deepctr.layers.normalization module

Author:

Weichen Shen,wcshen1994@163.com

class deepctr.layers.normalization.LayerNormalization(axis=-1, eps=1e-09, center=True, scale=True, **kwargs)

build(input_shape)

Creates the variables of the layer.

call(inputs)

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)

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()

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.