HW4_CNN.ipynb

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    "# Homework 4\n",
    "In this homework you will train your first convolutional neural network on  images from supertux. \n",
    "\n",
    "Development notes: \n",
    "\n",
    "1) If you are doing your homework in a Jupyter/iPython notebook you may need to 'Restart & Clear Output' after making a change and re-running a cell.  TensorFlow will not allow you to create multiple variables with the same name, which is what you are doing when you run a cell that creates a variable twice.<br/><br/>\n",
    "2) Be careful with your calls to global_variables_initializer(). If you call it after training one network it will re-initialize your variables erasing your training.  In general, double check the outputs of your model after all training and before turning your model in. Ending a session will discard all your variable values.\n",
    "\n",
    "## Part 0: Setup"
   ]
  },
  {
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   "execution_count": 1,
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   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "Input shape: (12257, 64, 64, 3)\n",
      "Labels shape: (12257,)\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import util\n",
    "\n",
    "# Load the data we are giving you\n",
    "def load(filename, W=64, H=64):\n",
    "    data = np.fromfile(filename, dtype=np.uint8).reshape((-1, W*H*3+1))\n",
    "    images, labels = data[:, :-1].reshape((-1,H,W,3)), data[:, -1]\n",
    "    return images, labels\n",
    "\n",
    "image_data, label_data = load('tux_train.dat')\n",
    "\n",
    "print('Input shape: ' + str(image_data.shape))\n",
    "print('Labels shape: ' + str(label_data.shape))\n",
    "\n",
    "num_classes = 6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 1: Define your convnet\n",
    "\n",
    "Make sure the total number of parameters is less than 100,000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
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     "text": [
      "Tensor(\"conv1/Relu:0\", shape=(?, 32, 32, 16), dtype=float32)\n",
      "Tensor(\"pool1/MaxPool:0\", shape=(?, 15, 15, 16), dtype=float32)\n",
      "Tensor(\"conv2/Relu:0\", shape=(?, 8, 8, 32), dtype=float32)\n",
      "Tensor(\"conv3/Relu:0\", shape=(?, 4, 4, 64), dtype=float32)\n",
      "Tensor(\"conv4/Relu:0\", shape=(?, 2, 2, 120), dtype=float32)\n",
      "Tensor(\"conv5/BiasAdd:0\", shape=(?, 1, 1, 6), dtype=float32)\n",
      "Total number of variables used  99310 / 100000\n"
     ]
    }
   ],
   "source": [
    "# IMPORTANT:\n",
    "# Give each of your layers a scope=(<layer_name>) argument\n",
    "#    with layer_name as 'conv1'...'conv5' or 'pool'\n",
    "\n",
    "# Lets clear the tensorflow graph, so that you don't have to restart the notebook every time you change the network\n",
    "tf.reset_default_graph()\n",
    "\n",
    "# Set up your input placeholder\n",
    "inputs = tf.placeholder(tf.float32, (None,64,64,3), name='input')\n",
    "\n",
    "# Whenever you deal with image data it's important to mean center it first and subtract the standard deviation\n",
    "white_inputs = (inputs - 100.) / 72.\n",
    "\n",
    "# Set up your label placeholders\n",
    "labels = tf.placeholder(tf.int64, (None), name='labels')\n",
    "\n",
    "# Step 1: define the compute graph of your CNN here\n",
    "#   Use 5 conv2d layers (tf.contrib.layers.conv2d) and one pooling layer tf.contrib.layers.max_pool2d or tf.contrib.layers.avg_pool2d.\n",
    "#   The output of the network should be a None x 1 x 1 x 6 tensor.\n",
    "#   Make sure the last conv2d does not have a ReLU: activation_fn=None\n",
    "\n",
    "conv1 = tf.contrib.layers.conv2d(\n",
    "  inputs=white_inputs,\n",
    "  num_outputs=16,\n",
    "  kernel_size=[3, 3],\n",
    "  stride=2,\n",
    "  padding=\"same\",\n",
    "  scope='conv1') # put in scope everywhere\n",
    "print(conv1)\n",
    "\n",
    "# Pooling Layer #1\n",
    "pool1 = tf.contrib.layers.max_pool2d(inputs=conv1, kernel_size=[3,3], stride=2, scope='pool1')\n",
    "print(pool1)\n",
    "\n",
    "# Convolutional Layer #2\n",
    "conv2 = tf.contrib.layers.conv2d(\n",
    "    inputs=pool1,\n",
    "    num_outputs=32,\n",
    "    kernel_size=[3, 3],\n",
    "    stride=2,\n",
    "    padding=\"same\",\n",
    "    scope='conv2')\n",
    "print(conv2)\n",
    "\n",
    "# Convolutional Layer #2\n",
    "conv3 = tf.contrib.layers.conv2d(\n",
    "    inputs=conv2,\n",
    "    num_outputs=64,\n",
    "    kernel_size=[3, 3],\n",
    "    stride=2,\n",
    "    padding=\"same\",\n",
    "    scope='conv3')\n",
    "print(conv3)\n",
    "\n",
    "conv4 = tf.contrib.layers.conv2d(\n",
    "    inputs=conv3,\n",
    "    num_outputs=120,\n",
    "    kernel_size=[3, 3],\n",
    "    stride=2,\n",
    "    padding=\"same\",\n",
    "    scope='conv4')\n",
    "print(conv4)\n",
    "\n",
    "conv5 = tf.contrib.layers.conv2d(\n",
    "    inputs=conv4,\n",
    "    num_outputs=6,\n",
    "    kernel_size=[3, 3],\n",
    "    activation_fn=None,\n",
    "    stride=2,\n",
    "    padding=\"same\", \n",
    "    scope='conv5')\n",
    "print(conv5)\n",
    "\n",
    "h= tf.identity(conv5, name='conv_out')\n",
    "\n",
    "# # The input here should be a   None x 1 x 1 x 6   tensor\n",
    "output = tf.identity(tf.contrib.layers.flatten(h), name='output')\n",
    "\n",
    "# # Step 2: use a classification loss function (from assignment 3)\n",
    "loss =  tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels=labels,logits=output))\n",
    "# # Step 3: create an optimizer (from assignment 3)\n",
    "optimizer = tf.train.AdamOptimizer(0.0001,0.9,0.999)\n",
    "# # Step 4: use that optimizer on your loss function (from assignment 3)\n",
    "minimizer = optimizer.minimize(loss)\n",
    "# # Step 5: calculate some metrics\n",
    "correct = tf.equal(tf.argmax(output, 1), labels)\n",
    "accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))\n",
    "\n",
    "print( \"Total number of variables used \", np.sum([v.get_shape().num_elements() for v in tf.trainable_variables()]), '/', 100000 )"
   ]
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   "source": [
    "## Part 2: Training\n",
    "\n",
    "Training might take up to 20 min depending on your architecture.  This time around you should get close to 100% trianing accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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   "source": [
    "def train_conv(batch_images,batch_labels):\n",
    "    _,acc_tc, loss_tc = sess.run([minimizer,accuracy,loss],feed_dict={inputs:batch_images,labels: batch_labels})\n",
    "    return acc_tc,loss_tc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
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     "text": [
      "Convnet\n",
      "[  0] Accuracy: 0.747  \t  Loss: 0.786\n",
      "[  1] Accuracy: 0.904  \t  Loss: 0.302\n",
      "[  2] Accuracy: 0.923  \t  Loss: 0.235\n",
      "[  3] Accuracy: 0.935  \t  Loss: 0.200\n",
      "[  4] Accuracy: 0.942  \t  Loss: 0.173\n",
      "[  5] Accuracy: 0.948  \t  Loss: 0.155\n",
      "[  6] Accuracy: 0.954  \t  Loss: 0.139\n",
      "[  7] Accuracy: 0.957  \t  Loss: 0.130\n",
      "[  8] Accuracy: 0.961  \t  Loss: 0.116\n",
      "[  9] Accuracy: 0.965  \t  Loss: 0.107\n",
      "[ 10] Accuracy: 0.967  \t  Loss: 0.098\n",
      "[ 11] Accuracy: 0.969  \t  Loss: 0.091\n",
      "[ 12] Accuracy: 0.973  \t  Loss: 0.083\n",
      "[ 13] Accuracy: 0.974  \t  Loss: 0.077\n",
      "[ 14] Accuracy: 0.978  \t  Loss: 0.071\n",
      "[ 15] Accuracy: 0.979  \t  Loss: 0.066\n",
      "[ 16] Accuracy: 0.981  \t  Loss: 0.061\n",
      "[ 17] Accuracy: 0.983  \t  Loss: 0.055\n",
      "[ 18] Accuracy: 0.984  \t  Loss: 0.051\n",
      "[ 19] Accuracy: 0.984  \t  Loss: 0.050\n"
     ]
    }
   ],
   "source": [
    "# Batch size\n",
    "BS = 32\n",
    "\n",
    "# Start a session\n",
    "sess = tf.Session()\n",
    "\n",
    "# Set up training\n",
    "sess.run(tf.global_variables_initializer())\n",
    "\n",
    "# This is a helper function that trains your model for several epochs un shuffled data\n",
    "# train_func should take a single step in the optmimzation and return accuracy and loss\n",
    "#   accuracy, loss = train_func(batch_images, batch_labels)\n",
    "# HINT: train_func should call sess.run\n",
    "def train(train_func):\n",
    "    # An epoch is a single pass over the training data\n",
    "    for epoch in range(20):\n",
    "        # Let's shuffle the data every epoch\n",
    "        np.random.seed(epoch)\n",
    "        np.random.shuffle(image_data)\n",
    "        np.random.seed(epoch)\n",
    "        np.random.shuffle(label_data)\n",
    "        # Go through the entire dataset once\n",
    "        accs, losss = [], []\n",
    "        for i in range(0, image_data.shape[0]-BS+1, BS):\n",
    "            # Train a single batch\n",
    "            batch_images, batch_labels = image_data[i:i+BS], label_data[i:i+BS]\n",
    "            acc, loss = train_func(batch_images, batch_labels)\n",
    "            accs.append(acc)\n",
    "            losss.append(loss)\n",
    "        print('[%3d] Accuracy: %0.3f  \\t  Loss: %0.3f'%(epoch, np.mean(accs), np.mean(losss)))\n",
    "\n",
    "\n",
    "# Train convnet\n",
    "print('Convnet')\n",
    "### Your Code Here (from assignment 3) ### \n",
    "train(train_conv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 3: Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### See your model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
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       "}\n",
       "</script>\n",
       "<link rel=&quot;import&quot; href=&quot;https://tensorboard.appspot.com/tf-graph-basic.build.html&quot; onload=load()>\n",
       "<div style=&quot;height:600px&quot;>\n",
       "<tf-graph-basic id=&quot;graph0.43304267445669353&quot;></tf-graph-basic>\n",
       "</div>\n",
       "\"></iframe>\n"
      ],
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       "<IPython.core.display.HTML object>"
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   ],
   "source": [
    "# Show the current graph\n",
    "util.show_graph(tf.get_default_graph().as_graph_def())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute the valiation accuracy\n",
    "The convnet still massively overfits. We will deal with this in assignment 5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input shape: (3912, 64, 64, 3)\n",
      "Labels shape: (3912,)\n",
      "ConvNet Validation Accuracy:  0.944785\n"
     ]
    }
   ],
   "source": [
    "image_val, label_val = load('tux_val.dat')\n",
    "\n",
    "print('Input shape: ' + str(image_val.shape))\n",
    "print('Labels shape: ' + str(label_val.shape))\n",
    "\n",
    "val_accuracy, val_loss = sess.run([accuracy, loss], feed_dict={inputs: image_val, labels: label_val})\n",
    "print(\"ConvNet Validation Accuracy: \", val_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 4: Save Model\n",
    "Please note that we also want you to turn in your ipynb for this assignment.  Zip up the ipynb along with the tfg for your submission."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "util.save('assignment4.tfg', session=sess)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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