merlin.models.tf.BinaryOutput#

class merlin.models.tf.BinaryOutput(*args, **kwargs)[source]#

Bases: merlin.models.tf.outputs.base.ModelOutput

Binary-classification prediction block.

Parameters

target (Union[str, Schema], optional) – The name of the target. If a Schema is provided, the target is inferred from the schema.
pre (Optional[Block], optional) – Optional block to transform predictions before computing the binary logits, by default None
post (Optional[Block], optional) – Optional block to transform the binary logits, by default None
name (str, optional) – The name of the task.
task_block (Block, optional) – The block to use for the task.
logits_temperature (float, optional) – Parameter used to reduce model overconfidence, so that logits / T. by default 1.
default_loss (Union[str, tf.keras.losses.Loss], optional) – Default loss to use for binary-classification by ‘binary_crossentropy’
default_metrics_fn (Callable) – A function returning the list of default metrics to use for binary-classification

__init__(target: typing.Optional[typing.Union[str, merlin.schema.schema.ColumnSchema]] = None, pre: typing.Optional[keras.engine.base_layer.Layer] = None, post: typing.Optional[keras.engine.base_layer.Layer] = None, logits_temperature: float = 1.0, name: typing.Optional[str] = None, default_loss: typing.Union[str, keras.losses.Loss] = 'binary_crossentropy', default_metrics_fn: typing.Callable[[], typing.Sequence[keras.metrics.base_metric.Metric]] = <function default_binary_metrics>, **kwargs)[source]#

Methods

`__init__`([target, pre, post, ...])
`add_loss`(losses, **kwargs)	Add loss tensor(s), potentially dependent on layer inputs.
`add_metric`(value[, name])	Adds metric tensor to the layer.
`add_update`(updates)	Add update op(s), potentially dependent on layer inputs.
`add_variable`(args, *kwargs)	Deprecated, do NOT use! Alias for add_weight.
`add_weight`([name, shape, dtype, ...])	Adds a new variable to the layer.
`build`([input_shape])	Builds the PredictionBlock.
`build_from_config`(config)
`call`(inputs[, training, testing])
`compute_mask`(inputs[, mask])	Computes an output mask tensor.
`compute_output_shape`(input_shape)
`compute_output_signature`(input_signature)	Compute the output tensor signature of the layer based on the inputs.
`count_params`()	Count the total number of scalars composing the weights.
`create_default_metrics`()
`finalize_state`()	Finalizes the layers state after updating layer weights.
`from_config`(config)
`get_build_config`()
`get_config`()
`get_input_at`(node_index)	Retrieves the input tensor(s) of a layer at a given node.
`get_input_mask_at`(node_index)	Retrieves the input mask tensor(s) of a layer at a given node.
`get_input_shape_at`(node_index)	Retrieves the input shape(s) of a layer at a given node.
`get_output_at`(node_index)	Retrieves the output tensor(s) of a layer at a given node.
`get_output_mask_at`(node_index)	Retrieves the output mask tensor(s) of a layer at a given node.
`get_output_shape_at`(node_index)	Retrieves the output shape(s) of a layer at a given node.
`get_task_name`(target_name)
`get_weights`()	Returns the current weights of the layer, as NumPy arrays.
`set_weights`(weights)	Sets the weights of the layer, from NumPy arrays.
`with_name_scope`(method)	Decorator to automatically enter the module name scope.

Attributes

`activity_regularizer`	Optional regularizer function for the output of this layer.
`compute_dtype`	The dtype of the layer's computations.
`dtype`	The dtype of the layer weights.
`dtype_policy`	The dtype policy associated with this layer.
`dynamic`	Whether the layer is dynamic (eager-only); set in the constructor.
`inbound_nodes`	Return Functional API nodes upstream of this layer.
`input`	Retrieves the input tensor(s) of a layer.
`input_mask`	Retrieves the input mask tensor(s) of a layer.
`input_shape`	Retrieves the input shape(s) of a layer.
`input_spec`	InputSpec instance(s) describing the input format for this layer.
`losses`	List of losses added using the add_loss() API.
`metrics`	List of metrics added using the add_metric() API.
`name`	Name of the layer (string), set in the constructor.
`name_scope`	Returns a tf.name_scope instance for this class.
`non_trainable_variables`
`non_trainable_weights`	List of all non-trainable weights tracked by this layer.
`outbound_nodes`	Return Functional API nodes downstream of this layer.
`output`	Retrieves the output tensor(s) of a layer.
`output_mask`	Retrieves the output mask tensor(s) of a layer.
`output_shape`	Retrieves the output shape(s) of a layer.
`stateful`
`submodules`	Sequence of all sub-modules.
`supports_masking`	Whether this layer supports computing a mask using compute_mask.
`task_name`
`trainable`
`trainable_variables`
`trainable_weights`	List of all trainable weights tracked by this layer.
`updates`
`variable_dtype`	Alias of Layer.dtype, the dtype of the weights.
`variables`	Returns the list of all layer variables/weights.
`weights`	Returns the list of all layer variables/weights.