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Wraps a python metric as a TF metric.

Inherits From: TFStepMetric

py_metric A batched python metric to wrap.
name Name of the metric.
dtype Data type of the metric.



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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.

trajectory A tf_agents.trajectory.Trajectory.

The arguments, for easy chaining.


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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.)

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.


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Resets the values being tracked by the metric.


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Computes and returns a final value for the metric.


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Generates summaries against train_step and all step_metrics.

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.

A list of summaries.


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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.


**kwargs A mini-batch of inputs to the Metric, passed on to call().