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pythontensorflowkerasconv-neural-networkdropout
Markus Weber
Markus Weber

Reputation: 1107

Keras Dropout Convolutional Filters

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.

Upvotes: 1

Views: 505

Answers (1)

Markus Weber
Markus Weber

Reputation: 1107

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.

Upvotes: 0

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