- Weichen Shen,email@example.com
Dice(axis=-1, epsilon=1e-09, **kwargs)[source]¶
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 (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.
- 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, training=None, **kwargs)[source]¶
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.
- inputs: Input tensor, or dict/list/tuple of input tensors.
The first positional inputs argument is subject to special rules: - inputs must be explicitly passed. A layer cannot have zeroarguments, and inputs cannot be provided via the default value of a keyword argument.
- NumPy array or Python scalar values in inputs get cast as tensors.
- Keras mask metadata is only collected from inputs.
- Layers are built (build(input_shape) method) using shape info from inputs only.
- input_spec compatibility is only checked against inputs.
- Mixed precision input casting is only applied to inputs. If a layer has tensor arguments in *args or **kwargs, their casting behavior in mixed precision should be handled manually.
- The SavedModel input specification is generated using inputs only.
- Integration with various ecosystem packages like TFMOT, TFLite, TF.js, etc is only supported for inputs and not for tensors in positional and keyword arguments.
- *args: Additional positional arguments. May contain tensors, although
- this is not recommended, for the reasons above.
- **kwargs: Additional keyword arguments. May contain tensors, although
this is not recommended, for the reasons above. The following optional keyword arguments are reserved: - training: Boolean scalar tensor of Python boolean indicatingwhether the call is meant for training or inference.
- mask: Boolean input mask. If the layer’s call() method takes a mask argument, its default value will be set to the mask generated for inputs by the previous layer (if input did come from a layer that generated a corresponding mask, i.e. if it came from a Keras layer with masking support).
- A tensor or list/tuple of tensors.
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.
- 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).
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.
- Python dictionary.