Keras Masking for RNN with Varying Time Steps

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.

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.