014 - Convolutional Networks.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./datasets/train-images-idx3-ubyte.gz\n",
      "Extracting ./datasets/train-labels-idx1-ubyte.gz\n",
      "Extracting ./datasets/t10k-images-idx3-ubyte.gz\n",
      "Extracting ./datasets/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "mnist = input_data.read_data_sets(\"./datasets\", one_hot=True, reshape=False)\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "# Parameters\n",
    "learning_rate = 0.00001\n",
    "epochs = 10\n",
    "batch_size = 128\n",
    "\n",
    "# Number of samples to calculate validation and accuracy\n",
    "# Decrease this if you're running out of memory to calculate accuracy\n",
    "test_valid_size = 256\n",
    "\n",
    "# Network Parameters\n",
    "n_classes = 10  # MNIST total classes (0-9 digits)\n",
    "dropout = 0.75  # Dropout, probability to keep units\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Weights and Biases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Store layers weight & bias\n",
    "weights = {\n",
    "    'wc1': tf.Variable(tf.random_normal([5, 5, 1, 32])),\n",
    "    'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64])),\n",
    "    'wd1': tf.Variable(tf.random_normal([7*7*64, 1024])),\n",
    "    'out': tf.Variable(tf.random_normal([1024, n_classes]))}\n",
    "\n",
    "biases = {\n",
    "    'bc1': tf.Variable(tf.random_normal([32])),\n",
    "    'bc2': tf.Variable(tf.random_normal([64])),\n",
    "    'bd1': tf.Variable(tf.random_normal([1024])),\n",
    "    'out': tf.Variable(tf.random_normal([n_classes]))}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convolutions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def conv2d(x, W, b, strides=1):\n",
    "    x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')\n",
    "    x = tf.nn.bias_add(x, b)\n",
    "    return tf.nn.relu(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Max Pooling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def maxpool2d(x, k=2):\n",
    "    return tf.nn.max_pool(\n",
    "        x,\n",
    "        ksize=[1, k, k, 1],\n",
    "        strides=[1, k, k, 1],\n",
    "        padding='SAME')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def conv_net(x, weights, biases, dropout):\n",
    "    # Layer 1 - 28*28*1 to 14*14*32\n",
    "    conv1 = conv2d(x, weights['wc1'], biases['bc1'])\n",
    "    conv1 = maxpool2d(conv1, k=2)\n",
    "\n",
    "    # Layer 2 - 14*14*32 to 7*7*64\n",
    "    conv2 = conv2d(conv1, weights['wc2'], biases['bc2'])\n",
    "    conv2 = maxpool2d(conv2, k=2)\n",
    "\n",
    "    # Fully connected layer - 7*7*64 to 1024\n",
    "    fc1 = tf.reshape(conv2, [-1, weights['wd1'].get_shape().as_list()[0]])\n",
    "    fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1'])\n",
    "    fc1 = tf.nn.relu(fc1)\n",
    "    fc1 = tf.nn.dropout(fc1, dropout)\n",
    "\n",
    "    # Output Layer - class prediction - 1024 to 10\n",
    "    out = tf.add(tf.matmul(fc1, weights['out']), biases['out'])\n",
    "    return out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch  1, Batch   1 -Loss: 68955.1562 Validation Accuracy: 0.128906\n",
      "Epoch  1, Batch   2 -Loss: 46622.2500 Validation Accuracy: 0.171875\n",
      "Epoch  1, Batch   3 -Loss: 45081.6250 Validation Accuracy: 0.175781\n",
      "Epoch  1, Batch   4 -Loss: 40587.3984 Validation Accuracy: 0.203125\n",
      "Epoch  1, Batch   5 -Loss: 33074.4844 Validation Accuracy: 0.195312\n",
      "Epoch  1, Batch   6 -Loss: 29567.8438 Validation Accuracy: 0.246094\n",
      "Epoch  1, Batch   7 -Loss: 27875.5000 Validation Accuracy: 0.257812\n",
      "Epoch  1, Batch   8 -Loss: 25540.8750 Validation Accuracy: 0.292969\n",
      "Epoch  1, Batch   9 -Loss: 22063.2422 Validation Accuracy: 0.273438\n",
      "Epoch  1, Batch  10 -Loss: 24570.9688 Validation Accuracy: 0.273438\n",
      "Epoch  1, Batch  11 -Loss: 21995.1582 Validation Accuracy: 0.285156\n",
      "Epoch  1, Batch  12 -Loss: 20450.8145 Validation Accuracy: 0.296875\n",
      "Epoch  1, Batch  13 -Loss: 17407.4922 Validation Accuracy: 0.289062\n",
      "Epoch  1, Batch  14 -Loss: 19531.2422 Validation Accuracy: 0.308594\n",
      "Epoch  1, Batch  15 -Loss: 22675.8652 Validation Accuracy: 0.312500\n",
      "Epoch  1, Batch  16 -Loss: 15106.1875 Validation Accuracy: 0.324219\n",
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      "Epoch 10, Batch 386 -Loss:   343.1379 Validation Accuracy: 0.851562\n",
      "Epoch 10, Batch 387 -Loss:   155.0746 Validation Accuracy: 0.855469\n",
      "Epoch 10, Batch 388 -Loss:   180.5411 Validation Accuracy: 0.851562\n",
      "Epoch 10, Batch 389 -Loss:   223.0739 Validation Accuracy: 0.855469\n",
      "Epoch 10, Batch 390 -Loss:   235.8642 Validation Accuracy: 0.859375\n",
      "Epoch 10, Batch 391 -Loss:   179.2617 Validation Accuracy: 0.855469\n",
      "Epoch 10, Batch 392 -Loss:   142.9981 Validation Accuracy: 0.851562\n",
      "Epoch 10, Batch 393 -Loss:   219.5248 Validation Accuracy: 0.855469\n",
      "Epoch 10, Batch 394 -Loss:   206.1728 Validation Accuracy: 0.855469\n",
      "Epoch 10, Batch 395 -Loss:   188.4494 Validation Accuracy: 0.851562\n",
      "Epoch 10, Batch 396 -Loss:   261.8113 Validation Accuracy: 0.855469\n",
      "Epoch 10, Batch 397 -Loss:   238.8462 Validation Accuracy: 0.851562\n",
      "Epoch 10, Batch 398 -Loss:   170.5622 Validation Accuracy: 0.851562\n",
      "Epoch 10, Batch 399 -Loss:   218.0620 Validation Accuracy: 0.847656\n",
      "Epoch 10, Batch 400 -Loss:   265.4469 Validation Accuracy: 0.843750\n",
      "Epoch 10, Batch 401 -Loss:   154.8608 Validation Accuracy: 0.843750\n",
      "Epoch 10, Batch 402 -Loss:   264.7904 Validation Accuracy: 0.843750\n",
      "Epoch 10, Batch 403 -Loss:   309.5742 Validation Accuracy: 0.847656\n",
      "Epoch 10, Batch 404 -Loss:   158.3135 Validation Accuracy: 0.843750\n",
      "Epoch 10, Batch 405 -Loss:   309.2166 Validation Accuracy: 0.843750\n",
      "Epoch 10, Batch 406 -Loss:   190.3871 Validation Accuracy: 0.843750\n",
      "Epoch 10, Batch 407 -Loss:   246.1684 Validation Accuracy: 0.843750\n",
      "Epoch 10, Batch 408 -Loss:   121.5727 Validation Accuracy: 0.843750\n",
      "Epoch 10, Batch 409 -Loss:   241.9741 Validation Accuracy: 0.847656\n",
      "Epoch 10, Batch 410 -Loss:   262.3614 Validation Accuracy: 0.843750\n",
      "Epoch 10, Batch 411 -Loss:    96.0894 Validation Accuracy: 0.847656\n",
      "Epoch 10, Batch 412 -Loss:   304.5662 Validation Accuracy: 0.839844\n",
      "Epoch 10, Batch 413 -Loss:   186.0686 Validation Accuracy: 0.843750\n",
      "Epoch 10, Batch 414 -Loss:   219.9587 Validation Accuracy: 0.839844\n",
      "Epoch 10, Batch 415 -Loss:   146.1722 Validation Accuracy: 0.839844\n",
      "Epoch 10, Batch 416 -Loss:   133.7537 Validation Accuracy: 0.839844\n",
      "Epoch 10, Batch 417 -Loss:   114.7576 Validation Accuracy: 0.839844\n",
      "Epoch 10, Batch 418 -Loss:   168.4944 Validation Accuracy: 0.839844\n",
      "Epoch 10, Batch 419 -Loss:   253.2984 Validation Accuracy: 0.839844\n",
      "Epoch 10, Batch 420 -Loss:   317.3074 Validation Accuracy: 0.839844\n",
      "Epoch 10, Batch 421 -Loss:   293.6424 Validation Accuracy: 0.839844\n",
      "Epoch 10, Batch 422 -Loss:   341.6960 Validation Accuracy: 0.839844\n",
      "Epoch 10, Batch 423 -Loss:   124.5185 Validation Accuracy: 0.839844\n",
      "Epoch 10, Batch 424 -Loss:   215.6124 Validation Accuracy: 0.839844\n",
      "Epoch 10, Batch 425 -Loss:   409.5372 Validation Accuracy: 0.839844\n",
      "Epoch 10, Batch 426 -Loss:   325.0568 Validation Accuracy: 0.839844\n",
      "Epoch 10, Batch 427 -Loss:   435.9743 Validation Accuracy: 0.839844\n",
      "Epoch 10, Batch 428 -Loss:   291.7277 Validation Accuracy: 0.839844\n",
      "Epoch 10, Batch 429 -Loss:   230.9131 Validation Accuracy: 0.843750\n",
      "Testing Accuracy: 0.83203125\n"
     ]
    }
   ],
   "source": [
    "# tf Graph input\n",
    "x = tf.placeholder(tf.float32, [None, 28, 28, 1])\n",
    "y = tf.placeholder(tf.float32, [None, n_classes])\n",
    "keep_prob = tf.placeholder(tf.float32)\n",
    "\n",
    "# Model\n",
    "logits = conv_net(x, weights, biases, keep_prob)\n",
    "\n",
    "# Define loss and optimizer\n",
    "cost = tf.reduce_mean(\\\n",
    "    tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y))\n",
    "optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\\\n",
    "    .minimize(cost)\n",
    "\n",
    "# Accuracy\n",
    "correct_pred = tf.equal(tf.argmax(logits, 1), tf.argmax(y, 1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n",
    "\n",
    "# Initializing the variables\n",
    "init = tf. global_variables_initializer()\n",
    "\n",
    "# Launch the graph\n",
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "\n",
    "    for epoch in range(epochs):\n",
    "        for batch in range(mnist.train.num_examples//batch_size):\n",
    "            batch_x, batch_y = mnist.train.next_batch(batch_size)\n",
    "            sess.run(optimizer, feed_dict={\n",
    "                x: batch_x,\n",
    "                y: batch_y,\n",
    "                keep_prob: dropout})\n",
    "\n",
    "            # Calculate batch loss and accuracy\n",
    "            loss = sess.run(cost, feed_dict={\n",
    "                x: batch_x,\n",
    "                y: batch_y,\n",
    "                keep_prob: 1.})\n",
    "            valid_acc = sess.run(accuracy, feed_dict={\n",
    "                x: mnist.validation.images[:test_valid_size],\n",
    "                y: mnist.validation.labels[:test_valid_size],\n",
    "                keep_prob: 1.})\n",
    "\n",
    "            print('Epoch {:>2}, Batch {:>3} -'\n",
    "                  'Loss: {:>10.4f} Validation Accuracy: {:.6f}'.format(\n",
    "                epoch + 1,\n",
    "                batch + 1,\n",
    "                loss,\n",
    "                valid_acc))\n",
    "\n",
    "    # Calculate Test Accuracy\n",
    "    test_acc = sess.run(accuracy, feed_dict={\n",
    "        x: mnist.test.images[:test_valid_size],\n",
    "        y: mnist.test.labels[:test_valid_size],\n",
    "        keep_prob: 1.})\n",
    "    print('Testing Accuracy: {}'.format(test_acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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