def execute(self, result_object_schema_type, result_object_schema_version):
# Create Result object
results_object = MLObject()
results_object.set_type(
schema_type=result_object_schema_type,
schema_version=result_object_schema_version,
)
# Mocked up results
return_dict = {
"training_execution_id": uuid.uuid4(),
"accuracy": float(f"{randrange(93000,99999)/100000}"),
"global_step": int(f"{randrange(50,150) * 100}"),
"loss": float(f"{randrange(10000,99999)/1000000}")
}
results_object.training_execution_id = return_dict[
"training_execution_id"]
results_object.accuracy = return_dict["accuracy"]
results_object.global_step = return_dict["global_step"]
results_object.loss = return_dict["loss"]
return results_object
def execute(self, result_object_schema_type, result_object_schema_version):
# Create Result object
results_object = MLObject()
results_object.set_type(
schema_type=result_object_schema_type,
schema_version=result_object_schema_version,
)
# Mocked up results
return_dict = {
"training_execution_id": str(uuid.uuid4()),
"accuracy": float(random.randrange(0, 100) / 100),
"global_step": 10**random.randrange(2, 4),
"loss": float(random.randrange(1000, 9999) / 1000),
}
results_object.training_execution_id = return_dict[
"training_execution_id"]
results_object.accuracy = return_dict["accuracy"]
results_object.global_step = return_dict["global_step"]
results_object.loss = return_dict["loss"]
errors = results_object.validate() # noqa
return results_object