MNIST_ANN_old.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-1-7287ce174405>:15: read_data_sets (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n",
      "WARNING:tensorflow:From D:\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\datasets\\mnist.py:260: maybe_download (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please write your own downloading logic.\n",
      "WARNING:tensorflow:From D:\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\datasets\\mnist.py:262: extract_images (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tf.data to implement this functionality.\n",
      "Extracting ./data/train-images-idx3-ubyte.gz\n",
      "WARNING:tensorflow:From D:\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\datasets\\mnist.py:267: extract_labels (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tf.data to implement this functionality.\n",
      "Extracting ./data/train-labels-idx1-ubyte.gz\n",
      "WARNING:tensorflow:From D:\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\datasets\\mnist.py:110: dense_to_one_hot (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tf.one_hot on tensors.\n",
      "Extracting ./data/t10k-images-idx3-ubyte.gz\n",
      "Extracting ./data/t10k-labels-idx1-ubyte.gz\n",
      "WARNING:tensorflow:From D:\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\datasets\\mnist.py:290: DataSet.__init__ (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n"
     ]
    }
   ],
   "source": [
    "\"\"\" Neural Network.\n",
    "A 2-Hidden Layers Fully Connected Neural Network (a.k.a Multilayer Perceptron)\n",
    "implementation with TensorFlow. This example is using the MNIST database\n",
    "of handwritten digits (http://yann.lecun.com/exdb/mnist/).\n",
    "Links:\n",
    "    [MNIST Dataset](http://yann.lecun.com/exdb/mnist/).\n",
    "Author: Aymeric Damien\n",
    "Project: https://github.com/aymericdamien/TensorFlow-Examples/\n",
    "\"\"\"\n",
    "\n",
    "from __future__ import print_function\n",
    "\n",
    "# Import MNIST data\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "mnist = input_data.read_data_sets(\"./data/\", one_hot=True)\n",
    "\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parameters\n",
    "learning_rate = 0.1\n",
    "num_steps = 500\n",
    "batch_size = 128\n",
    "display_step = 100\n",
    "\n",
    "# Network Parameters\n",
    "n_hidden_1 = 256 # 1st layer number of neurons\n",
    "n_hidden_2 = 256 # 2nd layer number of neurons\n",
    "num_input = 784 # MNIST data input (img shape: 28*28)\n",
    "num_classes = 10 # MNIST total classes (0-9 digits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From D:\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n"
     ]
    }
   ],
   "source": [
    "# tf Graph input\n",
    "X = tf.placeholder(\"float\", [None, num_input])\n",
    "Y = tf.placeholder(\"float\", [None, num_classes])\n",
    "\n",
    "# Store layers weight & bias\n",
    "weights = {\n",
    "    'h1': tf.Variable(tf.random_normal([num_input, n_hidden_1])),\n",
    "    'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),\n",
    "    'out': tf.Variable(tf.random_normal([n_hidden_2, num_classes]))\n",
    "}\n",
    "\n",
    "biases = {\n",
    "    'b1': tf.Variable(tf.random_normal([n_hidden_1])),\n",
    "    'b2': tf.Variable(tf.random_normal([n_hidden_2])),\n",
    "    'out': tf.Variable(tf.random_normal([num_classes]))\n",
    "}\n",
    "\n",
    "# Create model\n",
    "def neural_net(x):\n",
    "    # Hidden fully connected layer with 256 neurons\n",
    "    layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])\n",
    "    # Hidden fully connected layer with 256 neurons\n",
    "    layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])\n",
    "    # Output fully connected layer with a neuron for each class\n",
    "    out_layer = tf.matmul(layer_2, weights['out']) + biases['out']\n",
    "    return out_layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-4-d021e5145d3f>:6: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "\n",
      "Future major versions of TensorFlow will allow gradients to flow\n",
      "into the labels input on backprop by default.\n",
      "\n",
      "See `tf.nn.softmax_cross_entropy_with_logits_v2`.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Construct model\n",
    "logits = neural_net(X)\n",
    "prediction = tf.nn.softmax(logits)\n",
    "\n",
    "# Define loss and optimizer\n",
    "loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=Y))\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n",
    "train_op = optimizer.minimize(loss_op)\n",
    "\n",
    "# Evaluate model\n",
    "correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step 1, Minibatch Loss= 14136.1846, Training Accuracy= 0.336\n",
      "Step 100, Minibatch Loss= 404.8142, Training Accuracy= 0.828\n",
      "Step 200, Minibatch Loss= 177.0466, Training Accuracy= 0.836\n",
      "Step 300, Minibatch Loss= 71.5951, Training Accuracy= 0.883\n",
      "Step 400, Minibatch Loss= 81.1849, Training Accuracy= 0.812\n",
      "Step 500, Minibatch Loss= 77.5220, Training Accuracy= 0.836\n",
      "Optimization Finished!\n",
      "Model saved in file: model_old\n",
      "Testing Accuracy: 0.8491\n"
     ]
    }
   ],
   "source": [
    "# Initialize the variables (i.e. assign their default value)\n",
    "init = tf.global_variables_initializer()\n",
    "\n",
    "# 'Saver' op to save and restore all the variables\n",
    "saver = tf.train.Saver()\n",
    "\n",
    "# Start training\n",
    "with tf.Session() as sess:\n",
    "\n",
    "    # Run the initializer\n",
    "    sess.run(init)\n",
    "\n",
    "    for step in range(1, num_steps+1):\n",
    "        batch_x, batch_y = mnist.train.next_batch(batch_size)\n",
    "        # Run optimization op (backprop)\n",
    "        sess.run(train_op, feed_dict={X: batch_x, Y: batch_y})\n",
    "        if step % display_step == 0 or step == 1:\n",
    "            # Calculate batch loss and accuracy\n",
    "            loss, acc = sess.run([loss_op, accuracy], feed_dict={X: batch_x,\n",
    "                                                                 Y: batch_y})\n",
    "            print(\"Step \" + str(step) + \", Minibatch Loss= \" + \\\n",
    "                  \"{:.4f}\".format(loss) + \", Training Accuracy= \" + \\\n",
    "                  \"{:.3f}\".format(acc))\n",
    "\n",
    "    print(\"Optimization Finished!\")\n",
    "\n",
    "    # Save model weights to disk\n",
    "    save_path = saver.save(sess, \"model_old\")\n",
    "    print(\"Model saved in file: %s\" % save_path)\n",
    "    \n",
    "    # Calculate accuracy for MNIST test images\n",
    "    print(\"Testing Accuracy:\", \\\n",
    "        sess.run(accuracy, feed_dict={X: mnist.test.images, Y: mnist.test.labels}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From D:\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\training\\saver.py:1266: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use standard file APIs to check for files with this prefix.\n",
      "INFO:tensorflow:Restoring parameters from model_old\n",
      "Model restored from file: model_old\n",
      "Testing Accuracy: 0.8491\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "  \n",
    "    # Run the initializer\n",
    "    sess.run(init)\n",
    " \n",
    "    saver.restore(sess, \"model_old\")\n",
    "    print(\"Model restored from file: %s\" % save_path)\n",
    "     \n",
    "    # Calculate accuracy for MNIST test images\n",
    "    print(\"Testing Accuracy:\", \\\n",
    "    sess.run(accuracy, feed_dict={X: mnist.test.images,\n",
    "                                      Y: mnist.test.labels}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from model_old\n",
      "Model restored from file: model_old\n",
      "Answer: [7]\n"
     ]
    }
   ],
   "source": [
    "# Evaluate model\n",
    "# argmax returns the index with the largest value across axes of a tensor\n",
    "ans = tf.argmax(prediction, 1)\n",
    "\n",
    "# Show image that we want to predict\n",
    "plt.imshow(mnist.test.images[0].reshape((28, 28)))\n",
    "plt.show()\n",
    "\n",
    "# Running a test dataset by loading the model saved earlier\n",
    "with tf.Session() as sess:\n",
    "    # Run the initializer\n",
    "    sess.run(init)\n",
    " \n",
    "    saver.restore(sess, \"model_old\")\n",
    "    print(\"Model restored from file: %s\" % save_path)\n",
    " \n",
    "    # Calculate the answer for the image\n",
    "    print(\"Answer:\", sess.run(ans, feed_dict={X: mnist.test.images[0:1]}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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