deepctr.layers.normalization module

Author:

Weichen Shen,weichenswc@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 (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.

Args:

input_shape: Instance of TensorShape, or list of instances of

TensorShape if the layer expects a list of inputs (one instance per input).

call(inputs)

This is where the layer’s logic lives.

Note here that call() method in tf.keras is little bit different from keras API. In keras API, you can pass support masking for layers as additional arguments. Whereas tf.keras has compute_mask() method to support masking.

Args:

inputs: Input tensor, or list/tuple of input tensors. *args: Additional positional arguments. Currently unused. **kwargs: Additional keyword arguments. Currently unused.

Returns:

A tensor or list/tuple of tensors.

compute_output_shape(input_shape)

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.

Args:

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).

Note that get_config() does not guarantee to return a fresh copy of dict every time it is called. The callers should make a copy of the returned dict if they want to modify it.

Returns:

Python dictionary.