<gh_stars>0
import mnist
import scipy.misc
import matplotlib.pyplot as plt
import tensorflow as tf
# Importing the required Keras modules containing model and layers
from keras.models import Sequential
from keras.layers import Dense, Conv2D, Dropout, Flatten, MaxPooling2D

if __name__ == '__main__':
    images = mnist.train_images()
    scipy.misc.toimage(scipy.misc.imresize(images[0,:,:] * -1 + 256, 10.))

    (x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()

    #%matplotlib inline # Only use this if using iPython
    image_index = 7777 # You may select anything up to 60,000
    print(y_train[image_index]) # The label is 8
    plt.imshow(x_train[image_index], cmap='Greys')
    plt.show()
    x_train.shape

    # Reshaping the array to 4-dims so that it can work with the Keras API
    x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)
    x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)
    input_shape = (28, 28, 1)
    # Making sure that the values are float so that we can get decimal points after division
    x_train = x_train.astype('float32')
    x_test = x_test.astype('float32')
    # Normalizing the RGB codes by dividing it to the max RGB value.
    x_train /= 255
    x_test /= 255
    print('x_train shape:', x_train.shape)
    print('Number of images in x_train', x_train.shape[0])
    print('Number of images in x_test', x_test.shape[0])

    # Creating a Sequential Model and adding the layers
    model = Sequential()
    model.add(Conv2D(28, kernel_size=(3,3), input_shape=input_shape))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Flatten()) # Flattening the 2D arrays for fully connected layers
    model.add(Dense(128, activation=tf.nn.relu))
    model.add(Dropout(0.2))
    model.add(Dense(10,activation=tf.nn.softmax))

    model.compile(optimizer='adam', 
                loss='sparse_categorical_crossentropy', 
                metrics=['accuracy'])
    model.fit(x=x_train,y=y_train, epochs=10)
    
    model.evaluate(x_test, y_test)