merlin.models.tf.AvgPrecisionAt

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

Bases: merlin.models.tf.metrics.topk.TopkMetric

__init__(k=10, pre_sorted=False, name='map_at', **kwargs)[source]

Methods

__init__([k, pre_sorted, name])

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, aggregation, …])

Adds state variable.

build(input_shape)

Creates the variables of the layer (for subclass implementers).

build_from_config(config)

call(inputs, *args, **kwargs)

This is where the layer’s logic lives.

check_cast_inputs(labels, predictions)

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_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_weights()

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

merge_state(metrics)

Merges the state from one or more metrics.

reset_state()

Resets all of the metric state variables.

reset_states()

result()

set_weights(weights)

Sets the weights of the layer, from NumPy arrays.

update_state(y_true, y_pred[, sample_weight])

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

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.

label_relevant_counts

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

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_sorted

stateful

submodules

Sequence of all sub-modules.

supports_masking

Whether this layer supports computing a mask using compute_mask.

trainable

trainable_variables

trainable_weights

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.