tfma_eval.py

import tensorflow_model_analysis as tfma

# model.compile(..., metrics=[...]), etc) will be computed
# automatically.
metrics=[
    tfma.MetricConfig(class_name='ExampleCount')
],
# To add validation thresholds for metrics saved with the model,
# add them keyed by metric name to the thresholds map.
thresholds = {
    "binary_accuracy": tfma.MetricThreshold(
        value_threshold=tfma.GenericValueThreshold(
            lower_bound={'value': 0.5}),
        change_threshold=tfma.GenericChangeThreshold(
           direction=tfma.MetricDirection.HIGHER_IS_BETTER,
           absolute={'value': -1e-10}))
}