def __new__(cls, mode, predictions=None, loss=None, training_op=None,
default_metrics=None, signature_fn=None):
# Assert all ops are from the same graph.
get_graph_from_inputs((predictions, loss, training_op))
# Validate training_op.
if training_op is None:
if mode == ModeKeys.TRAIN:
raise ValueError('Missing training_op.')
elif not isinstance(training_op, ops.Operation):
# TODO(ptucker): Should this be allowed? Consider raising error.
training_op = ops.convert_to_tensor(training_op).op
# Validate loss.
if loss is None:
if mode in (ModeKeys.TRAIN, ModeKeys.EVAL):
raise ValueError('Missing loss.')
else:
loss = ops.convert_to_tensor(loss)
loss_shape = loss.get_shape()
if loss_shape.num_elements() not in (None, 1):
raise ValueError('Loss must be scalar: %s.' % loss)
if not loss_shape.is_compatible_with(tensor_shape.scalar()):
loss = array_ops.reshape(loss, [])
# Validate predictions.
if predictions is None:
if mode == ModeKeys.INFER or mode == ModeKeys.EVAL:
raise ValueError('Missing predictions.')
else:
if isinstance(predictions, dict):
predictions = {
k: contrib_framework.convert_to_tensor_or_sparse_tensor(v)
for k, v in six.iteritems(predictions)
}
else:
predictions = contrib_framework.convert_to_tensor_or_sparse_tensor(
predictions)
# Validate default_metrics
if default_metrics is None:
default_metrics = {}
else:
if not isinstance(default_metrics, dict):
raise ValueError('default_metrics must be a dict.')
for k, v in default_metrics.items():
if not isinstance(v, metric_spec.MetricSpec):
raise ValueError('Metric with key=%s is not MetricSpec' % k)
# validate signature_fn
if signature_fn:
if not callable(signature_fn):
raise ValueError('signature_fn is not callable.')
return super(ModelFnOps, cls).__new__(cls, predictions, loss, training_op,
default_metrics, signature_fn)
def __new__(cls, mode, predictions=None, loss=None, training_op=None,
default_metrics=None, signature_fn=None):
# Assert all ops are from the same graph.
get_graph_from_inputs((predictions, loss, training_op))
# Validate training_op.
if training_op is None:
if mode == ModeKeys.TRAIN:
raise ValueError('Missing training_op.')
elif not isinstance(training_op, ops.Operation):
# TODO(ptucker): Should this be allowed? Consider raising error.
training_op = ops.convert_to_tensor(training_op).op
# Validate loss.
if loss is None:
if mode in (ModeKeys.TRAIN, ModeKeys.EVAL):
raise ValueError('Missing loss.')
else:
loss = ops.convert_to_tensor(loss)
loss_shape = loss.get_shape()
if loss_shape.num_elements() not in (None, 1):
raise ValueError('Loss must be scalar: %s.' % loss)
if not loss_shape.is_compatible_with(tensor_shape.scalar()):
loss = array_ops.reshape(loss, [])
# Validate predictions.
if predictions is None:
if mode == ModeKeys.INFER or mode == ModeKeys.EVAL:
raise ValueError('Missing predictions.')
else:
if isinstance(predictions, dict):
predictions = {
k: contrib_framework.convert_to_tensor_or_sparse_tensor(v)
for k, v in six.iteritems(predictions)
}
else:
predictions = contrib_framework.convert_to_tensor_or_sparse_tensor(
predictions)
# Validate default_metrics
if default_metrics is None:
default_metrics = {}
else:
if not isinstance(default_metrics, dict):
raise ValueError('default_metrics must be a dict.')
for k, v in default_metrics.items():
if not isinstance(v, metric_spec.MetricSpec):
raise ValueError('Metric with key=%s is not MetricSpec' % k)
# validate signature_fn
if signature_fn:
if not callable(signature_fn):
raise ValueError('signature_fn is not callable.')
return super(ModelFnOps, cls).__new__(cls, predictions, loss, training_op,
default_metrics, signature_fn)
def _set_operation(a, b, set_operation, validate_indices=True):
"""Compute set operation of elements in last dimension of `a` and `b`.
All but the last dimension of `a` and `b` must match.
Args:
a: `Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices
must be sorted in row-major order.
b: `Tensor` or `SparseTensor` of the same type as `a`. Must be
`SparseTensor` if `a` is `SparseTensor`. If sparse, indices must be
sorted in row-major order.
set_operation: String indicating set operaiton. See
SetOperationOp::SetOperationFromContext for valid values.
validate_indices: Whether to validate the order and range of sparse indices
in `a` and `b`.
Returns:
A `SparseTensor` with the same rank as `a` and `b`, and all but the last
dimension the same. Elements along the last dimension contain the results
of the set operation.
Raises:
TypeError: If inputs are invalid types.
ValueError: If `a` is sparse and `b` is dense.
"""
a = framework.convert_to_tensor_or_sparse_tensor(a, name="a")
if a.dtype.base_dtype not in _VALID_DTYPES:
raise TypeError("'a' invalid dtype %s." % a.dtype)
b = framework.convert_to_tensor_or_sparse_tensor(b, name="b")
if b.dtype.base_dtype != a.dtype.base_dtype:
raise TypeError("Types don't match, %s vs %s." % (a.dtype, b.dtype))
# pylint: disable=protected-access
if isinstance(a, ops.SparseTensor):
if isinstance(b, ops.SparseTensor):
indices, values, shape = _set_ops.sparse_to_sparse_set_operation(
a.indices, a.values, a.shape, b.indices, b.values, b.shape,
set_operation, validate_indices)
else:
raise ValueError(
"Sparse,Dense is not supported, but Dense,Sparse is. "
"Please flip the order of your inputs.")
elif isinstance(b, ops.SparseTensor):
indices, values, shape = _set_ops.dense_to_sparse_set_operation(
a, b.indices, b.values, b.shape, set_operation, validate_indices)
else:
indices, values, shape = _set_ops.dense_to_dense_set_operation(
a, b, set_operation, validate_indices)
# pylint: enable=protected-access
return ops.SparseTensor(indices, values, shape)
def _call_model_fn(self, features, targets, mode):
"""Calls model function with support of 2, 3 or 4 arguments."""
features, targets = self._feature_engineering_fn(features, targets)
model_fn_args = _get_arguments(self._model_fn)
if 'mode' in model_fn_args:
if 'params' in model_fn_args:
predictions, loss, train_op = self._model_fn(
features, targets, mode=mode, params=self.params)
else:
predictions, loss, train_op = self._model_fn(features,
targets,
mode=mode)
else:
predictions, loss, train_op = self._model_fn(features, targets)
# Validate train_op.
if train_op is None:
if mode == ModeKeys.TRAIN:
raise ValueError('Missing train_op.')
elif not isinstance(train_op, ops.Operation):
train_op = ops.convert_to_tensor(train_op).op
# Validate loss.
if loss is None:
if mode in (ModeKeys.TRAIN, ModeKeys.EVAL):
raise ValueError('Missing loss.')
else:
loss = ops.convert_to_tensor(loss)
loss_shape = loss.get_shape()
if loss_shape.num_elements() not in (None, 1):
raise ValueError('Loss must be scalar: %s.' % loss)
if not loss_shape.is_compatible_with(tensor_shape.scalar()):
loss = array_ops.reshape(loss, [])
# Validate predictions.
if predictions is None:
if mode == ModeKeys.INFER:
raise ValueError('Missing predictions.')
else:
if isinstance(predictions, dict):
predictions = {
k: contrib_framework.convert_to_tensor_or_sparse_tensor(v)
for k, v in six.iteritems(predictions)
}
else:
predictions = contrib_framework.convert_to_tensor_or_sparse_tensor(
predictions)
return predictions, loss, train_op
def _set_operation(a, b, set_operation, validate_indices=True):
"""Compute set operation of elements in last dimension of `a` and `b`.
All but the last dimension of `a` and `b` must match.
Args:
a: `Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices
must be sorted in row-major order.
b: `Tensor` or `SparseTensor` of the same type as `a`. Must be
`SparseTensor` if `a` is `SparseTensor`. If sparse, indices must be
sorted in row-major order.
set_operation: String indicating set operaiton. See
SetOperationOp::SetOperationFromContext for valid values.
validate_indices: Whether to validate the order and range of sparse indices
in `a` and `b`.
Returns:
A `SparseTensor` with the same rank as `a` and `b`, and all but the last
dimension the same. Elements along the last dimension contain the results
of the set operation.
Raises:
TypeError: If inputs are invalid types.
ValueError: If `a` is sparse and `b` is dense.
"""
a = framework.convert_to_tensor_or_sparse_tensor(a, name="a")
if a.dtype.base_dtype not in _VALID_DTYPES:
raise TypeError("'a' invalid dtype %s." % a.dtype)
b = framework.convert_to_tensor_or_sparse_tensor(b, name="b")
if b.dtype.base_dtype != a.dtype.base_dtype:
raise TypeError("Types don't match, %s vs %s." % (a.dtype, b.dtype))
# pylint: disable=protected-access
if isinstance(a, ops.SparseTensor):
if isinstance(b, ops.SparseTensor):
indices, values, shape = _set_ops.sparse_to_sparse_set_operation(
a.indices, a.values, a.shape, b.indices, b.values, b.shape,
set_operation, validate_indices)
else:
raise ValueError("Sparse,Dense is not supported, but Dense,Sparse is. "
"Please flip the order of your inputs.")
elif isinstance(b, ops.SparseTensor):
indices, values, shape = _set_ops.dense_to_sparse_set_operation(
a, b.indices, b.values, b.shape, set_operation, validate_indices)
else:
indices, values, shape = _set_ops.dense_to_dense_set_operation(
a, b, set_operation, validate_indices)
# pylint: enable=protected-access
return ops.SparseTensor(indices, values, shape)
def _call_model_fn(self, features, targets, mode):
"""Calls model function with support of 2, 3 or 4 arguments."""
features, targets = self._feature_engineering_fn(features, targets)
model_fn_args = _get_arguments(self._model_fn)
if 'mode' in model_fn_args:
if 'params' in model_fn_args:
predictions, loss, train_op = self._model_fn(
features, targets, mode=mode, params=self.params)
else:
predictions, loss, train_op = self._model_fn(
features, targets, mode=mode)
else:
predictions, loss, train_op = self._model_fn(features, targets)
# Validate train_op.
if train_op is None:
if mode == ModeKeys.TRAIN:
raise ValueError('Missing train_op.')
elif not isinstance(train_op, ops.Operation):
train_op = ops.convert_to_tensor(train_op).op
# Validate loss.
if loss is None:
if mode in (ModeKeys.TRAIN, ModeKeys.EVAL):
raise ValueError('Missing loss.')
else:
loss = ops.convert_to_tensor(loss)
loss_shape = loss.get_shape()
if loss_shape.num_elements() not in (None, 1):
raise ValueError('Loss must be scalar: %s.' % loss)
if not loss_shape.is_compatible_with(tensor_shape.scalar()):
loss = array_ops.reshape(loss, [])
# Validate predictions.
if predictions is None:
if mode == ModeKeys.INFER:
raise ValueError('Missing predictions.')
else:
if isinstance(predictions, dict):
predictions = {
k: contrib_framework.convert_to_tensor_or_sparse_tensor(v)
for k, v in six.iteritems(predictions)
}
else:
predictions = contrib_framework.convert_to_tensor_or_sparse_tensor(
predictions)
return predictions, loss, train_op
def set_size(a, validate_indices=True):
"""Compute number of unique elements along last dimension of `a`.
Args:
a: `SparseTensor`, with indices sorted in row-major order.
validate_indices: Whether to validate the order and range of sparse indices
in `a`.
Returns:
For `a` ranked `n`, this is a `Tensor` with rank `n-1`, and the same 1st
`n-1` dimensions as `a`. Each value is the number of unique elements in
the corresponding `[0...n-1]` dimension of `a`.
Raises:
TypeError: If `a` is an invalid types.
"""
a = framework.convert_to_tensor_or_sparse_tensor(a, name="a")
if not isinstance(a, ops.SparseTensor):
raise TypeError("Expected `SparseTensor`, got %s." % a)
if a.values.dtype.base_dtype not in _VALID_DTYPES:
raise TypeError("Invalid dtype %s." % a.values.dtype)
# pylint: disable=protected-access
return _set_ops.set_size(a.indices, a.values, a.shape, validate_indices)
def set_size(a, validate_indices=True):
"""Compute number of unique elements along last dimension of `a`.
Args:
a: `SparseTensor`, with indices sorted in row-major order.
validate_indices: Whether to validate the order and range of sparse indices
in `a`.
Returns:
For `a` ranked `n`, this is a `Tensor` with rank `n-1`, and the same 1st
`n-1` dimensions as `a`. Each value is the number of unique elements in
the corresponding `[0...n-1]` dimension of `a`.
Raises:
TypeError: If `a` is an invalid types.
"""
a = framework.convert_to_tensor_or_sparse_tensor(a, name="a")
if not isinstance(a, ops.SparseTensor):
raise TypeError("Expected `SparseTensor`, got %s." % a)
if a.values.dtype.base_dtype not in _VALID_DTYPES:
raise TypeError("Invalid dtype %s." % a.values.dtype)
# pylint: disable=protected-access
return _set_ops.set_size(a.indices, a.values, a.shape, validate_indices)
def __new__(cls,
mode,
predictions=None,
loss=None,
train_op=None,
eval_metric_ops=None,
signature_fn=None,
output_alternatives=None):
"""Creates a validated `ModelFnOps` instance.
For a multi-headed model, the predictions dict here will contain the outputs
of all of the heads. However: at serving time, requests will be made
specifically for one or more heads, and the RPCs used for these requests may
differ by problem type (i.e., regression, classification, other). The
purpose of the output_alternatives dict is to aid in exporting a SavedModel
from which such head-specific queries can be served. These
output_alternatives will be combined with input_alternatives (see
`saved_model_export_utils`) to produce a set of `SignatureDef`s specifying
the valid requests that can be served from this model.
For a single-headed model, it is still adviseable to provide
output_alternatives with a single entry, because this is how the problem
type is communicated for export and serving. If output_alternatives is not
given, the resulting SavedModel will support only one head of unspecified
type.
Args:
mode: One of `ModeKeys`. Specifies if this training, evaluation or
prediction.
predictions: Predictions `Tensor` or dict of `Tensor`.
loss: Training loss `Tensor`.
train_op: Op for the training step.
eval_metric_ops: Dict of metric results keyed by name. The values of the
dict are the results of calling a metric function, such as `Tensor`.
signature_fn: The signature_fn used for exporting.
output_alternatives: a dict of
`{submodel_name: (problem_type, {tensor_name: Tensor})}`, where
`submodel_name` is a submodel identifier that should be consistent
across the pipeline (here likely taken from the name of each `Head`,
for models that use them), `problem_type` is a `ProblemType`,
`tensor_name` is a symbolic name for an output Tensor possibly but not
necessarily taken from `PredictionKey`, and `Tensor` is the
corresponding output Tensor itself.
Returns:
A validated `ModelFnOps` object.
Raises:
ValueError: If validation fails.
"""
# Assert all ops are from the same graph.
get_graph_from_inputs((predictions, loss, train_op))
# Validate train_op.
if train_op is None:
if mode == ModeKeys.TRAIN:
raise ValueError('Missing training_op.')
elif not isinstance(train_op, ops.Operation):
# TODO(ptucker): Should this be allowed? Consider raising error.
train_op = ops.convert_to_tensor(train_op).op
# Validate loss.
if loss is None:
if mode in (ModeKeys.TRAIN, ModeKeys.EVAL):
raise ValueError('Missing loss.')
else:
loss = ops.convert_to_tensor(loss)
loss_shape = loss.get_shape()
if loss_shape.num_elements() not in (None, 1):
raise ValueError('Loss must be scalar: %s.' % loss)
if not loss_shape.is_compatible_with(tensor_shape.scalar()):
loss = array_ops.reshape(loss, [])
# Validate predictions.
if predictions is None:
if mode == ModeKeys.INFER or mode == ModeKeys.EVAL:
raise ValueError('Missing predictions.')
else:
if isinstance(predictions, dict):
predictions = {
k: contrib_framework.convert_to_tensor_or_sparse_tensor(v)
for k, v in six.iteritems(predictions)
}
else:
predictions = contrib_framework.convert_to_tensor_or_sparse_tensor(
predictions)
# Validate eval_metric_ops
if eval_metric_ops is None:
eval_metric_ops = {}
else:
if not isinstance(eval_metric_ops, dict):
raise ValueError('eval_metric_ops must be a dict.')
# validate signature_fn
if signature_fn:
if not callable(signature_fn):
raise ValueError('signature_fn is not callable.')
return super(ModelFnOps, cls).__new__(cls, predictions, loss, train_op,
eval_metric_ops, signature_fn,
output_alternatives)
def __new__(cls,
mode,
predictions=None,
loss=None,
train_op=None,
eval_metric_ops=None,
signature_fn=None):
"""Creates a validated `ModelFnOps` instance.
Args:
mode: One of `ModeKeys`. Specifies if this training, evaluation or
prediction.
predictions: Predictions `Tensor` or dict of `Tensor`.
loss: Training loss `Tensor`.
train_op: Op for the training step.
eval_metric_ops: Dict of metric results keyed by name. The values of the
dict are the results of calling a metric function, such as `Tensor`.
signature_fn: The signature_fn used for exporting.
Returns:
A validated `ModelFnOps` object.
Raises:
ValueError: If validation fails.
"""
# Assert all ops are from the same graph.
get_graph_from_inputs((predictions, loss, train_op))
# Validate train_op.
if train_op is None:
if mode == ModeKeys.TRAIN:
raise ValueError('Missing training_op.')
elif not isinstance(train_op, ops.Operation):
# TODO(ptucker): Should this be allowed? Consider raising error.
train_op = ops.convert_to_tensor(train_op).op
# Validate loss.
if loss is None:
if mode in (ModeKeys.TRAIN, ModeKeys.EVAL):
raise ValueError('Missing loss.')
else:
loss = ops.convert_to_tensor(loss)
loss_shape = loss.get_shape()
if loss_shape.num_elements() not in (None, 1):
raise ValueError('Loss must be scalar: %s.' % loss)
if not loss_shape.is_compatible_with(tensor_shape.scalar()):
loss = array_ops.reshape(loss, [])
# Validate predictions.
if predictions is None:
if mode == ModeKeys.INFER or mode == ModeKeys.EVAL:
raise ValueError('Missing predictions.')
else:
if isinstance(predictions, dict):
predictions = {
k: contrib_framework.convert_to_tensor_or_sparse_tensor(v)
for k, v in six.iteritems(predictions)
}
else:
predictions = contrib_framework.convert_to_tensor_or_sparse_tensor(
predictions)
# Validate eval_metric_ops
if eval_metric_ops is None:
eval_metric_ops = {}
else:
if not isinstance(eval_metric_ops, dict):
raise ValueError('eval_metric_ops must be a dict.')
# validate signature_fn
if signature_fn:
if not callable(signature_fn):
raise ValueError('signature_fn is not callable.')
return super(ModelFnOps, cls).__new__(cls, predictions, loss, train_op,
eval_metric_ops, signature_fn)
def __new__(cls,
mode,
predictions=None,
loss=None,
train_op=None,
eval_metric_ops=None,
output_alternatives=None,
training_chief_hooks=None,
training_hooks=None,
scaffold=None):
"""Creates a validated `ModelFnOps` instance.
For a multi-headed model, the predictions dict here will contain the outputs
of all of the heads. However: at serving time, requests will be made
specifically for one or more heads, and the RPCs used for these requests may
differ by problem type (i.e., regression, classification, other). The
purpose of the output_alternatives dict is to aid in exporting a SavedModel
from which such head-specific queries can be served. These
output_alternatives will be combined with input_alternatives (see
`saved_model_export_utils`) to produce a set of `SignatureDef`s specifying
the valid requests that can be served from this model.
For a single-headed model, it is still adviseable to provide
output_alternatives with a single entry, because this is how the problem
type is communicated for export and serving. If output_alternatives is not
given, the resulting SavedModel will support only one head of unspecified
type.
Args:
mode: One of `ModeKeys`. Specifies if this training, evaluation or
prediction.
predictions: Predictions `Tensor` or dict of `Tensor`.
loss: Training loss `Tensor`.
train_op: Op for the training step.
eval_metric_ops: Dict of metric results keyed by name. The values of the
dict are the results of calling a metric function, such as `Tensor`.
output_alternatives: a dict of
`{submodel_name: (problem_type, {tensor_name: Tensor})}`, where
`submodel_name` is a submodel identifier that should be consistent
across the pipeline (here likely taken from the name of each `Head`,
for models that use them), `problem_type` is a `ProblemType`,
`tensor_name` is a symbolic name for an output Tensor possibly but not
necessarily taken from `PredictionKey`, and `Tensor` is the
corresponding output Tensor itself.
training_chief_hooks: A list of `SessionRunHook` objects that will be
run on the chief worker during training.
training_hooks: A list of `SessionRunHook` objects that will be run on
all workers during training.
scaffold: A `tf.train.Scaffold` object that can be used to set
initialization, saver, and more to be used in training.
Returns:
A validated `ModelFnOps` object.
Raises:
ValueError: If validation fails.
"""
# Assert all ops are from the same graph.
get_graph_from_inputs((predictions, loss, train_op))
# Validate train_op.
if train_op is None:
if mode == ModeKeys.TRAIN:
raise ValueError('Missing training_op.')
elif not isinstance(train_op, ops.Operation):
# TODO(ptucker): Should this be allowed? Consider raising error.
train_op = ops.convert_to_tensor(train_op).op
# Validate loss.
if loss is None:
if mode in (ModeKeys.TRAIN, ModeKeys.EVAL):
raise ValueError('Missing loss.')
else:
loss = ops.convert_to_tensor(loss)
loss_shape = loss.get_shape()
if loss_shape.num_elements() not in (None, 1):
raise ValueError('Loss must be scalar: %s.' % loss)
if not loss_shape.is_compatible_with(tensor_shape.scalar()):
loss = array_ops.reshape(loss, [])
# Validate predictions.
if predictions is None:
if mode == ModeKeys.INFER or mode == ModeKeys.EVAL:
raise ValueError('Missing predictions.')
else:
if isinstance(predictions, dict):
predictions = {
k: contrib_framework.convert_to_tensor_or_sparse_tensor(v)
for k, v in six.iteritems(predictions)
}
else:
predictions = contrib_framework.convert_to_tensor_or_sparse_tensor(
predictions)
# Validate eval_metric_ops
if eval_metric_ops is None:
eval_metric_ops = {}
else:
if not isinstance(eval_metric_ops, dict):
raise ValueError('eval_metric_ops must be a dict.')
# Validate hooks
if training_chief_hooks is None:
training_chief_hooks = []
if training_hooks is None:
training_hooks = []
for hook in training_hooks + training_chief_hooks:
if not isinstance(hook, session_run_hook.SessionRunHook):
raise TypeError('All hooks returned from model_fn must be '
'SessionRunHook instances, got instance of %s: %s' %
(type(hook), hook))
return super(ModelFnOps, cls).__new__(
cls,
predictions=predictions,
loss=loss,
train_op=train_op,
eval_metric_ops=eval_metric_ops,
output_alternatives=output_alternatives,
training_chief_hooks=training_chief_hooks,
training_hooks=training_hooks,
scaffold=scaffold)
def __new__(cls, mode, predictions=None, loss=None, train_op=None,
eval_metric_ops=None, signature_fn=None):
"""Creates a validated `ModelFnOps` instance.
Args:
mode: One of `ModeKeys`. Specifies if this training, evaluation or
prediction.
predictions: Predictions `Tensor` or dict of `Tensor`.
loss: Training loss `Tensor`.
train_op: Op for the training step.
eval_metric_ops: Dict of metric results keyed by name. The values of the
dict are the results of calling a metric function, such as `Tensor`.
signature_fn: The signature_fn used for exporting.
Returns:
A validated `ModelFnOps` object.
Raises:
ValueError: If validation fails.
"""
# Assert all ops are from the same graph.
get_graph_from_inputs((predictions, loss, train_op))
# Validate train_op.
if train_op is None:
if mode == ModeKeys.TRAIN:
raise ValueError('Missing training_op.')
elif not isinstance(train_op, ops.Operation):
# TODO(ptucker): Should this be allowed? Consider raising error.
train_op = ops.convert_to_tensor(train_op).op
# Validate loss.
if loss is None:
if mode in (ModeKeys.TRAIN, ModeKeys.EVAL):
raise ValueError('Missing loss.')
else:
loss = ops.convert_to_tensor(loss)
loss_shape = loss.get_shape()
if loss_shape.num_elements() not in (None, 1):
raise ValueError('Loss must be scalar: %s.' % loss)
if not loss_shape.is_compatible_with(tensor_shape.scalar()):
loss = array_ops.reshape(loss, [])
# Validate predictions.
if predictions is None:
if mode == ModeKeys.INFER or mode == ModeKeys.EVAL:
raise ValueError('Missing predictions.')
else:
if isinstance(predictions, dict):
predictions = {
k: contrib_framework.convert_to_tensor_or_sparse_tensor(v)
for k, v in six.iteritems(predictions)
}
else:
predictions = contrib_framework.convert_to_tensor_or_sparse_tensor(
predictions)
# Validate eval_metric_ops
if eval_metric_ops is None:
eval_metric_ops = {}
else:
if not isinstance(eval_metric_ops, dict):
raise ValueError('eval_metric_ops must be a dict.')
# validate signature_fn
if signature_fn:
if not callable(signature_fn):
raise ValueError('signature_fn is not callable.')
return super(ModelFnOps, cls).__new__(cls, predictions, loss, train_op,
eval_metric_ops, signature_fn)
