CNN RNN model with masking in Keras

CNN_RNN.py

# Create model
model = Sequential()
input_shape = (n_tel, img_w, img_h, n_channels)

# mask the inputs that correspond to padding
model.add(Masking(mask_value=0., input_shape=input_shape))

# Add CNN feature extractor
model.add(TimeDistributed(
        Conv2D(
                filters=16, 
                kernel_size=(3,3), 
                padding='same',
                activation='relu'
                )))
model.add(TimeDistributed(
        Conv2D(
                filters=32, 
                kernel_size=(3,3), 
                padding='same',
                activation='relu'
                )
        ))
model.add(TimeDistributed(MaxPooling2D(pool_size=(2,2), strides=None)))
model.add(Flatten())

# Add LSTM feature combinator
model.add(LSTM(units=100))

# Add FCN classifier
model.add(Dense(units=100, activation='relu'))
model.add(Dense(1, activation='sigmoid'))