- Weichen Shen,firstname.lastname@example.org
LayerNormalization(axis=-1, eps=1e-09, center=True, scale=True, **kwargs)¶
Creates the variables of the layer (optional, for subclass implementers).
This is a method that implementers of subclasses of Layer or Model can override if they need a state-creation step in-between layer instantiation and layer call.
This is typically used to create the weights of Layer subclasses.
- input_shape: Instance of TensorShape, or list of instances of
- TensorShape if the layer expects a list of inputs (one instance per input).
This is where the layer’s logic lives.
- inputs: Input tensor, or list/tuple of input tensors. **kwargs: Additional keyword arguments.
- A tensor or list/tuple of tensors.
Computes the output shape of the layer.
If the layer has not been built, this method will call build on the layer. This assumes that the layer will later be used with inputs that match the input shape provided here.
- input_shape: Shape tuple (tuple of integers)
- or list of shape tuples (one per output tensor of the layer). Shape tuples can include None for free dimensions, instead of an integer.
- An input shape tuple.
Returns the config of the layer.
A layer config is a Python dictionary (serializable) containing the configuration of a layer. The same layer can be reinstantiated later (without its trained weights) from this configuration.
The config of a layer does not include connectivity information, nor the layer class name. These are handled by Network (one layer of abstraction above).
- Python dictionary.