def _model_fn(features, labels, params, mode, config):
"""model_fn implementation."""
if mode == tf.estimator.ModeKeys.TRAIN:
self._register_word_counters(features, labels)
features_shards = dispatcher.shard(features)
labels_shards = dispatcher.shard(labels)
with tf.variable_scope(self.name,
initializer=self._initializer(params)):
losses_shards = dispatcher(_loss_op, features_shards,
labels_shards, params, mode,
config)
loss = _extract_loss(losses_shards)
train_op = optimize(loss, params)
return tf.estimator.EstimatorSpec(mode,
loss=loss,
train_op=train_op)
elif mode == tf.estimator.ModeKeys.EVAL:
with tf.variable_scope(self.name):
logits, predictions = self._build(features,
labels,
params,
mode,
config=config)
loss = self._compute_loss(features, labels, logits, params,
mode)
loss = _extract_loss(loss)
eval_metric_ops = self._compute_metrics(
features, labels, predictions)
if predictions is not None:
# Register predictions in a collection so that hooks can easily fetch them.
add_dict_to_collection("predictions", predictions)
return tf.estimator.EstimatorSpec(
mode, loss=loss, eval_metric_ops=eval_metric_ops)
elif mode == tf.estimator.ModeKeys.PREDICT:
with tf.variable_scope(self.name):
_, predictions = self._build(features,
labels,
params,
mode,
config=config)
export_outputs = {}
export_outputs[tf.saved_model.signature_constants.
DEFAULT_SERVING_SIGNATURE_DEF_KEY] = (
tf.estimator.export.PredictOutput(
predictions))
return tf.estimator.EstimatorSpec(
mode,
predictions=predictions,
export_outputs=export_outputs)
else:
raise RuntimeError("Invalid mode")
def _model_fn(features, labels, params, mode, config):
"""model_fn implementation."""
if mode == tf.estimator.ModeKeys.TRAIN:
counters = self._register_word_counters(features, labels)
counters_hook = hooks.CountersHook(
every_n_steps=config.save_summary_steps,
output_dir=config.model_dir,
counters=counters)
features_shards = dispatcher.shard(features)
labels_shards = dispatcher.shard(labels)
with tf.variable_scope(self.name, initializer=self._initializer(params)):
losses_shards = dispatcher(
_loss_op, features_shards, labels_shards, params, mode, config)
loss = _extract_loss(losses_shards)
train_op = optimize(loss, params, mixed_precision=(self.dtype == tf.float16))
return tf.estimator.EstimatorSpec(
mode,
loss=loss,
train_op=train_op,
training_hooks=[counters_hook])
elif mode == tf.estimator.ModeKeys.EVAL:
with tf.variable_scope(self.name):
logits, predictions = self._build(features, labels, params, mode, config=config)
loss = self._compute_loss(features, labels, logits, params, mode)
loss = _extract_loss(loss)
eval_metric_ops = self._compute_metrics(features, labels, predictions)
evaluation_hooks = []
if predictions is not None and eval_prediction_hooks_fn is not None:
evaluation_hooks.extend(eval_prediction_hooks_fn(predictions))
return tf.estimator.EstimatorSpec(
mode,
loss=loss,
eval_metric_ops=eval_metric_ops,
evaluation_hooks=evaluation_hooks)
elif mode == tf.estimator.ModeKeys.PREDICT:
with tf.variable_scope(self.name):
_, predictions = self._build(features, labels, params, mode, config=config)
export_outputs = {}
export_outputs[tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY] = (
tf.estimator.export.PredictOutput(predictions))
return tf.estimator.EstimatorSpec(
mode,
predictions=predictions,
export_outputs=export_outputs)
else:
raise RuntimeError("Invalid mode")
def __call__(self, features, labels, params, mode, config):
"""Creates the model.
See Also:
``tf.estimator.Estimator`` 's ``model_fn`` argument for more details about
arguments and the returned value.
"""
if mode == tf.estimator.ModeKeys.TRAIN:
self._register_word_counters(features, labels)
with tf.variable_scope(
self.name,
initializer=self._initializer(params)) as model_scope:
outputs, predictions = self._build(features, labels, params, mode,
config)
if predictions is not None:
# Register predictions in a collection so that hooks can easily fetch them.
add_dict_to_collection("predictions", predictions)
if mode != tf.estimator.ModeKeys.PREDICT:
with tf.variable_scope(model_scope):
loss = self._compute_loss(features, labels, outputs, params,
mode)
if isinstance(loss, tuple):
loss, display_loss = loss
else:
display_loss = loss
tf.summary.scalar("loss", display_loss)
if mode == tf.estimator.ModeKeys.TRAIN:
train_op = optimize(loss, params)
return tf.estimator.EstimatorSpec(mode,
loss=loss,
train_op=train_op)
else:
eval_metric_ops = self._compute_metrics(
features, labels, predictions)
return tf.estimator.EstimatorSpec(
mode, loss=loss, eval_metric_ops=eval_metric_ops)
else:
export_outputs = {}
export_outputs[tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY] = \
tf.estimator.export.PredictOutput(predictions)
return tf.estimator.EstimatorSpec(mode,
predictions=predictions,
export_outputs=export_outputs)