merlin.models.tf.CategoricalOutput

class merlin.models.tf.CategoricalOutput(*args, **kwargs)

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

Categorical output

Parameters

• prediction (Union[Schema, ColumnSchema,) –

  EmbeddingTable, ‘CategoricalTarget’,

  ’EmbeddingTablePrediction’]

  The target feature to predict. To perform weight-tying [1] technique, you should provide the EmbeddingTable or EmbeddingTablePrediction related to the target feature.

• negative_samplers (ItemSamplersType, optional) – List of samplers for negative sampling, by default None

• 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

• logits_temperature (float, optional) – Parameter used to reduce model overconfidence, so that logits / T. by default 1

• name (str, optional) – The name of the task, by default None

• default_loss (Union[str, tf.keras.losses.Loss], optional) – Default loss to use for categorical-classification by default ‘categorical_crossentropy’

• get_default_metrics (Callable, optional) – A function returning the list of default metrics to use for categorical-classification

• References –

• ---------- –

• Hakan Inan ([1]) –

• Khosravi (Khashayar) –

• Richard Socher. 2016. Tying word vectors (and) –

• word classifiers (and) –

• arXiv (1611.01462 (2016)) –

__init__(to_call: Union[merlin.schema.schema.Schema, merlin.schema.schema.ColumnSchema, merlin.models.tf.inputs.embedding.EmbeddingTable, merlin.models.tf.outputs.classification.CategoricalTarget, merlin.models.tf.outputs.classification.EmbeddingTablePrediction], target_name: Optional[str] = None, pre: Optional[keras.engine.base_layer.Layer] = None, post: Optional[keras.engine.base_layer.Layer] = None, logits_temperature: float = 1.0, name: Optional[str] = None, default_loss: Union[str, keras.losses.Loss] = 'categorical_crossentropy', default_metrics_fn: Callable[[], Sequence[keras.metrics.base_metric.Metric]] = <function default_categorical_prediction_metrics>, **kwargs)

Methods

__init__(to_call[, target_name, 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.
to_dataset([gpu])
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.

to_dataset(gpu=True) → merlin.io.dataset.Dataset

get_config()