Keras Masking For RNN With Varying Time Steps

I'm trying to fit an RNN in Keras using sequences that have varying time lengths. My data is in a Numpy array with format (sample, time, feature) = (20631, max_time, 24) where max_

Solution 1:

The way you implemented masking should be correct. If you have data with the shape (samples, timesteps, features), and you want to mask timesteps lacking data with a zero mask of the same size as the features argument, then you add Masking(mask_value=0., input_shape=(timesteps, features)). See here: keras.io/layers/core/#masking

Your model could potentially be too simple, and/or your number of epochs could be insufficient for the model to differentiate between all of your classes. Try this model:

model = Sequential()
model.add(Masking(mask_value=0., input_shape=(max_time, 24)))
model.add(LSTM(256, input_dim=24))
model.add(Dense(1024))
model.add(Dense(2))
model.add(Activation(activate))
model.compile(loss=weibull_loglik_discrete, optimizer=RMSprop(lr=.01))
model.fit(train_x, train_y, nb_epoch=100, batch_size=1000, verbose=2, validation_data=(test_x, test_y)) 

If that does not work, try doubling the epochs a few times (e.g. 200, 400) and see if that improves the results.

Solution 2:

I could not validate without actual data, but I had a similar experience with an RNN. In my case normalization solved the issue. Add a normalization layer to your model.