deepctr.layers.activation module

Author:

Weichen Shen,wcshen1994@163.com

class deepctr.layers.activation.Dice(axis=-1, epsilon=1e-09, **kwargs)

The Data Adaptive Activation Function in DIN,which can be viewed as a generalization of PReLu and can adaptively adjust the rectified point according to distribution of input data.

Input shape

• Arbitrary. Use the keyword argument input_shape (tuple of integers, does not include the samples axis) when using this layer as the first layer in a model.

Output shape

• Same shape as the input.

Arguments

• axis : Integer, the axis that should be used to compute data distribution (typically the features axis).
• epsilon : Small float added to variance to avoid dividing by zero.

References

• [Zhou G, Zhu X, Song C, et al. Deep interest network for click-through rate prediction[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 2018: 1059-1068.](https://arxiv.org/pdf/1706.06978.pdf)

build(input_shape)

Creates the variables of the layer.

call(inputs, training=None, **kwargs)

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.