Updating state to make predictions with a recurrent neural network

Hi everyone,

I'm working with a recurrent neural network in MATLAB, and I want to clarify the difference in predictions when using two different methods:

1. Calling predict(net, X) directly with the entire input sequence.
2. Iteratively updating the state of the network by looping through each time step and feeding the inputs one by one (e.g., net = predictAndUpdateState(net, X) for each step, or something like (as suggested in newer versions):

net=resetState(net);
for i=1:input_sequence_length
    [predictions,state]=predict(net,X(i,:));
    net.State=state;
end

Are the predictions from these two approaches supposed to be identical?

I tried with my own network for ts forecasting, the predictions are more or less identical (negligible differences), but not equal. What does this could mean? Does it mean that the "state" of the network does not have an important role on the predictions?

Thanks in advance for your insights!

Answer 1

[Umar]

Hi @Enrico Gambini,

I have reviewed your comments. When utilizing RNNs for time series forecasting, it is crucial to understand how state management affects predictions. The two methods you mentioned can yield different results due to how they handle the internal state of the network.

Predicting with Entire Input Sequence: When you call predict(net, X) with the entire input sequence, MATLAB processes all time steps at once. The RNN computes its hidden states and outputs based on the full context of the input data simultaneously. This method can leverage all available information from the input sequence to produce a single output for each time step, effectively using the entire temporal context.

Iterative State Update: In contrast, when you use predictAndUpdateState(net, X) iteratively for each time step, you are updating the network's state after each prediction. This means that each output is influenced by the previous state of the network, which was modified by the last input it received. This approach mimics a more natural sequential processing, where each prediction is made based on what has happened up to that point in time.

Key Observations on Differences in Predictions

State Dependency: The slight differences in predictions indicate that even though both methods utilize similar input data, they inherently operate under different assumptions about state evolution. The iterative method allows for a more dynamic interaction with the network’s hidden states.

Numerical Stability and Precision: The small discrepancies could also stem from numerical precision issues or how floating-point calculations are handled across multiple iterations versus a single batch operation. In many cases, slight variations in calculations can accumulate over many iterations.

Model Characteristics: If your model's architecture (e.g., number of layers, activation functions) is particularly sensitive to initial conditions or small perturbations, these variations may become more pronounced. Additionally, if your RNN has mechanisms like dropout or batch normalization applied during training but not during prediction, this could also influence output consistency.

Here are some additional insights that would help as well.

Importance of State Management: The fact that you observed "negligible differences" suggests that while state management does have an impact, it might not be critically significant for your specific use case. However, it's essential to consider that in some contexts (e.g., longer sequences or more complex dependencies), these differences could become more pronounced and affect performance.

Testing and Validation: To better understand how state impacts your predictions, consider conducting further experiments:

• Test with varying sequence lengths and complexities.
• Evaluate how changes in model hyperparameters affect the outputs.
• Explore other architectures such as LSTM or GRU which are designed to handle long-term dependencies more effectively.

Hope this helps.