keras-with-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",
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     "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 [==============================] - 12s - loss: 0.3322 - acc: 0.8997 - val_loss: 0.0818 - val_acc: 0.9753\n",
      "Epoch 2/12\n",
      "60000/60000 [==============================] - 11s - loss: 0.1196 - acc: 0.9651 - val_loss: 0.0546 - val_acc: 0.9828\n",
      "Epoch 3/12\n",
      "60000/60000 [==============================] - 11s - loss: 0.0867 - acc: 0.9749 - val_loss: 0.0420 - val_acc: 0.9856\n",
      "Epoch 4/12\n",
      "60000/60000 [==============================] - 11s - loss: 0.0732 - acc: 0.9782 - val_loss: 0.0428 - val_acc: 0.9857\n",
      "Epoch 5/12\n",
      "60000/60000 [==============================] - 11s - loss: 0.0637 - acc: 0.9808 - val_loss: 0.0366 - val_acc: 0.9870\n",
      "Epoch 6/12\n",
      "60000/60000 [==============================] - 11s - loss: 0.0561 - acc: 0.9832 - val_loss: 0.0366 - val_acc: 0.9882\n",
      "Epoch 7/12\n",
      "60000/60000 [==============================] - 11s - loss: 0.0525 - acc: 0.9848 - val_loss: 0.0344 - val_acc: 0.9874\n",
      "Epoch 8/12\n",
      "60000/60000 [==============================] - 11s - loss: 0.0473 - acc: 0.9862 - val_loss: 0.0321 - val_acc: 0.9881\n",
      "Epoch 9/12\n",
      "60000/60000 [==============================] - 11s - loss: 0.0444 - acc: 0.9871 - val_loss: 0.0303 - val_acc: 0.9896\n",
      "Epoch 10/12\n",
      "60000/60000 [==============================] - 11s - loss: 0.0426 - acc: 0.9873 - val_loss: 0.0297 - val_acc: 0.9894\n",
      "Epoch 11/12\n",
      "60000/60000 [==============================] - 11s - loss: 0.0383 - acc: 0.9884 - val_loss: 0.0284 - val_acc: 0.9896\n",
      "Epoch 12/12\n",
      "60000/60000 [==============================] - 11s - loss: 0.0361 - acc: 0.9889 - val_loss: 0.0272 - val_acc: 0.9912\n",
      "Test loss: 0.0272085557612\n",
      "Test accuracy: 0.9912\n",
      "CPU times: user 1min 40s, sys: 34.3 s, total: 2min 14s\n",
      "Wall time: 2min 21s\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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   "source": []
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