merlin.models.tf.AvgPrecisionAt
-
class
merlin.models.tf.
AvgPrecisionAt
(*args, **kwargs)[source] Bases:
merlin.models.tf.metrics.topk.TopkMetric
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 (optional, for subclass implementers).
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_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.