cnn_restore_0.1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
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   "source": [
    "import os\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "savedir = \"./nets\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "n_input  = [48,48]\n",
    "n_output = 8\n",
    "weights  = {\n",
    "    'wc1': tf.Variable(tf.random_normal([3, 3, 1, 96], stddev=0.1)),\n",
    "    'wd1': tf.Variable(tf.random_normal([24*24*96, n_output], stddev=0.1))\n",
    "}\n",
    "biases   = {\n",
    "    'bc1': tf.Variable(tf.random_normal([96], stddev=0.1)),\n",
    "    'bd1': tf.Variable(tf.random_normal([n_output], stddev=0.1))\n",
    "}\n",
    "# def conv_simple(_input, _w, _b):\n",
    "#     # Reshape input\n",
    "#     _input_r = tf.reshape(_input, shape=[-1, 28, 28, 1])\n",
    "#     # Convolution\n",
    "#     _conv1 = tf.nn.conv2d(_input_r, _w['wc1'], strides=[1, 1, 1, 1], padding='SAME')\n",
    "#     # Add-bias\n",
    "#     _conv2 = tf.nn.bias_add(_conv1, _b['bc1'])\n",
    "#     # Pass ReLu\n",
    "#     _conv3 = tf.nn.relu(_conv2)\n",
    "#     # Max-pooling\n",
    "#     _pool  = tf.nn.max_pool(_conv3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')\n",
    "#     # Vectorize\n",
    "#     _dense = tf.reshape(_pool, [-1, _w['wd1'].get_shape().as_list()[0]])\n",
    "#     # Fully-connected layer\n",
    "#     _out = tf.add(tf.matmul(_dense, _w['wd1']), _b['bd1'])\n",
    "#     # Return everything\n",
    "#     out = {\n",
    "#         'input_r': _input_r, 'conv1': _conv1, 'conv2': _conv2, 'conv3': _conv3\n",
    "#         , 'pool': _pool, 'dense': _dense, 'out': _out\n",
    "#     }\n",
    "    \n",
    "#     return out\n",
    "\n",
    "\n",
    "test_img = cv2.imread(\"./dataset/anger/0.jpg\")\n",
    "#    _pred = conv_simple(x, weights, biases)['out']\n",
    "x = tf.placeholder(tf.float32, n_input)\n",
    "y = tf.placeholder(tf.float32, [None, n_output])\n",
    "#output = conv_simple(x, weights, biases)['out']\n",
    "\n",
    "# Reshape input\n",
    "_input_r = tf.reshape(x, shape=[-1, 48, 48, 1])\n",
    "# Convolution\n",
    "_conv1 = tf.nn.conv2d(_input_r, weights['wc1'], strides=[1, 1, 1, 1], padding='SAME')\n",
    "# Add-bias\n",
    "_conv2 = tf.nn.bias_add(_conv1, biases['bc1'])\n",
    "# Pass ReLu\n",
    "_conv3 = tf.nn.relu(_conv2)\n",
    "# Max-pooling\n",
    "_pool  = tf.nn.max_pool(_conv3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')\n",
    "# Vectorize\n",
    "_dense = tf.reshape(_pool, [-1, weights['wd1'].get_shape().as_list()[0]])\n",
    "# Fully-connected layer\n",
    "_out = tf.add(tf.matmul(_dense, weights['wd1']), biases['bd1'])\n",
    "#output = tf.nn.softmax_cross_entropy_with_logits(_dense, weights['wd1'], biases['bd1'])\n",
    "saver = tf.train.Saver()\n",
    "# Return everything\n",
    "pred = tf.nn.softmax(_out)\n",
    "\n",
    "out = {\n",
    "    'input_r': _input_r, 'conv1': _conv1, 'conv2': _conv2, 'conv3': _conv3\n",
    "    , 'pool': _pool, 'dense': _dense, 'out': _out, 'pred': pred\n",
    "}\n",
    "    \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_img = cv2.imread('./dataset/anger/10.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image is not Gray\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(48, 48)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_img.shape\n",
    "\n",
    "def rgb2gray(rgb):\n",
    "    if len(rgb.shape) is 3:\n",
    "        print(\"Image is not Gray\")\n",
    "        return cv2.cvtColor(rgb, cv2.COLOR_BGR2GRAY)\n",
    "    else:\n",
    "        print(\"Current Image if Gray\")\n",
    "        return rgb\n",
    "\n",
    "imgsize = [48, 48]\n",
    "grayimg = rgb2gray(test_img)\n",
    "graysmall = cv2.resize(grayimg, (imgsize[0], imgsize[1]), interpolation = cv2.INTER_CUBIC) / 255. #Normalization\n",
    "#grayvec = np.reshape(graysmall, (1, -1))\n",
    "graysmall.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "emotion is  [[ 0.24023199  0.21703255  0.1340339   0.05086775  0.2120046   0.10552788\n",
      "   0.01985526  0.02044609]]\n",
      "Model restored.\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    # Restore variables from disk.\n",
    "    saver.restore(sess, \"./nets/cnn_mnist_simple.ckpt-4\")\n",
    "    feed_dict={x: graysmall}\n",
    "    print \"emotion is \", sess.run(pred, feed_dict )\n",
    "    \n",
    "    print(\"Model restored.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'Mean_4:0' shape=() dtype=float32>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Parameters\n",
    "learning_rate   = 0.001\n",
    "training_epochs = 5\n",
    "batch_size      = 100\n",
    "display_step    = 1\n",
    "# Functions! \n",
    "with tf.device(device_type): # <= This is optional\n",
    "    _pred = conv_simple(x, weights, biases)['out']\n",
    "    cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(_pred, y))\n",
    "    optm = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)\n",
    "    _corr = tf.equal(tf.argmax(_pred,1), tf.argmax(y,1)) # Count corrects\n",
    "    accr = tf.reduce_mean(tf.cast(_corr, tf.float32)) # Accuracy\n",
    "    init = tf.initialize_all_variables()\n"
   ]
  },
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   "execution_count": null,
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   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "scrolled": false
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   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'saver' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-c6c15eb5842f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mtraining_epochs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0msaver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrestore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msess\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"nets/cnn_mnist_simple.ckpt-\"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\"NETWORK RESTORED\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'saver' is not defined"
     ]
    }
   ],
   "source": [
    "training_epochs = 5\n",
    "saver.restore(sess, \"nets/cnn_mnist_simple.ckpt-\" + str(epoch))\n",
    "print (\"NETWORK RESTORED\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "No variables to save",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-13-4ae2ecd4e575>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msess\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0;31m# Restore variables from disk.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSaver\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrestore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msess\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"./nets/checkpoint\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Model restored.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/ryan/anaconda/lib/python2.7/site-packages/tensorflow/python/training/saver.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, var_list, reshape, sharded, max_to_keep, keep_checkpoint_every_n_hours, name, restore_sequentially, saver_def, builder)\u001b[0m\n\u001b[1;32m    835\u001b[0m         \u001b[0mvar_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvariables\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall_variables\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    836\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mvar_list\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 837\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"No variables to save\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    838\u001b[0m       saver_def = builder.build(\n\u001b[1;32m    839\u001b[0m           \u001b[0mvar_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: No variables to save"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    # Restore variables from disk.\n",
    "    tf.train.Saver().restore(sess, \"./nets/checkpoint\")\n",
    "    print(\"Model restored.\")"
   ]
  }
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