keras-without-gpu.ipynb

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     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://s3.amazonaws.com/img-datasets/mnist.npz\n"
     ]
    }
   ],
   "source": [
    "'''Trains a simple convnet on the MNIST dataset.\n",
    "\n",
    "Gets to 99.25% test accuracy after 12 epochs\n",
    "(there is still a lot of margin for parameter tuning).\n",
    "16 seconds per epoch on a GRID K520 GPU.\n",
    "'''\n",
    "\n",
    "from __future__ import print_function\n",
    "import keras\n",
    "from keras.datasets import mnist\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Flatten\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "from keras import backend as K\n",
    "\n",
    "# the data, shuffled and split between train and test sets\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
    "\n",
    "def mnist_cnn(x_train, y_train, x_test, y_test):\n",
    "    batch_size = 128\n",
    "    num_classes = 10\n",
    "    epochs = 12\n",
    "\n",
    "    # input image dimensions\n",
    "    img_rows, img_cols = 28, 28\n",
    "\n",
    "    if K.image_data_format() == 'channels_first':\n",
    "        x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)\n",
    "        x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)\n",
    "        input_shape = (1, img_rows, img_cols)\n",
    "    else:\n",
    "        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",
    "    # 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)\n",
    "\n",
    "    model = Sequential()\n",
    "    model.add(Conv2D(32, kernel_size=(3, 3),\n",
    "                     activation='relu',\n",
    "                     input_shape=input_shape))\n",
    "    model.add(Conv2D(64, (3, 3), activation='relu'))\n",
    "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "    model.add(Dropout(0.25))\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(128, activation='relu'))\n",
    "    model.add(Dropout(0.5))\n",
    "    model.add(Dense(num_classes, activation='softmax'))\n",
    "\n",
    "    model.compile(loss=keras.losses.categorical_crossentropy,\n",
    "                  optimizer=keras.optimizers.Adadelta(),\n",
    "                  metrics=['accuracy'])\n",
    "\n",
    "    model.fit(x_train, y_train,\n",
    "              batch_size=batch_size,\n",
    "              epochs=epochs,\n",
    "              verbose=1,\n",
    "              validation_data=(x_test, y_test))\n",
    "    score = model.evaluate(x_test, y_test, verbose=0)\n",
    "    print('Test loss:', score[0])\n",
    "    print('Test accuracy:', score[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train shape: (60000, 28, 28, 1)\n",
      "60000 train samples\n",
      "10000 test samples\n",
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/12\n",
      "60000/60000 [==============================] - 315s - loss: 0.3346 - acc: 0.8984 - val_loss: 0.0717 - val_acc: 0.9776\n",
      "Epoch 2/12\n",
      "60000/60000 [==============================] - 314s - loss: 0.1089 - acc: 0.9674 - val_loss: 0.0515 - val_acc: 0.9846\n",
      "Epoch 3/12\n",
      "60000/60000 [==============================] - 313s - loss: 0.0833 - acc: 0.9750 - val_loss: 0.0421 - val_acc: 0.9860\n",
      "Epoch 4/12\n",
      "60000/60000 [==============================] - 312s - loss: 0.0692 - acc: 0.9793 - val_loss: 0.0375 - val_acc: 0.9877\n",
      "Epoch 5/12\n",
      "60000/60000 [==============================] - 312s - loss: 0.0597 - acc: 0.9824 - val_loss: 0.0335 - val_acc: 0.9889\n",
      "Epoch 6/12\n",
      "60000/60000 [==============================] - 311s - loss: 0.0553 - acc: 0.9837 - val_loss: 0.0380 - val_acc: 0.9869\n",
      "Epoch 7/12\n",
      "60000/60000 [==============================] - 310s - loss: 0.0506 - acc: 0.9854 - val_loss: 0.0314 - val_acc: 0.9892\n",
      "Epoch 8/12\n",
      "60000/60000 [==============================] - 311s - loss: 0.0463 - acc: 0.9857 - val_loss: 0.0299 - val_acc: 0.9896\n",
      "Epoch 9/12\n",
      "60000/60000 [==============================] - 312s - loss: 0.0422 - acc: 0.9873 - val_loss: 0.0298 - val_acc: 0.9897\n",
      "Epoch 10/12\n",
      "60000/60000 [==============================] - 312s - loss: 0.0410 - acc: 0.9880 - val_loss: 0.0287 - val_acc: 0.9900\n",
      "Epoch 11/12\n",
      "60000/60000 [==============================] - 313s - loss: 0.0385 - acc: 0.9885 - val_loss: 0.0271 - val_acc: 0.9919\n",
      "Epoch 12/12\n",
      "60000/60000 [==============================] - 312s - loss: 0.0368 - acc: 0.9892 - val_loss: 0.0268 - val_acc: 0.9913\n",
      "Test loss: 0.0267528861207\n",
      "Test accuracy: 0.9913\n",
      "CPU times: user 1h 57min 37s, sys: 6min 22s, total: 2h 3min 59s\n",
      "Wall time: 1h 2min 48s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "mnist_cnn(x_train, y_train, x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "collapsed": true
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   "outputs": [],
   "source": []
  }
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