I have time series data of the following properties:
input shape: (num_timesteps, num_features) output shape: (num_timesteps, num_outputs)
I reshape it to batch form:
input shape: (num_batches, num_timesteps_in_batch, num features) output shape: (num_batches, num_timesteps_in_batch, num outputs)
I have a stateful RNN in Keras:
modelinput = Input(batch_shape=(num_batches,None,num_features)) prediction = GRU(10,return_sequences=True,stateful=True)(inputs) model = Model(inputs=modelinput,outputs=prediction)
After trainig (which works fine) I would like to predict on a sequence without cutting the data, so input shape (num_timesteps, num_features). How can I do that?
I thought about having a second model that shares the weights with the RNN and that has dynamic input shapes. Is that possible?