RowwiseMult

latent.layers.RowwiseMult

Performs row-wise multiplication between input vectors.

__init__(self, name=None) special

Parameters:

Name	Type	Description	Default
name	str	String indicating the name of the layer.	None

call(self, 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.

Parameters:

Name	Type	Description	Default
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 zero arguments, 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.	required
*args		Additional positional arguments. May contain tensors, although this is not recommended, for the reasons above.	required
**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 indicating whether 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).	required

Returns:

Type	Description
	A tensor or list/tuple of tensors.