simplemodel.py

from keras.layers import (Conv2D, BatchNormalization, Activation, Flatten)

# Build a model with 14 output nodes
model = Sequential()

model.add( Conv2D(8, (3,3), padding='same', input_shape=(28,28,1)))
model.add( BatchNormalization() )
model.add( Activation('relu') )


model.add( Conv2D(8, (3,3), strides=(2,2), padding='same') ) # -> 14,14,8
model.add( BatchNormalization() )
model.add( Activation('relu') )

model.add( Conv2D(8, (3,3), strides=(2,2), padding='same')) # -> 7,7,8
model.add( BatchNormalization() )
model.add( Activation('relu') )

model.add( Flatten() )

model.add( Dense(10, activation='softmax') )
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.summary()

model.fit(X_train.reshape((X_train.shape[0],28,28,1)), Y_train, batch_size=32, epochs=5, 
          validation_data=(X_test.reshape((X_test.shape[0],28,28,1)),Y_test))