deepctr.layers.normalization module

Author:
Weichen Shen,wcshen1994@163.com
class deepctr.layers.normalization.LayerNormalization(axis=-1, eps=1e-09, **kwargs)[source]
build(input_shape)[source]

Creates the variables of the layer.

call(x)[source]

This is where the layer’s logic lives.

Arguments:
inputs: Input tensor, or list/tuple of input tensors. **kwargs: Additional keyword arguments.
Returns:
A tensor or list/tuple of tensors.
compute_output_shape(input_shape)[source]

Computes the output shape of the layer.

Assumes that the layer will be built to match that input shape provided.

Arguments:
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.
Returns:
An input shape tuple.
get_config()[source]

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).

Returns:
Python dictionary.