04-CNN-IMGClassification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Image Classification using CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.examples.tutorials.mnist import input_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# parameters\n",
    "batch_size = 100\n",
    "learning_rate = 1e-4\n",
    "training_epochs = 2 #10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# layer functions\n",
    "def weight_variable(shape):\n",
    "    initial = tf.truncated_normal(shape, stddev=0.1)\n",
    "    return tf.Variable(initial)\n",
    "\n",
    "def bias_variable(shape):\n",
    "    initial = tf.constant(0.1, shape=shape)\n",
    "    return tf.Variable(initial)\n",
    "\n",
    "def conv2d(x, W):\n",
    "    return tf.nn.conv2d(x, W, strides=[1,1,1,1],\n",
    "                              padding='SAME')\n",
    "\n",
    "def max_pool_2x2(x):\n",
    "    return tf.nn.max_pool(x, ksize=[1,2,2,1],\n",
    "                             strides=[1,2,2,1],\n",
    "                             padding='SAME')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MNIST dataset\n",
    "mnist = input_data.read_data_sets(\"./MNIST_DATA\", one_hot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# input\n",
    "X = tf.placeholder(tf.float32, shape=[None, 784])\n",
    "Y_ = tf.placeholder(tf.float32, shape=[None, 10])\n",
    "# reshape (N, Height, Width, Channels)\n",
    "X_image = tf.reshape(X, [-1, 28, 28, 1])\n",
    "\n",
    "# dropout\n",
    "keep_prob = tf.placeholder(tf.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ConvNet-1\n",
    "# 28x28x1 -> 5x5x32 filter -> 2x2 max_pooling -> 14x14x32\n",
    "# Weight [Height, Width, IN_Channels, OUT_Channels]\n",
    "W_conv1 = weight_variable([5, 5, 1, 32])\n",
    "b_conv1 = bias_variable([32])\n",
    "h_conv1 = tf.nn.relu(conv2d(X_image, W_conv1) + b_conv1)\n",
    "h_pool1 = max_pool_2x2(h_conv1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ConvNet-2\n",
    "# 14x14x32 -> 5x5x64 filter -> 2x2 max_pooling -> 7x7x64\n",
    "W_conv2 = weight_variable([5, 5, 32, 64])\n",
    "b_conv2 = bias_variable([64])\n",
    "h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)\n",
    "h_pool2 = max_pool_2x2(h_conv2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# FC-1\n",
    "# 7x7x64 -> Flattening -> 1x1x1024\n",
    "W_fc1 = weight_variable([7*7*64, 1024])\n",
    "b_fc1 = bias_variable([1024])\n",
    "h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])\n",
    "h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)\n",
    "h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Output\n",
    "# 1024 -> 10\n",
    "W_fc2 = weight_variable([1024, 10])\n",
    "b_fc2 = bias_variable([10])\n",
    "Y = tf.matmul(h_fc1_drop, W_fc2) + b_fc2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loss (Cross Entropy) \n",
    "cross_entropy = tf.reduce_mean(\n",
    "                    tf.nn.softmax_cross_entropy_with_logits(\n",
    "                        logits=Y, labels=Y_))\n",
    "\n",
    "# Optimize (Adam)\n",
    "train_step = tf.train.AdamOptimizer(\n",
    "                    learning_rate).minimize(cross_entropy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# accuracy\n",
    "correct_prediction = tf.equal(tf.argmax(Y, 1), tf.argmax(Y_, 1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sess = tf.Session()\n",
    "sess.run(tf.global_variables_initializer())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "# train\n",
    "for epoch in range(training_epochs):\n",
    "    batch_count = int(mnist.train.num_examples/batch_size)\n",
    "    for i in range(batch_count):\n",
    "        batch_x, batch_y = mnist.train.next_batch(batch_size)\n",
    "        _, acc = sess.run([train_step, accuracy], \n",
    "                             feed_dict={X: batch_x,\n",
    "                                        Y_: batch_y,\n",
    "                                        keep_prob: 0.5})\n",
    "    print(\"Epoch: \", epoch, \"Train Accuracy: \", acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "# Test\n",
    "print(\"Test Accuracy: \", sess.run(accuracy,\n",
    "                                 feed_dict={X: mnist.test.images,\n",
    "                                            Y_: mnist.test.labels,\n",
    "                                            keep_prob: 1.0}))\n",
    "print(\"done\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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