def _build(self, # pylint: disable=arguments-differ
features, labels, params=None, config=None):
"""Build the different operation of the model."""
# Pre-process features and labels
features, labels = self._preprocess(features, labels)
results = self._call_graph_fn(features=features, labels=labels)
loss = None
train_op = None
eval_metrics = None
if Modes.is_infer(self.mode):
predictions = self._build_predictions(results=results, features=features, labels=labels)
extra_ops = self._build_extra_ops(results=results, features=features, labels=labels)
else:
_, loss = self._build_loss(results, features, labels)
eval_metrics = self._build_eval_metrics(results, features, labels)
if Modes.is_train(self.mode):
train_op = self._build_train_op(loss)
self._build_summary_op(results=results, features=features, labels=labels)
predictions = self._build_predictions(results=results, features=features, labels=labels)
extra_ops = self._build_extra_ops(results=results, features=features, labels=labels)
track(predictions, tf.GraphKeys.PREDICTIONS)
return EstimatorSpec(mode=self.mode,
predictions=predictions,
loss=loss,
extra_ops=extra_ops,
train_op=train_op,
eval_metric_ops=eval_metrics)
def _build(self, features, labels=None, params=None, config=None):
# Pre-process features and labels
features, labels = self._preprocess(features, labels)
results = self._call_graph_fn(inputs=features)
if not isinstance(results, BridgeSpec):
raise ValueError('`bridge_fn` should return a BridgeSpec.')
loss = None
train_op = None
eval_metrics = None
if Modes.is_infer(self.mode):
predictions = self._build_predictions(
results=results.results, features=features, labels=labels)
else:
losses, loss = self._build_loss(results, features, features)
eval_metrics = self._build_eval_metrics(results.results, features, features)
if Modes.is_train(self.mode):
train_op = self._build_train_op(loss)
self._build_summary_op(results=results.results, features=features, labels=labels)
predictions = self._build_predictions(
results=results.results, features=features, labels=labels)
# We add 'useful' tensors to the graph collection so that we
# can easly find them in our hooks/monitors.
track(predictions, tf.GraphKeys.PREDICTIONS)
return EstimatorSpec(mode=self.mode,
predictions=predictions,
loss=loss,
train_op=train_op,
eval_metric_ops=eval_metrics)
def _build(self, features, labels, params=None, config=None):
"""Build the different operation of the model."""
# Pre-process features and labels
features, labels = self._preprocess(features, labels)
results = self._call_graph_fn(inputs=features)
loss = None
train_op = None
eval_metrics = None
if Modes.is_infer(self.mode):
predictions = self._build_predictions(results=results,
features=features,
labels=labels)
extra_ops = self._build_extra_ops(results=results,
features=features,
labels=labels)
else:
losses, loss = self._build_loss(results, features, labels)
eval_metrics = self._build_eval_metrics(results, features, labels)
if Modes.is_train(self.mode):
train_op = self._build_train_op(loss)
self._build_summary_op(results=results,
features=features,
labels=labels)
predictions = self._build_predictions(results=results,
features=features,
labels=labels)
extra_ops = self._build_extra_ops(results=results,
features=features,
labels=labels)
# We add 'useful' tensors to the graph collection so that we
# can easily find them in our hooks/monitors.
track(predictions, tf.GraphKeys.PREDICTIONS)
return EstimatorSpec(mode=self.mode,
predictions=predictions,
loss=loss,
extra_ops=extra_ops,
train_op=train_op,
eval_metric_ops=eval_metrics)
def _build(self, features, labels=None, params=None, config=None):
# Pre-process features and labels
features, labels = self._preprocess(features, labels)
results = self._call_graph_fn(features=features, labels=labels)
if not isinstance(results, BridgeSpec):
raise ValueError('`bridge_fn` should return a BridgeSpec.')
loss = None
train_op = None
eval_metrics = None
if Modes.is_infer(self.mode):
predictions = self._build_predictions(results=results.results,
features=features,
labels=labels)
else:
_, loss = self._build_loss(results, features, features)
eval_metrics = self._build_eval_metrics(results.results, features,
features)
if Modes.is_train(self.mode):
train_op = self._build_train_op(loss)
self._build_summary_op(results=results.results,
features=features,
labels=labels)
predictions = self._build_predictions(results=results.results,
features=features,
labels=labels)
track(predictions, tf.GraphKeys.PREDICTIONS)
return EstimatorSpec(mode=self.mode,
predictions=predictions,
loss=loss,
train_op=train_op,
eval_metric_ops=eval_metrics)