model = Sequential()
nb_filter = 30
kernal_size = (5, 5)
input_shape = (img_rows, img_cols, 1)

model.add(Convolution2D(nb_filter, kernal_size[0], kernal_size[1], border_mode='valid', input_shape=input_shape))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size(2,2)))

model.add(Flatten())
model.add(Dense(100))
model.add(Activation('relu'))
model.add(Dense(10))
model.add(Activation('softmax'))

model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X_train, Y_train, batch_size=100, nb_epoch=10, verbose=1, validation_data=(X_test, Y_test))