Keras Dropout Convolutional Filters

Question

I understand that dropout doesn't have the same effect for kernels of convolutional filters of a neural network, as it does for FC layers :

But does the same fact apply, if you dropout the whole filter?

Let's assume a network structure like: Input, Conv2D, Conv2D, ..., Conv2D, Conv2D, Sigmoid. So there is no fully connected Layer in the whole network.

Question 1 Is it reasonable to apply conv filter dropouts to avoid co-adaptation between filters for improving the results of filter visualization .

Question 2 Is there a quick way to do dropout filters in keras.

Answer 1

Answer 1 Maybe.

Without Dropout:

With Dropout:

Answer 2 According to the keras documentations states, use keras.layers.Dropout(rate, noise_shape=None, seed=None) with noise_shape=(batch_size, 1, 1, features) . Use 1 if you want the dropout mask to be the same for a complete dimension.