09_MNIST_CNN_Practice_Save_Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "mnist = input_data.read_data_sets('MNIST_data', one_hot=True)\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sess = tf.InteractiveSession()\n",
    "\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], padding ='SAME')\n",
    "\n",
    "def max_pool_2x2(x):\n",
    "    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')\n",
    "    \n",
    "x = tf.placeholder(tf.float32, shape=[None, 784], name=\"input\")\n",
    "y_ = tf.placeholder(tf.float32, shape=[None, 10])\n",
    "\n",
    "x_image = tf.reshape(x, [-1, 28, 28, 1])\n",
    "\n",
    "w = tf.Variable(tf.zeros([784, 10]))\n",
    "b = tf.Variable(tf.zeros([10]))\n",
    "\n",
    "W_conv1 = weight_variable([5, 5, 1, 32])\n",
    "b_conv1 = bias_variable([32])\n",
    "\n",
    "h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)\n",
    "h_pool1 = max_pool_2x2(h_conv1)\n",
    "\n",
    "W_conv2 = weight_variable([5, 5, 32, 64])\n",
    "b_conv2 = bias_variable([64])\n",
    "\n",
    "h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)\n",
    "h_pool2 = max_pool_2x2(h_conv2)\n",
    "\n",
    "W_fc1 = weight_variable([7 * 7 * 64, 1024])\n",
    "b_fc1 = bias_variable([1024])\n",
    "\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",
    "\n",
    "keep_prob = tf.placeholder(tf.float32, name=\"keep_prob\")\n",
    "h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)\n",
    "\n",
    "W_fc2 = weight_variable([1024, 10])\n",
    "b_fc2 = bias_variable([10])\n",
    "\n",
    "y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2, name=\"output\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv))\n",
    "train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)\n",
    "correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-12-07574eadaa12>:1: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.\n",
      "Instructions for updating:\n",
      "Use `tf.global_variables_initializer` instead.\n",
      "WARNING:tensorflow:From <ipython-input-12-07574eadaa12>:7: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.\n",
      "Instructions for updating:\n",
      "Use `tf.global_variables_initializer` instead.\n"
     ]
    }
   ],
   "source": [
    "# sess.run(tf.initialize_all_variables())\n",
    "\n",
    "# cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y_conv), reduction_indices=[1]))\n",
    "# train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)\n",
    "# correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_, 1))\n",
    "# accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
    "# sess.run(tf.initialize_all_variables())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "step 0, training accuracy 0.06\n",
      "step 100, training accuracy 0.84\n",
      "step 200, training accuracy 0.9\n",
      "step 300, training accuracy 0.9\n",
      "step 400, training accuracy 0.86\n",
      "step 500, training accuracy 0.94\n",
      "step 600, training accuracy 0.94\n",
      "step 700, training accuracy 0.9\n",
      "step 800, training accuracy 0.94\n",
      "step 900, training accuracy 0.94\n",
      "step 1000, training accuracy 0.98\n",
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      "step 1400, training accuracy 1\n",
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      "step 2100, training accuracy 0.92\n",
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      "step 2500, training accuracy 1\n",
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      "step 18300, training accuracy 0.98\n",
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      "step 19800, training accuracy 1\n",
      "step 19900, training accuracy 1\n",
      "test accuracy 0.991\n"
     ]
    }
   ],
   "source": [
    "sess = tf.Session()\n",
    "\n",
    "sess.run(tf.global_variables_initializer())\n",
    "for i in range(20000):\n",
    "    batch = mnist.train.next_batch(50)\n",
    "    if i%100 == 0:\n",
    "        train_accuracy = sess.run( accuracy, feed_dict={x:batch[0], y_: batch[1], keep_prob:1.0})\n",
    "        print(\"step %d, training accuracy %g\"%(i, train_accuracy))\n",
    "    sess.run(train_step, feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})\n",
    "    \n",
    "print(\"test accuracy %g\"% sess.run(accuracy, feed_dict={ x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step 0, training accuracy 0.08\n",
      "step 100, training accuracy 0.8\n",
      "step 200, training accuracy 0.96\n",
      "step 300, training accuracy 0.96\n",
      "step 400, training accuracy 0.9\n",
      "step 500, training accuracy 0.9\n",
      "step 600, training accuracy 0.98\n",
      "step 700, training accuracy 0.96\n",
      "step 800, training accuracy 0.92\n",
      "step 900, training accuracy 0.98\n",
      "test accuracy 0.9672\n"
     ]
    }
   ],
   "source": [
    "# To make this run faster, we'll only run 1,000 iterations of the training process.\n",
    "\n",
    "for i in range(1000):\n",
    "    batch = mnist.train.next_batch(50)\n",
    "    if i%100 == 0:\n",
    "        train_accuracy = accuracy.eval(feed_dict={\n",
    "            x:batch[0], y_: batch[1], keep_prob: 1.0})\n",
    "        print(\"step %d, training accuracy %g\"%(i, train_accuracy))\n",
    "    train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})\n",
    "\n",
    "print(\"test accuracy %g\"%accuracy.eval(feed_dict={\n",
    "    x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x11da23f60>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Q1P8DesQ8Kn24nqwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a964ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image_a = mnist.validation.images[154]\n",
    "plt.imshow(image_a.reshape([28, 28]), cmap='Greys')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'input:0' shape=(?, 784) dtype=float32>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  1.15120891e-09   4.14841272e-10   7.59858754e-09   1.36865467e-07\n",
      "    1.09155670e-04   2.32434383e-09   1.10070902e-11   3.06436232e-05\n",
      "    7.33184288e-06   9.99852657e-01]]\n",
      "[9]\n"
     ]
    }
   ],
   "source": [
    "image_a = image_a.reshape([1, 784])\n",
    "result = sess.run(y_conv, feed_dict={x:image_a, keep_prob:1})\n",
    "print(result)\n",
    "print(sess.run(tf.argmax(result, 1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model saved to saved_mnist_cnn.ckpt\n"
     ]
    }
   ],
   "source": [
    "saver = tf.train.Saver()\n",
    "save_path = saver.save(sess, \"saved_mnist_cnn.ckpt\")\n",
    "print(\"Model saved to %s\" % save_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}