Sequence input layer

Minimum sequence length of input data, specified as a positive integer. When training or making predictions with the network, if the input data has fewer than MinLength time steps, then the software throws an error.

When you create a network that downsamples data in the time dimension, you must take care that the network supports your training data and any data for prediction. Some deep learning layers require that the input has a minimum sequence length. For example, a 1-D convolution layer requires that the input has at least as many time steps as the filter size.

As time series of sequence data propagates through a network, the sequence length can change. For example, downsampling operations such as 1-D convolutions can output data with fewer time steps than its input. This means that downsampling operations can cause later layers in the network to throw an error because the data has a shorter sequence length than the minimum length required by the layer.

When you train or assemble a network, the software automatically checks that sequences of length 1 can propagate through the network. Some networks might not support sequences of length 1, but can successfully propagate sequences of longer lengths. To check that a network supports propagating your training and expected prediction data, set the MinLength property to a value less than or equal to the minimum length of your data and the expected minimum length of your prediction data.

Tip

To prevent convolution and pooling layers from changing the size of the data, set the Padding option of the layer to "same" or "causal".

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64