merlin.models.tf.PredictionTask

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

Bases: keras.engine.base_layer.Layer, merlin.models.tf.core.base.ContextMixin

Base-class for prediction tasks.

Parameters
  • target_name (Optional[str], optional) – Label name, by default None

  • task_name (Optional[str], optional) – Task name, by default None

  • metrics (Optional[MetricOrMetrics], optional) – List of Keras metrics to be evaluated, by default None

  • pre (Optional[Block], optional) – Optional block to transform predictions before computing loss and metrics, by default None

  • pre_eval_topk (Optional[Block], optional) – Optional block to apply additional transform predictions before computing top-k evaluation loss and metrics, by default None

  • task_block (Optional[Layer], optional) – Optional block to apply to inputs before computing predictions, by default None

  • prediction_metrics (Optional[List[tf.keras.metrics.Metric]], optional) – List of Keras metrics used to summarize the predictions, by default None

  • label_metrics (Optional[List[tf.keras.metrics.Metric]], optional) – List of Keras metrics used to summarize the labels, by default None

  • compute_train_metrics (Optional[bool], optional) – Enable computing metrics during training, by default True

  • name (Optional[Text], optional) – Task name, by default None

__init__(target_name: Optional[str] = None, task_name: Optional[str] = None, pre: Optional[merlin.models.tf.core.base.Block] = None, pre_eval_topk: Optional[merlin.models.tf.core.base.Block] = None, task_block: Optional[keras.engine.base_layer.Layer] = None, name: Optional[str] = None, **kwargs)None[source]

Methods

__init__([target_name, task_name, pre, …])

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[, features_shape])

build_task(input_shape, schema, body, **kwargs)

call(inputs, *args, **kwargs)

This is where the layer’s logic lives.

child_name(name)

compute_mask(inputs[, mask])

Computes an output mask tensor.

compute_output_shape(input_shape)

Computes the output shape of the layer.

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.

finalize_state()

Finalizes the layers state after updating layer weights.

from_config(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_weights()

Returns the current weights of the layer, as NumPy arrays.

pre_call(inputs, **kwargs)

Apply PredictionTask to inputs to get predictions scores

pre_loss(outputs, **kwargs)

Apply call_outputs method of pre block to transform predictions and targets before computing loss and metrics.

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.

context

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.

pre_eval_topk

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.

property pre_eval_topk
pre_call(inputs: Union[tensorflow.python.framework.ops.Tensor, Dict[str, tensorflow.python.framework.ops.Tensor]], **kwargs)tensorflow.python.framework.ops.Tensor[source]

Apply PredictionTask to inputs to get predictions scores

Parameters

inputs (TensorOrTabularData) – inputs of the prediction task

Returns

Return type

tf.Tensor

pre_loss(outputs: merlin.models.tf.core.base.PredictionOutput, **kwargs)merlin.models.tf.core.base.PredictionOutput[source]

Apply call_outputs method of pre block to transform predictions and targets before computing loss and metrics.

Parameters

outputs (PredictionOutput) – The named tuple containing predictions and targets tensors

Returns

The named tuple containing transformed predictions and targets tensors

Return type

PredictionOutput

build(input_shape, features_shape=None)[source]
build_task(input_shape, schema: merlin.schema.schema.Schema, body: merlin.models.tf.core.base.Block, **kwargs)[source]
property task_name
child_name(name)[source]
classmethod from_config(config)[source]
get_config()[source]