Python tensorflow.python.saved_model.signature_constants.CLASSIFY_METHOD_NAME Examples
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Example #1
| Source File: signature_def_utils_impl.py From lambda-packs with MIT License | 5 votes |
|
def classification_signature_def(examples, classes, scores):
"""Creates classification signature from given examples and predictions.
Args:
examples: `Tensor`.
classes: `Tensor`.
scores: `Tensor`.
Returns:
A classification-flavored signature_def.
Raises:
ValueError: If examples is `None`.
"""
if examples is None:
raise ValueError('examples cannot be None for classification.')
if classes is None and scores is None:
raise ValueError('classes and scores cannot both be None for '
'classification.')
input_tensor_info = utils.build_tensor_info(examples)
signature_inputs = {signature_constants.CLASSIFY_INPUTS: input_tensor_info}
signature_outputs = {}
if classes is not None:
classes_tensor_info = utils.build_tensor_info(classes)
signature_outputs[signature_constants.CLASSIFY_OUTPUT_CLASSES] = (
classes_tensor_info)
if scores is not None:
scores_tensor_info = utils.build_tensor_info(scores)
signature_outputs[signature_constants.CLASSIFY_OUTPUT_SCORES] = (
scores_tensor_info)
signature_def = build_signature_def(
signature_inputs, signature_outputs,
signature_constants.CLASSIFY_METHOD_NAME)
return signature_def
Example #2
| Source File: signature_def_utils_test.py From auto-alt-text-lambda-api with MIT License | 5 votes |
|
def testClassificationSignatureDef(self):
input1 = constant_op.constant("a", name="input-1")
output1 = constant_op.constant("b", name="output-1")
output2 = constant_op.constant("c", name="output-2")
signature_def = signature_def_utils.classification_signature_def(input1,
output1,
output2)
self.assertEqual(signature_constants.CLASSIFY_METHOD_NAME,
signature_def.method_name)
# Check inputs in signature def.
self.assertEqual(1, len(signature_def.inputs))
x_tensor_info_actual = (
signature_def.inputs[signature_constants.CLASSIFY_INPUTS])
self.assertEqual("input-1:0", x_tensor_info_actual.name)
self.assertEqual(types_pb2.DT_STRING, x_tensor_info_actual.dtype)
self.assertEqual(0, len(x_tensor_info_actual.tensor_shape.dim))
# Check outputs in signature def.
self.assertEqual(2, len(signature_def.outputs))
classes_tensor_info_actual = (
signature_def.outputs[signature_constants.CLASSIFY_OUTPUT_CLASSES])
self.assertEqual("output-1:0", classes_tensor_info_actual.name)
self.assertEqual(types_pb2.DT_STRING, classes_tensor_info_actual.dtype)
self.assertEqual(0, len(classes_tensor_info_actual.tensor_shape.dim))
scores_tensor_info_actual = (
signature_def.outputs[signature_constants.CLASSIFY_OUTPUT_SCORES])
self.assertEqual("output-2:0", scores_tensor_info_actual.name)
self.assertEqual(types_pb2.DT_STRING, scores_tensor_info_actual.dtype)
self.assertEqual(0, len(scores_tensor_info_actual.tensor_shape.dim))
Example #3
| Source File: signature_def_utils_impl.py From auto-alt-text-lambda-api with MIT License | 5 votes |
|
def classification_signature_def(examples, classes, scores):
"""Creates classification signature from given examples and predictions.
Args:
examples: `Tensor`.
classes: `Tensor`.
scores: `Tensor`.
Returns:
A classification-flavored signature_def.
Raises:
ValueError: If examples is `None`.
"""
if examples is None:
raise ValueError('examples cannot be None for classification.')
if classes is None and scores is None:
raise ValueError('classes and scores cannot both be None for '
'classification.')
input_tensor_info = utils.build_tensor_info(examples)
signature_inputs = {signature_constants.CLASSIFY_INPUTS: input_tensor_info}
signature_outputs = {}
if classes is not None:
classes_tensor_info = utils.build_tensor_info(classes)
signature_outputs[signature_constants.CLASSIFY_OUTPUT_CLASSES] = (
classes_tensor_info)
if scores is not None:
scores_tensor_info = utils.build_tensor_info(scores)
signature_outputs[signature_constants.CLASSIFY_OUTPUT_SCORES] = (
scores_tensor_info)
signature_def = build_signature_def(
signature_inputs, signature_outputs,
signature_constants.CLASSIFY_METHOD_NAME)
return signature_def
Example #4
| Source File: bundle_shim_test.py From auto-alt-text-lambda-api with MIT License | 5 votes |
|
def testConvertDefaultSignatureClassificationToSignatureDef(self):
signatures_proto = manifest_pb2.Signatures()
classification_signature = manifest_pb2.ClassificationSignature()
classification_signature.input.CopyFrom(
manifest_pb2.TensorBinding(
tensor_name=signature_constants.CLASSIFY_INPUTS))
classification_signature.classes.CopyFrom(
manifest_pb2.TensorBinding(
tensor_name=signature_constants.CLASSIFY_OUTPUT_CLASSES))
classification_signature.scores.CopyFrom(
manifest_pb2.TensorBinding(
tensor_name=signature_constants.CLASSIFY_OUTPUT_SCORES))
signatures_proto.default_signature.classification_signature.CopyFrom(
classification_signature)
signatures_proto.default_signature.classification_signature.CopyFrom(
classification_signature)
signature_def = bundle_shim._convert_default_signature_to_signature_def(
signatures_proto)
# Validate classification signature correctly copied over.
self.assertEqual(signature_def.method_name,
signature_constants.CLASSIFY_METHOD_NAME)
self.assertEqual(len(signature_def.inputs), 1)
self.assertEqual(len(signature_def.outputs), 2)
self.assertProtoEquals(
signature_def.inputs[signature_constants.CLASSIFY_INPUTS],
meta_graph_pb2.TensorInfo(name=signature_constants.CLASSIFY_INPUTS))
self.assertProtoEquals(
signature_def.outputs[signature_constants.CLASSIFY_OUTPUT_SCORES],
meta_graph_pb2.TensorInfo(
name=signature_constants.CLASSIFY_OUTPUT_SCORES))
self.assertProtoEquals(
signature_def.outputs[signature_constants.CLASSIFY_OUTPUT_CLASSES],
meta_graph_pb2.TensorInfo(
name=signature_constants.CLASSIFY_OUTPUT_CLASSES))
Example #5
| Source File: bundle_shim.py From auto-alt-text-lambda-api with MIT License | 5 votes |
|
def _convert_default_signature_to_signature_def(signatures):
"""Convert default signature to object of type SignatureDef.
Args:
signatures: object of type manifest_pb2.Signatures()
Returns:
object of type SignatureDef which contains a converted version of default
signature from input signatures object
Raises:
RuntimeError: if default signature type is not classification or regression.
"""
default_signature = signatures.default_signature
signature_def = meta_graph_pb2.SignatureDef()
if default_signature.WhichOneof("type") == "regression_signature":
regression_signature = default_signature.regression_signature
signature_def.method_name = signature_constants.REGRESS_METHOD_NAME
_add_input_to_signature_def(regression_signature.input.tensor_name,
signature_constants.REGRESS_INPUTS,
signature_def)
_add_output_to_signature_def(regression_signature.output.tensor_name,
signature_constants.REGRESS_OUTPUTS,
signature_def)
elif default_signature.WhichOneof("type") == "classification_signature":
classification_signature = default_signature.classification_signature
signature_def.method_name = signature_constants.CLASSIFY_METHOD_NAME
_add_input_to_signature_def(classification_signature.input.tensor_name,
signature_constants.CLASSIFY_INPUTS,
signature_def)
_add_output_to_signature_def(classification_signature.classes.tensor_name,
signature_constants.CLASSIFY_OUTPUT_CLASSES,
signature_def)
_add_output_to_signature_def(classification_signature.scores.tensor_name,
signature_constants.CLASSIFY_OUTPUT_SCORES,
signature_def)
else:
raise RuntimeError("Only classification and regression default signatures "
"are supported for up-conversion. %s is not "
"supported" % default_signature.WhichOneof("type"))
return signature_def
Example #6
| Source File: signature_def_utils_impl.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 5 votes |
|
def _is_valid_classification_signature(signature_def):
"""Determine whether the argument is a servable 'classify' SignatureDef."""
if signature_def.method_name != signature_constants.CLASSIFY_METHOD_NAME:
return False
if (set(signature_def.inputs.keys())
!= set([signature_constants.CLASSIFY_INPUTS])):
return False
if (signature_def.inputs[signature_constants.CLASSIFY_INPUTS].dtype !=
types_pb2.DT_STRING):
return False
allowed_outputs = set([signature_constants.CLASSIFY_OUTPUT_CLASSES,
signature_constants.CLASSIFY_OUTPUT_SCORES])
if not signature_def.outputs.keys():
return False
if set(signature_def.outputs.keys()) - allowed_outputs:
return False
if (signature_constants.CLASSIFY_OUTPUT_CLASSES in signature_def.outputs
and
signature_def.outputs[signature_constants.CLASSIFY_OUTPUT_CLASSES].dtype
!= types_pb2.DT_STRING):
return False
if (signature_constants.CLASSIFY_OUTPUT_SCORES in signature_def.outputs
and
signature_def.outputs[signature_constants.CLASSIFY_OUTPUT_SCORES].dtype !=
types_pb2.DT_FLOAT):
return False
return True
Example #7
| Source File: signature_def_utils_test.py From keras-lambda with MIT License | 5 votes |
|
def testClassificationSignatureDef(self):
input1 = constant_op.constant("a", name="input-1")
output1 = constant_op.constant("b", name="output-1")
output2 = constant_op.constant("c", name="output-2")
signature_def = signature_def_utils.classification_signature_def(input1,
output1,
output2)
self.assertEqual(signature_constants.CLASSIFY_METHOD_NAME,
signature_def.method_name)
# Check inputs in signature def.
self.assertEqual(1, len(signature_def.inputs))
x_tensor_info_actual = (
signature_def.inputs[signature_constants.CLASSIFY_INPUTS])
self.assertEqual("input-1:0", x_tensor_info_actual.name)
self.assertEqual(types_pb2.DT_STRING, x_tensor_info_actual.dtype)
self.assertEqual(0, len(x_tensor_info_actual.tensor_shape.dim))
# Check outputs in signature def.
self.assertEqual(2, len(signature_def.outputs))
classes_tensor_info_actual = (
signature_def.outputs[signature_constants.CLASSIFY_OUTPUT_CLASSES])
self.assertEqual("output-1:0", classes_tensor_info_actual.name)
self.assertEqual(types_pb2.DT_STRING, classes_tensor_info_actual.dtype)
self.assertEqual(0, len(classes_tensor_info_actual.tensor_shape.dim))
scores_tensor_info_actual = (
signature_def.outputs[signature_constants.CLASSIFY_OUTPUT_SCORES])
self.assertEqual("output-2:0", scores_tensor_info_actual.name)
self.assertEqual(types_pb2.DT_STRING, scores_tensor_info_actual.dtype)
self.assertEqual(0, len(scores_tensor_info_actual.tensor_shape.dim))
Example #8
| Source File: signature_def_utils_impl.py From keras-lambda with MIT License | 5 votes |
|
def classification_signature_def(examples, classes, scores):
"""Creates classification signature from given examples and predictions.
Args:
examples: `Tensor`.
classes: `Tensor`.
scores: `Tensor`.
Returns:
A classification-flavored signature_def.
Raises:
ValueError: If examples is `None`.
"""
if examples is None:
raise ValueError('examples cannot be None for classification.')
if classes is None and scores is None:
raise ValueError('classes and scores cannot both be None for '
'classification.')
input_tensor_info = utils.build_tensor_info(examples)
signature_inputs = {signature_constants.CLASSIFY_INPUTS: input_tensor_info}
signature_outputs = {}
if classes is not None:
classes_tensor_info = utils.build_tensor_info(classes)
signature_outputs[signature_constants.CLASSIFY_OUTPUT_CLASSES] = (
classes_tensor_info)
if scores is not None:
scores_tensor_info = utils.build_tensor_info(scores)
signature_outputs[signature_constants.CLASSIFY_OUTPUT_SCORES] = (
scores_tensor_info)
signature_def = build_signature_def(
signature_inputs, signature_outputs,
signature_constants.CLASSIFY_METHOD_NAME)
return signature_def
Example #9
| Source File: bundle_shim_test.py From keras-lambda with MIT License | 5 votes |
|
def testConvertDefaultSignatureClassificationToSignatureDef(self):
signatures_proto = manifest_pb2.Signatures()
classification_signature = manifest_pb2.ClassificationSignature()
classification_signature.input.CopyFrom(
manifest_pb2.TensorBinding(
tensor_name=signature_constants.CLASSIFY_INPUTS))
classification_signature.classes.CopyFrom(
manifest_pb2.TensorBinding(
tensor_name=signature_constants.CLASSIFY_OUTPUT_CLASSES))
classification_signature.scores.CopyFrom(
manifest_pb2.TensorBinding(
tensor_name=signature_constants.CLASSIFY_OUTPUT_SCORES))
signatures_proto.default_signature.classification_signature.CopyFrom(
classification_signature)
signatures_proto.default_signature.classification_signature.CopyFrom(
classification_signature)
signature_def = bundle_shim._convert_default_signature_to_signature_def(
signatures_proto)
# Validate classification signature correctly copied over.
self.assertEqual(signature_def.method_name,
signature_constants.CLASSIFY_METHOD_NAME)
self.assertEqual(len(signature_def.inputs), 1)
self.assertEqual(len(signature_def.outputs), 2)
self.assertProtoEquals(
signature_def.inputs[signature_constants.CLASSIFY_INPUTS],
meta_graph_pb2.TensorInfo(name=signature_constants.CLASSIFY_INPUTS))
self.assertProtoEquals(
signature_def.outputs[signature_constants.CLASSIFY_OUTPUT_SCORES],
meta_graph_pb2.TensorInfo(
name=signature_constants.CLASSIFY_OUTPUT_SCORES))
self.assertProtoEquals(
signature_def.outputs[signature_constants.CLASSIFY_OUTPUT_CLASSES],
meta_graph_pb2.TensorInfo(
name=signature_constants.CLASSIFY_OUTPUT_CLASSES))
Example #10
| Source File: bundle_shim.py From keras-lambda with MIT License | 5 votes |
|
def _convert_default_signature_to_signature_def(signatures):
"""Convert default signature to object of type SignatureDef.
Args:
signatures: object of type manifest_pb2.Signatures()
Returns:
object of type SignatureDef which contains a converted version of default
signature from input signatures object
Raises:
RuntimeError: if default signature type is not classification or regression.
"""
default_signature = signatures.default_signature
signature_def = meta_graph_pb2.SignatureDef()
if default_signature.WhichOneof("type") == "regression_signature":
regression_signature = default_signature.regression_signature
signature_def.method_name = signature_constants.REGRESS_METHOD_NAME
_add_input_to_signature_def(regression_signature.input.tensor_name,
signature_constants.REGRESS_INPUTS,
signature_def)
_add_output_to_signature_def(regression_signature.output.tensor_name,
signature_constants.REGRESS_OUTPUTS,
signature_def)
elif default_signature.WhichOneof("type") == "classification_signature":
classification_signature = default_signature.classification_signature
signature_def.method_name = signature_constants.CLASSIFY_METHOD_NAME
_add_input_to_signature_def(classification_signature.input.tensor_name,
signature_constants.CLASSIFY_INPUTS,
signature_def)
_add_output_to_signature_def(classification_signature.classes.tensor_name,
signature_constants.CLASSIFY_OUTPUT_CLASSES,
signature_def)
_add_output_to_signature_def(classification_signature.scores.tensor_name,
signature_constants.CLASSIFY_OUTPUT_SCORES,
signature_def)
else:
raise RuntimeError("Only classification and regression default signatures "
"are supported for up-conversion. %s is not "
"supported" % default_signature.WhichOneof("type"))
return signature_def
Example #11
| Source File: bundle_shim.py From lambda-packs with MIT License | 4 votes |
|
def _convert_default_signature_to_signature_def(signatures):
"""Convert default signature to object of type SignatureDef.
Args:
signatures: object of type manifest_pb2.Signatures()
Returns:
object of type SignatureDef which contains a converted version of default
signature from input signatures object
Returns None if signature is of generic type because it cannot be converted
to SignatureDef.
"""
default_signature = signatures.default_signature
signature_def = meta_graph_pb2.SignatureDef()
if default_signature.WhichOneof("type") == "regression_signature":
regression_signature = default_signature.regression_signature
signature_def.method_name = signature_constants.REGRESS_METHOD_NAME
_add_input_to_signature_def(regression_signature.input.tensor_name,
signature_constants.REGRESS_INPUTS,
signature_def)
_add_output_to_signature_def(regression_signature.output.tensor_name,
signature_constants.REGRESS_OUTPUTS,
signature_def)
elif default_signature.WhichOneof("type") == "classification_signature":
classification_signature = default_signature.classification_signature
signature_def.method_name = signature_constants.CLASSIFY_METHOD_NAME
_add_input_to_signature_def(classification_signature.input.tensor_name,
signature_constants.CLASSIFY_INPUTS,
signature_def)
_add_output_to_signature_def(classification_signature.classes.tensor_name,
signature_constants.CLASSIFY_OUTPUT_CLASSES,
signature_def)
_add_output_to_signature_def(classification_signature.scores.tensor_name,
signature_constants.CLASSIFY_OUTPUT_SCORES,
signature_def)
else:
logging.error("Only classification and regression default signatures "
"are supported for up-conversion. %s is not "
"supported" % default_signature.WhichOneof("type"))
return None
return signature_def
Example #12
| Source File: rnn_classifier.py From text-antispam with MIT License | 4 votes |
|
def export(model_version, model_dir, sess, x, y_op):
"""导出tensorflow_serving可用的模型
SavedModel（tensorflow.python.saved_model）提供了一种跨语言格式来保存和恢复训练后的TensorFlow模型。它使用方法签名来定义Graph的输入和输出，使上层系统能够更方便地生成、调用或转换TensorFlow模型。
SavedModelBuilder类提供保存Graphs、Variables及Assets的方法。所保存的Graphs必须标注用途标签。在这个实例中我们打算将模型用于服务而非训练，因此我们用SavedModel预定义好的tag_constant.Serving标签。
为了方便地构建签名，SavedModel提供了signature_def_utils API。我们通过signature_def_utils.build_signature_def()来构建predict_signature。一个predict_signature至少包含以下参数：
* inputs = {'x': tensor_info_x} 指定输入的tensor信息
* outputs = {'y': tensor_info_y} 指定输出的tensor信息
* method_name = signature_constants.PREDICT_METHOD_NAME
method_name定义方法名，它的值应该是tensorflow/serving/predict、tensorflow/serving/classify和tensorflow/serving/regress三者之一。Builder标签用来明确Meta Graph被加载的方式，只接受serve和train两种类型。
"""
if model_version <= 0:
logging.warning('Please specify a positive value for version number.')
sys.exit()
path = os.path.dirname(os.path.abspath(model_dir))
if os.path.isdir(path) == False:
logging.warning('Path (%s) not exists, making directories...', path)
os.makedirs(path)
export_path = os.path.join(
compat.as_bytes(model_dir),
compat.as_bytes(str(model_version)))
if os.path.isdir(export_path) == True:
logging.warning('Path (%s) exists, removing directories...', export_path)
shutil.rmtree(export_path)
builder = saved_model_builder.SavedModelBuilder(export_path)
tensor_info_x = utils.build_tensor_info(x)
tensor_info_y = utils.build_tensor_info(y_op)
prediction_signature = signature_def_utils.build_signature_def(
inputs={'x': tensor_info_x},
outputs={'y': tensor_info_y},
# signature_constants.CLASSIFY_METHOD_NAME = "tensorflow/serving/classify"
# signature_constants.PREDICT_METHOD_NAME = "tensorflow/serving/predict"
# signature_constants.REGRESS_METHOD_NAME = "tensorflow/serving/regress"
# 如果缺失method_name会报错：
# grpc.framework.interfaces.face.face.AbortionError: AbortionError(code=StatusCode.INTERNAL, details="Expected prediction signature method_name to be one of {tensorflow/serving/predict, tensorflow/serving/classify, tensorflow/serving/regress}. Was: ")
method_name=signature_constants.PREDICT_METHOD_NAME)
builder.add_meta_graph_and_variables(
sess,
# tag_constants.SERVING = "serve"
# tag_constants.TRAINING = "train"
# 如果只有train标签，TensorFlow Serving加载时会报错：
# E tensorflow_serving/core/aspired_versions_manager.cc:351] Servable {name: default version: 2} cannot be loaded: Not found: Could not find meta graph def matching supplied tags.
[tag_constants.SERVING],
signature_def_map={
'predict_text': prediction_signature,
# 如果缺失会报错：
# grpc.framework.interfaces.face.face.AbortionError: AbortionError(code=StatusCode.FAILED_PRECONDITION, details="Default serving signature key not found.")
signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: prediction_signature
})
builder.save()
Example #13
| Source File: cnn_classifier.py From text-antispam with MIT License | 4 votes |
|
def export(model_version, model_dir, sess, x, y_op):
"""导出tensorflow_serving可用的模型
SavedModel（tensorflow.python.saved_model）提供了一种跨语言格式来保存和恢复训练后的TensorFlow模型。它使用方法签名来定义Graph的输入和输出，使上层系统能够更方便地生成、调用或转换TensorFlow模型。
SavedModelBuilder类提供保存Graphs、Variables及Assets的方法。所保存的Graphs必须标注用途标签。在这个实例中我们打算将模型用于服务而非训练，因此我们用SavedModel预定义好的tag_constant.Serving标签。
为了方便地构建签名，SavedModel提供了signature_def_utils API。我们通过signature_def_utils.build_signature_def()来构建predict_signature。一个predict_signature至少包含以下参数：
* inputs = {'x': tensor_info_x} 指定输入的tensor信息
* outputs = {'y': tensor_info_y} 指定输出的tensor信息
* method_name = signature_constants.PREDICT_METHOD_NAME
method_name定义方法名，它的值应该是tensorflow/serving/predict、tensorflow/serving/classify和tensorflow/serving/regress三者之一。Builder标签用来明确Meta Graph被加载的方式，只接受serve和train两种类型。
"""
if model_version <= 0:
logging.warning('Please specify a positive value for version number.')
sys.exit()
path = os.path.dirname(os.path.abspath(model_dir))
if os.path.isdir(path) == False:
logging.warning('Path (%s) not exists, making directories...', path)
os.makedirs(path)
export_path = os.path.join(
compat.as_bytes(model_dir),
compat.as_bytes(str(model_version)))
if os.path.isdir(export_path) == True:
logging.warning('Path (%s) exists, removing directories...', export_path)
shutil.rmtree(export_path)
builder = saved_model_builder.SavedModelBuilder(export_path)
tensor_info_x = utils.build_tensor_info(x)
tensor_info_y = utils.build_tensor_info(y_op)
prediction_signature = signature_def_utils.build_signature_def(
inputs={'x': tensor_info_x},
outputs={'y': tensor_info_y},
# signature_constants.CLASSIFY_METHOD_NAME = "tensorflow/serving/classify"
# signature_constants.PREDICT_METHOD_NAME = "tensorflow/serving/predict"
# signature_constants.REGRESS_METHOD_NAME = "tensorflow/serving/regress"
# 如果缺失method_name会报错：
# grpc.framework.interfaces.face.face.AbortionError: AbortionError(code=StatusCode.INTERNAL, details="Expected prediction signature method_name to be one of {tensorflow/serving/predict, tensorflow/serving/classify, tensorflow/serving/regress}. Was: ")
method_name=signature_constants.PREDICT_METHOD_NAME)
builder.add_meta_graph_and_variables(
sess,
# tag_constants.SERVING = "serve"
# tag_constants.TRAINING = "train"
# 如果只有train标签，TensorFlow Serving加载时会报错：
# E tensorflow_serving/core/aspired_versions_manager.cc:351] Servable {name: default version: 2} cannot be loaded: Not found: Could not find meta graph def matching supplied tags.
[tag_constants.SERVING],
signature_def_map={
'predict_text': prediction_signature,
# 如果缺失会报错：
# grpc.framework.interfaces.face.face.AbortionError: AbortionError(code=StatusCode.FAILED_PRECONDITION, details="Default serving signature key not found.")
signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: prediction_signature
})
builder.save()
Example #14
| Source File: signature_def_utils_impl.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 4 votes |
|
def classification_signature_def(examples, classes, scores):
"""Creates classification signature from given examples and predictions.
Args:
examples: `Tensor`.
classes: `Tensor`.
scores: `Tensor`.
Returns:
A classification-flavored signature_def.
Raises:
ValueError: If examples is `None`.
"""
if examples is None:
raise ValueError('Classification examples cannot be None.')
if not isinstance(examples, ops.Tensor):
raise ValueError('Classification examples must be a string Tensor.')
if classes is None and scores is None:
raise ValueError('Classification classes and scores cannot both be None.')
input_tensor_info = utils.build_tensor_info(examples)
if input_tensor_info.dtype != types_pb2.DT_STRING:
raise ValueError('Classification examples must be a string Tensor.')
signature_inputs = {signature_constants.CLASSIFY_INPUTS: input_tensor_info}
signature_outputs = {}
if classes is not None:
classes_tensor_info = utils.build_tensor_info(classes)
if classes_tensor_info.dtype != types_pb2.DT_STRING:
raise ValueError('Classification classes must be a string Tensor.')
signature_outputs[signature_constants.CLASSIFY_OUTPUT_CLASSES] = (
classes_tensor_info)
if scores is not None:
scores_tensor_info = utils.build_tensor_info(scores)
if scores_tensor_info.dtype != types_pb2.DT_FLOAT:
raise ValueError('Classification scores must be a float Tensor.')
signature_outputs[signature_constants.CLASSIFY_OUTPUT_SCORES] = (
scores_tensor_info)
signature_def = build_signature_def(
signature_inputs, signature_outputs,
signature_constants.CLASSIFY_METHOD_NAME)
return signature_def
