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A metric class for metrics that emit multiple values.
Inherits From: TFStepMetric
tf_agents.metrics.tf_metric.TFHistogramStepMetric(
name, prefix='Metrics'
)
The only difference between TFSTepMetric and TFHistogramStepMetric is that
the latter uses histogram summaries instead of scalar summaries.
Methods
call
call(
*args, **kwargs
)
Accumulates statistics for the metric. Users should use call instead.
| Args | |
|---|---|
*args
|
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**kwargs
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A mini-batch of inputs to the Metric, as passed to __call__().
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init_variables
init_variables()
Initializes this Metric's variables.
Should be called after variables are created in the first execution
of __call__(). If using graph execution, the return value should be
run() in a session before running the op returned by __call__().
(See example above.)
| Returns | |
|---|---|
| If using graph execution, this returns an op to perform the initialization. Under eager execution, the variables are reset to their initial values as a side effect and this function returns None. |
reset
reset()
Resets the values being tracked by the metric.
result
result()
Computes and returns a final value for the metric.
tf_summaries
tf_summaries(
train_step=None, step_metrics=()
)
Generates histogram summaries against train_step and all step_metrics.
| Args | |
|---|---|
train_step
|
(Optional) Step counter for training iterations. If None, no metric is generated against the global step. |
step_metrics
|
(Optional) Iterable of step metrics to generate summaries against. |
| Returns | |
|---|---|
| A list of histogram summaries. |
__call__
__call__(
*args, **kwargs
)
Returns op to execute to update this metric for these inputs.
Returns None if eager execution is enabled. Returns a graph-mode function if graph execution is enabled.
| Args | |
|---|---|
*args
|
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**kwargs
|
A mini-batch of inputs to the Metric, passed on to call().
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