mnist_softmax.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data loaddings\n",
    "\n",
    "from tensorflow.examples.tutorials.mnist import input_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-4-c908b284bd9b>:1: 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 /Users/phamvantoan/tensorflow_tutorial/lib/python3.5/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 /Users/phamvantoan/tensorflow_tutorial/lib/python3.5/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/mnist/train-images-idx3-ubyte.gz\n",
      "WARNING:tensorflow:From /Users/phamvantoan/tensorflow_tutorial/lib/python3.5/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/mnist/train-labels-idx1-ubyte.gz\n",
      "WARNING:tensorflow:From /Users/phamvantoan/tensorflow_tutorial/lib/python3.5/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/mnist/t10k-images-idx3-ubyte.gz\n",
      "Extracting data/mnist/t10k-labels-idx1-ubyte.gz\n",
      "WARNING:tensorflow:From /Users/phamvantoan/tensorflow_tutorial/lib/python3.5/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": [
    "mnist = input_data.read_data_sets('data/mnist', one_hot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = mnist.train.images\n",
    "X_test = mnist.test.images\n",
    "X_val = mnist.validation.images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = mnist.train.labels\n",
    "y_test = mnist.test.labels\n",
    "y_val = mnist.validation.labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(55000, 784)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 0., 0., 1., 0., 0.])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Placeholder\n",
    "\n",
    "X = tf.placeholder(tf.float32, [None, 784])\n",
    "y = tf.placeholder(tf.float32, [None, 10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Variable\n",
    "\n",
    "W = tf.Variable(tf.zeros([784, 10]))\n",
    "b = tf.Variable(tf.zeros([10]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model hyper parameters\n",
    "learning_rate = 0.01\n",
    "batch_size = 128\n",
    "nb_epochs = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define graph\n",
    "\n",
    "logits = tf.matmul(X, W) + b\n",
    "y_pred = tf.nn.softmax(logits=logits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loss function\n",
    "\n",
    "entropy = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y)\n",
    "\n",
    "loss = tf.reduce_mean(entropy)\n",
    "\n",
    "correct_preds = tf.equal(tf.argmax(y_pred, 1), tf.argmax(y, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Accuracy\n",
    "\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_preds, tf.float32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Optimizer\n",
    "\n",
    "optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Init variables\n",
    "\n",
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "sess = tf.Session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "sess.run(init)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epochs 0 val_loss = 0.839606523513794 val_accuracy = 0.8465999960899353\n",
      "Epochs 10 val_loss = 0.3785005509853363 val_accuracy = 0.8992000222206116\n",
      "Epochs 20 val_loss = 0.33478182554244995 val_accuracy = 0.909600019454956\n",
      "Epochs 30 val_loss = 0.3152567744255066 val_accuracy = 0.9136000275611877\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-41-8fe693a558e2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnb_epochs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnb_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m         \u001b[0mX_batch\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0my_batch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmnist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnext_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m         \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mX\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mX_batch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0my_batch\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/tensorflow_tutorial/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py\u001b[0m in \u001b[0;36mnext_batch\u001b[0;34m(self, batch_size, fake_data, shuffle)\u001b[0m\n\u001b[1;32m    211\u001b[0m         \u001b[0mperm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_examples\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    212\u001b[0m         \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mperm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 213\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_images\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimages\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mperm\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    214\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mperm\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    215\u001b[0m       \u001b[0;31m# Start next epoch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# Training\n",
    "\n",
    "nb_batch = X_train.shape[0] // batch_size\n",
    "\n",
    "for i in range(nb_epochs):\n",
    "    for _ in range(nb_batch):\n",
    "        X_batch , y_batch = mnist.train.next_batch(batch_size=batch_size)\n",
    "        _, batch_loss = sess.run([optimizer, loss], feed_dict={X : X_batch, y : y_batch})\n",
    "        \n",
    "    if i % 10 == 0:\n",
    "        _, val_loss, val_accuracy = sess.run([optimizer, loss, accuracy], feed_dict={X : X_val, y : y_val})\n",
    "        print(\"Epochs {} val_loss = {} val_accuracy = {}\".format(i, val_loss, val_accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epochs 35 test_loss = 0.3125869631767273 test_accuracy = 0.9143999814987183\n"
     ]
    }
   ],
   "source": [
    "_, test_loss, test_accuracy = sess.run([optimizer, loss, accuracy], feed_dict={X : X_test, y : y_test})\n",
    "print(\"Epochs {} test_loss = {} test_accuracy = {}\".format(i, test_loss, test_accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def show_result(X_true, y_true):\n",
    "    plt.imshow(X_true.reshape(28, 28))\n",
    "    print(np.argmax(y_true))\n",
    "    y_preds = sess.run(y_pred, feed_dict={X : [X_true]})\n",
    "    \n",
    "    print(np.argmax(y_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "3\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAADl1JREFUeJzt3X+QXXV5x/HPk2UTJJCSGNjJL5tIgzXSMXR2Qq3oyCA2UmqIdZA42uCkDUVwpMK0Ka1T7EwraNVRizghZIgtDTKVSMrQFhpLU61SFpoEAkKQWcpmkqxpmCZpNWx2n/6xJ3SBPd97ueece+7u837N7Oy957nnfh8u+ey5937vuV9zdwGIZ0rdDQCoB+EHgiL8QFCEHwiK8ANBEX4gKMIPBEX4gaAIPxDUSe0cbKpN85M1vZ1DAqH8TP+jl/yYNXPbQuE3s+WSviKpS9IGd78pdfuTNV3n2YVFhgSQ8LBva/q2LT/tN7MuSbdIer+kJZJWmdmSVu8PQHsVec2/TNKz7v6cu78k6S5JK8ppC0DVioR/nqQXxlwfyLa9gpmtNbM+M+sb0rECwwEoU+Xv9rv7enfvdffebk2rejgATSoS/r2SFoy5Pj/bBmACKBL+RyQtNrNFZjZV0uWStpbTFoCqtTzV5+7HzewaSf+o0am+je6+u7TOAFSq0Dy/u98v6f6SegHQRny8FwiK8ANBEX4gKMIPBEX4gaAIPxAU4QeCIvxAUIQfCIrwA0ERfiAowg8ERfiBoAg/EBThB4Ii/EBQhB8IivADQRF+ICjCDwRF+IGg2rpEN8b34hXvSNaPLkivuPzkVV/PrQ35cEs9NavbupL1IuO/a+eHk/Wp62cl62/4zr+3PHYEHPmBoAg/EBThB4Ii/EBQhB8IivADQRF+IKhC8/xm1i/piKRhScfdvbeMpiabgT/81WT9vt/9fLI+96RpyfqQ5/8NH9FIct+ihjxdLzL+v7x9c7K+/OoPJetd352RWxs+fLilniaTMj7kc4G7HyzhfgC0EU/7gaCKht8lPWBmj5rZ2jIaAtAeRZ/2n+/ue83sTEkPmtmP3H372BtkfxTWStLJOqXgcADKUujI7+57s9+DkrZIWjbObda7e6+793Yr/cYVgPZpOfxmNt3MTjtxWdL7JD1RVmMAqlXkaX+PpC1mduJ+/sbd/6GUrgBUruXwu/tzkt5eYi+T1qd/655kvdE8Psb3wJL04/qBRR/NL+5knp+pPiAowg8ERfiBoAg/EBThB4Ii/EBQfHU3Jq0fX356bm3RzjY20qE48gNBEX4gKMIPBEX4gaAIPxAU4QeCIvxAUMzzt8HNO34tWf/ouze0qZNYFi17oe4WOhpHfiAowg8ERfiBoAg/EBThB4Ii/EBQhB8Iinn+NliwIf0wf+DMlYXuf8qnTyu0f8rzn+lK1ne+Y1NlYxf1zJ65ubWzNdDGTjoTR34gKMIPBEX4gaAIPxAU4QeCIvxAUIQfCKrhPL+ZbZR0iaRBdz8n2zZL0rckLZTUL+kyd3+xujYntpO2PZq+wbZi9289Z+bWRuaekdz36atOSdY/90t/21JP7XDR7t9M1t/6+z/KrQ2X3cwE1MyR/w5Jy1+1bZ2kbe6+WKP/dNeV3BeAijUMv7tvl3ToVZtXSDrx0a5Nki4tuS8AFWv1NX+Pu+/LLu+X1FNSPwDapPAbfu7ukjyvbmZrzazPzPqGdKzocABK0mr4D5jZHEnKfg/m3dDd17t7r7v3dmtai8MBKFur4d8qaXV2ebWke8tpB0C7NAy/mW2W9ANJbzGzATNbI+kmSReZ2R5J782uA5hAGs7zu/uqnNKFJfeCFs3+zs9ya7e96Y6KR6/vc2LPD8xO1s8+3N+eRiYoPuEHBEX4gaAIPxAU4QeCIvxAUIQfCIqv7u4A3Q/NSda3LL4vvb/lf732kFf79z019uj4FQ5uVd755MeRHwiK8ANBEX4gKMIPBEX4gaAIPxAU4QeCYp6/DU5aMD9Zf+uMvcn6iEaS9dRceqN9i2o0j1/l+Pdd8JfJ+m+v+r3c2ozNPyy7nQmHIz8QFOEHgiL8QFCEHwiK8ANBEX4gKMIPBMU8Pyass7unJus3/9k3cmufHVyT3LfhsuqTAEd+ICjCDwRF+IGgCD8QFOEHgiL8QFCEHwiq4Ty/mW2UdImkQXc/J9t2o6TfkfST7GY3uPv9VTU50R1/YSBZ3/3fc9N30FNiMyVb858XJOtX9jyUW+udNlxyN6903rSh3NpPz+hO7nta2c10oGaO/HdIWj7O9i+7+9Lsh+ADE0zD8Lv7dkmH2tALgDYq8pr/GjPbZWYbzWxmaR0BaItWw3+rpLMkLZW0T9IX825oZmvNrM/M+oZ0rMXhAJStpfC7+wF3H3b3EUm3SVqWuO16d+91995uTWu1TwAlayn8ZjZ2WdmVkp4opx0A7dLMVN9mSe+RNNvMBiT9iaT3mNlSSS6pX9KVFfYIoAINw+/uq8bZfHsFvVSq621vSdb7P/jGZH3eQz/NrU351/9oqaeX92+wzvyUBk/Quq0rt3b30VnJfW/YOt7/3v931vWNvt/+cLJ6/Uc+kVvb/oVbGtx3Wuq/W0qvKeBWaOhJgU/4AUERfiAowg8ERfiBoAg/EBThB4IK89Xdb76jP1nfMvevk/W+j+dPK/3pR65ID/7DXcnysT9On7N7yWdXJOuWmCoc+cwZyX3P+n6xpaobTaH++rqHcmtFl+8usjx4g9nVEDjyA0ERfiAowg8ERfiBoAg/EBThB4Ii/EBQYeb5R1TsHM7U10x/fNPfJff9xrUfStZP3v+/6cGv/7l0PWGK8r++WpJ07tuS5ec+PCNZ/8Rv/H2yftXpe9LjozYc+YGgCD8QFOEHgiL8QFCEHwiK8ANBEX4gqDDz/P2r35Ss33J3+rz0q2c+nVtbeepgct+VG76erBeV+mrvoufMFxl7dPz6fPXFX8ytnb4rvfZstYuHdwaO/EBQhB8IivADQRF+ICjCDwRF+IGgCD8QVMN5fjNbIOmbknokuaT17v4VM5sl6VuSFkrql3SZu79YXavFDD/5TLL+3YPpef5Pzuzc89JTS1U3+m77Kseuevwv/NeSZP37F/9Cbm14IP3vIYJmjvzHJV3n7ksk/Yqkq81siaR1kra5+2JJ27LrACaIhuF3933u/lh2+YikpyTNk7RC0qbsZpskXVpVkwDK97pe85vZQknnSnpYUo+778tK+zX6sgDABNF0+M3sVEnflnStux8eW3N31+j7AePtt9bM+sysb0jHCjULoDxNhd/MujUa/Dvd/Z5s8wEzm5PV50ga9+wWd1/v7r3u3tutaWX0DKAEDcNvZibpdklPufuXxpS2SlqdXV4t6d7y2wNQlWZO6X2npI9JetzMdmTbbpB0k6S7zWyNpOclXVZNi+1x9Ob5yfrIhjpPTk1LTadVfUpvkWWyi7pz84XJ+vyBf6ts7MmgYfjd/XtS7pfepx99AB2LT/gBQRF+ICjCDwRF+IGgCD8QFOEHggrz1d2NnPKD9CmeF1z3ydza/vPTk91fW74pWX/vG44k65PVnx9cmqw/+Ll3Jevz72IevwiO/EBQhB8IivADQRF+ICjCDwRF+IGgCD8QlI1+A1d7zLBZfp7FOwu4a8nZyXr/B2cXuv+dV30tt1b1+fzn3vqplvddeM/BZL3R163jtR72bTrsh/JOwX8FjvxAUIQfCIrwA0ERfiAowg8ERfiBoAg/EBTz/MAkwjw/gIYIPxAU4QeCIvxAUIQfCIrwA0ERfiCohuE3swVm9s9m9qSZ7TazT2XbbzSzvWa2I/u5uPp2AZSlmUU7jku6zt0fM7PTJD1qZg9mtS+7+19U1x6AqjQMv7vvk7Qvu3zEzJ6SNK/qxgBU63W95jezhZLOlfRwtukaM9tlZhvNbGbOPmvNrM/M+oZ0rFCzAMrTdPjN7FRJ35Z0rbsflnSrpLMkLdXoM4Mvjrefu69391537+3WtBJaBlCGpsJvZt0aDf6d7n6PJLn7AXcfdvcRSbdJWlZdmwDK1sy7/SbpdklPufuXxmyfM+ZmKyU9UX57AKrSzLv975T0MUmPm9mObNsNklaZ2VJJLqlf0pWVdAigEs282/89SeOdH3x/+e0AaBc+4QcERfiBoAg/EBThB4Ii/EBQhB8IivADQRF+ICjCDwRF+IGgCD8QFOEHgiL8QFCEHwiqrUt0m9lPJD0/ZtNsSQfb1sDr06m9dWpfEr21qszeft7dz2jmhm0N/2sGN+tz997aGkjo1N46tS+J3lpVV2887QeCIvxAUHWHf33N46d0am+d2pdEb62qpbdaX/MDqE/dR34ANakl/Ga23MyeNrNnzWxdHT3kMbN+M3s8W3m4r+ZeNprZoJk9MWbbLDN70Mz2ZL/HXSatpt46YuXmxMrStT52nbbidduf9ptZl6RnJF0kaUDSI5JWufuTbW0kh5n1S+p199rnhM3s3ZKOSvqmu5+Tbfu8pEPuflP2h3Omu/9Bh/R2o6Sjda/cnC0oM2fsytKSLpV0hWp87BJ9XaYaHrc6jvzLJD3r7s+5+0uS7pK0ooY+Op67b5d06FWbV0jalF3epNF/PG2X01tHcPd97v5YdvmIpBMrS9f62CX6qkUd4Z8n6YUx1wfUWUt+u6QHzOxRM1tbdzPj6MmWTZek/ZJ66mxmHA1Xbm6nV60s3TGPXSsrXpeNN/xe63x3/2VJ75d0dfb0tiP56Gu2TpquaWrl5nYZZ2Xpl9X52LW64nXZ6gj/XkkLxlyfn23rCO6+N/s9KGmLOm/14QMnFknNfg/W3M/LOmnl5vFWllYHPHadtOJ1HeF/RNJiM1tkZlMlXS5paw19vIaZTc/eiJGZTZf0PnXe6sNbJa3OLq+WdG+NvbxCp6zcnLeytGp+7DpuxWt3b/uPpIs1+o7/jyX9UR095PT1Zkk7s5/ddfcmabNGnwYOafS9kTWS3ihpm6Q9kv5J0qwO6u2vJD0uaZdGgzanpt7O1+hT+l2SdmQ/F9f92CX6quVx4xN+QFC84QcERfiBoAg/EBThB4Ii/EBQhB8IivADQRF+IKj/A+Z9W7ksUOn1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_result(X_test[200], y_test[200])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Tensorflow Tutorial",
   "language": "python",
   "name": "myenv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}