This layer creates a convolution kernel that is convolved
with the layer input over a single spatial (or temporal) dimension
to produce a tensor of outputs.
If use_bias is True, a bias vector is created and added to the outputs.
If activation is not null, it is applied to the outputs as well.
When using this layer as the first layer in a model, provide an
inputShape argument Array or null.
For example, inputShape would be:
[10, 128] for sequences of 10 vectors of 128-dimensional vectors
[null, 128] for variable-length sequences of 128-dimensional vectors.
1D convolution layer (e.g., temporal convolution).
This layer creates a convolution kernel that is convolved with the layer input over a single spatial (or temporal) dimension to produce a tensor of outputs.
If
use_biasis True, a bias vector is created and added to the outputs.If
activationis notnull, it is applied to the outputs as well.When using this layer as the first layer in a model, provide an
inputShapeargumentArrayornull.For example,
inputShapewould be:[10, 128]for sequences of 10 vectors of 128-dimensional vectors[null, 128]for variable-length sequences of 128-dimensional vectors.