Dropout (rate, noise_shape=None, seed=None). A simple and powerful regularization technique for neural networks and deep learning models is dropout. After reading this post you will know: How the dropout regularization . You can see that dropout is only applied in train phase.

Not sure why you would want to do that, but here are is another simple solution: Do it outside keras. It might be good to add these features. Although CuDNN RNNs do not support dropout natively, it seems to be possible to implement it outside of . For instance, if your inputs have shape.

A Python integer to use as random seed. You can look at the code here and see that they use the dropped input in training and the actual input while testing. As far as I know you have to build your own training function from the layers and specify the training flag to predict with . Keras does this by default. The best way I can think of applying dropout only to specific features is to simply separate the features in different layers. For that, I suggest you simply divide your inputs in essential features and droppable features: from keras.

I found an answer myself, which seem to work. Here are a few examples to get you started! Multilayer Perceptron (MLP):. The implementation mainly resides in LSTM class. It is invoked for every batch in Recurrent.

The input dropout and recurrent dropout rates have been stored as . Initialising the ANN classifier = Sequential(). Using TensorFlow backend. If adjacent pixels within feature maps are strongly correlated (as is normally the case in early convolution layers) then regular dropout will not regularize the activations and will otherwise just result in . CIFAR-dataset from keras. This is called inverted dropout.

That is the reasoning and it holds assuming dropout is implemented as in what I believe was the original paper on it. However, it turns out that this is not exactly how dropout was implemented in keras. Because keras adjusts the weights appropriately at train time, no further changes need to happen if you . Use parameter recurrent_dropout for hidden state dropout (U matrices). PyTorch does not natively support variational . A second improvement is very simple. We decide to randomly drop with the dropout probability some of the values propagated inside our internal dense network of hidden . It could probably do better by tuning the hyperparameters, like the amount of dropout or the number of neural network layers.

Let us look into the code now.