mnist_conv.py.ipynb

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    "'''A deep convolutional neural networks with two convolution layers, one max pooling and two dense layers\n",
    "'''\n",
    "# step 1: Importing packages\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",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec"
   ]
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    "# step 2: Loading data and visualizing some examples\n",
    "\n",
    "# training parameters\n",
    "batch_size = 128\n",
    "num_classes = 10\n",
    "epochs = 5\n",
    "\n",
    "# data loading\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
    "\n",
    "# width and height of images\n",
    "img_rows, img_cols = x_train.shape[1], x_train.shape[2]\n",
    "\n",
    "# visualization of some examples\n",
    "def show_images(images, rows = 6, titles = None):\n",
    "    \"\"\"Display a list of images in a single figure with matplotlib.\n",
    "    \n",
    "    Args:\n",
    "        images: List of np.arrays compatible with plt.imshow.\n",
    "        rows (Default = 1): Number of columns in figure (number of rows is \n",
    "                        set to np.ceil(n_images/float(rows))).\n",
    "        titles: List of titles corresponding to each image. Must have\n",
    "            the same length as titles.\n",
    "    \"\"\"\n",
    "    n_images = len(images)\n",
    "    cols = int(n_images/rows)\n",
    "    plt.figure(figsize = (rows, cols))\n",
    "    gs1 = gridspec.GridSpec(rows, cols)\n",
    "    gs1.update(wspace=0.025, hspace=0.025) # set the spacing between axes. \n",
    "\n",
    "    for i in range(n_images):\n",
    "        ax1 = plt.subplot(gs1[i])\n",
    "        plt.axis('on')\n",
    "        ax1.set_xticklabels([])\n",
    "        ax1.set_yticklabels([])\n",
    "        ax1.set_aspect('equal')\n",
    "        plt.gray()\n",
    "        plt.imshow(images[i])\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "show_images(x_train[:48])"
   ]
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    "# step 3: Reshaping to 4D arrays and normalizing data\n",
    "x_train = x_train.reshape(-1, img_rows, img_cols, 1).astype('float32')\n",
    "x_test = x_test.reshape(-1, img_rows, img_cols, 1).astype('float32')\n",
    "input_shape = (img_rows, img_cols, 1)\n",
    "\n",
    "x_train /= 255\n",
    "x_test /= 255\n",
    "print('x_train shape:', x_train.shape)\n",
    "print('number of training examples: ',x_train.shape[0])\n",
    "print('number of testing examples: ',x_test.shape[0])\n"
   ]
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    "# step 4: Making categorical labels (one-hot labels)\n",
    "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
    "y_test = keras.utils.to_categorical(y_test, num_classes)"
   ]
  },
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   "source": [
    "# step 5: Building model\n",
    "model = Sequential()\n",
    "model.add(Conv2D(32, kernel_size=(5, 5),\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(0.25))\n",
    "model.add(Dense(num_classes, activation='softmax'))\n",
    "\n",
    "# summary of model's parameters\n",
    "model.summary()"
   ]
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   "source": [
    "# step 6: Compiling model\n",
    "model.compile(loss=keras.losses.categorical_crossentropy,\n",
    "              optimizer=keras.optimizers.Adam(lr=0.001),\n",
    "              metrics=['accuracy'])"
   ]
  },
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   "source": [
    "# step 7: Training\n",
    "model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_data=(x_test, y_test))"
   ]
  },
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   "source": [
    "# step 8: Evaluating\n",
    "score = model.evaluate(x_test, y_test, verbose=0)\n",
    "print('test_loss={:.4f} test_accuracy={:.4f}'.format(score[0], score[1]))"
   ]
  },
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