def append_new_subnetwork(self, ensemble_name, ensemble_spec,
subnetwork_builder, iteration_number,
iteration_step, summary, features, mode, labels):
del ensemble_name
del ensemble_spec
del subnetwork_builder
del iteration_number
del iteration_step
del summary
logits = [[.5]]
estimator_spec = self._head.create_estimator_spec(features=features,
mode=mode,
labels=labels,
logits=logits)
return _EnsembleSpec(name="test",
ensemble=None,
architecture=None,
predictions=estimator_spec.predictions,
loss=None,
adanet_loss=.1,
subnetwork_train_op=None,
ensemble_train_op=None,
eval_metric_ops=None,
export_outputs=estimator_spec.export_outputs)
def dummy_ensemble_spec(name,
random_seed=42,
num_subnetworks=1,
bias=0.,
loss=None,
adanet_loss=None,
eval_metric_ops=None,
dict_predictions=False,
export_output_key=None,
train_op=None):
"""Creates a dummy `_EnsembleSpec` instance.
Args:
name: _EnsembleSpec's name.
random_seed: A scalar random seed.
num_subnetworks: The number of fake subnetworks in this ensemble.
bias: Bias value.
loss: Float loss to return. When None, it's picked from a random
distribution.
adanet_loss: Float AdaNet loss to return. When None, it's picked from a
random distribution.
eval_metric_ops: Optional dictionary of metric ops.
dict_predictions: Boolean whether to return predictions as a dictionary of
`Tensor` or just a single float `Tensor`.
export_output_key: An `ExportOutputKeys` for faking export outputs.
train_op: A train op.
Returns:
A dummy `_EnsembleSpec` instance.
"""
if loss is None:
loss = dummy_tensor([], random_seed)
if adanet_loss is None:
adanet_loss = dummy_tensor([], random_seed * 2)
else:
adanet_loss = tf.convert_to_tensor(adanet_loss)
logits = dummy_tensor([], random_seed * 3)
if dict_predictions:
predictions = {
"logits": logits,
"classes": tf.cast(tf.abs(logits), dtype=tf.int64)
}
else:
predictions = logits
weighted_subnetworks = [
WeightedSubnetwork(
name=name,
iteration_number=1,
logits=dummy_tensor([2, 1], random_seed * 4),
weight=dummy_tensor([2, 1], random_seed * 4),
subnetwork=Subnetwork(
last_layer=dummy_tensor([1, 2], random_seed * 4),
logits=dummy_tensor([2, 1], random_seed * 4),
complexity=1.,
persisted_tensors={}))
]
export_outputs = _dummy_export_outputs(export_output_key, logits, predictions)
bias = tf.constant(bias)
return _EnsembleSpec(
name=name,
ensemble=Ensemble(
weighted_subnetworks=weighted_subnetworks * num_subnetworks,
bias=bias,
logits=logits,
),
predictions=predictions,
loss=loss,
adanet_loss=adanet_loss,
eval_metric_ops=eval_metric_ops,
subnetwork_train_op=train_op,
ensemble_train_op=train_op,
export_outputs=export_outputs)