import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten,  Conv2D, MaxPooling2D
from keras import backend as K

batch_size = 128
num_classes = 10
epochs = 12
img_rows, img_cols = 28, 28

(x_train, y_train),(x_test, y_test) = mnist.load_data()

x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)

input_shape = (img_rows, img_cols, 1)

x_train = x_train.astype('float32')
x_test = x_test.astype('float32')

x_train /= 255
x_test /= 255

y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras. utils. to_categorical(y_test, num_classes)

model = Sequential()
model.add(Conv2D(64,(3,3), activation = 'relu', input_shape = input_shape))
model.add(MaxPooling2D(pool_size = (2,2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation = 'relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation= 'softmax'))
model.compile(loss = keras.losses.categorical_crossentropy, optimizer = keras.optimizers.Adadelta(), metrics = ['accuracy'])
model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs,verbose = 1, validation_data=(x_test, y_test))

score = model.evaluate(x_test, y_test,verbose = 0)

print('Test loss: ', score[0])
print('Test Accuracy: ', score[1])
