keras_mnist_cnn.ipynb

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      "C:\\Users\\oscar\\Anaconda3\\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"
     ]
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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 [==============================] - 15s 256us/step - loss: 0.2563 - acc: 0.9196 - val_loss: 0.0598 - val_acc: 0.9808\n",
      "Epoch 2/12\n",
      "60000/60000 [==============================] - 12s 193us/step - loss: 0.0909 - acc: 0.9735 - val_loss: 0.0408 - val_acc: 0.9869\n",
      "Epoch 3/12\n",
      "60000/60000 [==============================] - 12s 196us/step - loss: 0.0668 - acc: 0.9803 - val_loss: 0.0356 - val_acc: 0.9883\n",
      "Epoch 4/12\n",
      "60000/60000 [==============================] - 12s 204us/step - loss: 0.0560 - acc: 0.9826 - val_loss: 0.0348 - val_acc: 0.9890\n",
      "Epoch 5/12\n",
      "60000/60000 [==============================] - 12s 194us/step - loss: 0.0486 - acc: 0.9856 - val_loss: 0.0288 - val_acc: 0.9908\n",
      "Epoch 6/12\n",
      "60000/60000 [==============================] - 12s 193us/step - loss: 0.0415 - acc: 0.9872 - val_loss: 0.0274 - val_acc: 0.9908\n",
      "Epoch 7/12\n",
      "60000/60000 [==============================] - 12s 194us/step - loss: 0.0374 - acc: 0.9887 - val_loss: 0.0312 - val_acc: 0.9904\n",
      "Epoch 8/12\n",
      "60000/60000 [==============================] - 12s 196us/step - loss: 0.0363 - acc: 0.9890 - val_loss: 0.0283 - val_acc: 0.9904\n",
      "Epoch 9/12\n",
      "60000/60000 [==============================] - 12s 195us/step - loss: 0.0333 - acc: 0.9900 - val_loss: 0.0289 - val_acc: 0.9906\n",
      "Epoch 10/12\n",
      "60000/60000 [==============================] - 12s 195us/step - loss: 0.0302 - acc: 0.9904 - val_loss: 0.0275 - val_acc: 0.9912\n",
      "Epoch 11/12\n",
      "60000/60000 [==============================] - 12s 194us/step - loss: 0.0276 - acc: 0.9914 - val_loss: 0.0260 - val_acc: 0.9917\n",
      "Epoch 12/12\n",
      "60000/60000 [==============================] - 12s 194us/step - loss: 0.0261 - acc: 0.9920 - val_loss: 0.0285 - val_acc: 0.9917\n",
      "Test loss: 0.028480751971762492\n",
      "Test accuracy: 0.9917\n"
     ]
    }
   ],
   "source": [
    "\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",
    "batch_size = 128\n",
    "num_classes = 10\n",
    "epochs = 12\n",
    "\n",
    "# input image dimensions\n",
    "img_rows, img_cols = 28, 28\n",
    "\n",
    "# the data, split between train and test sets\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\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])"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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