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Wraps a python metric as a TF metric.
Inherits From: TFStepMetric
tf_agents.metrics.tf_py_metric.TFPyMetric(
py_metric, name=None, dtype=tf.float32
)
Args | |
|---|---|
py_metric
|
A batched python metric to wrap. |
name
|
Name of the metric. |
dtype
|
Data type of the metric. |
Methods
call
call(
trajectory
)
Update the value of the metric using trajectory.
The trajectory can be either batched or un-batched depending on the expected inputs for the py_metric being wrapped.
| Args | |
|---|---|
trajectory
|
A tf_agents.trajectory.Trajectory. |
| Returns | |
|---|---|
| The arguments, for easy chaining. |
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 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 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
|
|
**kwargs
|
A mini-batch of inputs to the Metric, passed on to call().
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