Google I/O returns May 18-20! Reserve space and build your schedule Register now

tf_agents.metrics.tf_py_metric.TFPyMetric

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.

Methods

call

View source

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

View source

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

View source

Resets the values being tracked by the metric.

result

View source

Computes and returns a final value for the metric.

tf_summaries

View source

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__

View source

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().