \n","    test_image_tensor = torch.Tensor(test_images.transpose(0,3,1,2))\n","    test_label_tensor = torch.Tensor(test_labels)\n","   \n","    trainset = TensorDataset(train_image_tensor, train_label_tensor)\n","    valset = TensorDataset(val_image_tensor, val_label_tensor)\n","    testset = TensorDataset(test_image_tensor, test_label_tensor)\n","\n","    # resize and normalization\n","    transform = transforms.Compose(\n","    [transforms.Resize((32,32)),\n","      transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n","    \n","    #trainset.transform = transform\n","    #valset.transform = transform\n","    #testset.transform = transform\n","\n","    print(len(trainset))\n","    print(len(valset))\n","    # make data loaders\n","    train_loader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,\n","                                        num_workers=1)\n","    val_loader = torch.utils.data.DataLoader(valset, batch_size=batch_size,\n","                                        num_workers=1)\n","    test_loader = torch.utils.data.DataLoader(testset, batch_size=batch_size,\n","                                        num_workers=1)\n","    \n","    return train_loader, val_loader, test_loader, classes \n","\n","batch_size = 32\n","overfit_train_loader, overfit_val_loader, overfit_test_loader, classes = get_data_loader (batch_size)\n","print (classes)\n"],"execution_count":18,"outputs":[{"output_type":"stream","text":["72\n","72\n","('Speed limit (20km/h)', 'Speed limit (30km/h)', 'Speed limit (50km/h)', 'Speed limit (60km/h)', 'Speed limit (70km/h)', 'Speed limit (80km/h)', 'End of speed limit (80km/h)', 'Speed limit (100km/h)', 'Speed limit (120km/h)')\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"sPmJ1bCfSvZw","executionInfo":{"status":"ok","timestamp":1605619502990,"user_tz":300,"elapsed":10319,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}},"outputId":"f992cf87-4baa-44a1-f0bf-52ed2d0e7514","colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["### Sanity Check\n","\n","batch_size = 36\n","overfit_train_loader, overfit_val_loader, overfit_test_loader, classes = get_data_loader (batch_size)\n","\n","model_0 = SignClassifier()\n","use_cuda = False\n","train_acc, val_acc, train_loss, val_loss = train(model_0, overfit_train_loader, overfit_val_loader, batch_size=32, num_epochs=30, learning_rate = 0.001)\n","plot_training_curve(train_acc, val_acc, train_loss, val_loss)"],"execution_count":43,"outputs":[{"output_type":"stream","text":["72\n","72\n","Training Started...\n","\n","U S I N G   C U D A \n","\n","\n","Epoch:  0 \t Training acc: 0.1389 \t Val acc:0.1111 \t Training loss:2.3147 \t Val loss:2.0382\n","Epoch:  1 \t Training acc: 0.2778 \t Val acc:0.2222 \t Training loss:1.9810 \t Val loss:1.9609\n","Epoch:  2 \t Training acc: 0.3194 \t Val acc:0.2222 \t Training loss:1.8737 \t Val loss:1.8842\n","Epoch:  3 \t Training acc: 0.375 \t Val acc:0.2639 \t Training loss:1.7085 \t Val loss:1.8071\n","Epoch:  4 \t Training acc: 0.4444 \t Val acc:0.3056 \t Training loss:1.5107 \t Val loss:1.6999\n","Epoch:  5 \t Training acc: 0.7083 \t Val acc:0.4861 \t Training loss:1.2512 \t Val loss:1.7023\n","Epoch:  6 \t Training acc: 0.8889 \t Val acc:0.5278 \t Training loss:0.9701 \t Val loss:1.7075\n","Epoch:  7 \t Training acc: 0.9722 \t Val acc:0.6667 \t Training loss:0.6763 \t Val loss:1.8286\n","Epoch:  8 \t Training acc: 0.9583 \t Val acc:0.6111 \t Training loss:0.4633 \t Val loss:1.9395\n","Epoch:  9 \t Training acc: 0.7778 \t Val acc:0.6111 \t Training loss:0.3816 \t Val loss:1.8418\n","Epoch:  10 \t Training acc: 0.9444 \t Val acc:0.5000 \t Training loss:0.6044 \t Val loss:2.2643\n","Epoch:  11 \t Training acc: 0.9861 \t Val acc:0.6111 \t Training loss:0.3534 \t Val loss:2.0299\n","Epoch:  12 \t Training acc: 1.0 \t Val acc:0.7083 \t Training loss:0.2258 \t Val loss:1.7134\n","Epoch:  13 \t Training acc: 1.0 \t Val acc:0.7361 \t Training loss:0.2329 \t Val loss:1.9781\n","Epoch:  14 \t Training acc: 0.9861 \t Val acc:0.6806 \t Training loss:0.1565 \t Val loss:2.3623\n","Epoch:  15 \t Training acc: 0.9861 \t Val acc:0.6528 \t Training loss:0.1089 \t Val loss:2.0138\n","Epoch:  16 \t Training acc: 0.9861 \t Val acc:0.6667 \t Training loss:0.1108 \t Val loss:1.8639\n","Epoch:  17 \t Training acc: 1.0 \t Val acc:0.7500 \t Training loss:0.0854 \t Val loss:1.9035\n","Epoch:  18 \t Training acc: 1.0 \t Val acc:0.7083 \t Training loss:0.0441 \t Val loss:2.3075\n","Epoch:  19 \t Training acc: 1.0 \t Val acc:0.6944 \t Training loss:0.0442 \t Val loss:2.6534\n","Epoch:  20 \t Training acc: 1.0 \t Val acc:0.6944 \t Training loss:0.0301 \t Val loss:2.7212\n","Epoch:  21 \t Training acc: 1.0 \t Val acc:0.6528 \t Training loss:0.0196 \t Val loss:2.8702\n","Epoch:  22 \t Training acc: 1.0 \t Val acc:0.6389 \t Training loss:0.0180 \t Val loss:2.9533\n","Epoch:  23 \t Training acc: 1.0 \t Val acc:0.6944 \t Training loss:0.0168 \t Val loss:2.9490\n","Epoch:  24 \t Training acc: 1.0 \t Val acc:0.7222 \t Training loss:0.0118 \t Val loss:2.9625\n","Epoch:  25 \t Training acc: 1.0 \t Val acc:0.6944 \t Training loss:0.0081 \t Val loss:3.0635\n","Epoch:  26 \t Training acc: 1.0 \t Val acc:0.6944 \t Training loss:0.0071 \t Val loss:3.2061\n","Epoch:  27 \t Training acc: 1.0 \t Val acc:0.6944 \t Training loss:0.0071 \t Val loss:3.2839\n","Epoch:  28 \t Training acc: 1.0 \t Val acc:0.6806 \t Training loss:0.0064 \t Val loss:3.2657\n","Epoch:  29 \t Training acc: 1.0 \t Val acc:0.6806 \t Training loss:0.0049 \t Val loss:3.2273\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"HgWSmOTpttDG","executionInfo":{"status":"ok","timestamp":1605620058254,"user_tz":300,"elapsed":481554,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}},"outputId":"074b066b-a469-4c60-9170-ab643969c363","colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["model_1 = SignClassifier()\n","use_cuda = False\n","train_acc, val_acc, train_loss, val_loss = train(model_1, train_loader, val_loader, batch_size=32, num_epochs=30, learning_rate = 0.001)\n","plot_training_curve(train_acc, val_acc, train_loss, val_loss)"],"execution_count":44,"outputs":[{"output_type":"stream","text":["Training Started...\n","\n","U S I N G   C U D A \n","\n","\n","Epoch:  0 \t Training acc: 0.947 \t Val acc:0.9333 \t Training loss:0.4890 \t Val loss:0.2444\n","Epoch:  1 \t Training acc: 0.9726 \t Val acc:0.9652 \t Training loss:0.1250 \t Val loss:0.1255\n","Epoch:  2 \t Training acc: 0.9861 \t Val acc:0.9731 \t Training loss:0.0730 \t Val loss:0.1569\n","Epoch:  3 \t Training acc: 0.9864 \t Val acc:0.9775 \t Training loss:0.0719 \t Val loss:0.0964\n","Epoch:  4 \t Training acc: 0.9953 \t Val acc:0.9869 \t Training loss:0.0497 \t Val loss:0.0642\n","Epoch:  5 \t Training acc: 0.991 \t Val acc:0.9815 \t Training loss:0.0428 \t Val loss:0.0920\n","Epoch:  6 \t Training acc: 0.9865 \t Val acc:0.9733 \t Training loss:0.0407 \t Val loss:0.1204\n","Epoch:  7 \t Training acc: 0.9943 \t Val acc:0.9815 \t Training loss:0.0377 \t Val loss:0.1061\n","Epoch:  8 \t Training acc: 0.9849 \t Val acc:0.9728 \t Training loss:0.0294 \t Val loss:0.1974\n","Epoch:  9 \t Training acc: 0.9953 \t Val acc:0.9832 \t Training loss:0.0333 \t Val loss:0.0941\n","Epoch:  10 \t Training acc: 0.9942 \t Val acc:0.9822 \t Training loss:0.0351 \t Val loss:0.1075\n","Epoch:  11 \t Training acc: 0.9987 \t Val acc:0.9874 \t Training loss:0.0348 \t Val loss:0.0807\n","Epoch:  12 \t Training acc: 0.9965 \t Val acc:0.9842 \t Training loss:0.0235 \t Val loss:0.1662\n","Epoch:  13 \t Training acc: 0.9638 \t Val acc:0.9612 \t Training loss:0.0363 \t Val loss:0.2122\n","Epoch:  14 \t Training acc: 0.9992 \t Val acc:0.9886 \t Training loss:0.0255 \t Val loss:0.0750\n","Epoch:  15 \t Training acc: 0.9973 \t Val acc:0.9852 \t Training loss:0.0279 \t Val loss:0.0941\n","Epoch:  16 \t Training acc: 0.9967 \t Val acc:0.9889 \t Training loss:0.0199 \t Val loss:0.1277\n","Epoch:  17 \t Training acc: 0.9922 \t Val acc:0.9812 \t Training loss:0.0297 \t Val loss:0.1656\n","Epoch:  18 \t Training acc: 0.9997 \t Val acc:0.9914 \t Training loss:0.0159 \t Val loss:0.0814\n","Epoch:  19 \t Training acc: 0.9989 \t Val acc:0.9874 \t Training loss:0.0025 \t Val loss:0.1178\n","Epoch:  20 \t Training acc: 0.9951 \t Val acc:0.9840 \t Training loss:0.0561 \t Val loss:0.0967\n","Epoch:  21 \t Training acc: 0.9981 \t Val acc:0.9859 \t Training loss:0.0157 \t Val loss:0.0938\n","Epoch:  22 \t Training acc: 0.9978 \t Val acc:0.9869 \t Training loss:0.0216 \t Val loss:0.1130\n","Epoch:  23 \t Training acc: 0.9948 \t Val acc:0.9842 \t Training loss:0.0177 \t Val loss:0.1057\n","Epoch:  24 \t Training acc: 0.9996 \t Val acc:0.9921 \t Training loss:0.0147 \t Val loss:0.0760\n","Epoch:  25 \t Training acc: 0.9935 \t Val acc:0.9795 \t Training loss:0.0099 \t Val loss:0.2008\n","Epoch:  26 \t Training acc: 0.9894 \t Val acc:0.9800 \t Training loss:0.0443 \t Val loss:0.1706\n","Epoch:  27 \t Training acc: 0.9982 \t Val acc:0.9906 \t Training loss:0.0282 \t Val loss:0.0922\n","Epoch:  28 \t Training acc: 0.9975 \t Val acc:0.9881 \t Training loss:0.0209 \t Val loss:0.0846\n","Epoch:  29 \t Training acc: 0.9999 \t Val acc:0.9914 \t Training loss:0.0017 \t Val loss:0.0967\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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z7PoAsE8dF2lTL941PK/6KiosjLy9P/Ry8xxpCXl0dUVJRHzwuqeQRg+wgqqw1HyqvoEBl0b1+pNiUlJYWsrCxyc3P9XZR2IyoqipSUFI+eE3RnQuekskNHyjUQKOVn4eHh9O3b19/FCHrB1zTkTEWt/QRKKQUEZSDQNBNKKeUqCAOBpqJWSilXPg0EIjJdRDaLSIaI3NHIfheKiBGReme9eZOmolZKqdp8FghEJBR4EpgBDAFmi8iQevbrCNwEfOursrjSVNRKKVWbL2sE44AMY8x2Y0w5MBeYWc9+fwT+CjQvz6uHIsNCiYkI1RqBUko5+DIQJAO7Xe5nObbVEJHRQC9jzEeNHUhErhGRlSKy0hvjjeOjwzUVtVJKOfits1hEQoC/A7c2ta8x5hljTLoxJj0pKanFrx2nqaiVUqqGLwNBNtDL5X6KY5tTR+B4YKmIZAInAPNaq8O4QEcNKaUU4NtAsAJIE5G+IhIBXArMcz5ojCkwxiQaY1KNManAN8C5xpjmLzbgJk1FrZRSR/ksEBhjKoEbgIXAj8CbxpgNIvKAiJzb+LN9Kz4mXJuGlFLKwafJdowx84H5dbbd08C+U3xZFlfxMREUlJRjjEFEWutllVKqTQq6mcVg+wgqqgzF5VX+LopSSvldcAaCmjQT2jyklFJBGQjiXFJRK6VUsAvKQKCpqJVS6qigDASdNRW1UkrVCMpAoKmolVLqqKAMBHGailoppWoEZSCICg8lKjxEU1ErpRRBGgjALmKvNQKllArmQBCjqaiVUgqCPBAUaI1AKaWCOBBER+ioIaWUIpgDgaaiVkopIIgDQZyjacgY4++iKKWUXwVtIOgcE0F5VTUlFZqBVCkV3II2EMTrpDKllAKCORDEaCBQSikI4kDgTEWts4uVUsEuaAOBLk6jlFJW0AYCTUWtlFJW0AYCTUWtlFJW0AaCqPBQIsNCNM2EUiroBW0gAOfsYq0RKKWCW3AHAk1FrZRSQR4INBW1UkppINA+AqVUsAvuQKCpqJVSKsgDgSMVtWYgVUoFs6AOBHEx4ZRXVlNaUe3voiillN8EdSComV2szUNKqSAW1IFAU1ErpVSQB4I4TUWtlFK+DQQiMl1ENotIhojcUc/j14rIDyKyRkSWi8gQX5anrnhNRa2UUr4LBCISCjwJzACGALPrOdG/ZowZZowZCTwE/N1X5amPpqJWSinf1gjGARnGmO3GmHJgLjDTdQdjzGGXu7FAq47j1FXKlFIKwnx47GRgt8v9LGB83Z1E5FfALUAEcEp9BxKRa4BrAHr37u21AkaHhxIRFqKjhpRSQc3vncXGmCeNMf2B24G7G9jnGWNMujEmPSkpyWuvLSLER2uaCaVUcPNlIMgGerncT3Fsa8hc4DwflqdemopaKRXsfBkIVgBpItJXRCKAS4F5rjuISJrL3bOArT4sT700FbVSKtj5rI/AGFMpIjcAC4FQ4AVjzAYReQBYaYyZB9wgItOACuAQcKWvytOQuJhwdh8sbu2XVUqpNsOXncUYY+YD8+tsu8fl9k2+fH13dI4J54csrREopYKX3zuL/S0+RlNRK6WCW9AHgrjocEorqimtqPJ3UZRSyi+CPhDopDKlVLDTQBCtqaiVUsEt6ANBZ60RKKWCXNAHAk1FrZQKdkEfCOJjNBW1Uiq4aSCI1lTUSqng5lYgEJFYEQlx3B4oIueKSLhvi9Y6YiJCCQ8VbRpSSgUtd2sEXwBRIpIMfAJcDrzkq0K1JhEhPiaCAh01pJQKUu4GAjHGFAMXAE8ZY2YBQ31XrNYVHx2uNQKlVNByOxCIyInAT4CPHNtCfVOk1qepqJVSwczdQHAzcCfwniODaD9gie+K1briNBW1UiqIuZV91BjzOfA5gKPT+IAx5kZfFqw1xceEsyFHA4FSKji5O2roNRHpJCKxwHpgo4j81rdFaz2dY7SPQCkVvNxtGhpijDmMXUpyAdAXO3IocBTuhc0f1/tQfEwEJRVVmoFUKRWU3A0E4Y55A+cB84wxFYDxXbF8YM2r8PolUHzwmIfiHJPKCnRSmVIqCLkbCP4NZAKxwBci0gc47KtC+UTKWPs7e/UxD2kqaqVUMHMrEBhjHjPGJBtjzjTWTmCqj8vmXT1HgYRA1opjHqpJRa1DSJVSQcjdzuI4Efm7iKx0/PwNWzsIHJEdoeuQ+gNBjOYbUkoFL3ebhl4ACoGLHT+HgRd9VSifSR4D2augurrWZmcgKNCmIaVUEHI3EPQ3xtxrjNnu+Lkf6OfLgvlEylgozYeD22ptdqai1tnFSqlg5G4gKBGRic47IjIBKPFNkXzI2WFcp3koNiKUsBDRpiGlVFBya2YxcC3wsojEOe4fAq70TZF8KHEgRHaCrJUw8rKazTYDqU4qU0oFJ3dTTKwFRohIJ8f9wyJyM7DOl4XzupAQSB5db4dxXHS4pqJWSgUlj1YoM8YcdswwBrjFB+XxvZSxsG8DlB+ptblzjCaeU0oFp5YsVSleK0VrSk4HUwU5a2pttqmoNRAopYJPSwJBYKWYcEpJt7+zV9baHBcdQYGOGlJKBaFG+whEpJD6T/gCRPukRL4Wmwid+x7TTxAfE66jhoJd/m7o0BXCIv1dEqVaVaM1AmNMR2NMp3p+Ohpj3B1x1PakpNuRQy7io8MpLq+irFIzkAalA1vh8dHwzVP+LolSra4lTUOBK2UsFO6BguyaTfGxdlKZZiANUovugaryYy4QlAoGPg0EIjJdRDaLSIaI3FHP47eIyEYRWScinzqymvqes5/ApXkoPlrTTAStHV/A5vkQFg17A2tEtFLe4LNAICKhwJPADGAIMFtEhtTZ7Xsg3RgzHHgbeMhX5aml2zAIjawdCBz5hnTkUJCproKFd0Fcb5hwE+TvgpJ8f5dKqVblyxrBOCDDkZuoHJgLzHTdwRizxBhT7Lj7DZDiw/IcFRYBPUbYBHQOmoo6SK2dC3t/gGn3Hk1Bsm+9f8ukWo8xsOxvto8oiPkyECQDu13uZzm2NeTn2GUwjyEi1zhTYOfm5nqndCljIed7qLI1AE1F7aGqCijc5+9StEz5Efj0Afu3cPyF0H2Y3b73B/+WS7We3E32b+CVCwL/77kF2kRnsYj8FEgHHq7vcWPMM8aYdGNMelJSkndeNCUdKktrrv40FbWHlj8Kj485ZoZ2QPnyMSjaC2f8GUSgYzeI7Qp7tJ8gaDhXLCzMgdcvhfLixvdvp3wZCLKBXi73UxzbahGRacDvgXONMWU+LE9tNR3GdpRIh8gwQkOEfM035J5N/4PyQtj9rb9L0jyHc+DLf8LQ86HXuKPbewzXGkEwyVkNER1h1ku2heC9a45ZryQY+DIQrADSRKSviEQAlwLzXHcQkVHY9ZDPNcbs92FZjhXXCzp0qwkEIkJ8tKaZcEvRftjjSNGRudy/ZWmuz/5kU41Mu6/29u7DbHNBpV4QBIXs1dBzJBx3jq0Z/vghLL7X36VqdT4LBMaYSuAGYCHwI/CmMWaDiDwgIuc6dnsY6AC8JSJrRGReA4fzPhHbNuwyciguJlybhtyR8an9HdMlMANBzhpY8xqccB10Tq39WPdhUF1hg4Fq3yrLbdNwz1H2/gnXwdir4avHYGXgLcDYEj6dHWyMmQ/Mr7PtHpfb03z5+k1KHmObOIoPQkwC8dHh2jTkjozFti195Gz4+knbTxARIEtYGwOf3A0xCTDp1mMf7z7c/t67zjYTqfZr33o7iTB5tL0vAtP/Aocy4aNbIb43DDjVr0VsLW2is9hvnMMFHcNINRW1G6qrYNun9h+k78lQXRlY/QSb50PmMph6F0TFHft4Qj8Ij9V+gmCQ4+go7jn66LbQMJj1InQ9Dt6aA/s2+qVorS24A0HPUSAhNc1DcbpKWdNyvoeSQzBgGvQ6ASQ0cJqHKsvhkz9A4iAYPaf+fUJCodtQDQTBIPt727wZ37v29siOcNmbEB4Dr10cFMNKgzsQRHaArkNrAkF8dIROKGvK1kU2ePY/xX5+yaMDJxCsfAEOboPT/2Sv/BrSfZgNBCYwM60rN+WstrUBqWdplbhkuOwNKM4LimGlwR0IAFLGQNYqqK4mPiacI+VVlFcG3/Axt2Ustn0rMQn2fuok27RWVuTfcjWl+CAs/T/oNxXSTmt83x7DoeywbStW7VP5ETsgIHl0w/v0HAkXPm9rwe9e3a6HlWogSBkLZQWQl3F0UpnOLq7fkTx70h/g0sefOjEw+gm+eMSe3M94sP4rQFc6w7j927MWTHXt/oH6DD4Tpv+fHVSy+J7G9w1gGgicHcZZK4iPcaai1uahem1fAhgY4HJF3Ws8hIS17eahvG3w3TMw6nLb/t+UrkNs85cGgvbLOaO4sRqB0/hrHcNKH2+3w0o1EHRJg8g4Gwgcqai1w7gBWxdBdIKtMjtFdrBXVW05ECy6x646NvX37u0fHg2JAzUQtGc5q6FTil2RrinOYaX9T7GZagM5rUoDNBCEhNirguyVmoq6MdXVR4eNhoTWfix1ov3Haov9BJlf2mr9xJttLiF3dR/edtcmCIQ+mbYuexUkj3J//9AwmPgbqCi2F0TtjAYCsM1D+zbQOawS0FTU9dq7Fo7k1m4WcmrL/QSrXrRDBE+8wbPndR8Gh7Ntv0hbsn0pPHsKLP+Hv0sSuIoP2oEATfUP1NX7JIhJhI0f+KRY/qSBAGwCOlNN5wKbiVQ7i+uRsdj+7n/KsY+11X6CqgrY+gkMnG6bezzh7DDe14aah0oL4P1f2dub/uffsgQy50Sy5DGePS80zOYk2rIQKkq8Xy4/0kAAkGwzkcbmrrEZSLVp6FhbF9sJeB3qSQPeVvsJdn1jT56DZnj+3JpUE20oEHx8l02XPOxiO/Qxb5u/SxSYsr+3v137utw1ZCZUHDl6YdROaCAAiO0CCf2QrBXEeZJvKGMxzLsRqip9Wz5/KzkEWd/VHjZaV1vsJ9jyMYRG2LkDnortAp2S287aBJsXwJr/wsRb4JTfH92mPJez2g4SqS/FSFNSJ9kBE+2seUgDgZMjE2l8VJh7ncV718MbV8Dq/8Dmj3xfPn/avtSOua6vf8Cp76S21U9gjM0r1PdkW2NpDucMY387kmcvOLoNg8m324ypXYfa96c8l73avWGj9QkNg+POhs0fQ0Wpd8vlRxoInJLToWgf/aPym05FXZQLr8+GqE52CNq3/26dMvpLxmJ79dRYm2pb6yc4sBUObm9es5BT92FwYIv/24Pn32prZef/y663DXai066vbcenct/hHLsqnacdxa6GzLSLMm37zHvl8jMNBE6OFctGSkbjTUOV5fDmFXBkP1z6Koz/Jez8sm1cOfqCMXb9gf6nNJ6fJyLWBorMZa1XtsY4r5YHTm/+MboPt4vX7P/RO2Vqjh/ehg3vwdQ7ofvxR7cPmmFraVsW+q9sgciTiWQN6TsZouJh4/veKVMboIHAqdvxEBbFkOotDXcWGwPzb4NdX8G5T9gT3+jLbZbC9lor2LceCvc03j/glDrR/qO1hX6CLR/bE3lcSvOPUZNqwk/9BIV7bV78lLFw0k21H+sxCjr20OYhT+WstjVX53fbHKHhMPhs20dT2Xqr6/qSBgKnsAjoMYL+ZT823DT03TO2T2DiLTB8lt0W3RmGXww/vNU+q+nO0RHuBgJTBbu/8W2ZmnIkz/ZVtKRZCCC+D0R28k9tzxiY92t7ojnvX8fWxkJCbG0n49N21Vbtc9mr7VoDng4nrmvoeTZ31falXimWv2kgcJUylp4lWygtK6Wiqk6mwW1L4OM7YdCZcMofaj827pdQWWqDRHuzdbHtpOzYvel920o/wdZPbLNJS5qFwJ5sux3vn0Dw/Sv2fUy7DxIH1L/P4LPsUMYdX7RmyQKXMTaTaEv6B5z6TrapaTa0j+YhDQSuUtIJqy5jsOyqPaksbxu8dSUkDYILnrEnCFfdhthhZd89176GkpYetlf3aW6uKFrTT+DnQLBlgW026dGMceJ19RhuR4i1ZgriQzvtRUfqJBh3TcP79T0ZIjpo85C7Dm6H0vyW9Q84hUXYDvvNH9l+wwCngcCVY2LZqJCtR/sJSgvswhQSCrNft6sX1Wf8tXA4q339U+743A4JdadZyMnf/QSVZba5ZOAZxwbs5ug+zF51H9ze8mO5o7oaPvgVIDDzycbfQ1ik7cTfvKBd58r3mhznRDIvBAKAIefZ88OOz71zPD/SQOAqLoWy6K6MDA4lM4gAACAASURBVNlmU1FXV8HbV9mTwCWv2PHbDRk0A+J6t69O44zFENHRNvm4y9/9BJnLobzINuF5Q2t3GH/3bzvyavqfoXOfpvcffJYdDrnne9+XLdBlr4awKNtH4A39p9o+pHYwekgDgSsRSrqOYpQ4agSL7rEnwzMftie4xoSEwrhfwM7ltikh0Blj+wf6TbajJNzVazyEhPuveWjzAgiLts0m3pA02PZ7tEY/wYGtsPg+SDvDrp3gjrTTbW11UzuqifpKzmo7ksyTv+fGhEXaC8BNH9m8VgFMA0EdJjmdviH7SPzhWfj6CbsgRfpV7j151OX2JPRdO6gV5G62TV1NLetYlz/7CYyxw0b7n9LyUSFOYZGQdJzvA0FVJbx3rb1iPfexpldRc4pJgN4ntq8mSV+oqrSrknmaaK4pQ2bayX4B3mGvgaCO8N7jABix8WF7VTn9/9x/ckyCHUq67s3AH0qa4ci57kn/gJO/+gn2bYCC3TCohaOF6uo+zPdNQ8v+Btkr4ay/uTdCy9XgM2H/Rji4wzdlaw9yN9m1BLzRUeyq/ym2wz7Acw9pIKgjJjWdChPKocgUmPUfz6uR451DSV/2TQFbS8ZieyXcnAlZ/uoncCZhSzvDu8ftPgyK9kHhPu8e12nbZ7D0/2D4JTDsIs+f75wvoUnoGuZMPe2tjmKn8Gg7THnT/wJ6xKAGgjpCojrwty73cWXV7ykNb0Z2wm5D7bC/FQE8lLSsCHZ+5f6w0bp6jbP9BDtaOd3ElgV25JcnK5G5o4cjJbUv1iYoyIJ3fmH7Is5u5mIzCf1s0NbmoYZlr7bj/hP6ef/YQ2ZCcZ7tHwxQGgjqcfJZl7GuKI653+1q3gHG/9I2UWxpI1donz8M/zgeVjzvXnDKXA5V5c1rFgL/9BMU7rXLD3q7WQjspDLwfj9BZTm8NccOeb3kFfu5NdegGTZ4B3qTpK/krLbrD3hjSHFdaadBeGxANw9pIKjHif26MC41gac/30ZpRZXnBxg4A+J6tY2hpN8+A0v+ZGfafnQLPH2STaFrTMPPyVhk/7B7n9j8102daMdtlxU2/xiecCZf89awUVfR8RDf2/trEyz6A2StgJlPQGJay441+CzbHNec9XSLcuGZqbD0r+1zPkJFqe0/8nb/gFN4NAw8HX780A45D0AaCOohItw8LY19h8t4c+Vuzw8QGgZjf2HHg/tzKOn6d2HB72DQWXDTOrj0NXuyeP0S+M85kLPm2OcYY08mfU+2I2aay9lPsKuV1ifYvMDO4+g6xDfH7z7cuzWCH96Gb/8FJ1wPQ89v+fF6joYO3TxvHjIG5t1gg/bSP8Obl7eNpIHetG+9nRjp7f4BV0POs2t67/zKd6/hQxoIGnBi/y6MTe3MU0u2UVbZjCg/+grHUNJnvF84d2xbAu9eY6/qL3reBqfBZ8H138CZj9hRJs9MtvvkuwS7vG2Qv7P5/QNONfMJWqGfoLzYJv8aNMP9YZee6j4c8jKg/EjLj5W72S4002s8nPZAy48HLknoFnuWEXPl83bI7fT/gzP+bAPJ86fbxd1bQ+lheOdq+NtgePvnsOZ1KNrv3dfwRurppqSdZv/fA3RymQaCBogIN506kL2HS3lzRTNqBTEJNkOpP4aSZq+GN34KiQNtWgzXMfWh4TDuarjxe5tFdeMH8PgYO5GptKBlw0ZdRcTYNR5ao59gx+dQWeKb/gGn7sMAA/s2tuw4ZUXwxuX2O5n1kvcmN4EN9OVF7nfS526Ghb+33/X4a+HEX8FP3rbzR56Z6vvO/pzv4d8nw/p3bF6oHZ/D+9fCI2nwr0mw+H7I/LLlk7VyVkNsV7v0qK9ExNpgEKDNQxoIGjFhQBfS+3TmqaXNrBWM+6U9QX3/ivcL15C8bfDqLBuIfvqObd+uT1QcTLsXblhpmyaW/wMeG2X7NbqkNZ5Ow12e9BNUlMBXj9vUy55edW9eYFNh9Gli9ndL1KSaWNv8YxgDH94EeVttLa1TT++UzanvZLs2hjvNQ5Vl8M7P7Qls5lNHa1IDToWrl0BsIrxynh395m3GwDf/gudOs4MSfjYfLpsLt26Baz632X0jOsCX/4SXzoSH+tkLm1Uv1a69usu5NKWvaotOQ8+zw4zbynKtHvBpIBCR6SKyWUQyROSOeh4/WURWi0iliDRjALVviQg3TUtjT0Epb67M8vwA3Y+3J6fvnm2doaSH99h/Xgz89D3o1KPp58T3ggv+bf8Buw6BQztswjZvcKefoLLcfj7/HAmf3G3nX8y9zP0c+9WOVboGnHp0GUdfiEuxq1K1pJ9gxXOw/m2Yehf0m+Ktkh0VHnU0CV1jgwEAPvuTfS/nPnHscNsu/eEXi6H/qXZhnA9v9l6GzeKD9qT+8e22JnLtcuh9gn0sJMSO7Dn5NrhqAdy+Ay5+xV6oZH9vg+ijx8MXj7j/emWFdrlRX/YPOKWdYWeGB2Bqap8FAhEJBZ4EZgBDgNkiUrcnbxcwB3jNV+VoqYkDEhnTpzNPL8loXq2gtYaSluTDfy+0/2g/ebvhHPYN6TkSrvwQrv4MphwTs5snZVzD/QRVlfD9f22z1Pzb7PjuOfPhvKdte/+bV7h38tnzvU261tJFaJoi4khJ3cxAkLXSppZOOwMm3urdsrkafBYU5sCeegYCOG1fCl89ZlOnDG5glFVUnG1WnHAzrHoRXp5pRxe1xO4Vtiloy0LbHzH7dVtzbUhUHAw516bc+M16uP5bGxQ++yOsnevea+asAYxv+wecIjvY4PbjvIAbfeXLGsE4IMMYs90YUw7MBWa67mCMyTTGrAPa7Kdm+wrSyCko5a3m1AoGnen7Be4rSuD12fbK55L/Nv+PXsSO/28o1ban6usnqK62I2aeGm/TLcd2gZ++a5sHUifAyMvsxKqtC+Gdq5quSW3+GCTEJl/zte7D7TBET2t3R/LgzSttDe38f/lmLLtT2hn282goCV3xQXjvOtv8d/qDjR8rJBROux8ueM62sz87tXlDaKurbTPPi9Nt2a5aaPsjPGmqEYGug+H8Z+yItg9ugO1upH/21Yzihgw5zy7tmvVd67yel/gyECQDrg16WY5tHhORa0RkpYiszM1t4VVJM0xKS2RU73ieXrqN8koPY1ZomM1KmrnMnkS8rarSjrbY9bVdNKf/VO+/Rks4+wlKD9ssjf+aaNumQyPtcNarl9hmHdeTQvpVMP0vtuPtvV823vm2eQH0OqHxK0tv6T7Mpg/Jy3D/OdVV8O4v4Mh+uPhl35cztov9POrrJ3D2URzJhQufs4HaHcNnwc8W2Pfywhm2KS9rlW2KbKpj9EieHa686B5bW/nlF5DSgsRvYRG2uahLf9vpvn9T4/tnr7ZLjsZ2af5remLgGfZv29uTy6oqbC2ocK93j+sQEJ3FxphnjDHpxpj0pKSkVn99O69gINn5Jby9qhm1gtFX2qFlH9/h3b4CY+B/N9tVks58GI6/wHvH9hZnP8G/Jti2/8pSuPB52zY8+KyGrwpPuM4u07j+bduBXF9VO3+3Tfvgy9FCrro7Uk140jz0+UM2l9CMh6DnKN+Uq67BZ9qx84d21t7+/X9ts8Wpf7BNgZ5IHg3XLLGzrOffBs+dAn8fDH/qametP38GvPUzOwrpm6ftiXDjB/Z73/65TaY36z8ND17wRHQ8/OQt2yfy6qzGc0DlrG6dZiGnqE72wmbjB95pHiovtq0Jj42yF0Xr3mj5MesR1vQuzZYN9HK5n+LYFpBOTktkZK94nlySwUVjUogI8yCGxiTYf4QProfF98IZTVTJ3fXpA3ZE0sm/s0NC26KUcbat1xjbMTli9rELsTdk4m9sp/Hnf7GdcGf9rXbg2PKx/e2L2cT1SUyzV3t719mr5MZUlMDCu2DlC/Y9j5nTKkUE7Ofxyd22tnTCtXZb3jZYcLttVjnx1807bsfucNXHtmZ7OMcOMz2cAwXZcDjb9ktsnm+DvVNCf/jFm0fzNXlLfG+47A148Ux47WKY85Fto3d15ADk77Kp5FvTkJn2c1j1IgybZYODp4oP2prXd/+2eYx6n2j//n3UBOrLQLACSBORvtgAcClwmQ9fz6ecs43nvLiCd1ZnMXtcb88OMOon9h/l6yfsmOmmTiRN+epxWP53e4KZelfLjuVLETFw4xo7HLA5o3qm3GGH4H75TxsMznjwaDDYvMCeaFqansFdoeF2daumagS5m+3KdvvWw0m/hlPu8f3QRVdd+kPiIHsyOuFa26zw7tW2/Oe1sI8iJNSe1Bs6sRtjT2KHs20TVK/xx56gvaXnKLjoRZg72zY3XvJq7YuM1phIVp9BM+zw649uscG3z0m2ySjtjKYHcRRkwddP2aGyFUfsJMEJN0OfFqR7cYPPAoExplJEbgAWAqHAC8aYDSLyALDSGDNPRMYC7wGdgXNE5H5jzFBflamlJg9MYoSjVnDhaA9rBWBHSuzbYKf0Jw2EHiOaV5DVr9grviHnwVl/b92TTHO0pF1cBKbdb8e9f/OkbQ449R47LDBzWeOLu/tC92H2BGvMsZ+7MbDmVZj/Wzth7Cdve76wj7cMmmEvOkry7e/sVXYCW5wPJ1WB/Uxiu7Rem/yg6bZZ9KNb7ZDUMx85+r3krAak+f9nzRUVBzessvMJti60o6QW3mV/Evo7gsLp0GfC0Yuj3M32YmfdG/bvaNgsmHATdPNRypQ6fFkjwBgzH5hfZ9s9LrdXYJuMAoKIcPOpafzspRW8uzqLSz2tFYSG23bSZybD3J/ANUvtxB1PbJwHH95ox4tf8Ky9QmvvRGzncWWpXcAlLNoG0qpy3w8bravHCNscV7in9oSwskL43y3ww5s2DfkFz7o3j8NXBp8FXz5qh1qufAFG/tQ7OY3aorG/sP0hXz1mO4Yn3Gi3Z6+GpEHeGwXnidAwOwoudYJNI3JoJ2z9xDZnrngevnnKToLsP8X2JWz+yP5dp/8cTrrBNn21Ip8GgvZoyqAkRqTE8cSSDC4ck0J4qIe1gg5JdojnC9NtCuLL33e/zXzbElsFTk63x/DlBKq2RgTO+oetGSz5k83uGhVvR8i0ppoZxj8cDQQ5a+Dtn9n8PFN/D5Nu9X+ATk6H2CQ7ia1zX5jxF/+Wx9em3W/7Axb9wU6SHHKerREM8FONrK7OfWw/3rir7cz57Z87aguf2JXTJt9ua7eeXhh6SUCMGmpLnLONsw6V8O7qZowgAttmec6jtmlj0R/ce07WSluL6JIGP3mzZbnrA1VIiO1wHnq+naSXdrr7QdRbujlaLveuO5oq4fnTbKf2lf+Dyb/zfxAA+1kNOtMubH/hc/65Km5NISFw/r9tn8S7v4QN79o+itbuH3BHRKwd2XXOP+GWjXB7pu3n81MQAK0RNMvUQV0Z7qgVXDC6GbUCsBOnctbYKmKPETDi0ob33bfRzhru0BUufxeiOze/8IEuNMw2u3QfDsed2/qvH9nRzoLOXG7THmz+yHbonfd068xl8MS0++ycDE+Higaq8Ci49HUbmN/+ud3WWhPJmquN9O9pjaAZnLONdx8s4b3vWzAi9owHbS6iD2+qf20AsM0Nr5xvR8xc8b7nC5u3R6HhMOkWz9NoeEv34TZNw9ZPbN/F7LltLwiALVOwBAGn2C52jkF0Z5vepPvx/i5RQBDTVHKqNiY9Pd2sXLnS38XAGMO5T3xJ1qFihqfEExMRSnR4KNGO3zERoURFhBLj2BYbGcakAUnExdRJO1yUC89MsVcG1yytXT0s3GtncpYW2JmdXY9rxXeoGrThfTsS58yHW2+SmPLMvg12JE5bnGTpJyKyyhiTXu9jGgiab11WPg99vJnC0gpKKqooLq+ipLyKkgr7U/ej7ZUQzYtzxjGga51x1Tnf287jlLFw+Xv2irfkELx4lq0RXDnP5uxRSqlm0kDgB8YYyiqrbXCoqGJ7bhG/eWMN5ZXV/PvydE7sX2ec9dq5dgr5+GvtOPmXz7MT0C57s+3lD1JKBZzGAoH2EfiIiBAVHkpCbATJ8dFMSkvivesn0K1TFFe88O2xOYtGXArjr7Pr2D4zFbJX2pw8GgSUUj6mgaAV9UqI4e3rTmJc3wRue2stf/9kM7VqZKf/0U5GOrAZznnM5mJXSikf0+GjrSwuOpyXfjaOu99bz2OfZbDzYDEPXTScyLBQ2zdw2RuOFZW0E1Ip1To0EPhBeGgIf7lwGH0SY3jo483k5Jfw78vTSYiNsJNNNAgopVqRNg35iYhw/ZQBPHHZKNZmFXDBU1+y44CHi7Yr1cre/z6bq19e2bxlW1WbpYHAz84e3pPXrz6Bw6WVnP/Ul3y346C/i6RUvUrKq/jTRxtZtHEfTy3Z5u/iKC/SQNAGjOnTmfeuP4mE2Ah++ty3zP1uF6UVesWl2pa5K3ZxoKicYclxPLU0gy37Cv1dJOUlGgjaiD5dYnnvugmM7hPPHe/+wPD7P+GyZ7/hySUZrN2dT1V1YM33UO1LWWUV//58O+P6JvDiz8YSGxnGHe+so1r/LtsF7SxuQ+Jiwvnvz8fz5bY8vsw4wPKtB3h44WYeXriZTlFhnNQ/kQlpiUwckEhqlxikjSSsUu3fu6uz2Xu4lIcuGk5ih0j+cNYQbn1rLf/9didXnJjq7+K1iupqQ3lVNVHhbSC7rJdpIGhjwkJDmDwwickDkwA4UFTGV9vy+HLrAZZnHODjDXsB6BkXxYQBifTv2oGEmAgSYiNI6BBBl1h7u0NkmAYK5RWVVdU8vXQbI1LimJRmc2FdMDqZ99dk89cFm5h2XDd6xkf7uZS+ZYzhpjfWsGTTfn57xiB+ekIfQkPaz/+XppgIIMYYduYVszzjAF9mHOCrbXkUlFTUu29EaAidY8NJiI2kS2wEXTpE0CMumuT4KHrGRztuR9MpWgOGatx732fxmzfW8szlYzh96NHst7sPFnP6P77gpP5deO7K9Hb9d/SfrzK5d94G+iXFsj33CCNS4vjzBcMY2jPO30Vzm+YaaqeMMRSXV3HwSDl5R8o55Ph98EgZB49UOH7bbQeKythbUEpFVe3vOyYi1BEYokiOj6ZnfDTpfTozrm8CYc1ZZ0G1K9XVhtMf/YKwEGH+jZMIqXMV/Nyy7fzpox95fPYozhnRs4GjBLZ1Wflc9PTXTEpL5Nkr0vlwXQ5//N9GDhVXcNWEVG6eNpDYyLbfuNJYIGj7pVcNEhFiI8OIjQyjV0JMk/tXVxsOFJWRnV/CnoJScvJLyMl3/C4o4cc9hRwoKgMgITaC047rxvRh3ZnQP5GIMO8GBWMMRWWV5BdXUFBif/KLKzhSXkmICKEhECJS8xMaYt9vqAghIRAWEsKIXvHERYc3/WJNqKyq5sN1Obzy9U5SE2O54sRURvaK98K7DHwLN+wlY38Rj88edUwQAJhzUirz1uZw/4cbmJSWSHxM+1o+taCkgl+9tprEDhE8MmsEISHCzJHJTBnYlb98vIlnl+1g/g97eWDmUE49rpu/i9tsWiNQtRwpq+SLLbksWL+Xzzbtp6isko6RYZx6XFemH9+DyQOTiI5ovLOstKKKnXnFbM8tYvuBI+w4cIS8ojLySyooKK6wv0sqWjwSKiYilIvTezHnpFRSEz1furOyqpp5a3N4/LMMdhw4Qr+kWPYVlHKkvIoRKXFcfmIqZw/v0S47B91hjOHsx5dTUl7FolsmN9gmvjHnMOc8sZwLRiXz8KwRrVxK3zHGcP2rq1m0cR9v/PJExvQ5dmXAlZkHueu9H9iyr4gZx3fn3nOG0j0uyg+lbZo2DalmKa2o4suMA3y8fi+LftxHfnEF0eGhTB2cxPTjezAyJZ7dh+wJf1uuPeFvP1BE1qGSWmsxdO0YSbdOUcTHhBMXbX/iY8KJj44gzrEtPjqc+JgIYiJCMQaqjaHKGIwxVFU77lcbqo2h2tiA9c7qLD5cm0NlteHUwd34+cS+nNAvocm26sqqaj5Yk8MTS2wAOK5HJ246NY3Th3TjSHkl767O5uWvM9mWe4TOMeFcPLYXPx3fx61aV2vYd7iUhNiI5i2R6oElm/bzs5dW8PBFw5mV3qvRff/68SaeXrqN//58PBPT/Lf2rje99OUO7vtwI78/8ziuPrlfg/uVV1bz3PLt/HPxVsJDQ7jt9IFcfmJqm+tM1kCgWqyiqppvtx/k4w17WLhhH7mFZbUej4kIpV9SLH0TO9AvMZZ+SbH0T+pAamIsHXzYfrr/cCmvfLOTV7/dxcEj5Qzp0YmrJvblnBE9bCI/F84A8PhnW8nMK2ZIj07cNC2N047rdkyzhzGGr7fl8fLXO/lk414McMqgrlxxUiqTBiTW20xijOFwSSW5RWXkFpZxoKiMQ8XlDOrWkfTUhBadGApKKvhwbQ5vrdzN2qwC+nSJ4dbTB3H2sB71lqWljDFc8PRX7D9cxtLfTmky6JRWVDH90S+oNrDw5pObrDW2deuy8rnw6a+YPDCJZ69wryN8V14xd3+wni+25DIiJY45E1I5OS2JLh0iW6HETdNAoLyqqtqwetchMvYX0Schhn5JHejWKdKvo0ZKK6p4//tsnl++g637i0jqGMnlJ/ThJ+N7ExcdzvtrcnjCJQDcPC2N04Z0c6vMOfklvPbtrpqZtaldYph2XDcOl1ZwoMh2xOcWlpFXVE55VXW9x0iIjeCUwV05bUg3Tk5runkNbJ/O19vzeHPlbj5ev5eyymoGd+/IjON7sGD9HjbtLWRoz07cPn0wk9ISvfr5f5VxgMue+5Y/nnc8l5/Qx73nbDvAZc9+yy8n9+POGYG7rGpBSQVnP76M6mr46MaJHvV7GGP4cN0e/vzRj+w9XIoIDEuOY/LAJKYMSmJESrzfBmFoIFBBwxjDsq0HeH75Dj7fkktEWAiJsRHkFJQytGcnbp42kGnHdW3WSbOssoqP1+/l5a93snZ3PgmxESR1jCSxg+OnYwRJHSJrbYuLDmfVzkMs2mj7XA6XVhIVHsLEAUmcPqQbpx7X9Zgrxt0Hi3l7VRZvr8oiO7+ETlFhzByZzMXpvTg+uRMiQlW14YM12fx90RayDpVwYr8u3D5jsNc6uS979hsy9hfxxe+metRHcsc763hrVRYf/GoCxycHztBKJ2MM1/53FZ/+uL/BfgF3VFcb1ucU8PnmXD7fksvqXYeoNtApKoxJaXae0ORBSXTr1Hr9CRoIVFDK2F/IC19msiuvmCtPSm12AKiPMcbjY1VUVfPdjoMs2riPRRv3kZ1fQojYXFOnDelGl9hI3lmdxVfb8hCBiQMSmZXei9OHdGvwZFxWWcVr3+7iic8yyDtSzozju3Pr6YOOXRfbA6t2HuLCp7/i7rOO4xeTGm4br09BcQWn/v1zunWK5INfTWjw6je3sIxVOw+yMvMQK3ceIrewjL6JsfR1NCv2TbRNiz3jo1u1rf3FL3dwvxv9Ap4qKK5gecYBPt+yn8+35LLvsG1aHdy9I+eM6Mk1J/fzeZ+PBgKl2hhjDBtyDtcEhY17DgPQKyGaWWN6ceGYFJI9mK1bVFbJc8u28+wX2ymtrGbWmBRumpZGjzjPZ/xe9dIKvt91iC/vOIWYCM/7d+b/sIfrX13NnTMG88vJ/amuNmzLLWLlzkOszDzEqp0HycwrBiAiLIQRKXF0j4tmV94RtuceobCssuZYEWEhpHaJoV9ih5oAkdI5huT4aLrHRXl1WPPa3flc9C/P+gWawxjDpr2FfL4ll8827ee7HQcZ1Tuexy4d5dMBCRoIlGrjdh8sJu9IOcOT41rU+XugqIwnPsvg1W93EiLCnJNSuXZyfzrHutfOvT67gLMfX85tpw/khlPSmlUGYwzXvLKKZVtzOal/Iqt3HSK/2M6AT4iNYEyfzqT36Ux6ameOT46r1alvjOFAUTnbc4sco9BscNh+oIhdecVUugw5FoHEDpH0jI+mZ5ydMe96u3dCjNvvu6CkgrMeW4YxnvcLtNSHa3O4890fCBF46KIRTD++e9NPagYNBEoFmd0Hi/nHoi28tyab2Igwfj6xL7+Y1JeOUY1PwLv+1VUs23KA5Xec0qLJensLSjn3ieV0iAqzJ/0+CYxJ7Uy/xNhmX2lXVlWTdaiE7PySYyZDOu+X1Enf3iMuiiE9OjGkZyeG9uzEkB5x9EqIrlUG136BN689kdG9m9cv0BI7845ww2vf80N2AVee2Ic7zzzO6/NXNBAoFaS27Cvk759s4eMNe4mPCefayf258sTUekctZewv5LR/fMGvpgzgtjMG+aG0LWOMIb+4whEYStlxoIiNOYfZuOcwGfuLcFYmOkaGcVzPTjUBIutQCY99urVZfSLeVFZZxV8XbOaFL3cwpEcnnrhsFP2Smt/XU5cGAqWC3A9ZBfxt0WaWbs4lsUMkN0ztz+zxvWs1y9zyxhoWrN/Ll3ecYtfPbkdKK6rYvLeQjXsOsyGngI05h/lxT2FNDWLacV192i/gicUb93Hb22upqKzmwfOHcd6oZK8c12+BQESmA/8EQoHnjDF/qfN4JPAyMAbIAy4xxmQ2dkwNBEo134rMgzyycDPf7jhIcnw0N546gAtHp5CdX8Ipf/ucn52Uyt1nD/F3MVtFVbUhM+8IGfuLmDggsU0ljsvJL+Gmud+zIvMQs8akcP/Moc3quHfll0AgIqHAFuA0IAtYAcw2xmx02ed6YLgx5loRuRQ43xhzSWPH1UCgVMsYY1iecYBHPtnC2t359E2MpVunSFbvymfZ76a26th21bDKqmr++elWnliSQf+kDjx52WgGde/Y7OM1Fgh8OXB1HJBhjNlujCkH5gIz6+wzE/iP4/bbwKnSFupmSrVjIsKktCTev/4knr0inciwEL7ZfpCL01M0CLQhYaEh3Hr6IF65ajz5xRWc+8Ry/rcuxzev5ZOjWsnAbpf7WcD4hvYxxlSKSAHQBTjgw3IppbAB4bQh3Th1cFe+2ZHHqF6tP1pGaXTaxQAABlNJREFUNW1iWiILbprEHe+so28zsuy6o+00ijVCRK4BrgHo3bu3n0ujVPsSEiKc1L99ZAxtr5I6RvL8nLE+O74vm4ayAdfctSmObfXuIyJhQBy207gWY8wzxph0Y0x6UlKSj4qrlFLByZeBYAWQJiJ9RSQCuBSYV2efecCVjtsXAZ+ZQBvPqpRSAc5nTUOONv8bgIXY4aMvGGM2iMgDwEpjzDzgeeAVEckADmKDhVJKqVbk0z4CY8x8YH6dbfe43C4FZvmyDEoppRrnnxUSlFJKtRkaCJRSKshpIFBKqSCngUAppYJcwGUfFZFcYGedzYm0r9nI7e39QPt7T+3t/UD7e0/t7f1Ay95TH2NMvROxAi4Q1EdEVjaUTCkQtbf3A+3vPbW39wPt7z21t/cDvntP2jSklFJBTgOBUkoFufYSCJ7xdwG8rL29H2h/76m9vR9of++pvb0f8NF7ahd9BEoppZqvvdQIlFJKNZMGAqWUCnIBHQhEZLqIbBaRDBG5w9/l8QYRyRSRH0RkjYgE5OLMIvKCiOwXkfUu2xJEZJGIbHX8DpjlsBp4P/eJSLbje1ojImf6s4yeEJFeIrJERDaKyAYRucmxPZC/o4beU0B+TyISJSLfichax/u537G9r4h86zjnveFI8d/y1wvUPgIRCQW2AKdhl8FcAcw2xmz0a8FaSEQygXRjTMBOhBGRk4Ei4GVjzPGObQ8BB40xf3EE7c7GmNv9WU53NfB+7gOKjDGP+LNszSEiPYAexpjVItIRWAWcB8whcL+jht7TxQTg9+RYuz3WGFMkIuHAcuAm4BbgXWPMXBH5F7DWGPN0S18vkGsE44AMY8x2Y0w5MBeY6ecyKcAY8wV2fQlXM4H/OG7/B/tPGhAaeD8Byxizxxiz2nG7EPgRu354IH9HDb2ngGSsIsfdcMePAU4B3nZs99p3FMiBoGbhe4csAviLd2GAT0RklWOt5vaimzFmj+P2XqCbPwvjJTeIyDpH01HANKO4EpFUYBTwLe3kO6rzniBAvycRCRWRNcB+YBGwDcg3xlQ6dvHaOS+QA0F7NdEYMxqYAfzK0SzRrjiWIw3MNsmjngb6AyOBPcDf/Fscz4lIB+Ad4GZjzGHXxwL1O6rnPQXs92SMqTLGjMSu9z4OGOyr1wrkQFCz8L1DimNbQDPGZDt+7wfew/4BtAf7HO24zvbc/X4uT4sYY/Y5/lGrgWcJsO/J0e78DvCqMeZdx+aA/o7qe0+B/j0BGGPygSXAiUC8iDhXlvTaOS+QA8EKIM3Rix6BXe94np/L1CIiEuvo6EJEYoHTgfWNPytgzAOudNy+EvjAj2VpMecJ0+F8Auh7cnREPg/8aIz5u8tDAfsdNfSeAvV7EpEkEYl33I7GDor5ERsQLnLs5rXvKGBHDQE4hoI9CoQCLxhjHvRzkVpERPphawFg15N+LRDfk4i8DkzBpszdB9wLvA+8CfTGphG/2BgTEB2wDbyfKdjmBgNkAr90aV9v00RkIrAM+AGodmy+C9umHqjfUUPvaTYB+D2JyHBsZ3Ao9oL9TWPMA45zxFwgAfge+KkxpqzFrxfIgUAppVTLBXLTkFJKKS/QQKCUUkFOA4FSSgU5DQRKKRXkNBAopVSQ00CgVB0iUuWSrXKNNzPbikiqaxZTpdqCsKZ3USrolDim9isVFLRGoJSbHGtFPORYL+I7ERng2J4qIp85Ept9KiK9Hdu7ich7jpzya0XkJMehQkXkWUee+U8cM0eV8hsNBEodK7pO09AlLo8VGGOGAU9gZ7UDPA78xxgzHHgVeMyx/THgc2PMCGA0sMGxPQ140hgzFMgHLvTx+1GqUTqzWKk6RKTIGNOhnu2ZwCnGmO2OBGd7jTFdROQAdlGUCsf2PcaYRBHJBVJcUwA4UiQvMsakOe7fDoQbY/7k+3emVP20RqCUZ0wDtz3hmhumCu2rU36mgUApz1zi8vtrx+2vsNlvAX6CTX4G8ClwHdQsMhLXWoVUyhN6JaLUsaIdK0M5fWyMcQ4h7Swi67BX9bMd234NvCgivwVygZ85tt8EPCMiP8de+V+HXRxFqTZF+wiUcpOjjyDdGHPA32VRypu0aUgppYKc1giUUirIaY1AKaWCnAYCpZQKchoIlFIqyGkgUEqpIKeBQCmlgtz/A9c80XreFCCjAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"7gnwlAWx7Exf","executionInfo":{"status":"ok","timestamp":1605477784703,"user_tz":300,"elapsed":129367,"user":{"displayName":"Eric Ji","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhXIfdv0NwRhitAF6aaeGszxF2imvsyUqr1kk0tmQ=s64","userId":"13561871477578974388"}},"outputId":"bbfb22ab-c0af-405f-a694-7e6f7b428a4a","colab":{"base_uri":"https://localhost:8080/","height":777}},"source":["model_2 = SignClassifier()\n","use_cuda = False\n","train_acc, val_acc, train_loss, val_loss = train(model_2, train_loader, val_loader, batch_size=32, num_epochs=10, learning_rate = 0.01)\n","plot_training_curve(train_acc, val_acc, train_loss, val_loss)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Training Started...\n","U S I N G   C U D A \n","epoch:  0 training acc:  0.1111111111111111 val acc:  0.1111111111111111 training loss:  2.201994569168478 val loss:  2.1979341788554754\n","epoch:  1 training acc:  0.1111111111111111 val acc:  0.1111111111111111 training loss:  2.198701372808208 val loss:  2.1979337639696017\n","epoch:  2 training acc:  0.1111111111111111 val acc:  0.1111111111111111 training loss:  2.198704071254698 val loss:  2.197933889749482\n","epoch:  3 training acc:  0.1111111111111111 val acc:  0.1111111111111111 training loss:  2.198705068094476 val loss:  2.197933518041776\n","epoch:  4 training acc:  0.1111111111111111 val acc:  0.1111111111111111 training loss:  2.1987055372667395 val loss:  2.197934150695801\n","epoch:  5 training acc:  0.1111111111111111 val acc:  0.1111111111111111 training loss:  2.198705747446231 val loss:  2.1979337301779918\n","epoch:  6 training acc:  0.1111111111111111 val acc:  0.1111111111111111 training loss:  2.19870586403332 val loss:  2.197934049320972\n","epoch:  7 training acc:  0.1111111111111111 val acc:  0.1111111111111111 training loss:  2.1987059257558963 val loss:  2.1979339085225984\n","epoch:  8 training acc:  0.1111111111111111 val acc:  0.1111111111111111 training loss:  2.198705947540335 val loss:  2.1979337958838996\n","epoch:  9 training acc:  0.1111111111111111 val acc:  0.1111111111111111 training loss:  2.1987059814272394 val loss:  2.197933692631759\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"rpFAzzdr9jdy","executionInfo":{"status":"ok","timestamp":1605622395892,"user_tz":300,"elapsed":147512,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}},"outputId":"3b75c9f3-798f-4dc4-b3ba-79b69be5f63e","colab":{"base_uri":"https://localhost:8080/","height":827}},"source":["model_3 = SignClassifier()\n","use_cuda = False\n","train_acc, val_acc, train_loss, val_loss = train(model_3, train_loader, val_loader, batch_size=32, num_epochs=10, learning_rate = 0.001)\n","plot_training_curve(train_acc, val_acc, train_loss, val_loss)"],"execution_count":45,"outputs":[{"output_type":"stream","text":["Training Started...\n","\n","U S I N G   C U D A \n","\n","\n","Epoch:  0 \t Training acc: 0.947 \t Val acc:0.9333 \t Training loss:0.4890 \t Val loss:0.2444\n","Epoch:  1 \t Training acc: 0.9726 \t Val acc:0.9652 \t Training loss:0.1250 \t Val loss:0.1255\n","Epoch:  2 \t Training acc: 0.9861 \t Val acc:0.9731 \t Training loss:0.0730 \t Val loss:0.1569\n","Epoch:  3 \t Training acc: 0.9864 \t Val acc:0.9775 \t Training loss:0.0719 \t Val loss:0.0964\n","Epoch:  4 \t Training acc: 0.9953 \t Val acc:0.9869 \t Training loss:0.0497 \t Val loss:0.0642\n","Epoch:  5 \t Training acc: 0.991 \t Val acc:0.9815 \t Training loss:0.0428 \t Val loss:0.0920\n","Epoch:  6 \t Training acc: 0.9865 \t Val acc:0.9733 \t Training loss:0.0407 \t Val loss:0.1204\n","Epoch:  7 \t Training acc: 0.9943 \t Val acc:0.9815 \t Training loss:0.0377 \t Val loss:0.1061\n","Epoch:  8 \t Training acc: 0.9849 \t Val acc:0.9728 \t Training loss:0.0294 \t Val loss:0.1974\n","Epoch:  9 \t Training acc: 0.9953 \t Val acc:0.9832 \t Training loss:0.0333 \t Val loss:0.0941\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"2uQ2JFYL-O1e","executionInfo":{"status":"ok","timestamp":1605622908086,"user_tz":300,"elapsed":146774,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}},"outputId":"a5a42882-4357-4ae5-a341-4abb53385b43","colab":{"base_uri":"https://localhost:8080/","height":827}},"source":["model_4 = SignClassifier()\n","use_cuda = False\n","train_acc, val_acc, train_loss, val_loss = train(model_4, train_loader, val_loader, batch_size=32, num_epochs=10, learning_rate = 0.005)\n","plot_training_curve(train_acc, val_acc, train_loss, val_loss)"],"execution_count":46,"outputs":[{"output_type":"stream","text":["Training Started...\n","\n","U S I N G   C U D A \n","\n","\n","Epoch:  0 \t Training acc: 0.1111 \t Val acc:0.1111 \t Training loss:2.1992 \t Val loss:2.1975\n","Epoch:  1 \t Training acc: 0.1111 \t Val acc:0.1111 \t Training loss:2.1981 \t Val loss:2.1975\n","Epoch:  2 \t Training acc: 0.1111 \t Val acc:0.1111 \t Training loss:2.1981 \t Val loss:2.1975\n","Epoch:  3 \t Training acc: 0.1111 \t Val acc:0.1111 \t Training loss:2.1981 \t Val loss:2.1975\n","Epoch:  4 \t Training acc: 0.1111 \t Val acc:0.1111 \t Training loss:2.1981 \t Val loss:2.1975\n","Epoch:  5 \t Training acc: 0.1111 \t Val acc:0.1111 \t Training loss:2.1981 \t Val loss:2.1975\n","Epoch:  6 \t Training acc: 0.1111 \t Val acc:0.1111 \t Training loss:2.1981 \t Val loss:2.1975\n","Epoch:  7 \t Training acc: 0.1111 \t Val acc:0.1111 \t Training loss:2.1981 \t Val loss:2.1975\n","Epoch:  8 \t Training acc: 0.1111 \t Val acc:0.1111 \t Training loss:2.1981 \t Val loss:2.1975\n","Epoch:  9 \t Training acc: 0.1111 \t Val acc:0.1111 \t Training loss:2.1981 \t Val loss:2.1975\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"naz2U0q8Dx6Y","executionInfo":{"status":"ok","timestamp":1605636350649,"user_tz":300,"elapsed":203909,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}},"outputId":"4d86257a-9cdd-4334-f521-048986fa5f65","colab":{"base_uri":"https://localhost:8080/","height":911}},"source":["model_5 = SignClassifier()\n","use_cuda = False\n","train_acc, val_acc, train_loss, val_loss = train(model_5, train_loader, val_loader, batch_size=32, num_epochs=15, learning_rate = 0.0001)\n","plot_training_curve(train_acc, val_acc, train_loss, val_loss)"],"execution_count":21,"outputs":[{"output_type":"stream","text":["Training Started...\n","\n","U S I N G   C U D A \n","\n","\n","Epoch:  1 \t Training acc: 0.8985 \t Val acc:0.8847 \t Training loss:0.9972 \t Val loss:0.4481\n","Epoch:  2 \t Training acc: 0.9329 \t Val acc:0.9240 \t Training loss:0.3156 \t Val loss:0.2939\n","Epoch:  3 \t Training acc: 0.957 \t Val acc:0.9420 \t Training loss:0.2093 \t Val loss:0.2230\n","Epoch:  4 \t Training acc: 0.9693 \t Val acc:0.9548 \t Training loss:0.1553 \t Val loss:0.1813\n","Epoch:  5 \t Training acc: 0.9765 \t Val acc:0.9583 \t Training loss:0.1167 \t Val loss:0.1639\n","Epoch:  6 \t Training acc: 0.9792 \t Val acc:0.9585 \t Training loss:0.0903 \t Val loss:0.1546\n","Epoch:  7 \t Training acc: 0.9839 \t Val acc:0.9654 \t Training loss:0.0679 \t Val loss:0.1376\n","Epoch:  8 \t Training acc: 0.9851 \t Val acc:0.9627 \t Training loss:0.0527 \t Val loss:0.1529\n","Epoch:  9 \t Training acc: 0.9832 \t Val acc:0.9704 \t Training loss:0.0475 \t Val loss:0.1230\n","Epoch:  10 \t Training acc: 0.9947 \t Val acc:0.9770 \t Training loss:0.0337 \t Val loss:0.1103\n","Epoch:  11 \t Training acc: 0.9878 \t Val acc:0.9746 \t Training loss:0.0285 \t Val loss:0.1210\n","Epoch:  12 \t Training acc: 0.9948 \t Val acc:0.9793 \t Training loss:0.0323 \t Val loss:0.1005\n","Epoch:  13 \t Training acc: 0.9835 \t Val acc:0.9679 \t Training loss:0.0197 \t Val loss:0.1418\n","Epoch:  14 \t Training acc: 0.9974 \t Val acc:0.9800 \t Training loss:0.0186 \t Val loss:0.1020\n","Epoch:  15 \t Training acc: 0.9963 \t Val acc:0.9760 \t Training loss:0.0179 \t Val loss:0.1126\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"4IAANhqlFDT6","executionInfo":{"status":"ok","timestamp":1605638771882,"user_tz":300,"elapsed":203794,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}},"outputId":"dbce840a-3811-4a9c-e270-0da6fcb03d13","colab":{"base_uri":"https://localhost:8080/","height":911}},"source":["model_6 = SignClassifier()\n","use_cuda = False\n","train_acc, val_acc, train_loss, val_loss = train(model_6, train_loader, val_loader, batch_size=32, num_epochs=15, learning_rate = 0.00001)\n","plot_training_curve(train_acc, val_acc, train_loss, val_loss)"],"execution_count":22,"outputs":[{"output_type":"stream","text":["Training Started...\n","\n","U S I N G   C U D A \n","\n","\n","Epoch:  1 \t Training acc: 0.428 \t Val acc:0.4210 \t Training loss:2.0204 \t Val loss:1.8195\n","Epoch:  2 \t Training acc: 0.6194 \t Val acc:0.6264 \t Training loss:1.6027 \t Val loss:1.3874\n","Epoch:  3 \t Training acc: 0.732 \t Val acc:0.7358 \t Training loss:1.2195 \t Val loss:1.0920\n","Epoch:  4 \t Training acc: 0.7969 \t Val acc:0.7916 \t Training loss:0.9724 \t Val loss:0.8946\n","Epoch:  5 \t Training acc: 0.842 \t Val acc:0.8390 \t Training loss:0.7923 \t Val loss:0.7324\n","Epoch:  6 \t Training acc: 0.8715 \t Val acc:0.8627 \t Training loss:0.6537 \t Val loss:0.6217\n","Epoch:  7 \t Training acc: 0.8907 \t Val acc:0.8807 \t Training loss:0.5571 \t Val loss:0.5439\n","Epoch:  8 \t Training acc: 0.9064 \t Val acc:0.8916 \t Training loss:0.4846 \t Val loss:0.4847\n","Epoch:  9 \t Training acc: 0.9168 \t Val acc:0.9010 \t Training loss:0.4293 \t Val loss:0.4416\n","Epoch:  10 \t Training acc: 0.9237 \t Val acc:0.9054 \t Training loss:0.3866 \t Val loss:0.4075\n","Epoch:  11 \t Training acc: 0.9287 \t Val acc:0.9089 \t Training loss:0.3529 \t Val loss:0.3810\n","Epoch:  12 \t Training acc: 0.9334 \t Val acc:0.9156 \t Training loss:0.3256 \t Val loss:0.3588\n","Epoch:  13 \t Training acc: 0.9381 \t Val acc:0.9200 \t Training loss:0.3028 \t Val loss:0.3406\n","Epoch:  14 \t Training acc: 0.9422 \t Val acc:0.9247 \t Training loss:0.2840 \t Val loss:0.3253\n","Epoch:  15 \t Training acc: 0.9456 \t Val acc:0.9259 \t Training loss:0.2682 \t Val loss:0.3133\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"eil1ZkudGAv1","executionInfo":{"status":"ok","timestamp":1605639008776,"user_tz":300,"elapsed":428031,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}},"outputId":"9ca45d5e-ce0a-4b68-a071-d91f7d738ea9","colab":{"base_uri":"https://localhost:8080/","height":0}},"source":["model_7 = SignClassifier()\n","use_cuda = False\n","train_acc, val_acc, train_loss, val_loss = train(model_7, train_loader, val_loader, batch_size=32, num_epochs=15, learning_rate = 0.001)\n","plot_training_curve(train_acc, val_acc, train_loss, val_loss)"],"execution_count":23,"outputs":[{"output_type":"stream","text":["Training Started...\n","\n","U S I N G   C U D A \n","\n","\n","Epoch:  1 \t Training acc: 0.9681 \t Val acc:0.9528 \t Training loss:0.5516 \t Val loss:0.1858\n","Epoch:  2 \t Training acc: 0.9798 \t Val acc:0.9686 \t Training loss:0.1241 \t Val loss:0.1394\n","Epoch:  3 \t Training acc: 0.9897 \t Val acc:0.9760 \t Training loss:0.0789 \t Val loss:0.1107\n","Epoch:  4 \t Training acc: 0.9939 \t Val acc:0.9835 \t Training loss:0.0570 \t Val loss:0.1137\n","Epoch:  5 \t Training acc: 0.9904 \t Val acc:0.9778 \t Training loss:0.0587 \t Val loss:0.1099\n","Epoch:  6 \t Training acc: 0.9893 \t Val acc:0.9765 \t Training loss:0.0448 \t Val loss:0.1676\n","Epoch:  7 \t Training acc: 0.9572 \t Val acc:0.9514 \t Training loss:0.0359 \t Val loss:0.4780\n","Epoch:  8 \t Training acc: 0.9976 \t Val acc:0.9864 \t Training loss:0.0356 \t Val loss:0.1032\n","Epoch:  9 \t Training acc: 0.9908 \t Val acc:0.9830 \t Training loss:0.0348 \t Val loss:0.0970\n","Epoch:  10 \t Training acc: 0.9901 \t Val acc:0.9812 \t Training loss:0.0289 \t Val loss:0.0845\n","Epoch:  11 \t Training acc: 0.9944 \t Val acc:0.9852 \t Training loss:0.0260 \t Val loss:0.0877\n","Epoch:  12 \t Training acc: 0.9965 \t Val acc:0.9857 \t Training loss:0.0432 \t Val loss:0.1324\n","Epoch:  13 \t Training acc: 0.9996 \t Val acc:0.9896 \t Training loss:0.0117 \t Val loss:0.0953\n","Epoch:  14 \t Training acc: 0.974 \t Val acc:0.9630 \t Training loss:0.0493 \t Val loss:0.2684\n","Epoch:  15 \t Training acc: 0.9835 \t Val acc:0.9728 \t Training loss:0.0243 \t Val loss:0.1676\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"x2cplDXTP30G","executionInfo":{"status":"ok","timestamp":1605639014711,"user_tz":300,"elapsed":431931,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}},"outputId":"d2fe6655-49ee-4583-89bc-2cedaa8a1838","colab":{"base_uri":"https://localhost:8080/","height":0}},"source":["batch_size = 512\n","train_loader1, val_loader1, test_loader1, classes = get_data_loader (batch_size)\n","\n","model_8 = SignClassifier()\n","use_cuda = False\n","train_acc, val_acc, train_loss, val_loss = train(model_8, train_loader1, val_loader1, batch_size=512, num_epochs=15, learning_rate = 0.001)\n","plot_training_curve(train_acc, val_acc, train_loss, val_loss)"],"execution_count":24,"outputs":[{"output_type":"stream","text":["72\n","72\n","Training Started...\n","\n","U S I N G   C U D A \n","\n","\n","Epoch:  1 \t Training acc: 0.2222 \t Val acc:0.2222 \t Training loss:2.2498 \t Val loss:2.3234\n","Epoch:  2 \t Training acc: 0.1111 \t Val acc:0.1250 \t Training loss:2.1852 \t Val loss:2.0688\n","Epoch:  3 \t Training acc: 0.2222 \t Val acc:0.2222 \t Training loss:1.9656 \t Val loss:1.9673\n","Epoch:  4 \t Training acc: 0.2222 \t Val acc:0.2222 \t Training loss:1.8684 \t Val loss:1.8985\n","Epoch:  5 \t Training acc: 0.3472 \t Val acc:0.2361 \t Training loss:1.7826 \t Val loss:1.8424\n","Epoch:  6 \t Training acc: 0.4028 \t Val acc:0.2639 \t Training loss:1.6923 \t Val loss:1.7778\n","Epoch:  7 \t Training acc: 0.5139 \t Val acc:0.4722 \t Training loss:1.5993 \t Val loss:1.6969\n","Epoch:  8 \t Training acc: 0.6111 \t Val acc:0.5139 \t Training loss:1.4814 \t Val loss:1.6098\n","Epoch:  9 \t Training acc: 0.6528 \t Val acc:0.5694 \t Training loss:1.3425 \t Val loss:1.5136\n","Epoch:  10 \t Training acc: 0.8611 \t Val acc:0.6528 \t Training loss:1.1868 \t Val loss:1.3685\n","Epoch:  11 \t Training acc: 0.9167 \t Val acc:0.6528 \t Training loss:0.9995 \t Val loss:1.2528\n","Epoch:  12 \t Training acc: 0.9583 \t Val acc:0.7222 \t Training loss:0.8260 \t Val loss:1.1538\n","Epoch:  13 \t Training acc: 0.9583 \t Val acc:0.5972 \t Training loss:0.6380 \t Val loss:1.1653\n","Epoch:  14 \t Training acc: 0.8889 \t Val acc:0.5417 \t Training loss:0.5150 \t Val loss:1.3099\n","Epoch:  15 \t Training acc: 0.8611 \t Val acc:0.5972 \t Training loss:0.4701 \t Val loss:1.5832\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"uYdEyVCbQ3nW","executionInfo":{"status":"ok","timestamp":1605639021166,"user_tz":300,"elapsed":436184,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}},"outputId":"fecb8c40-9c1f-48bd-9275-187a0fe3e02b","colab":{"base_uri":"https://localhost:8080/","height":0}},"source":["batch_size = 256\n","train_loader2, val_loader2, test_loader2, classes = get_data_loader (batch_size)\n","\n","model_9 = SignClassifier()\n","use_cuda = False\n","train_acc, val_acc, train_loss, val_loss = train(model_9, train_loader2, val_loader2, batch_size=256, num_epochs=15, learning_rate = 0.001)\n","plot_training_curve(train_acc, val_acc, train_loss, val_loss)"],"execution_count":25,"outputs":[{"output_type":"stream","text":["72\n","72\n","Training Started...\n","\n","U S I N G   C U D A \n","\n","\n","Epoch:  1 \t Training acc: 0.2222 \t Val acc:0.2222 \t Training loss:2.2498 \t Val loss:2.3234\n","Epoch:  2 \t Training acc: 0.1111 \t Val acc:0.1250 \t Training loss:2.1852 \t Val loss:2.0688\n","Epoch:  3 \t Training acc: 0.2222 \t Val acc:0.2222 \t Training loss:1.9656 \t Val loss:1.9673\n","Epoch:  4 \t Training acc: 0.2222 \t Val acc:0.2222 \t Training loss:1.8684 \t Val loss:1.8985\n","Epoch:  5 \t Training acc: 0.3472 \t Val acc:0.2361 \t Training loss:1.7826 \t Val loss:1.8424\n","Epoch:  6 \t Training acc: 0.4028 \t Val acc:0.2639 \t Training loss:1.6923 \t Val loss:1.7778\n","Epoch:  7 \t Training acc: 0.5139 \t Val acc:0.4722 \t Training loss:1.5993 \t Val loss:1.6969\n","Epoch:  8 \t Training acc: 0.6111 \t Val acc:0.5139 \t Training loss:1.4814 \t Val loss:1.6098\n","Epoch:  9 \t Training acc: 0.6528 \t Val acc:0.5694 \t Training loss:1.3425 \t Val loss:1.5136\n","Epoch:  10 \t Training acc: 0.8611 \t Val acc:0.6528 \t Training loss:1.1868 \t Val loss:1.3685\n","Epoch:  11 \t Training acc: 0.9167 \t Val acc:0.6528 \t Training loss:0.9995 \t Val loss:1.2528\n","Epoch:  12 \t Training acc: 0.9583 \t Val acc:0.7222 \t Training loss:0.8260 \t Val loss:1.1538\n","Epoch:  13 \t Training acc: 0.9583 \t Val acc:0.5972 \t Training loss:0.6380 \t Val loss:1.1653\n","Epoch:  14 \t Training acc: 0.8889 \t Val acc:0.5417 \t Training loss:0.5150 \t Val loss:1.3099\n","Epoch:  15 \t Training acc: 0.8611 \t Val acc:0.5972 \t Training loss:0.4701 \t Val loss:1.5832\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"-OXGwDMSRfu9","executionInfo":{"status":"ok","timestamp":1605639027017,"user_tz":300,"elapsed":439942,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}},"outputId":"09da29d6-95ef-47f9-a868-4afb0f335d72","colab":{"base_uri":"https://localhost:8080/","height":0}},"source":["batch_size = 256\n","train_loader2, val_loader2, test_loader2, classes = get_data_loader (batch_size)\n","\n","model_10 = SignClassifier()\n","use_cuda = False\n","train_acc, val_acc, train_loss, val_loss = train(model_10, train_loader2, val_loader2, batch_size=256, num_epochs=12, learning_rate = 0.001)\n","plot_training_curve(train_acc, val_acc, train_loss, val_loss)"],"execution_count":26,"outputs":[{"output_type":"stream","text":["72\n","72\n","Training Started...\n","\n","U S I N G   C U D A \n","\n","\n","Epoch:  1 \t Training acc: 0.2222 \t Val acc:0.2222 \t Training loss:2.2498 \t Val loss:2.3234\n","Epoch:  2 \t Training acc: 0.1111 \t Val acc:0.1250 \t Training loss:2.1852 \t Val loss:2.0688\n","Epoch:  3 \t Training acc: 0.2222 \t Val acc:0.2222 \t Training loss:1.9656 \t Val loss:1.9673\n","Epoch:  4 \t Training acc: 0.2222 \t Val acc:0.2222 \t Training loss:1.8684 \t Val loss:1.8985\n","Epoch:  5 \t Training acc: 0.3472 \t Val acc:0.2361 \t Training loss:1.7826 \t Val loss:1.8424\n","Epoch:  6 \t Training acc: 0.4028 \t Val acc:0.2639 \t Training loss:1.6923 \t Val loss:1.7778\n","Epoch:  7 \t Training acc: 0.5139 \t Val acc:0.4722 \t Training loss:1.5993 \t Val loss:1.6969\n","Epoch:  8 \t Training acc: 0.6111 \t Val acc:0.5139 \t Training loss:1.4814 \t Val loss:1.6098\n","Epoch:  9 \t Training acc: 0.6528 \t Val acc:0.5694 \t Training loss:1.3425 \t Val loss:1.5136\n","Epoch:  10 \t Training acc: 0.8611 \t Val acc:0.6528 \t Training loss:1.1868 \t Val loss:1.3685\n","Epoch:  11 \t Training acc: 0.9167 \t Val acc:0.6528 \t Training loss:0.9995 \t Val loss:1.2528\n","Epoch:  12 \t Training acc: 0.9583 \t Val acc:0.7222 \t Training loss:0.8260 \t Val loss:1.1538\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"Eg1Eo9TsSGNF","executionInfo":{"status":"ok","timestamp":1605639035152,"user_tz":300,"elapsed":446416,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}},"outputId":"927c585f-7781-45be-9329-f988dcd5608e","colab":{"base_uri":"https://localhost:8080/","height":0}},"source":["batch_size = 128\n","train_loader3, val_loader3, test_loader3, classes = get_data_loader (batch_size)\n","\n","model_11 = SignClassifier()\n","use_cuda = False\n","train_acc, val_acc, train_loss, val_loss = train(model_11, train_loader3, val_loader3, batch_size=128, num_epochs=20, learning_rate = 0.001)\n","plot_training_curve(train_acc, val_acc, train_loss, val_loss)"],"execution_count":27,"outputs":[{"output_type":"stream","text":["72\n","72\n","Training Started...\n","\n","U S I N G   C U D A \n","\n","\n","Epoch:  1 \t Training acc: 0.1528 \t Val acc:0.2361 \t Training loss:2.4302 \t Val loss:2.1592\n","Epoch:  2 \t Training acc: 0.25 \t Val acc:0.1389 \t Training loss:2.0850 \t Val loss:2.0458\n","Epoch:  3 \t Training acc: 0.3333 \t Val acc:0.3194 \t Training loss:1.9519 \t Val loss:1.9193\n","Epoch:  4 \t Training acc: 0.3333 \t Val acc:0.3056 \t Training loss:1.8313 \t Val loss:1.8215\n","Epoch:  5 \t Training acc: 0.3889 \t Val acc:0.3333 \t Training loss:1.7380 \t Val loss:1.7353\n","Epoch:  6 \t Training acc: 0.4306 \t Val acc:0.3333 \t Training loss:1.6284 \t Val loss:1.6691\n","Epoch:  7 \t Training acc: 0.5278 \t Val acc:0.3750 \t Training loss:1.5164 \t Val loss:1.5983\n","Epoch:  8 \t Training acc: 0.6667 \t Val acc:0.5139 \t Training loss:1.3988 \t Val loss:1.4934\n","Epoch:  9 \t Training acc: 0.8056 \t Val acc:0.6111 \t Training loss:1.2477 \t Val loss:1.3932\n","Epoch:  10 \t Training acc: 0.8194 \t Val acc:0.5833 \t Training loss:1.0775 \t Val loss:1.3293\n","Epoch:  11 \t Training acc: 0.8472 \t Val acc:0.5278 \t Training loss:0.8917 \t Val loss:1.3087\n","Epoch:  12 \t Training acc: 0.9444 \t Val acc:0.5694 \t Training loss:0.6937 \t Val loss:1.2334\n","Epoch:  13 \t Training acc: 0.9167 \t Val acc:0.5417 \t Training loss:0.4931 \t Val loss:1.4297\n","Epoch:  14 \t Training acc: 0.8889 \t Val acc:0.5556 \t Training loss:0.4216 \t Val loss:1.5069\n","Epoch:  15 \t Training acc: 0.8889 \t Val acc:0.5000 \t Training loss:0.3369 \t Val loss:1.7860\n","Epoch:  16 \t Training acc: 0.9722 \t Val acc:0.5694 \t Training loss:0.3322 \t Val loss:1.6764\n","Epoch:  17 \t Training acc: 0.9583 \t Val acc:0.5139 \t Training loss:0.1995 \t Val loss:1.8160\n","Epoch:  18 \t Training acc: 1.0 \t Val acc:0.5139 \t Training loss:0.1433 \t Val loss:1.9625\n","Epoch:  19 \t Training acc: 1.0 \t Val acc:0.5139 \t Training loss:0.1089 \t Val loss:2.3317\n","Epoch:  20 \t Training acc: 1.0 \t Val acc:0.5278 \t Training loss:0.0791 \t Val loss:2.5838\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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VydoMow8FMYsQB8/OCrW2m65gnmjazP070asnjHaW549zfmbT6hXVIoVZyt+QgSTkGvN4r0OMSF4bIgMMacNMZsdDyPB3YB2dtXDQCmG8taoJyIVHVVTU5RpzOMXg1dn4ddC/D/uB0PB61g4SMdqVWxNI/N3sz90zdwOi7Z7kqVUs6WcMYxDvHNUKud3dU4jVuuEYhIGNASWJdtVnXgWJbXx/l7WCAiD4hIpIhERkcXrAdRl/ILgK7PWYFQtTn8+CQNFw7m+0HBjOnbmJX7oun57m98vf6YHh0oVZwsfwvSk6HHWLsrcSqXB4GIlAG+Ax43xsRdyTqMMRONMW2MMW1CQ0OdW+DVCGlgnSq65VOI2Y/vxM78I/kLfn6oDY2rluWZ77YyfMofHD9/0e5KlVJXK3ovbPjCaiUUUt/uapzKpUEgIv5YITDDGPN9DoucAGpmeV3DMc1ziEDEUHhkg/Xv7x8QNqc7s7vG8fqAa9hw5Dw3vreCL9ccJjNTjw6U8kiZGbD4JfAvZd03UMy4stWQAJOBXcaYd3NZbD4w3NF6qD0Qa4w56aqaXKpUBRjwEYz8EfwC8Zl1O8OOvcLifzSkVe3yvDRvB0MmrWXf6Xi7K1VKFUZcFEwfYI1p0uX/oEwROivhJOKqc9gich2wEtgGZDomvwDUAjDGfOoIiwlAb+AicI8xJjKv9bZp08ZERua5iP3SU+D38bDiHfALwHR/iW+lF6//tIeLqRnc0ymMR3s0ICjQ3+5KlVJ52f0jzHsI0lOtbmgi7vLYlkIissEY0ybHeZ52MdMjguCSmAOw8Ak49BtUb82FHm/z1qYSzIk8RkiZAF7o04hbIqojHvrBUqrYSkuCn1+EyMlQtQXcOsXjrwvkFQR6Z7ErVawHw+fBoElw/gjlvryBtxjPoqGhVAsO5Ik5W7j9szXsjLqia+hKKVc4vQMmdrVCoOMjcN8Sjw+B/OgRgbtcPAcr/wuRUyEtEdOoH79UvJvn1vgSm5TGsPa1efKGcIJL6ekipWxhDPwxCX4ZY3UiN/BTjxx2Mjd6aqgouXgO1n5ijWiUEktane5M8xvMv7aXo1ypEjzbO5zbWtfEx0dPFynlNokx1rWAvYugQS8Y8HGxuyisQVAUJcfC+s+t29UvxpBYtT3/TbqZKafCaFGzPK/1v4YWNcvZXaVSxd/B5fD9g5B0zhpzuN0oj70gnBcNgqIsNRE2TIPV4yH+JOfKNeONuL78kNSMIdfW4v9ubESF0p7fu6FSRU56Kix70+oyIqQBDJ7isWMOF4QGgSdIT4HNM2HVe3DhCKdL1uNf8X1Y4deJJ29szJ3tauOrp4uUco6YA/DdfdZQk61Hwo3/tnoaLsY0CDxJRjps/9a6sHx2L1F+NXgvqQ+7Q/vwyi0taBNWwe4KlfJcxsCW2fDT01Yvwv0/hCb97a7KLTQIPFFmJuyaj1k5Djm1jZOE8lFaP06F3cI93ZrSsV5Fvf9AqcJIjoUfn4Jt30DtTjBoIgTXsLsqt9Eg8GTGwL7FZPz2Nr4n1hNHaWand2VdxYEM6N6JPk2r4Oert4Molacjq+GHURB73OpC/vonwcfX7qrcSoOgODAGjq4lY+2nyO4FYDL5NSOCH0v2p0XnAdzetjalSvjZXaVSRUtasnVBePWHUL42DJxYrMYRKAwNguImLgqzfgqpf0whICWGA5lV+cb3Jsq0HcbQ66+hYpkAuytU6q8yM607dau3guqt3bPNk1vhhwfhzE6r6+heb0BAGfdsuwjSICiu0lNg5zwSVnxEmbObSTCBzDWdOdtkBLf07EZYSGm7K1TK8vsHsPhl63mTW6DHy1YXLK6QkQ6/v28NIlOqIgyYAA1ucM22PIgGgTc4sYG4FR9Tau88/EwaqzKbsq36HXTsfRctale0uzrlzU5sgMm9oGFvqNzUOk2TkQKtRlh9+wdVdt62Yg5YRwHH18M1g6Dvf60u4pUGgVdJiCZhzeeY9ZMJSo3mWGYov5XrT+0eo7iueQNtaaTcKzkOPusMGWkwehWULG+N+/vbf6zRvnwDoOPDVuduAUFXvh1jrDv1f3nJGkq273+h2WCn/RnFgQaBN8pIJ2n7fC4s+5CqFzaSbPxZXqIrFTrcxbVd+iG+2rmdcoPvH7Caa478CWp3+Ou8mAPw6+uw4wcoFQJdnrHO5fsV8k762BNWP0EHl0G9HtapoLLVnPc3FBMaBF4u7cRWjv/yAVWOLKAkKcRKWVIa9KFS29ugThfQUFCusGW2dZqm24vWl3xuTmyAxa/A4ZVQPgy6v2Sd1vHJp1m0MVbI/PS0dcTR6w1oc2+x7CfIGTQIFADpSfGs/eVrErd8T6eMSMpIMhkBwfg26gtNBkC9btZhtVJXK+YAfHo9VIuAEQvyb7NvDOxfCktegdPbrcFger5qfSZzkhgDPz4BO+dBjbZWl9GuuvhcTGgQqL9ISs1g2srdbF8xl26Za+jjv4mSmQlQIgjCe0Pj/lC/Z7Hve0W5SHoqTO4JF47CqN8huHrB35uZCdu+hl/fgNhjUK879BxrBcMle3+G+Y9YXbp3ewE6PeZ1N4ddCQ0ClaPzial8tGw/s9YcoIPPdh6usoPmCb/jk3QO/EtZTe6aDLD6Z7+aC3nKu/z8IqyZAHfMgMb9rmwdacnWfQcr3oGk89DsNuj0OPwxETZOg0rXwKDPinVvoc6mQaDydPz8Rd79ZS8/bD5B+QAfXos4T2+f9fjtWQiJZ6yWHfV7Wp1zhfeBwLJ2l6yKqn1LYMatcO0/rJY7Vys5Fla9bw3mlJ4EiHUE0O0FPY1ZSBoEqkB2RsXx9s+7Wb4n2hpTuWc9BoWcwHf3fNg5H+KjwC/QOkJodpv1r3+g3WWroiL+NHzaCUpXgvuXgn9J5607LspqHtqgF9Rq77z1ehENAlUoqw+c5T+LdrPleCwNK5fh2d6N6B4eghyPhO3fwY7vITEaAspCo37Q7Fao0xV8ta8jr5WZaR0JHFkDDyyHSo3srkhlo0GgCs0Yw6Ltp3jn5z0cOptI2zoVeO6mRrSqVd66hf/wCtj2HeyaDylxUDrU6jqg2WCrFUd+Tf9U8XKpC4l+70Obe+yuRuVAg0BdsbSMTGavP8YHS/ZxNiGFPs2q8MyNjf7sxygtGfYvhm3fwt7/QXoyBNeCpoOsUKjcVNt1F3eXupAI7wO3T9f/3kWUBoG6aokp6UxaeZCJKw6SlpHJXe1q82iPBn8dTzklHnb/aIXCgV/BZEBIuHU9odmtUKGufX+Aco2cupBQRZIGgXKaM3HJvLdkH3PWH6V0gB8PdavPyI5hBPpna8edGAM751qhcHS1Na16a6vFR6Ob9dRRcZFXFxKqSNEgUE6393Q8by3aza+7z1C9XEmevrEhA1pUx8cnh9MCscdh+/ewcTrE7IMqzaH7GKsFiJ5G8FyXupDo+gJ0fdbualQ+NAiUy6zef5Y3f9rFjqg4mlYvyws3NaZj/ZCcF87MsH49Lv83nD9sXVTuPgbqdnFrzcoJCtuFhLKdBoFyqcxMw7wtJxj3815OXEiie6NKPH9TIxpUzuVu5Iw02PSVdddo3AkIu97qaMxLhxD0OFfThYSyjQaBcovktAym/n6Yj5ftJzE1nTuurckTPRtSqWwuN52lJVt90q8cZ92X0KCX1VNltQi31q0KyRldSCi30yBQbnUuMZXxS/fx1dojlPDz4YHOdXmgc11KlcjlhrPURKsPmVXvQ/IFaHyzFQiVGru3cJU/Z3chodzGliAQkSlAP+CMMaZpDvO7AvOAQ45J3xtjXstvvRoEnuPQ2UTe/t9uFm0/RWhQAE/e0JDb29TEN6cLymD1K7PmY1jzEaQmWM1Ouz6n3QsXFa7sQkK5nF1B0BlIAKbnEQRPG2MKdWypQeB5Nhw5x5s/7mLj0Qs0rlqWV25uQvu6eYyjfPGcdafqus8gIxVa3gWdn4FyNd1XtPqrc4esrp+PR2oXEh7KtlNDIhIGLNQgUMYYftx2kn/9uIuo2GT6NqvK830aUaN8HmMexJ+GVe9C5BTrdeuRcN0TOgyhu6QmWp0NbvoKjqwCxBoGsuXddlemrkBRDoLvgONAFFYo7MhlPQ8ADwDUqlWr9ZEjR1xUsXK1pNQMJq44yCe/7ccYeLBLPUZ1yeP6AVj3Iax4x/pCEl9oNRyuexyCa7ivcG9hDBz7AzZ9CTvmQmo8lK9jHZW1GKr73IMV1SAoC2QaYxJEpA/wgTGmQX7r1COC4iHqQhJvLdrN/C1RVA0O5LmbGtG/RTUkrxvMzh+Gle/C5hmAQKth1hFCuVruKrv4ijsJW2bB5pnWTX/+peGaWyDiLqjdUW/8KwaKZBDksOxhoI0x5mxey2kQFC/rD59j7Pwd7IiKo03t8rxy8zU0qxGc95suHIVV78HGLwEDEXfC9U9ZA5+rgktPgT2LrGDdvwRMJtTqYJ36aTJAR6UrZopkEIhIFeC0McaISFvgW6C2yacgDYLiJyPT8O2GY7zz8x5iElO5vXVNnr4xnNCgfEagij1uNTndOM26a7nFUOj8lHZul5+TW60v/61fQ9I5CKoGEUOtX//aQqvYsqvV0CygKxACnAZeAfwBjDGfisjDwGggHUgCnjTGrM5vvRoExVdcchofLt3H1N8PU9Lfl0d7NGBExzBK+OXTQV1clNXKaMMX1l3LzW+H65+GkPpuqdsjZGbChqnW49Q28C0BjfpCxN1Qr5t2EeEFrjoIRKQ0kGSMyRSRhkAjYJExJs25peZPg6D4OxCdwJs/7uLX3WeoE1Kal/o1pnujyvm/Mf4UrP4Q1k+GjBRoOhg6Pw2h4a4vuijLzIAFj1kXgKs0ty62N70VSlWwuzLlRs4Igg3A9UB54HdgPZBqjLnLmYUWhAaB91i25wyvL9zJwehEuoaHMqZvE+pXKpP/GxPOOALhc0hLgmsGQpdnvPNO5Yx0mDsatn1t3YvR7QW98OulnBEEG40xrUTkEaCkMeZtEdlsjHF7pzAaBN4lLSOTaasP88HSfSSlZvBUr3Ae7Fw35+6us0s8a/WJ88ck607lxv2tC6F1uoB/Lv0fFScZafDdfbBzntWpX+en7a5I2cgZQbAJ+CfwHnCfMWaHiGwzxjRzbqn50yDwTmcTUnhl/g5+3HqSLg1Deff2FlQsk8/F5EsunoO1H8O6iZASCyXKQIMboFE/69/AfFopeaL0FPhmJOz5CW78F3R4yO6KlM2cEQRdgKeA340x/xGRusDjxphHnVtq/jQIvJcxhpl/HOXVBTspX8qfD4a0zLuriuzSU+DQCti9EHb/BIlnwMffGg+hUV8I7wtBBbgWUdSlXoQ5d8OBpdBnHLS93+6KVBHg1FZDIuIDlDHGxDmjuMLSIFA7o+J4eOZGDsck8njPhjzUrX7uHdnlJjMDjq+3QmHXQjh/CBCoca3VtXKjfp7ZlDIlAWYNgcOroP+H1k13SuGcI4KZwCggA+tCcVmsO4HfcWahBaFBoAASU9IZM3c7P2w6Qaf6FXnvjggqBV3heX9j4MxO2P0j7FoAp7Za00Mb/xkKVVsU/YusybEw43Yr4AZ+ajWjVcrBGUGw2RgTISJ3Aa2A54ANxpjmzi01fxoE6hJjDN9EHufl+dspE+DPB0Mi6JTbMJmFceGoIxQWwtHV1h23wTUh/CYIaQhBVR2PKlCmEvj6X/02r1bSefhykBVit062uodQKgtnBMEOIAKYCUwwxvwmIluMMS2cW2r+NAhUdntPx/PQjI3sj07gkW71ebRHA/x887kJraASY2DvIisYDvwK6cnZFhAoHWqFwqVwyOnf0iGuu2kr8Sx8eQtE74Hbp1uBpVQ2zgiCR4FngS1AX6AW8JUx5npnFloQGgQqJxdT03ll3g6+2XCctnUqMH5IS6oEO7mJaGaG9aUbf9K6eS23fxOjgWz/X4mvFQgNekKLO6FmW+ecaoo/DdP7Wx3yDZkB9Xte/TpVseSSLiZExM8Yk35VlV0BDQKVl+83HmfM3O0E+vvy7u0t6Bpeyf1FZKRZYZA9IGL2w96fIe0iVKhn9Y3U4o4r7z019oQVAnEn4c45UMftv8uUB3HGEUEwVl9BnR2TfgNeM8bEOq3KAtIgUPnZfyaBh2duZPepeEZ1qcdTvRri76xTRVcrJd4a7GXLLDi80poWdr3Vg2rj/hBQgDunAc4fgWk3W/dI3P0t1GrvuppVseCMIPgO2A5Mc0waBrQwxgxyWpUFpBQoXBQAABwaSURBVEGgCiI5LYPXFu5k5rqjtK5dnvFDW1K9XBEbY/f8Edg6xxoD4PwhawyAJv2tI4Ww68Enl/CKOQDT+luDxgz7Aaq3dm/dyiM5rdVQftPcQYNAFcb8LVG88P02fH2Ecbe14IYmRfCGMWPg2Dqra+gdcyElzmql1PwO60gh6/0M0XusEMhMg2FzoarbG+4pD+WMIFgD/J8xZpXjdSdgnDGmg1MrLQANAlVYh88m8vCsjWw/Eccj3evzRM+GBeuryA5pSVYLpc0z4eAyq+lqjbbWeAGhja07hn18Yfg87+xET10xZwRBC2A6cKlTlvPACGPMVqdVWUAaBOpKpKRn8PLcHcyJPEafZlX4720RlCxRxPvgjztpnTraMguid1vTgqrBiAU61oIqNKe1GnKMM4wxJk5EHjfGvO+kGgtMg0BdKWMMn688xL8W7aJptWAmDW/j/CamrmAMRG2yWhxFDNUhOdUVcVXz0aPGGLePGq5BoK7W0l2neXTWJsoE+vH58GvzHyNZqWIgryC4mjZ1RfQkq1J569G4Mt+O7oifjw+3fbaaRdtO2l2SUra6miBw3aj3SrlY46plmftQJ5pULcvoGRuZ8Os+XDV+t1JFXZ5BICLxIhKXwyMeqOamGpVyidCgAGbe355bIqox7pe9PDFnM8lpGXaXpZTb+eU10xgT5K5ClLJDoL8v790RQYPKQbzz8x6OnrvIZ8PaEBpUwNHPlCoGish990rZR0R4qFt9PrmrFTtPxnHLR7+z66Qt4y4pZQsNAqUcbmpWlW8e7Eh6ZiaDP1nNkp2n7S5JKbfQIFAqi2Y1gpn30HXUDS3D/V9GMmnFQb2IrIo9DQKlsqkSHMjXD3bgpqZVePOnXTz33TZS0zPtLkspl9EgUCoHJUv4MmFoKx7pXp85kccYNnkd5xNT7S5LKZfQIFAqFz4+wlO9wvlgSASbjl3glo9/52B0gt1lKeV0GgRK5WNARHVm3d+ehOR0Bn+6hq3HL9hdklJOpUGgVAG0rl2eb0d3pFQJX4ZOXMvv+8/aXZJSTqNBoFQB1QkpzXejO1KjfCnumbqen7SPIlVMuCwIRGSKiJwRke25zBcRGS8i+0Vkq4i0clUtSjlL5bJWi6LmNYJ5aOZGvlp7xO6SlLpqrjwi+ALoncf8m4AGjscDwCcurEUppwku5c+X97WjW3glxszdzvil2mGd8mwuCwJjzArgXB6LDACmG8taoJyIVHVVPUo5U8kSvnw2rDWDWlXn3cV7eXXBTjIzNQyUZ8qz0zkXqw4cy/L6uGPa3068isgDWEcN1Krl9rFwlMqRv68P4wa3oEKpEny+6hDnElMZd1sLSvjppTflWTziE2uMmWiMaWOMaRMaGmp3OUpd5uMjvNi3Mc/d1Ij5W6K4b9p6ElPS7S5LqUKxMwhOADWzvK7hmKaURxERRnWpx9u3Nuf3/We563O9C1l5FjuDYD4w3NF6qD0Qa4zR9njKY91+bU0+vbs1O0/Gcdtna4i6kGR3SUoViCubj84C1gDhInJcRO4TkVEiMsqxyE/AQWA/MAn4p6tqUcpdel1Then3tuV0bDK3frKa/Wfi7S5JqXyJpzV7a9OmjYmMjLS7DKXytCMqlhFT1pORmcnUe9oSUbOc3SUpLyciG4wxbXKa5xEXi5XyNNdUC+a70R0ICvTnzklrWbE32u6SlMqVBoFSLlK7Ymm+HdWB2hVLc9+09czfEmV3SUrlSINAKReqVDaQ2Q+0p2Wt8jw2exMz1mmXFKro0SBQysWCS/oz/d62dAuvxIs/bGfa6sN2l6TUX2gQKOUGgf6+fHp3a3o1qcwr83fw+cqDdpek1GUaBEq5SQk/Hz66qxV9m1XljR938dGy/XaXpBRgb19DSnkdf18fPhgSgZ+v8M7Pe0jLyOSxHg0QEbtLU15Mg0ApN/Pz9eHd2yPw8/Hh/SX7SM8wPNWroYaBso0GgVI28PUR3hncnBJ+woRl+0nNyOT5mxppGChbaBAoZRMfH+HNW5rh7+vDxBUHSU3P5JWbm2gYKLfTIFDKRj4+wqv9r8Hf14fJqw6RlpHJ6wOa4uOjYaDcR4NAKZuJCGP6Nsbf14dPfztAWkYm/x7UHF8NA+UmGgRKFQEiwrO9wynhK4z/dT/pGYZ3bmuhYaDcQoNAqSJCRHiyVzj+vj78d/Fe0jIN797eAn9fvd1HuZYGgVJFzCM9GuDv58Nbi3aTnpHJB0Na6jjIyqX006VUETSqSz1e6teERdtP8c8ZG0hJz7C7JFWMaRAoVUTdd10dXh9wDUt2neGB6RtITtMwUK6hQaBUETasQxhvDWrGin3R/GNaJEmpGgbK+TQIlCrihrStxTuDW7D6wFnu+eIPLqam212SKmY0CJTyAINb1+C9OyL449A5Rk5dT2KKhoFyHg0CpTzEgIjqvD+kJRuOnGfk1D9I0DBQTqJBoJQH6d+iGuOHtGTj0QuMmPIH8clpdpekigENAqU8TN/mVZkwtCVbjl1g+JQ/iNMwUFdJg0ApD3RTs6pMuLMV247HMmzyH8QmaRioK6dBoJSH6t20Cp/c3ZqdUbEMm7yO2IsaBurKaBAo5cFuaFKZT+9uze6T8dw1eS0XLqbaXZLyQBoESnm4Ho0r89mw1uw9ncCdk9ZxPlHDQBWOBoFSxUC3RpWYNLwN+6MTuPPzdZzTMFCFoEGgVDHRpWEoU0Zcy8HoBO6ctJaYhBS7S1IeQoNAqWLkugYhTB15LYdjEhk6aS3R8RoGKn8aBEoVMx3rhzB1ZFuOnUti6KS1nIlPtrskVcS5NAhEpLeI7BGR/SLyXA7zR4pItIhsdjz+4cp6lPIWHepV5It7riXqQhJDJq7ldJyGgcqdy4JARHyBj4CbgCbAUBFpksOic4wxEY7H566qRylv065uRabd25bTsckMmbiWU7EaBipnrjwiaAvsN8YcNMakArOBAS7cnlIqm2vDKjD9vrZEx6cwZOIaVu8/S0amsbssVcS4MgiqA8eyvD7umJbdrSKyVUS+FZGaOa1IRB4QkUgRiYyOjnZFrUoVW61rW2FwISmNOz9fR8e3lvLGwp1sOx6LMRoKCsRVHwQRGQz0Nsb8w/F6GNDOGPNwlmUqAgnGmBQReRC4wxjTPa/1tmnTxkRGRrqkZqWKs6TUDJbuPs28zVEs33OGtAxD3dDSDGhRnf4R1agTUtruEpULicgGY0ybHOe5MAg6AGONMTc6Xj8PYIz5dy7L+wLnjDHBea1Xg0Cpq3fhYiqLtp9i3uYTrDt0DmOgRY1gBkRUp1/zqlQqG2h3icrJ7AoCP2Av0AM4AawH7jTG7MiyTFVjzEnH84HAs8aY9nmtV4NAKec6GZvEgi1RzNscxY6oOHwEOtYLoX9ENXo3rULZQH+7S1ROYEsQODbcB3gf8AWmGGPeFJHXgEhjzHwR+TfQH0gHzgGjjTG781qnBoFSrrP/TDzzN0cxd3MUR89dpISfDz0aVWJARDW6hlci0N/X7hLVFbItCFwhpyBIS0vj+PHjJCdr8zhnCQwMpEaNGvj7669Bb2SMYfOxC8zbHMXCrVGcTUglKNCPWyKqM7xDbRpUDrK7RFVIxT4IDh06RFBQEBUrVkREbKqs+DDGEBMTQ3x8PHXq1LG7HGWz9IxMVh+I4fuNx/lp2ylSMzJpX7cCwzuEcUOTyvj7agcFniCvIPBzdzGukJycTFhYmIaAk4gIFStWRJvqKgA/Xx86Nwylc8NQXuqXwpzIY8xYe5R/zthI5bIBDG1bizvb1tILzB6sWAQBoCHgZLo/VU4qlgngn13r82DneizbfYbpa4/w/pJ9TPh1Pzc2rcLw9rVpW6eCfn48TLEJAqWU+/j6CD2bVKZnk8ocPpvIV2uP8HXkMX7cepLwykHc3aE2g1pWp3SAfsV4Aj255wQxMTFEREQQERFBlSpVqF69+uXXqal5DxASGRnJo48+6qZKlXK+sJDSjOnXhHUv9OQ/tzbDz1d4ae522v1rKa/M287+M/F2l6jyUSwuFu/atYvGjRvbVNFfjR07ljJlyvD0009fnpaeno6fn+f9MipK+1V5DmMMm45d4Ms1R/hx60lSMzLpWK8id7evTYNKZfD39cHfzwd/X6GEr4/12td6raeUXKfYXyzO6tUFO9gZFefUdTapVpZXbr6mUO8ZOXIkgYGBbNq0iU6dOjFkyBAee+wxkpOTKVmyJFOnTiU8PJzly5czbtw4Fi5cyNixYzl69CgHDx7k6NGjPP7443q0oDyOiNCqVnla1SrPi30b83WWi8v58feVvwRD1ueB/r7c2qoGIzqG4eujgeFMxS4IipLjx4+zevVqfH19iYuLY+XKlfj5+bFkyRJeeOEFvvvuu7+9Z/fu3Sxbtoz4+HjCw8MZPXq0tuVXHisky8XldQdjOHcxlbSMTNLSDakZmdbzjEzSMgyp6dleZ2SSlv7n66jYJF5buJOFW6N4e3Bz6lfSexmcpdgFQWF/ubvSbbfdhq+vdSdmbGwsI0aMYN++fYgIaWlpOb6nb9++BAQEEBAQQKVKlTh9+jQ1atRwZ9lKOZ2vj9CxfshVrcMYw9zNJ3h1wU76fLCKx3o24IHOdfU+BifQPehCpUv/2ZvjSy+9RLdu3di+fTsLFizI9S7ogICAy899fX1JT093eZ1KeQIRYWDLGix+ogs9m1TinZ/3cMtHv7MjKtbu0jyeBoGbxMbGUr26NRzDF198YW8xSnmw0KAAPr6rNZ/e3YrTcSkMmPA7437eQ0p6ht2leSwNAjd55plneP7552nZsqX+ylfKCXo3rcqSJzvTP6IaE5btp9/4VWw6et7usjySNh9VudL9qjzFsj1neOH7bZyOS+beTnV4qlc4JUtoT6lZ5dV8VI8IlFIer1t4JX55ojND29bi81WH6P3BCtYciLG7LI+hQaCUKhaCAv15c2AzZt1vjW01dNJaXvxhG/HJObfQU3/SIFBKFSsd6lXkf4915h/X1WHWH0e58b0VLNtzxu6yijQNAqVUsVOyhC9j+jXh29EdKRXgxz1T1/Pk15s5EJ2Ap10XdYdid0OZUkpd0qpWeX589Dom/LqfT5Yf4PuNJ6heriSdG4bSpWEIHeuH6JjMaBAopYq5AD9fnuoVzh3X1mTZnmhW7I1mwZYoZv1xFF8foWXNcnRuGMr1DUJoXqOcV/ZjpKeGnKBbt278/PPPf5n2/vvvM3r06ByX79q1K5eawPbp04cLFy78bZmxY8cybty4PLc7d+5cdu7cefn1yy+/zJIlSwpbvlJeoUb5UgxrX5tJw9uw6eUbmPNAe0Z1qUtKeibvLdnLwI9X0/qNxTw0cyNfrz/Gydgku0t2Gz0icIKhQ4cye/ZsbrzxxsvTZs+ezdtvv53ve3/66acr3u7cuXPp168fTZo0AeC111674nUp5U38fX1oV7ci7epW5P9uhJiEFFbtP8uKvWdZsS+aH7eeBKBh5TJ0bhDK9Q1DaVenAoH+xfPehOIXBIueg1PbnLvOKs3gprdynT148GDGjBlDamoqJUqU4PDhw0RFRTFr1iyefPJJkpKSGDx4MK+++urf3hsWFkZkZCQhISG8+eabTJs2jUqVKlGzZk1at24NwKRJk5g4cSKpqanUr1+fL7/8ks2bNzN//nx+++033njjDb777jtef/11+vXrx+DBg1m6dClPP/006enpXHvttXzyyScEBAQQFhbGiBEjWLBgAWlpaXzzzTc0atTIuftLKQ9TsUwAAyKqMyCiOsYYdp+KZ8XeaFbsi2b6miN8vuoQAX4+NKpaloaVyhBeJYgGlYMIrxxE5bIBHj+OQvELAhtUqFCBtm3bsmjRIgYMGMDs2bO5/fbbeeGFF6hQoQIZGRn06NGDrVu30rx58xzXsWHDBmbPns3mzZtJT0+nVatWl4Ng0KBB3H///QCMGTOGyZMn88gjj9C/f//LX/xZJScnM3LkSJYuXUrDhg0ZPnw4n3zyCY8//jgAISEhbNy4kY8//phx48bx+eefu3DvKOVZRITGVcvSuGpZHuxSj6TUDNYeimHVvrPsOhnHsj1n+GbD8cvLBwX60bBykONR5vLzkDIlPCYgil8Q5PHL3ZUunR66FASTJ0/m66+/ZuLEiaSnp3Py5El27tyZaxCsXLmSgQMHUqpUKQD69+9/ed727dsZM2YMFy5cICEh4S+noHKyZ88e6tSpQ8OGDQEYMWIEH3300eUgGDRoEACtW7fm+++/v+q/XanirGQJX7qFV6JbeKXL084lprL3dHyWRwKLtp9k1h9/3rxWvpT/3wKiXqUyVCxd9AKi+AWBTQYMGMATTzzBxo0buXjxIhUqVGDcuHGsX7+e8uXLM3LkyFy7ns7PyJEjmTt3Li1atOCLL75g+fLlV1Xrpa6utZtrpa5MhdIlaF+3Iu3rVrw8zRhDdEIK+04nsOdUPPvOxLPnVDxzN50gPuXP/8+CAvyoE1qasIqlqRPy5yMspDTBJe1pyqpB4CRlypShW7du3HvvvQwdOpS4uDhKly5NcHAwp0+fZtGiRXTt2jXX93fu3JmRI0fy/PPPk56ezoIFC3jwwQcBiI+Pp2rVqqSlpTFjxozL3VkHBQURH//3gcHDw8M5fPgw+/fvv3xNoUuXLi75u5VSFhGhUlAglYIC6ZRlEB5jDKfiktlzKp5DZxM5fDaRg2cT2Xj0PAu2RpH1/raKpUtcDoW/hETF0i7tRE+DwImGDh3KwIEDmT17No0aNaJly5Y0atSImjVr0qlTpzzf26pVK+644w5atGhBpUqVuPbaay/Pe/3112nXrh2hoaG0a9fu8pf/kCFDuP/++xk/fjzffvvt5eUDAwOZOnUqt9122+WLxaNGjXLNH62UypOIUDW4JFWDS9I1/K/zktMyOHbuIofOJlohEZPIwehEVuyN5tss1yEAqgYHct91dfjH9XWdX6On3W6t3VC7j+5XpeyTkJLOYUc4HIpO5FBMIl0ahjIgovoVrS+vbqj1iEAppYqgMgF+NK0eTNPqwS7flt5ZrJRSXs6lQSAivUVkj4jsF5HncpgfICJzHPPXiUjYlW7L005xFXW6P5XyHi4LAhHxBT4CbgKaAENFpEm2xe4Dzhtj6gPvAf+5km0FBgYSExOjX15OYowhJiaGwMBAu0tRSrmBK68RtAX2G2MOAojIbGAAsDPLMgOAsY7n3wITRERMIb/Ra9SowfHjx4mOjr76qhVghWuNGjXsLkMp5QauDILqwLEsr48D7XJbxhiTLiKxQEXgbNaFROQB4AGAWrVq/W1D/v7+1KlTx2mFK6WUN/GIi8XGmInGmDbGmDahoaF2l6OUUsWKK4PgBFAzy+sajmk5LiMifkAwEOPCmpRSSmXjyiBYDzQQkToiUgIYAszPtsx8YITj+WDg18JeH1BKKXV1XHpnsYj0Ad4HfIEpxpg3ReQ1INIYM19EAoEvgZbAOWDIpYvLeawzGjjisqKvTgjZrm8UMUW9Pij6NWp9V0fruzpXU19tY0yO59Y9rouJokxEInO7hbsoKOr1QdGvUeu7Olrf1XFVfR5xsVgppZTraBAopZSX0yBwrol2F5CPol4fFP0atb6ro/VdHZfUp9cIlFLKy+kRgVJKeTkNAqWU8nIaBIUkIjVFZJmI7BSRHSLyWA7LdBWRWBHZ7Hi87OYaD4vINse2I3OYLyIy3tH991YRaeXG2sKz7JfNIhInIo9nW8bt+09EpojIGRHZnmVaBRFZLCL7HP+Wz+W9IxzL7BORETkt46L63hGR3Y7/hj+ISLlc3pvn58GF9Y0VkRNZ/jv2yeW9eXZX78L65mSp7bCIbM7lvS7df7l9p7j182eM0UchHkBVoJXjeRCwF2iSbZmuwEIbazwMhOQxvw+wCBCgPbDOpjp9gVNYN7rYuv+AzkArYHuWaW8DzzmePwf8J4f3VQAOOv4t73he3k319QL8HM//k1N9Bfk8uLC+scDTBfgMHADqAiWALdn/f3JVfdnm/xd42Y79l9t3ijs/f3pEUEjGmJPGmI2O5/HALqxeVD3JAGC6sawFyolIVRvq6AEcMMbYfqe4MWYF1t3tWQ0ApjmeTwNuyeGtNwKLjTHnjDHngcVAb3fUZ4z5xRiT7ni5Fqs/L1vksv8K4nJ39caYVOBSd/VOlVd9IiLA7cAsZ2+3IPL4TnHb50+D4Co4RlRrCazLYXYHEdkiIotE5Bq3FgYG+EVENji68M4upy7C7QizIeT+P5+d+++SysaYk47np4DKOSxTVPblvVhHeTnJ7/PgSg87Tl1NyeXURlHYf9cDp40x+3KZ77b9l+07xW2fPw2CKyQiZYDvgMeNMXHZZm/EOt3RAvgQmOvm8q4zxrTCGh3uIRHp7Obt50usjgj7A9/kMNvu/fc3xjoOL5JtrUXkRSAdmJHLInZ9Hj4B6gERwEms0y9F0VDyPhpwy/7L6zvF1Z8/DYIrICL+WP/BZhhjvs8+3xgTZ4xJcDz/CfAXkRB31WeMOeH49wzwA9bhd1YF6SLc1W4CNhpjTmefYff+y+L0pVNmjn/P5LCMrftSREYC/YC7HF8Wf1OAz4NLGGNOG2MyjDGZwKRctmv3/vMDBgFzclvGHfsvl+8Ut33+NAgKyXE+cTKwyxjzbi7LVHEsh4i0xdrPbhlnQURKi0jQpedYFxS3Z1tsPjDc0XqoPRCb5RDUXXL9FWbn/ssmazfpI4B5OSzzM9BLRMo7Tn30ckxzORHpDTwD9DfGXMxlmYJ8HlxVX9brTgNz2W5Buqt3pZ7AbmPM8ZxmumP/5fGd4r7Pn6uuhBfXB3Ad1iHaVmCz49EHGAWMcizzMLADqwXEWqCjG+ur69juFkcNLzqmZ61PgI+wWmtsA9q4eR+WxvpiD84yzdb9hxVKJ4E0rPOs92ENm7oU2AcsASo4lm0DfJ7lvfcC+x2Pe9xY336s88OXPoefOpatBvyU1+fBTfV96fh8bcX6UquavT7H6z5YLWUOuLM+x/QvLn3usizr1v2Xx3eK2z5/2sWEUkp5OT01pJRSXk6DQCmlvJwGgVJKeTkNAqWU8nIaBEop5eU0CJTKRkQy5K89pDqtR0wRCcvaA6ZSRYGf3QUoVQQlGWMi7C5CKXfRIwKlCsjRL/3bjr7p/xCR+o7pYSLyq6NztaUiUssxvbJY4wRscTw6OlblKyKTHH3P/yIiJW37o5RCg0CpnJTMdmrojizzYo0xzYAJwPuOaR8C04wxzbE6fhvvmD4e+M1Ynee1wrozFaAB8JEx5hrgAnCri/8epfKkdxYrlY2IJBhjyuQw/TDQ3Rhz0NFJ2CljTEUROYvVfUKaY/pJY0yIiEQDNYwxKVnWEYbVf3wDx+tnAX9jzBuu/8uUypkeEShVOCaX54WRkuV5BnqtTtlMg0Cpwrkjy79rHM9XY/WaCXAXsNLxfCkwGkBEfEUk2F1FKlUY+ktEqb8rKX8dyPx/xphLTUjLi8hWrF/1Qx3THgGmisj/AdHAPY7pjwETReQ+rF/+o7F6wFSqSNFrBEoVkOMaQRtjzFm7a1HKmfTUkFJKeTk9IlBKKS+nRwRKKeXlNAiUUsrLaRAopZSX0yBQSikvp0GglFJe7v8B2LCMttZRqGoAAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"d7sbSiuNxn4t","executionInfo":{"status":"ok","timestamp":1605639043878,"user_tz":300,"elapsed":452858,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}},"outputId":"91ee0edc-5ee4-4e68-d00a-7bbff4688e07","colab":{"base_uri":"https://localhost:8080/","height":0}},"source":["batch_size = 64\n","train_loader4, val_loader4, test_loader4, classes = get_data_loader (batch_size)\n","\n","model_12 = SignClassifier()\n","use_cuda = False\n","train_acc, val_acc, train_loss, val_loss = train(model_12, train_loader4, val_loader4, batch_size=128, num_epochs=20, learning_rate = 0.001)\n","plot_training_curve(train_acc, val_acc, train_loss, val_loss)"],"execution_count":28,"outputs":[{"output_type":"stream","text":["72\n","72\n","Training Started...\n","\n","U S I N G   C U D A \n","\n","\n","Epoch:  1 \t Training acc: 0.2639 \t Val acc:0.2500 \t Training loss:2.1756 \t Val loss:2.0511\n","Epoch:  2 \t Training acc: 0.2361 \t Val acc:0.2361 \t Training loss:1.8193 \t Val loss:2.0314\n","Epoch:  3 \t Training acc: 0.4028 \t Val acc:0.3611 \t Training loss:1.5425 \t Val loss:1.9362\n","Epoch:  4 \t Training acc: 0.5972 \t Val acc:0.5139 \t Training loss:1.2452 \t Val loss:1.5973\n","Epoch:  5 \t Training acc: 0.7639 \t Val acc:0.6250 \t Training loss:0.9002 \t Val loss:1.3232\n","Epoch:  6 \t Training acc: 0.9028 \t Val acc:0.6111 \t Training loss:0.5829 \t Val loss:1.1492\n","Epoch:  7 \t Training acc: 0.875 \t Val acc:0.6806 \t Training loss:0.3703 \t Val loss:1.3750\n","Epoch:  8 \t Training acc: 0.7778 \t Val acc:0.5556 \t Training loss:0.2839 \t Val loss:2.2159\n","Epoch:  9 \t Training acc: 0.9167 \t Val acc:0.6111 \t Training loss:0.4969 \t Val loss:1.1959\n","Epoch:  10 \t Training acc: 0.9028 \t Val acc:0.5833 \t Training loss:0.1915 \t Val loss:2.4396\n","Epoch:  11 \t Training acc: 0.875 \t Val acc:0.7361 \t Training loss:0.2618 \t Val loss:1.4909\n","Epoch:  12 \t Training acc: 0.9306 \t Val acc:0.7083 \t Training loss:0.1945 \t Val loss:1.1126\n","Epoch:  13 \t Training acc: 0.9722 \t Val acc:0.7361 \t Training loss:0.1387 \t Val loss:0.8881\n","Epoch:  14 \t Training acc: 0.9722 \t Val acc:0.6944 \t Training loss:0.0845 \t Val loss:0.8998\n","Epoch:  15 \t Training acc: 1.0 \t Val acc:0.6250 \t Training loss:0.0825 \t Val loss:1.0266\n","Epoch:  16 \t Training acc: 0.9861 \t Val acc:0.6944 \t Training loss:0.0688 \t Val loss:1.1265\n","Epoch:  17 \t Training acc: 1.0 \t Val acc:0.7083 \t Training loss:0.0446 \t Val loss:1.1766\n","Epoch:  18 \t Training acc: 1.0 \t Val acc:0.7083 \t Training loss:0.0230 \t Val loss:1.2630\n","Epoch:  19 \t Training acc: 1.0 \t Val acc:0.7083 \t Training loss:0.0160 \t Val loss:1.3665\n","Epoch:  20 \t Training acc: 1.0 \t Val acc:0.7083 \t Training loss:0.0123 \t Val loss:1.4681\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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k6demBdXl4eCQkJFBcX89577x1YHhkZSV5e3iHH6t69O+np6axduxaASZMmcdxxx7npL62e3ySCoV1iCAkK4PFv3FfBojwkOw0K90CbvrB3O+Rt99x5Wx8BgdXcOCemwJbFUNo0hyb2JxMmTGDJkiVMmDCB5ORkBgwYQI8ePTj//PM55phjatw3JSWF8847j+TkZEaOHMmRRx55YN1DDz3E4MGDOeaYY+jRo8eB5ePHj+eJJ55gwIABrFu37sDysLAw3nrrLc455xz69u1LQEAAV155pfv/4Er8ahjqF39cyxPfrubDK4YwuHNrN0emHLPgLfjqBhjzPHx5LVzwCXQ92fnzPj8Q4nrBeZOqXr/0I/jscrjyV4jv43w8TZAOQ+0MHYa6Bpce04mEqDAenbaSsrLGlQD9WsY8aB4Hvcba91sXO3/Okv2wa0PV9QPlDlQYa/GQatz8KhGEhwRy0yndWZKZw9SlOk5Mo5ExF9oPsSN/tuwEWz1QYbxrPZjSqvsQlGvV2fY61o5lqpHzq0QAcOaAJHoltOCJb1dTVFLq7XDU4eRugT0bbSIAW0HriZZD2eUthqroQ1AuIMDOT6B3BA3S2IqnfV19rqffJYLAAOHOUT3J3F3Au3M2ejscdTjlrYUOJIJk2J0OBYeO8eJW5YPNta4hEYDtWLZ9ORTXr3mhvwsLC2Pnzp2aDNzEGMPOnTsJCwur035+1Y+g3NCuMRzXLZbnf1jDOaltiW4W4u2QVHUy5kFwM4h39eqMT7bP2/6ETsOcO29WGrRoa6enrEliCpSVwPZl0LbKejhVg7Zt25KZmUlWVpa3Q2kywsLCaNu2bZ328ctEAHDnqJ6MfHYWz/+wlntG9/J2OKo6GXPtF2xgsH2f4EoIW5c4mwiy0yC2horichWHpNZEUGfBwcF06tTJ22H4Pb8rGirXPT6Scwa249256WTszPd2OKoqRXn2l3b7ow4ui4iDyARn6wnKyv46PWVNWiTZFk1aT6AaMf+5I1gzE76+ESLjXY8E7o6OQQJ3MeWzzVw3dhhEtoGw6L8OOay8J/N3MGUH6wfKxfdztuVQ3hYo3le7RCCiU1eqRs9/EkF4NLQbDHlbYcdKWPcjkUW5PBYAbAZecm0XFHYgURDRxj6XJ4/oDvYYAX57I+VZGfNAAqDtkX9dnpAMa7+D/fkQ0sz9561qVrKaJKZA2rf2DiY00v3xKOUwxxKBiLQD3gXaAAZ41RjzbKVtBHgWGAXkAxONMc78tGqbemgZbtFe8ndt5vrXv6FXxD5uGByJ7N0OedvsY/syWPs97K8wJkhsTxh2E/Q+s/qhB5R7ZMy1wwhX/nJN6GfvFHascKZcPquKeYprkpQCGDvchJP1Fko5xMlvshLgJmPMIhGJBBaKyHfGmIrjuY4Euroeg4GXXc+eERpBs4TuDD8ljLs+X0bPFgMZcXT8odsV5dnxbTYvgF/+A5/9DX56FIbeCMnjD1ZkKvcpLYbMBTDgwkPXxVeoMHYiEWSn2c5rzWNrt315D+PNCzURqEbJsTIOY8zW8l/3xpg8YCWQVGmzscC7xpoHRItIglMxVee81HZ0iYvg8W9WUVxadugGoZEQ08V+6V81F86dBCER8OU18NwAmP+atiN3t21LoTj/0PoBgOj2ti7HqZFIs9PsHAS1rStq3toWG2qFsWqkPFLYLSIdgQHAb5VWJQGbKrzP5NBkgYhcISILRGSBE+2NgwIDuGNkDzZk7+OD+Rk1bxwQAL3GwN9nwfkf2zqEaTfDs8kw5wXYv8/t8fmlyh3JKhJxtodxdlrt6wfKJaXAZp2kRjVOjicCEYkAPgVuMMbk1ucYxphXjTGpxpjU2Nha3q7X0Qk94hjSuRXPzFxDbmHx4XcQgW6nwGUz4OKpts35jLvgmb4w60k7zaGqv4y59ld2i8Sq1yck2x69pbX4t6qLgj12qOva9CGoKDEFcjJgX7Z741HKAxxNBCISjE0C7xljPqtik81Auwrv27qWeZyIcNeoXuzat5///rTu8Dsc3BE6HWuTwaUzIGkg/PAQ/Kcv/PAI5O9yLuimyhh7R1Cx/0Bl8clQuv/gLGLucmB6ynrcEYA2I1WNkmOJwNUi6A1gpTHm6Wo2+xK4SKwhQI4xZqtTMR1O37ZRnNE/kTd+2cCWPQV1P0D7wXDBx3DFz9D5WJj1b/hPH5hxj+cmU2kKdq2HfVlVFwuVK+9h7O7ioewapqesSUIyIFpPoBolJ+8IjgEuBE4QkcWuxygRuVJEyqfcmQasB9YCrwFXOxhPrdx8ancM8OSMBvzSTOwP5/0Prp4HPUbB3Bfg2X4w/XYoKXJbrE3WgfqBGu4IWnexYxC5u8I4Ow0CQ2yxVF2ERtrmpnpHoBohx5qPGmN+AWpsdmHskIP/cCqG+mjbshmXHNORV2et57KhneidGFX/g8X1hLNeh+F3wOyn4beXISLW9kNQ1cuYC+Eta/5VHhAIbfq4v4dxVppNMvXpI5KYYju6GaO901Wjol1kq3D18C5Ehwfz6LSV7hket/URcMaL0GM0zHoKcr1W+tU4ZMyDdkMO34M7oZ8dhbSsiia/9ZWdVvMcBDVJSrFFWjmZ7otHKQ/QRFCFqPBgrj2hK7+u3clPaW5srnrKQ1BWDDPvd98xm5p92bBzja1vOZyEZNvre/cG95y7pMgeK6aWPYor06krVSOliaAa/zekAx1aN+Oxaasoddf8xq06w1HXwNLJsOl39xyzqalN/UC5ij2M3WHXejt0RV0rig/E0wcCgrWeQDU6mgiqERIUwG0jerB6ex6fLNx0+B1qa9hNEBEP0291b5FGU5ExFwJDIXHA4beN62m/eN2VCMqbota1D0G5oFBo01vvCFSjo4mgBiP7xJPSPpqnZqSRv7/EPQcNjYCTH7BfFks+cM8xm5KMebasPSj08NsGhUJcD/c1IS3vQ9C6S/2PkZRiB5/TJK8aEU0ENRAR7jqtJzvyinhtlpvKoQH6nmuHVp55PxTWq7N107Q/H7Yurrn/QGXxybblkDsq9bNXQ1R7CGle/2MkpkBRLuxc2/B4lPIQTQSHMbBDK0b2ieeVWevYkeemgeUCAmDk47BvB8x+0j3HbAq2LLLz/9amfqBcQjLkZ0PuloafvyEthsolaYWxanw0EdTCrSN6sL+kjP98t8Z9B00aCP0vgLkvwc46DGnRlGXMtc/tBtV+H3f1MC6fnrK2cxBUJ7YHBDfXCmPVqGgiqIVOMc35vyEd+PD3DJZtduNgcifeZ8u5v73LfcdszDLmQVwv25msttr0AaThHctyN9thrxt6RxAQaO9S9I5ANSKaCGrphpO6EhcZxnWT/6Bgf6l7DhrZBo69BdKmw9qZ7jlmY1VWCpvm161+AGzle+suDW85dGCMoQbeEYAtHtr2p/tHRlXKIZoIaim6WQhPn5vMhux9PPz1isPvUFtDrrL9C765w7+/OHassJWs7eqYCMA9cxPUd9TRqiQOgJJC+zcp1QhoIqiDo7vEcMWwzrz3WwbfrXDTaKJBoXDqv2xF5fzX3HPMxqimiWgOJyEZcjY1bMjvrNW2SKp5TP2PUU6HpFaNjCaCOrrxlG70TmzBbZ8uZUeum1oRdTsVjjgRfnrMfyc2yZgLkYl2Gsq6ckcP4+w19m7AHYPFtexkk4rWE6hGQhNBHYUGBfLs+P7k7y/hpo+XUOaO4SdEYMS/oHifndTGH2XMs3cD9fkiTki2zw0pHspe7Z5iIbB/Q+IAnbpSNRqaCOqhS1wkd5/Wi9lrsnl7Trp7DhrbHQZdAQvfcW5Sdl+1Z5NttVOX/gMVNWsFUe3qf93yd9lRQ92VCMB2LNuxwnaSU8rHaSKopwsGt+eknnE8Nn0VK7e6qXfwcbdBs9Yw/Tb39JRtLBpSP1Auvl/9m5CWVxQ3tA9BRUkpYEpt6yGlfJwmgnoSER47qx8twoO5YfJiCovd0KQ0PBpOvMeWly+vaornJipjLoRE2gHb6iuhnx3WoWhv3ffNTrPPDe1DUJEOSa0aEU0EDRATEcqT5/Rj9fY8Hpu+yj0HHXCh/XU7417/KVbImGd7EwcE1v8YCcmAge3L6r5v9mo74mldp6esSYsEiEyAzQvdd0ylHKKJoIGGd49j4tEdeXtOOj+u3tHwAwYE2nGIcjPh12cbfjxfV7DblqU3pFgIKrQcqkfxUPYa2ymtIYmoKokp2oRUNQqaCNzg9pE96N4mkls+Xkr2XjdMTt/haOhzFvz6DOzJaPjxfNmm3wHT8ETQIhGaxcC2elQYZ612b7FQuaQBsGsdFOxx/7GVciNNBG4QFhzIsxP6k1tYzO2fLnXPPMcnPwgIzLin4cfyZRlzISDIDsLXECK2nqCuLYeKC2HPRvdWFJc7UE+gzUiVb9NE4CY94ltw+4gezFy5g/d+c8Ov+Ki2MPSfsGIKpP/S8OP5qox5tny/IXMAlIvvBztW2bmHa2vXuoZNT1mT8lnWtMJY+ThNBG408eiOHNstloe/XsHaHXkNP+DR19r28dNvt4OyNTUlRbYytb79BypLSIayYtixsvb7HGgx5EAiaNbKjiOl9QTKx2kicKOAAOHJs/vRLCSI6z5YTFFJA7+8Q5rBKQ/B9j9h0TvuCdKXbF0CpUUNrx8oV58exllpgDRsesqaJKZo0ZDyeZoI3CyuRRiPn9WPFVtzeXpGWsMP2OsM6DAUvn/ItrBpSg5MROOmRNCyk+2PUJeWQ9lpEN3OJl0nJKXYXtN5bhqkUCkHaCJwwMm92nDB4Pa8Mms9v65t4CByIjDyMSjcYwela0oy5tlf4hGx7jleQADE961bhXF2mjPFQuW0Y5lqBDQROOTu03rRObY5N320hN379jfsYPF9YeBEO0x1Q2fi8hVlZQcHmnOnhH62U1lt6lTKp6d0x2Q0NcUjAVpPoHyaJgKHhIcE8tz4AezcV8Sdn//Z8CalJ9wDzWPh0781jR7HO9dAwS73FQuVS0i2U07WZh7o3EwoKXCmD0G5kOYQ21PvCJRP00TgoD5JUdx0SnemL9vGxwsyG3awZq1g3Cu2KGNGE5jjuLx+wF0thsrVZW6CLFcdjhN9CCpKGmDvCPxpIEHVqGgicNgVwzpzVOfW3D91ORuy9zXsYJ2H2yalC96ElV+5IzzvyZhnewK3PsK9x43tbscNqk0PYyebjlaUmGLvfnanO3sepepJE4HDAgKEp85NJjgwgBsm/0FxaVnDDnjCPbb448trIHeLe4L0hoy59Z+IpiaBwdCmV+3uCLJXQ3gr90xPWZMkrTBWvk0TgQckRofzr3F9WZKZw39/qkXZdU2CQuCsN21nrM//bis8G5u8bfbXsbuLhcqVz01wuKKY8ukpnRbXGwJDtMJY+SzHEoGIvCkiO0SkynGBRWS4iOSIyGLX416nYvEFo/omMLpfAs/9sIbV2xrY6zimix2hdMMsmPOcewL0pAMT0TiUCBL62ea2OZtq3i5rNcR6IBEEhdiWX9qxTPkoJ+8I3gZGHGab2caY/q7Hgw7G4hMeGNObFmHB3PzxEkoaWkQ04ELoNdbOcdzYfmlmzIOgcPuF7YSE/va5puKh/F2Qn+2ZOwJw9TBe3DSHClGNnmOJwBgzC9jl1PEbo9YRoTw4tg9/bs7h1dnrG3YwETj9WYiIt01K6zMzl7dkzIW2qbY83wlxvWzb/Zr6XByoKHa4xVC5pBQo3nfwvEr5EG/XERwlIktEZLqIVDtPoYhcISILRGRBVlaWJ+Nzu9P6JTCyTzzPfLeGNdsbWEQU3tI2Kd21Hr65zT0BOq0oz44F5FSxENjhImK61zzmkBPTU9ak7ZH2Oe0bz5xPqTrwZiJYBHQwxiQDzwNTqtvQGPOqMSbVGJMaG+um4Qi86MGxfWgeGsgtnyyltKyBbcs7DoVhN8Ef/4Pln7snQCdlLrDDPrcf7Ox5Djc3QdZqCAqD6PbOxlEupisccQL8+hwU5nrmnKrpKCuDZZ/B9uWOHN5ricAYk2uM2et6PQ0IFhGH2/H5htjIUO4f05vFm/bwxi8NLCICGH47JKXC1Othz2EqSL0tY54ttmk7yNnzxPeDvK2wt5rpQ52anrImJ9xj+xPMe9lz51SNmzGwejq8cix8cgkseMuR03gtEYhIvIhtRC4ig1yx7PRWPJ42JjmRU3q14ckZaazLamD5fmAwnPWarYj87ArfrpDMmAttekNYC2fPUz4kdXX1BNmrPVdRXC4pBXqMhupiTKEAACAASURBVLkv2MpqpapjDKz7AV4/ET4Yb+uXxr1uWws6wMnmox8Ac4HuIpIpIpeJyJUicqVrk7OBZSKyBHgOGG/cMsdj4yAiPHxmH8KDA7nVHUVErTrDaU9Bxhz45Wn3BOlupSW2aMjJ+oFy8X3tc1U9jIsLYfdGzycCgBPutvUkvz7r+XOrxiH9V3j7NJh0pr2jHfM8/GM+9DvHsTvYIEeOChhjJhxm/QvAC06dvzGIiwzjvtN7ceNHS3h7TjqXDe3UsAP2Ow/WzoQf/wWdhkO7I90Sp9ts/9P+snH3iKNVCY+G6A5V3xHsXAsYz/QhqCyuJ/Q9B357BYZcDZFtPB+D8k2ZC+HHh+2dQEQbGPUkpFwEQaGOn9rbrYb83pkDkjixRxxPfLuK9IaORSRi7wqikuDTy3yvUrK8I5m7RxytTkJy1RXGnhpjqDrDb4fS/TD7Ke+cX/mWbX/CBxPg9RPs5/WUh+G6xTDoco8kAdBE4HUiwiNn9iU4MIBbP1lKWUOLiMKibFliziaYdot7gnSXjLm2lU5UkmfOl9APdm+Awpy/Ls92eHrKw2l9BAz4Pzt44J4M78SgvC8rDT6eCP8daouDTrgbrl9iB5Z0asa8amgi8AHxUWHcO7oX89N38e7c9IYfsP1gOO52WDoZln7U8OO5gzGuiWg8UD9QrryH8bZKo5xkp9mEFBzuuVgqO+5Wewf3szOVf8qH7doAn18JLw2GtBkw7Ga4YQkcewuERnolJE0EPuLsgW0Z3j2Wx79ZTcZON0w8M+wmWwTz1Y2+MfzxtqWwdzu0c7j/QEXVzU2Qleb8HASHE9UWUi+DxR9A9lrvxqI8I2czTL0BXki1fX6GXA03LIUT77GdQ71IE4GPEBEePbMvgQHCrZ8uaXgRUWCQbVIqAfDp5bbFjrfszYIPL7TzD/Q4zXPnjWxjK90q9jAuK7Ozo3mrfqCiYTfaTm0/PertSJST9mXDN3fCcwNsx8+BE+G6P+DUR5wfAr2WNBH4kMTocO4+rSfz1u/ivfluKDuObg+jn4bM+TDr3w0/Xn3sz4cPzrPN4M7/CCLjPXv+yhXGORlQUugbiSAiDoZcCcs+PbT4SjV+hbnw46PwbDL89jL0PRuuXWgbdLRI9HZ0f6GJwMecd2Q7hnWN4bFpK8nc7YYior5nQ/L5MOsJ2Din4ceri7JS+OxyOzrq2W9A24GePT/Y4qGs1VBcYN9nr7HPvpAIwFYMhkbBj494OxLlLsUFMOd5mwB+ftwOLXL1PDjjJWjZwdvRVUkTgY8REf41znaGuv1TN0x6DzDq37ZN/WdX2GIaT/n2Llj1FYx4zLNFQhUl9ANTCjtW2PdZq+2zt+sIyoW3hGOuhdXTbGc71XiVFtshIJ5LgRl327vRy3+E8yb5zuetGrVKBCLSXEQCXK+7icgYEXFoDGHVtmUz7hjVk1/WZjP5dzeMHRQaaX+R78uGN06CnQ2cJa025r1sb4eHXG2LP7zlwFATruKh7DRo1hqatfJeTJUNvsrWn/zwkLcjUfVRVgZ/fgIvDoKvbrDNoy+eChdNOThNqY+r7R3BLCBMRJKAGcCF2IlnlEPOH9Seozq35pGvV7JlT0HDD5g0ECZ+ZYc3eONk2PR7w49ZnZVT4Zs77Lg6pzzs3HlqI7qD7VtR3sM4O81zcxDUVmiErThe/5OddU41DsZA2rd2QLhPL7MV/+M/gMu+g07Heju6OqltIhBjTD4wDnjJGHMOUO38AarhAgKEf5/djzJjuOMzNxURtU21H9LQFvDO6bBqWsOPWVnmAjtRTtJAGPeaZ0f3rIqIrSfYVjEReGgOgrpIvQwiE+H7hw4/17Lyvo1z4K2R8P65sD/Pftav/AV6jLKfuUam1olARI4CLgC+di3z8v/wpq9dq2bcNqIHP6dl8fHCTPcctPURNhnE9YQPL4DfX3fPccFOkPP+ebZl0ITJHu8dWa2EZDuOe952yN/pm+W1wWFw3C22hdeaGd6ORlVn6xL431k2CezaYFsA/eN36Heu93/0NEBtE8ENwB3A58aY5SLSGfjRubBUuQuHdGBQp1Y89NUKtuUUuuegEbG2mKjrKfD1TTDz/ob/Cs3fBe+dYytmL/jUnsNXxPezTUZXfWXf+0qLocoGXAgtO9q6grIGzmmt3CtzAXx0kS0GylwAJz1g+wIc+TcICvF2dA1Wq0RgjPnZGDPGGPO4q9I42xhzncOxKVxFRGf1o7i0jDs/d1MREUBIczjvPdu55Zf/wOd/h5L99TtWcSFMPt9OijP+A4jx0hg+1SmvMP7zE/vsq4kgMBiG32kHIVv5hbejUaUldlaw10+28wKs+9EOB3H9Ehh6g+/c8bpBbVsNvS8iLUSkObAMWCEiPjaiWdPVMaY5t5zagx9W7WD6sm3uO3BgEIx+xg52tfRDeO/sQwdoO5yyMphylR1Q7syXoYMHxxKqrZiuEBRu52oICoeodt6OqHp9z4bYHrYjki9PMNSUFey280U8m2xnBdu3A0Y8DjeucA0HEe3tCN2utkVDvYwxucAZwHSgE7blkPKQiUd3pEd8JI9OW0lhsRu/IETsYFdnvAwbf4W3RkHultrv//0DsPwze6vc5yz3xeVOAYF2VjSwdysBPtx9JiAQjr/LVmov/dDb0fiX7LXw9c3wdG/47l5o1QnGvw/XLrJNoL00IJwn1PZ/RLCr38AZwJfGmGJAmzZ4UGCAcO/oXmTuLuDNXze4/wT9z7dDQOxOh9dPgh0rD7/Pgjfh12cg9VI45nr3x+RO5cVDvlosVFHP0+3IqT/9q/7Fdap2jLHNdt8/D14YCIvegV5j4e+zbD1aj9MadSVwbdU2EbwCpAPNgVki0gHwsVlPmr6ju8Rwcq82vPjDWnbkuaniuKIuJ8Il022RxBunwobZ1W+bNsNWNHc9FUY+4ftN5hJcI5H6Wh+CqojYie73ZNgvJuV+xYWwaBK8fAy8O9ZWAB93G9ywzBZxlv9w8BO1rSx+zhiTZIwZZayNwPEOx6aqcOeonuwvLeOpb9OcOUFCP/jbd7YJ6P/GHaxgrWjLYjuhRnxfOPtNW9fg69q6pu0sn8vY13U50c7dMOtJO3Cfco+87bb+5T+94ctr7LKxL8I/l8Pxd/rt1KG1rSyOEpGnRWSB6/EU9u5AeVinmOZMPLojHy3cxLLNdazYra3o9nDpN5CUantMznn+YPPSPZtsJ5pmrWxRUmiEMzG4W5veduCv7iO9HUntlN8V7N3m3r4e/ign08778Onf4Jk+8PO/7Q+Di76Eq361s8UFh3k7Sq+S2jRHFJFPsa2Fyu9TLwSSjTHjHIytSqmpqWbBAv8enCunoJjjn/yJrnERTL5iCOJUsUxxoW1WumIKDL7S3jqXVyZf9q3tlKacNWkcbPnDNlkMa+HtaBqHvO2QPtsO15E+23Z0BDvAX5+zYchVtmOlnxGRhcaY1KrW1fae/ghjTMUmIQ+IyOKGh6bqIyo8mBtP7sbdU5bx7fJtjOiT4MyJgsPg7LdgRhLMexGWTIb9++DCzzQJeMoJd8Nrx9tB/Ibf5u1ofNO+nfYLP322rdfKdo0wG9oCOhxjO311HAZt+vh2izEvqm0iKBCRocaYXwBE5BjADSOhqfoaf2Q7Js3dyCPTVnJ8jzhCgxxq2RAQACMetSMqfv8gjH2h0Q2o1aglpdjB++a+AIMu961RU72lYI9t6rzB9eW/3TWpT3Bz24+l//nQaRjEJzeO+isfUNuioWTgXSDKtWg3cLExZmn1ezlDi4YOmr0miwvfmM/tI3tw5XEeuNUtLba9X5Vn7VgJLx1lm+ie/IC3o/GOvTvgt1dg7Uw7gKAps6N9thtsv/Q7HmuTpn4+q9XgoiFjzBIgWURauN7nisgNgMcTgTpoWNdYTuoZxws/rOWslLbERoY6e0L9T+YdcT2h7zn2i3DIVZ6f7tOb9mXbXr7zX4PSItuS6thb7Zd/2yMhyOHPvJ+oU4GZMSbX1cMY4EYH4lF1dOeonhQWl/L0d6u9HYpy0vDb7a/gjyfaSvymLn8XzHwAnulni8V6jYFrFsAl0+D4O6DjUE0CbtSQmhMf70HkHzrHRnDx0R2Z/Psmlm9xqDmp8r7WR8CZ/7VjOk25sumOTlqwG3542CaAX/5jm/te/RuMe9UvW/p4SkMSgQ4x4SOuO6Er0eHBPPTVCveNTqp8T59xcPJDsPxzmHmvt6Nxr8Ic+OkxeCYZZj1hO9RdPddOsRrbCIYFaeRqrCMQkTyq/sIXINyRiFSdRTWzzUnv+WI5M1Zs59TeflSG7G+OvhZyNtlOflHtYfAV3o6oYYry4Lf/2r+nMMe2kBp+B8T38XZkfqXGRGCMabrD7TUxEwa15925G3l02kqGd491rjmp8i4RGPGY7S37zW22WW+P07wdVd0V7YXfX4Nfn4OCXdBtpK0HSezv7cj8kvauaCKCAgO4Z3QvNu7M55056d4ORzkpIBDOegMSB8Anl0HmQm9HVHv78+2v/2eT7cx4bVPh8h/g/MmaBLzIsUQgIm+KyA4RWVbNehGR50RkrYgsFZEUp2LxF8d2i+WEHnE8//1asvcWeTsc5aSQZjDhQ4iIs2M/lQ+j4KtKS2Def20CmHG3HdzwsplwwceQNNDb0fk9J+8I3gZG1LB+JNDV9bgCeNnBWPzGnaN6UlBcytPfOTQ6qfIdEbHwf5/aeaL/d7YdasEXbV0Kr59gi7Jiu8Ml38CFn0O7I70dmXJxLBEYY2YBu2rYZCzwrmtY63lAtIg4NGiO/+gSF8GFR3Vg8vwMVm7VKSOavJiuMGGyrTOYPAGKfWjkl+ICW/zz6nDI3Qrnvmsne/HF6Uz9nDfrCJKATRXeZ7qWHUJErigfAjsrK8sjwTVm15/YlRbhwTz8tTYn9Qvth9h29pvm29FifaGPQfqvdtKXX/4D/SfANfPtzF/KJzWKymJjzKvGmFRjTGpsbKy3w/F50c1C+OdJ3fh17U5mrtzh7XCUJ/Q+A055GFZ8Ad/d4704CnNg6g3w9ihbZHXRF3bil/CW3otJHZY3E8FmoF2F921dy5QbnD+4PV3iInjk6xXsL/GBX4jKeUf9Awb93Q7J8Nsrnj//qmnw4hA7veZR18BVc6DzcM/HoerMm4ngS+AiV+uhIUCOMWarF+NpUoIDA7j7tJ6k78zn3bnp3g5HeYIIjPgXdD8Npt8GK7/yzHn37rBjIE2eYH/5/20mnPoIhOgkho2Fk81HPwDmAt1FJFNELhORK0XkStcm04D1wFrgNeBqp2LxV8O7xzG8eyzPfr+Gndqc1D8EBMJZr9smmZ9eBpt+d+5cxtgpIF8cBKu+huPvhit+0uagjVCt5iPwJTofQd2s3ZHHqc/MZsKgdjx8RiOZuF013L5seP0kKMq1v9BbdXbv8XdvhK/+Ceu+t3MCjHneNg1VPqum+QgaRWWxqr8ucZFcOKQD7/+Wwepted4OR3lK8xi44BP7q92dfQzKSu20mS8dBZt+g5FP2H4BmgQaNU0EfuD6E7sSGaajk/qdmC4H+xh8ML7+fQyMsUNDbF0Cb54K39wOHY6Gq+fZQe90HuBGTyf09AMtm4dww0ldeWDqCr5dvp0RfXR0Ur/RfjCc9Rp8dDF8djkM/ScU5toio8KcCq8rLss5dFlZiT1eeCsY95qdMU10SpKmQusI/ERxaRmnP/8LOQXFzLzxOJqH6m8AvzL3Rfj2zurXh7awj7AWEBZ18HXFZWHR0PN0W+ykGp0Gz1msGr/gwAAeObMPZ708l/98l8bdo3t5OyTlSUOuhoRkO/5/5S/60Ejb2kj5LU0EfmRgh1ZMGNSet+akc2ZKEr0To7wdkvIUETvPr1JV0FoeP3P7iB5Ehwdz5+fLKC1rXMWCSilnaCLwM1HNgrl7dE+WbNrD+/MzvB2OUsoHaCLwQ2f0T+KYLq359zer2JFX6O1wlFJeponAD4kID43tQ1FxGQ9/tdLb4SilvEwTgZ/qHBvBVcOP4MslW5iVpnM8KOXPNBH4sauGH0GnmObc88UyCotLvR2OUspLNBH4sbDgQB4+ow8bd+bz0o9rvR2OUspLNBH4uWO6xHBG/0Re/nkda3fs9XY4Sikv0ESguOu0XoQHB3LX53/qoHRK+SFNBIrYyFBuH9mT3zbs4rNFOluoUv5GE4ECYPyR7UhpH80j01aye99+b4ejlPIgTQQKgIAA4ZEz+5JTUMxj01d5OxyllAdpIlAH9Exowd+GduLDBZv4PX2Xt8NRSnmIJgL1F9ef1JWk6HDu+vxP9peUeTscpZQHaCJQf9EsJIgHxvQmbfteXv9lvbfDUUp5gCYCdYiTerXhlF5teO77NWzale/tcJRSDtNEoKp0/5jeBIpw7xfLtG+BUk2cJgJVpcTocP55cjd+XJ3F9GXbvB2OUspBmghUtSYe3ZFeCS14YOpy8gqLvR2OUsohmghUtYICA3h0XF925BXx1Iw0b4ejlHKIJgJVo/7tovm/wR14d246f2bmeDscpZQDNBGow7r51O60jgjlzs//1AnvlWqCNBGow4oKD+be0b34c3MOD0xdrq2IlGpigrwdgGocTk9OZNnmHF6ZtZ7oZiHceHI3b4eklHITTQSq1m4f2YM9+cU89/0aWjYL5pJjOnk7JKWUG2giULUmIjxyZh9yCop5YOoKosKDGZfS1tthKaUayNE6AhEZISKrRWStiNxexfqJIpIlIotdj785GY9quKDAAJ4Z35+jj2jNLZ8sZeaK7d4OSSnVQI4lAhEJBF4ERgK9gAki0quKTT80xvR3PV53Kh7lPmHBgbx6USp9Elvwj/cX8dv6nd4OSSnVAE7eEQwC1hpj1htj9gOTgbEOnk95UERoEG9dMoh2rZrxt3cWsGyzb/cxMMawalsuZdr8ValDOJkIkoBNFd5nupZVdpaILBWRT0SkXVUHEpErRGSBiCzIyspyIlZVD62ahzDpskG0CA/m4jfnsz5rr7dDqtaz369hxDOzee6HNd4ORSmf4+1+BFOBjsaYfsB3wDtVbWSMedUYk2qMSY2NjfVogKpmCVHhTLpsEAAXvjGfrTkFXo7oUO/MSeeZmWto1TyEF39cS9r2PG+HpJRPcTIRbAYq/sJv61p2gDFmpzGmyPX2dWCgg/Eoh3SOjeCdSweRU1DMRW/MZ/e+/d4O6YAvFm/mvi+Xc3KvNky/fhjNQ4O47dOl2kNaqQqcTAS/A11FpJOIhADjgS8rbiAiCRXejgFWOhiPclCfpChevziVjF35THz7d/YWlXg7JH5ctYObPlrC4E6teH7CANq0COPe0b34I2MPk+amezs8pXyGY4nAGFMCXAN8i/2C/8gYs1xEHhSRMa7NrhOR5SKyBLgOmOhUPMp5Qzq35oXzU1i2OYe/T1pAUUmp12JZkL6Lq95bSI+ESF6/OJWw4EAAzhyQxLHdYvn3t6vJ3K2zrykFII1t3JjU1FSzYMECb4ehavDpwkxu+ngJI/vE88L5KQQGiEfPv3JrLue9MpeYiFA+uvIoYiJC/7I+c3c+p/xnFkd2bMXblxyJiGfjU8obRGShMSa1qnXerixWTdBZA9tyz+heTF+2jTs/+9Ojg9Rt3LmPi96cT7OQIN69bNAhSQCgbctm3HJqd35Oy2LK4s1VHEUp/6KJQDnisqGduPaELny4YBOPf7PaI+fckVvIhW/Mp7i0jEmXDaJty2bVbnvRUR0Z0D6aB6euYOfeomq3U8ofaCJQjrnx5G5cOKQD//15Ha/8vM7Rc+XkF3PRm/PJ3lvE25cMomubyBq3DwwQHj+rH3uLSnhg6gpHY1PK12kiUI4RER4Y05vTkxP51/RVfPh7hiPnKdhfyqXv/M66rL28emEq/dtF12q/bm0iuXp4F75csoUfVumYScp/aSJQjgoIEJ46J5njusVyx2d/cuOHi906HEVxaRlXvbeQPzJ28+z4AQztGlOn/a8+/gi6xkVw9+fLfKLJq1LeoIlAOS4kKID//t9ALjqqI98s38bo53/h3Ffm8u3ybQ3q2FVWZrj54yX8tDqLR87sy6i+CYffqZLQoEAeO6sfW3ML+fc3q+odi1KNmSYC5RHhIYHcP6Y3c+84kbtG9WTz7gL+Pmkhxz/5E2/+soG8wuI6Hc8YwwNTl/PF4i3cOqI7Ewa1r3dsAzu05OKjOjJp3kYWpO+q93GUaqy0H4HyipLSMmas2M6bv2xgwcbdRIYGce6R7Zh4dEfataq+tU+5Z2am8czMNVxxbGfuGNmjwX0B9hWVcMp/ZhEWHMDX1w070AFNqaZC+xEonxMUGMCovgl8ctXRTPnHMZzQM4535qRz3BM/cuWkhczfsKva/gdv/7qBZ2au4ZyBbd2SBACahwbx6Li+rMvax4s/rm3w8ZRqTPSOQPmMrTkFTJq7kffnZ7Anv5i+SVFcOrQjp/VNJCTI/maZ8sdmbvhwMaf0asNLF6QQFOje3zI3friYL5dsYeq1Q+mZ0MKtx1bKm2q6I9BEoHxOwf5SPvsjkzd/2cC6rH3ERYZy0VEdaNeqGTd9tITUji15+5JBjhTf7N63n5Oe/pm2LcP57OpjPD48hlJO0USgGqWyMsOsNVm8+Ws6s9LshER9k6J4//LBRIYFO3beL5ds4boP/uDu03ryt2GdHTuPUp5UUyII8nQwStVWQIAwvHscw7vHsWZ7HjNWbGf8ke0cTQIAp/dL4Is/NvPUjDRO7R1fq8rr2ti5t4iWzUII0LsM5WP0jkCpKmzNKeDkp2fRv100ky4bVO8K6e25hUxdsoXP/9jM8i25JESFMbpfAqcnJ9I3KUpHPlUeo3cEStVRQlQ4t43ozj1fLOeThZmck1rldNpVyiss5ptl2/hi8RbmrMumzEC/tlHceHI3lmbu4e056bw2ewPtWzXj9OQERvdLpEd8pCYF5TV6R6BUNcrKDOe9Ope07XuZeeNxxEYeOqR1uf0lZcxKy+LzxZuZuWI7RSVltG/VjDMGJDG2fyJHxEYc2DYnv5hvl29j6tItzFm3k9IyQ5e4CE7vl8jo5IS/bKuUu2hlsVL1tHbHXkY9O5uTe7XhxQtS/rLOGMPCjbuZsngzXy3dyp78Ylo1D2F0vwTOGJDEgHbRh/2Vv3NvEdOXbWPqki3MT9+FMdAroQWnJycyul+C2+onlNJEoFQDvPDDGp6ckcarFw7klN7xrN2Rx5Q/tvDFks1s2lVAWHAAp/SK58wBSQztGkNwPfs2bM8t5OulW5m6dAt/ZOwBoH+7aEb3s8VH8VFh7vyzlJ/RRKBUAxSXlnH687+QvXc/8VGhLNucS4DA0K6xnNE/kVN6xxMR6t7qtk278vn6z618tXQLyzbnIgJHdmjFSb1sK6qucRFap6DqRBOBUg20NHMP5/x3Lt3jIzmjfxKjkxOIi/TML/T1WXv5eulWvv5zK6u25QGQGBXGcd1jOa5bLMd0iXG8Sa1q/DQRKOUGxaVl9S72cZetOQX8vDqLn9Oy+GVNNnlFJQQFCAM7tOS47rEM7xZHzwRtgaQOpYlAqSaouLSMRRt383NaFj+tzmLF1lwA4iJDOa5bLMd1j2VYl1iimundgtJEoJRf2JFbyM9p9m5h9ppscgqKCRAY0L4lx3WLZXj3WPokRmnPZj+liUApP1NSWsaSzD38vDqLn9KyWJpppwdt1TyEoV1iGNY1hmO7xdKmhbZE8heaCJTyc9l7i5i9JovZadnMWpNN9t4iAHrERx5ICkd2bKUT8jRhmgiUUgeUlRlWbstl9ppsZqVlsSB9N/tLywgNCmBw59Yc60oM2kS1adFEoJSqVv7+En5bv4tZa7KYlZbFuqx9AMS3CDtwtzC0Swwtm4d4OVLVEJoIlFK1tnlPAbPTspi1xjZRzS0sQQT6JEYRExFCYIAgIgSKEBAAASIEBggBUv7g4DYBECjlr4WI0CCimwXbR3gIUc2CiQ4PJrpZCC3Cgtw+45w6SEcfVUrVWlJ0OOMHtWf8oPaUlhmWZO5hdlo2c9dnk713P2XGUFpmKDOGMmOLmsqModQYysqosB7XNvZ9aZkhf39pjeeODA2yyeGQRGHft4kKo23LcNpGhxMTEaotoNxEE4FSqlqBAUJK+5aktG/J9XRt8PFKSsvILSxhT/5+cgqK2VNQTE5+MXvy97OnoJg9+cV2uev9lj0FB7YrLftr6UVIUABJ0eG0bRl+4Llty2YktbSv4yLDdKrRWtJEoJTymKDAAFo1D6FVHesbjDHkFpawLaeQzN35bN5TQObuAjbvLiBzdz4rt+aSvXf/X88VICRGH0wSSS3DSYwOJyEqjISoMOKjwt0+RlRjpVdBKeXzRISo8GCiwoPpHh9Z5TYF+0tdCeLQRDFrTRbbc4sO2ScyNIj4qDDiXckhISrclSTs6/ioMFqEBTX51lOOJgIRGQE8CwQCrxtjHqu0PhR4FxgI7ATOM8akOxmTUqppCg8JpEtcBF3iqp7Yp6iklB25RWzNKWRrTgHbcgrZmlNon3MLWb0ti6y9RVRuP9MsJNAmixZhRIYFERYcSHhwIGHBgYQGBxAWZF+HBQccfA6qsD440PU+gJCgAIICAggKFIJdz4EBQnBggFeLsRxLBCISCLwInAxkAr+LyJfGmBUVNrsM2G2M6SIi44HHgfOcikkp5b9CgwJp16pZjZP9FJeWsSOviG05BQeTxIHnAnbu3U9hSSkF+0spLC6lsKSM/SVlbolPBIIDbEIICrTJIahCkggKFCYc2Z7Lj+3slvNV5OQdwSBgrTFmPYCITAbGAhUTwVjgftfrT4AXRERMY2vTqpRqEoIDbQV0UnR4rfcpKzMUlZS5EkMphcWu18WVXpeUUlxqKCk1lJSVuV6XUVL212Wl5evKylzL7XbFZabG6VIbwslEkARsqvA+Exhc3TbGmBIRyQFaA9kVNxKRK4ArANq3b+9UvEopR/yMFAAAByBJREFUVWcBAUJ4SCDhIY13eI5G0XvDGPOqMSbVGJMaGxvr7XCUUqpJcTIRbAbaVXjf1rWsym1EJAiIwlYaK6WU8hAnE8HvQFcR6SQiIcB44MtK23wJXOx6fTbwg9YPKKWUZzlWR+Aq878G+BbbfPRNY8xyEXkQWGCM+RJ4A5gkImuBXdhkoZRSyoMc7UdgjJkGTKu07N4KrwuBc5yMQSmlVM0aRWWxUkop52giUEopP6eJQCml/Fyjm5hGRLKAjd6OoxoxVOoM52N8PT7w/Rg1vobR+BqmIfF1MMZU2RGr0SUCXyYiC6qbAcgX+Hp84PsxanwNo/E1jFPxadGQUkr5OU0ESinl5zQRuNer3g7gMHw9PvD9GDW+htH4GsaR+LSOQCml/JzeESillJ/TRKCUUn5OE0EdiUg7EflRRFaIyHIRub6KbYaLSI6ILHY97q3qWA7GmC4if7rOvaCK9SIiz4nIWhFZKiIpHoyte4XrslhEckXkhkrbePz6icibIrJDRJZVWNZKRL4TkTWu55bV7Huxa5s1InJxVds4FN8TIrLK9W/4uYhEV7NvjZ8HB+O7X0Q2V/h3HFXNviNEZLXr83i7B+P7sEJs6SKyuJp9Hb1+1X2nePTzZ4zRRx0eQAKQ4nodCaQBvSptMxz4yosxpgMxNawfBUwHBBgC/OalOAOBbdiOLl69fsCxQAqwrMKyfwO3u17fDjxexX6tgPWu55au1y09FN8pQJDr9eNVxVebz4OD8d0P3FyLz8A6oDMQAiyp/P/JqfgqrX8KuNcb16+67xRPfv70jqCOjDFbjTGLXK/zgJXYKTcbk7HAu8aaB0SLSIIX4jgRWGeM8XpPcWPMLOxQ6BWNBd5xvX4HOKOKXU8FvjPG7DLG7Aa+A0Z4Ij5jzAxjTInr7Tzs5E9eUc31q40Dc5sbY/YD5XObu1VN8YmIAOcCH7j7vLVRw3eKxz5/mggaQEQ6AgOA36pYfZSILBGR6SLS26OBgQFmiMhC13zPlVU1n7Q3ktl4qv/P583rV66NMWar6/U2oE0V2/jKtbwUe5dXlcN9Hpx0javo6s1qijZ84foNA7YbY9ZUs95j16/Sd4rHPn+aCOpJRCKAT4EbjDG5lVYvwhZ3JAPPA1M8HN5QY0wKMBL4h4gc6+HzH5bYWevGAB9Xsdrb1+8Qxt6H+2RbaxG5CygB3qtmE299Hl4GjgD6A1uxxS++aAI13w145PrV9J3i9OdPE0E9iEgw9h/sPWPMZ5XXG2NyjTF7Xa+nAcEiEuOp+Iwxm13PO4DPsbffFdVmPmmnjQQWGWO2V17h7etXwfbyIjPX844qtvHqtRSRicBo4ALXl8UhavF5cIQxZrsxptQYUwa8Vs15vX39goBxwIfVbeOJ61fNd4rHPn+aCOrIVZ74BrDSGPN0NdvEu7ZDRAZhr/NOD8XXXEQiy19jKxSXVdrsS+AiV+uhIUBOhVtQT6n2V5g3r18lFefUvhj4ooptvgVOEZGWrqKPU1zLHCciI4BbgTHGmPxqtqnN58Gp+CrWO51ZzXlrM7e5k04CVhljMqta6YnrV8N3iuc+f07VhDfVBzAUe4u2FFjseowCrgSudG1zDbAc2wJiHnC0B+Pr7DrvElcMd7mWV4xPgBexrTX+BFI9fA2bY7/Yoyos8+r1wyalrUAxtpz1MqA18D2wBpgJtHJtmwq8XmHfS4G1rsclHoxvLbZ8uPxz+F/XtonAtJo+Dx6Kb5Lr87UU+6WWUDk+1/tR2JYy6zwZn2v52+WfuwrbevT61fCd4rHPnw4xoZRSfk6LhpRSys9pIlBKKT+niUAppfycJgKllPJzmgiUUsrPaSJQqhIRKZW/jpDqthExRaRjxREwlfIFQd4OQCkfVGCM6e/tIJTylP9v745VowrCMAy/H8FiQRAxYCOSwlRBC0mV0luwCJJKrFKIlXgDqawkamMK8S6CEEEEBbt4AWKnkBQKNkHkTzEjrHGVLBhXOO8Dy87+C4cz1cycOec7rgikY+q59Pd7Nv3bJJd6fSHJix6utpPkYq+fT3tPwG7/rPRDzSXZ6tnzz5OMZtYpCQcCaZLRkUtDq2P/famqy8Aj4EGvPQSeVdUVWvDbZq9vAi+rheddpT2ZCrAIPK6qJeAzcP2E+yP9kU8WS0ck+VpVpyfUPwDXqup9Dwn7VFXnkuzT4hO+9frHqppPsgdcqKqDsWMs0PLjF/vve8Cpqto4+Z5Jk7kikKZTv2lP42Cs/R336jRjDgTSdFbHvt/09mtaaibAGvCqt3eAdYAkc0nO/KuTlKbhTET61Sg/v8h8u6p+3EJ6Nsk72qz+Rq/dBp4muQvsATd7/Q7wJMkt2sx/nZaAKf1X3COQjqnvESxX1f6sz0X6m7w0JEkD54pAkgbOFYEkDZwDgSQNnAOBJA2cA4EkDZwDgSQN3CFaz85bKMl3jwAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]}]}
\ No newline at end of file
diff --git a/006 Improvements.ipynb b/006 Improvements.ipynb
new file mode 100644
index 0000000..24f9bb0
--- /dev/null
+++ b/006 Improvements.ipynb	
@@ -0,0 +1 @@
+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"006 Improvements.ipynb","provenance":[{"file_id":"18YGciOMG_OZDR7-rMwUU2D6by8VgEql5","timestamp":1606610875693},{"file_id":"1YKQ3ItCmgfpn66WhGy9ey9jpWQ5o2nus","timestamp":1605399621858}],"collapsed_sections":[],"toc_visible":true},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"markdown","metadata":{"id":"Qjc-UWAOCify"},"source":["# Data Preprocessing\n","\n","Tasks\n","1.   Load Kaggle traffic sign data from drive\n","2.   Unpickle files\n","3.   Filter out classes not related to speed\n","4.   Normalize number of samples per class\n","5.   Split into train / val / test sets\n","6.   Shuffle datasets\n","7.   Resize\n","8.   Normalize pixel values\n","9.   Make pytorch dataloaders"]},{"cell_type":"code","metadata":{"id":"cPKNBe0NUG71","executionInfo":{"status":"ok","timestamp":1607226685424,"user_tz":300,"elapsed":4019,"user":{"displayName":"Shadman Siddiqui","photoUrl":"","userId":"01981550049686806435"}}},"source":["import pickle\n","import numpy as np\n","import torch\n","import torchvision.transforms as transforms\n","from torch.utils.data import TensorDataset, DataLoader\n","import matplotlib.pyplot as plt"],"execution_count":1,"outputs":[]},{"cell_type":"code","metadata":{"id":"ljaGNZa8HUcq","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1607226724492,"user_tz":300,"elapsed":383,"user":{"displayName":"Shadman Siddiqui","photoUrl":"","userId":"01981550049686806435"}},"outputId":"9e34c8aa-3332-4ce7-d79d-f68c71fd6721"},"source":["# mount drive\n","from google.colab import drive\n","drive.mount('/content/gdrive')"],"execution_count":3,"outputs":[{"output_type":"stream","text":["Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Dy0lMjtXasiZ","executionInfo":{"status":"ok","timestamp":1607226726645,"user_tz":300,"elapsed":439,"user":{"displayName":"Shadman Siddiqui","photoUrl":"","userId":"01981550049686806435"}}},"source":["# Data directory\n","# Change this as needed\n","data_dir = '/content/gdrive/My Drive/APS360 Project/'"],"execution_count":4,"outputs":[]},{"cell_type":"code","metadata":{"id":"Si9g90e0Ud0i","executionInfo":{"status":"ok","timestamp":1607226731135,"user_tz":300,"elapsed":833,"user":{"displayName":"Shadman Siddiqui","photoUrl":"","userId":"01981550049686806435"}}},"source":["# Data loaders\n","\n","def get_data_loader(batch_size):\n","    ''' The Kaggle dataset is split into three pickle files: train.pickle,\n","    valid.pickle, and test.pickle.\n","    The function will combine the three datasets and resplit them such that the\n","    resulting split is approximately 70% training, 15% validation, and 15% testing.\n","    The function filters classes 0-8,13,14,32 only, as they are related to speed.\n","    The splitting ratio will be applied to each class, to avoid imbalance of \n","    classes in the training/validation/testing samples.'''\n","\n","    classes = ('Speed limit (20km/h)',\n","            'Speed limit (30km/h)',\n","            'Speed limit (50km/h)',\n","            'Speed limit (60km/h)',\n","            'Speed limit (70km/h)',\n","            'Speed limit (80km/h)',\n","            'End of speed limit (80km/h)',\n","            'Speed limit (100km/h)',\n","            'Speed limit (120km/h)',\n","            'Yield',\n","            'Stop',\n","            'End of all speed and passing limits')\n","    # Index of chosen classes in the original dataset\n","    # In our reconstructed dataset, the same classes will have indexes 0-11\n","    original_labels = (0,1,2,3,4,5,6,7,8,13,14,32)\n","    # load pickle files\n","    # will combine datasets from three seperate pikcle files\n","    \n","    with open(data_dir+'train.pickle', 'rb') as file:\n","        data1 = pickle.load(file)\n","\n","    with open(data_dir+'valid.pickle', 'rb') as file:\n","        data2 = pickle.load(file)\n","\n","    with open (data_dir+'test.pickle', 'rb') as file:\n","        data3 = pickle.load(file)\n","\n","    images = np.concatenate((data1['features'], data2['features'], data3['features']))\n","    labels = np.concatenate((data1['labels'], data2['labels'], data3['labels']))\n","\n","    # shuffle\n","    np.random.seed(9001)\n","    indices = list(range(images.shape[0]))\n","    np.random.shuffle(indices)\n","    images = images[indices]\n","    labels = labels[indices]\n","\n","    # split into train /  valid / test\n","    train_split = 0.7\n","    valid_split = 0.85\n","\n","    train_images = images[0:int(train_split*images.shape[0])]\n","    train_labels = labels[0:int(train_split*images.shape[0])]\n","\n","    valid_images = images[int(train_split*images.shape[0]):int(valid_split*images.shape[0])]\n","    valid_labels = labels[int(train_split*images.shape[0]):int(valid_split*images.shape[0])]\n","\n","    test_images = images[int(valid_split*images.shape[0]):]\n","    test_labels = labels[int(valid_split*images.shape[0]):]\n","\n","   # sort into classes\n","    class_train_images = []\n","    class_train_labels = []\n","    for i in range(len(classes)):\n","        class_train_indices = np.where(train_labels==original_labels[i])[0]\n","        class_train_images.append(train_images[class_train_indices])\n","        \n","        # Don't want the original labels\n","        # This is where we convert to our own labels: 0-11\n","        class_train_labels.append(np.full(len(class_train_indices),i)) \n","\n","    class_valid_images = []\n","    class_valid_labels = []\n","    for i in range(len(classes)):\n","        class_valid_indices = np.where(valid_labels==original_labels[i])[0]\n","        class_valid_images.append(valid_images[class_valid_indices])\n","        \n","        # Don't want the original labels\n","        # This is where we convert to our own labels: 0-11\n","        class_valid_labels.append(np.full(len(class_valid_indices),i)) \n","\n","    class_test_images = []\n","    class_test_labels = []\n","    for i in range(len(classes)):\n","        class_test_indices = np.where(test_labels==original_labels[i])[0]\n","        class_test_images.append(test_images[class_test_indices])\n","\n","        # Don't want the original labels\n","        # This is where we convert to our own labels: 0-11\n","        class_test_labels.append(np.full(len(class_test_indices),i)) \n","\n","    # normalize number of samples in each class\n","    \n","    # train\n","    desired_size=int(600*0.7)\n","    extra_train_images = []\n","    extra_train_labels = []\n","    for i in range(len(classes)):\n","        # Randomly sample from the original class images to duplicate to extra\n","        if class_train_images[i].shape[0] < desired_size:\n","            extra_train_images.append(\n","                class_train_images[i][np.random.randint(\n","                    low=0,\n","                    high=class_train_images[i].shape[0],\n","                    size=desired_size-class_train_images[i].shape[0])])\n","\n","            # Add random noise to create variation from originals\n","            noise_intensity = 0.1\n","\n","            extra_train_images[i] = (1-noise_intensity)*extra_train_images[i]+noise_intensity*(255*torch.rand(*extra_train_images[i].shape).detach().numpy())\n","            extra_train_images[i] = np.clip(extra_train_images[i], 0, 255)\n","\n","            # add labels for extra samples\n","            extra_train_labels.append(np.full(extra_train_images[i].shape[0], i))\n","\n","            # append to original\n","            class_train_images[i] = np.concatenate((class_train_images[i],extra_train_images[i]))\n","            class_train_labels[i] = np.concatenate((class_train_labels[i],extra_train_labels[i]))\n","        else:\n","            # if more than desired_size images, truncate\n","            extra_train_images.append([])\n","            extra_train_labels.append([])\n","            class_train_images[i] = class_train_images[i][:desired_size]\n","            class_train_labels[i] = class_train_labels[i][:desired_size]\n","    # valid\n","    desired_size=int(600*0.15)\n","    extra_valid_images = []\n","    extra_valid_labels = []\n","    for i in range(len(classes)):\n","        # Randomly sample from the original class images to duplicate to extra\n","        if class_valid_images[i].shape[0] < desired_size:\n","            extra_valid_images.append(\n","                class_valid_images[i][np.random.randint(\n","                    low=0,\n","                    high=class_valid_images[i].shape[0],\n","                    size=desired_size-class_valid_images[i].shape[0])])\n","\n","\n","            # add labels for extra samples\n","            extra_valid_labels.append(np.full(extra_valid_images[i].shape[0], i))\n","\n","            # append to original\n","            class_valid_images[i] = np.concatenate((class_valid_images[i],extra_valid_images[i]))\n","            class_valid_labels[i] = np.concatenate((class_valid_labels[i],extra_valid_labels[i]))\n","        else:\n","            # if more than desired_size images, truncate\n","            extra_valid_images.append([])\n","            extra_valid_labels.append([])\n","            class_valid_images[i] = class_valid_images[i][:desired_size]\n","            class_valid_labels[i] = class_valid_labels[i][:desired_size]\n","\n","    # test\n","    desired_size=int(600*0.15)\n","    extra_test_images = []\n","    extra_test_labels = []\n","    for i in range(len(classes)):\n","        # Randomly sample from the original class images to duplicate to extra\n","        if class_test_images[i].shape[0] < desired_size:\n","            extra_test_images.append(\n","                class_test_images[i][np.random.randint(\n","                    low=0,\n","                    high=class_test_images[i].shape[0],\n","                    size=desired_size-class_test_images[i].shape[0])])\n","            # Add random noise to create variation from originals\n","            noise_intensity = 0.3\n","\n","            extra_test_images[i] = extra_test_images[i]+noise_intensity*(-255*np.ones(extra_test_images[i].shape)+2*255*torch.rand(*extra_test_images[i].shape).detach().numpy())\n","            extra_test_images[i] = np.clip(extra_test_images[i], 0, 255)\n","\n","            # Add random noise to create variation from originals\n","            noise_intensity = 0.05\n","\n","            extra_test_images[i] = (1-noise_intensity)*extra_test_images[i]+noise_intensity*(255*torch.rand(*extra_test_images[i].shape).detach().numpy())\n","            extra_test_images[i] = np.clip(extra_test_images[i], 0, 255)\n","            # add labels for extra samples\n","            extra_test_labels.append(np.full(extra_test_images[i].shape[0], i))\n","\n","            # append to original\n","            class_test_images[i] = np.concatenate((class_test_images[i],extra_test_images[i]))\n","            class_test_labels[i] = np.concatenate((class_test_labels[i],extra_test_labels[i]))\n","        else:\n","            # if more than desired_size images, truncate\n","            extra_test_images.append([])\n","            extra_test_labels.append([])\n","            class_test_images[i] = class_test_images[i][:desired_size]\n","            class_test_labels[i] = class_test_labels[i][:desired_size]\n","\n","    # combine class arrays\n","    train_images = np.concatenate(class_train_images)\n","    train_labels = np.concatenate(class_train_labels)\n","\n","    valid_images = np.concatenate(class_valid_images)\n","    valid_labels = np.concatenate(class_valid_labels)\n","\n","    test_images = np.concatenate(class_test_images)\n","    test_labels = np.concatenate(class_test_labels)\n","\n","    # shuffle again\n","    indices = list(range(train_images.shape[0]))\n","    np.random.shuffle(indices)\n","    train_images = train_images[indices]\n","    train_labels = train_labels[indices]\n","    \n","    indices = list(range(valid_images.shape[0]))\n","    np.random.shuffle(indices)\n","    valid_images = valid_images[indices]\n","    valid_labels = valid_labels[indices]\n","   \n","    indices = list(range(test_images.shape[0]))\n","    np.random.shuffle(indices)\n","    test_images = test_images[indices]\n","    test_labels = test_labels[indices]\n","\n","    # SHADMAN'S CHANGES ABOVE THIS\n","\n","    # make into torch datasets\n","    train_image_tensor = torch.Tensor(train_images.transpose(0,3,1,2))\n","    train_label_tensor = torch.Tensor(train_labels)\n","    \n","    val_image_tensor = torch.Tensor(valid_images.transpose(0,3,1,2))\n","    val_label_tensor = torch.Tensor(valid_labels)\n","    \n","    test_image_tensor = torch.Tensor(test_images.transpose(0,3,1,2))\n","    test_label_tensor = torch.Tensor(test_labels)\n","   \n","    trainset = TensorDataset(train_image_tensor, train_label_tensor)\n","    valset = TensorDataset(val_image_tensor, val_label_tensor)\n","    testset = TensorDataset(test_image_tensor, test_label_tensor)\n","\n","    # resize and normalization\n","    transform = transforms.Compose(\n","    [transforms.Resize((32,32)),\n","      transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n","    \n","    trainset.transform = transform\n","    valset.transform = transform\n","    testset.transform = transform\n","\n","    # make data loaders\n","    train_loader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,\n","                                        num_workers=1)\n","    val_loader = torch.utils.data.DataLoader(valset, batch_size=batch_size,\n","                                        num_workers=1)\n","    test_loader = torch.utils.data.DataLoader(testset, batch_size=batch_size,\n","                                        num_workers=1)\n","    \n","    return train_loader, val_loader, test_loader, classes "],"execution_count":5,"outputs":[]},{"cell_type":"code","metadata":{"id":"W_HTih2z9ZUo","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1607226745371,"user_tz":300,"elapsed":3306,"user":{"displayName":"Shadman Siddiqui","photoUrl":"","userId":"01981550049686806435"}},"outputId":"8d2483ae-c78b-42ed-dcee-a59fbca1661f"},"source":["batch_size = 32\n","train_loader, val_loader, test_loader, classes = get_data_loader (batch_size)\n","print (classes)\n","print (len(train_loader))\n","print (len(val_loader))\n","print (len(test_loader))"],"execution_count":6,"outputs":[{"output_type":"stream","text":["('Speed limit (20km/h)', 'Speed limit (30km/h)', 'Speed limit (50km/h)', 'Speed limit (60km/h)', 'Speed limit (70km/h)', 'Speed limit (80km/h)', 'End of speed limit (80km/h)', 'Speed limit (100km/h)', 'Speed limit (120km/h)', 'Yield', 'Stop', 'End of all speed and passing limits')\n","158\n","34\n","34\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"JjdkO6f_Gw3v","executionInfo":{"status":"ok","timestamp":1607226747115,"user_tz":300,"elapsed":382,"user":{"displayName":"Shadman Siddiqui","photoUrl":"","userId":"01981550049686806435"}}},"source":["# Check one batch\n","dataiter = iter(train_loader)\n"],"execution_count":7,"outputs":[]},{"cell_type":"code","metadata":{"id":"1J6vbY4YETHN","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1607226750535,"user_tz":300,"elapsed":1136,"user":{"displayName":"Shadman Siddiqui","photoUrl":"","userId":"01981550049686806435"}},"outputId":"aacfc91a-8408-48a6-c71a-d64e5dd5a3cc"},"source":["images, labels = dataiter.next()\n","images = images.numpy().astype(int) # convert images to numpy for display\n","labels = labels.int()\n","\n","# plot the images in the batch, along with the corresponding labels\n","fig = plt.figure(figsize=(25, 4))\n","for idx in np.arange(20):\n","    ax = fig.add_subplot(2, 20/2, idx+1, xticks=[], yticks=[])\n","    plt.imshow(np.transpose(images[idx], (1, 2, 0)))\n","    ax.set_title(classes[labels[idx]])\n","    print(images[idx])"],"execution_count":8,"outputs":[{"output_type":"stream","text":["[[[110 123 134 ...  75  86  89]\n","  [ 92 105 106 ...  95  94  79]\n","  [ 79  86  85 ... 104  96  73]\n","  ...\n","  [ 67  61  60 ...  45  44  44]\n","  [ 72  70  66 ...  48  47  48]\n","  [ 73  68  67 ...  51  52  52]]\n","\n"," [[ 89 100 103 ...  66  76  69]\n","  [ 79  89  89 ...  80  73  61]\n","  [ 68  73  73 ...  86  69  56]\n","  ...\n","  [ 52  46  45 ...  31  31  30]\n","  [ 56  54  52 ...  32  32  33]\n","  [ 58  55  55 ...  36  36  36]]\n","\n"," [[ 99 109 107 ...  70  84  77]\n","  [ 92 104 101 ...  79  76  65]\n","  [ 79  85  86 ...  84  73  62]\n","  ...\n","  [ 52  46  48 ...  34  34  32]\n","  [ 57  53  52 ...  34  34  35]\n","  [ 58  53  56 ...  36  36  37]]]\n","[[[ 47  50  50 ...  54  59  58]\n","  [ 42  45  48 ...  53  54  52]\n","  [ 52  50  47 ...  52  54  55]\n","  ...\n","  [ 77  85  92 ... 186 197 202]\n","  [ 89  92 107 ... 180 194 213]\n","  [101  99 111 ... 179 201 216]]\n","\n"," [[ 42  46  44 ...  49  54  52]\n","  [ 37  41  43 ...  44  46  45]\n","  [ 45  43  42 ...  47  49  50]\n","  ...\n","  [ 72  76  82 ... 182 195 205]\n","  [ 83  85 102 ... 174 195 214]\n","  [ 99  94 101 ... 172 199 215]]\n","\n"," [[ 38  38  36 ...  48  53  48]\n","  [ 34  35  37 ...  40  41  41]\n","  [ 39  37  40 ...  41  45  46]\n","  ...\n","  [ 69  67  72 ... 168 186 193]\n","  [ 74  76 101 ... 157 185 205]\n","  [ 86  82 102 ... 162 191 203]]]\n","[[[140 196 179 ... 161 160 172]\n","  [170 178 179 ... 152 145 158]\n","  [182 177 181 ... 161 151 137]\n","  ...\n","  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16 17 ... 13 12 13]\n","  [16 15 16 ... 12 13 14]\n","  ...\n","  [26 34 36 ... 14 14 13]\n","  [35 39 40 ... 13 14 12]\n","  [38 38 40 ... 12 12 12]]]\n","[[[ 49  51  49 ... 145 184 181]\n","  [ 50  51  48 ... 113 139 176]\n","  [ 50  51  50 ... 138 163 182]\n","  ...\n","  [ 59  48  48 ... 188 190 191]\n","  [ 60  50  47 ... 224 202 220]\n","  [ 58  52  46 ... 209 193 229]]\n","\n"," [[ 49  50  48 ... 127 160 163]\n","  [ 50  50  46 ... 105 130 158]\n","  [ 49  50  49 ... 115 146 163]\n","  ...\n","  [ 54  49  51 ... 158 166 159]\n","  [ 58  51  49 ... 185 179 181]\n","  [ 58  50  47 ... 172 171 189]]\n","\n"," [[ 48  53  52 ...  80  96  96]\n","  [ 51  52  48 ...  64  71  92]\n","  [ 52  53  49 ...  68  88 105]\n","  ...\n","  [ 53  51  52 ... 140 142 125]\n","  [ 60  53  50 ... 159 166 185]\n","  [ 60  50  47 ... 146 153 172]]]\n","[[[118 103  72 ... 101  97  95]\n","  [114  99  78 ...  90  88  94]\n","  [ 98 102  95 ...  78  81  93]\n","  ...\n","  [ 83  86  86 ...  54  57  62]\n","  [ 90  99  88 ...  62  59  64]\n","  [104 107  98 ...  62  55  58]]\n","\n"," [[111 104  79 ...  89  86  86]\n","  [106  95  74 ...  82  76  80]\n","  [ 90  93  87 ...  71  71  78]\n","  ...\n","  [ 83  85  84 ...  62  64  68]\n","  [ 88  93  87 ...  71  67  72]\n","  [ 95  97  93 ...  72  66  68]]\n","\n"," [[100  95  71 ...  72  70  70]\n","  [ 98  88  65 ...  69  65  65]\n","  [ 86  87  77 ...  64  64  66]\n","  ...\n","  [ 71  75  75 ...  58  58  61]\n","  [ 70  76  71 ...  69  65  66]\n","  [ 74  77  74 ...  70  64  65]]]\n","[[[103  95  70 ...  65  66  48]\n","  [103  84  55 ...  61  59  49]\n","  [121 109  84 ...  63  57  49]\n","  ...\n","  [ 44  46  41 ...  58  61  73]\n","  [ 43  49  51 ...  54  56  62]\n","  [ 47  51  52 ...  66  64  66]]\n","\n"," [[107 106  85 ...  74  76  63]\n","  [ 99  94  70 ...  72  69  57]\n","  [112 103  83 ...  74  67  55]\n","  ...\n","  [ 48  48  42 ...  72  73  81]\n","  [ 47  47  47 ...  68  70  76]\n","  [ 51  48  47 ...  75  73  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13]]]\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1800x288 with 20 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Ofp9IpxrNr2R","executionInfo":{"status":"ok","timestamp":1607227347078,"user_tz":300,"elapsed":380,"user":{"displayName":"Shadman Siddiqui","photoUrl":"","userId":"01981550049686806435"}},"outputId":"67262c4c-62f7-49c0-c24f-bc078f539562"},"source":["print (images[7])"],"execution_count":9,"outputs":[{"output_type":"stream","text":["[[[ 43  80  72 ... 255 255 255]\n","  [ 43  69  60 ... 255 255 255]\n","  [ 48  88  83 ... 255 255 255]\n","  ...\n","  [ 46  39  38 ...  89  72  60]\n","  [ 54  49  46 ...  54  60  58]\n","  [ 45  42  38 ...  54  56  61]]\n","\n"," [[ 45  78  71 ... 255 255 255]\n","  [ 43  56  56 ... 255 255 255]\n","  [ 48  85  80 ... 255 255 255]\n","  ...\n","  [ 42  42  39 ...  91  71  58]\n","  [ 48  49  45 ...  64  62  55]\n","  [ 37  39  37 ...  61  58  56]]\n","\n"," [[ 42  74  65 ... 243 255 255]\n","  [ 42  49  45 ... 255 255 255]\n","  [ 49  77  62 ... 255 255 255]\n","  ...\n","  [ 38  43  40 ...  83  51  50]\n","  [ 49  51  46 ...  52  54  54]\n","  [ 35  41  39 ...  54  50  55]]]\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"k5rbyYhrHTAJ"},"source":["# close all figures to prevent memory leak\n","plt.close('all')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"d6UmCUytrUdf"},"source":["def get_model_name(name, batch_size, learning_rate, epoch):\n","    \"\"\" Generate a name for the model consisting of all the hyperparameter values\n","\n","    Args:\n","        config: Configuration object containing the hyperparameters\n","    Returns:\n","        path: A string with the hyperparameter name and value concatenated\n","    \"\"\"\n","    path = \"model_{0}_bs{1}_lr{2}_epoch{3}\".format(name,\n","                                                   batch_size,\n","                                                   learning_rate,\n","                                                   epoch)\n","    return path\n","\n","def normalize_label(labels):\n","    \"\"\"\n","    Given a tensor containing 2 possible values, normalize this to 0/1\n","\n","    Args:\n","        labels: a 1D tensor containing two possible scalar values\n","    Returns:\n","        A tensor normalize to 0/1 value\n","    \"\"\"\n","    max_val = torch.max(labels)\n","    min_val = torch.min(labels)\n","    norm_labels = (labels - min_val)//(max_val - min_val)             #this is kinda brilliant\n","    return norm_labels\n","\n","\n"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Yy0uWVQws3ig"},"source":["#TRAIN\n","\n","Tasks:\n"," - Calculate training accuracy\n"," - Calculate training loss\n"," - Calculate validation accuracy\n"," - Calculate validation loss\n"," - Store weights in epoch files (we may have to be smart about WHERE we're saving this so that we don't clutter up the drive)\n"," - Get accuracy of model after training is complete\n"," - Plot everything\n"]},{"cell_type":"code","metadata":{"id":"rPjuItwogPhe"},"source":["def train(model, train_loader, val_loader, batch_size=27, num_epochs=21, learning_rate = 0.001):\n","\n","    torch.manual_seed(1000)\n","    criterion = nn.CrossEntropyLoss()\n","    optimizer = optim.Adam(model.parameters(), lr=learning_rate)\n","\n","    train_acc, val_acc, train_loss, val_loss = [], [], [], []\n","\n","    # training\n","    print (\"Training Started...\\n\")\n","    if torch.cuda.is_available():\n","        print(\"U S I N G   C U D A \\n\\n\")\n","\n","    for epoch in range(num_epochs):                                     # the number of iterations\n","        sum_train_loss = 0.0\n","        sum_val_loss = 0.0\n","\n","        n = 0                                                           # Number of training iterations in this epoch\n","        m = 0                                                           # Number of validation iterations in this epoch\n","\n","        for imgs, labels in iter(train_loader):\n","                \n","            if use_cuda and torch.cuda.is_available():\n","              imgs = imgs.cuda()\n","              labels = labels.cuda()\n","\n","            out = model(imgs)                                           # forward pass\n","            loss = criterion(out, labels.long())                               # compute the total loss\n","            loss.backward()                                             # backward pass (compute parameter updates)\n","            optimizer.step()                                            # make the updates for each parameter\n","            optimizer.zero_grad()                                       # a clean up step for PyTorch\n","            sum_train_loss += loss.item() \n","            n += 1\n","        \n","        for imgs, labels in iter(val_loader):\n","            \n","            if use_cuda and torch.cuda.is_available():\n","              imgs = imgs.cuda()\n","              labels = labels.cuda()                                    # cudafication for speeeeed\n","\n","            out = model(imgs)                                            \n","            loss = criterion(out, labels.long())                               # compute loss with Cross Entropy\n","            sum_val_loss += loss.item()\n","            m += 1\n","\n","        # track accuracy and loss\n","        train_acc.append(get_accuracy(model, train_loader))\n","        val_acc.append(get_accuracy(model, val_loader))\n","        train_loss.append(sum_train_loss/n)\n","        val_loss.append(sum_val_loss/m)\n","\n","        ################################################################################################################\n","        model_path = get_model_name(model.name, batch_size, learning_rate, epoch+1) \n","        torch.save(model.state_dict(), model_path)\n","\n","        print('Epoch: ', epoch + 1,                                                 #A little text formatting goes a long way\n","              '\\t Training acc:', round(train_acc[-1],4),\n","              '\\t Val acc:%.4f' % val_acc[-1],\n","              '\\t Training loss:%.4f' % train_loss[-1],\n","              '\\t Val loss:%.4f' % val_loss[-1])\n","\n","    return train_acc, val_acc, train_loss, val_loss\n","\n","def get_accuracy(model, data_loader):\n","\n","  correct = 0\n","  total = 0\n","  for imgs, labels in data_loader:\n","      \n","      if use_cuda and torch.cuda.is_available():\n","        imgs = imgs.cuda()\n","        labels = labels.cuda()\n","\n","      output = model(imgs)\n","      #select index with maximum prediction score\n","      pred = output.max(1, keepdim=True)[1]\n","      correct += pred.eq(labels.view_as(pred)).sum().item()\n","      total += imgs.shape[0]\n","\n","  return correct / total\n","\n","\n","\n","\n","def plot_training_curve(train_acc, val_acc, train_loss, val_loss):\n","    \"\"\" Plots the training curve for a model run, given the csv files\n","    containing the train/validation error/loss.\n","\n","    Args:\n","        path: The base path of the csv files produced during training\n","    \"\"\"\n","    import matplotlib.pyplot as plt\n","\n","    plt.title(\"Train vs Validation Accuracy\")\n","    n = len(train_acc) # number of epochs\n","    plt.plot(range(1,n+1), train_acc, label=\"Train\")\n","    plt.plot(range(1,n+1), val_acc, label=\"Validation\")\n","    plt.xlabel(\"Epoch\")\n","    plt.ylabel(\"Accuracy\")\n","    plt.legend(loc='best')\n","    plt.show()\n","    plt.title(\"Train vs Validation Loss\")\n","    plt.plot(range(1,n+1), train_loss, label=\"Train\")\n","    plt.plot(range(1,n+1), val_loss, label=\"Validation\")\n","    plt.xlabel(\"Epoch\")\n","    plt.ylabel(\"Loss\")\n","    plt.legend(loc='best')\n","    plt.show()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"0Q5T62z8XhGh"},"source":["#Primary Model Architecture and Testing\n","\n","Tasks:\n"," - Build a CNN model with 3 convolutional layers and 3 fully connected layers\n"," - Perform a sanity check to test if model is capable of overfitting\n"," - Use training code to test on model and tune hyperparameters to obtain best results\n"," - Print and Plot Results\n"," - Try 10 different hyperparameter settings and compare\n"]},{"cell_type":"code","metadata":{"id":"GqVmU6tztUUL"},"source":["import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","\n","import torch.optim as optim                                                     # For gradient descent\n","import matplotlib.pyplot as plt\n","import numpy as np\n","\n","# Creating a CNN\n","class SignClassifier(nn.Module):\n","\n","    def __init__(self):\n","        super(SignClassifier, self).__init__()\n","        self.name = \"Net\"                                                       \n","        self.conv1 = nn.Conv2d(3,5,5)                                           # First kernel is a 5 by 5, 3 color channels, it has output of 5    -> given 32x32x3, you are left with 28x28x5\n","        self.pool = nn.MaxPool2d(2,2)                                           # Max pooling layer with kernel size 2 and stride 2                 -> you are left with 14x14x5\n","        self.conv2 = nn.Conv2d(5,10,3)                                          # Second kernel is 5 by 5, it changes input depth from 5 to 10      -> you are left with 10x10x10\n","        self.conv3 = nn.Conv2d(10,12,5)                                         # Third kernel is 5 by 5, it changes input depth from 10 to 12      -> you are left with 6x6x12\n","                                                                                \n","        self.fc1 = nn.Linear(8*8*12, 200)                                       # Fully Connected Layers\n","        self.fc2 = nn.Linear(200, 100)\n","        self.fc3 = nn.Linear(100,12)                                             # 12 possible outputs\n","\n","    def forward(self, x):\n","        x = self.pool(F.relu(self.conv1(x)))                                    # Apply first kernel, then activation function, then max pooling \n","        x = F.relu(self.conv2(x))                                               # Apply second kernel, then activation function\n","        x = F.relu(self.conv3(x))                                               # Apply second kernel, then activation function\n","        x = x.view(-1, 8*8*12)                                                  # flatten tensor for ANN portion\n","        x = F.relu(self.fc1(x))                                                 # Apply activation function on first fully connected layer\n","        x = F.relu(self.fc2(x))                                                 # Apply activation function on second fully connected layer\n","        x = self.fc3(x)                                                         # final activation function is included with criterion\n","        x = x.squeeze(1) # Flatten to [batch_size]\n","        return x\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"DjFo214RVNS1","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1606616428898,"user_tz":300,"elapsed":3282,"user":{"displayName":"Shadman Siddiqui","photoUrl":"","userId":"01981550049686806435"}},"outputId":"21f855de-fab5-4eea-c9a0-195b9c02c080"},"source":["# Modified Dataloader's so that trainset and valset have only 72 images each to check if model is capable of overfitting\n","\n","def get_data_loader(batch_size):\n","    ''' The Kaggle dataset is split into three pickle files: train.pickle,\n","    valid.pickle, and test.pickle.\n","    The function will combine the three datasets and resplit them such that the\n","    resulting split is approximately 70% training, 15% validation, and 15% testing.\n","    The function filters classes 0-8,13,14,32 only, as they are related to speed.\n","\n","    The splitting ratio will be applied to each class, to avoid imbalance of \n","    classes in the training/validation/testing samples.'''\n","\n","    classes = ('Speed limit (20km/h)',\n","            'Speed limit (30km/h)',\n","            'Speed limit (50km/h)',\n","            'Speed limit (60km/h)',\n","            'Speed limit (70km/h)',\n","            'Speed limit (81km/h)',\n","            'End of speed limit (80km/h)',\n","            'Speed limit (100km/h)',\n","            'Speed limit (120km/h)',\n","            'Yield',\n","            'Stop',\n","            'End of all speed and passing limits')\n","    # Index of chosen classes in the original dataset\n","    # In our reconstructed dataset, the same classes will have indexes 0-11\n","    original_labels = (0,1,2,3,4,5,6,7,8,13,14,32)\n","\n","    # load pickle files\n","    # will combine datasets from three seperate pikcle files\n","   \n","    with open(data_dir+'train.pickle', 'rb') as file:\n","        data1 = pickle.load(file)\n","\n","    with open(data_dir+'valid.pickle', 'rb') as file:\n","        data2 = pickle.load(file)\n","\n","    with open (data_dir+'test.pickle', 'rb') as file:\n","        data3 = pickle.load(file)\n","\n","    images = np.concatenate((data1['features'], data2['features'], data3['features']))\n","    labels = np.concatenate((data1['labels'], data2['labels'], data3['labels']))\n","    \n","    # sort into classes\n","    class_images = []\n","    class_labels = []\n","    for i in range(len(classes)):\n","        class_indices = np.where(labels==original_labels[i])[0]\n","        class_images.append(images[class_indices])\n","        #class_labels.append(labels[class_indices])\n","        # Don't want the original labels\n","        # This is where we convert to our own labels: 0-11\n","        class_labels.append(np.full(len(class_indices),i)) \n","\n","    # normalize number of samples in each class\n","    desired_size=3000\n","    extra_samples = []\n","    extra_labels = []\n","    for i in range(len(classes)):\n","        # Randomly sample from the original class images to duplicate to extra\n","        # Duplicate enough samples to make the total for the class 3000\n","        extra_samples.append(\n","            class_images[i][np.random.randint(\n","                low=0,\n","                high=class_images[i].shape[0],\n","                size=desired_size-class_images[i].shape[0])])\n","        # Add random noise to create variation from originals\n","        noise = np.random.normal(0,1, extra_samples[i].size)\n","        noise = noise.reshape(extra_samples[i].shape[0],extra_samples[i].shape[1],extra_samples[i].shape[2],extra_samples[i].shape[3]).astype('uint8')\n","        extra_samples[i] = extra_samples[i]+noise\n","\n","        # add labels for extra samples\n","        extra_labels.append(np.full(extra_samples[i].shape[0], i))\n","\n","        # append to original\n","        class_images[i] = np.concatenate((class_images[i],extra_samples[i]))\n","        class_labels[i] = np.concatenate((class_labels[i],extra_labels[i]))\n","\n","    # split into train /  val / test\n","    train_split = float(1/360)\n","    val_split = float(2/360) ###################################################################################################################################################################\n","\n","    train_image_arrays = [class_images[i][0:int(train_split*class_images[i].shape[0])] for i in range(len(classes))]\n","    train_label_arrays = [class_labels[i][0:int(train_split*class_images[i].shape[0])] for i in range(len(classes))]\n","    train_images = np.concatenate(train_image_arrays)\n","    train_labels = np.concatenate(train_label_arrays)\n","    \n","    val_image_arrays = [class_images[i][int(train_split*class_images[i].shape[0]):int(val_split*class_images[i].shape[0])] for i in range(len(classes))]\n","    val_label_arrays = [class_labels[i][int(train_split*class_images[i].shape[0]):int(val_split*class_images[i].shape[0])] for i in range(len(classes))]\n","    val_images = np.concatenate(val_image_arrays)\n","    val_labels = np.concatenate(val_label_arrays)\n","\n","    test_image_arrays = [class_images[i][int(val_split*class_images[i].shape[0]):] for i in range(len(classes))]\n","    test_label_arrays = [class_labels[i][int(val_split*class_images[i].shape[0]):] for i in range(len(classes))]\n","    test_images = np.concatenate(test_image_arrays)\n","    test_labels = np.concatenate(test_label_arrays)\n","\n","    # shuffle\n","    np.random.seed(9001)\n","    indices = list(range(train_images.shape[0]))\n","    np.random.shuffle(indices)\n","    train_images = train_images[indices]\n","    train_labels = train_labels[indices]\n","    \n","    indices = list(range(val_images.shape[0]))\n","    np.random.shuffle(indices)\n","    val_images = val_images[indices]\n","    val_labels = val_labels[indices]\n","   \n","    indices = list(range(test_images.shape[0]))\n","    np.random.shuffle(indices)\n","    test_images = test_images[indices]\n","    test_labels = test_labels[indices]\n","\n","    # make into torch datasets\n","    train_image_tensor = torch.Tensor(train_images.transpose(0,3,1,2))\n","    train_label_tensor = torch.Tensor(train_labels)\n","    \n","    val_image_tensor = torch.Tensor(val_images.transpose(0,3,1,2))\n","    val_label_tensor = torch.Tensor(val_labels)\n","    \n","    test_image_tensor = torch.Tensor(test_images.transpose(0,3,1,2))\n","    test_label_tensor = torch.Tensor(test_labels)\n","   \n","    trainset = TensorDataset(train_image_tensor, train_label_tensor)\n","    valset = TensorDataset(val_image_tensor, val_label_tensor)\n","    testset = TensorDataset(test_image_tensor, test_label_tensor)\n","\n","    # resize and normalization\n","    transform = transforms.Compose(\n","    [transforms.Resize((32,32)),\n","      transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n","    \n","    trainset.transform = transform\n","    valset.transform = transform\n","    testset.transform = transform\n","\n","    print(len(trainset))\n","    print(len(valset))\n","    # make data loaders\n","    train_loader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,\n","                                        num_workers=1)\n","    val_loader = torch.utils.data.DataLoader(valset, batch_size=batch_size,\n","                                        num_workers=1)\n","    test_loader = torch.utils.data.DataLoader(testset, batch_size=batch_size,\n","                                        num_workers=1)\n","    \n","    return train_loader, val_loader, test_loader, classes \n","\n","batch_size = 32\n","overfit_train_loader, overfit_val_loader, overfit_test_loader, classes = get_data_loader (batch_size)\n","print (classes)\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["96\n","96\n","('Speed limit (20km/h)', 'Speed limit (30km/h)', 'Speed limit (50km/h)', 'Speed limit (60km/h)', 'Speed limit (70km/h)', 'Speed limit (81km/h)', 'End of speed limit (80km/h)', 'Speed limit (100km/h)', 'Speed limit (120km/h)', 'Yield', 'Stop', 'End of all speed and passing limits')\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"sPmJ1bCfSvZw","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1605619502990,"user_tz":300,"elapsed":10319,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}},"outputId":"f992cf87-4baa-44a1-f0bf-52ed2d0e7514"},"source":["### Sanity Check\n","\n","batch_size = 36\n","overfit_train_loader, overfit_val_loader, overfit_test_loader, classes = get_data_loader (batch_size)\n","\n","model_0 = SignClassifier()\n","use_cuda = False\n","train_acc, val_acc, train_loss, val_loss = train(model_0, overfit_train_loader, overfit_val_loader, batch_size=32, num_epochs=30, learning_rate = 0.001)\n","plot_training_curve(train_acc, val_acc, train_loss, val_loss)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["72\n","72\n","Training Started...\n","\n","U S I N G   C U D A \n","\n","\n","Epoch:  0 \t Training acc: 0.1389 \t Val acc:0.1111 \t Training loss:2.3147 \t Val loss:2.0382\n","Epoch:  1 \t Training acc: 0.2778 \t Val acc:0.2222 \t Training loss:1.9810 \t Val loss:1.9609\n","Epoch:  2 \t Training acc: 0.3194 \t Val acc:0.2222 \t Training loss:1.8737 \t Val loss:1.8842\n","Epoch:  3 \t Training acc: 0.375 \t Val acc:0.2639 \t Training loss:1.7085 \t Val loss:1.8071\n","Epoch:  4 \t Training acc: 0.4444 \t Val acc:0.3056 \t Training loss:1.5107 \t Val loss:1.6999\n","Epoch:  5 \t Training acc: 0.7083 \t Val acc:0.4861 \t Training loss:1.2512 \t Val loss:1.7023\n","Epoch:  6 \t Training acc: 0.8889 \t Val acc:0.5278 \t Training loss:0.9701 \t Val loss:1.7075\n","Epoch:  7 \t Training acc: 0.9722 \t Val acc:0.6667 \t Training loss:0.6763 \t Val loss:1.8286\n","Epoch:  8 \t Training acc: 0.9583 \t Val acc:0.6111 \t Training loss:0.4633 \t Val loss:1.9395\n","Epoch:  9 \t Training acc: 0.7778 \t Val acc:0.6111 \t Training loss:0.3816 \t Val loss:1.8418\n","Epoch:  10 \t Training acc: 0.9444 \t Val acc:0.5000 \t Training loss:0.6044 \t Val loss:2.2643\n","Epoch:  11 \t Training acc: 0.9861 \t Val acc:0.6111 \t Training loss:0.3534 \t Val loss:2.0299\n","Epoch:  12 \t Training acc: 1.0 \t Val acc:0.7083 \t Training loss:0.2258 \t Val loss:1.7134\n","Epoch:  13 \t Training acc: 1.0 \t Val acc:0.7361 \t Training loss:0.2329 \t Val loss:1.9781\n","Epoch:  14 \t Training acc: 0.9861 \t Val acc:0.6806 \t Training loss:0.1565 \t Val loss:2.3623\n","Epoch:  15 \t Training acc: 0.9861 \t Val acc:0.6528 \t Training loss:0.1089 \t Val loss:2.0138\n","Epoch:  16 \t Training acc: 0.9861 \t Val acc:0.6667 \t Training loss:0.1108 \t Val loss:1.8639\n","Epoch:  17 \t Training acc: 1.0 \t Val acc:0.7500 \t Training loss:0.0854 \t Val loss:1.9035\n","Epoch:  18 \t Training acc: 1.0 \t Val acc:0.7083 \t Training loss:0.0441 \t Val loss:2.3075\n","Epoch:  19 \t Training acc: 1.0 \t Val acc:0.6944 \t Training loss:0.0442 \t Val loss:2.6534\n","Epoch:  20 \t Training acc: 1.0 \t Val acc:0.6944 \t Training loss:0.0301 \t Val loss:2.7212\n","Epoch:  21 \t Training acc: 1.0 \t Val acc:0.6528 \t Training loss:0.0196 \t Val loss:2.8702\n","Epoch:  22 \t Training acc: 1.0 \t Val acc:0.6389 \t Training loss:0.0180 \t Val loss:2.9533\n","Epoch:  23 \t Training acc: 1.0 \t Val acc:0.6944 \t Training loss:0.0168 \t Val loss:2.9490\n","Epoch:  24 \t Training acc: 1.0 \t Val acc:0.7222 \t Training loss:0.0118 \t Val loss:2.9625\n","Epoch:  25 \t Training acc: 1.0 \t Val acc:0.6944 \t Training loss:0.0081 \t Val loss:3.0635\n","Epoch:  26 \t Training acc: 1.0 \t Val acc:0.6944 \t Training loss:0.0071 \t Val loss:3.2061\n","Epoch:  27 \t Training acc: 1.0 \t Val acc:0.6944 \t Training loss:0.0071 \t Val loss:3.2839\n","Epoch:  28 \t Training acc: 1.0 \t Val acc:0.6806 \t Training loss:0.0064 \t Val loss:3.2657\n","Epoch:  29 \t Training acc: 1.0 \t Val acc:0.6806 \t Training loss:0.0049 \t Val loss:3.2273\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"HgWSmOTpttDG","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1607146113304,"user_tz":300,"elapsed":565290,"user":{"displayName":"Eric Ji","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhXIfdv0NwRhitAF6aaeGszxF2imvsyUqr1kk0tmQ=s64","userId":"13561871477578974388"}},"outputId":"c6aec144-e0be-4477-faf2-150879a371a6"},"source":["model_1 = SignClassifier()\n","use_cuda = False\n","train_acc, val_acc, train_loss, val_loss = train(model_1, train_loader, val_loader, batch_size=32, num_epochs=30, learning_rate = 0.001)\n","plot_training_curve(train_acc, val_acc, train_loss, val_loss)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Training Started...\n","\n","U S I N G   C U D A \n","\n","\n","Epoch:  1 \t Training acc: 0.9622 \t Val acc:0.9417 \t Training loss:0.4054 \t Val loss:0.2025\n","Epoch:  2 \t Training acc: 0.9775 \t Val acc:0.9602 \t Training loss:0.0949 \t Val loss:0.1508\n","Epoch:  3 \t Training acc: 0.9882 \t Val acc:0.9707 \t Training loss:0.0695 \t Val loss:0.1093\n","Epoch:  4 \t Training acc: 0.9908 \t Val acc:0.9733 \t Training loss:0.0514 \t Val loss:0.1124\n","Epoch:  5 \t Training acc: 0.9762 \t Val acc:0.9552 \t Training loss:0.0466 \t Val loss:0.2502\n","Epoch:  6 \t Training acc: 0.9848 \t Val acc:0.9637 \t Training loss:0.0400 \t Val loss:0.2186\n","Epoch:  7 \t Training acc: 0.9824 \t Val acc:0.9681 \t Training loss:0.0517 \t Val loss:0.1668\n","Epoch:  8 \t Training acc: 0.9979 \t Val acc:0.9833 \t Training loss:0.0312 \t Val loss:0.0713\n","Epoch:  9 \t Training acc: 0.9955 \t Val acc:0.9789 \t Training loss:0.0313 \t Val loss:0.1020\n","Epoch:  10 \t Training acc: 0.997 \t Val acc:0.9800 \t Training loss:0.0294 \t Val loss:0.1582\n","Epoch:  11 \t Training acc: 0.9976 \t Val acc:0.9804 \t Training loss:0.0311 \t Val loss:0.1140\n","Epoch:  12 \t Training acc: 0.991 \t Val acc:0.9698 \t Training loss:0.0430 \t Val loss:0.1883\n","Epoch:  13 \t Training acc: 0.9944 \t Val acc:0.9769 \t Training loss:0.0248 \t Val loss:0.1327\n","Epoch:  14 \t Training acc: 0.9972 \t Val acc:0.9820 \t Training loss:0.0325 \t Val loss:0.1281\n","Epoch:  15 \t Training acc: 0.9996 \t Val acc:0.9865 \t Training loss:0.0181 \t Val loss:0.1174\n","Epoch:  16 \t Training acc: 0.9889 \t Val acc:0.9672 \t Training loss:0.0310 \t Val loss:0.1870\n","Epoch:  17 \t Training acc: 0.9937 \t Val acc:0.9778 \t Training loss:0.0355 \t Val loss:0.1775\n","Epoch:  18 \t Training acc: 0.9996 \t Val acc:0.9863 \t Training loss:0.0266 \t Val loss:0.1106\n","Epoch:  19 \t Training acc: 0.9963 \t Val acc:0.9822 \t Training loss:0.0185 \t Val loss:0.1136\n","Epoch:  20 \t Training acc: 0.9939 \t Val acc:0.9802 \t Training loss:0.0212 \t Val loss:0.1539\n","Epoch:  21 \t Training acc: 0.9989 \t Val acc:0.9830 \t Training loss:0.0132 \t Val loss:0.1314\n","Epoch:  22 \t Training acc: 0.9957 \t Val acc:0.9776 \t Training loss:0.0359 \t Val loss:0.1729\n","Epoch:  23 \t Training acc: 0.9973 \t Val acc:0.9800 \t Training loss:0.0459 \t Val loss:0.1711\n","Epoch:  24 \t Training acc: 0.9977 \t Val acc:0.9822 \t Training loss:0.0169 \t Val loss:0.1539\n","Epoch:  25 \t Training acc: 0.9998 \t Val acc:0.9856 \t Training loss:0.0046 \t Val loss:0.1516\n","Epoch:  26 \t Training acc: 0.9965 \t Val acc:0.9820 \t Training loss:0.0544 \t Val loss:0.1452\n","Epoch:  27 \t Training acc: 0.9883 \t Val acc:0.9665 \t Training loss:0.0363 \t Val loss:0.1986\n","Epoch:  28 \t Training acc: 0.9997 \t Val acc:0.9863 \t Training loss:0.0115 \t Val loss:0.1235\n","Epoch:  29 \t Training acc: 0.999 \t Val acc:0.9865 \t Training loss:0.0080 \t Val loss:0.1803\n","Epoch:  30 \t Training acc: 0.9963 \t Val acc:0.9796 \t Training loss:0.0309 \t Val loss:0.1593\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"7gnwlAWx7Exf","colab":{"base_uri":"https://localhost:8080/","height":777},"executionInfo":{"status":"ok","timestamp":1605477784703,"user_tz":300,"elapsed":129367,"user":{"displayName":"Eric Ji","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhXIfdv0NwRhitAF6aaeGszxF2imvsyUqr1kk0tmQ=s64","userId":"13561871477578974388"}},"outputId":"bbfb22ab-c0af-405f-a694-7e6f7b428a4a"},"source":["model_2 = SignClassifier()\n","use_cuda = False\n","train_acc, val_acc, train_loss, val_loss = train(model_2, train_loader, val_loader, batch_size=32, num_epochs=10, learning_rate = 0.01)\n","plot_training_curve(train_acc, val_acc, train_loss, val_loss)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Training Started...\n","U S I N G   C U D A \n","epoch:  0 training acc:  0.1111111111111111 val acc:  0.1111111111111111 training loss:  2.201994569168478 val loss:  2.1979341788554754\n","epoch:  1 training acc:  0.1111111111111111 val acc:  0.1111111111111111 training loss:  2.198701372808208 val loss:  2.1979337639696017\n","epoch:  2 training acc:  0.1111111111111111 val acc:  0.1111111111111111 training loss:  2.198704071254698 val loss:  2.197933889749482\n","epoch:  3 training acc:  0.1111111111111111 val acc:  0.1111111111111111 training loss:  2.198705068094476 val loss:  2.197933518041776\n","epoch:  4 training acc:  0.1111111111111111 val acc:  0.1111111111111111 training loss:  2.1987055372667395 val loss:  2.197934150695801\n","epoch:  5 training acc:  0.1111111111111111 val acc:  0.1111111111111111 training loss:  2.198705747446231 val loss:  2.1979337301779918\n","epoch:  6 training acc:  0.1111111111111111 val acc:  0.1111111111111111 training loss:  2.19870586403332 val loss:  2.197934049320972\n","epoch:  7 training acc:  0.1111111111111111 val acc:  0.1111111111111111 training loss:  2.1987059257558963 val loss:  2.1979339085225984\n","epoch:  8 training acc:  0.1111111111111111 val acc:  0.1111111111111111 training loss:  2.198705947540335 val loss:  2.1979337958838996\n","epoch:  9 training acc:  0.1111111111111111 val acc:  0.1111111111111111 training loss:  2.1987059814272394 val loss:  2.197933692631759\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"rpFAzzdr9jdy","colab":{"base_uri":"https://localhost:8080/","height":827},"executionInfo":{"status":"ok","timestamp":1605622395892,"user_tz":300,"elapsed":147512,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}},"outputId":"3b75c9f3-798f-4dc4-b3ba-79b69be5f63e"},"source":["model_3 = SignClassifier()\n","use_cuda = False\n","train_acc, val_acc, train_loss, val_loss = train(model_3, train_loader, val_loader, batch_size=32, num_epochs=10, learning_rate = 0.001)\n","plot_training_curve(train_acc, val_acc, train_loss, val_loss)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Training Started...\n","\n","U S I N G   C U D A \n","\n","\n","Epoch:  0 \t Training acc: 0.947 \t Val acc:0.9333 \t Training loss:0.4890 \t Val loss:0.2444\n","Epoch:  1 \t Training acc: 0.9726 \t Val acc:0.9652 \t Training loss:0.1250 \t Val loss:0.1255\n","Epoch:  2 \t Training acc: 0.9861 \t Val acc:0.9731 \t Training loss:0.0730 \t Val loss:0.1569\n","Epoch:  3 \t Training acc: 0.9864 \t Val acc:0.9775 \t Training loss:0.0719 \t Val loss:0.0964\n","Epoch:  4 \t Training acc: 0.9953 \t Val acc:0.9869 \t Training loss:0.0497 \t Val loss:0.0642\n","Epoch:  5 \t Training acc: 0.991 \t Val acc:0.9815 \t Training loss:0.0428 \t Val loss:0.0920\n","Epoch:  6 \t Training acc: 0.9865 \t Val acc:0.9733 \t Training loss:0.0407 \t Val loss:0.1204\n","Epoch:  7 \t Training acc: 0.9943 \t Val acc:0.9815 \t Training loss:0.0377 \t Val loss:0.1061\n","Epoch:  8 \t Training acc: 0.9849 \t Val acc:0.9728 \t Training loss:0.0294 \t Val loss:0.1974\n","Epoch:  9 \t Training acc: 0.9953 \t Val acc:0.9832 \t Training loss:0.0333 \t Val loss:0.0941\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"2uQ2JFYL-O1e","colab":{"base_uri":"https://localhost:8080/","height":827},"executionInfo":{"status":"ok","timestamp":1605622908086,"user_tz":300,"elapsed":146774,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}},"outputId":"a5a42882-4357-4ae5-a341-4abb53385b43"},"source":["model_4 = SignClassifier()\n","use_cuda = False\n","train_acc, val_acc, train_loss, val_loss = train(model_4, train_loader, val_loader, batch_size=32, num_epochs=10, learning_rate = 0.005)\n","plot_training_curve(train_acc, val_acc, train_loss, val_loss)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Training Started...\n","\n","U S I N G   C U D A \n","\n","\n","Epoch:  0 \t Training acc: 0.1111 \t Val acc:0.1111 \t Training loss:2.1992 \t Val loss:2.1975\n","Epoch:  1 \t Training acc: 0.1111 \t Val acc:0.1111 \t Training loss:2.1981 \t Val loss:2.1975\n","Epoch:  2 \t Training acc: 0.1111 \t Val acc:0.1111 \t Training loss:2.1981 \t Val loss:2.1975\n","Epoch:  3 \t Training acc: 0.1111 \t Val acc:0.1111 \t Training loss:2.1981 \t Val loss:2.1975\n","Epoch:  4 \t Training acc: 0.1111 \t Val acc:0.1111 \t Training loss:2.1981 \t Val loss:2.1975\n","Epoch:  5 \t Training acc: 0.1111 \t Val acc:0.1111 \t Training loss:2.1981 \t Val loss:2.1975\n","Epoch:  6 \t Training acc: 0.1111 \t Val acc:0.1111 \t Training loss:2.1981 \t Val loss:2.1975\n","Epoch:  7 \t Training acc: 0.1111 \t Val acc:0.1111 \t Training loss:2.1981 \t Val loss:2.1975\n","Epoch:  8 \t Training acc: 0.1111 \t Val acc:0.1111 \t Training loss:2.1981 \t Val loss:2.1975\n","Epoch:  9 \t Training acc: 0.1111 \t Val acc:0.1111 \t Training loss:2.1981 \t Val loss:2.1975\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"naz2U0q8Dx6Y","colab":{"base_uri":"https://localhost:8080/","height":911},"executionInfo":{"status":"ok","timestamp":1605636350649,"user_tz":300,"elapsed":203909,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}},"outputId":"4d86257a-9cdd-4334-f521-048986fa5f65"},"source":["model_5 = SignClassifier()\n","use_cuda = False\n","train_acc, val_acc, train_loss, val_loss = train(model_5, train_loader, val_loader, batch_size=32, num_epochs=15, learning_rate = 0.0001)\n","plot_training_curve(train_acc, val_acc, train_loss, val_loss)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Training Started...\n","\n","U S I N G   C U D A \n","\n","\n","Epoch:  1 \t Training acc: 0.8985 \t Val acc:0.8847 \t Training loss:0.9972 \t Val loss:0.4481\n","Epoch:  2 \t Training acc: 0.9329 \t Val acc:0.9240 \t Training loss:0.3156 \t Val loss:0.2939\n","Epoch:  3 \t Training acc: 0.957 \t Val acc:0.9420 \t Training loss:0.2093 \t Val loss:0.2230\n","Epoch:  4 \t Training acc: 0.9693 \t Val acc:0.9548 \t Training loss:0.1553 \t Val loss:0.1813\n","Epoch:  5 \t Training acc: 0.9765 \t Val acc:0.9583 \t Training loss:0.1167 \t Val loss:0.1639\n","Epoch:  6 \t Training acc: 0.9792 \t Val acc:0.9585 \t Training loss:0.0903 \t Val loss:0.1546\n","Epoch:  7 \t Training acc: 0.9839 \t Val acc:0.9654 \t Training loss:0.0679 \t Val loss:0.1376\n","Epoch:  8 \t Training acc: 0.9851 \t Val acc:0.9627 \t Training loss:0.0527 \t Val loss:0.1529\n","Epoch:  9 \t Training acc: 0.9832 \t Val acc:0.9704 \t Training loss:0.0475 \t Val loss:0.1230\n","Epoch:  10 \t Training acc: 0.9947 \t Val acc:0.9770 \t Training loss:0.0337 \t Val loss:0.1103\n","Epoch:  11 \t Training acc: 0.9878 \t Val acc:0.9746 \t Training loss:0.0285 \t Val loss:0.1210\n","Epoch:  12 \t Training acc: 0.9948 \t Val acc:0.9793 \t Training loss:0.0323 \t Val loss:0.1005\n","Epoch:  13 \t Training acc: 0.9835 \t Val acc:0.9679 \t Training loss:0.0197 \t Val loss:0.1418\n","Epoch:  14 \t Training acc: 0.9974 \t Val acc:0.9800 \t Training loss:0.0186 \t Val loss:0.1020\n","Epoch:  15 \t Training acc: 0.9963 \t Val acc:0.9760 \t Training loss:0.0179 \t Val loss:0.1126\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYgAAAEWCAYAAAB8LwAVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdd3hUZfbA8e9JJw0ICTV0CEU6oVhAEEWaINhAEbBgWfvquqvrKovrz12XXcvalY6ICoICIiKCqKi0UKS3CEkoCQESAunv7497A0NImUAmM8mcz/PkyZ3b5swQ5szbxRiDUkopVZiPuwNQSinlmTRBKKWUKpImCKWUUkXSBKGUUqpImiCUUkoVSROEUkqpImmCUC4lIktEZKy747gYIjJNRP5hb/cSkZ3OnHuRz3VKRJpd7PVKuYImCHUB+8Oq4CdfRM44PL6jLPcyxgw0xkx3VawlEZGRIhIvIlJov5+IHBWRIc7eyxjzgzGmVTnFtVJE7i10/1BjzL7yuH8Jz3lcRAJd9Ryq6tEEoS5gf1iFGmNCgQPADQ77Pio4T0T83BelUxYANYCrC+0fABjg6wqPyA1EpAnQC+s1D63g5/b0vxFVAk0Qymki0kdEEkTkzyJyGJgqIjVFZJGIJNvfUBeJSLTDNWe/LYvIOBH5UUQm2efuF5GBxTzXn0VkbqF9r4vIGw732ici6fZ9LijZGGMygU+BMYUOjQFmG2NyReQzETksIidFZJWIXFbSa3d43FlENtjP/wkQ5HCs2PdERF7C+rB+0y6RvWnvNyLSwt6uLiIz7Ot/F5HnRMSnrO9hodf7CzANOK+6T0Qaisjn9nMdK4jHPjZeRLbbr3GbiHQpHKv92LEq7mL+RiJEZKqIJNnHF9j7fxORGxzO8xeRFBHpXMrrVeVEE4Qqq7pABNAYuA/rb2iq/bgRcAZ4s9iroQewE4gEXgEmF64Css0BBolIGICI+AK3ArNFJAR4AxhojAkDrgA2FvN804GbRaSafZ/qwA32foAlQEugNrAB+KiomzgSkQCs0slMrPfiM+Amh1OKfU+MMX8FfgAetktkDxfxFP8DqgPNsEo/Y4C7HI47+x4WGGO/ro+A60Wkjv06fIFFwO9AE6AB1vuOiNwCTLCvDccqeRwr6X1xUNa/kZlAMHAZ1r/Dq/b+GcBoh/MGAYeMMXFOxqEulTFGf/Sn2B8gHrjW3u4DZANBJZzfCTju8HglcK+9PQ7Y43AsGKvao24x9/oRGGNvXwfstbdDgBNYH8rVnHgNu4Hb7e3xwKZizqthx1PdfjwN+IfDa0+wt3sDSYA4XLu64NyyvCcO+wzQAvC13+O2DsfuB1Ze5Ht4FZADRNqPdwBP2NuXA8mAXxHXLQUeK+aeBmjh8Ljw++T03whQD8gHahZxXn0gHQi3H88Fnnb3/wlv+tEShCqrZGNV3QAgIsEi8p5dFZIGrAJq2N9Oi3K4YMMYc9reDC3m3NnAKHv7dvsxxpgM4DbgAeCQiCwWkdYlxDyDc9VMd9qPERFfEfmniOy1Y4+3z4ks4V5gfXAlGvtTy/Z7wcZFvCeOIgF/x/vZ2w0cHpflPRwLfGOMSbEfz+ZcNVND4HdjTG4R1zUE9joRb1HK8jfSEEg1xhwvfBNjTBLwE3CTiNQABuJECU+VH00QqqwKT//7JNAK6GGMCcf6dg1QUpWHsz4D+tj11cOxEwSAMWapMeY6rG+gO4APSrjPTKCfiFwO9OTch8ztwDDgWqwqnSZOxn4IaFCoWqeRw3Zp70lJUyinYH3jb1zo3omlxHQBu1rtVuBqu53lMPAE0FFEOgIHgUZSdEPyQaB5Mbc+jVVyKVC30PGy/I0cBCLsBFCU6VjVTLcAPxtjyvw+qIunCUJdqjCsOuUTIhIBvFBeNzbGJGNVx0wF9htjtgOISB0RGWa3RWQBp7CqKYq7TzxWddXHwDJjTME38DD7+mNYH3j/52RoPwO5wKN2w+kIoLvD8dLekyNY7QtFxZqH1bD+koiEiUhj4I/ALCdjc3QjkAe0xarW6QS0wWoDGQOswUp2/xSREBEJEpEr7Ws/BJ4Ska5iaWHHAlZ7z+12CWwAF/YSK6zY98MYcwirHehtuzHbX0R6O1y7AOgCPIZd8lMVRxOEulSvAdWwvvn+Qvl3HZ2N9Q1/tsM+H6wPzSQgFesD6sFS7jMd61u544fMDKzqm0RgG1b8pTLGZAMjsNoDUrGquz53OKW09+R1rIbz42L3yirkESAD2IeV2GYDU5yJrZCxwFRjzAFjzOGCH6wG4juwvsHfgNX2cQBIsF8LxpjPgJfs507H+qCOsO/7mH3dCfs+C0qJo7T3406sUtMO4CjweMEBY8wZYB7QlPPfY1UB5PxqVKWU8iwi8jwQY4wZXerJqlzpIBallMeyq6TuwSplqAqmVUxKKY8kIuOxGrGXGGNWuTseb6RVTEoppYqkJQillFJFqjJtEJGRkaZJkybuDkMppSqV9evXpxhjooo6VmUSRJMmTVi3bp27w1BKqUpFRH4v7pjLqphEZIpYc+7/VsxxEZE3RGSPiGwumCnSPjZWRHbbP5VysRmllKrsXNkGMQ1r3v3iDMSaRbMl1oyP78DZbm0vYM1Y2R14QURqujBOpZRSRXBZgrC7paWWcMowYIax/II1eVc94Hqs6RAKJvBaRsmJRimllAu4sw2iAVYf5wIJ9r7i9pdZTk4OCQkJZGZmln6yckpQUBDR0dH4+/u7OxSllItV6kZqEbkPq3qKRo0aXXA8ISGBsLAwmjRpQsnrqShnGGM4duwYCQkJNG3a1N3hKKVczJ3jIBKx5oIvEG3vK27/BYwx7xtjYo0xsVFRF/bSyszMpFatWpocyomIUKtWLS2RKeUl3JkgvgTG2L2ZegIn7al/lwL97al/awL97X0XRZND+dL3Uynv4bIqJhH5GGv5wUixFnt/AWulLIwx7wJfYa0xuwdrAZK77GOpIvIisNa+1URjTEmN3Uop5fEST5zhy41JhAb6EhESSERIwNmfmsH++Pl63sQWLksQxphRpRw3wEPFHJvCxc1/71GOHTtGv379ADh8+DC+vr4UVIWtWbOGgICAYq9dt24dM2bM4I03ilouQClVmSSnZzHq/V84kHq62HNqBPsTEXwuadQKDaBm8LntiJBA63hoALVCAgjyd2YF20tTqRupPV2tWrXYuHEjABMmTCA0NJSnnnrq7PHc3Fz8/Ir+J4iNjSU2NrZC4lRKuU5GVi53T1tLcnoWcx+4nEa1gjmekcOxjCxSM7JJzcjm2Klsjp/O5lhGNqmnsvn92Gk2HDjB8dPZ5OUXPaFqcIDv2WTSMboGL97Yrtxj1wRRwcaNG0dQUBBxcXFceeWVjBw5kscee4zMzEyqVavG1KlTadWqFStXrmTSpEksWrSICRMmcODAAfbt28eBAwd4/PHHefTRR939UpRSpcjJy+fBjzaw7VAaH4zpSmwTa1G+2mFBWCuxlswYQ9qZ3LPJ5FhGNsft32eTS0Y2eS6aldtrEsTfF25lW1Jaud6zbf1wXrjhsjJfl5CQwOrVq/H19SUtLY0ffvgBPz8/vv32W5599lnmzZt3wTU7duxgxYoVpKen06pVKx588EEdi6C8Xn6+wcfHMztOGGP487zNrNqVzL9uas81reuU+R4iQvVgf6oH+9OsyOn0XMtrEoQnueWWW/D1teoPT548ydixY9m9ezciQk5OTpHXDB48mMDAQAIDA6lduzZHjhwhOjq6IsNWXmDFjqPMWXuAe3s1o1uTiNIvcJOcvHxm/3qA177dxeXNa/HfWztVSJ18WbyydCefb0jkiWtjuK3bheO0KgOvSRAX803fVUJCQs5u/+1vf6Nv377Mnz+f+Ph4+vTpU+Q1gYGBZ7d9fX3Jzc11dZjKyyyIS+TJzzZhjGHp1iMMuKwufx7YmqaRIaVfXIFW7DzKPxZtY29yBpfVD+erLYdJzVjDB2NiCQvyjFL19NXxvLNyL6O6N+LRfi3cHc5F87x+VV7m5MmTNGhgzSQybdo09wajvNa0n/bz+Ccb6dE0gl+fvZYnr4th1e5krvvv9/x94VaOZ2S7O0R2HUlnzJQ13DV1LfkGPhgTy6JHruK12zqxLv44I9//heT0LHeHyZIth5iwcCvXtqnDi8Muq9RjhzRBuNnTTz/NM888Q+fOnbVUoCqcMYZXl+1iwsJt9G9bhynjuhEVFsgj/Vqy8k99uLVbQ6avjqf3v1fw/qq9ZObkVXiMqRnZ/G3Bbwx8/Qc2HjjOc4PbsPTx3lzXtg4iwo2dG/Dh2Fj2JWdw87urOXCs+K6krrZmfyqPfbKRzg1r8L9RnT1ybENZVJk1qWNjY03hBYO2b99OmzZt3BRR1aXva9WQn2+YuGgb01bHc3PXaP45on2RH2i7jqTz8lfbWbEzmeia1Xh6QGtu6FDP5d+Ms3PzmfFzPK8v383p7Dzu6NGIx6+NISKk6PFDGw4c5+5pa/H39WH6Xd1pWz/cpfEVtutIOje/s5rIsEDmPXAFNYuJ09OIyHpjTJF96it3elNKXZScvHz++OlGpq2O596rmvLKTR2K/bYbUyeMqXd1Z9Y9PQgL8ufRj+O48e3VrNnvmgkOrDaQw/R/9Xv+sXg7XRrV5OvHejFxWLtikwNAl0Y1+ez+y/HzEW5772d+3XfMJfEVJenEGcZOWUOgvy/T7+peaZJDaTRBKOVlMnPyuH/mehZsTOJP17fir4PbONVV9KqWkSx65Com3dKRIyczufW9n7l/5jr2JZ8qt9i2JaVx+we/cv/M9fj5+jDtrm5Mv7s7LeuUPmYAoGWdMOY+eAVR4YHcOWUN32w9XG6xFefkmRzGTV1DemYu0+7qRsOIYJc/Z0XRBKGUF0nLzGHM5DVWT6Ab2/FQ3xZlqiry9RFu7hrNiqf68FT/GH7cnUL/V1cx4cutpF5CQ/bR9Ez+Mm8zg//3AzsOpzFx2GV8/Vgv+rSqXeZ7NahRjbkPXEGbeuE8MGs9n649WPpFFykzJ4/xM9axPyWD9+/symX1q7vsudzBa7q5KuXtktOzGDtlDbuPpvPGyM7c0LH+Rd+rWoAvD1/Tktu6NeK1b3cx4+d45m1I4OG+LRh7RROnxyRk5uQx+cf9vL1iD1m5+dxzZVMeuaYl1YMvrbtqREgAs+/twQOz1vP0vM2kns7m/t7NyrXdJC/f8MdPN7JmfypvjOrMFS0iy+3enkIThFJe4GDqae6c/CtH0rL4cGw3ro4pn2G5UWGBvDS8PeOuaMLLS3bw8pIdzPj5d54e0IobOtQvturKGMPiLYf455IdJBw/w3Vt6/DsoDblOuYiJNCPyWO78eRnm/jnkh0cO5XFMwOdq04rjTGGFxdt46sth3lucBuGXkKy9WSaIJSq4nYfSefOyWs4nZ3LrHu707Vx+Y+QblknjCnjuvHTnhReWrydx+ZsZMqP+3l2UBt6NKt13rmbE04wceE21v1+nNZ1w5h9bw+XffsO8PPh9ds6ERHszwc/7OdYRjb/uqkD/pfY/fTd7/cxbXU891zVlHt7NSunaD2PtkG4WN++fVm69Pz1jl577TUefPDBIs/v06cPBd11Bw0axIkTJy44Z8KECUyaNKnE512wYAHbtm07+/j555/n22+/LWv4qpLbePAEt7z3M3nG8Mn9l7skOTi6soXVkP2fWzpyJC2L297/hftmWA3Zh09m8sdPNzL0zZ+IP5bBP0e0Z/GjvVxeNePjI0wYehlPXhfD5xsSuX/mes5kX/x4js83JPCvr3dwQ8f6/HVQ1e7urSUIFxs1ahRz5szh+uuvP7tvzpw5vPLKK6Ve+9VXX1308y5YsIAhQ4bQtm1bACZOnHjR91KV0097Uhg/Yx2RoYHMvKc7jWtVzJQZPj7CTV2jGdS+HlN+stoX+r+6Cj9fIT8fHuzTnD/0aV6h02KICI/0a0lEaADPLfiN0ZN/ZfLYWGoEl6076qpdyTw9dzOXN6vFpFs6eOxEgeVFSxAudvPNN7N48WKys60eHvHx8SQlJfHxxx8TGxvLZZddxgsvvFDktU2aNCElJQWAl156iZiYGK666ip27tx59pwPPviAbt260bFjR2666SZOnz7N6tWr+fLLL/nTn/5Ep06d2Lt3L+PGjWPu3LkALF++nM6dO9O+fXvuvvtusrKyzj7fCy+8QJcuXWjfvj07duxw5VujXOjr3w5x19S1NKwZzNwHLq+w5OCoWoAvD/Vtwco/9WV0z8YM7Vif5U9ezZ8HtHbbnEl39GjMW7d3YUvCSW5972cOn3R+ffXfEk/y4Kz1tKgdyntjuhLo51mTA7qC95QglvwFDm8p33vWbQ8D/1niKREREXTv3p0lS5YwbNgw5syZw6233sqzzz5LREQEeXl59OvXj82bN9OhQ4ci77F+/XrmzJnDxo0byc3NpUuXLnTt2hWAESNGMH78eACee+45Jk+ezCOPPMLQoUMZMmQIN99883n3yszMZNy4cSxfvpyYmBjGjBnDO++8w+OPPw5AZGQkGzZs4O2332bSpEl8+OGHl/ouqQr26dqD/OXzzXRqWIOp47pfco+gSxUVFsiEoZ4zWeag9vWoUc2f+2au56Z3VjPjnu40jwot8ZoDx04zbuoaagQHMP3u7oR7yKSArqYliApQUM0EVvXSqFGj+PTTT+nSpQudO3dm69at57UXFPbDDz8wfPhwgoODCQ8PZ+jQoWeP/fbbb/Tq1Yv27dvz0UcfsXXr1hJj2blzJ02bNiUmJgaAsWPHsmrVqrPHR4wYAUDXrl2Jj4+/2Jes3OT9VXt5et5mrmwRyax7e7g9OXiqK1pEMue+nmTm5HHLuz+zOeHCtr4Cx05lMWbKr+TkGabf3Y064UEVGKl7eU8JopRv+q40bNgwnnjiCTZs2MDp06eJiIhg0qRJrF27lpo1azJu3DgyM50v6joaN24cCxYsoGPHjkybNo2VK1deUqwF04rrlOLlJzMnjwBfH5fWVxtjeGXpTt5ZuZfBHerx6q2dCPDT738ladegOnMfvII7J//KqPd/4b07Y7mq5fkN5qezc7l7+joOncxk9vgetKjt3IjuqsJ7EoQbhYaG0rdvX+6++25GjRpFWloaISEhVK9enSNHjrBkyZJi14EA6N27N+PGjeOZZ54hNzeXhQsXcv/99wOQnp5OvXr1yMnJ4aOPPjo7dXhYWBjp6ekX3KtVq1bEx8ezZ88eWrRowcyZM7n66qtd8rqrupy8fFJOZXEkLYvDJzM5mp7JkbRMjqRl2b+t7ZNncgj086FZVCgtaofSPCrE/h1K08iQS17oJi/f8NyC3/h4zQFu79GIF4e1w7eKN56Wl6aRIcx78ArGTlnDXdPW8OptnRjSwRrTkJuXz8Oz49iScIJ3R3d1eQ8wT6QJooKMGjWK4cOHM2fOHFq3bk3nzp1p3bo1DRs25Morryzx2i5dunDbbbfRsWNHateuTbdu3c4ee/HFF+nRowdRUVH06NHjbFIYOXIk48eP54033jjbOA0QFBTE1KlTueWWW8jNzaVbt2488MADrnnRlVR+vuH46WwOp2Vy1P6wP2x/2B9Ny+RIeiaHT2ZxLCOLwpMh+/oItcMCqR0eRJNaIfRsVouo0EBOnslhb/IpNh48zqLNSWevE4GGNYPPSxrNa4fSIirUqQnfsnLz+OMnm1i85RAP9W3OU/1bVer1B9yhTngQn9x/OfdOX8sjH8dx/HQOo3s04tn5W/hux1FeGt6O/pfVdXeYbqHTfasyq2rva16+4Yfdycxdn0DcgRMcTc8kJ+/C/xeRoQHUDguiTnggdcKDHH7OPY4ICSj123tmTh77kjPYm3yKvcmn2HP0FHuTM9iXfIqs3Pyz50WEBNAiKpTmtUPOSxwNalTDx0fIyMrlgVnr+WF3Cn8d1IbxvavugK2KkJmTx8OzN/Dt9qN0bxrBmv2pPHpNC/7Yv5W7Q3Opkqb71hKE8lp7jp5i7voE5sclcCQtixrB/vSJiaJejWrUtT/4a9sf/FGhgeVWpx/k70vb+uEXrFeQl29IOnHGThjnksfSrUdIzTg34VxBdVVWbh7xKRm8cnMHbo1tWC6xebMgf1/eHd2Vv3y+hbnrE7g1Nponrotxd1hupQlCeZWTZ3JYtDnpbGnB10fo2yqKvw+Npm/r2m7t2+7rIzSMCKZhRDB9W58/i2lqRva50oadQI6mZ/HO6K5c76XVH67g5+vDv2/uwB09GtG+QXWvr66r8gnCGOP1/8jlqTJWSeblG37ck8Lc9Qks3XqY7Nx8WtUJ47nBbRjWqQFRYYHuDrFUESEBRIRE0K2J9zWUVjQRoXOjmu4OwyNU6QQRFBTEsWPHqFWrliaJcmCM4dixYwQFVY5+4HuTTzFvfQKfb0jkcFomNYL9GdWtITd3bUi7BuH6N6FUKVyaIERkAPA64At8aIz5Z6HjjYEpQBSQCow2xiTYx14BBmMN5lsGPGbK+PU1OjqahIQEkpOTL/m1KEtQUBDR0dHuDqNYaZk5LNp0iLnrD7LBrkK6OiaKF25oyzVt3FuFpFRl47IEISK+wFvAdUACsFZEvjTGOA4ZngTMMMZMF5FrgJeBO0XkCuBKoGDuiR+Bq4GVZYnB39+fpk2bXtoLUR4vL9+wem8Kn62zqpCycvNpWTuUZwe15sZODajtRSNflYczxurbXEm4sgTRHdhjjNkHICJzgGGAY4JoC/zR3l4BLLC3DRAEBAAC+ANHXBirqoT2JZ9i3garCunQyUzCg/y4NbYhN3eNpkO0NjAqD5KfD0ufgQ0zoPGV0GYItBoMoeWzcJOruDJBNAAcF4NNAHoUOmcTMAKrGmo4ECYitYwxP4vICuAQVoJ40xizvfATiMh9wH0AjRo1Kv9XoDxKWmYOGw+cYMOB4/ywO4X1vx/HR6B3TBTPDW5Lvza1L3lUsqokcrNh91L4bR60uA463+HuiIqXnw+LHrOSQ8vrIWUnLHwMFj0BDXtCmxushFHD8z7D3N1I/RTwpoiMA1YBiUCeiLQA2gAFld3LRKSXMeYHx4uNMe8D74M1UK7ColYul59v2Jt8ig0HjhNnJ4XdR0+dLaG3rhvOXwa2ZnjnBl41eZrXO/wbbPwINn8Cp4+BXxBsnQ/ph6DXk55XfZOfB188DJtmQ+8/Qd+/WvuP/AbbF8H2hVbJYukzUK+jnSyGQpRnDM5zZYJIBBxH70Tb+84yxiRhlSAQkVDgJmPMCREZD/xijDllH1sCXA6clyBU1eFYOthw4AQbDxwnLdOaLLB6NX86N6rBkA716dKoJh0aVvea6ZYVcDoVtsyFjbPg0Cbw8YdWA6HzaGjaG758FL570UoY/V8CHw+ZpDAvFxY8AFs+sxLD1U+fO1a3vfXT9xk4thd22Mniu39YP7Va2sniBqjf2W2Jz2VTbYiIH7AL6IeVGNYCtxtjtjqcEwmkGmPyReQlIM8Y87yI3AaMBwZgVTF9DbxmjFlY3PMVNdWG8kyOpYMNv1tJYU/yudJBTO0wujSuQedGNenSqCbNIkOq/MpdqpC8XNj7nVVa2PkV5GVD3Q5WUmh/CwQ7jAfJz4elz8Kv70CH22DYW+Dr5i8QeTkw717YtgCunQBXPeHcdWlJsGOxlSzifwSTB+HRVhVUmxug0eXgU77VqCVNteHSuZhEZBDwGlY31ynGmJdEZCKwzhjzpYjcjNVzyWBVMT1kjMmye0C9DfS2j31tjPlj0c9i0QThuU6eyWHjwRPElVA66GIng44Nq7tttTGvl3kSfnoD9iyDyBio1wnqd7I+mIPCS7++PKTshrhZVhVS+iGoFmF96He+w/rGXRxj4If/WCWJlv3hlukQEFwxMReWmwWf3QU7F8P1/weXP3Rx9zmdCru+tpLF3u8gNxOCa0GrQVayaNYH/C59kKfbEkRF0gThWYwxLN9+lFe/3cW2Q2lnSwet6oTRuZGWDjxKbhas/RBWTYIzqVbD6YkDkJ5knyBQq/m5hFGvk1VfXl5JIzMNtn4OcR9BwhoQX2h5HXS6A2IGgF8Z1o1eNxUW/xGiu8GoOeeXNCpCTiZ8OsZqQB80CbqPL5/7Zp2CPd9aVVG7lkJWGgSEQUx/aD3Eer8CL26tCk0QqkLtOZrOxEXbWbUrmeZRIQzr1EBLB54oP8+qH//uJTh5AJr1tapD6neyjp86Ckkb4dDGc7/THJoRI5qfSxj1C5JGdSefOx/if7CqkLZ9CblnILKVVVLoMBLC6lz869r2hVW9U6sFjJ4H4fUv/l5lkXMG5txufdsf8hrE3uWa58nNgv2rrJLFjsVwOgVqt4U//HxRt9MEoSrEyTM5vP7tbmb8HE+1AF8evzaGMZc3xt/XQxoNlcUY2L0Mlv/d6k1Tr5OVGJr3Lf3aU8nnJ4ykjZCWcO54RLMLSxrVapw7fjweNs6GjR9bSSmwOrS/ySotNOhafo2x+763PqyrRcCd8yGyRfnctzjZGTD7NqvdYNibVltJRcjPgwO/WCWKVgMv6haaIJRL5eUbPll7kEnf7OT46WxGdmvEU/1jqBXq+ZPgeZ2EdbDsBfj9R6jZFPr9DdoOv7SeP6eSrd5Fh+LsxLEJTjoMgarZ1EoYGSlWqQGx6s87j4bWg8G/2iW+qGIkxcGsm63t0XOt3kCukJUOH90KB3+BG9+Fjre55nlcRBOEcpk1+1OZ8OVWth1Ko3uTCJ6/oS3tGjhZzaAqTspuq8SwfSGERMHVf4YuY8tWv18WGSmFShqbrOfqMBI6joQaFbR+RcoemDkczhyHkR9Bs3JeXjfzpJWEEtfDTR9CuxHle/8KoAlClbvEE2d4+avtLNp8iPrVg3hmUBuGdKin01t4mrRD8P0/YcNM65v6FY9avWoCQ90dWcVJS4KZIyB1r/Uh3nZY+dz3zHHrvoe3wC1TrZ5FlZCuKKfKTWZOHu9+v5d3v9+LMfBYv5Y8cHVzqgXoFBce5cwJ+Ol1+OUdyM+1etP0esrj5/5xifD6cNdXVhvBZ+NgyKvQddyl3fN0KswYBsk74LaZF13/7+k0QSinGGP4asth/u+r7SSeOMPgDvV4ZmBromu6qa+5KlpOJqz9wBoTcOa4Nais7yy509oAACAASURBVF8hwstnNQ6OgDFfWF1QFz5mVYFd7NQcp5Kt5HBsD4z8GFpeW/7xeghNEKpU25LS+PvCrfy6P5U29cL5z60d6dmslrvDUo7y86zBZd+9ZPUqat4Prn3B6kWkLAHBMOpjWPAHa0BdRoo1kK0sDfTph2H6UGucyB2fWo3tVZgmCFWs1Ixs/vPNTj5ec4Dq1fz5x43tGNW9Eb46sM1zGAO7v4FvJ8DRbVbX0hvfqvIfXBfN1x+Gv2eNSP71HWtgoLNTc6QlwfQbrHad0XOhyVWuj9fNNEGoC+Tk5TPrl995ddkuMrLzGHN5E564NobqwTrIzaMcXGN1WT2w2hp/cPNUaHuj50xW56l8fGDAyxASaU/ylwq3ToeAkOKvOXHQSg4ZKXDn59CoZ8XF60aaINR5ftidzMSF29h99BRXtYjk+RvaElPn4obwKxfJSIFv/mZNIR1SGwb/x+qy6u4J6ioTEej9lFWSWPxHmHEj3P5J0VNzHI+3ksOZkzBmAUQX2eGnStIEoQA4cOw0Ly7exrJtR2gUEcz7d3blurZ1tNuqJ8nPh7iZsOx5yD5lzRDa6ynv6rJa3mLvspLCvHth6iCrdOA4NcexvVZyyM6AsV+4brCdh9IEodh48AR3Tv6VvHzD0wNacc9VTQn0026rHuXINmsFsoO/QKMrYMh/oXYbd0dVNbQdBkE1rKk5Jl9/bmqO5F1WcsjPgXGLSp5NtorSBOHlNhw4ztjJa6gZEsBH9/agYYR2W/Uo2Rnw/b/g57cgMByGvQ2dbve8ldMqu2ZXW0lg1s0wpT8M+Je1xgTA2EVQp61743MTbc3yYut/T2XM5DVEhAYw576emhw8zc4l8FYPa8Bbx5HwyHprtlNNDq5RvzPcvRT8Q+Dze0F8YNxir00OoCUIr7Vmfyp3TV1D7fAgPh7fk7rVdV1nj3EyAZb82Zr7P6oN3LUEGl/h7qi8Q2QLuGcp/Pga9LjfWgfDi2mC8EK/7DvG3dPWUrd6EHPG96R2uCYHj5CXC7++Cyv+D0y+NQV3z4dcN6GeKlp4fRj0iruj8AiaILzM6j0p3D19LdE1g5k9vge1wzQ5eISDa61G6CNboOX1MOjfULOxu6NSXk4ThBf5cXcK90xfS+Nawcwe35NIXa/hfPtXQfJOqN/F6rFSEd/czxyHb/8O66dBWD24daY1K6i2MygPoAnCS3y/K5n7ZqyjaWQIH93bQxfzcXR0uzXwbM+yc/t8A615jKK7WQOjortB9ejy++A2xlruc+mzcPoY9PwD9H3motcVVsoVNEF4gRU7j3L/zPW0iApl1r09iAjROm3AWnN5xUuwYYa1AHz/f1jf3g9tgoS11upr6ybDL29Z54fWhYbd7KTRzZr3KOAien6l7LZG7+5fZS2zOXqeTqqnPJImiCpu+fYjPDhrAzF1Q5l1Tw9qBGtyIPu09aH/42uQmwnd77NWWCuYZqFmk3OLyuTlWAvCJKyzk8Zaa1U2APGFuu3shNHdKmlENCu+lJGTCT/+F358FfyqweD/WusS+OigROWZdEW5KuybrYd5aPYG2tQLZ+bdPXSyvfx82PIpLJ8IaYnQeghc+/eyL2ifkXJ+wkhcb019AVAt4lwJIzrWKiEEhcPe72Dxk5C6z1qjof9LEFan/F+jUmWkK8p5oa9/O8zDszdwWYPqzLi7O9WreXlyiP8Rlv7VWh+5XicY8f7FT9ccEgmtBlg/YK3FkLwTEtacq5ravdQ+WazeSMfjIaI53LkAmvctj1eklMtpgqiCFm8+xKNz4ugQXZ3pd3cnPMiLk0PKbmtyu51fQXg0jPgA2t1cvlNi+/hao23rtD23lOWZE5C0wUoWSXHQaTRc8Qj4a7diVXlogqhiFm5K4vFPNtK5YQ2m3tWNMG9NDhnH4Pt/wropVn1/v+etnkL+1Srm+avVgObXWD9KVVIuTRAiMgB4HfAFPjTG/LPQ8cbAFCAKSAVGG2MS7GONgA+BhoABBhlj4l0Zb2X3xcZEnvhkI7GNI5hyVzdCA70w/+dkwpr3YNV/rHaBruOgzzMQGuXuyJSqdFz2CSIivsBbwHVAArBWRL40xmxzOG0SMMMYM11ErgFeBu60j80AXjLGLBORUCDfVbFWBZ9vSOCpzzbRvWkEU8Z1IzjAy5KDMfDbPFj+d2u94JbXw3UToXZrd0emVKXlyk+R7sAeY8w+ABGZAwwDHBNEW+CP9vYKYIF9blvAzxizDMAYc8qFcVZ6n607yNPzNnN5s1pMHtuNagFe1m3ywC9WA3TiOqjTXhuClSonrpzuuwFw0OFxgr3P0SZghL09HAgTkVpADHBCRD4XkTgR+bddIjmPiNwnIutEZF1ycrILXoLn+2TtAZ6et5mrWkR6X3JI3QefjoEp11szoA57C+7/XpODUuXE3fUQTwFvisg4YBWQCORhxdUL6AwcAD4BxgGTHS82xrwPvA/WOIiKCtpTzP71AM/O30LvmCjev7MrQf4enhyMsbqE5udYA9Dyc+3f9mPH7cLHCj9OioM1H1jrMPd5Fq54uORF55VSZebKBJGI1cBcINred5YxJgm7BGG3M9xkjDkhIgnARofqqQVATwolCG828+d4/vbFVvq2iuKd0R6WHNIPWwPD9nwL8T9BVjrkZVsf7uVGoPNouOY5CKtbjvdVShVwZYJYC7QUkaZYiWEkcLvjCSISCaQaY/KBZ7B6NBVcW0NEoowxycA1gA6Ttk37aT8TFm7j2ja1eeuOLu5fPzo3y2oH2Lsc9nxnTVkNEFLbqu4JibK+6fv427/9HB77gW9A8cfO7nd8HABB1bVnklIu5rIEYYzJFZGHgaVY3VynGGO2ishEYJ0x5kugD/CyiBisKqaH7GvzROQpYLmICLAe+MBVsVYmn647yISF27iubR3eur0LAX5uWjX22N5zpYT9P0BOhvXh3aintdBN835Qp135DkhTSlUonYupEjmVlUvvV1bQonYoH93bA3/fCvzwzUq3EsHe5bBnORzfb+2v2QRaXGv9NLlKp6tWqpLRuZiqiOmr40nNyOaZga1dnxyMsWYxLUgIB36x2hD8Q6BpL7j8IWuUsJev2atUVaYJopI4eSaH977fS7/WtencqKZrniTjGOxbYVUb7f0OTh2x9tdpB5f/wao2atQT/HSxIaW8gSaISmLyj/tJy8zlietiyv/m2adh3j2wcwlgrCmrm/e1EkLzayC8Xvk/p1LK42mCqASOZ2Qz5cf9DGxXl3YNqpfvzXMyYc4o2Pc9XPWEtUZC/U66iI1SShNEZfDeqn1kZLug9JCbbY1E3rcShr0Nne8o3/srpSo17YPo4ZLTs5i+Op6hHesTU6ccewjl5VrVSruXWktfanJQShWiCcLDvbNyL1m5eTzWr2X53TQ/DxY8ANu/hOtfhm73lN+9lVJVRqkJQkRuEBFNJG5w+GQms379nZu6RNMsKrR8bpqfDwsfhS2fWYvoXP6H8rmvUqrKceaD/zZgt4i8IiI6uX4FenPFbvLzDY+WV+nBGFjyJ4ibBb2fhl5Pls99lVJVUqkJwhgzGmtW1b3ANBH52Z5mW4fMutDB1NN8svYgt3VrSMOI4Eu/oTHwzXOw9kNrbeS+z176PZVSVZpTVUfGmDRgLjAHqIe1dsMGEXnEhbF5tf99txsR4eFrWpTPDVe8BD+/Cd3vg+teBJHyua9Sqspypg1iqIjMB1YC/kB3Y8xAoCOgdRQusD8lg3kbErmjRyPqVa926TdcNQlW/Ru6jIEB/9LkoJRyijPjIG4CXjXGrHLcaYw5LSLa/cUFXv92F/6+woN9ymGeo5/fgu9ehPa3wpDXdHZVpZTTnPm0mACsKXggItVEpAmAMWa5S6LyYruPpPPFpiTGXtGE2mFBl3aztR/C0meh7TC48R0dHa2UKhNnEsRnQL7D4zx7n3KBV7/dRbC/L/f3vsTSQ9wsWPwkxAyEER9ai+0opVQZOJMg/Iwx2QUP7O0A14XkvbYmneSrLYe556qmRIRcwlu8ZS588TA06wu3TAM//edSSpWdMwkiWUSGFjwQkWFAiutC8l6vLttFeJAf9/RqdvE32fYlfH4fNL4SRs4G/0usplJKeS1n6h0eAD4SkTcBAQ4CY1walRfaePAE324/ylP9Y6hezf/ibrLrG5h7NzToCrfPgYByGD+hlPJapSYIY8xeoKeIhNqPT7k8Ki/032W7qBnsz7grm17cDfaugE9GQ522cMdnuvSnUuqSOdVyKSKDgcuAILH70BtjJrowLq+yNj6VVbuSeXZQa0IDL6Ix+ffV8PEoqNUC7lwA1WqUf5BKKa/jzEC5d7HmY3oEq4rpFqCxi+PyGsYYJi3dSVRYIHf2bFL2GySsg49ugRoNYcwCCI4o9xiVUt7JmUbqK4wxY4Djxpi/A5cDLlj30jut3nuMX/en8lCf5lQLKOM4hUObYNYICImEMV9AaG3XBKmU8krOJIhM+/dpEakP5GDNx6QukTGG/3yzk3rVgxjZvVHZLj6yDWbcCIHhMHYhhNd3TZBKKa/lTIJYKCI1gH8DG4B4YLYrg/IWK3cms+HACR65piVB/mUoPaTsgRnDwDfAKjnUKGNyUUopJ5TYImovFLTcGHMCmCcii4AgY8zJComuCjPG8J9lO2kYUY1bYqOdv/B4PEy/AUw+jFsEtcphviallCpCiSUIY0w+8JbD4yxNDuVj6dYj/JaYxmP9YvD3dXICvdxs+OROyDltlRyiWrk2SKWUV3Pmk2m5iNwkUvY5okVkgIjsFJE9IvKXIo43FpHlIrJZRFaKSHSh4+EikmAP0qsy8vMNry7bRbPIEG7sVIa2g1WvwOHNcOPbULed6wJUSimcSxD3Y03OlyUiaSKSLiJppV0kIr5YpY+BQFtglIi0LXTaJGCGMaYDMBF4udDxF4FVVDGLthxi55F0Hr8uBj9nSw8H18IP/4FOd0Drwa4NUCmlcG7J0TBjjI8xJsAYE24/Dnfi3t2BPcaYffYEf3OAYYXOaQt8Z2+vcDwuIl2BOsA3zryQyiI3L5/Xvt1FqzphDGnvZGew7NMw/34IbwADCudQpZRyjVKH7YpI76L2F15AqAgNsOZtKpAA9Ch0ziZgBPA61jKmYSJSCzgO/AcYDVxbQmz3AfcBNGpUOXryLNiYxL7kDN4d3RUfHydr7b59AVL3Wt1Zg6q7NkCllLI5M6/Dnxy2g7BKBuuBa8rh+Z8C3hSRcVhVSYlY6038AfjKGJNQUtOHMeZ94H2A2NhYUw7xuFROXj5vLN9NuwbhXH9ZHecu2vsdrHkfev4BmhaZq5VSyiWcmazvBsfHItIQeM2JeycCDR0eR9v7HO+dhFWCwJ4M8CZjzAkRuRzoJSJ/AEKBABE5ZYy5oKG7Mpm7PoEDqaeZMi4Wp9r8zxyHBQ9BZAz0e971ASqllIOLWWYsAWjjxHlrgZYi0hQrMYwEbnc8QUQigVS7O+0zwBQAY8wdDueMA2Ire3LIys3jf8t307lRDfq2cnJKjCV/hlNHYORH4F/NtQEqpVQhzrRB/A8oqL7xATphjagukTEmV0QeBpYCvsAUY8xWEZkIrDPGfAn0AV4WEYNVxfTQRb2KSmDOmoMknczklZs7Old62LoANn8CfZ6BBl1cH6BSShUixpRcdS8iYx0e5gLxxpifXBrVRYiNjTXr1q1zdxhFOpOdR+9/r6BZZAhz7utZeoJIPwJv94SajeGeZeB7kQsIKaVUKURkvTEmtqhjzlQxzQUyjTF59s18RSTYGHO6PIOsymb98jvJ6Vm8dXuX0pODMbDwUWu09PD3NDkopdzGqZHUgGMFeDXgW9eEU/Wcysrlne/30qtlJN2bOrFWQ9xM2PU1XDtBp9JQSrmVMwkiyHGZUXtbFzt20vTV8aRmZPNkfyc+7I/Hw9fPQJNe0P1+l8emlFIlcSZBZIjI2VZSe4TzGdeFVHWcPJPDe9/v5do2tenUsJRlQPPzYP6DID5w4zvg4+QUHEop5SLOtEE8DnwmIklYS47WxVqCVJVi8o/7ScvM5YnrnFiA75e34cBqKznUaFj6+Uop5WLODJRbKyKtgYI6kp3GmBzXhlX5Hc/IZsqP+xnUvi6X1S9leowj22D5RGg9BDqOqpgAlVKqFKXWY4jIQ0CIMeY3Y8xvQKg9wlmV4NN1BzmVlctj/UopPeRmWxPxBYbDkNeg7LOqK6WUSzhT0T3eXlEOAGPMcWC860KqGubHJdK5UQ1a1Q0r+cSCNR6GvgGhURUTnFJKOcGZBOHruFiQvc5DgOtCqvy2H0pjx+F0hnduUPKJusaDUsqDOdNI/TXwiYi8Zz++H1jiupAqvwVxifj5CINLWu9B13hQSnk4ZxLEn7HWXHjAfrwZqyeTKkJevuGLjUlcHRNFrdDA4k/UNR6UUh7OmRXl8oFfgXistSCuAba7NqzK69d9xziclsmNJVUv6RoPSqlKoNgShIjEAKPsnxTgEwBjTN+KCa1ymh+XSGigH9e1LWZBIF3jQSlVSZRUxbQD+AEYYozZAyAiT1RIVJVUZk4eS347zMB2dQny9y36JF3jQSlVSZRUxTQCOASsEJEPRKQf1khqVYxvtx/hVFZu8b2XCtZ4uPppXeNBKeXxik0QxpgFxpiRQGtgBdaUG7VF5B0R6V9RAVYmC+ISqRseRI9mtS48mH4EFj0B9TtDrycrPjillCojZxqpM4wxs+21qaOBOKyeTcpBakY2K3cmM6xTfXx9ChW0dI0HpVQlVKYpQ40xx40x7xtj+rkqoMpq8eYkcvNN0b2XdI0HpVQlpHNKl5P5cYm0rhtGm3rh5x/QNR6UUpWUJohy8PuxDDYcOHFh6UHXeFBKVWLOjKRWpZgfl4gIDO1Y//wDusaDUqoS06+0l8gYw4K4RHo2rUX9Gg7jGlJ26xoPSqlKTRPEJdp48ATxx05fOPYhbhaYfBj8X13jQSlVKWmCuEQL4hIJ9PNhQHuH+QuNgR2LoMlVEFbMlBtKKeXhNEFcgpy8fBZuPsS1besQHuQwtiFlFxzbY1UvKaVUJaUJ4hL8sDuZ1IxshncqVL20faH1WxcBUkpVYi5NECIyQER2isgeEflLEccbi8hyEdksIitFJNre30lEfhaRrfax21wZ58WaH5dEzWB/escUWip0xyJo0BXC6xd9oVJKVQIuSxD20qRvAQOBtsAoEWlb6LRJwAxjTAdgIlCwtNppYIwx5jJgAPCaiNRwVawXIz0zh2+2HmZIh/oE+Dm8jScTIClOq5eUUpWeK0sQ3YE9xph9xphsYA4wrNA5bYHv7O0VBceNMbuMMbvt7STgKFDoa7p7Ld16hKzc/AsHx+1YbP1uc0PFB6WUUuXIlQmiAXDQ4XGCvc/RJqxpxQGGA2Eict5UqCLSHQgA9hZ+AhG5T0TWici65OTkcgvcGQviEmkUEUyXRoUKNjsWWYsBRbas0HiUUqq8ubuR+ingahGJA64GEoG8goMiUg+YCdxlL316HnviwFhjTGxUVMUVMA6fzOSnvSnc2LkB4jjG4XQqxP+k1UtKqSrBlVNtJAKO80tE2/vOsquPRgCISChwkzHmhP04HFgM/NUY84sL4yyzLzclYgzc2KlQI/Sur8HkQRtNEEqpys+VJYi1QEsRaSoiAcBI4EvHE0QkUkQKYngGmGLvDwDmYzVgz3VhjBdlflwSHRvWoFlU6PkHti+C8AZQX1eLU0pVfi5LEMaYXOBhYCmwHfjUGLNVRCaKyFD7tD7AThHZBdQBXrL33wr0BsaJyEb7p5OrYi2LHYfT2H4ojeGFSw/Zp2Hvd9bYB51aQylVBbh0NldjzFfAV4X2Pe+wPRe4oIRgjJkFzHJlbBdrQVwSvj7CkMIzt+5dDrlndHCcUqrKcHcjdaWSn2/4YmMiV8dEERkaeP7B7YsgqAY0vtI9wSmlVDnTBFEGv+5P5dDJzAvHPuTlwK4l0GqgrjetlKoyNEGUwYK4REICfLmuTaEZWuN/hMyT2r1VKVWlaIJwUmZOHl9tOcSAdvWoFuB7/sEdi8GvGjS/xj3BKaWUC2iCcNJ3O46SnpV74cJA+flWgmjRDwKC3ROcUkq5gCYIJ32+IZHaYYFc3rzW+QeS4iA9SauXlFJVjiYIJ6RmZLNy51GGdaqPr0+hMQ47FoL4Qsz17glOKaVcRBOEExZvOURuvrmw9xJY3VubXAXBERUfmFJKuZAmCCcsiEskpk4obeuFn38geRcc261TeyulqiRNEKU4cOw0638/fuHMrWBVLwG0GlTxgSmllItpgijFgo3WBLTDCq87DVb1Uv0uUL2IY0opVclpgiiBMYYFcYn0bBZBgxrVzj94MhGSNujU3kqpKksTRAk2J5xkX0rGhWMf4NzSoq21/UEpVTVpgijB/LhEAvx8GNCu3oUHC5YWjYqp+MCUUqoCaIIoRk5ePgs3JXFtm9pUr1ZoAr7Tqdb8Szo4TilVhWmCKMaPu1M4lpHNjUU1Tu9aai0tqglCKVWFaYIoxvy4RGoE+9OnVe0LD+5YBGH1oX7nig9MKaUqiCaIIpzKyuWbbYcZ3L4eAX6F3qLs07BnubVynI++fUqpqks/4Yqw9LfDZObkF917ae931tKi2r1VKVXFaYIowoKNiUTXrEbXxjUvPLhDlxZVSnkHTRCFHEnL5Kc9KQwvamqNvBzYuQRiBujSokqpKk8TRCELNyWRbyh65tbff4LME1q9pJTyCpogCpkfl0jH6Oo0jwq98OD2RfbSov0qPjCllKpgmiAc7DqSztaktKJLD8bo0qJKKa+iCcLBgrhEfH2EIR3qX3gwaYMuLaqU8iqaIGz5+YYvNibRq2UkUWGBF56wfZEuLaqU8iouTRAiMkBEdorIHhH5SxHHG4vIchHZLCIrRSTa4dhYEdlt/4x1ZZwAa+JTSTxxpuixD2B1b21ypS4tqpTyGi5LECLiC7wFDATaAqNEpG2h0yYBM4wxHYCJwMv2tRHAC0APoDvwgogUMSih/CyISyQ4wJfr2ta58GDyLkjZpVN7K6W8iitLEN2BPcaYfcaYbGAOMKzQOW2B7+ztFQ7HrweWGWNSjTHHgWXAAFcFmpmTx+IthxhwWV2CA/wuPGHHIut368GuCkEppTyOKxNEA+Cgw+MEe5+jTcAIe3s4ECYitZy8FhG5T0TWici65OTkiw50xY6jpGfmFt17CawEoUuLKqW8jLsbqZ8CrhaROOBqIBHIc/ZiY8z7xphYY0xsVFTURQcxPy6RqLBArmhe68KDaUmQuF5LD0opr+PKBJEINHR4HG3vO8sYk2SMGWGM6Qz81d53wplry8uJ09ms2HmUoR3r4+dbxNtRsLRoG21/UEp5F1cmiLVASxFpKiIBwEjgS8cTRCRSRApieAaYYm8vBfqLSE27cbq/va/cCcJj/VpyS2x00SfsWAS1WkJUK1c8vVJKeSyXJQhjTC7wMNYH+3bgU2PMVhGZKCJD7dP6ADtFZBdQB3jJvjYVeBEryawFJtr7yl31YH8evqYlreuGX3jwzHFraVGde0kp5YWK6LJTfowxXwFfFdr3vMP2XGBuMddO4VyJwj12LYX8XO3eqpTySu5upPZs2xdCWD1dWlQp5ZU0QRRHlxZVSnk5/eQrzr4V1tKiOjmfUspLaYIoznZ7adEmV7k7EqWUcgtNEEXJy4VdurSoUsq7aYIoyu8/WV1cdfS0UsqLaYIoyo5F4BdkrR6nlFJeShNEYQVLizbvBwEh7o5GKaXcRhNEYUlxkJaoo6eVUl5PE0RhOwqWFnXZ8hNKKVUpaIIobLsuLaqUUqAJ4nwpuyFlpw6OU0opNEGcT5cWVUqpszRBONq+yJqYr3oxa0MopZQX0QRRIC0JEtdp9ZJSStk0QRTQpUWVUuo8miAK7FgEtVpAZIy7I1FKKY+gCQLOLS3aegiIuDsapZTyCJogAHZ9Yy0tqtVLSil1liYIgB0FS4t2cXckSinlMTRB5JzRpUWVUqoI+omYeRJaDYLLhrs7EqWU8ih+7g7A7cLqws2T3R2FUkp5HC1BKKWUKpImCKWUUkXSBKGUUqpILk0QIjJARHaKyB4R+UsRxxuJyAoRiRORzSIyyN7vLyLTRWSLiGwXkWdcGadSSqkLuSxBiIgv8BYwEGgLjBKRtoVOew741BjTGRgJvG3vvwUINMa0B7oC94tIE1fFqpRS6kKuLEF0B/YYY/YZY7KBOcCwQucYINzerg4kOewPERE/oBqQDaS5MFallFKFuDJBNAAOOjxOsPc5mgCMFpEE4CvgEXv/XCADOAQcACYZY1ILP4GI3Cci60RkXXJycjmHr5RS3s3djdSjgGnGmGhgEDBTRHywSh95QH2gKfCkiDQrfLEx5n1jTKwxJjYqKqoi41ZKqSrPlQPlEoGGDo+j7X2O7gEGABhjfhaRICASuB342hiTAxwVkZ+AWGBfcU+2fv36FBH5vRzjLw+RQIq7gyiDyhRvZYoVKle8lSlWqFzxemKsjYs74MoEsRZoKSJNsRLDSKwPfkcHgH7ANBFpAwQByfb+a7BKFCFAT+C1kp7MGONxRQgRWWeMiXV3HM6qTPFWplihcsVbmWKFyhVvZYoVXFjFZIzJBR4GlgLbsXorbRWRiSIy1D7tSWC8iGwCPgbGGWMMVu+nUBHZipVophpjNrsqVqWUUhdy6VxMxpivsBqfHfc977C9DbiyiOtOYXV1VUop5SbubqSu6t53dwBlVJnirUyxQuWKtzLFCpUr3soUK2LV6CillFLn0xKEUkqpImmCUEopVSRNEC4gIg3tSQi3ichWEXnM3TGVRkR87UkTF7k7ltKISA0RmSsiO+zJHC93d0zFEZEn7L+B30TkY3usj8cQkSkiclREfnPYFyEiy0Rkt/27pjtjdFRMvP+2/xY2i8h8EanhzhgLFBWrw7EnRcSISKQ7YnOWJgjXyAWeNMa00lpYIgAABIVJREFUxRrD8VARExV6msewuiNXBq9jDaRsDXTEQ+MWkQbAo0CsMaYd4Is1HsiTTMMerOrgL8ByY0xLYLn92FNM48J4lwHtjDEdgF2Ap8z+PI0LY0VEGgL9scZ7eTRNEC5gjDlkjNlgb6djfYAVnofKY4hINDAY+NDdsZRGRKoDvYHJAMaYbGPMCfdGVSI/oJo98WQw5yak9AjGmFVA4XnOhgHT7e3pwI0VGlQJiorXGPONPe4K4BesWRvcrpj3FuBV4GmsSUk9miYIF7OnKe8M/OreSEr0GtYfbL67A3FCU6zR9lPtKrEP7dH2HscYkwj8f3v3ExpXFUdx/HvQLlIqIoqxEiSg0oWIpbgQu2sVREq7cFGkSv2zclF0I6JCVyJFRKQqiq4Eg5ta0I3S0oIIFgRLYlAXggaNJLZdWPAPQcJx8W7qUN8kGZjpfdbzgWFubkI4D+bxe/e+efe+THOluACct32sbqp1Gbe9UNqLwHjNMAN6DPi4doh+JO0BfrY9UzvLeqRAjJCkTcAHwFO2O7lcuaRdwBnbX9bOsk5XAtuAN8s+Ir/TrSmQC8rc/R6aonYjzRL2D9VNNZiyskHnr3QBJD1PM707VTtLG0kbgeeAg2v9bVekQIyIpA00xWHK9tHaeVaxHdgtaY5mz44dkt6rG2lV88C87ZUR2RGagtFF9wA/2D5bFp48CtxdOdN6/CJpM0B5P1M5z5okPQLsAva5uw933UxzsTBTzrcJ4LSkG6qmWkUKxAhIEs0c+be2X6mdZzW2n7U9YXuS5gbqSdudvcq1vQj8JGlL6doJfFMx0mp+BO6StLF8JnbS0RvqF/kI2F/a+4EPK2ZZk6T7aKZId9v+o3aefmzP2r7e9mQ53+aBbeUz3UkpEKOxHXiY5mp8urzurx3qMnIAmJL0FbAVeLFynlZllHMEOA3M0pxvnVpqQdL7wClgi6R5SY8Dh4B7JX1HMwo6VDNjrz55XweuAo6Xc+2tqiGLPln/U7LURkREtMoIIiIiWqVAREREqxSIiIholQIRERGtUiAiIqJVCkTEACQt93x1eVrS0J7iljTZtvJnRC0j3ZM64jL0p+2ttUNEXAoZQUQMgaQ5SS9JmpX0haRbSv+kpJNlr4ITkm4q/eNl74KZ8lpZguMKSe+UPSSOSRqrdlDxv5cCETGYsYummPb2/O687dtpnux9tfS9Brxb9iqYAg6X/sPAp7bvoFlL6uvSfyvwhu3bgF+BB0Z8PBF95UnqiAFI+s32ppb+OWCH7e/LQo2Ltq+VdA7YbPuv0r9g+zpJZ4EJ20s9/2MSOF426kHSM8AG2y+M/sgi/i0jiIjhcZ/2IJZ62svkPmFUlAIRMTx7e95Plfbn/LPN6D7gs9I+ATwBF/YDv/pShYxYr1ydRAxmTNJ0z8+f2F75qus1ZYXZJeDB0neAZve7p2l2wnu09D8JvF1W+FymKRYLRHRI7kFEDEG5B3Gn7XO1s0QMS6aYIiKiVUYQERHRKiOIiIholQIRERGtUiAiIqJVCkRERLRKgYiIiFZ/AzfW9SzEeM2rAAAAAElFTkSuQmCC\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"4IAANhqlFDT6","colab":{"base_uri":"https://localhost:8080/","height":911},"executionInfo":{"status":"ok","timestamp":1605638771882,"user_tz":300,"elapsed":203794,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}},"outputId":"dbce840a-3811-4a9c-e270-0da6fcb03d13"},"source":["model_6 = SignClassifier()\n","use_cuda = False\n","train_acc, val_acc, train_loss, val_loss = train(model_6, train_loader, val_loader, batch_size=32, num_epochs=15, learning_rate = 0.00001)\n","plot_training_curve(train_acc, val_acc, train_loss, val_loss)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Training Started...\n","\n","U S I N G   C U D A \n","\n","\n","Epoch:  1 \t Training acc: 0.428 \t Val acc:0.4210 \t Training loss:2.0204 \t Val loss:1.8195\n","Epoch:  2 \t Training acc: 0.6194 \t Val acc:0.6264 \t Training loss:1.6027 \t Val loss:1.3874\n","Epoch:  3 \t Training acc: 0.732 \t Val acc:0.7358 \t Training loss:1.2195 \t Val loss:1.0920\n","Epoch:  4 \t Training acc: 0.7969 \t Val acc:0.7916 \t Training loss:0.9724 \t Val loss:0.8946\n","Epoch:  5 \t Training acc: 0.842 \t Val acc:0.8390 \t Training loss:0.7923 \t Val loss:0.7324\n","Epoch:  6 \t Training acc: 0.8715 \t Val acc:0.8627 \t Training loss:0.6537 \t Val loss:0.6217\n","Epoch:  7 \t Training acc: 0.8907 \t Val acc:0.8807 \t Training loss:0.5571 \t Val loss:0.5439\n","Epoch:  8 \t Training acc: 0.9064 \t Val acc:0.8916 \t Training loss:0.4846 \t Val loss:0.4847\n","Epoch:  9 \t Training acc: 0.9168 \t Val acc:0.9010 \t Training loss:0.4293 \t Val loss:0.4416\n","Epoch:  10 \t Training acc: 0.9237 \t Val acc:0.9054 \t Training loss:0.3866 \t Val loss:0.4075\n","Epoch:  11 \t Training acc: 0.9287 \t Val acc:0.9089 \t Training loss:0.3529 \t Val loss:0.3810\n","Epoch:  12 \t Training acc: 0.9334 \t Val acc:0.9156 \t Training loss:0.3256 \t Val loss:0.3588\n","Epoch:  13 \t Training acc: 0.9381 \t Val acc:0.9200 \t Training loss:0.3028 \t Val loss:0.3406\n","Epoch:  14 \t Training acc: 0.9422 \t Val acc:0.9247 \t Training loss:0.2840 \t Val loss:0.3253\n","Epoch:  15 \t Training acc: 0.9456 \t Val acc:0.9259 \t Training loss:0.2682 \t Val loss:0.3133\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"eil1ZkudGAv1","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1605639008776,"user_tz":300,"elapsed":428031,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}},"outputId":"9ca45d5e-ce0a-4b68-a071-d91f7d738ea9"},"source":["model_7 = SignClassifier()\n","use_cuda = False\n","train_acc, val_acc, train_loss, val_loss = train(model_7, train_loader, val_loader, batch_size=32, num_epochs=15, learning_rate = 0.001)\n","plot_training_curve(train_acc, val_acc, train_loss, val_loss)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Training Started...\n","\n","U S I N G   C U D A \n","\n","\n","Epoch:  1 \t Training acc: 0.9681 \t Val acc:0.9528 \t Training loss:0.5516 \t Val loss:0.1858\n","Epoch:  2 \t Training acc: 0.9798 \t Val acc:0.9686 \t Training loss:0.1241 \t Val loss:0.1394\n","Epoch:  3 \t Training acc: 0.9897 \t Val acc:0.9760 \t Training loss:0.0789 \t Val loss:0.1107\n","Epoch:  4 \t Training acc: 0.9939 \t Val acc:0.9835 \t Training loss:0.0570 \t Val loss:0.1137\n","Epoch:  5 \t Training acc: 0.9904 \t Val acc:0.9778 \t Training loss:0.0587 \t Val loss:0.1099\n","Epoch:  6 \t Training acc: 0.9893 \t Val acc:0.9765 \t Training loss:0.0448 \t Val loss:0.1676\n","Epoch:  7 \t Training acc: 0.9572 \t Val acc:0.9514 \t Training loss:0.0359 \t Val loss:0.4780\n","Epoch:  8 \t Training acc: 0.9976 \t Val acc:0.9864 \t Training loss:0.0356 \t Val loss:0.1032\n","Epoch:  9 \t Training acc: 0.9908 \t Val acc:0.9830 \t Training loss:0.0348 \t Val loss:0.0970\n","Epoch:  10 \t Training acc: 0.9901 \t Val acc:0.9812 \t Training loss:0.0289 \t Val loss:0.0845\n","Epoch:  11 \t Training acc: 0.9944 \t Val acc:0.9852 \t Training loss:0.0260 \t Val loss:0.0877\n","Epoch:  12 \t Training acc: 0.9965 \t Val acc:0.9857 \t Training loss:0.0432 \t Val loss:0.1324\n","Epoch:  13 \t Training acc: 0.9996 \t Val acc:0.9896 \t Training loss:0.0117 \t Val loss:0.0953\n","Epoch:  14 \t Training acc: 0.974 \t Val acc:0.9630 \t Training loss:0.0493 \t Val loss:0.2684\n","Epoch:  15 \t Training acc: 0.9835 \t Val acc:0.9728 \t Training loss:0.0243 \t Val loss:0.1676\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 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GMWAgvb7bvTZ/s24LZgxtARHVSmlFIHOG5kMcCQ5DjiY6MoqtBEoJRSjkwEIkJueiLbq3QsgSOtfA6qt4c7CuXR11ZJjHQ9+X06MhGA7UKq01E70L7d8PJ18Nlj4Y5EYRtHKysrNRkEiDGGyspK4uPju/W5cI8jCJtcVyJLvijH7TZERUm4w1GhUlponyu+DG8cCoCsrCyKi4sJxEBRZcXHx5OVldWtzzg2EWSnJdDU4qaspoERqQPDHY4KlbJV9rl8Y9fHqZCIjY1l1KhR4Q7D8RxbNZSbrgvZO5K3RFC9DZp7NlpUqf7GsYmgrQupthM4S1khRMeBcUPV5kMfr5QDODYRDE+JJzZatMHYSRpqoGoL5J1tX2v1kFKAgxNBTHQUWYMTtAupk+xaY5+PmAWINhgr5eHYRAC2ekgHlTmIt31g5LGQOhIqtESgFDg8EdhBZfXah9kpygohMQOSh4ErDyq+CHdESkUERyeC7LQE9jW2UFnXFO5QVCiUFsKwI0EEXPlQsQncOhW5Uo5OBLku23NIVytzgJZGKF8Pwz3LDWbkQct+2Luj688p5QCOTgTZaXYswTYdS9D/7V4P7hZbIgBbNQRaPaQUDk8EI9MGIoJ2IXUC76LowyfZZ1e+fdYupEo5OxHExUQzImUg27VE0P+VFcKAZBjsmc4gMR0GpmmJQCkcngjA04VUSwT9X2khDDsConz+5DPyNREohSYCctITdKWy/s7ttoPJvO0DXq6xmgiUQhMBOemJVNU1UdPQHO5QVLBUbYGmfQd6DHm58qG+EuoqwxOXUhHC8YkgVyef6/+8U0+3LxFkeBqMtVSgHM7xicDbhVSno+7HSgshKhYyxh283zXWPutUE8rhHJ8IvNNR66CyfqysEIYcDjEDDt6fkg0x8VCuJQLlbI5PBIlxMbiS4nRQWX9ljC0RtG8fANuDKF0bjJVyfCIA206gXUj7qdpSqK+AYZM6fj8jT6uGlONpIgCy0xO0sbi/8k493VGJAOxUE9U7oEn//ZVzaSLATkddVtNAQ3NruENRgVZWCAgMPaLj9115gIHKTaGMSqmIookAn/WLdWBZ/1O6CtLHQFxSx+/r5HNKaSIAO6gMoKhCG4z7nbLCr44f8JV+GEiUJgLlaEFNBCJyjohsFJFNInJrF8ddLCJGRAqCGU9nctK0RNAv7d8D1ds7bx8AiI2H1BxNBMrRgpYIRCQaeBiYDowHZovI+A6OSwZuBD4NViyHkpoQy6D4GB1U1t94p57uqkQAtnpIxxIoBwtmieBoYJMxZosxpgmYB8zs4LhfAr8BGoIYS5dEhFxXog4q62/aegx10nXUKyPPNha7tbOAcqZgJoJMwHcdwGLPvjYiMhUYaYx5vasTicg1IrJMRJaVl5cHPlLs+sWaCPqZskJIHgGJrq6Pc+VBayNUbwtNXEpFmLA1FotIFPB74MeHOtYY85gxpsAYU5CRkRGUeHLTEymp3k9zqy5m3m90NqK4vbbVyrR6SDlTMBNBCTDS53WWZ59XMnAEsEREioBjgVfC1WCcnZ5Aq9tQsmd/OC6vAq15v20APlT7APhMPqeJQDlTMBPBUmCsiIwSkQHAFcAr3jeNMXuNMS5jTK4xJhf4BJhhjFkWxJg6lZuus5D2K7vWgWn1r0SQkAaJGTrVhHKsoCUCY0wLcD3wBrAe+JcxZq2I3CMiM4J13Z7SQWX9TGdrEHTGlQ8VXwYvHqUiWEwwT26MWQgsbLfvzk6OPTWYsRzKkOQ44mOjKKrQRNAvlBZCfCqkZvt3vGssrH3JzlYqEtzYlIowOrLYQ0TISUtke5VWDfULZYUwbKL/X+oZ+dBQDXUVwY1LqQikicBHjk5H3T+0tsCutf5XC4GuVqYcTROBj1xXItur6nG7TbhDUb1R+SW0NPjXUOzV1oVUE4FyHk0EPrLTEmhqcVNWE7ZBzioQvCOKu1MiGJQJsQnaYKwcSROBD+1C2k+UFdq1iL1TTPsjKspWD2nVkHIgTQQ+2rqQajtB31a6CoaMh+hudorTLqTKoTQR+BieEk9stGiDcV9mjC0RdKd9wMuVB3t3QOO+wMelVATTROAjJjqKrMEJ2oW0L6veDg17u9c+4JXhqUrSZSuVw2giaCcnPUEHlfVlZX5OPd0RXbZSOZQmgnZy0hLYXlWPMdqFtE8qLbRLTw75yhpIh5Y2GiRau5Aqx9FE0E5OeiL7GluorGsKdyiqJ8oK7Z39gITufzYmDgbnaolAOY4mgnZyXfYLRBep6aNKD7FY/aFk5GsiUI6jiaCd7DQ7lmCbjiXoe+oqoHZnz3oMebnGQuVmO02FUg6hiaCdkWkDEUG7kPZFpd2cerojrnxwN8OeooCEpFRfoImgnbiYaEakDGS7lgj6Hm+PoWETe36ODM+cQ1o9pBxEE0EHdBbSPqq0EFKy7YpjPaWzkCoH0kTQgZz0BF2prC/q6YhiX/EpkDRMp5pQjqKJoAM56YlU1TVR09Ac7lCUvxr32Ube3rQPeLnG6lgC5SiaCDqQk6aTz/U5u9YCpvclAjjQhVQHFSqH0ETQgRydjrrvKevBGgSdceVBYw3s29X7cynVB2gi6IB3OmodVNaHlK6ChHQYNKL35/LOOaTVQ8ohNBF0IDEuBldSnA4q60vKPCOK/V2svivahVQ5jCaCTuSmJ2iJoK9obYbd6wPTPgCQPBwGJGsiUI6hiaAT2ZoI+o7yDdDaFJj2AbClCtdYTQTKMTQRdCI3PZGymgYamlvDHYo6lNJerEHQGVcelGsiUM6giaATbesX68CyyFdWCLGJkDYmcOfMyLMT2DXWBu6cSkUoTQSdaOtCWqENxhGvtBCGHQFRAfxz1tXKlIMENRGIyDkislFENonIrR28f62IrBaRlSLyoYj0YFmp4GgbVKYlgsjmdkPZ6sC1D3i5PD2HtHpIOUDQEoGIRAMPA9OB8cDsDr7onzPGTDTGTAZ+C/w+WPF0V2pCLIPiY3RQWaTbsxWaagPXY8grbRRExWiJQDmCX4lARBJFJMqznSciM0Qk9hAfOxrYZIzZYoxpAuYBM30PMMbU+LxMBCJmTL+IkOtK1J5DkS6QI4p9RcfaNYw1ESgH8LdE8D4QLyKZwJvAN4G5h/hMJrDD53WxZ99BROR/RWQztkRwQ0cnEpFrRGSZiCwrLy/3M+Tey07TLqQRr7TQ3rkPOTzw53blaSJQjuBvIhBjTD0wC3jEGHMpMCEQARhjHjbGjAF+CtzRyTGPGWMKjDEFGRkZgbisX3LTEymp3k9zqztk11TdVFYIGePswvOB5sqDqi12wJpS/ZjfiUBEjgO+Drzu2Rd9iM+UACN9Xmd59nVmHnChn/GERHZ6Aq1uQ8me/eEORXWmt4vVdyUjH9wtULU1OOdXKkL4mwh+CNwGvGSMWSsio4HFh/jMUmCsiIwSkQHAFcArvgeIyFifl+cBEbUaSK7OQhrZasugbnfgG4q9dLUy5RAx/hxkjHkPeA/A02hcYYzpsD7f5zMtInI98Aa29PCkJ4ncAywzxrwCXC8iZwLNwB7gqp7/KIGng8oiXGmQGoq9fGchPfyC4FxDqQjgVyIQkeeAa4FW7J3+IBH5ozHm/q4+Z4xZCCxst+9On+0bux1xCA1JjiM+NoqiCk0EEalslX3uzWL1XYlLhkGZumyl6vf8rRoa7+nqeSGwCBiF7TnUr4kIOWmJbK/SqqGIVFoIg0dB/KDgXcM1VquGVL/nbyKI9YwbuBB4xRjTTAT1+Q+mnPQEirQLaWQKxGL1h+LKtyUCXbZS9WP+JoK/AkXYQV/vi0gOUNPlJ/qJXFci26vqcbv1iyCiNOyFPUXBax/wco2Fpn1QszO411EqjPxKBMaYh4wxmcaYc421DTgtyLFFhOy0BJpa3JTVNIQ7FOWrbLV9DuTU0x3R1cqUA/g7xUSKiPzeO7pXRH6HLR30e94upDrCOMIEu8eQl85CqhzA36qhJ4Fa4DLPowb4e7CCiiQHFrLXBuOIUlYISUMheWhwr5M0FOJSNBEo/715B3zyaLij6Ba/uo8CY4wxF/u8/oWIrAxGQJFmeEo8sdGiDcaRJpgjin15l60s155Dyg/7dsPHD4NEw2FnHhiUGOH8LRHsF5ETvS9E5ATAEfMuxERHkTU4QbuQRpLmBrtOcbB7DHll5GuJQPln3b/BuO1EiP+5tc/0NvM3EVwLPCwiRSJSBPwZ+H7QooowOekJOqgskuxeB6Y1NCUCsO0E+3bB/urQXE/1XWvmw5DxcMadsOlt2Lgo3BH5xd9eQ6uMMZOAI4EjjTFTgNODGlkEyUlLYHtVPaaPZPd+z7sGQahKBG0NxjrCWHVhbzFs/xiOmAVHf8/OivvGbbYEG+G6tUKZMabGZzGZHwUhnoiUk57IvsYWKuuawh2KAts+EDcIUnNDcz3tQqr8sfYl+zxhll3YaPpv7FiXj/8U1rD80ZulKiVgUUS4XJe355BWD0WEskI7v1AgF6vvSmoORA/QqSZU19bMh+GTIX2MfT36VDh8Bnzwe1taiGC9+Z/kmHqS7DTvWAJtMA47dyvsWhu69gGA6BhIG6NVQ6pzVVtg5+dwxMUH7z/7Xtt4/GaHa25FjC4TgYjUikhNB49aYESIYgy7kWkDEdESQUSo3ATN9aFrH/DSLqSqK2sW2OcJFx28PzUbTrzJVhtt/SD0cfmpy0RgjEk2xgzq4JFsjPF3DEKfFxcTzYiUgVoiiAShGlHcXkY+7NkKLY2hva7qG9YsgJHHQurIr753wo02ISz6KbS2hD42P4SokrXv01lII0TZKoiOO9CAGyqufFvEr9oS2uuqyLd7Pexe+9VqIa/YgXD2/9ljlv0ttLH5SROBn3LSE3SlskhQWghDDre9MkLJO0JUq4dUe2sWgETB+JmdHzPufBh9Giy+F+oqQhebnzQR+CknPZGquiZqGprDHYpzGROaNQg60rZ+sTYYKx/GwNoFkHti1/NeidjupE118M4vQhefnzQR+CknzbN+sVYPhc/eYti/J/TtAwADEiFlpHYhVQcrK7QdGDqrFvKVkQ/HXAsrnoGSFcGPrRs0EfgpxzMddZE2GIdP24jiIK9B0BlXng4qUwdbM9/OK3T4DP+OP+WnkJgBi24Btzu4sXWDJgI/HZiOWksEYVO2GhAYOiE813fl2aqhCPoPrMLIGNs+MPo0SEjz7zPxg+CsX0DxUiicF9z4ukETgZ8S42JwJcVpF9JwKi20dfUDwrQmUkaeHcNQUxKe66vIUrwU9u7wr1rI15FXQNZR8NZddsnVCKCJoBty0xO0RBBOZSFag6AzLu+cQ9pOoLClgeg4GHdu9z4XFQXTfwt15fDeb4MTWzdpIuiGbE0E4VNfZe++wtFjyMs7C2m5thM4nrvVjhYeexbEp3T/85lTYeo34dO/RESXZE0E3ZCbnkhZTQMNza3hDsV5ysI0othXogsGDtYGYwXbPoJ9Zd2vFvJ1xl0Qm2hHHId5intNBN3gbTDWgWVhEK6pJXyJaM8hZa2ZD7EJkHd2z8+R6ILTbocti2HDa4GLrQc0EXRDWxfSCm0wDrmyQhiUCYnp4Y1DE4FqbYb1r0D+9N53XDjqu3ZFszduh+bwrf6riaAb2gaVaYkg9EK1WP2huPJsI199VbgjUeGy9T2or+xdtZBXdIxtOK7eDv99qPfn66GgJgIROUdENorIJhG5tYP3fyQi60SkUETeEZGcYMbTW6kJsQyKj9FBZaHWVA+VX4a3odirbbUynWrCsdYsgLgUOOzMwJxv1El2+uoPf28TQhgELRGISDTwMDAdGA/MFpHx7Q77HCgwxhwJvAhERl+qTogIua5E7TkUarvW2pk/I6JE4J1zKPw9PVQYtDTC+ldh3HkQExe48571S0DgjZ8F7pzdEMwSwdHAJmPMFmNMEzAPOGh6PmPMYmOM91v1EyAriPEERHaadiENubJV9jkSSgSpObbvuLYTONOmt6GxJjDVQr5SR8JJP7ZtD1uWBPbcfghmIsgEdvi8Lvbs68x3gEUdvSEi14jIMhFZVl5eHsAQuy83PZGS6v00t+o0AyFTWgjxqXbSt3CLivasVqaJwJHWLICBaTD6lMCf+/gfwOBczwI2oZ3lOCIai0XkG0ABcH9H7xtjHjPGFBhjCjIyMkIbXDvZ6Qm0ug0le8LXwu843qmnRcIdieUaq1VDTtRUBxsX2nUHgrEeRmw8nP1rKN8Anz0e+PN3IZTj1LMAAB8QSURBVJiJoATwvYXL8uw7iIicCfwMmGGMifh1AHN1FtLQam2GXesio33Ay5UPe7ZBc0O4I1Gh9MUbdq6pQFcL+cqfDmPOgCW/hn27g3eddoKZCJYCY0VklIgMAK4AXvE9QESmAH/FJoHQ/dS9oIPKQqziC2htDN/U0x1xjQWMnYdeOcea+ZA0FHKOD941vAvYNO+Ht0O3gE3QEoExpgW4HngDWA/8yxizVkTuERHv5N33A0nACyKyUkRe6eR0vbdrHbx+c6/v4oYkxxEfG0VRhSaCkIiEEcXttXUh1XYCx2iogS/fst08o6KDey3XWDj2Olj5DyheHtxrecQE8+TGmIXAwnb77vTZDlBHXD8UfQBLH7dTx17+DKRm9+g0IkJOWiLbq7RqKCTKCiFm4IFum5Eg/TBANBE4ycaFtmQazGohX6fcAoX/hIU3w3ffsTOWBlFENBaHxDHfhyueh6ot8NeTYdM7PT5VTnoCRdqFNDRKC+1CNMG+C+uO2IH2RkITgXOsmQ8p2XYdgVCIS4az7oGdK2Dls0G/nHMSAdh5w69ZAskj4B8Xw/v392i1qVxXItur6nG7wztjYL9njF2VLBLGD7SXka9dSJ2ivgo2vwtHXBTanmtHXg4jj4G374b91UG9lLMSAUD6GPjuWzDxUnj3VzDvym7/krPTEmhqcVNWo71GgmpPETTujaz2AS9Xnp32wq1Tkvd7618BdwtMmBXa64rYeYjqK+G93wT1Us5LBGBnDJz1GEy/Hza9BY+dCmVr/P64twupjjAOsrbF6iM0EbQ02MVyVP+2Zj6kjQlPz7URk2HaHPj0r7B7fdAu48xEADbbHnMNzFlo/0M/cSas+qdfHz2wkL02GAdVaSFINAwJ02L1XdHVypyhdhcUfWgbicM1oPH0n9s2g0W3BG0BG+cmAq/sY+Ca9yBzGrx0DSz8CbQ0dfmR4SnxxEaLNhgHU0sjbH7H1sXHxoc7mq/SLqTOsO7fdsLDUPUW6khiOpx+B2x938YTBJoIAJKHwrf+DcddD589BnPPg5qdnR4eEx1F1uAE7UIaLM374fnZsPNzO/9KJEpIg4R0nWqiv1sz3y4cM2RceOOY9m2YNNtOThcEmgi8omPg7Hvh0rl22uO/ngxbP+j08Jz0BB1UFgxNdfDspbaXxow/w+Qrwx1R51z5ui5Bf1a9A3Z8AkeEuJG4I9ExcNFfbM1FEGgiaG/CRXDNYrtI+dMz4aM/dVgvl5OWwPaqekyYF53uVxpqbLfebf+Fi/4KU78Z7oi6lpEH5Voi6LfWvmSfQ91bKAw0EXQkIx++965dfOLNO+CFq6Cx9qBDctIT2dfYQmVd1+0Jyk/7q+GZi2DHZ3Dx32DS5eGO6NBcebC/Cuoqwx2JCoa1C2DEFNvlvJ/TRNCZuGS47Gm7ctD6V+Hx0w+6+8t1eXsOafVQr9VXwdMzoHSV/Z1HQlHcHy5vg7GWCvqdys22jSqcjcQhpImgKyJwwg22IXn/HpsMPMXF7DTvWAJtMO6VfeXw1AWwewNc8Rwcfn64I/Kfd/4jrR7qf9YusM8TLgpvHCGiicAfo062XUyHHA4vzIE3fsbI1FhEtETQK7Vl8NT59u7rynmQ97VwR9Q9KSPthHjaYNz/rFkAI4+FlIhfPTcgNBH4KyXTDj476nvw8Z+Je3YWEwY1aomgp/aWwN/PtT0zvvEijDk93BF1X1QUuA7TqqH+Zvd62L3OMdVCoImge2IGwHkP2B4tJct5uvlm4sqWhTuqvmfPNvj7dLsC0zcXQO6J4Y6o51z5Oqisv1mzACTKLknpEJoIemLSFfDdtzAxcfyq+qfwwe9s/3d1aFVb7IC9hmrb9pJ9bLgj6p2MfFuqadIqwn7BGDuILPckO9DUITQR9NSwifz76Od4t3UKvHMPPDgR3n8AGvaGO7LIVfGlrQ5qqoOrXoWs4AyOCSldtrJ/KV0FVZv7Ts+1ANFE0AvDhw7j+80/YssF82HEVHj3l/CHiXZ6a+1bfrBd62wScLfAnNciaw3i3nDpnEMhEaqBm2vmQ1QMHD7j0Mf2I5oIeiHHMx31utjxtsHzmvdg9Cl2wZsHj4A3fmZ7xjhdaaHtHSRRtsF9aATOJtpT6WPsz6WJIDjcrfDuvXBfNiz5jZ2MMFiMsd3Dx5xu55JyEE0EvXBgOmpP/fCIyXY95P/5FA6/AD55BB48El7/MVRvD2OkYVSywo4TiBkI315op2XoT2LiYHCujiUIhtoyO83L+7+FwTmw5P/gLydC0X+Dc73ipXZ9CQf1FvLSRNALiXExuJLivtqFdMg4u/DN9cvsVAnLn4KHpsDL/wsVDqpL3vGZ/Y8cP8gmgf46VN+VpyWCQNuyxH7pFy+DCx+Faz+Er8+3JYK558K//9eOSA+kNfMhOg7yzw3sefsATQS9lJuewMdbKtlR1UGvkfQxMONPcONKOOq7sOZFePgoeOHb3VoRrU8q+i88fSEkuuDbi+wdXX/lyrONxbpsZe+5W2Hxr+3fzsA0OwGkdwbasWfC/3wCJ94Eq+bBnwtg5fOBaT9wt9pqobFn2RsXh9FE0Es3njmW6rpmzv/Th7yzflfHB6VkwfTfwA9Xw/E3wJdvwl9OgOevhJLloQ04FDYvtrOIpmTaJNDfR2e68qC1ya6xrHpu32478eB799ku2tcstqP5fQ1IgDPvhu+/b5ePfPlaO09Vb0va2/4L+3Y5sloINBH02kljM3jthhPJGjyQ7zy1jPsWbaCl1d3xwUlD4Kxf2IRw6m32j+/x0+0ff7DqPUPty7fgucshbRTMeR2Sh4U7ouBrW61Mp5rosa0f2KqgHZ/adSgufNSuLd6ZoRPg6jfg/D/AzlXw6PHw3m973pi8ZgHEJkLe2T37fB+niSAActITmX/d8cw+Opu/vLeZKx//lF01DZ1/ICENTr0VbloDZ/4Cylbbes8nz4FNbwe2q5wxdrDTvt12MNeutXZk7/5qcHeSsHpqw0KYd6X9YrzqNZv4nMA7+ZxONdF9bje8d7+9q48bZKd/n/pN/9YHjoqCgqvh+qV2ssLF98KjJ9g1hrujtdkuAZk/vevk049JX1tYpaCgwCxbFrnTOrz0eTG3L1hDYlw0f7xiCicc5jr0h5rq4fNn4L9/hJoSGD4ZTr7Z3vU07oOmfQeefbcbaz376jz7an2OqzuwbTqpu5Yo+58vPgUGpkJ8qs92in3dtj/14P3xKXbKDa+1L8P879jxAd+Ybxf2cZL7x9pJ82Y+HO5I+o595bDge7BlMUy8zN7dxyX1/Hxfvg2v/wiqt8Hkb8DXfulfN9Av34ZnL7az3447r+fXj3AistwYU9Dhe5oIAu/LXbVc9+wKNpfv46Yz87j+tMOIivLjDqelCVY9Dx/+3r/65ug4+x9nQJJdP2FAkud1IgxI9nnP8zwgyS4E31RnSwQN1XYk9Fe299rXLV2UagBiEw4khYovIOso+PoLjmxsY+75tlriu2+FO5K+oei/9sahvgrO/S1Mvcq/UsChNNXb7qYf/cn+XX7tXtve0NW5X7oONrwOP/nSdgfup8KWCETkHOCPQDTwhDHmvnbvnww8CBwJXGGMefFQ5+wLiQCgrrGFn720mpdX7uSksS4evHwy6Ul+/pG1tsCXb9ilG32/xNt/sUfHBveHaG44kBR8E4Tvtvd1YgZ87Ve9u6Pry167yXY//Om2wHyh9VduN/z3D3b0/eBRcNlTMGxi4K+zay28+kMo/sxOI3/eH+xMse01N8ADY+24nwsfCXwcEaSrRBATxItGAw8DZwHFwFIRecUYs87nsO3AHODmYMURLolxMfzh8skcPSqdu19dy3kPfcjDX5/CtBw/iqrRMZFRRI2Ntw8HTb7VY658mxy/eMO2GSQPtz1c1AF1lfDSNbYdbMIsuOCPwSs9ehuTV8yFt+6GR4+Dk26GE3948F3/5negscZxcwu1F7REABwNbDLGbAEQkXnATKAtERhjijzvBbjVMjKICFcek82RWSn8z7MruPyvn3Dr9HF858RRiN419i8jptjn533WWo5LgUHDbVJIHt7B9gjboB4VHZ6YQ2n7J3b8TH0FnPc7KPhO8EtO3sbk/PPgjdvsyOTVL8AFDx6Y+nzNfEhIh1GnBDeWCBfMRJAJ7PB5XQwcE8TrRawjMlN47YYT+ckLq/jV6+v5bGsV9186iZSBQa7aUaGTfQzcsNI2VNaUQu1OO0VCzU6oLbVtKLVlX224lyhIGupJDiNsd9uDtkdA6si+25vF7YaPHrIz9KZmw3ffDv2Eg8lD4ZInYdKVtjF57nm2MfnUW2HjIjjy8uBXs0a4YCaCgBGRa4BrALKzs8McTc8Mio/lL9+Yxt8+3Mp9izZw/p8+4JErpzExKyXcoalASRtlH51xt0JduU0MByULz3blZtv1saH64M9JFGSMg8ypkDnNPoaMj/wvr/oqeOla2941fqYdZR8fxr9378hkb2Py6n/ZgYAOHUTmK2iNxSJyHHC3MeZsz+vbAIwxv+7g2LnAa/2psbgry7ft4frnVlC5r4k7LxjP14/J1qoidUBTvU0WtWX2uXKTHYFeshzqPdObxwy0d9aZ02yCyCqA1JzIaaje8ZmtCqrbbXvuHP29yIkNbGPyazfZjg7/87EjqufC0mtIRGKAL4AzgBJgKXClMWZtB8fOxUGJAKCqromb/rmS974oZ+bkEfzfRRNJjOsTBTQVLsbYqqfiZXZW15LlULryQDffhPQDJYbMApsgQj2dsjHw8Z/h7bthUCZcOtfGEamMiawEFUTh7D56LrZ7aDTwpDHmXhG5B1hmjHlFRI4CXgIGAw1AmTGmy8nq+0siAHC7DY8s2cTv3/qC0RlJPPr1qYwdmhzusFRf0tpsF1r3lhiKl0P5BsDz/3rwKJsYsgrs87CJEDvQ//Mb4xmcWOvzqDkwoNH72rtdvtFOnTLufDu4bmBqUH5s1X06oCzCfbSpghvmraSusYV7LzqCWVP7+SRtKrgaa2HnSk9y8JQeakrse1Extmtl5jQ79qP9F3nbwztyvRaMH536ogfYQY3xKXD09+GY7zvmTruv0ETQB+yuaeAHz3/Op1urmH30SO66YALxsf2/3lKFSE3pgVJDyXLY+blNAAOS7Rf4QY8kO/XIV/YPOjCK3XdfXFK/HpHbX2gi6CNaWt38/q0veGTJZsYPH8QvL5zA1OzB2pCsAs874WCUzjvpFJoI+pjFG3Zz079WUl3fzChXIrOmZHLR1EyyButIVaVUz2gi6IP2NbawaHUpC1aU8PEW22Xw2NFpzJqaxbkTh5OkPYyUUt2giaCPK95Tz0srSljweQlbK+qIj43inAnDuHhaFsePcRHtz8ymSilH00TQTxhjWLG9mgUrinl11U5qGloYNiieC6dkcvHUTO16qpTqlCaCfqihuZV3N+xm/vJilnxRTqvbcGRWCrOmZDJjciZpiQMOfRKllGNoIujnKvY18u+VO1mwopi1O2uIiRJOGzeEi6dmcfq4IQyI0Z4hSjmdJgIH2VBWw4IVJbz0eQnltY2kJsQyY9IILp6axZFZKQHvimqMocVtECAmWhOOUpFKE4EDtbS6+WBTBQtWlPDm2jIaW9yMyUjkgkkjSIqLobHFTWOLm6YWN40trTS1bdvnptYD+9v2ebbt61b73OrGGBgQHcXErBQKcgdzVE4a03IGM1irp5SKGJoIHK6moZmFhaXMX1HM0qI9B703ICaKuOgo+xxjn+129MH7oqOIi432PHte+7xf09DC8m17KCyuprnV/k2NHZJEQW4aR+UO5qjcNLIGD9TBcUqFiSYC1aamoRnAfoFHRwX8i7mhuZXC4r0sLapiWVEVy7btobahBYChg+JsYsgZTEFuGocPH6RdX5UKkbCsWawi06D44C5mEh8bzdGj0jh6lJ3+2O02fLG7lqVFe2xiKNrD64WlACTFxTAlO5WjctMoyB3MlJGDGThA51dSfVdzq5sXlhUTJXDBpBF9Zmp5LRGokCup3t+WFJYWVbFxVy3GQEyUMCEzpa3EUJA7GFeSTmam+oZVO6q5dcFq1pfWAJAcH8Ml07L45rE5jM5ICnN0WjWkItze/c2s2G5LDEuL9rByRzVNLXZStMzUgYzOSGSU68BjTEYSI1IHarWSQ2yvrGdTeS0nHOYiLibySoz7Glt44I2NPPVxEUOS4/jFjAlkJMfx1EfbWLSmlOZWw8l5GXzr2BxOGzckbH+3mghUn9LY0sqakhpbWiirZUv5PraU11Hb2NJ2zIDoKHLSE2xyyEhkjCuJUZ6EkZ44QBul+7CmFjdLi6pYvGE3izfuZnN5HWBvCn545lhmTc2KmJuAt9ft4uf/XkNZTQPfOCaHn5yTf1D16+7aBuZ9toNnP93GrppGsgYP5BvH5nB5wciQ96rTRKD6PGMMlXVNbCmvY2vFPrZU1LG1vI4tFXVsq6xr66kEMCg+hlEZSYz2KUV4SxUJA/pGna3TlO1tYMnG3by7YTf/3VRBXVMrA2KiOHZ0OqflZzA8ZSCPLNlEYfFexg5J4uaz8/na+KFhS/i7axq4+9W1LFxdRt7QJH49ayLTcjpfFrS51c1b63bx1EdFfLq1iriYKGZMGsFVx+dyRGZKSGLWRKD6tZZWNzurG9hSsc+TKA48Sqr3H3TssEHx5LoSSIqLQUQQ7EJaUSKIgOB5FiFK8Lx/4L0o4cB2FOA5PspzjigRoqN8HiJERQkxntdRYrejooRogejoKKJ990VBdJTd592Oj41iSHI8QwfFkTIwtl+Udlpa3azcUc3ijbt5d0N5W736iJR4Ths3hNPyh3D8YekHJW5jDP9ZU8b9b25kS3kdk0em8tNzxnHcmPSQxe12G55fup37Fm2gscXNDacfxjUnj+nW6P2NZbU8/XERC1aUsL+5lSnZqVx1XC7TJw4LatWXJgLlWPubWimqPJAYNpfvo6iijoZmNwb75WIMGAxu4/vabrs97xmD53HwPrcBPNutxuB2G1rdxrMNLW6355jAGBATxZDkOIYOij/wPCiuLVF490diwqjc18j7X5azeEM5731Rzt79zURHCQU5g9u+/POGJh0y7pZWNwtWlPCHt7+gdG8DJ411ccvZ45iYFdw76y931XLbgtUs27aH40anc+9FR/SqEXjv/mbmLy/mmU+2sbWiDlfSAK44Kpsrj8lmRGo31pX2kyYCpcLImzxa3O4DycFtE4fvdmurTSCt7oMf9U0t7K5ttI+aBnbVNLC7ttE+1zQe1HbiNSAmiqE+CWJIsk0YQ5Pj25JHakIsSXExDIyNDkrScLsNa3fWeO76d7OquBpjwJU0gFPyhnD6uCGcONZFysCedWluaG7lH59s4+HFm9hT38x5Rw7nx2flBbyHTkNzK48s2cyjSzaRGBfDz849nEumZQXsd+Z2Gz7cVMHTHxfxzobdRIlw1uFD+dbxORw3Oj1g19FEoFQ/Vt/Uwu6axrbksKumgfK27UZ213aeMMBWdSUOiCFhQDSJcTEkxkWTMCCGpDjPvgExbfsT42JI9ByXMMB3n92OjY5i+bY9vLthN0s2llOxrxERODIrldPzh3DauAyOGJFCVAAbe2samnni/S088eFWGlvcXFYwkhvPGMuwlPhen/uTLZXc/tJqtpTXceHkEdxx/vigdmneUVXPPz7dxj+X7qC6vpmxQ5L41vG5XDQls9eLUWkiUEq1JYxdNQ3sqm1k7/5m6hpbqG9soa6plTrf58YW6tv2tVDX2EpdUwv+fl0Mio/h5LwMTh83hJPzMkIyHqS8tpGHF2/i2U+3ESXCnONzue7UMaQmdL93zt76Zn69aD3zlu4ga/BA7r1oIqfkZQQh6o41NLfy6qqdPPVxEWtKakiKs2MSrjo+l1GuxB6dUxOBUqrXjDE0NLvZ19hCfVOL59mbOGyi2N/UyuHDBzE1OzVss9HuqKrnD29/wUufl5AUF8O1p4zh2yfk+tVjzBjDa4Wl/OLVdeypb+K7J47ixjPHhq23mTGGz3dU88zH23i9sJR7Zk7giqOze3QuTQRKKcfZWFbL/W9s5O31u3AlxXHjGYdx+VHZnfbwKd5Tz89fXsPijeVMzEzh17Mmhqxrpz8q9jWSFBdDfGzPehZpIlBKOdbybVX85j8b+WxrFdlpCfzorDxmTBrR1k7R0upm7kdF/O7NLxCBm7+Wz1XH50bMoLVA0USglHI0YwzvfVHOb/+zkXWlNYwblswt5+QzJDme2xasZnXJXk4fN4RfXngEmUHouhkJNBEopRS2q+Zrq0v53Zsb2VZZD4Aryc4PdO7EYRE39iKQdBpqpZQCoqKEGZNGMP2IYbywrJhdNQ1cfcIoUhKCOz17pAtqIhCRc4A/AtHAE8aY+9q9Hwc8DUwDKoHLjTFFwYxJKaVio6O48pie9b7pj4LWv0tEooGHgenAeGC2iIxvd9h3gD3GmMOAPwC/CVY8SimlOhbMjr5HA5uMMVuMMU3APGBmu2NmAk95tl8EzpD+XEmnlFIRKJiJIBPY4fO62LOvw2OMMS3AXuArUwmKyDUiskxElpWXlwcpXKWUcqbwDP3rJmPMY8aYAmNMQUZG6IZ5K6WUEwQzEZQAI31eZ3n2dXiMiMQAKdhGY6WUUiESzESwFBgrIqNEZABwBfBKu2NeAa7ybF8CvGv62sAGpZTq44LWfdQY0yIi1wNvYLuPPmmMWSsi9wDLjDGvAH8DnhGRTUAVNlkopZQKoaCOIzDGLAQWttt3p892A3BpMGNQSinVtT43xYSIlAPbwh1HOy6gItxBdENfildjDZ6+FG9fihUiM94cY0yHvW36XCKIRCKyrLM5PCJRX4pXYw2evhRvX4oV+l68faL7qFJKqeDRRKCUUg6niSAwHgt3AN3Ul+LVWIOnL8Xbl2KFPhavthEopZTDaYlAKaUcThOBUko5nCaCXhCRkSKyWETWichaEbkx3DEdiohEi8jnIvJauGM5FBFJFZEXRWSDiKwXkePCHVNnROQmz9/AGhF5XkTiwx2TLxF5UkR2i8gan31pIvKWiHzpeR4czhi9Oon1fs/fQaGIvCQiqeGM0VdH8fq892MRMSLiCkds/tJE0DstwI+NMeOBY4H/7WDxnUhzI7A+3EH46Y/Af4wx44BJRGjcIpIJ3AAUGGOOwE6pEmnTpcwFzmm371bgHWPMWOAdz+tIMJevxvoWcIQx5kjgC+C2UAfVhbl8NV5EZCTwNWB7qAPqLk0EvWCMKTXGrPBs12K/qNqvuRAxRCQLOA94ItyxHIqIpAAnY+ejwhjTZIypDm9UXYoBBnpm0U0AdoY5noMYY97Hzufly3dhqKeAC0MaVCc6itUY86ZnzRKAT7CzGUeETn63YFddvAWI+B45mggCRERygSnAp+GNpEsPYv8w3eEOxA+jgHLg756qrCdEJDHcQXXEGFMCPIC98ysF9hpj3gxvVH4Zaowp9WyXAUPDGUw3XA0sCncQXRGRmUCJMWZVuGPxhyaCABCRJGA+8ENjTE244+mIiJwP7DbGLA93LH6KAaYCjxpjpgB1RE7VxUE8deszsclrBJAoIt8Ib1Td45n+PeLvXEXkZ9gq2WfDHUtnRCQBuB2481DHRgpNBL0kIrHYJPCsMWZBuOPpwgnADBEpwq4ffbqI/CO8IXWpGCg2xnhLWC9iE0MkOhPYaowpN8Y0AwuA48Mckz92ichwAM/z7jDH0yURmQOcD3w9wtctGYO9KVjl+f+WBawQkWFhjaoLmgh6QUQEW4e93hjz+3DH0xVjzG3GmCxjTC62IfNdY0zE3rUaY8qAHSKS79l1BrAujCF1ZTtwrIgkeP4mziBCG7bb8V0Y6irg32GMpUsicg62WnOGMaY+3PF0xRiz2hgzxBiT6/n/VgxM9fxNRyRNBL1zAvBN7N31Ss/j3HAH1Y/8AHhWRAqBycD/hTmeDnlKLS8CK4DV2P9XETXFgIg8D3wM5ItIsYh8B7gPOEtEvsSWau4LZ4xencT6ZyAZeMvz/+wvYQ3SRyfx9ik6xYRSSjmclgiUUsrhNBEopZTDaSJQSimH00SglFIOp4lAKaUcThOBUu2ISKtPd+CVIhKwEc0iktvRLJVKhVNMuANQKgLtN8ZMDncQSoWKlgiU8pOIFInIb0VktYh8JiKHefbnisi7nrny3xGRbM/+oZ6581d5Ht5pJ6JF5HHP+gVvisjAsP1QSqGJQKmODGxXNXS5z3t7jTETsSNdH/Ts+xPwlGeu/GeBhzz7HwLeM8ZMws6TtNazfyzwsDFmAlANXBzkn0epLunIYqXaEZF9xpikDvYXAacbY7Z4JhssM8aki0gFMNwY0+zZX2qMcYlIOZBljGn0OUcu8JZnMRhE5KdArDHmV8H/yZTqmJYIlOoe08l2dzT6bLeibXUqzDQRKNU9l/s8f+zZ/ogDS1N+HfjAs/0OcB20rRWdEqogleoOvRNR6qsGishKn9f/McZ4u5AO9syG2gjM9uz7AXYltZ9gV1X7tmf/jcBjntkoW7FJoRSlIoy2ESjlJ08bQYExpiLcsSgVSFo1pJRSDqclAqWUcjgtESillMNpIlBKKYfTRKCUUg6niUAppRxOE4FSSjnc/wPs0H8HVRtfigAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"x2cplDXTP30G","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1605639014711,"user_tz":300,"elapsed":431931,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}},"outputId":"d2fe6655-49ee-4583-89bc-2cedaa8a1838"},"source":["batch_size = 512\n","train_loader1, val_loader1, test_loader1, classes = get_data_loader (batch_size)\n","\n","model_8 = SignClassifier()\n","use_cuda = False\n","train_acc, val_acc, train_loss, val_loss = train(model_8, train_loader1, val_loader1, batch_size=512, num_epochs=15, learning_rate = 0.001)\n","plot_training_curve(train_acc, val_acc, train_loss, val_loss)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["72\n","72\n","Training Started...\n","\n","U S I N G   C U D A \n","\n","\n","Epoch:  1 \t Training acc: 0.2222 \t Val acc:0.2222 \t Training loss:2.2498 \t Val loss:2.3234\n","Epoch:  2 \t Training acc: 0.1111 \t Val acc:0.1250 \t Training loss:2.1852 \t Val loss:2.0688\n","Epoch:  3 \t Training acc: 0.2222 \t Val acc:0.2222 \t Training loss:1.9656 \t Val loss:1.9673\n","Epoch:  4 \t Training acc: 0.2222 \t Val acc:0.2222 \t Training loss:1.8684 \t Val loss:1.8985\n","Epoch:  5 \t Training acc: 0.3472 \t Val acc:0.2361 \t Training loss:1.7826 \t Val loss:1.8424\n","Epoch:  6 \t Training acc: 0.4028 \t Val acc:0.2639 \t Training loss:1.6923 \t Val loss:1.7778\n","Epoch:  7 \t Training acc: 0.5139 \t Val acc:0.4722 \t Training loss:1.5993 \t Val loss:1.6969\n","Epoch:  8 \t Training acc: 0.6111 \t Val acc:0.5139 \t Training loss:1.4814 \t Val loss:1.6098\n","Epoch:  9 \t Training acc: 0.6528 \t Val acc:0.5694 \t Training loss:1.3425 \t Val loss:1.5136\n","Epoch:  10 \t Training acc: 0.8611 \t Val acc:0.6528 \t Training loss:1.1868 \t Val loss:1.3685\n","Epoch:  11 \t Training acc: 0.9167 \t Val acc:0.6528 \t Training loss:0.9995 \t Val loss:1.2528\n","Epoch:  12 \t Training acc: 0.9583 \t Val acc:0.7222 \t Training loss:0.8260 \t Val loss:1.1538\n","Epoch:  13 \t Training acc: 0.9583 \t Val acc:0.5972 \t Training loss:0.6380 \t Val loss:1.1653\n","Epoch:  14 \t Training acc: 0.8889 \t Val acc:0.5417 \t Training loss:0.5150 \t Val loss:1.3099\n","Epoch:  15 \t Training acc: 0.8611 \t Val acc:0.5972 \t Training loss:0.4701 \t Val loss:1.5832\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"uYdEyVCbQ3nW","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1605639021166,"user_tz":300,"elapsed":436184,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}},"outputId":"fecb8c40-9c1f-48bd-9275-187a0fe3e02b"},"source":["batch_size = 256\n","train_loader2, val_loader2, test_loader2, classes = get_data_loader (batch_size)\n","\n","model_9 = SignClassifier()\n","use_cuda = False\n","train_acc, val_acc, train_loss, val_loss = train(model_9, train_loader2, val_loader2, batch_size=256, num_epochs=15, learning_rate = 0.001)\n","plot_training_curve(train_acc, val_acc, train_loss, val_loss)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["72\n","72\n","Training Started...\n","\n","U S I N G   C U D A \n","\n","\n","Epoch:  1 \t Training acc: 0.2222 \t Val acc:0.2222 \t Training loss:2.2498 \t Val loss:2.3234\n","Epoch:  2 \t Training acc: 0.1111 \t Val acc:0.1250 \t Training loss:2.1852 \t Val loss:2.0688\n","Epoch:  3 \t Training acc: 0.2222 \t Val acc:0.2222 \t Training loss:1.9656 \t Val loss:1.9673\n","Epoch:  4 \t Training acc: 0.2222 \t Val acc:0.2222 \t Training loss:1.8684 \t Val loss:1.8985\n","Epoch:  5 \t Training acc: 0.3472 \t Val acc:0.2361 \t Training loss:1.7826 \t Val loss:1.8424\n","Epoch:  6 \t Training acc: 0.4028 \t Val acc:0.2639 \t Training loss:1.6923 \t Val loss:1.7778\n","Epoch:  7 \t Training acc: 0.5139 \t Val acc:0.4722 \t Training loss:1.5993 \t Val loss:1.6969\n","Epoch:  8 \t Training acc: 0.6111 \t Val acc:0.5139 \t Training loss:1.4814 \t Val loss:1.6098\n","Epoch:  9 \t Training acc: 0.6528 \t Val acc:0.5694 \t Training loss:1.3425 \t Val loss:1.5136\n","Epoch:  10 \t Training acc: 0.8611 \t Val acc:0.6528 \t Training loss:1.1868 \t Val loss:1.3685\n","Epoch:  11 \t Training acc: 0.9167 \t Val acc:0.6528 \t Training loss:0.9995 \t Val loss:1.2528\n","Epoch:  12 \t Training acc: 0.9583 \t Val acc:0.7222 \t Training loss:0.8260 \t Val loss:1.1538\n","Epoch:  13 \t Training acc: 0.9583 \t Val acc:0.5972 \t Training loss:0.6380 \t Val loss:1.1653\n","Epoch:  14 \t Training acc: 0.8889 \t Val acc:0.5417 \t Training loss:0.5150 \t Val loss:1.3099\n","Epoch:  15 \t Training acc: 0.8611 \t Val acc:0.5972 \t Training loss:0.4701 \t Val loss:1.5832\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"-OXGwDMSRfu9","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1605639027017,"user_tz":300,"elapsed":439942,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}},"outputId":"09da29d6-95ef-47f9-a868-4afb0f335d72"},"source":["batch_size = 256\n","train_loader2, val_loader2, test_loader2, classes = get_data_loader (batch_size)\n","\n","model_10 = SignClassifier()\n","use_cuda = False\n","train_acc, val_acc, train_loss, val_loss = train(model_10, train_loader2, val_loader2, batch_size=256, num_epochs=12, learning_rate = 0.001)\n","plot_training_curve(train_acc, val_acc, train_loss, val_loss)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["72\n","72\n","Training Started...\n","\n","U S I N G   C U D A \n","\n","\n","Epoch:  1 \t Training acc: 0.2222 \t Val acc:0.2222 \t Training loss:2.2498 \t Val loss:2.3234\n","Epoch:  2 \t Training acc: 0.1111 \t Val acc:0.1250 \t Training loss:2.1852 \t Val loss:2.0688\n","Epoch:  3 \t Training acc: 0.2222 \t Val acc:0.2222 \t Training loss:1.9656 \t Val loss:1.9673\n","Epoch:  4 \t Training acc: 0.2222 \t Val acc:0.2222 \t Training loss:1.8684 \t Val loss:1.8985\n","Epoch:  5 \t Training acc: 0.3472 \t Val acc:0.2361 \t Training loss:1.7826 \t Val loss:1.8424\n","Epoch:  6 \t Training acc: 0.4028 \t Val acc:0.2639 \t Training loss:1.6923 \t Val loss:1.7778\n","Epoch:  7 \t Training acc: 0.5139 \t Val acc:0.4722 \t Training loss:1.5993 \t Val loss:1.6969\n","Epoch:  8 \t Training acc: 0.6111 \t Val acc:0.5139 \t Training loss:1.4814 \t Val loss:1.6098\n","Epoch:  9 \t Training acc: 0.6528 \t Val acc:0.5694 \t Training loss:1.3425 \t Val loss:1.5136\n","Epoch:  10 \t Training acc: 0.8611 \t Val acc:0.6528 \t Training loss:1.1868 \t Val loss:1.3685\n","Epoch:  11 \t Training acc: 0.9167 \t Val acc:0.6528 \t Training loss:0.9995 \t Val loss:1.2528\n","Epoch:  12 \t Training acc: 0.9583 \t Val acc:0.7222 \t Training loss:0.8260 \t Val loss:1.1538\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"Eg1Eo9TsSGNF","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1605639035152,"user_tz":300,"elapsed":446416,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}},"outputId":"927c585f-7781-45be-9329-f988dcd5608e"},"source":["batch_size = 128\n","train_loader3, val_loader3, test_loader3, classes = get_data_loader (batch_size)\n","\n","model_11 = SignClassifier()\n","use_cuda = False\n","train_acc, val_acc, train_loss, val_loss = train(model_11, train_loader3, val_loader3, batch_size=128, num_epochs=20, learning_rate = 0.001)\n","plot_training_curve(train_acc, val_acc, train_loss, val_loss)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["72\n","72\n","Training Started...\n","\n","U S I N G   C U D A \n","\n","\n","Epoch:  1 \t Training acc: 0.1528 \t Val acc:0.2361 \t Training loss:2.4302 \t Val loss:2.1592\n","Epoch:  2 \t Training acc: 0.25 \t Val acc:0.1389 \t Training loss:2.0850 \t Val loss:2.0458\n","Epoch:  3 \t Training acc: 0.3333 \t Val acc:0.3194 \t Training loss:1.9519 \t Val loss:1.9193\n","Epoch:  4 \t Training acc: 0.3333 \t Val acc:0.3056 \t Training loss:1.8313 \t Val loss:1.8215\n","Epoch:  5 \t Training acc: 0.3889 \t Val acc:0.3333 \t Training loss:1.7380 \t Val loss:1.7353\n","Epoch:  6 \t Training acc: 0.4306 \t Val acc:0.3333 \t Training loss:1.6284 \t Val loss:1.6691\n","Epoch:  7 \t Training acc: 0.5278 \t Val acc:0.3750 \t Training loss:1.5164 \t Val loss:1.5983\n","Epoch:  8 \t Training acc: 0.6667 \t Val acc:0.5139 \t Training loss:1.3988 \t Val loss:1.4934\n","Epoch:  9 \t Training acc: 0.8056 \t Val acc:0.6111 \t Training loss:1.2477 \t Val loss:1.3932\n","Epoch:  10 \t Training acc: 0.8194 \t Val acc:0.5833 \t Training loss:1.0775 \t Val loss:1.3293\n","Epoch:  11 \t Training acc: 0.8472 \t Val acc:0.5278 \t Training loss:0.8917 \t Val loss:1.3087\n","Epoch:  12 \t Training acc: 0.9444 \t Val acc:0.5694 \t Training loss:0.6937 \t Val loss:1.2334\n","Epoch:  13 \t Training acc: 0.9167 \t Val acc:0.5417 \t Training loss:0.4931 \t Val loss:1.4297\n","Epoch:  14 \t Training acc: 0.8889 \t Val acc:0.5556 \t Training loss:0.4216 \t Val loss:1.5069\n","Epoch:  15 \t Training acc: 0.8889 \t Val acc:0.5000 \t Training loss:0.3369 \t Val loss:1.7860\n","Epoch:  16 \t Training acc: 0.9722 \t Val acc:0.5694 \t Training loss:0.3322 \t Val loss:1.6764\n","Epoch:  17 \t Training acc: 0.9583 \t Val acc:0.5139 \t Training loss:0.1995 \t Val loss:1.8160\n","Epoch:  18 \t Training acc: 1.0 \t Val acc:0.5139 \t Training loss:0.1433 \t Val loss:1.9625\n","Epoch:  19 \t Training acc: 1.0 \t Val acc:0.5139 \t Training loss:0.1089 \t Val loss:2.3317\n","Epoch:  20 \t Training acc: 1.0 \t Val acc:0.5278 \t Training loss:0.0791 \t Val loss:2.5838\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"d7sbSiuNxn4t","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1605639043878,"user_tz":300,"elapsed":452858,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}},"outputId":"91ee0edc-5ee4-4e68-d00a-7bbff4688e07"},"source":["batch_size = 64\n","train_loader4, val_loader4, test_loader4, classes = get_data_loader (batch_size)\n","\n","model_12 = SignClassifier()\n","use_cuda = False\n","train_acc, val_acc, train_loss, val_loss = train(model_12, train_loader4, val_loader4, batch_size=128, num_epochs=20, learning_rate = 0.001)\n","plot_training_curve(train_acc, val_acc, train_loss, val_loss)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["72\n","72\n","Training Started...\n","\n","U S I N G   C U D A \n","\n","\n","Epoch:  1 \t Training acc: 0.2639 \t Val acc:0.2500 \t Training loss:2.1756 \t Val loss:2.0511\n","Epoch:  2 \t Training acc: 0.2361 \t Val acc:0.2361 \t Training loss:1.8193 \t Val loss:2.0314\n","Epoch:  3 \t Training acc: 0.4028 \t Val acc:0.3611 \t Training loss:1.5425 \t Val loss:1.9362\n","Epoch:  4 \t Training acc: 0.5972 \t Val acc:0.5139 \t Training loss:1.2452 \t Val loss:1.5973\n","Epoch:  5 \t Training acc: 0.7639 \t Val acc:0.6250 \t Training loss:0.9002 \t Val loss:1.3232\n","Epoch:  6 \t Training acc: 0.9028 \t Val acc:0.6111 \t Training loss:0.5829 \t Val loss:1.1492\n","Epoch:  7 \t Training acc: 0.875 \t Val acc:0.6806 \t Training loss:0.3703 \t Val loss:1.3750\n","Epoch:  8 \t Training acc: 0.7778 \t Val acc:0.5556 \t Training loss:0.2839 \t Val loss:2.2159\n","Epoch:  9 \t Training acc: 0.9167 \t Val acc:0.6111 \t Training loss:0.4969 \t Val loss:1.1959\n","Epoch:  10 \t Training acc: 0.9028 \t Val acc:0.5833 \t Training loss:0.1915 \t Val loss:2.4396\n","Epoch:  11 \t Training acc: 0.875 \t Val acc:0.7361 \t Training loss:0.2618 \t Val loss:1.4909\n","Epoch:  12 \t Training acc: 0.9306 \t Val acc:0.7083 \t Training loss:0.1945 \t Val loss:1.1126\n","Epoch:  13 \t Training acc: 0.9722 \t Val acc:0.7361 \t Training loss:0.1387 \t Val loss:0.8881\n","Epoch:  14 \t Training acc: 0.9722 \t Val acc:0.6944 \t Training loss:0.0845 \t Val loss:0.8998\n","Epoch:  15 \t Training acc: 1.0 \t Val acc:0.6250 \t Training loss:0.0825 \t Val loss:1.0266\n","Epoch:  16 \t Training acc: 0.9861 \t Val acc:0.6944 \t Training loss:0.0688 \t Val loss:1.1265\n","Epoch:  17 \t Training acc: 1.0 \t Val acc:0.7083 \t Training loss:0.0446 \t Val loss:1.1766\n","Epoch:  18 \t Training acc: 1.0 \t Val acc:0.7083 \t Training loss:0.0230 \t Val loss:1.2630\n","Epoch:  19 \t Training acc: 1.0 \t Val acc:0.7083 \t Training loss:0.0160 \t Val loss:1.3665\n","Epoch:  20 \t Training acc: 1.0 \t Val acc:0.7083 \t Training loss:0.0123 \t Val loss:1.4681\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":317},"id":"ZVvik9YwKu82","executionInfo":{"status":"ok","timestamp":1607148603463,"user_tz":300,"elapsed":443,"user":{"displayName":"Eric Ji","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhXIfdv0NwRhitAF6aaeGszxF2imvsyUqr1kk0tmQ=s64","userId":"13561871477578974388"}},"outputId":"ab6fa291-cec6-4a54-a170-2e71b1e2adef"},"source":["from PIL import Image\n","from torchvision.transforms import ToTensor\n","import matplotlib.pyplot as plt\n","\n","\n","img = Image.open(\"yield.jpg\")\n","image = ToTensor()(img).unsqueeze(0)\n","print(image.size())\n","\n","out = model_1(image)\n","idx = torch.argmax(out)\n","\n","classes = ['Speed limit (20km/h)',\n","        'Speed limit (30km/h)',\n","        'Speed limit (50km/h)',\n","        'Speed limit (60km/h)',\n","        'Speed limit (70km/h)',\n","        'Speed limit (81km/h)',\n","        'End of speed limit (80km/h)',\n","        'Speed limit (100km/h)',\n","        'Speed limit (120km/h)',\n","        'Yield',\n","        'Stop',\n","        'End of all speed and passing limits']\n","\n","\n","print(classes[idx])\n","\n","image = ToTensor()(img).squeeze(0)\n","plt.imshow(image.permute(1,2,0))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["torch.Size([1, 3, 32, 32])\n","Speed limit (81km/h)\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7fb39acf9eb8>"]},"metadata":{"tags":[]},"execution_count":51},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":334},"id":"NeDRzyUoUzBt","executionInfo":{"status":"ok","timestamp":1607147337756,"user_tz":300,"elapsed":630,"user":{"displayName":"Eric Ji","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhXIfdv0NwRhitAF6aaeGszxF2imvsyUqr1kk0tmQ=s64","userId":"13561871477578974388"}},"outputId":"8a748d91-a61a-4615-bcd5-ff0156b388ea"},"source":["train_data = iter(train_loader)\n","imgs, labels = train_data.next()\n","\n","print(labels)\n","\n","#for img in imgs:\n","plt.imshow(np.transpose(imgs[21].numpy().astype(int), (1, 2, 0)))\n","out = model_1(imgs[21].unsqueeze(0))\n","idx = torch.argmax(out)\n","print(classes[idx])"],"execution_count":null,"outputs":[{"output_type":"stream","text":["tensor([11.,  0.,  3.,  8.,  3., 11.,  0.,  0.,  2.,  7.,  2., 11.,  3.,  2.,\n","        11.,  5.,  6.,  8.,  2.,  2.,  5., 10.,  0.,  3.,  3.,  9.,  1.,  0.,\n","         4., 11., 11.,  9.])\n","Stop\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAPsAAAD5CAYAAADhukOtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAfxElEQVR4nO2da4xlV5Xf/+vec9+PenZVV7/b7rbBkMGYjkUCImRGM3LQSAYpQvAB+QOaHkWDFKTJB4tIgUj5wEQBxCciE6zxRIRHBhBWhDJDrJGsiRRD29htm8avpt3u6np0VdfzVt3XuSsf6nbStvb/VLmr61YP5/+TSnVrr9rn7LPPWffcu/9nrWXuDiHE7z6Z/R6AEGIwyNmFSAlydiFSgpxdiJQgZxciJcjZhUgJ0W46m9lDAL4JIAvgv7j7VxN3lo08H+WCtiQBMJMLvyf1ul0+tl6W2noJ+wLvhkwmDrc7f890C/cBAOcmwIyassb3182E+1mbzxWMH7QnzUfCOHLkPLd7CecsU6C2ovN+ccJc9Xrhs91L2FeUsL1chk9I3GtQWydOGH+HXP3kXAKAW/i4Oq0Out042NFuVWc3syyAVwH8IYArAH4J4LPu/mvWp1wo+XumTgRtrYRJrB4qB9vX5hZpn+JGhdrWsnxf2Ro1IV++HmyvdninuLBCba3lhLlPGGMtz/e3VAz3i64u0D69aJjbhvkFV8nwOZ6YmAi2X9lcon1y5WPU9p4O77ccccfd2Aw7YLN4gvYZyeepbao0wsfR/AW1za7M837T7WB7tsSPq5VbC7b/9sIVbDaawZO2m4/xDwJ43d0vunsbwPcBPLyL7Qkh9pDdOPthAG/d9PeVfpsQ4g5kV9/Zd4KZnQVwFgBy2T3fnRCCsJs7+zSAozf9faTf9jbc/TF3P+PuZyI5uxD7xm6c/ZcATpvZSTPLA/gMgCdvz7CEELebW77VunvXzL4A4G+wJVg97u4vJ/XJWAYlspK8XuHSRG9tI9g+NMxXTddLm9Q2VuKaV9X56nNjM7z6nOmUaJ9uJ6wkAEAme43aRmr8fbixEJa1AGA8G1YMlkarfBzhxWAAQHeNGy2qU1ubyHn5iKsMJ8pcFC1N/GNqW29dpTZfDS8jHYz5OcvV+bW4MdyitswV3i9zNXxeACAmqkZ1iSsQPXIJWJcrPLv6XO3uPwPws91sQwgxGPQEnRApQc4uREqQswuREuTsQqQEObsQKWGwT7lkYvSq4cCQoQ0uh/WisExSzHJ5LdvmslArW6S2mSgcYAAAhdxqsL1X5lJYLe5QW8a4TPLWAp+P+tA6tTXmw0EtlcPLtM/GEg+4GDnQpLb1tTeprWDh+Z8s8OAlHztAbZvFsPwKAJGPUttYPXzczVXep9Thcx/HXO6NS8epbWiIb3MzCst568bHmIlIYFNCpJzu7EKkBDm7EClBzi5ESpCzC5ES5OxCpITBrsb3DNkGCYRo81VOy4UDDK6v8OCOVsRXJQttvjJaWuEr9V2EV01zeR7c0azyle5cmwdVlA/yU5Nb49usDYVtldoQ7RP3eJqr5QIPTjlV5kE+zYPjwfb8Il9Vr3R5yqfeJldJ1vk0YmQtPMZogqsk621+DdQSlJd4mo+xXUkIbFoMqzyo82t4JA4rOdmE27fu7EKkBDm7EClBzi5ESpCzC5ES5OxCpAQ5uxApYaDSm6GHLMKBFY2EKieZfFgKiTa5XDdkXAZBQh6xVkIOrxzCslYzy/t0S7xqyuHJg9SGMT4fhxPyuLUq4VPaNi55TaxyKbJb5PeDjSI/7jhcgQjR+Bjt00uontPY5Jdqbe0ota1Vw8ddWeHSW5uUGwOAbpvb6hVeNqG9cJnaxi0cvLSwkRDwVAqPP6m0me7sQqQEObsQKUHOLkRKkLMLkRLk7EKkBDm7EClhV9KbmV0CsAYgBtB19zNJ/9/NAAuVsCQTx1wqq7fC70krtYTyT3NcTsp1eemf4iYXL9rD4VJIxR6Pkjp97D5qm0ooedW4wmUXXyf5xwCUi3PBdgPf12qHz+OVOX6JVMs8B2CnES5d1MnySLkuieQCgBMf+qfUhmF+7VgxfGxxlpe18m5CHrdlfl2NFPl8rOf4NdLIhcc/tMQj7FAOR8oZ9qj8U59/7u786hNC3BHoY7wQKWG3zu4A/tbMnjWzs7djQEKIvWG3H+M/6u7TZjYB4Odm9ht3f/rmf+i/CZwFgFxusIlxhBD/n13d2d19uv97HsBPADwY+J/H3P2Mu5/JRvrWIMR+ccveZ2YVM6vdeA3gjwC8dLsGJoS4vezmc/UkgJ+Y2Y3t/Dd3/59JHTLmqGTCEWeZIpfDWhthOeFQnss414/xyLZ8mydsjOpcPsFmeIwHPngX7ZIrcIln9ldPU9vCNC+7VHAeHbaxGZbYigcSEnp2uZyEmN8P1loJZa8QlpN6DX7JVYyXf7r4DL+PHPwQjzYbzobLUNk6l1hb5DwDQDfPo+VeneXXXGOG29YQlkvdeALO6nL4GnYSbQjswtnd/SKAD9xqfyHEYNGXaCFSgpxdiJQgZxciJcjZhUgJcnYhUsJAH2mLexmsNMNyWa7Jh9IkKkl+jUdyNfNcQrOI9+uVeP24Q4fDyRJHnO8rfvFZalv/LZfQmkM8esnmExJcZsM11ja7POFkUs25oRV+Xq5l+XFv5sNSn1V4zbmVaJHaihk+/pVXwtIVAHROHgu210gtOgAYW79GbVd/w20zS1wu9RyX+g5E4WtuPsevxbWNcOxZnBD1pju7EClBzi5ESpCzC5ES5OxCpAQ5uxApYaCr8Q5HNw6v/JacryKOtMMrmR7xgIVyzMsuxaUGtR2thkvxAMDBI+GSTK3zL9M+M4t89bbjfPW23uTBOo0CLw1VrYQDUBrgQSvddV5OarPC5zGX52pCLhc+N5mEkkadDL8GCgnBOqsJRY/s+dlge/Uw317v2L3UVh3lysWw8/MSLc9T23USlJMZ5mpHvRQ+n9OZhPx51CKE+J1Czi5ESpCzC5ES5OxCpAQ5uxApQc4uREoYqPSWg2OSyCT5Ef7Qv8fhPG7N6zwfWKnO38cqYyepbfzQJLWtzJ4PtrffDJfiAYA4IZihYNzWjrlENWo8YMR74QAPX+KBJN0CD06JwWXK8no4vxsA5ArhS2u9zcsgoczHuFDn57o+z2XKXCkslU3PXKJ9Dibk3avfywNo8sPcneYScu9FM2EJs5zj0mZxbTTYnnG+H93ZhUgJcnYhUoKcXYiUIGcXIiXI2YVICXJ2IVLCttKbmT0O4I8BzLv7+/ttowB+AOAEgEsAPu3uS9ttK7YslvLhyKDS5jTt5+1whM9sh0tG91bDuccAYOrQKWorrb1GbasXwxFlcYPLUxjjkVCbPZ4Lb7zN34e7zYT36HZYrhmp86i3xVUebdYZ5WWj1iMueU32iJw0xI/5+hIvd1ROKNdUTsgp2LXwXBWu8ei1hc6b1JZb4/Lg2PEPUltUW6a2Tjd8/bSu8WjEaRK52QGP5tvJnf0vATz0jrZHATzl7qcBPNX/WwhxB7Ots/frrV9/R/PDAJ7ov34CwCdv87iEELeZW/3OPunuM/3Xs9iq6CqEuIPZ9eOy7u5mRr/0mdlZAGcBIMoN9OlcIcRN3Oqdfc7MpgCg/5vm3HH3x9z9jLufiSI5uxD7xa06+5MAHum/fgTAT2/PcIQQe8VOpLfvAfg4gHEzuwLgywC+CuCHZvZ5AG8C+PROdpb1GCPdsASxWuCJHptxOKps4gSPoDp+8i5qq7V41NhbCeWaukth+ao2mRCxt8hlrVKNv9fON3mywajKJapcOXxKS8tcGc3XwlGFABCtcMmucoBLXpnM0WB7r5UQqTjGk3N2q3yuGkNcZj1ZDEepxS0eqTh/hctk65uXqW1tmS9d1cd5tFy7GD7XxTpPUnmgFZYOZ51LlNs6u7t/lpj+YLu+Qog7Bz1BJ0RKkLMLkRLk7EKkBDm7EClBzi5EShjsUy49IEMS72VrPJqoHIUTRL7v6Bjtk3Uurbz2v/+G2kpc8UIUhRMsdrI8gip3iEfmNVtc8poa5nJe/SSP2usOheXIyYQkissXX+Dbi6eorfLe91JbfCC8v5VXL9I+pcu89p0f+gC1DZ84Tm1rmXCtt8zCQdqnNnSF2pqvUBOKuXBUJAAUslwmPlIOX3QXl/i1kxkPJyT1adV6EyL1yNmFSAlydiFSgpxdiJQgZxciJcjZhUgJA5XeehnDaiUsDeS7PGKo2AlLE3nj0V9Liy9RmyckbCzVeB244Y89EGzPJtQhs6s8wq65wBNV9oYq1FY+wCMEu5PhKLW58zyirJHlEVnH3svln16eb/PK+XBE3PBwOBoOANZO8mSJQ0s8Aqy0HK57BgAzly8F21vlCdrnriKf37jwBrWtx2Vq21zldfHmGuFzne/xY95ohqVZd56kUnd2IVKCnF2IlCBnFyIlyNmFSAlydiFSwmADYTJAphTeZX0l/GA/AMxOhFdHmys8kKS0yleRo3EecFE+zYNrygiP8bXneH63yXG+6lut8PG/cZ5HXETHeCBMnAuvxncuX6B9LGEVuQ2+r4Vrl6gtvz4TbO/l+b6GEoJFVgo8wKNmPOipsxEOsIricHkqANgY50EyjWG+qr6R4cFcJ7J8ldxHwmOZXuLXR7O1EGzvJZQU051diJQgZxciJcjZhUgJcnYhUoKcXYiUIGcXIiXspPzT4wD+GMC8u7+/3/YVAH8C4EYkxJfc/WfbbSvrGdS7YXmlaVx6M4QljY0KD0CprXOpplPgJZkaNS691dthucOuT9M+i3M8mGEpP0dt3SbPZzaT44EaJ+MDwfZMhctCuMpLPPW6vJxQIeFesbkZljfdeLBLpsAl0eoMDyhamOKBMJV7woE3h7pcCuvW+LWzfI3PY2mEz+MKeNBTuxuWiaMy75Mnlz4f+c7u7H8J4KFA+zfc/f7+z7aOLoTYX7Z1dnd/GsD1AYxFCLGH7OY7+xfM7LyZPW6WEFguhLgjuFVn/xaAuwHcD2AGwNfYP5rZWTM7Z2bnOl3+fU0IsbfckrO7+5y7x+7eA/BtAA8m/O9j7n7G3c/kIr4oIoTYW27J2c3s5jIhnwLAc0AJIe4IdiK9fQ/AxwGMm9kVAF8G8HEzux+AA7gE4E93tLdeD94IyyubNR4NNebhSJ6ok/BeleUiRMUTZL6LXOJZvSec9+vYfffRPs21y9Q2P8fLRrXjArXV3ypR2wYp/zTX4ssqBePRVesJOc14XCEwHYfn/0CbS4oW8Yit+QKXG0/luZTqh8KyXOsCv/Svnz9Hbb1Vfu3kwM/Z0CivK9bJE3mzwecjPxrOW2dZ7hPbOru7fzbQ/J3t+gkh7iz0BJ0QKUHOLkRKkLMLkRLk7EKkBDm7EClhoAknPZtFrx4WbEaLPDFj73h4mNUNXiIpV+FP6zWucOnKWr+hNrxwT7jPqcO0S+3UvdTWyfIxZme4ZLdu4WSOADA1fnd4X/M8omxmNkH2jHlYRL7A5byxTlgq2+jycYx3efTaXPZVaousRm2Ni+HIwmv/hx/XTOMqtR0gCT0BoD3Ejy3Kc5l1Y5mEsE1w98zMhqVN42qd7uxCpAU5uxApQc4uREqQswuREuTsQqQEObsQKWGwtd7iGPFqOKpsZY0nNmyvhqWyzhSXjD40ziO5Zja4bbPLpyS7GpblCqs8qWR0hMuDtZNT1LY5do3avMClw1wrHJXlG/x9fcx44s6VOS4ndQ7xY4uJBhQN8Si66x0eEVdb4jXWFme5ZJchNedWR7l8eaDK4/mW4oRzvckl2HwhnAgUAEql8BxfnOfjyA2ThJ78sHRnFyItyNmFSAlydiFSgpxdiJQgZxciJQx0Nb6XMWzWwoEE6y2eM24sF15irHjC8O0ENeUyPJ9Zt8JXn08dOhhsf22Gl3GK2nzlfMrCOe0AIJvwPrzRXaK2+Ua4XwS+r06Tr/x3c6vUVke4tBIAlE6fDrYXjh2ifTZevUht002+Cj5u/HyWonCw0ajxwKvpWV7Oa3gsfA0AwGqeqwndVZ4nj+XeGz/Og26iTli9ihJyL+rOLkRKkLMLkRLk7EKkBDm7EClBzi5ESpCzC5ESdlL+6SiAvwIwia1yT4+5+zfNbBTADwCcwFYJqE+7O9eEAPQcaDXDgRDVTS7x9ErhkkbtOE/7XEsoDVWMeCBMJuIBC51DYdnodJ3LMdlieOwAEIHLMUvgQT4rHX7a7ovCcs38QV6aqHOZjyN7hctQrTYfY+5kOBeeJchaS1e4hFnd4IFS5Td5SaZZUtqqdo2XY6oVeU47gF/itTyX82oRn+NmOywdXn+Jl6FaaoUDg5qbfJ52cmfvAvhzd78PwIcB/JmZ3QfgUQBPuftpAE/1/xZC3KFs6+zuPuPuz/VfrwG4AOAwgIcBPNH/tycAfHKvBimE2D3v6ju7mZ0A8EEAzwCYdP9/0bOz2PqYL4S4Q9mxs5tZFcCPAHzR3d/2BdvdHQh/ATWzs2Z2zszOxV2eJ10IsbfsyNnNLIctR/+uu/+43zxnZlN9+xSA4MPL7v6Yu59x9zPZiGcpEULsLds6u5kZtuqxX3D3r99kehLAI/3XjwD46e0fnhDidrGTqLePAPgcgBfN7Pl+25cAfBXAD83s8wDeBPDp7TaU7WVQbYalqJUJ/hF/eDEsy0VDfPiFUjjXHQC0ejwy6HqLlxmqXw1LMpXjXKrZaPMoutnnX+P95laobajMJcfp34Tz5MUbPLItE/OaQWvLCTLlHJfRojfDEWxJ49hoj1NbYZRLZY03XqK2VRLFWIx53r31Ko82y+SOU9vB4yeorZxbprbfzoSvuSsJEWzD1XAkqK3w49rW2d397wGwvf7Bdv2FEHcGeoJOiJQgZxciJcjZhUgJcnYhUoKcXYiUYFsPvw2GQqngh06FSx5Fy1wyAEnaeOQwl7yOHHs/tS013uK7eoPXzylig3Qi7QCWSjwZYiGepbb8Ou+3kRABlquEZSPjlZpgziWe/CaXRLOdBrU1uuEHqEbG+fUWd3jkY2uVy43tPI/oa+XC0tvQOj/mxdIRarv3I3dRW77Go95+/RaPYLtyLXzc2XWeJLSxHj6uhaWraHfC2Vt1ZxciJcjZhUgJcnYhUoKcXYiUIGcXIiXI2YVICYOt9dZ1bMyHZaPhGpdWlsphWS67zKOTinddp7bSCS5pVBvhRH4AgEZYhrq+wqPG8lcWqK2dEL2Wifn4ozx/j+42w2OsbfBcAkujo9QWO5dEh2Jex65IEjP2FrlMtlzhyT5zVS5vFtd4Us9MNTz+zYTkkAfv4QlEm3UemXf1tV9R2+Ycl0snsmGbV/n14eVwss/MGq+Jpzu7EClBzi5ESpCzC5ES5OxCpAQ5uxApYaCr8ZmsozgcfoA/2x2h/SZXwgEXm3Ue0HLpDZ6z7PDIYWobOXgPtVVWw/ndRhfWaJ+ZAzyQJGryYy5XuGKwuNCitupIONAk53zlHE2e767s/H7QGOFj7JD99Rb5eSku8Xx92UyCYpAQ5BPF4fnPJQSt5EiZLwC4tsDnajXP8/XlKrxUVrUansdGm6/gv5dENi1k+PnSnV2IlCBnFyIlyNmFSAlydiFSgpxdiJQgZxciJWwrvZnZUQB/ha2SzA7gMXf/ppl9BcCfALhRz+dL7v6zpG1lekB5LZz7q1riOcFyJI9bXOSyli/z/Ghv9a5SW7PI5Y5TtdPB9voZHojhr7xJbQsRL4W0vsClshyfKrQ3w7JcpppQXqvF99Vqcqms1+LbNBKs0y7wuarUuYa2scbnqtjjUhmGTwWbJ993gnaZm+PBJF7i98fRLpd0j0zxfIkLk2E3PLjOg4YyY2G5N7rIA552orN3Afy5uz9nZjUAz5rZz/u2b7j7f9rBNoQQ+8xOar3NAJjpv14zswsA+FuYEOKO5F19ZzezEwA+COCZftMXzOy8mT1uZvxxMCHEvrNjZzezKoAfAfiiu68C+BaAuwHcj607/9dIv7Nmds7MznV7/PuwEGJv2ZGzm1kOW47+XXf/MQC4+5y7x+7eA/BtAA+G+rr7Y+5+xt3PRAnP7Qoh9pZtvc/MDMB3AFxw96/f1H5zaZdPAXjp9g9PCHG72Mlq/EcAfA7Ai2b2fL/tSwA+a2b3Y0uOuwTgT7fbkEWG3EQ4b1wuoaRROR+Wf5o9Lk10x3musN4Gj06yVV6CaC4O5wQbPsXXK4sPhMtdAUD1VzxqD5XL1NTs8txkNXZGWzwybz7Ho+jKXT6P7TqPUhtdDUdydbnyhmtdHlEW1fmSUHOER5S97/TvBds7cThHHgAcPTpEbZ2Y56er3Z1wcCQ3IAAcicMn7XqZz2+5FL6uctlf0D47WY3/ewAhZTdRUxdC3FnoS7QQKUHOLkRKkLMLkRLk7EKkBDm7EClhoAknI89ishWOYFuZTIgcq4f7TDmXjPIJWQivH+QRce68X2clLIVkNrhc1y0co7ZDp7iMc3k5nDgSALqXZ6ntKpG8SsblpANVHlHWdi7zbVT4/K/mwnKpGw/Zm6zz0lvdwhi11Y/yeYSFpd7MET4fh43LpfkqLzmGXMI5q3Pbcis8JwcLvBxWIQrLx1HE51d3diFSgpxdiJQgZxciJcjZhUgJcnYhUoKcXYiUMFDprZfLYXMqXGNrrJ0QiZYLR0ONVLk00crxBH8TXX7YvVWesG9jKmxrJch8nu1QW1ThEs+BMR4FuDmREO231g22D9W5dNVbfYDayhl+XoaGeCTdaj6cxLJ6nc9HL8vPS67JI9taCfXocuXwfEzUwnIuAMR5XsNubY2Pf5NPBzK98DgAoNsJS59LVR5xuNFYDLa3EvxId3YhUoKcXYiUIGcXIiXI2YVICXJ2IVKCnF2IlDBQ6S2fdRwdIhLEJo94WsF6sD2bIHUUczw6qTfK+5nxxIAjHo6Uin2Z9ml3+HHVIx4tN18IS5QA0O1y+We0FJYHrcvnY2GKj3+lwcdfyU1SW8fCJ6eV5RLae/4Rj0TzHk84GYHLrD4brtv28isv0j4bizwCc36ZJ6ocjvixRT1+rrNdEsEGLr35RDjisN3iSSp1ZxciJcjZhUgJcnYhUoKcXYiUIGcXIiVsuxpvZkUATwMo9P//r939y2Z2EsD3AYwBeBbA59ydP4UPIIoKGD1wV9C27OGcZQBwtB1ewV9c5AEoownBEdl1HhSSsNCNbu96sL3Z44ddjXhJo1aBB1ycGuUruxNT91Dbei0cJJMr8Nxk9y7yuW8nzEdc5LJGlAmrAuNVHsTTmeSKwWSOr0yvLs1R2+XqRrD98OzdtM/rV1+ntqGYB0rNOp+scj6sKAFAsRT2iQ//s4/QPpWV8Or+c8++Sfvs5M7eAvD77v4BbJVnfsjMPgzgLwB8w91PAVgC8PkdbEsIsU9s6+y+xY23pVz/xwH8PoC/7rc/AeCTezJCIcRtYaf12bP9Cq7zAH4O4A0Ay+5+4/P1FQC8lKkQYt/ZkbO7e+zu9wM4AuBBAO/Z6Q7M7KyZnTOzc5tN/nSPEGJveVer8e6+DODvAPwTAMNmdmOB7wiAadLnMXc/4+5nSsWE+tVCiD1lW2c3swNmNtx/XQLwhwAuYMvp/2X/3x4B8NO9GqQQYvfsJBBmCsATZpbF1pvDD939f5jZrwF838z+A4BfAfjOdhvKRIbyeFh6qURcWhnNnQq2nxhOKIPU41Ey9S6XjOI8D3RY7oTLDI10eH6x9VH+1eVwk0tvXV51Cevg/YZI4EquzqW34eM8H9viNV62KK7xyycfh6WmSpvn1lvL85x8cZXLgyMxXy6qIiyzvhZdoH3ml2b4OBpcejva5mPsNrhMXCqHJceRCX5cQ/Xw3Ed5Pr5tnd3dzwP4YKD9Ira+vwsh/gGgJ+iESAlydiFSgpxdiJQgZxciJcjZhUgJ5s6lldu+M7NrAG6E5YwDWBjYzjkax9vRON7OP7RxHHf3YF20gTr723Zsds7dz+zLzjUOjSOF49DHeCFSgpxdiJSwn87+2D7u+2Y0jrejcbyd35lx7Nt3diHEYNHHeCFSwr44u5k9ZGavmNnrZvbofoyhP45LZvaimT1vZucGuN/HzWzezF66qW3UzH5uZq/1f/N6R3s7jq+Y2XR/Tp43s08MYBxHzezvzOzXZvaymf3rfvtA5yRhHAOdEzMrmtkvzOyF/jj+fb/9pJk90/ebH5hZQmxkAHcf6A+ALLbSWt0FIA/gBQD3DXoc/bFcAjC+D/v9GIAHALx0U9t/BPBo//WjAP5in8bxFQD/ZsDzMQXggf7rGoBXAdw36DlJGMdA5wSAAaj2X+cAPAPgwwB+COAz/fb/DOBfvZvt7sed/UEAr7v7Rd9KPf19AA/vwzj2DXd/GsA781I/jK3EncCAEniScQwcd59x9+f6r9ewlRzlMAY8JwnjGCi+xW1P8rofzn4YwFs3/b2fySodwN+a2bNmdnafxnCDSXe/kTVhFgAvkbr3fMHMzvc/5u/514mbMbMT2Mqf8Az2cU7eMQ5gwHOyF0le075A91F3fwDAvwDwZ2b2sf0eELD1zo6tN6L94FsA7sZWjYAZAF8b1I7NrArgRwC+6O5vq4IwyDkJjGPgc+K7SPLK2A9nnwZw9Ka/abLKvcbdp/u/5wH8BPubeWfOzKYAoP87XFh8j3H3uf6F1gPwbQxoTswshy0H+667/7jfPPA5CY1jv+akv+93neSVsR/O/ksAp/sri3kAnwHw5KAHYWYVM6vdeA3gjwC8lNxrT3kSW4k7gX1M4HnDufp8CgOYEzMzbOUwvODuX7/JNNA5YeMY9JzsWZLXQa0wvmO18RPYWul8A8C/3acx3IUtJeAFAC8PchwAvoetj4MdbH33+jy2auY9BeA1AP8LwOg+jeO/AngRwHlsOdvUAMbxUWx9RD8P4Pn+zycGPScJ4xjonAD4PWwlcT2PrTeWf3fTNfsLAK8D+O8ACu9mu3qCToiUkPYFOiFSg5xdiJQgZxciJcjZhUgJcnYhUoKcXYiUIGcXIiXI2YVICf8X5BQ4H60hAGgAAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]}]}
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diff --git a/007 Final Test.ipynb b/007 Final Test.ipynb
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+{"nbformat":4,"nbformat_minor":0,"metadata":{"accelerator":"GPU","colab":{"name":"007 Final Test.ipynb","provenance":[{"file_id":"1E6w6-XOv9Eu0VaXKLbZDV-Cl4NbLu5Jg","timestamp":1607208812230},{"file_id":"18YGciOMG_OZDR7-rMwUU2D6by8VgEql5","timestamp":1606610875693},{"file_id":"1YKQ3ItCmgfpn66WhGy9ey9jpWQ5o2nus","timestamp":1605399621858}],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","name":"python3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"Qjc-UWAOCify"},"source":["# Data Preprocessing\n","\n","Tasks\n","1.   Load Kaggle traffic sign data from drive\n","2.   Unpickle files\n","3.   Filter out classes not related to speed\n","4.   Normalize number of samples per class\n","5.   Split into train / val / test sets\n","6.   Shuffle datasets\n","7.   Resize\n","8.   Normalize pixel values\n","9.   Make pytorch dataloaders"]},{"cell_type":"code","metadata":{"id":"cPKNBe0NUG71"},"source":["import pickle\n","import numpy as np\n","import torch\n","import torchvision.transforms as transforms\n","from torch.utils.data import TensorDataset, DataLoader\n","import matplotlib.pyplot as plt"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ljaGNZa8HUcq","executionInfo":{"status":"ok","timestamp":1607488975435,"user_tz":300,"elapsed":22118,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}},"outputId":"79c0d1ad-1e68-4bea-a1e3-8bf922043004"},"source":["# mount drive\n","from google.colab import drive\n","drive.mount('/content/gdrive')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Mounted at /content/gdrive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Dy0lMjtXasiZ"},"source":["# Data directory\n","# Change this as needed\n","# data_dir = '/content/gdrive/My Drive/2020-21 School Year/APS360/APS360 Project/'\n","data_dir = '/content/gdrive/My Drive/APS360 Project/'"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Si9g90e0Ud0i"},"source":["# Data loaders\n","\n","def get_data_loader(batch_size):\n","    ''' The Kaggle dataset is split into three pickle files: train.pickle,\n","    valid.pickle, and test.pickle.\n","    The function will combine the three datasets and resplit them such that the\n","    resulting split is approximately 70% training, 15% validation, and 15% testing.\n","    The function filters classes 0-8,13,14,32 only, as they are related to speed.\n","    The splitting ratio will be applied to each class, to avoid imbalance of \n","    classes in the training/validation/testing samples.'''\n","\n","    classes = ('Speed limit (20km/h)',\n","            'Speed limit (30km/h)',\n","            'Speed limit (50km/h)',\n","            'Speed limit (60km/h)',\n","            'Speed limit (70km/h)',\n","            'Speed limit (80km/h)',\n","            'End of speed limit (80km/h)',\n","            'Speed limit (100km/h)',\n","            'Speed limit (120km/h)',\n","            'Yield',\n","            'Stop',\n","            'End of all speed and passing limits')\n","    # Index of chosen classes in the original dataset\n","    # In our reconstructed dataset, the same classes will have indexes 0-11\n","    original_labels = (0,1,2,3,4,5,6,7,8,13,14,32)\n","    # load pickle files\n","    # will combine datasets from three seperate pikcle files\n","    \n","    with open(data_dir+'train.pickle', 'rb') as file:\n","        data1 = pickle.load(file)\n","\n","    with open(data_dir+'valid.pickle', 'rb') as file:\n","        data2 = pickle.load(file)\n","\n","    with open (data_dir+'test.pickle', 'rb') as file:\n","        data3 = pickle.load(file)\n","\n","    images = np.concatenate((data1['features'], data2['features'], data3['features']))\n","    labels = np.concatenate((data1['labels'], data2['labels'], data3['labels']))\n","    images = images.astype(np.float32)\n","    images = images / 255.0\n","\n","\n","    # normalization\n","    for i in range(len(images)):\n","        images[i] = (images[i]-np.mean(images[i]))/np.std(images[i])\n","\n","\n","\n","    # shuffle\n","    np.random.seed(9001)\n","    indices = list(range(images.shape[0]))\n","    np.random.shuffle(indices)\n","    images = images[indices]\n","    labels = labels[indices]\n","\n","    # split into train /  valid / test\n","    train_split = 0.7\n","    valid_split = 0.85\n","\n","    train_images = images[0:int(train_split*images.shape[0])]\n","    train_labels = labels[0:int(train_split*images.shape[0])]\n","\n","    valid_images = images[int(train_split*images.shape[0]):int(valid_split*images.shape[0])]\n","    valid_labels = labels[int(train_split*images.shape[0]):int(valid_split*images.shape[0])]\n","\n","    test_images = images[int(valid_split*images.shape[0]):]\n","    test_labels = labels[int(valid_split*images.shape[0]):]\n","\n","   # sort into classes\n","    class_train_images = []\n","    class_train_labels = []\n","    for i in range(len(classes)):\n","        class_train_indices = np.where(train_labels==original_labels[i])[0]\n","        class_train_images.append(train_images[class_train_indices])\n","        \n","        # Don't want the original labels\n","        # This is where we convert to our own labels: 0-11\n","        class_train_labels.append(np.full(len(class_train_indices),i)) \n","\n","    class_valid_images = []\n","    class_valid_labels = []\n","    for i in range(len(classes)):\n","        class_valid_indices = np.where(valid_labels==original_labels[i])[0]\n","        class_valid_images.append(valid_images[class_valid_indices])\n","        \n","        # Don't want the original labels\n","        # This is where we convert to our own labels: 0-11\n","        class_valid_labels.append(np.full(len(class_valid_indices),i)) \n","\n","    class_test_images = []\n","    class_test_labels = []\n","    for i in range(len(classes)):\n","        class_test_indices = np.where(test_labels==original_labels[i])[0]\n","        class_test_images.append(test_images[class_test_indices])\n","\n","        # Don't want the original labels\n","        # This is where we convert to our own labels: 0-11\n","        class_test_labels.append(np.full(len(class_test_indices),i)) \n","\n","    # normalize number of samples in each class\n","    \n","    # train\n","    desired_size=int(600*0.7)\n","    extra_train_images = []\n","    extra_train_labels = []\n","    for i in range(len(classes)):\n","        # Randomly sample from the original class images to duplicate to extra\n","        if class_train_images[i].shape[0] < desired_size:\n","            extra_train_images.append(\n","                class_train_images[i][np.random.randint(\n","                    low=0,\n","                    high=class_train_images[i].shape[0],\n","                    size=desired_size-class_train_images[i].shape[0])])\n","\n","            # Add random noise to create variation from originals\n","            # noise_intensity = 0.1\n","\n","            # extra_train_images[i] = (1-noise_intensity)*extra_train_images[i]+noise_intensity*(255*torch.rand(*extra_train_images[i].shape).detach().numpy())\n","            # extra_train_images[i] = np.clip(extra_train_images[i], 0, 255)\n","            \n","\n","            # add labels for extra samples\n","            extra_train_labels.append(np.full(extra_train_images[i].shape[0], i))\n","\n","            # append to original\n","            class_train_images[i] = np.concatenate((class_train_images[i],extra_train_images[i]))\n","            class_train_labels[i] = np.concatenate((class_train_labels[i],extra_train_labels[i]))\n","        else:\n","            # if more than desired_size images, truncate\n","            extra_train_images.append([])\n","            extra_train_labels.append([])\n","            class_train_images[i] = class_train_images[i][:desired_size]\n","            class_train_labels[i] = class_train_labels[i][:desired_size]\n","    # valid\n","    desired_size=int(600*0.15)\n","    extra_valid_images = []\n","    extra_valid_labels = []\n","    for i in range(len(classes)):\n","        # Randomly sample from the original class images to duplicate to extra\n","        if class_valid_images[i].shape[0] < desired_size:\n","            extra_valid_images.append(\n","                class_valid_images[i][np.random.randint(\n","                    low=0,\n","                    high=class_valid_images[i].shape[0],\n","                    size=desired_size-class_valid_images[i].shape[0])])\n","\n","\n","            # add labels for extra samples\n","            extra_valid_labels.append(np.full(extra_valid_images[i].shape[0], i))\n","\n","            # append to original\n","            class_valid_images[i] = np.concatenate((class_valid_images[i],extra_valid_images[i]))\n","            class_valid_labels[i] = np.concatenate((class_valid_labels[i],extra_valid_labels[i]))\n","        else:\n","            # if more than desired_size images, truncate\n","            extra_valid_images.append([])\n","            extra_valid_labels.append([])\n","            class_valid_images[i] = class_valid_images[i][:desired_size]\n","            class_valid_labels[i] = class_valid_labels[i][:desired_size]\n","\n","    # test\n","    desired_size=int(600*0.15)\n","    extra_test_images = []\n","    extra_test_labels = []\n","    for i in range(len(classes)):\n","        # Randomly sample from the original class images to duplicate to extra\n","        if class_test_images[i].shape[0] < desired_size:\n","            extra_test_images.append(\n","                class_test_images[i][np.random.randint(\n","                    low=0,\n","                    high=class_test_images[i].shape[0],\n","                    size=desired_size-class_test_images[i].shape[0])])\n","            # Add random noise to create variation from originals\n","            noise_intensity = 0.1\n","\n","            extra_train_images[i] = (1-noise_intensity)*extra_train_images[i]+noise_intensity*(1*torch.rand(*extra_train_images[i].shape).detach().numpy())\n","            extra_train_images[i] = np.clip(extra_train_images[i], 0, 1)\n","\n","            # Add random noise to create variation from originals\n","            noise_intensity = 0.05\n","\n","            extra_test_images[i] = (1-noise_intensity)*extra_test_images[i]+noise_intensity*(1*torch.rand(*extra_test_images[i].shape).detach().numpy())\n","            extra_test_images[i] = np.clip(extra_test_images[i], 0, 1)\n","            # add labels for extra samples\n","            extra_test_labels.append(np.full(extra_test_images[i].shape[0], i))\n","\n","            # append to original\n","            class_test_images[i] = np.concatenate((class_test_images[i],extra_test_images[i]))\n","            class_test_labels[i] = np.concatenate((class_test_labels[i],extra_test_labels[i]))\n","        else:\n","            # if more than desired_size images, truncate\n","            extra_test_images.append([])\n","            extra_test_labels.append([])\n","            class_test_images[i] = class_test_images[i][:desired_size]\n","            class_test_labels[i] = class_test_labels[i][:desired_size]\n","\n","    # combine class arrays\n","    train_images = np.concatenate(class_train_images)\n","    train_labels = np.concatenate(class_train_labels)\n","\n","    valid_images = np.concatenate(class_valid_images)\n","    valid_labels = np.concatenate(class_valid_labels)\n","\n","    test_images = np.concatenate(class_test_images)\n","    test_labels = np.concatenate(class_test_labels)\n","\n","    # shuffle again\n","    indices = list(range(train_images.shape[0]))\n","    np.random.shuffle(indices)\n","    train_images = train_images[indices]\n","    train_labels = train_labels[indices]\n","    \n","    indices = list(range(valid_images.shape[0]))\n","    np.random.shuffle(indices)\n","    valid_images = valid_images[indices]\n","    valid_labels = valid_labels[indices]\n","   \n","    indices = list(range(test_images.shape[0]))\n","    np.random.shuffle(indices)\n","    test_images = test_images[indices]\n","    test_labels = test_labels[indices]\n","\n","    # SHADMAN'S CHANGES ABOVE THIS\n","\n","    # make into torch datasets\n","    train_image_tensor = torch.Tensor(train_images.transpose(0,3,1,2))\n","    train_label_tensor = torch.Tensor(train_labels)\n","    \n","    val_image_tensor = torch.Tensor(valid_images.transpose(0,3,1,2))\n","    val_label_tensor = torch.Tensor(valid_labels)\n","    \n","    test_image_tensor = torch.Tensor(test_images.transpose(0,3,1,2))\n","    test_label_tensor = torch.Tensor(test_labels)\n","   \n","    trainset = TensorDataset(train_image_tensor, train_label_tensor)\n","    valset = TensorDataset(val_image_tensor, val_label_tensor)\n","    testset = TensorDataset(test_image_tensor, test_label_tensor)\n","\n","\n","\n","    # make data loaders\n","    train_loader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,\n","                                        num_workers=1)\n","    val_loader = torch.utils.data.DataLoader(valset, batch_size=batch_size,\n","                                        num_workers=1)\n","    test_loader = torch.utils.data.DataLoader(testset, batch_size=batch_size,\n","                                        num_workers=1)\n","    \n","    return train_loader, val_loader, test_loader, classes "],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"W_HTih2z9ZUo","executionInfo":{"status":"ok","timestamp":1607488984769,"user_tz":300,"elapsed":28206,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}},"outputId":"f141aaee-b525-4be3-b967-5d1ce6f4b574"},"source":["batch_size = 32\n","train_loader, val_loader, test_loader, classes = get_data_loader (batch_size)\n","print (classes)\n","print (len(train_loader))\n","print (len(val_loader))\n","print (len(test_loader))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["('Speed limit (20km/h)', 'Speed limit (30km/h)', 'Speed limit (50km/h)', 'Speed limit (60km/h)', 'Speed limit (70km/h)', 'Speed limit (80km/h)', 'End of speed limit (80km/h)', 'Speed limit (100km/h)', 'Speed limit (120km/h)', 'Yield', 'Stop', 'End of all speed and passing limits')\n","158\n","34\n","34\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"JjdkO6f_Gw3v"},"source":["# Check one batch\n","dataiter = iter(val_loader)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"1J6vbY4YETHN","executionInfo":{"status":"ok","timestamp":1607488985726,"user_tz":300,"elapsed":13174,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}},"outputId":"ac08a3dd-8b00-4335-ac08-577fa33c2ae9"},"source":["images, labels = dataiter.next()\n","images = images.numpy()\n","labels = labels.int()\n","# plot the images in the batch, along with the corresponding labels\n","fig = plt.figure(figsize=(25, 4))\n","for idx in np.arange(20):\n","    ax = fig.add_subplot(2, 20/2, idx+1, xticks=[], yticks=[])\n","    plt.imshow(np.transpose(images[idx], (1, 2, 0)))\n","    ax.set_title(classes[labels[idx]])\n","print (images[1])"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"],"name":"stderr"},{"output_type":"stream","text":["[[[-0.5331847  -0.69084597 -0.7302613  ... -0.5331847  -0.65143067\n","   -0.7696766 ]\n","  [-0.5331847  -0.69084597 -0.7302613  ...  0.01862992  0.09746056\n","   -0.25727734]\n","  [-0.5331847  -0.69084597 -0.7302613  ... -0.21786202 -0.25727734\n","   -0.3361081 ]\n","  ...\n","  [ 2.6988723   2.5806262   2.659457   ... -0.61201537 -0.5331847\n","   -0.5331847 ]\n","  [ 2.7777028   2.6200416   2.659457   ... -0.5726     -0.5331847\n","   -0.5331847 ]\n","  [ 2.6988723   2.5806262   2.541211   ... -0.61201537 -0.5331847\n","   -0.5726    ]]\n","\n"," [[-0.49376938 -0.69084597 -0.7302613  ... -0.29669267 -0.41493872\n","   -0.49376938]\n","  [-0.5331847  -0.69084597 -0.7302613  ...  0.25512186  0.33395252\n","   -0.02078541]\n","  [-0.5331847  -0.69084597 -0.7302613  ...  0.01862992 -0.02078541\n","   -0.09961606]\n","  ...\n","  [ 2.2653036   2.186473    2.304719   ... -0.5726     -0.5726\n","   -0.5726    ]\n","  [ 2.2653036   2.2258883   2.2653036  ... -0.5331847  -0.5331847\n","   -0.5726    ]\n","  [ 2.2653036   2.1470575   2.1470575  ... -0.5726     -0.5331847\n","   -0.5331847 ]]\n","\n"," [[-0.17844671 -0.45435405 -0.45435405 ...  0.2945372   0.2945372\n","    0.25512186]\n","  [-0.21786202 -0.41493872 -0.45435405 ...  0.84635174  1.0040132\n","    0.7281057 ]\n","  [-0.21786202 -0.41493872 -0.41493872 ...  0.8069364   0.7675211\n","    0.6886904 ]\n","  ...\n","  [ 1.8711503   1.831735    1.949981   ... -0.49376938 -0.45435405\n","   -0.3755234 ]\n","  [ 1.831735    1.8711503   1.9105656  ... -0.45435405 -0.45435405\n","   -0.41493872]\n","  [ 1.831735    1.831735    1.7923197  ... -0.49376938 -0.45435405\n","   -0.3755234 ]]]\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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size 1800x288 with 20 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"k5rbyYhrHTAJ"},"source":["# close all figures to prevent memory leak\n","plt.close('all')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"d6UmCUytrUdf"},"source":["def get_model_name(name, batch_size, learning_rate, epoch):\n","    \"\"\" Generate a name for the model consisting of all the hyperparameter values\n","\n","    Args:\n","        config: Configuration object containing the hyperparameters\n","    Returns:\n","        path: A string with the hyperparameter name and value concatenated\n","    \"\"\"\n","    path = \"model_{0}_bs{1}_lr{2}_epoch{3}\".format(name,\n","                                                   batch_size,\n","                                                   learning_rate,\n","                                                   epoch)\n","    return path\n","\n","def normalize_label(labels):\n","    \"\"\"\n","    Given a tensor containing 2 possible values, normalize this to 0/1\n","\n","    Args:\n","        labels: a 1D tensor containing two possible scalar values\n","    Returns:\n","        A tensor normalize to 0/1 value\n","    \"\"\"\n","    max_val = torch.max(labels)\n","    min_val = torch.min(labels)\n","    norm_labels = (labels - min_val)//(max_val - min_val)             #this is kinda brilliant\n","    return norm_labels\n","\n","\n"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Yy0uWVQws3ig"},"source":["#TRAIN\n","\n","Tasks:\n"," - Calculate training accuracy\n"," - Calculate training loss\n"," - Calculate validation accuracy\n"," - Calculate validation loss\n"," - Store weights in epoch files (we may have to be smart about WHERE we're saving this so that we don't clutter up the drive)\n"," - Get accuracy of model after training is complete\n"," - Plot everything\n"]},{"cell_type":"code","metadata":{"id":"rPjuItwogPhe"},"source":["def train(model, train_loader, val_loader, batch_size=27, num_epochs=21, learning_rate = 0.001):\n","\n","    torch.manual_seed(1000)\n","    criterion = nn.CrossEntropyLoss()\n","    optimizer = optim.Adam(model.parameters(), lr=learning_rate)\n","\n","    train_acc, val_acc, train_loss, val_loss = [], [], [], []\n","\n","    # training\n","    print (\"Training Started...\\n\")\n","    if torch.cuda.is_available():\n","        print(\"U S I N G   C U D A \\n\\n\")\n","\n","    for epoch in range(num_epochs):                                     # the number of iterations\n","        sum_train_loss = 0.0\n","        sum_val_loss = 0.0\n","\n","        n = 0                                                           # Number of training iterations in this epoch\n","        m = 0                                                           # Number of validation iterations in this epoch\n","\n","        for imgs, labels in iter(train_loader):\n","                \n","            if use_cuda and torch.cuda.is_available():\n","              imgs = imgs.cuda()\n","              labels = labels.cuda()\n","\n","            out = model(imgs)                                           # forward pass\n","            loss = criterion(out, labels.long())                               # compute the total loss\n","            loss.backward()                                             # backward pass (compute parameter updates)\n","            optimizer.step()                                            # make the updates for each parameter\n","            optimizer.zero_grad()                                       # a clean up step for PyTorch\n","            sum_train_loss += loss.item() \n","            n += 1\n","        \n","        for imgs, labels in iter(val_loader):\n","            \n","            if use_cuda and torch.cuda.is_available():\n","              imgs = imgs.cuda()\n","              labels = labels.cuda()                                    # cudafication for speeeeed\n","\n","            out = model(imgs)                                            \n","            loss = criterion(out, labels.long())                               # compute loss with Cross Entropy\n","            sum_val_loss += loss.item()\n","            m += 1\n","\n","        # track accuracy and loss\n","        train_acc.append(get_accuracy(model, train_loader))\n","        val_acc.append(get_accuracy(model, val_loader))\n","        train_loss.append(sum_train_loss/n)\n","        val_loss.append(sum_val_loss/m)\n","\n","        ################################################################################################################\n","        model_path = get_model_name(model.name, batch_size, learning_rate, epoch+1) \n","        torch.save(model.state_dict(), model_path)\n","\n","        print('Epoch: ', epoch + 1,                                                 #A little text formatting goes a long way\n","              '\\t Training acc:', round(train_acc[-1],4),\n","              '\\t Val acc:%.4f' % val_acc[-1],\n","              '\\t Training loss:%.4f' % train_loss[-1],\n","              '\\t Val loss:%.4f' % val_loss[-1])\n","\n","    return train_acc, val_acc, train_loss, val_loss\n","\n","def get_accuracy(model, data_loader):\n","\n","  correct = 0\n","  total = 0\n","  for imgs, labels in data_loader:\n","      \n","      if use_cuda and torch.cuda.is_available():\n","        imgs = imgs.cuda()\n","        labels = labels.cuda()\n","\n","      output = model(imgs)\n","      #select index with maximum prediction score\n","      pred = output.max(1, keepdim=True)[1]\n","      correct += pred.eq(labels.view_as(pred)).sum().item()\n","      total += imgs.shape[0]\n","\n","  return correct / total\n","\n","\n","\n","\n","def plot_training_curve(train_acc, val_acc, train_loss, val_loss):\n","    \"\"\" Plots the training curve for a model run, given the csv files\n","    containing the train/validation error/loss.\n","\n","    Args:\n","        path: The base path of the csv files produced during training\n","    \"\"\"\n","    import matplotlib.pyplot as plt\n","\n","    plt.title(\"Train vs Validation Accuracy\")\n","    n = len(train_acc) # number of epochs\n","    plt.plot(range(1,n+1), train_acc, label=\"Train\")\n","    plt.plot(range(1,n+1), val_acc, label=\"Validation\")\n","    plt.xlabel(\"Epoch\")\n","    plt.ylabel(\"Accuracy\")\n","    plt.legend(loc='best')\n","    plt.show()\n","    plt.title(\"Train vs Validation Loss\")\n","    plt.plot(range(1,n+1), train_loss, label=\"Train\")\n","    plt.plot(range(1,n+1), val_loss, label=\"Validation\")\n","    plt.xlabel(\"Epoch\")\n","    plt.ylabel(\"Loss\")\n","    plt.legend(loc='best')\n","    plt.show()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"e3JMNibuNadN"},"source":["import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","\n","import torch.optim as optim                                                     # For gradient descent\n","import matplotlib.pyplot as plt\n","import numpy as np"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"35CCLa-9MtiM"},"source":["#Baseline"]},{"cell_type":"code","metadata":{"id":"rPUYNeWvM4hH"},"source":["class BaseANN(nn.Module):\n","  \n","  def __init__(self):\n","    super(BaseANN, self).__init__()\n","\n","    self.name = \"BaseANN\"\n","    self.num_classes = 12\n","\n","    #model layers\n","    self.fc1 = nn.Linear(32*32*3, 32)\n","    self.fc2 = nn.Linear(32, self.num_classes)\n","  \n","  def forward(self, x):\n","    #flatten image for input into fc layers\n","    x = x.view(-1, 32*32*3)\n","    x = F.relu(self.fc1(x))\n","    x = self.fc2(x)\n","    x = x.squeeze(1)\n","\n","    return x"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"KiAJv5AkNH3o","executionInfo":{"status":"ok","timestamp":1607452834965,"user_tz":300,"elapsed":47704,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}},"outputId":"fe508fe6-1fc4-45a8-d939-099c36a68c68"},"source":["model4 = BaseANN()\n","use_cuda = False\n","\n","batch_size = 256\n","train_loader, val_loader, test_loader, classes = get_data_loader(batch_size)\n","\n","train_acc, val_acc, train_loss, val_loss = train(model4, train_loader, val_loader, batch_size=256, num_epochs=70, learning_rate = 0.0055)\n","plot_training_curve(train_acc, val_acc, train_loss, val_loss)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Training Started...\n","\n","U S I N G   C U D A \n","\n","\n","Epoch:  1 \t Training acc: 0.6141 \t Val acc:0.5611 \t Training loss:1.7904 \t Val loss:1.2620\n","Epoch:  2 \t Training acc: 0.7885 \t Val acc:0.7259 \t Training loss:0.8413 \t Val loss:0.7712\n","Epoch:  3 \t Training acc: 0.8754 \t Val acc:0.8000 \t Training loss:0.5147 \t Val loss:0.5414\n","Epoch:  4 \t Training acc: 0.9137 \t Val acc:0.8380 \t Training loss:0.3471 \t Val loss:0.4752\n","Epoch:  5 \t Training acc: 0.9218 \t Val acc:0.8333 \t Training loss:0.2623 \t Val loss:0.4872\n","Epoch:  6 \t Training acc: 0.9454 \t Val acc:0.8667 \t Training loss:0.2140 \t Val loss:0.4080\n","Epoch:  7 \t Training acc: 0.9536 \t Val acc:0.8731 \t Training loss:0.1810 \t Val loss:0.4120\n","Epoch:  8 \t Training acc: 0.9627 \t Val acc:0.8843 \t Training loss:0.1543 \t Val loss:0.3867\n","Epoch:  9 \t Training acc: 0.9544 \t Val acc:0.8731 \t Training loss:0.1350 \t Val loss:0.3788\n","Epoch:  10 \t Training acc: 0.9706 \t Val acc:0.8981 \t Training loss:0.1088 \t Val loss:0.3445\n","Epoch:  11 \t Training acc: 0.9633 \t Val acc:0.8926 \t Training loss:0.0975 \t Val loss:0.4202\n","Epoch:  12 \t Training acc: 0.9681 \t Val acc:0.8880 \t Training loss:0.1190 \t Val loss:0.4133\n","Epoch:  13 \t Training acc: 0.9548 \t Val acc:0.8546 \t Training loss:0.1034 \t Val loss:0.4872\n","Epoch:  14 \t Training acc: 0.98 \t Val acc:0.8926 \t Training loss:0.0864 \t Val loss:0.4056\n","Epoch:  15 \t Training acc: 0.9827 \t Val acc:0.8917 \t Training loss:0.0792 \t Val loss:0.4136\n","Epoch:  16 \t Training acc: 0.9812 \t Val acc:0.8833 \t Training loss:0.0625 \t Val loss:0.4221\n","Epoch:  17 \t Training acc: 0.9867 \t Val acc:0.9056 \t Training loss:0.0587 \t Val loss:0.3948\n","Epoch:  18 \t Training acc: 0.9913 \t Val acc:0.9083 \t Training loss:0.0442 \t Val loss:0.3881\n","Epoch:  19 \t Training acc: 0.9895 \t Val acc:0.8963 \t Training loss:0.0433 \t Val loss:0.4217\n","Epoch:  20 \t Training acc: 0.9855 \t Val acc:0.8954 \t Training loss:0.0517 \t Val loss:0.4924\n","Epoch:  21 \t Training acc: 0.9687 \t Val acc:0.8769 \t Training loss:0.0753 \t Val loss:0.6244\n","Epoch:  22 \t Training acc: 0.9659 \t Val acc:0.8694 \t Training loss:0.0734 \t Val loss:0.6492\n","Epoch:  23 \t Training acc: 0.9635 \t Val acc:0.8880 \t Training loss:0.0856 \t Val loss:0.7014\n","Epoch:  24 \t Training acc: 0.9808 \t Val acc:0.8676 \t Training loss:0.1095 \t Val loss:0.5688\n","Epoch:  25 \t Training acc: 0.9768 \t Val acc:0.8833 \t Training loss:0.1100 \t Val loss:0.4887\n","Epoch:  26 \t Training acc: 0.9645 \t Val acc:0.8722 \t Training loss:0.0765 \t Val loss:0.5222\n","Epoch:  27 \t Training acc: 0.9841 \t Val acc:0.8861 \t Training loss:0.0575 \t Val loss:0.6576\n","Epoch:  28 \t Training acc: 0.9935 \t Val acc:0.9111 \t Training loss:0.0419 \t Val loss:0.4613\n","Epoch:  29 \t Training acc: 0.9867 \t Val acc:0.8917 \t Training loss:0.0378 \t Val loss:0.5053\n","Epoch:  30 \t Training acc: 0.9891 \t Val acc:0.8981 \t Training loss:0.0392 \t Val loss:0.4496\n","Epoch:  31 \t Training acc: 0.9722 \t Val acc:0.8676 \t Training loss:0.0434 \t Val loss:0.6199\n","Epoch:  32 \t Training acc: 0.9871 \t Val acc:0.8889 \t Training loss:0.0739 \t Val loss:0.6396\n","Epoch:  33 \t Training acc: 0.9853 \t Val acc:0.8898 \t Training loss:0.0534 \t Val loss:0.5534\n","Epoch:  34 \t Training acc: 0.9861 \t Val acc:0.8935 \t Training loss:0.0433 \t Val loss:0.5381\n","Epoch:  35 \t Training acc: 0.9982 \t Val acc:0.9028 \t Training loss:0.0243 \t Val loss:0.5497\n","Epoch:  36 \t Training acc: 0.994 \t Val acc:0.8907 \t Training loss:0.0142 \t Val loss:0.6547\n","Epoch:  37 \t Training acc: 0.9883 \t Val acc:0.8898 \t Training loss:0.0213 \t Val loss:0.6104\n","Epoch:  38 \t Training acc: 0.9911 \t Val acc:0.8944 \t Training loss:0.0220 \t Val loss:0.6142\n","Epoch:  39 \t Training acc: 0.9905 \t Val acc:0.8907 \t Training loss:0.0214 \t Val loss:0.6071\n","Epoch:  40 \t Training acc: 0.9919 \t Val acc:0.8861 \t Training loss:0.0150 \t Val loss:0.6941\n","Epoch:  41 \t Training acc: 0.9891 \t Val acc:0.8963 \t Training loss:0.0168 \t Val loss:0.5860\n","Epoch:  42 \t Training acc: 0.9903 \t Val acc:0.8907 \t Training loss:0.0230 \t Val loss:0.6677\n","Epoch:  43 \t Training acc: 0.9859 \t Val acc:0.8963 \t Training loss:0.0219 \t Val loss:0.7049\n","Epoch:  44 \t Training acc: 0.9935 \t Val acc:0.8972 \t Training loss:0.0204 \t Val loss:0.6250\n","Epoch:  45 \t Training acc: 0.9935 \t Val acc:0.9028 \t Training loss:0.0132 \t Val loss:0.7008\n","Epoch:  46 \t Training acc: 0.9923 \t Val acc:0.8963 \t Training loss:0.0227 \t Val loss:0.6409\n","Epoch:  47 \t Training acc: 0.9901 \t Val acc:0.8833 \t Training loss:0.0371 \t Val loss:0.6714\n","Epoch:  48 \t Training acc: 0.9819 \t Val acc:0.8926 \t Training loss:0.0341 \t Val loss:0.6427\n","Epoch:  49 \t Training acc: 0.9956 \t Val acc:0.9046 \t Training loss:0.0247 \t Val loss:0.6621\n","Epoch:  50 \t Training acc: 0.9935 \t Val acc:0.8907 \t Training loss:0.0184 \t Val loss:0.6640\n","Epoch:  51 \t Training acc: 0.9942 \t Val acc:0.9028 \t Training loss:0.0145 \t Val loss:0.5679\n","Epoch:  52 \t Training acc: 0.9952 \t Val acc:0.9074 \t Training loss:0.0195 \t Val loss:0.5628\n","Epoch:  53 \t Training acc: 0.9921 \t Val acc:0.8926 \t Training loss:0.0325 \t Val loss:0.6978\n","Epoch:  54 \t Training acc: 0.9897 \t Val acc:0.8898 \t Training loss:0.0265 \t Val loss:0.6533\n","Epoch:  55 \t Training acc: 0.9948 \t Val acc:0.8991 \t Training loss:0.0187 \t Val loss:0.6818\n","Epoch:  56 \t Training acc: 0.9929 \t Val acc:0.8870 \t Training loss:0.0174 \t Val loss:0.8365\n","Epoch:  57 \t Training acc: 0.9925 \t Val acc:0.9130 \t Training loss:0.0162 \t Val loss:0.6635\n","Epoch:  58 \t Training acc: 0.9907 \t Val acc:0.8963 \t Training loss:0.0132 \t Val loss:0.9097\n","Epoch:  59 \t Training acc: 0.9847 \t Val acc:0.8852 \t Training loss:0.0192 \t Val loss:0.7858\n","Epoch:  60 \t Training acc: 0.9845 \t Val acc:0.8861 \t Training loss:0.0525 \t Val loss:0.7775\n","Epoch:  61 \t Training acc: 0.9917 \t Val acc:0.8870 \t Training loss:0.0481 \t Val loss:0.6855\n","Epoch:  62 \t Training acc: 0.9907 \t Val acc:0.8981 \t Training loss:0.0232 \t Val loss:0.8242\n","Epoch:  63 \t Training acc: 0.9937 \t Val acc:0.8861 \t Training loss:0.0222 \t Val loss:0.8357\n","Epoch:  64 \t Training acc: 0.9968 \t Val acc:0.9028 \t Training loss:0.0228 \t Val loss:0.6488\n","Epoch:  65 \t Training acc: 0.9931 \t Val acc:0.9009 \t Training loss:0.0178 \t Val loss:0.7409\n","Epoch:  66 \t Training acc: 0.9958 \t Val acc:0.9019 \t Training loss:0.0182 \t Val loss:0.7368\n","Epoch:  67 \t Training acc: 0.9861 \t Val acc:0.8907 \t Training loss:0.0268 \t Val loss:0.8829\n","Epoch:  68 \t Training acc: 0.9919 \t Val acc:0.9093 \t Training loss:0.0298 \t Val loss:0.7734\n","Epoch:  69 \t Training acc: 0.9942 \t Val acc:0.9065 \t Training loss:0.0256 \t Val loss:0.8337\n","Epoch:  70 \t Training acc: 0.9944 \t Val acc:0.8991 \t Training loss:0.0356 \t Val loss:0.8951\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"0Q5T62z8XhGh"},"source":["#Primary Model Architecture and Testing\n","\n","Tasks:\n"," - Build a CNN model with 3 convolutional layers and 3 fully connected layers\n"," - Perform a sanity check to test if model is capable of overfitting\n"," - Use training code to test on model and tune hyperparameters to obtain best results\n"," - Print and Plot Results\n"," - Try 10 different hyperparameter settings and compare\n"]},{"cell_type":"code","metadata":{"id":"GqVmU6tztUUL"},"source":["import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","\n","import torch.optim as optim                                                     # For gradient descent\n","import matplotlib.pyplot as plt\n","import numpy as np\n","\n","# Creating a CNN\n","class SignClassifier(nn.Module):\n","\n","    def __init__(self):\n","        super(SignClassifier, self).__init__()\n","        self.name = \"Net\"                                                       \n","        self.conv1 = nn.Conv2d(3,5,5)                                           # First kernel is a 5 by 5, 3 color channels, it has output of 5    -> given 32x32x3, you are left with 28x28x5\n","        self.pool = nn.MaxPool2d(2,2)                                           # Max pooling layer with kernel size 2 and stride 2                 -> you are left with 14x14x5\n","        self.conv2 = nn.Conv2d(5,10,3)                                          # Second kernel is 5 by 5, it changes input depth from 5 to 10      -> you are left with 10x10x10\n","        self.conv3 = nn.Conv2d(10,12,5)                                         # Third kernel is 5 by 5, it changes input depth from 10 to 12      -> you are left with 6x6x12\n","                                                                                \n","        self.fc1 = nn.Linear(8*8*12, 200)                                       # Fully Connected Layers\n","        self.fc2 = nn.Linear(200, 100)\n","        self.fc3 = nn.Linear(100,12)                                             # 12 possible outputs\n","\n","    def forward(self, x):\n","        x = self.pool(F.relu(self.conv1(x)))                                    # Apply first kernel, then activation function, then max pooling \n","        x = F.relu(self.conv2(x))                                               # Apply second kernel, then activation function\n","        x = F.relu(self.conv3(x))                                               # Apply second kernel, then activation function\n","        x = x.view(-1, 8*8*12)                                                  # flatten tensor for ANN portion\n","        x = F.relu(self.fc1(x))                                                 # Apply activation function on first fully connected layer\n","        x = F.relu(self.fc2(x))                                                 # Apply activation function on second fully connected layer\n","        x = self.fc3(x)                                                         # final activation function is included with criterion\n","        x = x.squeeze(1) # Flatten to [batch_size]\n","        return x\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"wsAaLccZ0kk0","executionInfo":{"status":"ok","timestamp":1607489180640,"user_tz":300,"elapsed":571,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}}},"source":["model_1 = SignClassifier()"],"execution_count":16,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"HgWSmOTpttDG","executionInfo":{"status":"ok","timestamp":1607473433490,"user_tz":300,"elapsed":69646,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}},"outputId":"009ca641-f75a-4475-dd31-c1dc6b00d279"},"source":["model_1 = SignClassifier()\n","use_cuda = False\n","\n","\n","batch_size = 32\n","train_loader, val_loader, test_loader, classes = get_data_loader(batch_size)\n","\n","train_acc, val_acc, train_loss, val_loss = train(model_1, train_loader, val_loader, batch_size=32, num_epochs=20, learning_rate = 0.001)\n","plot_training_curve(train_acc, val_acc, train_loss, val_loss)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Training Started...\n","\n","U S I N G   C U D A \n","\n","\n","Epoch:  1 \t Training acc: 0.8062 \t Val acc:0.7824 \t Training loss:1.1906 \t Val loss:0.6170\n","Epoch:  2 \t Training acc: 0.9458 \t Val acc:0.9222 \t Training loss:0.3330 \t Val loss:0.2586\n","Epoch:  3 \t Training acc: 0.9573 \t Val acc:0.9398 \t Training loss:0.1721 \t Val loss:0.1978\n","Epoch:  4 \t Training acc: 0.9758 \t Val acc:0.9593 \t Training loss:0.0980 \t Val loss:0.1428\n","Epoch:  5 \t Training acc: 0.9871 \t Val acc:0.9630 \t Training loss:0.0758 \t Val loss:0.1302\n","Epoch:  6 \t Training acc: 0.9766 \t Val acc:0.9519 \t Training loss:0.0464 \t Val loss:0.1872\n","Epoch:  7 \t Training acc: 0.9927 \t Val acc:0.9704 \t Training loss:0.0318 \t Val loss:0.1289\n","Epoch:  8 \t Training acc: 0.9907 \t Val acc:0.9676 \t Training loss:0.0281 \t Val loss:0.1804\n","Epoch:  9 \t Training acc: 0.9808 \t Val acc:0.9685 \t Training loss:0.0369 \t Val loss:0.1436\n","Epoch:  10 \t Training acc: 0.9954 \t Val acc:0.9685 \t Training loss:0.0378 \t Val loss:0.1127\n","Epoch:  11 \t Training acc: 0.9986 \t Val acc:0.9778 \t Training loss:0.0145 \t Val loss:0.0962\n","Epoch:  12 \t Training acc: 0.9873 \t Val acc:0.9704 \t Training loss:0.0170 \t Val loss:0.1298\n","Epoch:  13 \t Training acc: 0.996 \t Val acc:0.9648 \t Training loss:0.0222 \t Val loss:0.1651\n","Epoch:  14 \t Training acc: 0.9966 \t Val acc:0.9722 \t Training loss:0.0274 \t Val loss:0.1180\n","Epoch:  15 \t Training acc: 0.9962 \t Val acc:0.9741 \t Training loss:0.0168 \t Val loss:0.1019\n","Epoch:  16 \t Training acc: 1.0 \t Val acc:0.9806 \t Training loss:0.0056 \t Val loss:0.0866\n","Epoch:  17 \t Training acc: 1.0 \t Val acc:0.9815 \t Training loss:0.0005 \t Val loss:0.0848\n","Epoch:  18 \t Training acc: 1.0 \t Val acc:0.9815 \t Training loss:0.0002 \t Val loss:0.0889\n","Epoch:  19 \t Training acc: 1.0 \t Val acc:0.9815 \t Training loss:0.0002 \t Val loss:0.0922\n","Epoch:  20 \t Training acc: 1.0 \t Val acc:0.9815 \t Training loss:0.0001 \t Val loss:0.0948\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"r2owMmHKQ1J_","executionInfo":{"status":"ok","timestamp":1607453022388,"user_tz":300,"elapsed":229132,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}},"outputId":"9fb0e118-637a-4514-82e2-9da3dab2ca6b"},"source":["model_2 = SignClassifier()\n","use_cuda = False\n","\n","batch_size = 256\n","train_loader, val_loader, test_loader, classes = get_data_loader(batch_size)\n","\n","train_acc, val_acc, train_loss, val_loss = train(model_2, train_loader, val_loader, batch_size=256, num_epochs=30, learning_rate = 0.0009)\n","plot_training_curve(train_acc, val_acc, train_loss, val_loss)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Training Started...\n","\n","U S I N G   C U D A \n","\n","\n","Epoch:  1 \t Training acc: 0.4298 \t Val acc:0.3935 \t Training loss:2.2948 \t Val loss:1.7916\n","Epoch:  2 \t Training acc: 0.6611 \t Val acc:0.6333 \t Training loss:1.3764 \t Val loss:1.0756\n","Epoch:  3 \t Training acc: 0.7829 \t Val acc:0.7639 \t Training loss:0.8230 \t Val loss:0.7217\n","Epoch:  4 \t Training acc: 0.8468 \t Val acc:0.8296 \t Training loss:0.5702 \t Val loss:0.5340\n","Epoch:  5 \t Training acc: 0.8948 \t Val acc:0.8824 \t Training loss:0.4406 \t Val loss:0.4091\n","Epoch:  6 \t Training acc: 0.9153 \t Val acc:0.9037 \t Training loss:0.3550 \t Val loss:0.3617\n","Epoch:  7 \t Training acc: 0.9304 \t Val acc:0.9120 \t Training loss:0.2951 \t Val loss:0.3116\n","Epoch:  8 \t Training acc: 0.9401 \t Val acc:0.9213 \t Training loss:0.2521 \t Val loss:0.2779\n","Epoch:  9 \t Training acc: 0.9468 \t Val acc:0.9324 \t Training loss:0.2162 \t Val loss:0.2510\n","Epoch:  10 \t Training acc: 0.9538 \t Val acc:0.9361 \t Training loss:0.1889 \t Val loss:0.2279\n","Epoch:  11 \t Training acc: 0.9621 \t Val acc:0.9407 \t Training loss:0.1706 \t Val loss:0.2086\n","Epoch:  12 \t Training acc: 0.9629 \t Val acc:0.9435 \t Training loss:0.1591 \t Val loss:0.1973\n","Epoch:  13 \t Training acc: 0.9698 \t Val acc:0.9509 \t Training loss:0.1444 \t Val loss:0.1819\n","Epoch:  14 \t Training acc: 0.9726 \t Val acc:0.9556 \t Training loss:0.1147 \t Val loss:0.1714\n","Epoch:  15 \t Training acc: 0.9744 \t Val acc:0.9519 \t Training loss:0.0965 \t Val loss:0.1713\n","Epoch:  16 \t Training acc: 0.9796 \t Val acc:0.9574 \t Training loss:0.0819 \t Val loss:0.1630\n","Epoch:  17 \t Training acc: 0.9839 \t Val acc:0.9593 \t Training loss:0.0703 \t Val loss:0.1549\n","Epoch:  18 \t Training acc: 0.9855 \t Val acc:0.9537 \t Training loss:0.0628 \t Val loss:0.1504\n","Epoch:  19 \t Training acc: 0.9865 \t Val acc:0.9657 \t Training loss:0.0571 \t Val loss:0.1406\n","Epoch:  20 \t Training acc: 0.9812 \t Val acc:0.9602 \t Training loss:0.0525 \t Val loss:0.1501\n","Epoch:  21 \t Training acc: 0.9883 \t Val acc:0.9648 \t Training loss:0.0485 \t Val loss:0.1436\n","Epoch:  22 \t Training acc: 0.9851 \t Val acc:0.9611 \t Training loss:0.0391 \t Val loss:0.1607\n","Epoch:  23 \t Training acc: 0.9954 \t Val acc:0.9667 \t Training loss:0.0320 \t Val loss:0.1319\n","Epoch:  24 \t Training acc: 0.9954 \t Val acc:0.9694 \t Training loss:0.0220 \t Val loss:0.1384\n","Epoch:  25 \t Training acc: 0.9954 \t Val acc:0.9685 \t Training loss:0.0202 \t Val loss:0.1311\n","Epoch:  26 \t Training acc: 0.9966 \t Val acc:0.9676 \t Training loss:0.0152 \t Val loss:0.1400\n","Epoch:  27 \t Training acc: 0.9966 \t Val acc:0.9639 \t Training loss:0.0126 \t Val loss:0.1384\n","Epoch:  28 \t Training acc: 0.9937 \t Val acc:0.9657 \t Training loss:0.0140 \t Val loss:0.1623\n","Epoch:  29 \t Training acc: 0.997 \t Val acc:0.9704 \t Training loss:0.0112 \t Val loss:0.1516\n","Epoch:  30 \t Training acc: 0.996 \t Val acc:0.9639 \t Training loss:0.0110 \t Val loss:0.1646\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"r28wMxa8XmWo","executionInfo":{"status":"ok","timestamp":1607453147221,"user_tz":300,"elapsed":352337,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}},"outputId":"8633ef30-b8f1-4831-fe1e-12b7533d8aa2"},"source":["model_3 = SignClassifier()\n","model_3.name = \"model3\"\n","use_cuda = False\n","\n","batch_size = 256\n","train_loader, val_loader, test_loader, classes = get_data_loader(batch_size)\n","\n","train_acc, val_acc, train_loss, val_loss = train(model_3, train_loader, val_loader, batch_size=256, num_epochs=40, learning_rate = 0.001)\n","plot_training_curve(train_acc, val_acc, train_loss, val_loss)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Training Started...\n","\n","U S I N G   C U D A \n","\n","\n","Epoch:  1 \t Training acc: 0.4016 \t Val acc:0.3750 \t Training loss:2.2646 \t Val loss:1.8148\n","Epoch:  2 \t Training acc: 0.5494 \t Val acc:0.5657 \t Training loss:1.5570 \t Val loss:1.2885\n","Epoch:  3 \t Training acc: 0.6893 \t Val acc:0.6769 \t Training loss:1.1097 \t Val loss:0.9403\n","Epoch:  4 \t Training acc: 0.7421 \t Val acc:0.7222 \t Training loss:0.8072 \t Val loss:0.7779\n","Epoch:  5 \t Training acc: 0.8256 \t Val acc:0.8222 \t Training loss:0.6247 \t Val loss:0.5492\n","Epoch:  6 \t Training acc: 0.8619 \t Val acc:0.8639 \t Training loss:0.4949 \t Val loss:0.4292\n","Epoch:  7 \t Training acc: 0.9 \t Val acc:0.8833 \t Training loss:0.4038 \t Val loss:0.3544\n","Epoch:  8 \t Training acc: 0.9155 \t Val acc:0.9037 \t Training loss:0.3195 \t Val loss:0.3000\n","Epoch:  9 \t Training acc: 0.9335 \t Val acc:0.9185 \t Training loss:0.2651 \t Val loss:0.2535\n","Epoch:  10 \t Training acc: 0.9373 \t Val acc:0.9167 \t Training loss:0.2218 \t Val loss:0.2562\n","Epoch:  11 \t Training acc: 0.9486 \t Val acc:0.9250 \t Training loss:0.1895 \t Val loss:0.2300\n","Epoch:  12 \t Training acc: 0.9534 \t Val acc:0.9250 \t Training loss:0.1616 \t Val loss:0.2221\n","Epoch:  13 \t Training acc: 0.9518 \t Val acc:0.9222 \t Training loss:0.1408 \t Val loss:0.2327\n","Epoch:  14 \t Training acc: 0.9621 \t Val acc:0.9333 \t Training loss:0.1289 \t Val loss:0.2002\n","Epoch:  15 \t Training acc: 0.9736 \t Val acc:0.9556 \t Training loss:0.1307 \t Val loss:0.1512\n","Epoch:  16 \t Training acc: 0.9605 \t Val acc:0.9509 \t Training loss:0.1271 \t Val loss:0.1702\n","Epoch:  17 \t Training acc: 0.978 \t Val acc:0.9565 \t Training loss:0.0975 \t Val loss:0.1439\n","Epoch:  18 \t Training acc: 0.9804 \t Val acc:0.9491 \t Training loss:0.0824 \t Val loss:0.1472\n","Epoch:  19 \t Training acc: 0.9798 \t Val acc:0.9528 \t Training loss:0.0758 \t Val loss:0.1623\n","Epoch:  20 \t Training acc: 0.9845 \t Val acc:0.9593 \t Training loss:0.0617 \t Val loss:0.1451\n","Epoch:  21 \t Training acc: 0.9881 \t Val acc:0.9611 \t Training loss:0.0569 \t Val loss:0.1318\n","Epoch:  22 \t Training acc: 0.9899 \t Val acc:0.9565 \t Training loss:0.0497 \t Val loss:0.1349\n","Epoch:  23 \t Training acc: 0.9913 \t Val acc:0.9593 \t Training loss:0.0412 \t Val loss:0.1406\n","Epoch:  24 \t Training acc: 0.9817 \t Val acc:0.9500 \t Training loss:0.0419 \t Val loss:0.1599\n","Epoch:  25 \t Training acc: 0.9744 \t Val acc:0.9537 \t Training loss:0.0380 \t Val loss:0.1690\n","Epoch:  26 \t Training acc: 0.9927 \t Val acc:0.9630 \t Training loss:0.0476 \t Val loss:0.1188\n","Epoch:  27 \t Training acc: 0.9859 \t Val acc:0.9556 \t Training loss:0.0337 \t Val loss:0.1594\n","Epoch:  28 \t Training acc: 0.979 \t Val acc:0.9444 \t Training loss:0.0338 \t Val loss:0.1867\n","Epoch:  29 \t Training acc: 0.9867 \t Val acc:0.9500 \t Training loss:0.0351 \t Val loss:0.1683\n","Epoch:  30 \t Training acc: 0.994 \t Val acc:0.9639 \t Training loss:0.0252 \t Val loss:0.1398\n","Epoch:  31 \t Training acc: 0.9921 \t Val acc:0.9639 \t Training loss:0.0185 \t Val loss:0.1548\n","Epoch:  32 \t Training acc: 0.9875 \t Val acc:0.9593 \t Training loss:0.0160 \t Val loss:0.1764\n","Epoch:  33 \t Training acc: 0.9956 \t Val acc:0.9713 \t Training loss:0.0131 \t Val loss:0.1318\n","Epoch:  34 \t Training acc: 0.9909 \t Val acc:0.9620 \t Training loss:0.0111 \t Val loss:0.1683\n","Epoch:  35 \t Training acc: 0.9964 \t Val acc:0.9685 \t Training loss:0.0099 \t Val loss:0.1545\n","Epoch:  36 \t Training acc: 0.9913 \t Val acc:0.9565 \t Training loss:0.0108 \t Val loss:0.1892\n","Epoch:  37 \t Training acc: 0.9923 \t Val acc:0.9593 \t Training loss:0.0103 \t Val loss:0.1681\n","Epoch:  38 \t Training acc: 0.9964 \t Val acc:0.9694 \t Training loss:0.0112 \t Val loss:0.1340\n","Epoch:  39 \t Training acc: 0.9962 \t Val acc:0.9611 \t Training loss:0.0123 \t Val loss:0.1575\n","Epoch:  40 \t Training acc: 0.9909 \t Val acc:0.9500 \t Training loss:0.0155 \t Val loss:0.1935\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"r5OcZGwYYN2X","executionInfo":{"status":"ok","timestamp":1607453271122,"user_tz":300,"elapsed":474611,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}},"outputId":"9a72cbd2-d004-4a7a-e02d-d7ef6c22e2ce"},"source":["model_4 = SignClassifier()\n","use_cuda = False\n","\n","batch_size = 256\n","train_loader, val_loader, test_loader, classes = get_data_loader(batch_size)\n","\n","train_acc, val_acc, train_loss, val_loss = train(model_4, train_loader, val_loader, batch_size=256, num_epochs=40, learning_rate = 0.00016)\n","plot_training_curve(train_acc, val_acc, train_loss, val_loss)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Training Started...\n","\n","U S I N G   C U D A \n","\n","\n","Epoch:  1 \t Training acc: 0.2601 \t Val acc:0.2481 \t Training loss:2.4730 \t Val loss:2.4503\n","Epoch:  2 \t Training acc: 0.3304 \t Val acc:0.3213 \t Training loss:2.3982 \t Val loss:2.3191\n","Epoch:  3 \t Training acc: 0.401 \t Val acc:0.3657 \t Training loss:2.1797 \t Val loss:2.0174\n","Epoch:  4 \t Training acc: 0.4651 \t Val acc:0.4306 \t Training loss:1.8416 \t Val loss:1.7048\n","Epoch:  5 \t Training acc: 0.5595 \t Val acc:0.5361 \t Training loss:1.5590 \t Val loss:1.4593\n","Epoch:  6 \t Training acc: 0.6149 \t Val acc:0.6120 \t Training loss:1.3436 \t Val loss:1.2627\n","Epoch:  7 \t Training acc: 0.6526 \t Val acc:0.6500 \t Training loss:1.1854 \t Val loss:1.1175\n","Epoch:  8 \t Training acc: 0.6813 \t Val acc:0.6852 \t Training loss:1.0649 \t Val loss:1.0045\n","Epoch:  9 \t Training acc: 0.7145 \t Val acc:0.7222 \t Training loss:0.9677 \t Val loss:0.9133\n","Epoch:  10 \t Training acc: 0.7403 \t Val acc:0.7444 \t Training loss:0.8865 \t Val loss:0.8361\n","Epoch:  11 \t Training acc: 0.7688 \t Val acc:0.7685 \t Training loss:0.8162 \t Val loss:0.7741\n","Epoch:  12 \t Training acc: 0.7871 \t Val acc:0.7685 \t Training loss:0.7557 \t Val loss:0.7269\n","Epoch:  13 \t Training acc: 0.799 \t Val acc:0.7861 \t Training loss:0.7038 \t Val loss:0.6882\n","Epoch:  14 \t Training acc: 0.8103 \t Val acc:0.7972 \t Training loss:0.6583 \t Val loss:0.6565\n","Epoch:  15 \t Training acc: 0.8208 \t Val acc:0.8065 \t Training loss:0.6177 \t Val loss:0.6233\n","Epoch:  16 \t Training acc: 0.8353 \t Val acc:0.8324 \t Training loss:0.5810 \t Val loss:0.5913\n","Epoch:  17 \t Training acc: 0.8458 \t Val acc:0.8417 \t Training loss:0.5466 \t Val loss:0.5588\n","Epoch:  18 \t Training acc: 0.8589 \t Val acc:0.8556 \t Training loss:0.5159 \t Val loss:0.5296\n","Epoch:  19 \t Training acc: 0.8679 \t Val acc:0.8611 \t Training loss:0.4883 \t Val loss:0.5053\n","Epoch:  20 \t Training acc: 0.8776 \t Val acc:0.8704 \t Training loss:0.4638 \t Val loss:0.4825\n","Epoch:  21 \t Training acc: 0.8831 \t Val acc:0.8796 \t Training loss:0.4417 \t Val loss:0.4623\n","Epoch:  22 \t Training acc: 0.8887 \t Val acc:0.8870 \t Training loss:0.4214 \t Val loss:0.4442\n","Epoch:  23 \t Training acc: 0.8944 \t Val acc:0.8944 \t Training loss:0.4032 \t Val loss:0.4267\n","Epoch:  24 \t Training acc: 0.901 \t Val acc:0.8972 \t Training loss:0.3861 \t Val loss:0.4116\n","Epoch:  25 \t Training acc: 0.9056 \t Val acc:0.9000 \t Training loss:0.3704 \t Val loss:0.3970\n","Epoch:  26 \t Training acc: 0.9105 \t Val acc:0.9037 \t Training loss:0.3558 \t Val loss:0.3832\n","Epoch:  27 \t Training acc: 0.9135 \t Val acc:0.9056 \t Training loss:0.3421 \t Val loss:0.3703\n","Epoch:  28 \t Training acc: 0.9165 \t Val acc:0.9074 \t Training loss:0.3296 \t Val loss:0.3584\n","Epoch:  29 \t Training acc: 0.9198 \t Val acc:0.9093 \t Training loss:0.3179 \t Val loss:0.3475\n","Epoch:  30 \t Training acc: 0.9232 \t Val acc:0.9111 \t Training loss:0.3070 \t Val loss:0.3377\n","Epoch:  31 \t Training acc: 0.9266 \t Val acc:0.9093 \t Training loss:0.2967 \t Val loss:0.3289\n","Epoch:  32 \t Training acc: 0.9298 \t Val acc:0.9139 \t Training loss:0.2872 \t Val loss:0.3183\n","Epoch:  33 \t Training acc: 0.9317 \t Val acc:0.9167 \t Training loss:0.2783 \t Val loss:0.3107\n","Epoch:  34 \t Training acc: 0.9353 \t Val acc:0.9167 \t Training loss:0.2698 \t Val loss:0.3026\n","Epoch:  35 \t Training acc: 0.9383 \t Val acc:0.9176 \t Training loss:0.2615 \t Val loss:0.2952\n","Epoch:  36 \t Training acc: 0.9407 \t Val acc:0.9250 \t Training loss:0.2537 \t Val loss:0.2884\n","Epoch:  37 \t Training acc: 0.9423 \t Val acc:0.9250 \t Training loss:0.2463 \t Val loss:0.2822\n","Epoch:  38 \t Training acc: 0.9442 \t Val acc:0.9278 \t Training loss:0.2394 \t Val loss:0.2766\n","Epoch:  39 \t Training acc: 0.946 \t Val acc:0.9296 \t Training loss:0.2325 \t Val loss:0.2706\n","Epoch:  40 \t Training acc: 0.948 \t Val acc:0.9306 \t Training loss:0.2262 \t Val loss:0.2653\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"N3pb2xa5bzL9","executionInfo":{"status":"ok","timestamp":1607453425777,"user_tz":300,"elapsed":627839,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}},"outputId":"aadb3c52-c019-4d94-8006-f410032a581f"},"source":["model_5 = SignClassifier()\n","use_cuda = False\n","\n","batch_size = 256\n","train_loader, val_loader, test_loader, classes = get_data_loader(batch_size)\n","\n","train_acc, val_acc, train_loss, val_loss = train(model_5, train_loader, val_loader, batch_size=256, num_epochs=50, learning_rate = 0.00018)\n","plot_training_curve(train_acc, val_acc, train_loss, val_loss)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Training Started...\n","\n","U S I N G   C U D A \n","\n","\n","Epoch:  1 \t Training acc: 0.276 \t Val acc:0.2593 \t Training loss:2.4705 \t Val loss:2.4427\n","Epoch:  2 \t Training acc: 0.3335 \t Val acc:0.3120 \t Training loss:2.3751 \t Val loss:2.2729\n","Epoch:  3 \t Training acc: 0.4105 \t Val acc:0.3713 \t Training loss:2.1013 \t Val loss:1.9171\n","Epoch:  4 \t Training acc: 0.4948 \t Val acc:0.4722 \t Training loss:1.7409 \t Val loss:1.6041\n","Epoch:  5 \t Training acc: 0.5917 \t Val acc:0.5704 \t Training loss:1.4594 \t Val loss:1.3537\n","Epoch:  6 \t Training acc: 0.6389 \t Val acc:0.6269 \t Training loss:1.2556 \t Val loss:1.1735\n","Epoch:  7 \t Training acc: 0.6702 \t Val acc:0.6778 \t Training loss:1.1087 \t Val loss:1.0402\n","Epoch:  8 \t Training acc: 0.7056 \t Val acc:0.7102 \t Training loss:0.9955 \t Val loss:0.9409\n","Epoch:  9 \t Training acc: 0.7339 \t Val acc:0.7435 \t Training loss:0.9039 \t Val loss:0.8554\n","Epoch:  10 \t Training acc: 0.7633 \t Val acc:0.7565 \t Training loss:0.8271 \t Val loss:0.7830\n","Epoch:  11 \t Training acc: 0.7837 \t Val acc:0.7787 \t Training loss:0.7611 \t Val loss:0.7273\n","Epoch:  12 \t Training acc: 0.798 \t Val acc:0.7861 \t Training loss:0.7038 \t Val loss:0.6881\n","Epoch:  13 \t Training acc: 0.8073 \t Val acc:0.7963 \t Training loss:0.6550 \t Val loss:0.6547\n","Epoch:  14 \t Training acc: 0.8226 \t Val acc:0.8130 \t Training loss:0.6119 \t Val loss:0.6223\n","Epoch:  15 \t Training acc: 0.8361 \t Val acc:0.8222 \t Training loss:0.5729 \t Val loss:0.5839\n","Epoch:  16 \t Training acc: 0.8514 \t Val acc:0.8426 \t Training loss:0.5364 \t Val loss:0.5484\n","Epoch:  17 \t Training acc: 0.8637 \t Val acc:0.8537 \t Training loss:0.5034 \t Val loss:0.5177\n","Epoch:  18 \t Training acc: 0.8722 \t Val acc:0.8676 \t Training loss:0.4746 \t Val loss:0.4921\n","Epoch:  19 \t Training acc: 0.8794 \t Val acc:0.8787 \t Training loss:0.4491 \t Val loss:0.4685\n","Epoch:  20 \t Training acc: 0.8869 \t Val acc:0.8806 \t Training loss:0.4258 \t Val loss:0.4479\n","Epoch:  21 \t Training acc: 0.8921 \t Val acc:0.8861 \t Training loss:0.4052 \t Val loss:0.4300\n","Epoch:  22 \t Training acc: 0.8982 \t Val acc:0.8917 \t Training loss:0.3859 \t Val loss:0.4125\n","Epoch:  23 \t Training acc: 0.906 \t Val acc:0.9009 \t Training loss:0.3684 \t Val loss:0.3964\n","Epoch:  24 \t Training acc: 0.9105 \t Val acc:0.9065 \t Training loss:0.3522 \t Val loss:0.3826\n","Epoch:  25 \t Training acc: 0.9149 \t Val acc:0.9083 \t Training loss:0.3371 \t Val loss:0.3688\n","Epoch:  26 \t Training acc: 0.9177 \t Val acc:0.9056 \t Training loss:0.3237 \t Val loss:0.3572\n","Epoch:  27 \t Training acc: 0.9226 \t Val acc:0.9074 \t Training loss:0.3112 \t Val loss:0.3447\n","Epoch:  28 \t Training acc: 0.9258 \t Val acc:0.9093 \t Training loss:0.2993 \t Val loss:0.3346\n","Epoch:  29 \t Training acc: 0.9294 \t Val acc:0.9139 \t Training loss:0.2880 \t Val loss:0.3251\n","Epoch:  30 \t Training acc: 0.9319 \t Val acc:0.9148 \t Training loss:0.2775 \t Val loss:0.3161\n","Epoch:  31 \t Training acc: 0.9367 \t Val acc:0.9176 \t Training loss:0.2676 \t Val loss:0.3070\n","Epoch:  32 \t Training acc: 0.9403 \t Val acc:0.9204 \t Training loss:0.2582 \t Val loss:0.2984\n","Epoch:  33 \t Training acc: 0.9431 \t Val acc:0.9231 \t Training loss:0.2493 \t Val loss:0.2908\n","Epoch:  34 \t Training acc: 0.9442 \t Val acc:0.9259 \t Training loss:0.2408 \t Val loss:0.2834\n","Epoch:  35 \t Training acc: 0.9472 \t Val acc:0.9241 \t Training loss:0.2329 \t Val loss:0.2763\n","Epoch:  36 \t Training acc: 0.9486 \t Val acc:0.9241 \t Training loss:0.2253 \t Val loss:0.2701\n","Epoch:  37 \t Training acc: 0.9502 \t Val acc:0.9278 \t Training loss:0.2180 \t Val loss:0.2641\n","Epoch:  38 \t Training acc: 0.9516 \t Val acc:0.9287 \t Training loss:0.2111 \t Val loss:0.2592\n","Epoch:  39 \t Training acc: 0.9526 \t Val acc:0.9287 \t Training loss:0.2046 \t Val loss:0.2544\n","Epoch:  40 \t Training acc: 0.954 \t Val acc:0.9287 \t Training loss:0.1983 \t Val loss:0.2498\n","Epoch:  41 \t Training acc: 0.9562 \t Val acc:0.9306 \t Training loss:0.1923 \t Val loss:0.2455\n","Epoch:  42 \t Training acc: 0.9573 \t Val acc:0.9306 \t Training loss:0.1866 \t Val loss:0.2415\n","Epoch:  43 \t Training acc: 0.9585 \t Val acc:0.9269 \t Training loss:0.1814 \t Val loss:0.2388\n","Epoch:  44 \t Training acc: 0.9593 \t Val acc:0.9278 \t Training loss:0.1761 \t Val loss:0.2351\n","Epoch:  45 \t Training acc: 0.9609 \t Val acc:0.9287 \t Training loss:0.1713 \t Val loss:0.2319\n","Epoch:  46 \t Training acc: 0.9625 \t Val acc:0.9287 \t Training loss:0.1667 \t Val loss:0.2291\n","Epoch:  47 \t Training acc: 0.9625 \t Val acc:0.9324 \t Training loss:0.1618 \t Val loss:0.2261\n","Epoch:  48 \t Training acc: 0.9627 \t Val acc:0.9333 \t Training loss:0.1574 \t Val loss:0.2243\n","Epoch:  49 \t Training acc: 0.9639 \t Val acc:0.9333 \t Training loss:0.1532 \t Val loss:0.2206\n","Epoch:  50 \t Training acc: 0.9639 \t Val acc:0.9398 \t Training loss:0.1485 \t Val loss:0.2186\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"hYNG6mXgdarC","executionInfo":{"status":"ok","timestamp":1607453580505,"user_tz":300,"elapsed":780511,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}},"outputId":"e30d4f6f-da51-4f40-b4fb-5676c0bb1d3d"},"source":["model_6 = SignClassifier()\n","use_cuda = False\n","\n","batch_size = 256\n","train_loader, val_loader, test_loader, classes = get_data_loader(batch_size)\n","\n","train_acc, val_acc, train_loss, val_loss = train(model_6, train_loader, val_loader, batch_size=256, num_epochs=50, learning_rate = 0.0002)\n","plot_training_curve(train_acc, val_acc, train_loss, val_loss)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Training Started...\n","\n","U S I N G   C U D A \n","\n","\n","Epoch:  1 \t Training acc: 0.177 \t Val acc:0.1593 \t Training loss:2.4724 \t Val loss:2.4504\n","Epoch:  2 \t Training acc: 0.3306 \t Val acc:0.3056 \t Training loss:2.3821 \t Val loss:2.2810\n","Epoch:  3 \t Training acc: 0.3986 \t Val acc:0.3611 \t Training loss:2.1136 \t Val loss:1.9236\n","Epoch:  4 \t Training acc: 0.47 \t Val acc:0.4398 \t Training loss:1.7122 \t Val loss:1.5745\n","Epoch:  5 \t Training acc: 0.5496 \t Val acc:0.5250 \t Training loss:1.4377 \t Val loss:1.3642\n","Epoch:  6 \t Training acc: 0.6032 \t Val acc:0.5861 \t Training loss:1.2619 \t Val loss:1.2023\n","Epoch:  7 \t Training acc: 0.655 \t Val acc:0.6491 \t Training loss:1.1180 \t Val loss:1.0668\n","Epoch:  8 \t Training acc: 0.7062 \t Val acc:0.6926 \t Training loss:0.9920 \t Val loss:0.9447\n","Epoch:  9 \t Training acc: 0.75 \t Val acc:0.7259 \t Training loss:0.8807 \t Val loss:0.8409\n","Epoch:  10 \t Training acc: 0.7806 \t Val acc:0.7657 \t Training loss:0.7859 \t Val loss:0.7591\n","Epoch:  11 \t Training acc: 0.804 \t Val acc:0.7787 \t Training loss:0.7066 \t Val loss:0.6915\n","Epoch:  12 \t Training acc: 0.8202 \t Val acc:0.7954 \t Training loss:0.6423 \t Val loss:0.6367\n","Epoch:  13 \t Training acc: 0.8401 \t Val acc:0.8130 \t Training loss:0.5896 \t Val loss:0.5867\n","Epoch:  14 \t Training acc: 0.855 \t Val acc:0.8324 \t Training loss:0.5439 \t Val loss:0.5413\n","Epoch:  15 \t Training acc: 0.8698 \t Val acc:0.8500 \t Training loss:0.5050 \t Val loss:0.5021\n","Epoch:  16 \t Training acc: 0.878 \t Val acc:0.8657 \t Training loss:0.4707 \t Val loss:0.4674\n","Epoch:  17 \t Training acc: 0.8875 \t Val acc:0.8741 \t Training loss:0.4392 \t Val loss:0.4371\n","Epoch:  18 \t Training acc: 0.8921 \t Val acc:0.8769 \t Training loss:0.4116 \t Val loss:0.4158\n","Epoch:  19 \t Training acc: 0.8986 \t Val acc:0.8833 \t Training loss:0.3876 \t Val loss:0.3958\n","Epoch:  20 \t Training acc: 0.9058 \t Val acc:0.8935 \t Training loss:0.3653 \t Val loss:0.3765\n","Epoch:  21 \t Training acc: 0.9119 \t Val acc:0.9019 \t Training loss:0.3451 \t Val loss:0.3621\n","Epoch:  22 \t Training acc: 0.9177 \t Val acc:0.9056 \t Training loss:0.3263 \t Val loss:0.3492\n","Epoch:  23 \t Training acc: 0.9236 \t Val acc:0.9102 \t Training loss:0.3102 \t Val loss:0.3379\n","Epoch:  24 \t Training acc: 0.9252 \t Val acc:0.9111 \t Training loss:0.2955 \t Val loss:0.3279\n","Epoch:  25 \t Training acc: 0.9292 \t Val acc:0.9148 \t Training loss:0.2820 \t Val loss:0.3191\n","Epoch:  26 \t Training acc: 0.9319 \t Val acc:0.9148 \t Training loss:0.2690 \t Val loss:0.3113\n","Epoch:  27 \t Training acc: 0.9363 \t Val acc:0.9167 \t Training loss:0.2574 \t Val loss:0.3049\n","Epoch:  28 \t Training acc: 0.9395 \t Val acc:0.9185 \t Training loss:0.2463 \t Val loss:0.2987\n","Epoch:  29 \t Training acc: 0.9419 \t Val acc:0.9194 \t Training loss:0.2366 \t Val loss:0.2932\n","Epoch:  30 \t Training acc: 0.9452 \t Val acc:0.9204 \t Training loss:0.2277 \t Val loss:0.2863\n","Epoch:  31 \t Training acc: 0.9472 \t Val acc:0.9213 \t Training loss:0.2192 \t Val loss:0.2791\n","Epoch:  32 \t Training acc: 0.9506 \t Val acc:0.9287 \t Training loss:0.2112 \t Val loss:0.2732\n","Epoch:  33 \t Training acc: 0.9528 \t Val acc:0.9324 \t Training loss:0.2035 \t Val loss:0.2676\n","Epoch:  34 \t Training acc: 0.9534 \t Val acc:0.9352 \t Training loss:0.1957 \t Val loss:0.2611\n","Epoch:  35 \t Training acc: 0.9565 \t Val acc:0.9417 \t Training loss:0.1885 \t Val loss:0.2519\n","Epoch:  36 \t Training acc: 0.9603 \t Val acc:0.9444 \t Training loss:0.1822 \t Val loss:0.2416\n","Epoch:  37 \t Training acc: 0.9615 \t Val acc:0.9444 \t Training loss:0.1765 \t Val loss:0.2336\n","Epoch:  38 \t Training acc: 0.9599 \t Val acc:0.9407 \t Training loss:0.1690 \t Val loss:0.2306\n","Epoch:  39 \t Training acc: 0.9603 \t Val acc:0.9380 \t Training loss:0.1592 \t Val loss:0.2280\n","Epoch:  40 \t Training acc: 0.9621 \t Val acc:0.9398 \t Training loss:0.1498 \t Val loss:0.2238\n","Epoch:  41 \t Training acc: 0.9661 \t Val acc:0.9426 \t Training loss:0.1418 \t Val loss:0.2187\n","Epoch:  42 \t Training acc: 0.9677 \t Val acc:0.9435 \t Training loss:0.1352 \t Val loss:0.2154\n","Epoch:  43 \t Training acc: 0.9708 \t Val acc:0.9426 \t Training loss:0.1294 \t Val loss:0.2141\n","Epoch:  44 \t Training acc: 0.9712 \t Val acc:0.9454 \t Training loss:0.1237 \t Val loss:0.2120\n","Epoch:  45 \t Training acc: 0.9728 \t Val acc:0.9481 \t Training loss:0.1184 \t Val loss:0.2110\n","Epoch:  46 \t Training acc: 0.9744 \t Val acc:0.9481 \t Training loss:0.1135 \t Val loss:0.2089\n","Epoch:  47 \t Training acc: 0.9756 \t Val acc:0.9491 \t Training loss:0.1089 \t Val loss:0.2071\n","Epoch:  48 \t Training acc: 0.975 \t Val acc:0.9491 \t Training loss:0.1044 \t Val loss:0.2058\n","Epoch:  49 \t Training acc: 0.9756 \t Val acc:0.9500 \t Training loss:0.1002 \t Val loss:0.2042\n","Epoch:  50 \t Training acc: 0.9758 \t Val acc:0.9491 \t Training loss:0.0960 \t Val loss:0.2030\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"nDbnqQqR1Cck","executionInfo":{"status":"ok","timestamp":1607453678580,"user_tz":300,"elapsed":876762,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}},"outputId":"a102c6ec-2d8b-4039-d342-fab41c9a493f"},"source":["model_7 = SignClassifier()\n","use_cuda = False\n","\n","batch_size = 128\n","train_loader, val_loader, test_loader, classes = get_data_loader(batch_size)\n","\n","train_acc, val_acc, train_loss, val_loss = train(model_7, train_loader, val_loader, batch_size=128, num_epochs=30, learning_rate = 0.0002)\n","plot_training_curve(train_acc, val_acc, train_loss, val_loss)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Training Started...\n","\n","U S I N G   C U D A \n","\n","\n","Epoch:  1 \t Training acc: 0.3208 \t Val acc:0.2991 \t Training loss:2.4431 \t Val loss:2.3351\n","Epoch:  2 \t Training acc: 0.4498 \t Val acc:0.4148 \t Training loss:2.0484 \t Val loss:1.7181\n","Epoch:  3 \t Training acc: 0.5694 \t Val acc:0.5509 \t Training loss:1.5017 \t Val loss:1.3505\n","Epoch:  4 \t Training acc: 0.6552 \t Val acc:0.6370 \t Training loss:1.2210 \t Val loss:1.1097\n","Epoch:  5 \t Training acc: 0.7192 \t Val acc:0.6981 \t Training loss:1.0130 \t Val loss:0.9153\n","Epoch:  6 \t Training acc: 0.7659 \t Val acc:0.7435 \t Training loss:0.8417 \t Val loss:0.7747\n","Epoch:  7 \t Training acc: 0.7952 \t Val acc:0.7852 \t Training loss:0.7137 \t Val loss:0.6813\n","Epoch:  8 \t Training acc: 0.8268 \t Val acc:0.8102 \t Training loss:0.6213 \t Val loss:0.6050\n","Epoch:  9 \t Training acc: 0.849 \t Val acc:0.8398 \t Training loss:0.5492 \t Val loss:0.5469\n","Epoch:  10 \t Training acc: 0.8716 \t Val acc:0.8546 \t Training loss:0.4892 \t Val loss:0.4863\n","Epoch:  11 \t Training acc: 0.8885 \t Val acc:0.8685 \t Training loss:0.4392 \t Val loss:0.4436\n","Epoch:  12 \t Training acc: 0.9014 \t Val acc:0.8852 \t Training loss:0.3988 \t Val loss:0.4094\n","Epoch:  13 \t Training acc: 0.9085 \t Val acc:0.8926 \t Training loss:0.3649 \t Val loss:0.3807\n","Epoch:  14 \t Training acc: 0.9161 \t Val acc:0.8991 \t Training loss:0.3372 \t Val loss:0.3567\n","Epoch:  15 \t Training acc: 0.922 \t Val acc:0.9028 \t Training loss:0.3120 \t Val loss:0.3382\n","Epoch:  16 \t Training acc: 0.9286 \t Val acc:0.9102 \t Training loss:0.2895 \t Val loss:0.3213\n","Epoch:  17 \t Training acc: 0.9317 \t Val acc:0.9157 \t Training loss:0.2698 \t Val loss:0.3075\n","Epoch:  18 \t Training acc: 0.9367 \t Val acc:0.9185 \t Training loss:0.2516 \t Val loss:0.2983\n","Epoch:  19 \t Training acc: 0.9389 \t Val acc:0.9204 \t Training loss:0.2351 \t Val loss:0.2897\n","Epoch:  20 \t Training acc: 0.9435 \t Val acc:0.9259 \t Training loss:0.2202 \t Val loss:0.2814\n","Epoch:  21 \t Training acc: 0.947 \t Val acc:0.9269 \t Training loss:0.2068 \t Val loss:0.2745\n","Epoch:  22 \t Training acc: 0.9498 \t Val acc:0.9259 \t Training loss:0.1950 \t Val loss:0.2660\n","Epoch:  23 \t Training acc: 0.9522 \t Val acc:0.9296 \t Training loss:0.1835 \t Val loss:0.2582\n","Epoch:  24 \t Training acc: 0.9563 \t Val acc:0.9315 \t Training loss:0.1738 \t Val loss:0.2492\n","Epoch:  25 \t Training acc: 0.9603 \t Val acc:0.9352 \t Training loss:0.1635 \t Val loss:0.2389\n","Epoch:  26 \t Training acc: 0.9611 \t Val acc:0.9343 \t Training loss:0.1551 \t Val loss:0.2325\n","Epoch:  27 \t Training acc: 0.9643 \t Val acc:0.9380 \t Training loss:0.1462 \t Val loss:0.2275\n","Epoch:  28 \t Training acc: 0.9647 \t Val acc:0.9417 \t Training loss:0.1394 \t Val loss:0.2242\n","Epoch:  29 \t Training acc: 0.9675 \t Val acc:0.9444 \t Training loss:0.1314 \t Val loss:0.2165\n","Epoch:  30 \t Training acc: 0.9712 \t Val acc:0.9500 \t Training loss:0.1238 \t Val loss:0.2053\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":908},"id":"a09go22G8r5Q","executionInfo":{"status":"ok","timestamp":1607453735401,"user_tz":300,"elapsed":931413,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}},"outputId":"2affcd9e-9bc3-46d7-8e0b-8cdf8ccd8a53"},"source":["model_8 = SignClassifier()\n","use_cuda = False\n","\n","batch_size = 64\n","train_loader, val_loader, test_loader, classes = get_data_loader(batch_size)\n","\n","train_acc, val_acc, train_loss, val_loss = train(model_8, train_loader, val_loader, batch_size=64, num_epochs=15, learning_rate = 0.00008)\n","plot_training_curve(train_acc, val_acc, train_loss, val_loss)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Training Started...\n","\n","U S I N G   C U D A \n","\n","\n","Epoch:  1 \t Training acc: 0.2629 \t Val acc:0.2463 \t Training loss:2.4562 \t Val loss:2.3783\n","Epoch:  2 \t Training acc: 0.4024 \t Val acc:0.3880 \t Training loss:2.1410 \t Val loss:1.8747\n","Epoch:  3 \t Training acc: 0.4984 \t Val acc:0.4852 \t Training loss:1.6750 \t Val loss:1.5203\n","Epoch:  4 \t Training acc: 0.5766 \t Val acc:0.5500 \t Training loss:1.4223 \t Val loss:1.3274\n","Epoch:  5 \t Training acc: 0.6323 \t Val acc:0.6056 \t Training loss:1.2490 \t Val loss:1.1670\n","Epoch:  6 \t Training acc: 0.6742 \t Val acc:0.6694 \t Training loss:1.1057 \t Val loss:1.0394\n","Epoch:  7 \t Training acc: 0.7121 \t Val acc:0.7028 \t Training loss:0.9908 \t Val loss:0.9385\n","Epoch:  8 \t Training acc: 0.7375 \t Val acc:0.7278 \t Training loss:0.8981 \t Val loss:0.8579\n","Epoch:  9 \t Training acc: 0.7657 \t Val acc:0.7491 \t Training loss:0.8233 \t Val loss:0.7908\n","Epoch:  10 \t Training acc: 0.7855 \t Val acc:0.7694 \t Training loss:0.7626 \t Val loss:0.7366\n","Epoch:  11 \t Training acc: 0.798 \t Val acc:0.7852 \t Training loss:0.7118 \t Val loss:0.6909\n","Epoch:  12 \t Training acc: 0.8131 \t Val acc:0.7944 \t Training loss:0.6684 \t Val loss:0.6508\n","Epoch:  13 \t Training acc: 0.8242 \t Val acc:0.8028 \t Training loss:0.6292 \t Val loss:0.6167\n","Epoch:  14 \t Training acc: 0.8351 \t Val acc:0.8102 \t Training loss:0.5953 \t Val loss:0.5834\n","Epoch:  15 \t Training acc: 0.8415 \t Val acc:0.8222 \t Training loss:0.5644 \t Val loss:0.5543\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYIAAAEWCAYAAABrDZDcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3dd3hVVdbA4d8ikASSQAgplCQEgVCkEwTEUVR0UFGsCDaYsY/dmXHUcRzrfOo4OjpWbGBBFAtiQUcZsQAKoUPoJSGUJBAggZC+vj/OBS4h5QZyc1LW+zx5csq+5657CXuds/c5e4uqYowxpvFq4nYAxhhj3GWJwBhjGjlLBMYY08hZIjDGmEbOEoExxjRylgiMMaaRs0RgaoSIzBSR8W7HcSxEZJKIPOZZ/o2IrPGl7DG+1z4ROeFYX2+MP1giaMQ8ldLBn1IROeC1fmV1jqWq56jqZH/FWhkRGSsim0VEymxvKiKZIjLK12Op6k+q2q2G4potIteVOX6oqm6sieNX8p67RSTIX+9hGh5LBI2Yp1IKVdVQIA0432vbewfLiUhT96L0yXQgHDitzPaRgAJf13pELhCRBOA3OJ/5glp+77r+N2IqYYnAHEVEhotIuoj8RUR2AG+JSGsR+UJEsjxnnF+ISKzXaw6d/YrIBBH5WUSe9pTdJCLnVPBefxGRj8pse05Envc61kYRyfUc56grFVXNBz4Erimz6xpgiqoWi8g0EdkhIntF5EcRObGyz+613l9EFnne/wMg2Gtfhd+JiDyOUym/4LnCesGzXUWki2e5lYi87Xl9qog8ICJNqvsdlvm8vwCTgCOa6UQkTkQ+8bzXroPxePZdLyKrPJ8xRUQGlI3Vs+7dhHYsfyMRIvKWiGzz7J/u2b5CRM73KtdMRHaKSP8qPq+pIZYITEXaAhFAR+AGnL+Vtzzr8cAB4IUKXw2DgTVAJPAU8EbZphuPqcC5IhIGICIBwBhgioiEAM8D56hqGHAysKSC95sMXCoizT3HaQWc79kOMBPoCkQDi4D3yjuINxEJxLnaeAfnu5gGXOJVpMLvRFX/CvwE3Oq5wrq1nLf4D9AKOAHnauYa4Hde+339Dg+6xvO53gN+KyIxns8RAHwBpAIJQAec7x0RuQx4yPPaljhXErsq+168VPdv5B2gBXAizr/Ds57tbwNXeZU7F9iuqot9jMMcL1W1H/sB2AyM8CwPBwqB4ErK9wN2e63PBq7zLE8A1nvta4HTXNG2gmP9DFzjWT4L2OBZDgH24FS+zX34DOuAKzzL1wNLKygX7omnlWd9EvCY12dP9yyfCmwDxOu1cw+Wrc534rVNgS5AgOc77um170Zg9jF+h6cARUCkZ301cJdneSiQBTQt53XfAHdUcEwFunitl/2efP4bAdoBpUDrcsq1B3KBlp71j4B73P4/0Zh+7IrAVCRLnSYXAESkhYi86mnCyAF+BMI9Z5vl2XFwQVXzPIuhFZSdAozzLF/hWUdV9wOXAzcB20XkSxHpXknMb3O4eehqzzoiEiAiT4jIBk/smz1lIis5FjgV1Fb11E4eqQcXjuE78RYJNPM+nme5g9d6db7D8cB/VXWnZ30Kh5uH4oBUVS0u53VxwAYf4i1Pdf5G4oBsVd1d9iCqug2YA1wiIuHAOfhwxWZqjiUCU5Gyw9L+EegGDFbVljhnywCVNVX4ahow3NOefBGeRACgqt+o6lk4Z5SrgdcqOc47wJkiMhQYwuHK5ApgNDACpykmwcfYtwMdyjTHxHstV/WdVDa0706cM/iOZY69tYqYjuJpDhsDnObpB9kB3AX0FZG+wBYgXsrv0N0CdK7g0Hk4VyIHtS2zvzp/I1uACE9FX57JOM1DlwHzVLXa34M5dpYIjK/CcNp894hIBPD3mjqwqmbhNKO8BWxS1VUAIhIjIqM9fQUFwD6c5oWKjrMZp5npfeBbVT14Rh3mef0unIrtHz6GNg8oBm73dGBeDJzktb+q7yQDp/2/vFhLcDq4HxeRMBHpCNwNvOtjbN4uBEqAnjjNMf2AHjh9FNcA83GS2hMiEiIiwSIyzPPa14E/ichAcXTxxAJOf8wVniuqkRx9V1ZZFX4fqrodp5/mJU+ncjMROdXrtdOBAcAdeK7kTO2xRGB89W+gOc6Z7C/U/C2ZU3DO2Kd4bWuCUzluA7JxKqKbqzjOZJyzbO/K5G2cZpetQApO/FVS1ULgYpz2+mycZqpPvIpU9Z08h9OBvVs8d0GVcRuwH9iIk8CmAG/6ElsZ44G3VDVNVXcc/MHpqL0S54z8fJy+iTQg3fNZUNVpwOOe987FqZAjPMe9w/O6PZ7jTK8ijqq+j6txroJWA5nAnQd3qOoB4GOgE0d+x6YWyJHNn8YY4w4ReRBIVNWrqixsapQ9BGKMcZ2nKelanKsGU8usacgY4yoRuR6nM3mmqv7odjyNkTUNGWNMI2dXBMYY08jVuz6CyMhITUhIcDsMY4ypVxYuXLhTVaPK21fvEkFCQgLJycluh2GMMfWKiKRWtM+ahowxppGzRGCMMY2cJQJjjGnk6l0fQXmKiopIT08nPz+/6sLGJ8HBwcTGxtKsWTO3QzHG+FmDSATp6emEhYWRkJBA5fN2GF+oKrt27SI9PZ1OnTq5HY4xxs8aRNNQfn4+bdq0sSRQQ0SENm3a2BWWMY1Eg0gEgCWBGmbfpzGNR4NoGjLGmIakqKSUnfsKyMwpIDO3gKzcAjJz8zmjezR9Yiua2+fYWSKoAbt27eLMM88EYMeOHQQEBBAV5TzAN3/+fAIDAyt8bXJyMm+//TbPP1/ecPXGmIYkr7CYzJwCsg5V8vlk5h5ezvJU+tl5hZQ3DFyb0CBLBHVVmzZtWLJkCQAPPfQQoaGh/OlPfzq0v7i4mKZNy/+qk5KSSEpKqpU4jTH+U1BcwuadeazNyGXrngNeFX6+54y+gH0FR08b3bSJEBUWRHRYELGtWzCgY2uiw4KIDgs+tD26ZRCRoUE0C/BPa74lAj+ZMGECwcHBLF68mGHDhjF27FjuuOMO8vPzad68OW+99RbdunVj9uzZPP3003zxxRc89NBDpKWlsXHjRtLS0rjzzju5/fbb3f4oxhgv3hX+uoxc1mXuY21GLpt35VFSevg0vkVgwKEKvUf7lpwa6lTo0WHBhyr36LBgwps3o0kTd/vkGlwiePjzlaRsy6nRY/Zs35K/n39itV+Xnp7O3LlzCQgIICcnh59++ommTZvy3Xffcf/99/Pxxx8f9ZrVq1fz/fffk5ubS7du3bj55pvtXn5jXOBLhd9EIKFNCF2iQzmnVzu6xoTSNTqM+DYtCA2qP9Vr/Ym0HrrssssICAgAYO/evYwfP55169YhIhQVFZX7mvPOO4+goCCCgoKIjo4mIyOD2NjY2gzbmEbleCr8E6JCCG4W4PInOH4NLhEcy5m7v4SEhBxa/tvf/sbpp5/Op59+yubNmxk+fHi5rwkKCjq0HBAQQHHx0W2KxpjqKyopZfPO/azNcCr6gz/lVfhdYw5X+IkxYXSKdLnCV4XcHdCsOTS3zuJ6a+/evXTo0AGASZMmuRuMMQ1YSamSlp3Hmh3OGf7azH2s3ZHLxp37KCo5XOF3bBNC1+g6VuEDFBfCzjWwYwVkrIAdy53febvg/Odg4IQaf0tLBLXknnvuYfz48Tz22GOcd955bodjTL1XWqps3XPAc2Z/+Cx/feY+CopLD5WLi2hOYnQYZ/SIJtFT4XeOCnW/wgfYv/NwRX+w4s9aA6WepuOAIIjuAd3OgbZ9oOMpfgmj3s1ZnJSUpGUnplm1ahU9evRwKaKGy75XUxeoKhk5BazJyGXtjsNNOusy95FXWHKoXLtWwXSNCaNbTKjndxhdokMJqQudtiXFkL3h6Eo/d/vhMqFtoW0viOkFbXs7v9t0gYCaiV9EFqpqufeq14FvyBhjDisuKWXV9lwWbM5mYepuklOzycgpOLQ/MjSIbm1DGZMUR7e2YSTGhNIlOoxWzevA3XWqkJcNWauPbNbJXAXFnrG7mjSFqO7Q6bQjK/6QSNfCtkRgjHFVTn4Ri1J3syh1N8mpu1myZc+hM/32rYI5qVMbBsaH071dSxJjwogIqfhJfb9Shfy9kLMVcrbB3nRnee9WyEn3/N4GxQcOv6Z5hFPJD7rOU+H3gshu0NSlz1ABvyYCERkJPAcEAK+r6hNl9scDk4FwT5l7VfUrf8ZkjHGPqpK++wDJqdkkb97NwtTdrMnIRdXpwO3RriWXDYxlYEIESR1b0z68ee0FV7i/TKW+tUxlvxUK9x35GmkCYe2gZQdo18dpy28VCxGdnUo/rB3UgwEc/ZYIRCQAeBE4C0gHFojIDFVN8Sr2APChqr4sIj2Br4AEf8VkjKldRSWlpGzLITl1NwtTnaaeg808oUFN6R8fzshebUnqGEG/+HD/PYSlCgd2w55U2J0Ke9Kc5T1bDlf4+XuOfl1INLTqAJFdofPpToXfqgO0jHV+h7atsTZ8N/nzE5wErFfVjQAiMhUYDXgnAgVaepZbAdv8GI8xxs/2Hihicdpukjc7bftLt+zlQJHTzNMhvDmDO7UhKaE1Azu2pnvblgTU5NAK+TleFXyap8L3Wi7MPbJ8UCsIj3fO4OMGH1nBt+wALdtD06Dy36uB8Wci6ABs8VpPBwaXKfMQ8F8RuQ0IAUaUdyARuQG4ASA+Pr7GAzXGVI+qkplbQMq2HFZu28vKbTms3JZDWnYeAAFNhB7twrh8UBwDO7YmKaE17VodZzNPYR7s3eJVwZc5uz+w+8jyzVpAeEdo3RE6nnx4OTzeWfbDg1n1ldvXNOOASar6LxEZCrwjIr1UtdS7kKpOBCaCc/uoC3FW6vTTT+fee+/lt7/97aFt//73v1mzZg0vv/zyUeWHDx/O008/TVJSEueeey5TpkwhPPzIP8ryRjEta/r06SQmJtKzZ08AHnzwQU499VRGjCg3nxpzTEpLldTsvCMq/JRte9m5r/BQmY5tWtCrQ0suHxRHv7hw+sWFH9ttm/l7IXsj7NoA2Zuc5YM/+zOPLBsQ5KnU46HDgMMVfOuOzu8WbepF+3xd4M9EsBWI81qP9Wzzdi0wEkBV54lIMBAJlPkXr9vGjRvH1KlTj0gEU6dO5amnnqrytV99dex949OnT2fUqFGHEsEjjzxyzMcyBqCwuJS1GbmHzvRTtuewanvuoeGTmzYRusaEMbxbNCe2b8mJ7VvRvV0YLYOrcetmXnaZSn7D4eW8XUeWDWsPESdA4tnQOsGp4MM9Z/WhMdCkwUyy6Cp/JoIFQFcR6YSTAMYCV5QpkwacCUwSkR5AMJDlx5j84tJLL+WBBx6gsLCQwMBANm/ezLZt23j//fe5++67OXDgAJdeeikPP/zwUa9NSEggOTmZyMhIHn/8cSZPnkx0dDRxcXEMHDgQgNdee42JEydSWFhIly5deOedd1iyZAkzZszghx9+4LHHHuPjjz/m0UcfZdSoUVx66aXMmjWLP/3pTxQXFzNo0CBefvllgoKCSEhIYPz48Xz++ecUFRUxbdo0unfvXttfmakD9hUUs2p7Diu3Hj7TX5eZe2gYhhaBAfRs15KLB3Q4VOl3jQklqGkVT+QevJe+vIo+e+PRTTgtYyGiE/Q436n0D/60ToDAkHLfwtQsvyUCVS0WkVuBb3BuDX1TVVeKyCNAsqrOAP4IvCYid+F0HE/Q433Ueea9zkMcNaltbzjniQp3R0REcNJJJzFz5kxGjx7N1KlTGTNmDPfffz8RERGUlJRw5plnsmzZMvr06VPuMRYuXMjUqVNZsmQJxcXFDBgw4FAiuPjii7n++usBeOCBB3jjjTe47bbbuOCCCw5V/N7y8/OZMGECs2bNIjExkWuuuYaXX36ZO++8E4DIyEgWLVrESy+9xNNPP83rr79eE9+SqeNKS5Wl6Xv4blUGs1ZlsnrH4c7TNiGB9GzfklMTT/BU+i1JaBPi2zj5edmQNg9S50LaL7BzHRTs9SogEB7nVO4nXuSp6Dt7KvuOzkBqxlV+7SPwPBPwVZltD3otpwDD/BlDbTnYPHQwEbzxxht8+OGHTJw4keLiYrZv305KSkqFieCnn37ioosuokWLFgBccMEFh/atWLGCBx54gD179rBv374jmqDKs2bNGjp16kRiYiIA48eP58UXXzyUCC6++GIABg4cyCeffHLcn93UXQcKS/h5/U6+S8lg1upMdu4rIKCJMCihNXeflUivDs6ZfnRYEOJre/rerZ6Kfw6kzoOsVc72gCDoMBD6XHa4oj9Y2TeSu2/qK7c7i2teJWfu/jR69GjuuusuFi1aRF5eHhERETz99NMsWLCA1q1bM2HCBPLz84/p2BMmTGD69On07duXSZMmMXv27OOK9eBQ1zbMdcOUkZPPrFWZzFqVwc/rd1JQXEpYUFNO6xbFWT1jGJ4YTasWPrbpqzrNOQcr/dQ5zh06AIFhED8Yel8KHYdB+/7QLNh/H8z4TcNLBC4JDQ3l9NNP5/e//z3jxo0jJyeHkJAQWrVqRUZGBjNnzqxwDgKAU089lQkTJnDfffdRXFzM559/zo033ghAbm4u7dq1o6ioiPfee+/QcNZhYWHk5uYedaxu3bqxefNm1q9ff6hP4bTTTvPL5zbuU1VWbc/1NPlksDTdaZaJi2jOuJPiOatnDIMSIghs6kPHamkpZK48XOmnzj18t06LNhA/FAbf5NyOGdOrQTxMZSwR1Khx48Zx0UUXMXXqVLp3707//v3p3r07cXFxDBtWeQvYgAEDuPzyy+nbty/R0dEMGjTo0L5HH32UwYMHExUVxeDBgw9V/mPHjuX666/n+eef56OPPjpUPjg4mLfeeovLLrvsUGfxTTfd5J8PbVxRUFzCrxuz+W5VBt+lZLBtbz4i0C8unD//thsjesSQGBNadXNPcSFsX+JU+KlzYcsvzi2c4HTinjDcqfQ7ngyRiXY7ZgNlw1CbCtn3Wrdk7y/k+9WZzFqdwQ9rsthfWELzZgGc0jWSs3rEcHr3aKLCqmiLV3VGwlzzJWz6EbYsODxIWmSic8bfcRh0HOrcomkaDBuG2ph6atueA3y+dBuzVmWSnJpNqUJMyyBG9+/AiB7RnNw5suoJVkpLYMuvsPpL52f3Jmd72z7ObFcdT3YSQGiU3z+PqZssERhTx6gqyam7eWvOJr5ZmUFJqdKzXUtuPaMrI3pE06t9q6pv6yw6ABu+d87813wNeTshINAZA3/YHc4omWFta+cDmTqvwSQCVfX99jdTpfrWZNgQ5BeV8PnSbUyau5mV23JoGdyU607pxJWDOxLfpkXVB8jLhrVfO2f9G/4HRXnOwGqJZ0O3c6HLCAhuWfVxTKPTIBJBcHAwu3btok2bNpYMaoCqsmvXLoKD7VbA2rBjbz7v/pLKlPlpZO8vJDEmlH9c1JsL+7enRWAV/0V3b4bVX8Gar5zOXi1xhmXodyV0P89p769jk6CYuqdBJILY2FjS09PJyqp3o1PUWcHBwcTGxrodRoOlqixK28OkuZuZuXw7JaqM6BHD705OYGjnSk5oVGHHMk97/1eQ4XmKPron/OZu58y/fX+7u8dUS4NIBM2aNaNTp05uh2FMlQqKS/hy2XYmzd3MsvS9hAU3ZcLJCVwzNKHi5p+SIudsf/WXzpn/3i3OzFhxQ+Dsx6H7uc4TvMYcowaRCIyp6zJz8nnv1zTe+zWNnfsK6BwVwqMX9uLi/h3KH665KN9p5181A9bMdGbPahoMnc+A4fdC4khXJzs3DYslAmP8aMmWPUyas4kvl2+nqEQ5o3s0E05O4JQukUff+VO4H9Z961T+a79x5scNbuU093Qf5UyVaKNxGj+wRGBMDSssLmXmCqf5Z3HaHkKDmnLVkI5cMzSBTpFlKvL8HKfSX/UZrPvOebirRST0ugR6joZOp0JANcb6N+YYWCIwpoZk5Rbw/vw03v0llczcAjpFhvDQ+T25ZGAsYd4Tt+RlO809q2Y4zT8lhc4k6P2vcir/+KE2ho+pVfbXZsxxKC1V5mzYyQcLtvDflRkUlpRyWmIUT16awGldow43/+zLgtVfQMpnsPknKC2GVnEw6Hqn8o8dZLNtGddYIjDmGGzbc4BpyelMW7iF9N0HaNW8GVcMjueqIR3pEh3qFMrZBqs8lX/aXNBS5+6eobc6lb/d5mnqCEsExviosLiU71Zl8MGCLfy4LgtVOKVLJPeM7M7ZPWOcMX92p8LcSU7lnz7feWFUdzj1z9DjAog50Sp/U+dYIjCmCusycvlgwRY+WbyV7P2FtGsVzG2nd+GypDjiIlo4bf4LX4NlU2HbYudFbXvDGQ9Aj9EQlejuBzCmCpYIjCnHvoJivly2jakLtrA4bQ/NAoQRPWK4fFAcv+kaRQClzqBu373jPORVUgjt+sJZjxyehN2YesISgTEeB4d9+GBBGl8s205eYQldokN54LweXNS/A21Cg2DXBvj+FVjyPuRug+YRMOg6Z2yftr3c/gjGHBNLBKbR27WvgE8Xb2Xqgi2sz9xHi8AAzu/TnjGD4hgQH44U7oeUj2Dxu06nrzSBLmc582MnnmODupl6zxKBaZRKSpWf1mXxwYItfLcqg6ISZUB8OE9e0pvz+rQnNDAAtsyHGe/Ayk+dp3wjOsOZf4e+46BlO7c/gjE1xhKBaVT2FRTz5s+bmDo/jW1784kICWT80AQuHxRH15gwyN0BC/4Di9+DXeugWQj0ugj6Xw1xg+2OH9MgWSIwjUJpqfLp4q08+fVqMnMLOC0xigdG9WREjxgCKXYmdJn1njPWj5ZA/Mlwyp3Q80IICnU7fGP8yq+JQERGAs8BAcDrqvpEmf3PAqd7VlsA0aoa7s+YTOOzOG03D32ewtIte+gbF86rVw+kf3xryFgJ3/3Hue0zbxeEtXOmcex3JUR2cTtsY2qN3xKBiAQALwJnAenAAhGZoaopB8uo6l1e5W8D+vsrHtP4ZOTk8+TM1XyyeCvRYUE8M6YvF/aKpMmy9+Hryc49/02aOeP5978aTjjdxvgxjZI//+pPAtar6kYAEZkKjAZSKig/Dvi7H+MxjUR+UQlv/LyJF79fT3GJ8ofhnfnDqfGEpkyFF56GnK0Q0wtGPgG9x0BIG7dDNsZV/kwEHYAtXuvpwODyCopIR6AT8L8K9t8A3AAQHx9fs1GaBkNV+WZlBo9/lcKW7AOc3TOGB0Z2JT59Bky8DPakOYO7jX4RThhuHb/GeNSV6+CxwEeqWlLeTlWdCEwESEpK0toMzNQPa3bk8sgXK5mzfheJMaG8+7skTsn/Ht6/DnZvcgZ4O+9Z6HKmJQBjyvBnItgKxHmtx3q2lWcscIsfYzEN1J68Qp75di3v/pJKWHAzHr2gO1eELCTgv390bv9s2xvGTXWmdrQEYEy5/JkIFgBdRaQTTgIYC1xRtpCIdAdaA/P8GItpYIpLSpkyP41nvl1LzoEirh4cx5/j1xI670rIWgXRPWHMO84UjzbOvzGV8lsiUNViEbkV+Abn9tE3VXWliDwCJKvqDE/RscBUVbUmH+OTOet38vDnK1mbsY+TT4jgqd7pxC75AyxZDpGJcOmb0PMiSwDG+EjqW/2blJSkycnJbodhXJC2K4/Hv0rhm5UZxLUO5t8DdzJgw0vI9iXO8A/D73Xm+m0S4HaoxtQ5IrJQVZPK21dXOouNqdD+gmJe/H49r/+0iaYB8Nyg3Zyf/RZNfk6G8I4w+iXoc7k9A2DMMbL/OabOKi1Vpi/ZyhMznWEh7umWwXXFUwlc/iu0jIXzn3OeAg5oVvXBjDEVskRg6qR1Gbnc+8lyFqbuZmzbrfw18hPCUuc5w0Cc+zQMuAaaBrkdpjENgiUCU6cUFJfw0vcbeGn2egYEbuGXuOm0zZoLRdEw8kkYOAGaBbsdpjENiiUCU2ckb87m3k+Wsz5zH39OzOAP2x5C9jeHsx+DpGshsIXbIRrTIFkiMK7LzS/iqa/X8M4vqXQIb85nI/Pp+/N90LoTjJ8BodFuh2hMg2aJwLjq25QM/jZ9BRm5+fx+WCfuOWEzwZ/cBJHd4JrpEBLpdojGNHiWCIwrMnPzeXhGCl8u3073tmG8cvVA+u2fAx+Oh5gT4epPoUWE22Ea0yhYIjC1SlWZlpzOY1+mkF9cyp9/240bTj2BZqs/g4+vg3b94KqPobnNT2RMbbFEYGrN5p37ue+T5czbuIuTOkXwfxf3pnNUKCybBp/eALEnwZXTILil26Ea06hYIjB+V1RSyus/beLf360lsGkT/u/i3lyeFEeTJgJLpsBntzhzBF/xgc0PbIwLLBEYv1qevpe/fLyMlO05jDyxLQ+PPpGYlp7nABZOhs/vgBNOg7Hv2+2hxrjEEoHxi7zCYp79di1v/LyJyNAgXrlqICN7tT1cYP5r8NWfoMtZcPm79pCYMS6yRGBq3E/rsrj/0+VsyT7AFYPj+cvI7rRq7jUe0LyX4Jv7IPEcGDPZhoowxmWWCEyN2b2/kEe/TOGTRVs5ITKED24YwuATykwMP+c5+PZB6HE+XPImNA10J1hjzCGWCMxxU1VmLN3GI5+nsPdAEbed0YVbTu9CcLMy8wL8+E/432POnAEXvWqjhhpTR1giMMclv6iEez9exvQl2+gbF857l/Sme9syt3+qwuz/gx+ehD5jYfSLNneAMXWI/W80x2zXvgJufGchyam7ufusRG45vQsBTcpMEK8Ksx6Gn5+F/lfB+c/bDGLG1DGWCMwxWZ+5j99PWkBGTj4vXjGA8/q0O7qQKvz3AZj3AiT9Hs79l80jbEwdZInAVNuc9Tu5+d2FBDZtwtQbhtA/vvXRhVRh5j0wfyIMvglGPgEiR5czxrjOEoGplg8WpPHXT1dwQlQIb4wfRFxEOQ+BlZbCl3fDwrdg6K3OfAKWBIypsywRGJ+UlipPfrOaV3/YyG+6RvLilQNoGVzOXT+lJTDjdljyLpxyN5z5oCUBY+o4SwSmSgcKS7jrgyV8vXIHVw2J56HzT6RpQDlt/SXFMP1mWP4hDL8PTvuLJQFj6gFLBKZSmTn5XPd2Msu37uVvo3ry+2EJSHmVe0kRfHIDrPwEzvgbnPqn2g/WGJjSnGgAABuRSURBVHNM/HoLh4iMFJE1IrJeRO6toMwYEUkRkZUiMsWf8ZjqSdmWw4UvzmF95j5euzqJa0/pVH4SKC6Ej37nJIGzHrUkYEw947crAhEJAF4EzgLSgQUiMkNVU7zKdAXuA4ap6m4Rsclp64jvV2dy65RFhAU3Y9pNQzmxfavyCx7YA9PGw8bZMPJJGHJTrcZpjDl+VV4RiMj5InIsVw4nAetVdaOqFgJTgdFlylwPvKiquwFUNfMY3sfUsElzNnHt5AV0igrhs1uHVZwEsjfCG2fB5jlw4cuWBIypp3yp4C8H1onIUyLSvRrH7gBs8VpP92zzlggkisgcEflFREaWdyARuUFEkkUkOSsrqxohmOooLinl75+t4KHPUzizRwwf3jj08NwBZaX9Aq+PgP1ZziTz/a6o3WCNMTWmykSgqlcB/YENwCQRmeepmMNq4P2bAl2B4cA44DUROWqyWlWdqKpJqpoUFRVVA29rytpXUMz1byczeV4q1/+mE69cNZAWgRW0HC77ECafD81bw3WzIOGU2g3WGFOjfGryUdUc4COc5p12wEXAIhG5rZKXbQXivNZjPdu8pQMzVLVIVTcBa3ESg6lFW/cc4NKX5/Ljup3846Le/PW8nkePGQTO08L/exw+uR7iBsO130KbzrUfsDGmRvnSR3CBiHwKzAaaASep6jlAX+CPlbx0AdBVRDqJSCAwFphRpsx0nKsBRCQSp6loYzU/gzkOS7fs4cIX57B1zwEm/W4QVwyOL79gUT58fC38+JQzeNxVn0CLiNoN1hjjF77cNXQJ8Kyq/ui9UVXzROTail6kqsUicivwDRAAvKmqK0XkESBZVWd49p0tIilACfBnVd11rB/GVM/XK7Zz5wdLiAwNYsp1g+kaU0Fr375MmHoFpC+AEQ/DsDvsQTFjGhBR1coLiHQCtqtqvme9ORCjqpv9H97RkpKSNDk52Y23bjBUlVd/3MgTM1czID6cidckERlawXSRGSkw5XKnU/jiidDzgtoN1hhTI0RkoaomlbfPlz6CaUCp13qJZ5uphwqLS7n34+U8MXM15/dtz5Trh1ScBNZ/B2+cDSWF8LuvLAkY00D50jTU1PMcAACqWuhp8zf1zL6CYm54O5m5G3Zx+5lduWtE1/KfFAaY/xrM/AtE94QrPoBWZe/8NcY0FL4kgiwRucDTpo+IjAZ2+jcsU9NKSpU73l/Mr5uyeWZMXy4eEFt+wdIS+Oav8OvLkDgSLnkDgkJrN1hjTK3yJRHcBLwnIi8AgvOQ2DV+jcrUuCe/Xs2s1Zk8OvrEipNAQS58dC2s+waG3AJnP2rTShrTCFSZCFR1AzBEREI96/v8HpWpUR8u2MLEHzdyzdCOXD00ofxCe7bA+2MhcxWMetaZWtIY0yj4NOiciJwHnAgEH2xTVtVH/BiXqSG/bNzFX6cv5zddI3lwVM/yC21dCFPGQnEBXPURdD6jdoM0xriqykQgIq8ALYDTgdeBS4H5fo7L1IDUXfu5+d2FxEW04IUrBpQ/mczK6fDpTRAaDeM/h+jqDCdljGkIfLl99GRVvQbYraoPA0NxngA2dVhOfhHXTk5GgTfHD6JV8zLTSqrCT/9yhpBu1weu/58lAWMaKV+ahvI9v/NEpD2wC2e8IVNHFZeUcuuUxWzeuZ93rh1MQmRImQKF8PkdsHQK9L4MLngBmlUwyqgxpsHzJRF87hkR9J/AIkCB1/walTkuj325ih/XZvHExb0Z2rnNkTvzsuGDqyB1Dgy/H067x4aLMKaRqzQReCakmaWqe4CPReQLIFhV99ZKdKba3v0llUlzN3PtKZ0Ye1KZAeR2b4Z3LoK9W53nA3pf6kqMxpi6pdJEoKqlIvIiznwEqGoBUFAbgZnqm7N+J3+fsZLTu0Vx/7k9jtyZvcmZQ6Ag1+kUjh/sTpDGmDrHl87iWSJyiVQ4FoGpCzZm7ePmdxfSOSqE58f1P3I+geyNMGkUFO6zJGCMOYovieBGnEHmCkQkR0RyRSTHz3GZatib59wh1DSgCW+MH0RYsNcdQrs2wFvnQVGekwTa9XEvUGNMneTLk8U1MSWl8ZOiklJufm8hW3cf4L3rBxMX0eLwzp3rYfIoZ/TQCV9AzInuBWqMqbN8eaDs1PK2l52oxtQ+VeXvM1Yyd8Mu/nVZXwYleM0YlrXW6RMoLYbxX0BMBU8VG2MaPV9uH/2z13IwcBKwELBxCFw2ee5mpvyaxs3DO3PJQK+B5LLWOElAFSZ8aQ+KGWMq5UvT0Pne6yISB/zbbxEZn8xek8kjX6Rwds8Y/nx2t8M7Mlc7SUDEaQ6K6lbxQYwxBt86i8tKB3pUWcr4zbqMXG6bsphubVvy7OX9aHLwDqGMFJh0HkgT50rAkoAxxge+9BH8B+dpYnASRz+cJ4yNC7L3F3Lt5GSCmgXwxvgkQoI8/4QZK50rgYBAp08gsou7gRpj6g1f+gi8Z4ovBt5X1Tl+isdUorC4lJveXciOnHw+uGEI7cObOzt2LIfJF0DTYKc5qE1ndwM1xtQrviSCj4B8VS0BEJEAEWmhqnn+Dc14U1X++uly5m/K5rmx/egf39rZsX0ZvH0BNGvhPCdgScAYU00+PVkMNPdabw58559wTEVe/2kT0xamc/uZXRndzzOR/LYlTnNQYKjTJ2BJwBhzDHxJBMHe01N6lltUUt7UsO9SMvjHzFWc17sdd57Z1dm4bbFzJRDU0mkOiujkbpDGmHrLl0SwX0QGHFwRkYHAAV8OLiIjRWSNiKwXkXvL2T9BRLJEZInn5zrfQ28cVm3P4Y6pi+ndoRVPX9bXuUNo60KYPBqCWzlJoHWC22EaY+oxX/oI7gSmicg2QIC2wOVVvUhEAoAXgbNwbjldICIzVDWlTNEPVPXW6oXdOGTlFnDd5GRCg5vy2jVJNA8MgPRkZyjp5q2dJBAeX/WBjDGmEr48ULZARLoDB29KX6OqRT4c+yRgvapuBBCRqcBooGwiMOXILyrhxneS2bW/gGk3nkxMy2DYMh/evQRaRDh9Aq1iqz6QMcZUocqmIRG5BQhR1RWqugIIFZE/+HDsDsAWr/V0z7ayLhGRZSLykeep5UZPVbn/k+UsStvDM2P60Tu2FaT9Cu9cDCGRMOErSwLGmBrjSx/B9Z4ZygBQ1d3A9TX0/p8DCaraB/gWmFxeIRG5QUSSRSQ5Kyurht667vpsyTY+WbyVu0Ykcm7vdpA6D969GEKjPVcC5eVTY4w5Nr4kggDvSWk8bf+BPrxuK+B9hh/r2XaIqu7yzHoG8DowsLwDqepEVU1S1aSoqCgf3rr+2rE3nwc/W8HAjq259YwukDrXaQ4Ka+ckgZbt3Q7RGNPA+JIIvgY+EJEzReRM4H1gpg+vWwB0FZFOIhIIjAVmeBcQkXZeqxcAq3wLu2FSVe75eBlFJcq/LutLQNocePdS5wpgwhfQsl3VBzHGmGry5a6hvwA3ADd51pfh3DlUKVUtFpFbgW+AAOBNVV0pIo8Ayao6A7hdRC7AGboiG5hQ/Y/QcEyZn8aPa7N4dPSJJOQuhCmXO3cFXTMDwmLcDs8Y00D5ctdQqYj8CnQGxgCRwMe+HFxVvwK+KrPtQa/l+4D7qhNwQ5W2K4/Hv1zFKV0iuTJR4dVxEN4Rxs9w+gaMMcZPKkwEIpIIjPP87AQ+AFDV02sntMajpFT507SlBIjw1CW9aDJ9jDOU9JXTLAkYY/yusiuC1cBPwChVXQ8gInfVSlSNzFtzNjF/czZPX9aX9mvegdSf4YIXINzupjXG+F9lncUXA9uB70XkNU9HsVRS3hyDdRm5PPXNGkb0iOGSjvnw3UPQ9Wzof5XboRljGokKE4GqTlfVsUB34HucoSaiReRlETm7tgJsyIpKSvnjtKWEBAbwfxf2RD67BZoGwvnPO1NNGmNMLajy9lFV3a+qUzxzF8cCi3HuJDLH6aXvN7AsfS+PX9SbqJVvwJZf4Jx/2m2ixphaVa05i1V1t+fhrjP9FVBjsWLrXv7zv3WM7teec9vmwqxHodt50GeM26EZYxoZX54jMDUsv6iEuz9cQkRIIA+P6gbvj4LAFjDqWWsSMsbUOksELnj2u7WszdjHW78bRPjiV5z5BS590x4aM8a4olpNQ+b4LUzNZuKPGxl3Uhynh++E2f8HPUfDiRe7HZoxppGyK4JalFdYzN0fLqVDeHP+OrIrvPNbZ6rJ856xJiFjjGssEdSiJ2auJi07j/evH0Logv/A9qUw5m1njgFjjHGJNQ3Vkp/X7eTtean8flgnhjTfCj88Cb0udZqFjDHGRXZFUAty8ov480dL6RwVwp9HdIK3RkCLNnDuP90OzRhjLBHUhodnpJCZW8DHN59M8NxnIGMFjH3fmXvYGGNcZk1DfvbflTv4eFE6fxjemX5NNsJP/4K+46D7uW6HZowxgF0R+NWufQXc/+lyerZryW2nxsMbw51hpUc+4XZoxhhziCUCP1FVHpi+gr0Hinj3usEE/vwkZK2GKz+C5uFuh2eMMYdY05CfzFi6jZkrdnDXWYl0L1oDc56D/ldD17PcDs0YY45gicAPMnLy+dv0FfSPD+fGoe1h+s0Q1h5++w+3QzPGmKNY01ANU1Xu+WgZhSWlPDOmHwGzH4dd6+Dq6RDc0u3wjDHmKHZFUMOmLtjCD2uzuO+cHnTavwzmvQhJ10Jnm+rZGFM32RVBDdqSncdjX6Rwcuc2XD0gEiZe5Mw7fNYjbodmjDEVskRQQ0pLlT9OW4qI8M/L+tLkf3+H7I0w/gsICnU7PGOMqZA1DdWQN+dsYv6mbB48vycddifD/Fdh8E3Q6Tduh2aMMZWyRFAD1mfm8tQ3axjRI5rLeofDZ3+AiBPgzAfdDs0YY6rk10QgIiNFZI2IrBeReyspd4mIqIgk+TMefyguKeWPHy4lJDCAf1zcG/n2QdizBS58GQJD3A7PGGOq5LdEICIBwIvAOUBPYJyI9CynXBhwB/Crv2Lxp1d/3MjS9L08dmFvojPnQvKbMPQWiB/idmjGGOMTf14RnASsV9WNqloITAXKG3z/UeBJIN+PsfhFTn4Rr8zewFk9YzgvsQV8dhtEJsIZD7gdmjHG+MyfiaADsMVrPd2z7RARGQDEqeqXlR1IRG4QkWQRSc7Kyqr5SI/Re7+kkVtQzB1ndoVv/gq525wmoWbN3Q7NGGN85lpnsYg0AZ4B/lhVWVWdqKpJqpoUFRXl/+B8kF9Uwhs/b+I3XSPplTcfFr8Dw+6A2HrXzWGMaeT8mQi2AnFe67GebQeFAb2A2SKyGRgCzKgvHcYfL0pn574Cbh0aCTNug6geMPw+t8Myxphq8+cDZQuAriLSCScBjAWuOLhTVfcCh2ZtF5HZwJ9UNdmPMdWI4pJSXv1hI33jwjlp/XOwLxPGvQ9Ng9wOzRhjqs1vVwSqWgzcCnwDrAI+VNWVIvKIiFzgr/etDTNX7CAtO4+7BrVAlrwHg66D9v3dDssYY46JX4eYUNWvgK/KbCv3KStVHe7PWGqKqvLS7A10jgrhtOxpoAon3+p2WMYYc8zsyeJq+mFtFqu253Db0Ehk0WTodQmEx7sdljHGHDNLBNX08uwNtGsVzKjCmVC4z7lTyBhj6jFLBNWwMHU3v27K5saTO9B0wavQZQS07eV2WMYYc1wsEVTDKz9sILxFM8YFzYH9WXY1YIxpECwR+GhdRi7fpmQwYUgcQfNfcO4SSrAhpo0x9Z8lAh+98sNGmjcL4NrIFGfCmWF3gojbYRljzHGzROCDrXsO8NmSrYwdFEtY8gvQuhP0ON/tsIwxpkZYIvDBaz9uBOCWTjtg2yI4+TZoEuByVMYYUzMsEVQhe38hUxekcWH/DkQufQVCoqDfFVW/0Bhj6glLBFWYNHcz+UWl3H5iPqz/FgbfaMNMG2MaFEsEldhfUMzkuZs5u2cM8avfhGYhkHSt22EZY0yNskRQiffnp7H3QBG3JQXDio9g4HhoEeF2WMYYU6MsEVSgsLiU13/axJATIuid9p6zccgf3A3KGGP8wBJBBaYv3sqOnHxuGxoJCydDr0shPK7qFxpjTD1jiaAcJaXKKz9u4MT2LTk5+1Mo2g/Dbnc7LGOM8QtLBOX4NmUHG7P2c8tvOiC/vgpdz4aYE90Oyxhj/MISQRmqysuzN9CxTQtGFn0PeTttcDljTINmiaCMeRt2sTR9Lzf9JoEm8/4DHQZCx2Fuh2WMMX5jiaCMl3/YQFRYEJe0WAS7NzlXAza4nDGmAbNE4GV5+l5+WreTa4clEDjveYjoDN1HuR2WMcb4lSUCL6/8sIGw4KZc0y4Nti+xweWMMY2CJQKPjVn7+GrFdq4e0pEW819wBpfrO87tsIwxxu8sEXhM/HEjgQFNuK7rftgwCwbfBM2C3Q7LGGP8zhIBsGNvPh8vSmdMUhwRS16BwFAYZIPLGWMaB78mAhEZKSJrRGS9iNxbzv6bRGS5iCwRkZ9FpKc/46nIm3M2Uapwc79msOJjGDgBmrd2IxRjjKl1fksEIhIAvAicA/QExpVT0U9R1d6q2g94CnjGX/FUZG9eEe/9ksqoPu1ov+pN51bRITfXdhjGGOMaf14RnASsV9WNqloITAVGexdQ1Ryv1RBA/RhPud75ZTP7C0u4ZXAELHobeo+BVrG1HYYxxrimqR+P3QHY4rWeDgwuW0hEbgHuBgKBM8o7kIjcANwAEB8fX2MBHigs4a05mzm9WxSJaR9AUZ5zy6gxxjQirncWq+qLqtoZ+AvwQAVlJqpqkqomRUVF1dh7f5i8hV37C7nllA7w6yvQ9bcQ40o3hTHGuMafiWAr4D2Af6xnW0WmAhf6MZ4jFJWUMvHHjSR1bE3S7q8gb5cNLmeMaZT8mQgWAF1FpJOIBAJjgRneBUSkq9fqecA6P8ZzhC+WbWPrngP84dSOMO8FiB0EHU+urbc3xpg6w299BKpaLCK3At8AAcCbqrpSRB4BklV1BnCriIwAioDdwHh/xeOttNQZarpbTBjDS3+F3Zvh7MdscDljTKPkz85iVPUr4Ksy2x70WnalLeb7NZmszdjHs2P60GTuA9CmC3Q7141QjDHGda53Frvh5dkb6BDenPNbroftS+Hk221wOWNMo9XoEsGCzdkkp+7mhlNPoOm85yE0Bvpc7nZYxhjjmkaXCF76fj1tQgK5PHY3bPifDS5njGn0GlUiWLU9h+/XZPG7YQkEz38BAsMg6fduh2WMMa5qVInglR82EBIYwDXdBVZ+CkkToHm422EZY4yrGk0iSNuVx+dLt3HlkI60XDIRpAkMtsHljDGm0SSCjxZuoWmTJlw3sKUzuFyfMdCqg9thGWOM6/z6HEFdcseIREb0jCF61StQfMC5ZdQYY0zjuSIIaCL0iQ6EX1+FxHMgurvbIRljTJ3QaBIBAIvfhQPZNricMcZ4aTyJoKQY5v0HYk+C+CFuR2OMMXVG40kEKdNhTxqccqcNLmeMMV4aTyIIDIXuo5z+AWOMMYc0mruG6DbS+THGGHOExnNFYIwxplyWCIwxppGzRGCMMY2cJQJjjGnkLBEYY0wjZ4nAGGMaOUsExhjTyFkiMMaYRk5U1e0YqkVEsoBUt+MoIxLY6XYQ1VCf4rVY/ac+xVufYoW6GW9HVY0qb0e9SwR1kYgkq2qS23H4qj7Fa7H6T32Ktz7FCvUvXmsaMsaYRs4SgTHGNHKWCGrGRLcDqKb6FK/F6j/1Kd76FCvUs3itj8AYYxo5uyIwxphGzhKBMcY0cpYIjoOIxInI9yKSIiIrReQOt2OqiogEiMhiEfnC7ViqIiLhIvKRiKwWkVUiMtTtmCoiInd5/gZWiMj7IhLsdkzeRORNEckUkRVe2yJE5FsRWef53drNGA+qINZ/ev4OlonIpyIS7maM3sqL12vfH0VERSTSjdh8ZYng+BQDf1TVnsAQ4BYR6elyTFW5A1jldhA+eg74WlW7A32po3GLSAfgdiBJVXsBAcBYd6M6yiSg7BR99wKzVLUrMMuzXhdM4uhYvwV6qWofYC1wX20HVYlJHB0vIhIHnA2k1XZA1WWJ4Dio6nZVXeRZzsWpqDq4G1XFRCQWOA943e1YqiIirYBTgTcAVLVQVfe4G1WlmgLNRaQp0ALY5nI8R1DVH4HsMptHA5M9y5OBC2s1qAqUF6uq/ldViz2rvwCxtR5YBSr4bgGeBe4B6vwdOZYIaoiIJAD9gV/djaRS/8b5wyx1OxAfdAKygLc8TVmvi0iI20GVR1W3Ak/jnPltB/aq6n/djconMaq63bO8A4hxM5hq+D0w0+0gKiMio4GtqrrU7Vh8YYmgBohIKPAxcKeq5rgdT3lEZBSQqaoL3Y7FR02BAcDLqtof2E/dabo4gqdtfTRO8moPhIjIVe5GVT3q3Ede589cReSvOE2y77kdS0VEpAVwP/Cg27H4yhLBcRKRZjhJ4D1V/cTteCoxDLhARDYDU4EzRORdd0OqVDqQrqoHr7A+wkkMddEIYJOqZqlqEfAJcLLLMfkiQ0TaAXh+Z7ocT6VEZAIwCrhS6/YDUJ1xTgqWev6/xQKLRKStq1FVwhLBcRARwWnDXqWqz7gdT2VU9T5VjVXVBJyOzP+pap09a1XVHcAWEenm2XQmkOJiSJVJA4aISAvP38SZ1NGO7TJmAOM9y+OBz1yMpVIiMhKnWfMCVc1zO57KqOpyVY1W1QTP/7d0YIDnb7pOskRwfIYBV+OcXS/x/JzrdlANyG3AeyKyDOgH/MPleMrluWr5CFgELMf5f1WnhhgQkfeBeUA3EUkXkWuBJ4CzRGQdzlXNE27GeFAFsb4AhAHfev6fveJqkF4qiLdesSEmjDGmkbMrAmOMaeQsERhjTCNnicAYYxo5SwTGGNPIWSIwxphGzhKBMWWISInX7cBLRKTGnmgWkYTyRqk0xk1N3Q7AmDrogKr2czsIY2qLXREY4yMR2SwiT4nIchGZLyJdPNsTROR/nrHyZ4lIvGd7jGfs/KWen4PDTgSIyGue+Qv+KyLNXftQxmCJwJjyNC/TNHS51769qtob50nXf3u2/QeY7Bkr/z3gec/254EfVLUvzjhJKz3buwIvquqJwB7gEj9/HmMqZU8WG1OGiOxT1dBytm8GzlDVjZ7BBneoahsR2Qm0U9Uiz/btqhopIllArKoWeB0jAfjWMxkMIvIXoJmqPub/T2ZM+eyKwJjq0QqWq6PAa7kE66szLrNEYEz1XO71e55neS6Hp6a8EvjJszwLuBkOzRXdqraCNKY67EzEmKM1F5ElXutfq+rBW0hbe0ZDLQDGebbdhjOT2p9xZlX7nWf7HcBEz2iUJThJYTvG1DHWR2CMjzx9BEmqutPtWIypSdY0ZIwxjZxdERhjTCNnVwTGGNPIWSIwxphGzhKBMcY0cpYIjDGmkbNEYIwxjdz/A+2muuOEm7t3AAAAAElFTkSuQmCC\n","text/plain":["<Figure size 432x288 with 1 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"viSKIDzKyqXI"},"source":["def get_class_accuracies(model, data_loader):\n","\n","  corrects= [0]*12\n","  totals = [0]*12\n","  for imgs, labels in data_loader:\n","      \n","      if use_cuda and torch.cuda.is_available():\n","        imgs = imgs.cuda()\n","        labels = labels.cuda()\n","\n","      output = model(imgs)\n","\n","      for i in range(len(labels)):\n","        label = int(labels[i].item())\n","        pred = torch.argmax(output[i])\n","        totals[label] += 1\n","        if pred == label:\n","          corrects[label] += 1\n","\n","  acc = [ i / j for i, j in zip(corrects, totals)]\n","      \n","  return acc"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Uj8RWkV723Ct","executionInfo":{"status":"ok","timestamp":1607489189728,"user_tz":300,"elapsed":2384,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}},"outputId":"74afe043-2ab3-41c6-a7b5-43dfca8ae7c4"},"source":["testModel = SignClassifier()\n","use_cuda=False\n","model_path = ('/content/gdrive/My Drive/APS360 Project/the best model/'+get_model_name(model_1.name, 32, 0.001, 18))\n","\n","\n","state = torch.load(model_path)\n","testModel.load_state_dict(state)\n","total_test_acc = get_accuracy(testModel, test_loader)\n","class_test_acc = get_class_accuracies(testModel, test_loader)\n","print(f\"The overall test accuracy is: {total_test_acc}\\n\")\n","\n","for i in range(len(class_test_acc)):\n","  print(\"Test accuracy for {}: {:.4f}\".format(classes[i], class_test_acc[i]))"],"execution_count":17,"outputs":[{"output_type":"stream","text":["The overall test accuracy is: 0.9740740740740741\n","\n","Test accuracy for Speed limit (20km/h): 0.9444\n","Test accuracy for Speed limit (30km/h): 0.9667\n","Test accuracy for Speed limit (50km/h): 0.9778\n","Test accuracy for Speed limit (60km/h): 0.9556\n","Test accuracy for Speed limit (70km/h): 0.9778\n","Test accuracy for Speed limit (80km/h): 0.9778\n","Test accuracy for End of speed limit (80km/h): 0.9778\n","Test accuracy for Speed limit (100km/h): 0.9889\n","Test accuracy for Speed limit (120km/h): 0.9778\n","Test accuracy for Yield: 0.9889\n","Test accuracy for Stop: 0.9889\n","Test accuracy for End of all speed and passing limits: 0.9667\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"FbTR-17-6kAN","executionInfo":{"status":"ok","timestamp":1607489193354,"user_tz":300,"elapsed":370,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}}},"source":["def get_confusion(model, data_loader):\r\n","  confusion = np.zeros((12,12))\r\n","  for imgs, labels in data_loader:\r\n","    output = model(imgs)\r\n","    for i in range(len(labels)):\r\n","      label = int(labels[i].item())\r\n","      pred = torch.argmax(output[i])\r\n","      confusion[label, pred] += 1\r\n","  return confusion\r\n","  "],"execution_count":18,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ibIU0JUS6lah","executionInfo":{"status":"ok","timestamp":1607472983038,"user_tz":300,"elapsed":474,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}},"outputId":"dfcb1d95-f40b-427a-a6bf-fa67cbb27698"},"source":["print(get_confusion(testModel, test_loader))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["[[85.  0.  0.  0.  2.  0.  0.  0.  1.  0.  0.  2.]\n"," [ 0. 87.  2.  0.  0.  1.  0.  0.  0.  0.  0.  0.]\n"," [ 0.  1. 88.  0.  0.  1.  0.  0.  0.  0.  0.  0.]\n"," [ 0.  0.  0. 86.  0.  0.  0.  0.  0.  1.  1.  2.]\n"," [ 0.  1.  0.  0. 88.  1.  0.  0.  0.  0.  0.  0.]\n"," [ 0.  0.  1.  0.  0. 88.  1.  0.  0.  0.  0.  0.]\n"," [ 0.  0.  0.  0.  0.  0. 88.  1.  1.  0.  0.  0.]\n"," [ 0.  0.  0.  0.  0.  1.  0. 89.  0.  0.  0.  0.]\n"," [ 0.  0.  0.  0.  0.  0.  0.  2. 88.  0.  0.  0.]\n"," [ 0.  0.  0.  1.  0.  0.  0.  0.  0. 89.  0.  0.]\n"," [ 0.  0.  0.  1.  0.  0.  0.  0.  0.  0. 89.  0.]\n"," [ 0.  0.  0.  3.  0.  0.  0.  0.  0.  0.  0. 87.]]\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"8XVyWz7-5igw"},"source":["### Demo Setup\n"]},{"cell_type":"code","metadata":{"id":"xlcWtt98vJYQ","executionInfo":{"status":"ok","timestamp":1607489313434,"user_tz":300,"elapsed":288,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}}},"source":["import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","import matplotlib.pyplot as plt\n","import numpy as np\n","import os\n","from PIL import Image\n","from torchvision.transforms import ToTensor\n","import torchvision"],"execution_count":24,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"g93n1X5exqv1","executionInfo":{"status":"ok","timestamp":1607489306649,"user_tz":300,"elapsed":349,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}},"outputId":"3ca29f77-2fe1-49cf-ab6b-e1c14400d16e"},"source":["from google.colab import drive\n","drive.mount('/content/gdrive')\n","path = '/content/gdrive/My Drive/APS360 Project/Test_Images/'"],"execution_count":22,"outputs":[{"output_type":"stream","text":["Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"8-qa2HMGxAZQ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1607489316355,"user_tz":300,"elapsed":1785,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}},"outputId":"501c0b59-2fc5-4291-8932-f53b6b115a92"},"source":["#load the day test images\n","day_path = path + 'day_drive/'\n","day_imgs = []\n","\n","for i in sorted(os.listdir(day_path)):\n","    img = Image.open(day_path + i)\n","    img = ToTensor()(img).unsqueeze(0)\n","    img = img.numpy()\n","    img = img.astype(np.float32)\n","    img = img / 255.0\n","\n","\n","    # normalization\n","    \n","    img = (img-np.mean(img))/np.std(img)\n","    img = torch.from_numpy(img)\n","    print()\n","    day_imgs.append(img)\n"],"execution_count":25,"outputs":[{"output_type":"stream","text":["\n","\n","\n","\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"VeulyxPPzomI","executionInfo":{"status":"ok","timestamp":1607489319005,"user_tz":300,"elapsed":305,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}},"outputId":"639922d4-dec8-46fe-b3fa-097a38dc9242"},"source":["# check the predictions\n","for img in day_imgs:\n","  out = testModel(img)\n","  idx = torch.argmax(out)\n","  print(classes[idx])"],"execution_count":26,"outputs":[{"output_type":"stream","text":["Yield\n","Speed limit (70km/h)\n","Speed limit (20km/h)\n","Stop\n","Speed limit (70km/h)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"GpdeQK5h1mD1","executionInfo":{"status":"ok","timestamp":1607489322658,"user_tz":300,"elapsed":2644,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}}},"source":["#load the night test images\n","night_path = path + 'night_drive/'\n","night_imgs = []\n","\n","for i in sorted(os.listdir(night_path)):\n","    img = Image.open(night_path + i)\n","    img = ToTensor()(img).unsqueeze(0)\n","    img = img.numpy()\n","    img = img.astype(np.float32)\n","    # normalization\n","    img = (img-np.mean(img))/np.std(img)\n","    img = torch.from_numpy(img)\n","    night_imgs.append(img)"],"execution_count":27,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":302},"id":"h4tFlb2yCSfI","executionInfo":{"status":"ok","timestamp":1607489323625,"user_tz":300,"elapsed":489,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}},"outputId":"ceceda58-31ec-4105-f04a-181747ea747e"},"source":["#Just to make sure Normalization worked as intended\r\n","night_imgs[0].size()\r\n","plt.imshow(night_imgs[0].squeeze(0).permute(1,2,0))\r\n"],"execution_count":28,"outputs":[{"output_type":"stream","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"],"name":"stderr"},{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7f35093e52e8>"]},"metadata":{"tags":[]},"execution_count":28},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"zu9ZY_Do10Xr","executionInfo":{"status":"ok","timestamp":1607489402847,"user_tz":300,"elapsed":295,"user":{"displayName":"Rishabh G","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GivHnx-kw-QUzXT8jzfjC3Jm0w-Lq3sPrwZMXbRa5o=s64","userId":"13598507082383917485"}},"outputId":"e2f1114a-1c85-4584-d0d2-a613e76b1f68"},"source":["#check the predictions\n","\n","for i in range(len(night_imgs)):\n","  #normalized_image=torchvision.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)).forward(img)\n","  # if i==0:\n","  #   out = model(night_imgs[1])\n","  #else:\n","  out = testModel(night_imgs[i])\n","  idx = torch.argmax(out)\n","  print(classes[idx])"],"execution_count":30,"outputs":[{"output_type":"stream","text":["Speed limit (50km/h)\n","Speed limit (50km/h)\n","Yield\n","End of speed limit (80km/h)\n","Speed limit (80km/h)\n","Speed limit (70km/h)\n","Speed limit (60km/h)\n"],"name":"stdout"}]}]}
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