04_k__FER_initial-cnn-no-weights-02-dropout.ipynb

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   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "#for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    " #   for filename in filenames:\n",
    "  #      print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
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     "text": [
      "Collecting kaleido\r\n",
      "  Downloading kaleido-0.2.1-py2.py3-none-manylinux1_x86_64.whl (79.9 MB)\r\n",
      "     |████████████████████████████████| 79.9 MB 202 kB/s            \r\n",
      "\u001b[?25hInstalling collected packages: kaleido\r\n",
      "Successfully installed kaleido-0.2.1\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\r\n"
     ]
    }
   ],
   "source": [
    "!pip install kaleido"
   ]
  },
  {
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "import tensorflow.keras.backend as K\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras import datasets, layers, models\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "from sklearn.model_selection import KFold, StratifiedKFold\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "import plotly.graph_objects as go\n",
    "import glob\n",
    "import os"
   ]
  },
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   "source": [
    "def create_dir (direct):\n",
    "  if not os.path.exists(direct):\n",
    "    os.mkdir(direct)"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "ROOT_DIR = \"/kaggle/input/ferset\" #MT Directory\n",
    "DATASET_02 = ROOT_DIR + \"/02_FER\"\n",
    "DATASET_02_TRAIN = DATASET_02 + \"/train\"\n",
    "DATASET_02_TEST = DATASET_02+ \"/test\""
   ]
  },
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    {
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     "text": [
      "L\n",
      "48 48\n"
     ]
    }
   ],
   "source": [
    "import PIL\n",
    "\n",
    "image = PIL.Image.open(DATASET_02_TRAIN + \"/surprise/Training_99984132.jpg\")\n",
    "#image to open\n",
    "\n",
    "width, height = image.size\n",
    "\n",
    "print(image.mode)\n",
    "print(width, height)"
   ]
  },
  {
   "cell_type": "code",
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   "id": "f16f502b",
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   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "Found 7178 images belonging to 7 classes.\n",
      "Found 28709 images belonging to 7 classes.\n"
     ]
    }
   ],
   "source": [
    "datagen = ImageDataGenerator()\n",
    "image_size = (48, 48)\n",
    "colormode = 'rgb'\n",
    "test_set = datagen.flow_from_directory(DATASET_02_TEST, target_size=image_size, color_mode =colormode, batch_size=32, shuffle=False)\n",
    "train_set = datagen.flow_from_directory(DATASET_02_TRAIN, target_size=image_size, color_mode =colormode, batch_size=32)"
   ]
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   "source": [
    "def create_model_VF():\n",
    "    model = models.Sequential()\n",
    "    model.add(layers.Conv2D(64, (3, 3), activation='relu', input_shape=(48, 48, 3)))\n",
    "    model.add(layers.MaxPooling2D((2, 2)))\n",
    "    #model.add(layers.Dropout(0.4))\n",
    "    model.add(layers.Conv2D(128, (3, 3), activation='relu'))\n",
    "    model.add(layers.MaxPooling2D((2, 2)))\n",
    "    #model.add(layers.Dropout(0.4))\n",
    "    model.add(layers.Conv2D(256, (3, 3), activation='relu'))\n",
    "    model.add(layers.MaxPooling2D((2, 2)))\n",
    "    model.add(layers.Flatten())\n",
    "    model.add(layers.Dense(256, activation='relu'))\n",
    "    model.add(layers.Dense(7, activation='softmax'))\n",
    "    \n",
    "    return model\n",
    "\n"
   ]
  },
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   "source": [
    "def create_model_VF_02():\n",
    "    model = models.Sequential()\n",
    "    model.add(layers.Conv2D(64, (3, 3), activation='relu', input_shape=(48, 48, 3)))\n",
    "    model.add(layers.MaxPooling2D((2, 2)))\n",
    "    model.add(layers.Dropout(0.4))\n",
    "    model.add(layers.Conv2D(128, (3, 3), activation='relu'))\n",
    "    model.add(layers.MaxPooling2D((2, 2)))\n",
    "    model.add(layers.Dropout(0.4))\n",
    "    model.add(layers.Conv2D(256, (3, 3), activation='relu'))\n",
    "    model.add(layers.MaxPooling2D((2, 2)))\n",
    "    model.add(layers.Flatten())\n",
    "    model.add(layers.Dense(256, activation='relu'))\n",
    "    model.add(layers.Dense(7, activation='softmax'))\n",
    "    \n",
    "    return model\n",
    "\n"
   ]
  },
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   "source": [
    "def create_model_VF_04():\n",
    "    model = models.Sequential()\n",
    "    model.add(layers.Conv2D(64, (3, 3), activation='relu', input_shape=(48, 48, 3)))\n",
    "    model.add(layers.MaxPooling2D((2, 2)))\n",
    "    model.add(layers.Dropout(0.4))\n",
    "    model.add(layers.Conv2D(128, (3, 3), activation='relu'))\n",
    "    model.add(layers.MaxPooling2D((2, 2)))\n",
    "    model.add(layers.Dropout(0.4))\n",
    "    model.add(layers.Conv2D(256, (3, 3), activation='relu'))\n",
    "    model.add(layers.MaxPooling2D((2, 2)))\n",
    "    model.add(layers.Flatten())\n",
    "    model.add(layers.Dense(256, activation='relu'))\n",
    "    model.add(layers.Dense(7, activation='softmax'))\n",
    "    \n",
    "    return model\n"
   ]
  },
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   "source": [
    "def get_data_df(dir):\n",
    "  # modified code from: https://www.kaggle.com/namgalielei/simple-load-images-and-count-number-of-each-class\n",
    "\n",
    "  train_df = pd.DataFrame()\n",
    "\n",
    "  trainset = glob.glob(dir)\n",
    "  #print(trainset)\n",
    "  train_df['filename'] = [img.split(\"/\")[-2]+\"/\"+img.split(\"/\")[-1] for img in trainset]\n",
    "  train_df['label'] = [img.split(\"/\")[-2] for img in trainset]\n",
    "\n",
    "  return train_df"
   ]
  },
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   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>filename</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>surprise/Training_66056468.jpg</td>\n",
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       "      <td>surprise</td>\n",
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       "      <th>3</th>\n",
       "      <td>surprise/Training_67725241.jpg</td>\n",
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       "      <td>surprise/Training_71308969.jpg</td>\n",
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       "      <td>happy/Training_18866880.jpg</td>\n",
       "      <td>happy</td>\n",
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       "    <tr>\n",
       "      <th>28705</th>\n",
       "      <td>happy/Training_16513509.jpg</td>\n",
       "      <td>happy</td>\n",
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       "      <th>28706</th>\n",
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       "<p>28709 rows × 2 columns</p>\n",
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      "text/plain": [
       "                             filename     label\n",
       "0      surprise/Training_66056468.jpg  surprise\n",
       "1      surprise/Training_26522394.jpg  surprise\n",
       "2      surprise/Training_63239185.jpg  surprise\n",
       "3      surprise/Training_67725241.jpg  surprise\n",
       "4      surprise/Training_71308969.jpg  surprise\n",
       "...                               ...       ...\n",
       "28704     happy/Training_18866880.jpg     happy\n",
       "28705     happy/Training_16513509.jpg     happy\n",
       "28706     happy/Training_10834907.jpg     happy\n",
       "28707     happy/Training_30459679.jpg     happy\n",
       "28708     happy/Training_95673040.jpg     happy\n",
       "\n",
       "[28709 rows x 2 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_train = get_data_df(DATASET_02_TRAIN+\"/*/*.jpg\")\n",
    "test_train"
   ]
  },
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     "status": "completed"
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    "tags": []
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "surprise\n",
      "fear\n",
      "angry\n",
      "neutral\n",
      "sad\n",
      "disgust\n",
      "happy\n",
      "35887\n",
      "[4002, 5121, 4953, 6198, 6077, 547, 8989]\n",
      "{0: 1.2810380524023703, 1: 1.001115853488437, 2: 1.035072539009547, 3: 0.8271562255105334, 4: 0.8436258492207152, 5: 9.372420997649517, 6: 0.5703319930708962}\n"
     ]
    }
   ],
   "source": [
    "total = 0\n",
    "value_list = []\n",
    "for item in os.listdir(DATASET_02_TRAIN):\n",
    "    y = len(os.listdir(DATASET_02_TRAIN+'/'+item))+len(os.listdir(DATASET_02_TEST+'/'+item))\n",
    "    value_list.append(y)\n",
    "    total += y\n",
    "    print(item)\n",
    "print(total)\n",
    "print(value_list)\n",
    "weights = {}\n",
    "for n in range(0,len(value_list)):\n",
    "    w = total/(len(value_list)*value_list[n])\n",
    "    weights[n] = w\n",
    "    \n",
    "print(weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5866e360",
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   "outputs": [],
   "source": [
    "create_dir('/kaggle/working/Saved_CNN_Models')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a5174143",
   "metadata": {
    "execution": {
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   "source": [
    "from sklearn.model_selection import KFold, StratifiedKFold\n",
    "\n",
    "train_data = get_data_df(DATASET_02_TRAIN+\"/*/*.jpg\")\n",
    "Y = train_data[['label']]\n",
    "\n",
    "#Y = final_train[['label']]\n",
    "#n = len(final_train)   \n",
    "\n",
    "skf = StratifiedKFold(n_splits = 5, random_state = 7, shuffle = True) \n",
    "\n",
    "#Un needed preprocessing aside from rescale\n",
    "#idg = ImageDataGenerator(rescale=1./255)\n",
    "create_dir('/kaggle/working/Saved_CNN_Models')\n",
    "#model name\n",
    "def get_model_name(k):\n",
    "    return 'model_'+str(k)+'.h5'\n",
    "def model_test(fold_count, epoch, model_method, image_size, mode):\n",
    "    skf = StratifiedKFold(n_splits = fold_count, random_state = 7, shuffle = True)\n",
    "    train_data = get_data_df(DATASET_02_TRAIN+\"/*/*.jpg\")\n",
    "      #Incorperate preprocessing in the method \n",
    "      #####################################################################\n",
    "      #aug_train = get_data_df(DATASET_01_ROOT+\"/Aug_train/*/*.jpg\")\n",
    "      #aug_train['filename'] = aug_train['filename'].str[7:]\n",
    "      #final_train = test_train.append(aug_train, sort=False, ignore_index=True)\n",
    "\n",
    "\n",
    "      #######################################################################\n",
    "      #idg = ImageDataGenerator(rescale=1./255)\n",
    "    idg = ImageDataGenerator()\n",
    "    Y = train_data[['label']]\n",
    "    n = len(train_data)    \n",
    "    VALIDATION_ACCURACY = []\n",
    "    VALIDATION_LOSS = []\n",
    "\n",
    "      #create a directory\n",
    "    save_dir = '/kaggle/working/Saved_CNN_Models/'+model_method.__name__+'_'+str(epoch)+'epochs/'\n",
    "    create_dir(save_dir)\n",
    "    \n",
    "    img_dir = DATASET_02_TRAIN\n",
    "\n",
    "    fold_var = 1\n",
    "\n",
    "      #n is number of samples, doesn't care about x values for split, just y\n",
    "    for train_index, val_index in skf.split(np.zeros(n),Y):\n",
    "\n",
    "        #Index value for fold\n",
    "        training_data = train_data.iloc[train_index]\n",
    "        validation_data = train_data.iloc[val_index]\n",
    "        #generator for train\n",
    "        print(\"hi\")\n",
    "        print(validation_data.head())\n",
    "        #Modify the training size, have an input adjustability \n",
    "        train_data_generator = idg.flow_from_dataframe(training_data, directory = img_dir,\n",
    "                                                       target_size=image_size, batch_size=32, color_mode =mode,\n",
    "                                                       x_col = \"filename\", y_col = \"label\",shuffle = True)\n",
    "        #print(\"hi\")\n",
    "        #generator for validation\n",
    "        valid_data_generator  = idg.flow_from_dataframe(validation_data, directory = img_dir,\n",
    "                                                        target_size=image_size, color_mode =mode,batch_size=32,\n",
    "                                                        x_col = \"filename\", y_col = \"label\",shuffle = True)\n",
    "        #print(\"hi\")\n",
    "        #create a method for this\n",
    "        # CREATE NEW MODEL\n",
    "        model = model_method()\n",
    "        # COMPILE NEW MODEL\n",
    "        model.compile(loss=tf.keras.losses.CategoricalCrossentropy(),\n",
    "                optimizer= tf.keras.optimizers.Adam(learning_rate=0.0001),\n",
    "                metrics=['accuracy'])\n",
    "\n",
    "        # CREATE CALLBACKS\n",
    "        #Unsure about checpoint\n",
    "        checkpoint = tf.keras.callbacks.ModelCheckpoint(save_dir+model_method.__name__+\"_\"+get_model_name(fold_var), \n",
    "                    monitor='val_accuracy', verbose=1, \n",
    "                    save_best_only=True, mode='max')\n",
    "\n",
    "        callbacks_list = [checkpoint]\n",
    "        # There can be other callbacks, but just showing one because it involves the model name\n",
    "        # This saves the best model\n",
    "        # FIT THE MODEL\n",
    "        #Include the weights for classes\n",
    "        history = model.fit(train_data_generator,\n",
    "                epochs=epoch,\n",
    "                callbacks=callbacks_list,\n",
    "                validation_data=valid_data_generator\n",
    "                ,verbose = 2)\n",
    "\n",
    "        #PLOT HISTORY\n",
    "        #Plots for whichever\n",
    "        #\t\t:\n",
    "        #plt.plot(history.history['binary_accuracy'], label='accuracy')\n",
    "        #plt.plot(history.history['val_binary_accuracy'], label = 'val_accuracy')\n",
    "        #plt.xlabel('Epoch')\n",
    "        #plt.ylabel('Accuracy')\n",
    "        #plt.ylim([0.3, 1])\n",
    "        #plt.legend(loc='lower right')\n",
    "        #plt.show()\n",
    "        #frame = {'Accuracy': history.history['binary_accuracy'], 'Val_Accuracy': history.history['val_binary_accuracy']}\n",
    "        #fig = px.line(frame)\n",
    "        #fig.show()\n",
    "        fig = go.Figure()\n",
    "        fig.add_trace(go.Scatter(y= history.history['accuracy'],\n",
    "                          mode='lines+markers',\n",
    "                          name='Training Accuracy'))\n",
    "        fig.add_trace(go.Scatter(y= history.history['val_accuracy'],\n",
    "                          mode='lines+markers',\n",
    "                          name='Validation Accuracy'))\n",
    "        fig.update_yaxes(range=[0, 1])\n",
    "        fig.update_layout(\n",
    "            title=\"Fold \"+ str(fold_var)+ \"'s performance\",\n",
    "            title_x=0.5,\n",
    "            xaxis_title=\"Epochs\",\n",
    "            yaxis_title=\"Accuracy\",\n",
    "            autosize=False,\n",
    "            width=800,\n",
    "            height=400,\n",
    "            margin=dict(l=20, r=20, t=40, b=20),)\n",
    "        fig.show()\n",
    "        graph_dir = '/kaggle/working/img'\n",
    "        create_dir(graph_dir)\n",
    "        fig.write_image(graph_dir+'/fold'+str(fold_var)+'.png') \n",
    "\n",
    "        #\t\t:\n",
    "        # LOAD BEST MODEL to evaluate the performance of the model\n",
    "        model.load_weights(save_dir+model_method.__name__+\"_\" + \"model_\"+str(fold_var)+\".h5\")\n",
    "\n",
    "        results = model.evaluate(test_set)\n",
    "        results = dict(zip(model.metrics_names,results))\n",
    "\n",
    "        VALIDATION_ACCURACY.append(results['accuracy'])\n",
    "        VALIDATION_LOSS.append(results['loss'])\n",
    "\n",
    "        tf.keras.backend.clear_session()\n",
    "\n",
    "        fold_var += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2ad22264",
   "metadata": {
    "execution": {
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    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#model_test(5, 1, create_model_V1, (48,48), 'rgb')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "31ef4322",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-06T03:23:42.382202Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#model_test(5, 30, create_model_VF, (48,48,), 'rgb')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a930d35d",
   "metadata": {
    "execution": {
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     "iopub.status.idle": "2022-05-06T08:23:27.404263Z",
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     "shell.execute_reply.started": "2022-04-09T15:15:09.882669Z"
    },
    "papermill": {
     "duration": 17984.948449,
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     "exception": false,
     "start_time": "2022-05-06T03:23:42.456628",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hi\n",
      "                          filename     label\n",
      "10  surprise/Training_52655852.jpg  surprise\n",
      "16  surprise/Training_44186778.jpg  surprise\n",
      "21  surprise/Training_87248788.jpg  surprise\n",
      "31  surprise/Training_32069005.jpg  surprise\n",
      "45   surprise/Training_4473463.jpg  surprise\n",
      "Found 22967 validated image filenames belonging to 7 classes.\n",
      "Found 5742 validated image filenames belonging to 7 classes.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-05-06 03:23:55.924660: I tensorflow/core/common_runtime/process_util.cc:146] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance.\n",
      "2022-05-06 03:23:56.376834: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "718/718 - 206s - loss: 6.1189 - accuracy: 0.1841 - val_loss: 1.9285 - val_accuracy: 0.1759\n",
      "\n",
      "Epoch 00001: val_accuracy improved from -inf to 0.17590, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_1.h5\n",
      "Epoch 2/30\n",
      "718/718 - 105s - loss: 1.8774 - accuracy: 0.2127 - val_loss: 1.8889 - val_accuracy: 0.2264\n",
      "\n",
      "Epoch 00002: val_accuracy improved from 0.17590 to 0.22640, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_1.h5\n",
      "Epoch 3/30\n",
      "718/718 - 103s - loss: 1.8276 - accuracy: 0.2504 - val_loss: 1.8941 - val_accuracy: 0.2240\n",
      "\n",
      "Epoch 00003: val_accuracy did not improve from 0.22640\n",
      "Epoch 4/30\n",
      "718/718 - 103s - loss: 1.8194 - accuracy: 0.2513 - val_loss: 1.8928 - val_accuracy: 0.2290\n",
      "\n",
      "Epoch 00004: val_accuracy improved from 0.22640 to 0.22901, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_1.h5\n",
      "Epoch 5/30\n",
      "718/718 - 102s - loss: 1.8170 - accuracy: 0.2511 - val_loss: 1.8792 - val_accuracy: 0.2381\n",
      "\n",
      "Epoch 00005: val_accuracy improved from 0.22901 to 0.23807, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_1.h5\n",
      "Epoch 6/30\n",
      "718/718 - 105s - loss: 1.8131 - accuracy: 0.2520 - val_loss: 1.8802 - val_accuracy: 0.2348\n",
      "\n",
      "Epoch 00006: val_accuracy did not improve from 0.23807\n",
      "Epoch 7/30\n",
      "718/718 - 105s - loss: 1.8077 - accuracy: 0.2518 - val_loss: 1.8687 - val_accuracy: 0.2557\n",
      "\n",
      "Epoch 00007: val_accuracy improved from 0.23807 to 0.25566, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_1.h5\n",
      "Epoch 8/30\n",
      "718/718 - 105s - loss: 1.7978 - accuracy: 0.2531 - val_loss: 1.8500 - val_accuracy: 0.2837\n",
      "\n",
      "Epoch 00008: val_accuracy improved from 0.25566 to 0.28370, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_1.h5\n",
      "Epoch 9/30\n",
      "718/718 - 105s - loss: 1.7798 - accuracy: 0.2616 - val_loss: 1.7989 - val_accuracy: 0.2811\n",
      "\n",
      "Epoch 00009: val_accuracy did not improve from 0.28370\n",
      "Epoch 10/30\n",
      "718/718 - 105s - loss: 1.7615 - accuracy: 0.2682 - val_loss: 1.7771 - val_accuracy: 0.2851\n",
      "\n",
      "Epoch 00010: val_accuracy improved from 0.28370 to 0.28509, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_1.h5\n",
      "Epoch 11/30\n",
      "718/718 - 104s - loss: 1.7340 - accuracy: 0.2767 - val_loss: 1.7319 - val_accuracy: 0.2907\n",
      "\n",
      "Epoch 00011: val_accuracy improved from 0.28509 to 0.29067, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_1.h5\n",
      "Epoch 12/30\n",
      "718/718 - 106s - loss: 1.7126 - accuracy: 0.2870 - val_loss: 1.7078 - val_accuracy: 0.3070\n",
      "\n",
      "Epoch 00012: val_accuracy improved from 0.29067 to 0.30704, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_1.h5\n",
      "Epoch 13/30\n",
      "718/718 - 104s - loss: 1.6884 - accuracy: 0.3000 - val_loss: 1.6784 - val_accuracy: 0.3091\n",
      "\n",
      "Epoch 00013: val_accuracy improved from 0.30704 to 0.30913, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_1.h5\n",
      "Epoch 14/30\n",
      "718/718 - 102s - loss: 1.6714 - accuracy: 0.3048 - val_loss: 1.6598 - val_accuracy: 0.3227\n",
      "\n",
      "Epoch 00014: val_accuracy improved from 0.30913 to 0.32271, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_1.h5\n",
      "Epoch 15/30\n",
      "718/718 - 103s - loss: 1.6527 - accuracy: 0.3119 - val_loss: 1.6543 - val_accuracy: 0.3370\n",
      "\n",
      "Epoch 00015: val_accuracy improved from 0.32271 to 0.33699, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_1.h5\n",
      "Epoch 16/30\n",
      "718/718 - 105s - loss: 1.6368 - accuracy: 0.3178 - val_loss: 1.6354 - val_accuracy: 0.3337\n",
      "\n",
      "Epoch 00016: val_accuracy did not improve from 0.33699\n",
      "Epoch 17/30\n",
      "718/718 - 105s - loss: 1.6213 - accuracy: 0.3195 - val_loss: 1.6085 - val_accuracy: 0.3189\n",
      "\n",
      "Epoch 00017: val_accuracy did not improve from 0.33699\n",
      "Epoch 18/30\n",
      "718/718 - 104s - loss: 1.6066 - accuracy: 0.3252 - val_loss: 1.6044 - val_accuracy: 0.3356\n",
      "\n",
      "Epoch 00018: val_accuracy did not improve from 0.33699\n",
      "Epoch 19/30\n",
      "718/718 - 105s - loss: 1.5957 - accuracy: 0.3305 - val_loss: 1.5908 - val_accuracy: 0.3309\n",
      "\n",
      "Epoch 00019: val_accuracy did not improve from 0.33699\n",
      "Epoch 20/30\n",
      "718/718 - 104s - loss: 1.5799 - accuracy: 0.3368 - val_loss: 1.5685 - val_accuracy: 0.3579\n",
      "\n",
      "Epoch 00020: val_accuracy improved from 0.33699 to 0.35789, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_1.h5\n",
      "Epoch 21/30\n",
      "718/718 - 105s - loss: 1.5286 - accuracy: 0.3908 - val_loss: 1.5079 - val_accuracy: 0.4080\n",
      "\n",
      "Epoch 00021: val_accuracy improved from 0.35789 to 0.40805, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_1.h5\n",
      "Epoch 22/30\n",
      "718/718 - 105s - loss: 1.4191 - accuracy: 0.4364 - val_loss: 1.3810 - val_accuracy: 0.4768\n",
      "\n",
      "Epoch 00022: val_accuracy improved from 0.40805 to 0.47684, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_1.h5\n",
      "Epoch 23/30\n",
      "718/718 - 105s - loss: 1.3517 - accuracy: 0.4726 - val_loss: 1.3271 - val_accuracy: 0.4875\n",
      "\n",
      "Epoch 00023: val_accuracy improved from 0.47684 to 0.48746, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_1.h5\n",
      "Epoch 24/30\n",
      "718/718 - 104s - loss: 1.3091 - accuracy: 0.4897 - val_loss: 1.3060 - val_accuracy: 0.5031\n",
      "\n",
      "Epoch 00024: val_accuracy improved from 0.48746 to 0.50313, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_1.h5\n",
      "Epoch 25/30\n",
      "718/718 - 105s - loss: 1.2813 - accuracy: 0.5004 - val_loss: 1.2962 - val_accuracy: 0.4981\n",
      "\n",
      "Epoch 00025: val_accuracy did not improve from 0.50313\n",
      "Epoch 26/30\n",
      "718/718 - 104s - loss: 1.2566 - accuracy: 0.5128 - val_loss: 1.2744 - val_accuracy: 0.5186\n",
      "\n",
      "Epoch 00026: val_accuracy improved from 0.50313 to 0.51863, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_1.h5\n",
      "Epoch 27/30\n",
      "718/718 - 102s - loss: 1.2317 - accuracy: 0.5247 - val_loss: 1.2688 - val_accuracy: 0.5127\n",
      "\n",
      "Epoch 00027: val_accuracy did not improve from 0.51863\n",
      "Epoch 28/30\n",
      "718/718 - 101s - loss: 1.2097 - accuracy: 0.5359 - val_loss: 1.2482 - val_accuracy: 0.5268\n",
      "\n",
      "Epoch 00028: val_accuracy improved from 0.51863 to 0.52682, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_1.h5\n",
      "Epoch 29/30\n",
      "718/718 - 104s - loss: 1.1792 - accuracy: 0.5475 - val_loss: 1.2309 - val_accuracy: 0.5308\n",
      "\n",
      "Epoch 00029: val_accuracy improved from 0.52682 to 0.53083, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_1.h5\n",
      "Epoch 30/30\n",
      "718/718 - 104s - loss: 1.1532 - accuracy: 0.5596 - val_loss: 1.2249 - val_accuracy: 0.5315\n",
      "\n",
      "Epoch 00030: val_accuracy improved from 0.53083 to 0.53152, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_1.h5\n"
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      "225/225 [==============================] - 50s 222ms/step - loss: 1.2191 - accuracy: 0.5315\n",
      "hi\n",
      "                          filename     label\n",
      "2   surprise/Training_63239185.jpg  surprise\n",
      "6   surprise/Training_69351834.jpg  surprise\n",
      "7   surprise/Training_67440750.jpg  surprise\n",
      "12  surprise/Training_46386777.jpg  surprise\n",
      "18  surprise/Training_43664635.jpg  surprise\n",
      "Found 22967 validated image filenames belonging to 7 classes.\n",
      "Found 5742 validated image filenames belonging to 7 classes.\n",
      "Epoch 1/30\n",
      "718/718 - 107s - loss: 4.6689 - accuracy: 0.1840 - val_loss: 1.9044 - val_accuracy: 0.1930\n",
      "\n",
      "Epoch 00001: val_accuracy improved from -inf to 0.19296, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_2.h5\n",
      "Epoch 2/30\n",
      "718/718 - 106s - loss: 1.8510 - accuracy: 0.2289 - val_loss: 1.8683 - val_accuracy: 0.2550\n",
      "\n",
      "Epoch 00002: val_accuracy improved from 0.19296 to 0.25496, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_2.h5\n",
      "Epoch 3/30\n",
      "718/718 - 106s - loss: 1.8092 - accuracy: 0.2514 - val_loss: 1.8319 - val_accuracy: 0.2971\n",
      "\n",
      "Epoch 00003: val_accuracy improved from 0.25496 to 0.29711, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_2.h5\n",
      "Epoch 4/30\n",
      "718/718 - 103s - loss: 1.7475 - accuracy: 0.2914 - val_loss: 1.7487 - val_accuracy: 0.3514\n",
      "\n",
      "Epoch 00004: val_accuracy improved from 0.29711 to 0.35145, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_2.h5\n",
      "Epoch 5/30\n",
      "718/718 - 102s - loss: 1.6604 - accuracy: 0.3479 - val_loss: 1.6203 - val_accuracy: 0.3910\n",
      "\n",
      "Epoch 00005: val_accuracy improved from 0.35145 to 0.39098, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_2.h5\n",
      "Epoch 6/30\n",
      "718/718 - 106s - loss: 1.5818 - accuracy: 0.3876 - val_loss: 1.5492 - val_accuracy: 0.4368\n",
      "\n",
      "Epoch 00006: val_accuracy improved from 0.39098 to 0.43678, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_2.h5\n",
      "Epoch 7/30\n",
      "718/718 - 106s - loss: 1.5323 - accuracy: 0.4090 - val_loss: 1.4951 - val_accuracy: 0.4521\n",
      "\n",
      "Epoch 00007: val_accuracy improved from 0.43678 to 0.45211, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_2.h5\n",
      "Epoch 8/30\n",
      "718/718 - 106s - loss: 1.4848 - accuracy: 0.4281 - val_loss: 1.4484 - val_accuracy: 0.4606\n",
      "\n",
      "Epoch 00008: val_accuracy improved from 0.45211 to 0.46064, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_2.h5\n",
      "Epoch 9/30\n",
      "718/718 - 106s - loss: 1.4411 - accuracy: 0.4493 - val_loss: 1.4078 - val_accuracy: 0.4815\n",
      "\n",
      "Epoch 00009: val_accuracy improved from 0.46064 to 0.48154, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_2.h5\n",
      "Epoch 10/30\n",
      "718/718 - 106s - loss: 1.4048 - accuracy: 0.4635 - val_loss: 1.3829 - val_accuracy: 0.4822\n",
      "\n",
      "Epoch 00010: val_accuracy improved from 0.48154 to 0.48224, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_2.h5\n",
      "Epoch 11/30\n",
      "718/718 - 105s - loss: 1.3652 - accuracy: 0.4786 - val_loss: 1.4049 - val_accuracy: 0.4882\n",
      "\n",
      "Epoch 00011: val_accuracy improved from 0.48224 to 0.48816, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_2.h5\n",
      "Epoch 12/30\n",
      "718/718 - 106s - loss: 1.3378 - accuracy: 0.4936 - val_loss: 1.3224 - val_accuracy: 0.5190\n",
      "\n",
      "Epoch 00012: val_accuracy improved from 0.48816 to 0.51898, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_2.h5\n",
      "Epoch 13/30\n",
      "718/718 - 106s - loss: 1.3022 - accuracy: 0.5048 - val_loss: 1.2939 - val_accuracy: 0.5296\n",
      "\n",
      "Epoch 00013: val_accuracy improved from 0.51898 to 0.52961, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_2.h5\n",
      "Epoch 14/30\n",
      "718/718 - 104s - loss: 1.2820 - accuracy: 0.5142 - val_loss: 1.2979 - val_accuracy: 0.5303\n",
      "\n",
      "Epoch 00014: val_accuracy improved from 0.52961 to 0.53030, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_2.h5\n",
      "Epoch 15/30\n",
      "718/718 - 104s - loss: 1.2512 - accuracy: 0.5280 - val_loss: 1.2672 - val_accuracy: 0.5428\n",
      "\n",
      "Epoch 00015: val_accuracy improved from 0.53030 to 0.54284, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_2.h5\n",
      "Epoch 16/30\n",
      "718/718 - 105s - loss: 1.2261 - accuracy: 0.5377 - val_loss: 1.2623 - val_accuracy: 0.5320\n",
      "\n",
      "Epoch 00016: val_accuracy did not improve from 0.54284\n",
      "Epoch 17/30\n",
      "718/718 - 106s - loss: 1.1935 - accuracy: 0.5503 - val_loss: 1.2149 - val_accuracy: 0.5455\n",
      "\n",
      "Epoch 00017: val_accuracy improved from 0.54284 to 0.54545, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_2.h5\n",
      "Epoch 18/30\n",
      "718/718 - 103s - loss: 1.1732 - accuracy: 0.5595 - val_loss: 1.2262 - val_accuracy: 0.5449\n",
      "\n",
      "Epoch 00018: val_accuracy did not improve from 0.54545\n",
      "Epoch 19/30\n",
      "718/718 - 104s - loss: 1.1409 - accuracy: 0.5740 - val_loss: 1.1924 - val_accuracy: 0.5538\n",
      "\n",
      "Epoch 00019: val_accuracy improved from 0.54545 to 0.55381, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_2.h5\n",
      "Epoch 20/30\n",
      "718/718 - 106s - loss: 1.1174 - accuracy: 0.5821 - val_loss: 1.1943 - val_accuracy: 0.5563\n",
      "\n",
      "Epoch 00020: val_accuracy improved from 0.55381 to 0.55625, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_2.h5\n",
      "Epoch 21/30\n",
      "718/718 - 106s - loss: 1.0910 - accuracy: 0.5905 - val_loss: 1.1825 - val_accuracy: 0.5608\n",
      "\n",
      "Epoch 00021: val_accuracy improved from 0.55625 to 0.56078, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_2.h5\n",
      "Epoch 22/30\n",
      "718/718 - 106s - loss: 1.0671 - accuracy: 0.6010 - val_loss: 1.1904 - val_accuracy: 0.5625\n",
      "\n",
      "Epoch 00022: val_accuracy improved from 0.56078 to 0.56252, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_2.h5\n",
      "Epoch 23/30\n",
      "718/718 - 107s - loss: 1.0341 - accuracy: 0.6129 - val_loss: 1.1941 - val_accuracy: 0.5611\n",
      "\n",
      "Epoch 00023: val_accuracy did not improve from 0.56252\n",
      "Epoch 24/30\n",
      "718/718 - 108s - loss: 1.0098 - accuracy: 0.6223 - val_loss: 1.1803 - val_accuracy: 0.5632\n",
      "\n",
      "Epoch 00024: val_accuracy improved from 0.56252 to 0.56322, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_2.h5\n",
      "Epoch 25/30\n",
      "718/718 - 107s - loss: 0.9803 - accuracy: 0.6353 - val_loss: 1.1817 - val_accuracy: 0.5691\n",
      "\n",
      "Epoch 00025: val_accuracy improved from 0.56322 to 0.56914, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_2.h5\n",
      "Epoch 26/30\n",
      "718/718 - 105s - loss: 0.9438 - accuracy: 0.6499 - val_loss: 1.1903 - val_accuracy: 0.5650\n",
      "\n",
      "Epoch 00026: val_accuracy did not improve from 0.56914\n",
      "Epoch 27/30\n",
      "718/718 - 105s - loss: 0.9168 - accuracy: 0.6583 - val_loss: 1.1689 - val_accuracy: 0.5761\n",
      "\n",
      "Epoch 00027: val_accuracy improved from 0.56914 to 0.57611, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_2.h5\n",
      "Epoch 28/30\n",
      "718/718 - 105s - loss: 0.8875 - accuracy: 0.6744 - val_loss: 1.1755 - val_accuracy: 0.5707\n",
      "\n",
      "Epoch 00028: val_accuracy did not improve from 0.57611\n",
      "Epoch 29/30\n",
      "718/718 - 105s - loss: 0.8586 - accuracy: 0.6815 - val_loss: 1.1924 - val_accuracy: 0.5695\n",
      "\n",
      "Epoch 00029: val_accuracy did not improve from 0.57611\n",
      "Epoch 30/30\n",
      "718/718 - 105s - loss: 0.8293 - accuracy: 0.6948 - val_loss: 1.2031 - val_accuracy: 0.5789\n",
      "\n",
      "Epoch 00030: val_accuracy improved from 0.57611 to 0.57889, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_2.h5\n"
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      "225/225 [==============================] - 18s 79ms/step - loss: 1.2132 - accuracy: 0.5684\n",
      "hi\n",
      "                          filename     label\n",
      "4   surprise/Training_71308969.jpg  surprise\n",
      "8   surprise/Training_93942694.jpg  surprise\n",
      "9   surprise/Training_11693705.jpg  surprise\n",
      "22  surprise/Training_11550894.jpg  surprise\n",
      "23  surprise/Training_91279496.jpg  surprise\n",
      "Found 22967 validated image filenames belonging to 7 classes.\n",
      "Found 5742 validated image filenames belonging to 7 classes.\n",
      "Epoch 1/30\n",
      "718/718 - 108s - loss: 4.1476 - accuracy: 0.1983 - val_loss: 1.8621 - val_accuracy: 0.2687\n",
      "\n",
      "Epoch 00001: val_accuracy improved from -inf to 0.26872, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_3.h5\n",
      "Epoch 2/30\n",
      "718/718 - 108s - loss: 1.8224 - accuracy: 0.2586 - val_loss: 1.7763 - val_accuracy: 0.3299\n",
      "\n",
      "Epoch 00002: val_accuracy improved from 0.26872 to 0.32985, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_3.h5\n",
      "Epoch 3/30\n",
      "718/718 - 107s - loss: 1.7122 - accuracy: 0.3205 - val_loss: 1.6581 - val_accuracy: 0.3589\n",
      "\n",
      "Epoch 00003: val_accuracy improved from 0.32985 to 0.35893, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_3.h5\n",
      "Epoch 4/30\n",
      "718/718 - 105s - loss: 1.6166 - accuracy: 0.3704 - val_loss: 1.5776 - val_accuracy: 0.4363\n",
      "\n",
      "Epoch 00004: val_accuracy improved from 0.35893 to 0.43626, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_3.h5\n",
      "Epoch 5/30\n",
      "718/718 - 107s - loss: 1.5555 - accuracy: 0.4032 - val_loss: 1.5340 - val_accuracy: 0.4286\n",
      "\n",
      "Epoch 00005: val_accuracy did not improve from 0.43626\n",
      "Epoch 6/30\n",
      "718/718 - 107s - loss: 1.5005 - accuracy: 0.4260 - val_loss: 1.4917 - val_accuracy: 0.4610\n",
      "\n",
      "Epoch 00006: val_accuracy improved from 0.43626 to 0.46099, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_3.h5\n",
      "Epoch 7/30\n",
      "718/718 - 108s - loss: 1.4603 - accuracy: 0.4410 - val_loss: 1.4625 - val_accuracy: 0.4636\n",
      "\n",
      "Epoch 00007: val_accuracy improved from 0.46099 to 0.46360, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_3.h5\n",
      "Epoch 8/30\n",
      "718/718 - 107s - loss: 1.4198 - accuracy: 0.4597 - val_loss: 1.4082 - val_accuracy: 0.4902\n",
      "\n",
      "Epoch 00008: val_accuracy improved from 0.46360 to 0.49025, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_3.h5\n",
      "Epoch 9/30\n",
      "718/718 - 109s - loss: 1.3820 - accuracy: 0.4772 - val_loss: 1.3956 - val_accuracy: 0.5047\n",
      "\n",
      "Epoch 00009: val_accuracy improved from 0.49025 to 0.50470, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_3.h5\n",
      "Epoch 10/30\n",
      "718/718 - 109s - loss: 1.3507 - accuracy: 0.4906 - val_loss: 1.3578 - val_accuracy: 0.5096\n",
      "\n",
      "Epoch 00010: val_accuracy improved from 0.50470 to 0.50958, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_3.h5\n",
      "Epoch 11/30\n",
      "718/718 - 107s - loss: 1.3222 - accuracy: 0.5006 - val_loss: 1.3318 - val_accuracy: 0.5152\n",
      "\n",
      "Epoch 00011: val_accuracy improved from 0.50958 to 0.51515, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_3.h5\n",
      "Epoch 12/30\n",
      "718/718 - 109s - loss: 1.2832 - accuracy: 0.5150 - val_loss: 1.3028 - val_accuracy: 0.5160\n",
      "\n",
      "Epoch 00012: val_accuracy improved from 0.51515 to 0.51602, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_3.h5\n",
      "Epoch 13/30\n",
      "718/718 - 107s - loss: 1.2574 - accuracy: 0.5266 - val_loss: 1.2742 - val_accuracy: 0.5308\n",
      "\n",
      "Epoch 00013: val_accuracy improved from 0.51602 to 0.53083, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_3.h5\n",
      "Epoch 14/30\n",
      "718/718 - 109s - loss: 1.2262 - accuracy: 0.5382 - val_loss: 1.2681 - val_accuracy: 0.5291\n",
      "\n",
      "Epoch 00014: val_accuracy did not improve from 0.53083\n",
      "Epoch 15/30\n",
      "718/718 - 107s - loss: 1.1954 - accuracy: 0.5485 - val_loss: 1.2427 - val_accuracy: 0.5333\n",
      "\n",
      "Epoch 00015: val_accuracy improved from 0.53083 to 0.53326, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_3.h5\n",
      "Epoch 16/30\n",
      "718/718 - 108s - loss: 1.1608 - accuracy: 0.5664 - val_loss: 1.2523 - val_accuracy: 0.5366\n",
      "\n",
      "Epoch 00016: val_accuracy improved from 0.53326 to 0.53657, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_3.h5\n",
      "Epoch 17/30\n",
      "718/718 - 108s - loss: 1.1332 - accuracy: 0.5770 - val_loss: 1.2316 - val_accuracy: 0.5486\n",
      "\n",
      "Epoch 00017: val_accuracy improved from 0.53657 to 0.54859, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_3.h5\n",
      "Epoch 18/30\n",
      "718/718 - 109s - loss: 1.1051 - accuracy: 0.5827 - val_loss: 1.2152 - val_accuracy: 0.5495\n",
      "\n",
      "Epoch 00018: val_accuracy improved from 0.54859 to 0.54946, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_3.h5\n",
      "Epoch 19/30\n",
      "718/718 - 108s - loss: 1.0715 - accuracy: 0.6047 - val_loss: 1.2157 - val_accuracy: 0.5528\n",
      "\n",
      "Epoch 00019: val_accuracy improved from 0.54946 to 0.55277, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_3.h5\n",
      "Epoch 20/30\n",
      "718/718 - 108s - loss: 1.0369 - accuracy: 0.6121 - val_loss: 1.2084 - val_accuracy: 0.5549\n",
      "\n",
      "Epoch 00020: val_accuracy improved from 0.55277 to 0.55486, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_3.h5\n",
      "Epoch 21/30\n",
      "718/718 - 108s - loss: 0.9952 - accuracy: 0.6305 - val_loss: 1.2173 - val_accuracy: 0.5576\n",
      "\n",
      "Epoch 00021: val_accuracy improved from 0.55486 to 0.55765, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_3.h5\n",
      "Epoch 22/30\n",
      "718/718 - 108s - loss: 0.9624 - accuracy: 0.6453 - val_loss: 1.2225 - val_accuracy: 0.5519\n",
      "\n",
      "Epoch 00022: val_accuracy did not improve from 0.55765\n",
      "Epoch 23/30\n",
      "718/718 - 105s - loss: 0.9234 - accuracy: 0.6556 - val_loss: 1.2105 - val_accuracy: 0.5604\n",
      "\n",
      "Epoch 00023: val_accuracy improved from 0.55765 to 0.56043, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_3.h5\n",
      "Epoch 24/30\n",
      "718/718 - 106s - loss: 0.8888 - accuracy: 0.6671 - val_loss: 1.2111 - val_accuracy: 0.5629\n",
      "\n",
      "Epoch 00024: val_accuracy improved from 0.56043 to 0.56287, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_3.h5\n",
      "Epoch 25/30\n",
      "718/718 - 106s - loss: 0.8522 - accuracy: 0.6879 - val_loss: 1.2332 - val_accuracy: 0.5726\n",
      "\n",
      "Epoch 00025: val_accuracy improved from 0.56287 to 0.57262, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_3.h5\n",
      "Epoch 26/30\n",
      "718/718 - 105s - loss: 0.8113 - accuracy: 0.7033 - val_loss: 1.2469 - val_accuracy: 0.5639\n",
      "\n",
      "Epoch 00026: val_accuracy did not improve from 0.57262\n",
      "Epoch 27/30\n",
      "718/718 - 105s - loss: 0.7766 - accuracy: 0.7159 - val_loss: 1.2800 - val_accuracy: 0.5632\n",
      "\n",
      "Epoch 00027: val_accuracy did not improve from 0.57262\n",
      "Epoch 28/30\n",
      "718/718 - 105s - loss: 0.7384 - accuracy: 0.7290 - val_loss: 1.3015 - val_accuracy: 0.5686\n",
      "\n",
      "Epoch 00028: val_accuracy did not improve from 0.57262\n",
      "Epoch 29/30\n",
      "718/718 - 105s - loss: 0.7031 - accuracy: 0.7415 - val_loss: 1.3174 - val_accuracy: 0.5676\n",
      "\n",
      "Epoch 00029: val_accuracy did not improve from 0.57262\n",
      "Epoch 30/30\n",
      "718/718 - 105s - loss: 0.6702 - accuracy: 0.7578 - val_loss: 1.3375 - val_accuracy: 0.5664\n",
      "\n",
      "Epoch 00030: val_accuracy did not improve from 0.57262\n"
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      "225/225 [==============================] - 18s 80ms/step - loss: 1.1900 - accuracy: 0.5780\n",
      "hi\n",
      "                          filename     label\n",
      "0   surprise/Training_66056468.jpg  surprise\n",
      "3   surprise/Training_67725241.jpg  surprise\n",
      "11  surprise/Training_62829838.jpg  surprise\n",
      "14  surprise/Training_97275390.jpg  surprise\n",
      "19  surprise/Training_29912845.jpg  surprise\n",
      "Found 22967 validated image filenames belonging to 7 classes.\n",
      "Found 5742 validated image filenames belonging to 7 classes.\n",
      "Epoch 1/30\n",
      "718/718 - 107s - loss: 4.5248 - accuracy: 0.2000 - val_loss: 1.8873 - val_accuracy: 0.2468\n",
      "\n",
      "Epoch 00001: val_accuracy improved from -inf to 0.24678, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_4.h5\n",
      "Epoch 2/30\n",
      "718/718 - 108s - loss: 1.8441 - accuracy: 0.2510 - val_loss: 1.8446 - val_accuracy: 0.2807\n",
      "\n",
      "Epoch 00002: val_accuracy improved from 0.24678 to 0.28074, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_4.h5\n",
      "Epoch 3/30\n",
      "718/718 - 108s - loss: 1.7650 - accuracy: 0.2968 - val_loss: 1.7730 - val_accuracy: 0.3422\n",
      "\n",
      "Epoch 00003: val_accuracy improved from 0.28074 to 0.34222, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_4.h5\n",
      "Epoch 4/30\n",
      "718/718 - 108s - loss: 1.6743 - accuracy: 0.3493 - val_loss: 1.6547 - val_accuracy: 0.3899\n",
      "\n",
      "Epoch 00004: val_accuracy improved from 0.34222 to 0.38993, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_4.h5\n",
      "Epoch 5/30\n",
      "718/718 - 108s - loss: 1.6005 - accuracy: 0.3844 - val_loss: 1.5681 - val_accuracy: 0.4187\n",
      "\n",
      "Epoch 00005: val_accuracy improved from 0.38993 to 0.41867, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_4.h5\n",
      "Epoch 6/30\n",
      "718/718 - 108s - loss: 1.5297 - accuracy: 0.4157 - val_loss: 1.5144 - val_accuracy: 0.4375\n",
      "\n",
      "Epoch 00006: val_accuracy improved from 0.41867 to 0.43748, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_4.h5\n",
      "Epoch 7/30\n",
      "718/718 - 108s - loss: 1.4815 - accuracy: 0.4352 - val_loss: 1.4716 - val_accuracy: 0.4538\n",
      "\n",
      "Epoch 00007: val_accuracy improved from 0.43748 to 0.45385, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_4.h5\n",
      "Epoch 8/30\n",
      "718/718 - 108s - loss: 1.4343 - accuracy: 0.4545 - val_loss: 1.4316 - val_accuracy: 0.4716\n",
      "\n",
      "Epoch 00008: val_accuracy improved from 0.45385 to 0.47161, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_4.h5\n",
      "Epoch 9/30\n",
      "718/718 - 108s - loss: 1.3928 - accuracy: 0.4741 - val_loss: 1.3901 - val_accuracy: 0.4929\n",
      "\n",
      "Epoch 00009: val_accuracy improved from 0.47161 to 0.49286, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_4.h5\n",
      "Epoch 10/30\n",
      "718/718 - 109s - loss: 1.3563 - accuracy: 0.4855 - val_loss: 1.3563 - val_accuracy: 0.4981\n",
      "\n",
      "Epoch 00010: val_accuracy improved from 0.49286 to 0.49808, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_4.h5\n",
      "Epoch 11/30\n",
      "718/718 - 108s - loss: 1.3278 - accuracy: 0.5000 - val_loss: 1.3401 - val_accuracy: 0.5007\n",
      "\n",
      "Epoch 00011: val_accuracy improved from 0.49808 to 0.50070, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_4.h5\n",
      "Epoch 12/30\n",
      "718/718 - 108s - loss: 1.2935 - accuracy: 0.5127 - val_loss: 1.3101 - val_accuracy: 0.5200\n",
      "\n",
      "Epoch 00012: val_accuracy improved from 0.50070 to 0.52003, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_4.h5\n",
      "Epoch 13/30\n",
      "718/718 - 108s - loss: 1.2665 - accuracy: 0.5230 - val_loss: 1.2831 - val_accuracy: 0.5244\n",
      "\n",
      "Epoch 00013: val_accuracy improved from 0.52003 to 0.52438, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_4.h5\n",
      "Epoch 14/30\n",
      "718/718 - 108s - loss: 1.2384 - accuracy: 0.5366 - val_loss: 1.2843 - val_accuracy: 0.5310\n",
      "\n",
      "Epoch 00014: val_accuracy improved from 0.52438 to 0.53100, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_4.h5\n",
      "Epoch 15/30\n",
      "718/718 - 108s - loss: 1.2094 - accuracy: 0.5457 - val_loss: 1.2389 - val_accuracy: 0.5421\n",
      "\n",
      "Epoch 00015: val_accuracy improved from 0.53100 to 0.54215, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_4.h5\n",
      "Epoch 16/30\n",
      "718/718 - 107s - loss: 1.1806 - accuracy: 0.5602 - val_loss: 1.2248 - val_accuracy: 0.5355\n",
      "\n",
      "Epoch 00016: val_accuracy did not improve from 0.54215\n",
      "Epoch 17/30\n",
      "718/718 - 108s - loss: 1.1486 - accuracy: 0.5716 - val_loss: 1.2060 - val_accuracy: 0.5448\n",
      "\n",
      "Epoch 00017: val_accuracy improved from 0.54215 to 0.54476, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_4.h5\n",
      "Epoch 18/30\n",
      "718/718 - 109s - loss: 1.1275 - accuracy: 0.5787 - val_loss: 1.2144 - val_accuracy: 0.5505\n",
      "\n",
      "Epoch 00018: val_accuracy improved from 0.54476 to 0.55051, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_4.h5\n",
      "Epoch 19/30\n",
      "718/718 - 108s - loss: 1.0898 - accuracy: 0.5933 - val_loss: 1.1956 - val_accuracy: 0.5463\n",
      "\n",
      "Epoch 00019: val_accuracy did not improve from 0.55051\n",
      "Epoch 20/30\n",
      "718/718 - 105s - loss: 1.0657 - accuracy: 0.6065 - val_loss: 1.1749 - val_accuracy: 0.5578\n",
      "\n",
      "Epoch 00020: val_accuracy improved from 0.55051 to 0.55782, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_4.h5\n",
      "Epoch 21/30\n",
      "718/718 - 105s - loss: 1.0348 - accuracy: 0.6127 - val_loss: 1.1869 - val_accuracy: 0.5604\n",
      "\n",
      "Epoch 00021: val_accuracy improved from 0.55782 to 0.56043, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_4.h5\n",
      "Epoch 22/30\n",
      "718/718 - 106s - loss: 0.9922 - accuracy: 0.6343 - val_loss: 1.1722 - val_accuracy: 0.5636\n",
      "\n",
      "Epoch 00022: val_accuracy improved from 0.56043 to 0.56357, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_4.h5\n",
      "Epoch 23/30\n",
      "718/718 - 106s - loss: 0.9682 - accuracy: 0.6394 - val_loss: 1.1916 - val_accuracy: 0.5606\n",
      "\n",
      "Epoch 00023: val_accuracy did not improve from 0.56357\n",
      "Epoch 24/30\n",
      "718/718 - 108s - loss: 0.9274 - accuracy: 0.6578 - val_loss: 1.1691 - val_accuracy: 0.5648\n",
      "\n",
      "Epoch 00024: val_accuracy improved from 0.56357 to 0.56479, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_4.h5\n",
      "Epoch 25/30\n",
      "718/718 - 108s - loss: 0.8936 - accuracy: 0.6687 - val_loss: 1.1838 - val_accuracy: 0.5700\n",
      "\n",
      "Epoch 00025: val_accuracy improved from 0.56479 to 0.57001, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_4.h5\n",
      "Epoch 26/30\n",
      "718/718 - 109s - loss: 0.8619 - accuracy: 0.6830 - val_loss: 1.1969 - val_accuracy: 0.5603\n",
      "\n",
      "Epoch 00026: val_accuracy did not improve from 0.57001\n",
      "Epoch 27/30\n",
      "718/718 - 110s - loss: 0.8308 - accuracy: 0.6940 - val_loss: 1.2192 - val_accuracy: 0.5651\n",
      "\n",
      "Epoch 00027: val_accuracy did not improve from 0.57001\n",
      "Epoch 28/30\n",
      "718/718 - 107s - loss: 0.8008 - accuracy: 0.7029 - val_loss: 1.2108 - val_accuracy: 0.5693\n",
      "\n",
      "Epoch 00028: val_accuracy did not improve from 0.57001\n",
      "Epoch 29/30\n",
      "718/718 - 107s - loss: 0.7639 - accuracy: 0.7213 - val_loss: 1.2607 - val_accuracy: 0.5658\n",
      "\n",
      "Epoch 00029: val_accuracy did not improve from 0.57001\n",
      "Epoch 30/30\n",
      "718/718 - 107s - loss: 0.7254 - accuracy: 0.7327 - val_loss: 1.2519 - val_accuracy: 0.5728\n",
      "\n",
      "Epoch 00030: val_accuracy improved from 0.57001 to 0.57280, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_4.h5\n"
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      "225/225 [==============================] - 16s 70ms/step - loss: 1.2373 - accuracy: 0.5807\n",
      "hi\n",
      "                          filename     label\n",
      "1   surprise/Training_26522394.jpg  surprise\n",
      "5   surprise/Training_49242724.jpg  surprise\n",
      "13   surprise/Training_7022565.jpg  surprise\n",
      "15  surprise/Training_37026288.jpg  surprise\n",
      "17  surprise/Training_97667504.jpg  surprise\n",
      "Found 22968 validated image filenames belonging to 7 classes.\n",
      "Found 5741 validated image filenames belonging to 7 classes.\n",
      "Epoch 1/30\n",
      "718/718 - 108s - loss: 6.1230 - accuracy: 0.1910 - val_loss: 1.9140 - val_accuracy: 0.1930\n",
      "\n",
      "Epoch 00001: val_accuracy improved from -inf to 0.19300, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_5.h5\n",
      "Epoch 2/30\n",
      "718/718 - 108s - loss: 1.8943 - accuracy: 0.2216 - val_loss: 1.8936 - val_accuracy: 0.2538\n",
      "\n",
      "Epoch 00002: val_accuracy improved from 0.19300 to 0.25379, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_5.h5\n",
      "Epoch 3/30\n",
      "718/718 - 108s - loss: 1.7973 - accuracy: 0.2640 - val_loss: 1.8298 - val_accuracy: 0.3006\n",
      "\n",
      "Epoch 00003: val_accuracy improved from 0.25379 to 0.30064, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_5.h5\n",
      "Epoch 4/30\n",
      "718/718 - 106s - loss: 1.7303 - accuracy: 0.3136 - val_loss: 1.7385 - val_accuracy: 0.3512\n",
      "\n",
      "Epoch 00004: val_accuracy improved from 0.30064 to 0.35116, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_5.h5\n",
      "Epoch 5/30\n",
      "718/718 - 105s - loss: 1.6741 - accuracy: 0.3432 - val_loss: 1.6472 - val_accuracy: 0.3816\n",
      "\n",
      "Epoch 00005: val_accuracy improved from 0.35116 to 0.38164, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_5.h5\n",
      "Epoch 6/30\n",
      "718/718 - 105s - loss: 1.6157 - accuracy: 0.3777 - val_loss: 1.5843 - val_accuracy: 0.4085\n",
      "\n",
      "Epoch 00006: val_accuracy improved from 0.38164 to 0.40847, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_5.h5\n",
      "Epoch 7/30\n",
      "718/718 - 107s - loss: 1.5647 - accuracy: 0.3993 - val_loss: 1.5317 - val_accuracy: 0.4370\n",
      "\n",
      "Epoch 00007: val_accuracy improved from 0.40847 to 0.43703, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_5.h5\n",
      "Epoch 8/30\n",
      "718/718 - 106s - loss: 1.5201 - accuracy: 0.4199 - val_loss: 1.4990 - val_accuracy: 0.4513\n",
      "\n",
      "Epoch 00008: val_accuracy improved from 0.43703 to 0.45132, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_5.h5\n",
      "Epoch 9/30\n",
      "718/718 - 107s - loss: 1.4746 - accuracy: 0.4386 - val_loss: 1.4365 - val_accuracy: 0.4621\n",
      "\n",
      "Epoch 00009: val_accuracy improved from 0.45132 to 0.46211, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_5.h5\n",
      "Epoch 10/30\n",
      "718/718 - 106s - loss: 1.4336 - accuracy: 0.4602 - val_loss: 1.4091 - val_accuracy: 0.4771\n",
      "\n",
      "Epoch 00010: val_accuracy improved from 0.46211 to 0.47709, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_5.h5\n",
      "Epoch 11/30\n",
      "718/718 - 108s - loss: 1.3970 - accuracy: 0.4740 - val_loss: 1.3701 - val_accuracy: 0.4980\n",
      "\n",
      "Epoch 00011: val_accuracy improved from 0.47709 to 0.49800, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_5.h5\n",
      "Epoch 12/30\n",
      "718/718 - 109s - loss: 1.3617 - accuracy: 0.4888 - val_loss: 1.3443 - val_accuracy: 0.5083\n",
      "\n",
      "Epoch 00012: val_accuracy improved from 0.49800 to 0.50827, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_5.h5\n",
      "Epoch 13/30\n",
      "718/718 - 109s - loss: 1.3368 - accuracy: 0.4981 - val_loss: 1.3178 - val_accuracy: 0.5194\n",
      "\n",
      "Epoch 00013: val_accuracy improved from 0.50827 to 0.51942, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_5.h5\n",
      "Epoch 14/30\n",
      "718/718 - 108s - loss: 1.3071 - accuracy: 0.5086 - val_loss: 1.2819 - val_accuracy: 0.5278\n",
      "\n",
      "Epoch 00014: val_accuracy improved from 0.51942 to 0.52778, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_5.h5\n",
      "Epoch 15/30\n",
      "718/718 - 108s - loss: 1.2785 - accuracy: 0.5190 - val_loss: 1.2762 - val_accuracy: 0.5405\n",
      "\n",
      "Epoch 00015: val_accuracy improved from 0.52778 to 0.54050, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_5.h5\n",
      "Epoch 16/30\n",
      "718/718 - 107s - loss: 1.2558 - accuracy: 0.5315 - val_loss: 1.2701 - val_accuracy: 0.5379\n",
      "\n",
      "Epoch 00016: val_accuracy did not improve from 0.54050\n",
      "Epoch 17/30\n",
      "718/718 - 108s - loss: 1.2208 - accuracy: 0.5410 - val_loss: 1.2456 - val_accuracy: 0.5438\n",
      "\n",
      "Epoch 00017: val_accuracy improved from 0.54050 to 0.54381, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_5.h5\n",
      "Epoch 18/30\n",
      "718/718 - 110s - loss: 1.2017 - accuracy: 0.5528 - val_loss: 1.2266 - val_accuracy: 0.5497\n",
      "\n",
      "Epoch 00018: val_accuracy improved from 0.54381 to 0.54973, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_5.h5\n",
      "Epoch 19/30\n",
      "718/718 - 109s - loss: 1.1690 - accuracy: 0.5637 - val_loss: 1.2124 - val_accuracy: 0.5581\n",
      "\n",
      "Epoch 00019: val_accuracy improved from 0.54973 to 0.55809, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_5.h5\n",
      "Epoch 20/30\n",
      "718/718 - 109s - loss: 1.1435 - accuracy: 0.5754 - val_loss: 1.2099 - val_accuracy: 0.5518\n",
      "\n",
      "Epoch 00020: val_accuracy did not improve from 0.55809\n",
      "Epoch 21/30\n",
      "718/718 - 108s - loss: 1.1120 - accuracy: 0.5867 - val_loss: 1.1805 - val_accuracy: 0.5656\n",
      "\n",
      "Epoch 00021: val_accuracy improved from 0.55809 to 0.56558, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_5.h5\n",
      "Epoch 22/30\n",
      "718/718 - 109s - loss: 1.0832 - accuracy: 0.6007 - val_loss: 1.1879 - val_accuracy: 0.5551\n",
      "\n",
      "Epoch 00022: val_accuracy did not improve from 0.56558\n",
      "Epoch 23/30\n",
      "718/718 - 108s - loss: 1.0581 - accuracy: 0.6070 - val_loss: 1.1616 - val_accuracy: 0.5691\n",
      "\n",
      "Epoch 00023: val_accuracy improved from 0.56558 to 0.56906, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_5.h5\n",
      "Epoch 24/30\n",
      "718/718 - 108s - loss: 1.0228 - accuracy: 0.6203 - val_loss: 1.1860 - val_accuracy: 0.5570\n",
      "\n",
      "Epoch 00024: val_accuracy did not improve from 0.56906\n",
      "Epoch 25/30\n",
      "718/718 - 108s - loss: 0.9978 - accuracy: 0.6334 - val_loss: 1.1679 - val_accuracy: 0.5678\n",
      "\n",
      "Epoch 00025: val_accuracy did not improve from 0.56906\n",
      "Epoch 26/30\n",
      "718/718 - 108s - loss: 0.9629 - accuracy: 0.6472 - val_loss: 1.1628 - val_accuracy: 0.5727\n",
      "\n",
      "Epoch 00026: val_accuracy improved from 0.56906 to 0.57272, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_5.h5\n",
      "Epoch 27/30\n",
      "718/718 - 109s - loss: 0.9299 - accuracy: 0.6571 - val_loss: 1.1626 - val_accuracy: 0.5771\n",
      "\n",
      "Epoch 00027: val_accuracy improved from 0.57272 to 0.57708, saving model to /kaggle/working/Saved_CNN_Models/create_model_VF_02_30epochs/create_model_VF_02_model_5.h5\n",
      "Epoch 28/30\n",
      "718/718 - 109s - loss: 0.9002 - accuracy: 0.6661 - val_loss: 1.1795 - val_accuracy: 0.5715\n",
      "\n",
      "Epoch 00028: val_accuracy did not improve from 0.57708\n",
      "Epoch 29/30\n",
      "718/718 - 107s - loss: 0.8706 - accuracy: 0.6770 - val_loss: 1.1854 - val_accuracy: 0.5764\n",
      "\n",
      "Epoch 00029: val_accuracy did not improve from 0.57708\n",
      "Epoch 30/30\n",
      "718/718 - 109s - loss: 0.8321 - accuracy: 0.6942 - val_loss: 1.1925 - val_accuracy: 0.5717\n",
      "\n",
      "Epoch 00030: val_accuracy did not improve from 0.57708\n"
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      "225/225 [==============================] - 16s 71ms/step - loss: 1.1622 - accuracy: 0.5762\n"
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    "model_test(5, 30, create_model_VF_02, (48,48,), 'rgb')"
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