Confusion about regression or classification head

Hey,

First of all, keep in mind that TCNs were not initially designed to handle non-causal time series. At least, not in the original paper.

how should I initialize the TCN class or modify the network?

You can just specify padding=same and you don't need to modify the network after that.

What I try to do is simply take the last time step of the output, but isn't all the calculation before the last time step all wasted? i.e. the upper left triangle, if my understanding is right.

If you refer to the visual below, yes some calculations are wasted. The useful calculations are marked with arrows. The rest will still be calculated but will not be used.

Can't we down-sample the temporal dimension in the encoding phase (like most CNN networks where it gets downsampled towards the end of the layers)?

It is possible to do that too and that will work as well even better but:

Imagine how many layers of CNN you will need if your input size is 48,000 (one second of audio sound at 48KHz) to down-sample to something manageable that you can feed to Dense layers. You can obviously use Max Pool layers but you might lose some fine-grained temporal information (by averaging out or ignoring). Here is a quick summary of what TCNs are good for:

Temporal Order Preservation: TCN: TCNs are explicitly designed to handle time series data, preserving the temporal order of inputs. This is crucial when the sequence of events is important for the prediction. CNN: While CNNs can be applied to time series data, they do not inherently preserve the temporal order in the same way. They treat the temporal dimension similarly to spatial dimensions in images, which might not capture the sequential nature of time series as effectively.
Receptive Field Control: TCN: TCNs use dilated convolutions, which allow them to have a large receptive field (covering long time spans) without a deep network. This is particularly beneficial when you need to capture long-term dependencies in the data. CNN: Standard CNNs require deeper networks or pooling operations to achieve a large receptive field, which can make training more challenging and less efficient for time series data.
Handling Variable-Length Sequences: TCN: TCNs can naturally handle variable-length sequences due to their fully convolutional nature. They don't require padding or truncation to a fixed size, making them more flexible for real-world time series data. CNN: CNNs can also handle variable-length sequences, but typically through additional steps like padding or using global pooling, which might not be as efficient.
Causal and Non-Causal Configurations: TCN: TCNs can be configured to be causal (where each time step only depends on past inputs) or non-causal, making them versatile for both real-time predictions and tasks where the entire sequence is available. CNN: CNNs are generally non-causal and don’t have a built-in mechanism to enforce causality, which might not be suitable for tasks requiring real-time predictions based only on past data.
Better Stability and Gradient Flow: TCN: TCNs, with their residual connections and dilated convolutions, tend to have better stability during training, especially for long sequences. They help in avoiding vanishing or exploding gradients, which are common issues in sequence models. CNN: Standard CNNs may struggle with long sequences due to issues with gradient flow, particularly if the network is very deep.
Interpretability: TCN: Due to the structured dilation and temporal convolution, TCNs can offer more interpretability in terms of which time steps (or features) influence the output, which can be valuable for understanding the model’s decision-making process. CNN: While interpretable, CNNs do not provide the same level of clarity in temporal influence since they are primarily designed for spatial hierarchies.

philipperemy / keras-tcn

Confusion about regression or classification head #257