x = Convolution2D(32, (3, 3), padding ='same', kernel_initializer='he_normal')(model_input)
x = Activation('relu')(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
x = Convolution2D(32, (3, 3), kernel_initializer='he_normal')(x)
x = Activation('relu')(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
x = Dropout(0.25)(x)
x = Flatten()(x)
conv_out = (Dense(512, activation='relu', kernel_constraint=maxnorm(3)))(x)

lst = [x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, x11, x12, x13]

sgd = SGD(lr=lrate, momentum=0.9, decay=lrate/nb_epoch, nesterov=False)
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])