MoE_MNIST2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mixture of Experts Network for MNIST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import keras\n",
    "from keras.models import Sequential, Model\n",
    "from keras.layers import Dense, Dropout, Flatten, BatchNormalization, Input\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "from keras.layers import concatenate, Lambda, Reshape\n",
    "from keras.optimizers import Adam\n",
    "import keras.backend as K\n",
    "from keras.datasets import mnist\n",
    "\n",
    "import pydot\n",
    "from keras.utils.vis_utils import model_to_dot\n",
    "from IPython.display import SVG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 64\n",
    "num_classes = 10\n",
    "epochs = 3\n",
    "\n",
    "# input image dimensions\n",
    "img_rows, img_cols = 28, 28\n",
    "\n",
    "# the data, shuffled and split between train and test sets\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train shape: (60000, 28, 28, 1)\n",
      "60000 train samples\n",
      "10000 test samples\n"
     ]
    }
   ],
   "source": [
    "x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\n",
    "x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\n",
    "input_shape = (img_rows, img_cols, 1)\n",
    "\n",
    "x_train = x_train.astype('float32')\n",
    "x_test = x_test.astype('float32')\n",
    "x_train /= 255\n",
    "x_test /= 255\n",
    "print('x_train shape:', x_train.shape)\n",
    "print(x_train.shape[0], 'train samples')\n",
    "print(x_test.shape[0], 'test samples')\n",
    "\n",
    "y_train0 = y_train[:]\n",
    "y_test0 = y_test[:]\n",
    "# convert class vectors to binary class matrices\n",
    "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
    "y_test = keras.utils.to_categorical(y_test, num_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "input1 = Input(shape=input_shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "def baseModel(input1, num_classes):\n",
    "    x = Conv2D(32, kernel_size=(3, 3), activation='relu')(input1)\n",
    "    x = Conv2D(64, (3, 3), activation='relu')(x)\n",
    "    x = MaxPooling2D(pool_size=(2, 2))(x)\n",
    "    x = Dropout(0.25)(x)\n",
    "    x = Flatten()(x)\n",
    "    x = Dense(128, activation='relu')(x)\n",
    "    x = Dropout(0.5)(x)\n",
    "    x = Dense(num_classes, activation='softmax')(x)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "x1 = baseModel(input1, num_classes)\n",
    "x1Model = Model(input1, x1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "x1Model.compile(loss='categorical_crossentropy',\n",
    "                optimizer=Adam(),\n",
    "                metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/3\n",
      "60000/60000 [==============================] - 61s 1ms/step - loss: 0.2245 - acc: 0.9314 - val_loss: 0.0462 - val_acc: 0.9864\n",
      "Epoch 2/3\n",
      "60000/60000 [==============================] - 59s 991us/step - loss: 0.0885 - acc: 0.9737 - val_loss: 0.0366 - val_acc: 0.9868\n",
      "Epoch 3/3\n",
      "60000/60000 [==============================] - 59s 984us/step - loss: 0.0667 - acc: 0.9800 - val_loss: 0.0349 - val_acc: 0.9885\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fa666d07e80>"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1Model.fit(x_train, y_train,\n",
    "            batch_size=batch_size,\n",
    "            epochs=epochs,\n",
    "            validation_data=(x_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 4s 400us/step\n",
      "[0.034910372883686792, 0.98850000000000005]\n"
     ]
    }
   ],
   "source": [
    "x1Score = x1Model.evaluate(x_test, y_test)\n",
    "print(x1Score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "zeroToFourTr = [i for i in range(len(y_train0)) if y_train0[i] in [0,1,2,3,4]]\n",
    "fiveToNineTr = [i for i in range(len(y_train0)) if y_train0[i] in [5,6,7,8,9]]\n",
    "zeroToFourTe = [i for i in range(len(y_test0)) if y_test0[i] in [0,1,2,3,4]]\n",
    "fiveToNineTe = [i for i in range(len(y_test0)) if y_test0[i] in [5,6,7,8,9]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train00 = x_train[zeroToFourTr]\n",
    "x_test00 = x_test[zeroToFourTe]\n",
    "y_train00 = y_train[zeroToFourTr]\n",
    "y_test00 = y_test[zeroToFourTe]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train00.shape, y_train00, x_test00.shape, y_test00"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "x00 = baseModel(input1, num_classes)\n",
    "x00Model = Model(input1, x00)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "x00Model.compile(loss='categorical_crossentropy',\n",
    "                optimizer=Adam(),\n",
    "                metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 30596 samples, validate on 5139 samples\n",
      "Epoch 1/3\n",
      "30596/30596 [==============================] - 33s 1ms/step - loss: 0.1080 - acc: 0.9658 - val_loss: 0.0119 - val_acc: 0.9963\n",
      "Epoch 2/3\n",
      "30596/30596 [==============================] - 32s 1ms/step - loss: 0.0324 - acc: 0.9900 - val_loss: 0.0101 - val_acc: 0.9965\n",
      "Epoch 3/3\n",
      "30596/30596 [==============================] - 31s 1ms/step - loss: 0.0247 - acc: 0.9926 - val_loss: 0.0065 - val_acc: 0.9984\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fa66c8449e8>"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x00Model.fit(x_train00, y_train00,\n",
    "            batch_size=batch_size,\n",
    "            epochs=epochs,\n",
    "            validation_data=(x_test00, y_test00))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5139/5139 [==============================] - 2s 443us/step\n",
      "[0.0064898463812420003, 0.99844327690212109]\n"
     ]
    }
   ],
   "source": [
    "x00Score = x00Model.evaluate(x_test00, y_test00)\n",
    "print(x00Score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train11 = x_train[fiveToNineTr]\n",
    "x_test11 = x_test[fiveToNineTe]\n",
    "y_train11 = y_train[fiveToNineTr]\n",
    "y_test11 = y_test[fiveToNineTe]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((29404, 28, 28, 1), (29404, 10), (4861, 28, 28, 1), (4861, 10))"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train11.shape, y_train11.shape, x_test11.shape, y_test11.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "x11 = baseModel(input1, 10)\n",
    "x11Model = Model(input1, x11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [],
   "source": [
    "x11Model.compile(loss='categorical_crossentropy',\n",
    "                optimizer=Adam(),\n",
    "                metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 29404 samples, validate on 4861 samples\n",
      "Epoch 1/3\n",
      "29404/29404 [==============================] - 32s 1ms/step - loss: 0.1666 - acc: 0.9455 - val_loss: 0.0394 - val_acc: 0.9875\n",
      "Epoch 2/3\n",
      "29404/29404 [==============================] - 30s 1ms/step - loss: 0.0512 - acc: 0.9835 - val_loss: 0.0347 - val_acc: 0.9889\n",
      "Epoch 3/3\n",
      "29404/29404 [==============================] - 29s 999us/step - loss: 0.0361 - acc: 0.9889 - val_loss: 0.0206 - val_acc: 0.9930\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fa66c04a780>"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x11Model.fit(x_train11, y_train11,\n",
    "            batch_size=batch_size,\n",
    "            epochs=epochs,\n",
    "            validation_data=(x_test11, y_test11))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4861/4861 [==============================] - 2s 432us/step\n",
      "[0.020630182366605715, 0.99300555441267224]\n"
     ]
    }
   ],
   "source": [
    "x11Score = x11Model.evaluate(x_test11, y_test11)\n",
    "print(x11Score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train01 = np.array([0 if i in [0,1,2,3,4] else 1 for i in y_train0])\n",
    "y_test01 = np.array([0 if i in [0,1,2,3,4] else 1 for i in y_test0])\n",
    "y_train01 = keras.utils.to_categorical(y_train01, 2)\n",
    "y_test01 = keras.utils.to_categorical(y_test01, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "g0 = Conv2D(32, kernel_size=(3, 3), activation='relu')(input1)\n",
    "g0 = Conv2D(64, (3, 3), activation='relu')(g0)\n",
    "g0 = MaxPooling2D(pool_size=(2, 2))(g0)\n",
    "g0 = Dropout(0.25)(g0)\n",
    "g0 = Flatten()(g0)\n",
    "g0 = Dense(128, activation='relu')(g0)\n",
    "g0 = Dropout(0.5)(g0)\n",
    "g0 = Dense(2, activation='softmax')(g0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [],
   "source": [
    "g0 = baseModel(input1, 2)\n",
    "g0Model = Model(input1, g0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [],
   "source": [
    "g0Model.compile(loss='categorical_crossentropy',\n",
    "                optimizer=Adam(),\n",
    "                metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/3\n",
      "60000/60000 [==============================] - 62s 1ms/step - loss: 0.1008 - acc: 0.9617 - val_loss: 0.0419 - val_acc: 0.9848\n",
      "Epoch 2/3\n",
      "60000/60000 [==============================] - 61s 1ms/step - loss: 0.0409 - acc: 0.9861 - val_loss: 0.0318 - val_acc: 0.9887\n",
      "Epoch 3/3\n",
      "60000/60000 [==============================] - 62s 1ms/step - loss: 0.0309 - acc: 0.9895 - val_loss: 0.0275 - val_acc: 0.9920\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fa66b9a5b38>"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g0Model.fit(x_train, y_train01,\n",
    "            batch_size=batch_size,\n",
    "            epochs=epochs,\n",
    "            validation_data=(x_test, y_test01))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 4s 437us/step\n",
      "[0.027455827408827464, 0.99199999999999999]\n"
     ]
    }
   ],
   "source": [
    "g0Score = g0Model.evaluate(x_test, y_test01)\n",
    "print(g0Score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([Dimension(None), Dimension(2)])"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g0.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def subGate(input1, num_classes):\n",
    "    g01 = Flatten()(input1)\n",
    "    g01 = Dense(128, activation=\"relu\")(g01)\n",
    "    g01 = Dense(num_classes*2, activation=\"softmax\")(g01)\n",
    "    g01 = Reshape((num_classes, 2))(g01)\n",
    "    return g01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [],
   "source": [
    "g00 = subGate(input1, num_classes)\n",
    "g11 = subGate(input1, num_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [],
   "source": [
    "g0Switch = Lambda(lambda gx: K.switch(gx[2][:,0] > gx[2][:,1], \n",
    "                                      gx[0], \n",
    "                                      gx[1]), \n",
    "                output_shape=(num_classes,))([x00, x11, g0, x1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [],
   "source": [
    "def subGateLambda(x0, x1, g01):\n",
    "    output = Lambda(lambda gx: (gx[0]*gx[2][:,:,0]) + (gx[1]*gx[2][:,:,1]), output_shape=(num_classes,))([x0, x1, g01])\n",
    "    return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "output = Lambda(lambda gx: K.switch(gx[2][:,0] > gx[2][:,1], \n",
    "                                    subGateLambda(gx[0],gx[3],gx[4]), \n",
    "                                    subGateLambda(gx[1],gx[3],gx[5])), \n",
    "                output_shape=(num_classes,))([x00, x11, g0, x1, g00, g11])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [],
   "source": [
    "for l in x1Model.layers:\n",
    "    l.trainable = False\n",
    "for l in x00Model.layers:\n",
    "    l.trainable = False\n",
    "for l in x11Model.layers:\n",
    "    l.trainable = False\n",
    "for l in g0Model.layers:\n",
    "    l.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = Lambda(lambda gx: (gx[0]*gx[2][:,:,0]) + (gx[1]*gx[2][:,:,1]), output_shape=(num_classes,))([x1, x2, g0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = Lambda(lambda gx: K.switch(gx[2][:,:,0] > 0.4, \n",
    "                                    gx[0]*gx[2][:,:,0], \n",
    "                                    gx[0]*0) + K.switch(gx[2][:,:,1] > 0.4, \n",
    "                                                        gx[1]*gx[2][:,:,1], \n",
    "                                                        gx[1]*0), \n",
    "                output_shape=(num_classes,))([x1, x2, g0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "model = Model(input1, output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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     "execution_count": 272,
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   "source": [
    "SVG(model_to_dot(model).create(prog='dot', format='svg'))"
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  {
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   "execution_count": 273,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer=Adam(),\n",
    "              metrics=['accuracy'])"
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  {
   "cell_type": "code",
   "execution_count": 274,
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      "<keras.layers.core.Dense object at 0x7fa66c0e78d0> False\n",
      "<keras.layers.core.Dense object at 0x7fa66ba6b7f0> False\n",
      "<keras.layers.core.Dense object at 0x7fa666dee978> False\n",
      "<keras.layers.core.Dense object at 0x7fa66b4882b0> True\n",
      "<keras.layers.core.Dense object at 0x7fa66b45e940> True\n",
      "<keras.layers.core.Dropout object at 0x7fa66c8c2a90> False\n",
      "<keras.layers.core.Dropout object at 0x7fa66c14b780> False\n",
      "<keras.layers.core.Dropout object at 0x7fa66ba2c6d8> False\n",
      "<keras.layers.core.Dropout object at 0x7fa666daf828> False\n",
      "<keras.layers.core.Dense object at 0x7fa66b486860> True\n",
      "<keras.layers.core.Dense object at 0x7fa66b332cc0> True\n",
      "<keras.layers.core.Dense object at 0x7fa66c8c27f0> False\n",
      "<keras.layers.core.Dense object at 0x7fa66c14b4e0> False\n",
      "<keras.layers.core.Dense object at 0x7fa66baf0668> False\n",
      "<keras.layers.core.Dense object at 0x7fa666f3ba90> False\n",
      "<keras.layers.core.Reshape object at 0x7fa66b491e48> True\n",
      "<keras.layers.core.Reshape object at 0x7fa66b34c6a0> True\n",
      "<keras.layers.core.Lambda object at 0x7fa666becc50> True\n"
     ]
    }
   ],
   "source": [
    "for l in model.layers:\n",
    "    print(l, l.trainable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/3\n",
      "60000/60000 [==============================] - 69s 1ms/step - loss: 0.0361 - acc: 0.9889 - val_loss: 0.0307 - val_acc: 0.9901\n",
      "Epoch 2/3\n",
      "60000/60000 [==============================] - 67s 1ms/step - loss: 0.0339 - acc: 0.9901 - val_loss: 0.0319 - val_acc: 0.9895\n",
      "Epoch 3/3\n",
      "60000/60000 [==============================] - 65s 1ms/step - loss: 0.0297 - acc: 0.9913 - val_loss: 0.0295 - val_acc: 0.9908\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fa66688b8d0>"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train,\n",
    "          batch_size=batch_size,\n",
    "          epochs=epochs,\n",
    "          validation_data=(x_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 9s 886us/step\n",
      "[0.029530485406908339, 0.99080000000000001]\n"
     ]
    }
   ],
   "source": [
    "score = model.evaluate(x_test, y_test)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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