FINISHED 04 Using Keras to Recognize Digits.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\admin\\Envs\\deepvision\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "import keras\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.datasets import mnist\n",
    " \n",
    "(pics, labels), _ = mnist.load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LET'S GET THE IMAGES READY FOR THE AI MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "print(pics.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x174536985c0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "plt.imshow(pics[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to flatten each \"28 pixel x 28 pixel\" matrix into a long list of pixel values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 784)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pics = np.resize(pics, (60000, 784))\n",
    "pics.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "         0,   0,   0,   0], dtype=uint8)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pics[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the greyscale image lists brightness from 0 to 255. \n",
    "\n",
    "Since we are going to be doing math with these pixels, we should make it more flexible by turning 0 - 255 into a decimal from 0.0000 to 1.0000:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 784)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pics = pics.astype('float32')\n",
    "pics /= 255\n",
    "pics.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
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       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.01176471, 0.07058824, 0.07058824,\n",
       "       0.07058824, 0.49411765, 0.53333336, 0.6862745 , 0.10196079,\n",
       "       0.6509804 , 1.        , 0.96862745, 0.49803922, 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.11764706, 0.14117648, 0.36862746, 0.6039216 ,\n",
       "       0.6666667 , 0.99215686, 0.99215686, 0.99215686, 0.99215686,\n",
       "       0.99215686, 0.88235295, 0.6745098 , 0.99215686, 0.9490196 ,\n",
       "       0.7647059 , 0.2509804 , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.19215687, 0.93333334,\n",
       "       0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.99215686,\n",
       "       0.99215686, 0.99215686, 0.99215686, 0.9843137 , 0.3647059 ,\n",
       "       0.32156864, 0.32156864, 0.21960784, 0.15294118, 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.07058824, 0.85882354, 0.99215686, 0.99215686,\n",
       "       0.99215686, 0.99215686, 0.99215686, 0.7764706 , 0.7137255 ,\n",
       "       0.96862745, 0.94509804, 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.3137255 , 0.6117647 , 0.41960785, 0.99215686, 0.99215686,\n",
       "       0.8039216 , 0.04313726, 0.        , 0.16862746, 0.6039216 ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.05490196,\n",
       "       0.00392157, 0.6039216 , 0.99215686, 0.3529412 , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.54509807,\n",
       "       0.99215686, 0.74509805, 0.00784314, 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.04313726, 0.74509805, 0.99215686,\n",
       "       0.27450982, 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.13725491, 0.94509804, 0.88235295, 0.627451  ,\n",
       "       0.42352942, 0.00392157, 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.31764707, 0.9411765 , 0.99215686, 0.99215686, 0.46666667,\n",
       "       0.09803922, 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.1764706 ,\n",
       "       0.7294118 , 0.99215686, 0.99215686, 0.5882353 , 0.10588235,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.0627451 , 0.3647059 ,\n",
       "       0.9882353 , 0.99215686, 0.73333335, 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.9764706 , 0.99215686,\n",
       "       0.9764706 , 0.2509804 , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.18039216, 0.50980395,\n",
       "       0.7176471 , 0.99215686, 0.99215686, 0.8117647 , 0.00784314,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.15294118,\n",
       "       0.5803922 , 0.8980392 , 0.99215686, 0.99215686, 0.99215686,\n",
       "       0.98039216, 0.7137255 , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.09411765, 0.44705883, 0.8666667 , 0.99215686, 0.99215686,\n",
       "       0.99215686, 0.99215686, 0.7882353 , 0.30588236, 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.09019608, 0.25882354, 0.8352941 , 0.99215686,\n",
       "       0.99215686, 0.99215686, 0.99215686, 0.7764706 , 0.31764707,\n",
       "       0.00784314, 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.07058824, 0.67058825, 0.85882354,\n",
       "       0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.7647059 ,\n",
       "       0.3137255 , 0.03529412, 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.21568628, 0.6745098 ,\n",
       "       0.8862745 , 0.99215686, 0.99215686, 0.99215686, 0.99215686,\n",
       "       0.95686275, 0.52156866, 0.04313726, 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.53333336, 0.99215686, 0.99215686, 0.99215686,\n",
       "       0.83137256, 0.5294118 , 0.5176471 , 0.0627451 , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        ], dtype=float32)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pics[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NOW LET'S 1_HOT_ENCODE THE LABELS FOR TRAINING"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we saw above, the first pic in the MNIST database was clearly a 5. Let's make sure that the first label says '5':"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we are growing an A.I. model to make a prediction across all 10 digits, we need to one hot encode each label. Keras has a built in utility that will do this for us."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 1., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# NOW WE NEED TO ONE HOT ENCODE THE LABELS\n",
    "from keras.utils import np_utils\n",
    "\n",
    "labels = np_utils.to_categorical(labels, 10)\n",
    "labels[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NOW LET'S BUILD OUR MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Dense(512, input_dim=784, activation='relu'))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(512, activation='relu'))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(10, activation='softmax'))\n",
    "\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 54000 samples, validate on 6000 samples\n",
      "Epoch 1/10\n",
      "54000/54000 [==============================] - 8s 141us/step - loss: 0.2695 - acc: 0.9182 - val_loss: 0.0976 - val_acc: 0.9702\n",
      "Epoch 2/10\n",
      "54000/54000 [==============================] - 4s 74us/step - loss: 0.1061 - acc: 0.9669 - val_loss: 0.0760 - val_acc: 0.9790\n",
      "Epoch 3/10\n",
      "54000/54000 [==============================] - 4s 74us/step - loss: 0.0757 - acc: 0.9758 - val_loss: 0.0646 - val_acc: 0.9798\n",
      "Epoch 4/10\n",
      "54000/54000 [==============================] - 4s 71us/step - loss: 0.0579 - acc: 0.9820 - val_loss: 0.0706 - val_acc: 0.9792\n",
      "Epoch 5/10\n",
      "54000/54000 [==============================] - 4s 73us/step - loss: 0.0467 - acc: 0.9853 - val_loss: 0.0697 - val_acc: 0.9817\n",
      "Epoch 6/10\n",
      "54000/54000 [==============================] - 4s 72us/step - loss: 0.0398 - acc: 0.9869 - val_loss: 0.0617 - val_acc: 0.9838\n",
      "Epoch 7/10\n",
      "54000/54000 [==============================] - 4s 71us/step - loss: 0.0350 - acc: 0.9883 - val_loss: 0.0581 - val_acc: 0.9838\n",
      "Epoch 8/10\n",
      "54000/54000 [==============================] - 4s 71us/step - loss: 0.0332 - acc: 0.9892 - val_loss: 0.0612 - val_acc: 0.9838\n",
      "Epoch 9/10\n",
      "54000/54000 [==============================] - 4s 72us/step - loss: 0.0283 - acc: 0.9905 - val_loss: 0.0617 - val_acc: 0.9827\n",
      "Epoch 10/10\n",
      "54000/54000 [==============================] - 4s 72us/step - loss: 0.0233 - acc: 0.9919 - val_loss: 0.0646 - val_acc: 0.9845\n"
     ]
    }
   ],
   "source": [
    "def TrainingLoop(number_of_epochs, verbose):\n",
    "    model.fit(pics, labels, \\\n",
    "              batch_size=128, \\\n",
    "              epochs=number_of_epochs, \\\n",
    "              verbose=verbose, \\\n",
    "              validation_split=.1 )\n",
    "\n",
    "TrainingLoop(10, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LET'S VISUALLY VALIDATE OUR MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(784,)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_image = pics[300]\n",
    "\n",
    "test_image.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x174042c2390>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QhB8IivADQRF+ICjCDwRF+IGg/h/ANOu6fiF+QQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.resize(test_image, (28, 28))\n",
    "plt.imshow( np.resize(test_image, (28, 28)) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 784)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_image_batch = np.expand_dims(test_image, axis=0)\n",
    "test_image_batch.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 4s 4s/step\n",
      "[[4.80685536e-08 1.50097810e-06 4.44500216e-07 6.63612752e-07\n",
      "  1.59714894e-10 1.31232400e-06 2.84290387e-08 2.27004762e-08\n",
      "  9.99995947e-01 1.26593225e-08]]\n"
     ]
    }
   ],
   "source": [
    "result = model.predict(test_image_batch, batch_size=1, verbose=1)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n"
     ]
    }
   ],
   "source": [
    "print(np.argmax(result, axis=1)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save_weights('keras_digits.hd5')"
   ]
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}