Is there any way to combine two models GRU and DropBlock? - keras

Question

Combine two models DropBlock(dimesnions=4), and CuDNNGRU(dimension=3) for MNIST image Classification. How can I combine different dimensions of Drop Block and GRU models?
GRU mostly used for Document classification but here I'm trying to classify MNIST dataset. Both Models separately score ~98%. I want to combine both DropBlock and GRU Model in a single classification Model.
The codes of combining the DropBlock and GRU model for MNIST classification.
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train/255.0
x_test = x_test/255.0
print(x_train.shape)
print(x_train[0].shape)
#load the model
model = Sequential()
gru = Sequential()
gru.add(CuDNNGRU(128,input_shape=(x_train.shape[1:]),
return_sequences=True))
gru.add(BatchNormalization())
gru.add(CuDNNGRU(128))
gru.add(BatchNormalization())
print(gru.output_shape)
drop = Sequential()
drop.add(DropBlock2D(input_shape=(28, 28, 1), block_size=7,
keep_prob=0.8, name='Input-Dropout'))
drop.add(Conv2D(filters=64, kernel_size=3, activation='relu',
padding='same', name='Conv-1'))
drop.add(MaxPool2D(pool_size=2, name='Pool-1'))
drop.add(DropBlock2D(block_size=5, keep_prob=0.8, name='Dropout-1'))
drop.add(Conv2D(filters=32, kernel_size=3, activation='relu',
padding='same', name='Conv-2'))
drop.add(MaxPool2D(pool_size=2, name='Pool-2'))
print(drop.output_shape)
model.add(merge([drop, gru], mode='concat', concat_axis=2))
print(model.output_shape)
model.add(Flatten(name='Flatten'))
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
score = model.evaluate(x_test, y_test, verbose=0)
The expected result ~98% results on this classification model.