- Weichen Shen,email@example.com
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.
- 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.
- [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)
Creates the variables of the layer.
call(inputs, training=None, **kwargs)¶
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.
Assumes that the layer will be built to match that input shape provided.
- 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.