import numpy as np

from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.utils import np_utils
from keras.optimizers import Adam

from keras.datasets import mnist
from matplotlib import pyplot as plt

np.random.seed(666)
(X_train, y_train), (X_test, y_test) = mnist.load_data()

# Input data proprocessing
X_train = X_train.reshape(X_train.shape[0], 1, 28, 28)
X_test = X_test.reshape(X_test.shape[0], 1, 28, 28)
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255

# Class data preprocessing
Y_train = np_utils.to_categorical(y_train, 10)
Y_test = np_utils.to_categorical(y_test, 10)

model = Sequential()

model.add(Convolution2D(32, kernel_size=(3, 3), activation="relu", input_shape=(1,28,28)))
model.add(Convolution2D(32, kernel_size=(3, 3), activation="relu"))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))

model.add(Flatten())
model.add(Dense(128, activation='softmax'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))

model.compile(loss='categorical_crossentropy',
              optimizer=Adam(),
              metrics=['accuracy'])

model.fit(X_train, Y_train, batch_size=32, epochs=10, verbose=1)
score = model.evaluate(X_test, Y_test)
print(score)

