def _serving_input_receiver_fn():
"""A receiver function to be passed to export_savedmodel."""
placeholders = {}
time_placeholder = array_ops.placeholder(
name=feature_keys.TrainEvalFeatures.TIMES,
dtype=dtypes.int64,
shape=[default_batch_size, default_series_length])
placeholders[
feature_keys.TrainEvalFeatures.TIMES] = time_placeholder
# Values are only necessary when filtering. For prediction the default
# value will be ignored.
placeholders[feature_keys.TrainEvalFeatures.VALUES] = (
array_ops.placeholder_with_default(
name=feature_keys.TrainEvalFeatures.VALUES,
input=array_ops.zeros(shape=[
default_batch_size if default_batch_size else 0,
default_series_length if default_series_length else 0,
self._model.num_features
],
dtype=self._model.dtype),
shape=(default_batch_size, default_series_length,
self._model.num_features)))
if self._model.exogenous_feature_columns:
with ops.Graph().as_default():
# Default placeholders have only an unknown batch dimension. Make them
# in a separate graph, then splice in the series length to the shapes
# and re-create them in the outer graph.
parsed_features = (feature_column.make_parse_example_spec(
self._model.exogenous_feature_columns))
placeholder_features = parsing_ops.parse_example(
serialized=array_ops.placeholder(shape=[None],
dtype=dtypes.string),
features=parsed_features)
exogenous_feature_shapes = {
key: (value.get_shape(), value.dtype)
for key, value in placeholder_features.items()
}
for feature_key, (batch_only_feature_shape, value_dtype) in (
exogenous_feature_shapes.items()):
batch_only_feature_shape = (
batch_only_feature_shape.with_rank_at_least(
1).as_list())
feature_shape = (
[default_batch_size, default_series_length] +
batch_only_feature_shape[1:])
placeholders[feature_key] = array_ops.placeholder(
dtype=value_dtype,
name=feature_key,
shape=feature_shape)
batch_size_tensor = array_ops.shape(time_placeholder)[0]
placeholders.update(
self._model_start_state_placeholders(
batch_size_tensor, static_batch_size=default_batch_size))
return export_lib.ServingInputReceiver(placeholders, placeholders)
def _serving_input_receiver_fn():
"""A receiver function to be passed to export_savedmodel."""
placeholders = {}
placeholders[feature_keys.TrainEvalFeatures.TIMES] = (
array_ops.placeholder(
name=feature_keys.TrainEvalFeatures.TIMES,
dtype=dtypes.int64,
shape=[default_batch_size, default_series_length]))
# Values are only necessary when filtering. For prediction the default
# value will be ignored.
placeholders[feature_keys.TrainEvalFeatures.VALUES] = (
array_ops.placeholder_with_default(
name=feature_keys.TrainEvalFeatures.VALUES,
input=array_ops.zeros(
shape=[
default_batch_size
if default_batch_size else 0, default_series_length
if default_series_length else 0, self._model.num_features
],
dtype=self._model.dtype),
shape=(default_batch_size, default_series_length,
self._model.num_features)))
with ops.Graph().as_default():
# Default placeholders have only an unknown batch dimension. Make them
# in a separate graph, then splice in the series length to the shapes
# and re-create them in the outer graph.
exogenous_feature_shapes = {
key: (value.get_shape(), value.dtype) for key, value
in feature_column.make_place_holder_tensors_for_base_features(
self._model.exogenous_feature_columns).items()}
for feature_key, (batch_only_feature_shape, value_dtype) in (
exogenous_feature_shapes.items()):
batch_only_feature_shape = batch_only_feature_shape.with_rank_at_least(
1).as_list()
feature_shape = ([default_batch_size, default_series_length]
+ batch_only_feature_shape[1:])
placeholders[feature_key] = array_ops.placeholder(
dtype=value_dtype, name=feature_key, shape=feature_shape)
# Models may not know the shape of their state without creating some
# variables/ops. Avoid polluting the default graph by making a new one. We
# use only static metadata from the returned Tensors.
with ops.Graph().as_default():
self._model.initialize_graph()
model_start_state = self._model.get_start_state()
for prefixed_state_name, state_tensor in ts_head_lib.state_to_dictionary(
model_start_state).items():
state_shape_with_batch = tensor_shape.TensorShape(
(default_batch_size,)).concatenate(state_tensor.get_shape())
placeholders[prefixed_state_name] = array_ops.placeholder(
name=prefixed_state_name,
shape=state_shape_with_batch,
dtype=state_tensor.dtype)
return export_lib.ServingInputReceiver(placeholders, placeholders)
def _serving_input_receiver_fn():
"""A receiver function to be passed to export_savedmodel."""
times_column = feature_column.numeric_column(
key=feature_keys.TrainEvalFeatures.TIMES, dtype=dtypes.int64)
values_column = feature_column.numeric_column(
key=feature_keys.TrainEvalFeatures.VALUES,
dtype=values_input_dtype,
shape=(self._model.num_features, ))
parsed_features_no_sequence = (
feature_column.make_parse_example_spec(
list(self._model.exogenous_feature_columns) +
[times_column, values_column]))
parsed_features = {}
for key, feature_spec in parsed_features_no_sequence.items():
if isinstance(feature_spec, parsing_ops.FixedLenFeature):
if key == feature_keys.TrainEvalFeatures.VALUES:
parsed_features[key] = feature_spec._replace(
shape=((values_proto_length, ) +
feature_spec.shape))
else:
parsed_features[key] = feature_spec._replace(
shape=((filtering_length + prediction_length, ) +
feature_spec.shape))
elif feature_spec.dtype == dtypes.string:
parsed_features[key] = parsing_ops.FixedLenFeature(
shape=(filtering_length + prediction_length, ),
dtype=dtypes.string)
else: # VarLenFeature
raise ValueError(
"VarLenFeatures not supported, got %s for key %s" %
(feature_spec, key))
tfexamples = array_ops.placeholder(shape=[default_batch_size],
dtype=dtypes.string,
name="input")
features = parsing_ops.parse_example(serialized=tfexamples,
features=parsed_features)
features[feature_keys.TrainEvalFeatures.TIMES] = array_ops.squeeze(
features[feature_keys.TrainEvalFeatures.TIMES], axis=-1)
features[feature_keys.TrainEvalFeatures.VALUES] = math_ops.cast(
features[feature_keys.TrainEvalFeatures.VALUES],
dtype=self._model.dtype)[:, :filtering_length]
features.update(
self._model_start_state_placeholders(
batch_size_tensor=array_ops.shape(
features[feature_keys.TrainEvalFeatures.TIMES])[0],
static_batch_size=default_batch_size))
return export_lib.ServingInputReceiver(features,
{"examples": tfexamples})
def _serving_input_receiver_fn():
"""A receiver function to be passed to export_savedmodel."""
placeholders = {}
placeholders[feature_keys.TrainEvalFeatures.TIMES] = (
array_ops.placeholder(
name=feature_keys.TrainEvalFeatures.TIMES,
dtype=dtypes.int64,
shape=[default_batch_size, default_series_length]))
# Values are only necessary when filtering. For prediction the default
# value will be ignored.
placeholders[feature_keys.TrainEvalFeatures.VALUES] = (
array_ops.placeholder_with_default(
name=feature_keys.TrainEvalFeatures.VALUES,
input=array_ops.zeros(shape=[
default_batch_size if default_batch_size else 0,
default_series_length if default_series_length else 0,
self._model.num_features
],
dtype=self._model.dtype),
shape=(default_batch_size, default_series_length,
self._model.num_features)))
for feature_key, feature_value in exogenous_features.items():
value_tensor = ops.convert_to_tensor(feature_value)
value_tensor.get_shape().with_rank_at_least(2)
feature_shape = value_tensor.get_shape().as_list()
feature_shape[0] = default_batch_size
feature_shape[1] = default_series_length
placeholders[feature_key] = array_ops.placeholder(
dtype=value_tensor.dtype,
name=feature_key,
shape=feature_shape)
# Models may not know the shape of their state without creating some
# variables/ops. Avoid polluting the default graph by making a new one. We
# use only static metadata from the returned Tensors.
with ops.Graph().as_default():
self._model.initialize_graph()
model_start_state = self._model.get_start_state()
for prefixed_state_name, state_tensor in model_utils.state_to_dictionary(
model_start_state).items():
state_shape_with_batch = tensor_shape.TensorShape(
(default_batch_size, )).concatenate(
state_tensor.get_shape())
placeholders[prefixed_state_name] = array_ops.placeholder(
name=prefixed_state_name,
shape=state_shape_with_batch,
dtype=state_tensor.dtype)
return export_lib.ServingInputReceiver(placeholders, placeholders)
def _input_fn():
with ops.name_scope('inputs'):
x = array_ops.placeholder_with_default(0.0, shape=[], name='x')
y = array_ops.placeholder_with_default(0.0, shape=[], name='y')
label = constant_op.constant(0.0)
features = {'x': x, 'y': y}
if core:
if train:
return features, label
return export_lib.ServingInputReceiver(features=features,
receiver_tensors=features)
else:
if train:
return features, label
return input_fn_utils.InputFnOps(features=features,
labels={},
default_inputs=features)
def _serving_input_receiver_fn():
"""A receiver function to be passed to export_savedmodel."""
placeholders = {}
time_placeholder = array_ops.placeholder(
name=feature_keys.TrainEvalFeatures.TIMES,
dtype=dtypes.int64,
shape=[default_batch_size, default_series_length])
placeholders[feature_keys.TrainEvalFeatures.TIMES] = time_placeholder
# Values are only necessary when filtering. For prediction the default
# value will be ignored.
placeholders[feature_keys.TrainEvalFeatures.VALUES] = (
array_ops.placeholder_with_default(
name=feature_keys.TrainEvalFeatures.VALUES,
input=array_ops.zeros(
shape=[
default_batch_size
if default_batch_size else 0, default_series_length
if default_series_length else 0, self._model.num_features
],
dtype=self._model.dtype),
shape=(default_batch_size, default_series_length,
self._model.num_features)))
if self._model.exogenous_feature_columns:
with ops.Graph().as_default():
# Default placeholders have only an unknown batch dimension. Make them
# in a separate graph, then splice in the series length to the shapes
# and re-create them in the outer graph.
parsed_features = (
feature_column.make_parse_example_spec(
self._model.exogenous_feature_columns))
placeholder_features = parsing_ops.parse_example(
serialized=array_ops.placeholder(
shape=[None], dtype=dtypes.string),
features=parsed_features)
exogenous_feature_shapes = {
key: (value.get_shape(), value.dtype) for key, value
in placeholder_features.items()}
for feature_key, (batch_only_feature_shape, value_dtype) in (
exogenous_feature_shapes.items()):
batch_only_feature_shape = (
batch_only_feature_shape.with_rank_at_least(1).as_list())
feature_shape = ([default_batch_size, default_series_length]
+ batch_only_feature_shape[1:])
placeholders[feature_key] = array_ops.placeholder(
dtype=value_dtype, name=feature_key, shape=feature_shape)
# Models may not know the shape of their state without creating some
# variables/ops. Avoid polluting the default graph by making a new one. We
# use only static metadata from the returned Tensors.
with ops.Graph().as_default():
self._model.initialize_graph()
# Evaluate the initial state as same-dtype "zero" values. These zero
# constants aren't used, but are necessary for feeding to
# placeholder_with_default for the "cold start" case where state is not
# fed to the model.
def _zeros_like_constant(tensor):
return tensor_util.constant_value(array_ops.zeros_like(tensor))
start_state = nest.map_structure(
_zeros_like_constant, self._model.get_start_state())
batch_size_tensor = array_ops.shape(time_placeholder)[0]
for prefixed_state_name, state in ts_head_lib.state_to_dictionary(
start_state).items():
state_shape_with_batch = tensor_shape.TensorShape(
(default_batch_size,)).concatenate(state.shape)
default_state_broadcast = array_ops.tile(
state[None, ...],
multiples=array_ops.concat(
[batch_size_tensor[None],
array_ops.ones(len(state.shape), dtype=dtypes.int32)],
axis=0))
placeholders[prefixed_state_name] = array_ops.placeholder_with_default(
input=default_state_broadcast,
name=prefixed_state_name,
shape=state_shape_with_batch)
return export_lib.ServingInputReceiver(placeholders, placeholders)
