src.model.deeplearn.layer.layer
Classes
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- class src.model.deeplearn.layer.layer.Layer(*args, **kwargs)
- Author:
Alberto M. Esmoris Pena
A layer can be seen as a map \(f\) from an arbitrary tuple of input tensors \(\mathcal{X}_{1}, \ldots, \mathcal{X}_{m}\) to an arbitrary tuple of output tensors \(\mathcal{Y}_{1}, \ldots, \mathcal{Y}_{n}\), i.e., \(f(\mathcal{X}_1, \ldots, \mathcal{X}_m) = (\mathcal{Y}_1, \ldots, \mathcal{Y}_n)\).
The Layer class provides an interface that must be realized by any class that must assume the role of a layer inside a neural network.
- __init__(**kwargs)
Initialize the member attributes of the layer and the internal weights that do not depend on the input dimensionality.
- Parameters:
kwargs – The key-word specification to parametrize the layer.
- build(dim_in)
Logic to build the layer before the first call is executed.
This method can be overloaded by any derived class to either change or extend the logic.
- Parameters:
dim_in – The dimensionality of the input tensor.
- call(inputs, training=False, mask=False)
The layer’s computation logic.
- Parameters:
inputs – The input tensor or the list of input tensors.
training (bool) – True when the layer is called during training, False otherwise.
mask – Boolean mask (one boolean per input timestep).
- Returns:
The output tensor.
- get_config()
Obtain the dictionary specifying how to serialize the layer.
- Returns:
The dictionary with the necessary data to serialize the layer.
- Return type:
dict