ProbAttention#

class tfts.layers.attention_layer.ProbAttention(*args, **kwargs)[source]#

Bases: Layer

Methods

`add_loss`(loss)	Can be called inside of the call() method to add a scalar loss.
`add_metric`(args, *kwargs)
`add_variable`(shape, initializer[, dtype, ...])	Add a weight variable to the layer.
`add_weight`([shape, initializer, dtype, ...])	Add a weight variable to the layer.
`build`(input_shape)
`build_from_config`(config)	Builds the layer's states with the supplied config dict.
`call`(q, k, v[, mask])	Prob attention
`compute_mask`(inputs, previous_mask)
`compute_output_shape`(input_shape)
`compute_output_spec`(args, *kwargs)
`count_params`()	Count the total number of scalars composing the weights.
`from_config`(config)	Creates an operation from its config.
`get_build_config`()	Returns a dictionary with the layer's input shape.
`get_config`()	Returns the config of the object.
`get_weights`()	Return the values of layer.weights as a list of NumPy arrays.
`load_own_variables`(store)	Loads the state of the layer.
`quantize`(mode[, type_check])
`quantized_build`(input_shape, mode)
`quantized_call`(args, *kwargs)
`rematerialized_call`(layer_call, args, *kwargs)	Enable rematerialization dynamically for layer's call method.
`save_own_variables`(store)	Saves the state of the layer.
`set_weights`(weights)	Sets the values of layer.weights from a list of NumPy arrays.
`stateless_call`(trainable_variables, ...[, ...])	Call the layer without any side effects.
`symbolic_call`(args, *kwargs)

Attributes

`compute_dtype`	The dtype of the computations performed by the layer.
`dtype`	Alias of layer.variable_dtype.
`dtype_policy`
`input`	Retrieves the input tensor(s) of a symbolic operation.
`input_dtype`	The dtype layer inputs should be converted to.
`input_spec`
`losses`	List of scalar losses from add_loss, regularizers and sublayers.
`metrics`	List of all metrics.
`metrics_variables`	List of all metric variables.
`non_trainable_variables`	List of all non-trainable layer state.
`non_trainable_weights`	List of all non-trainable weight variables of the layer.
`output`	Retrieves the output tensor(s) of a layer.
`path`	The path of the layer.
`quantization_mode`	The quantization mode of this layer, None if not quantized.
`supports_masking`	Whether this layer supports computing a mask using compute_mask.
`trainable`	Settable boolean, whether this layer should be trainable or not.
`trainable_variables`	List of all trainable layer state.
`trainable_weights`	List of all trainable weight variables of the layer.
`variable_dtype`	The dtype of the state (weights) of the layer.
`variables`	List of all layer state, including random seeds.
`weights`	List of all weight variables of the layer.

get_config()[source]#

Returns the config of the object.

An object config is a Python dictionary (serializable) containing the information needed to re-instantiate it.