DeepNeuralNetwork_Restore_Ver.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "< How to Save and Restore the model >\n",
    "=================================================\n",
    "Deep Neural Network - Restore\n",
    "----------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting /tmp/data/train-images-idx3-ubyte.gz\n",
      "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n",
      "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n",
      "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot = True)\n",
    "import matplotlib.pylab as plt\n",
    "\n",
    "import sys, os\n",
    "sys.path.append(os.pardir)\n",
    "\n",
    "from mnist import load_mnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_nodes_hl1 = 500\n",
    "n_nodes_hl2 = 500\n",
    "n_nodes_hl3 = 500\n",
    "\n",
    "n_classes = 10\n",
    "batch_size = 100\n",
    "\n",
    "x = tf.placeholder('float', [None, 784], name = 'x_')\n",
    "y = tf.placeholder('float', name = 'y_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b9339b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(x_train, t_train), (x_test, t_test) = load_mnist(flatten=True, normalize=False)\n",
    "\n",
    "a=x_train[5500]\n",
    "aa = a.reshape(28,28)\n",
    "plt.imshow(aa)\n",
    "plt.show()\n",
    "\n",
    "x = x_train[5500]\n",
    "x = x.reshape(-1,784)\n",
    "x = tf.cast(x, 'float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hidden_1_layer = {'weights':tf.Variable(tf.random_normal([784, n_nodes_hl1]), name = 'w1'),\n",
    "                      'biases':tf.Variable(tf.random_normal([n_nodes_hl1]), name = 'b1')}\n",
    "\n",
    "hidden_2_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2]), name = 'w2'),\n",
    "                      'biases':tf.Variable(tf.random_normal([n_nodes_hl2]), name = 'b2')}\n",
    "\n",
    "hidden_3_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl2, n_nodes_hl3]), name = 'w3'),\n",
    "                      'biases':tf.Variable(tf.random_normal([n_nodes_hl3]), name = 'b3')}\n",
    "\n",
    "output_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl3, n_classes]), name = 'w4'),\n",
    "                    'biases':tf.Variable(tf.random_normal([n_classes]), name = 'b4')}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from pyhelp/sy\n"
     ]
    }
   ],
   "source": [
    "save_path = 'pyhelp/'\n",
    "model_name = 'sy'\n",
    "if not os.path.exists(save_path):\n",
    "    os.makedirs(save_path)\n",
    "save_path_full = os.path.join(save_path, model_name)\n",
    "\n",
    "sess= tf.Session()\n",
    "saver = tf.train.Saver()\n",
    "saver = tf.train.import_meta_graph('/Users/moonsooyoung/Desktop/pyhelp/pyhelp/sy.meta')\n",
    "saver.restore(sess,save_path_full)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "l1 = tf.add(tf.matmul(x,hidden_1_layer['weights']), hidden_1_layer['biases'],name = 'l1_')\n",
    "l1 = tf.nn.relu(l1, name = 'l1')\n",
    "\n",
    "l2 = tf.add(tf.matmul(l1,hidden_2_layer['weights']), hidden_2_layer['biases'],name = 'l2_')\n",
    "l2 = tf.nn.relu(l2, name = 'l2')\n",
    "\n",
    "l3 = tf.add(tf.matmul(l2,hidden_3_layer['weights']), hidden_3_layer['biases'],name = 'l3_')\n",
    "l3 = tf.nn.relu(l3, name = 'l3')\n",
    "\n",
    "output = tf.matmul(l3,output_layer['weights']) + output_layer['biases']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*****************************************\n",
      "[[ -5425075.       10510584.       -1175049.875    -1985677.125\n",
      "    -942181.75     -1102140.625    -3021251.25      1881475.25\n",
      "    1560218.625       69987.09375]]\n",
      "=========================================\n",
      "[1]\n"
     ]
    }
   ],
   "source": [
    "print('*****************************************')\n",
    "print(sess.run(output))\n",
    "print('=========================================')\n",
    "print(sess.run(tf.argmax(output,1)))"
   ]
  }
 ],
 "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}