def testTensorSignatureExampleParserSingle(self):
examples = tf.placeholder(name='example', shape=[None], dtype=tf.string)
placeholder_a = tf.placeholder(name='test',
shape=[None, 100],
dtype=tf.int32)
signatures = tensor_signature.create_signatures(placeholder_a)
result = tensor_signature.create_example_parser_from_signatures(
signatures, examples)
self.assertTrue(tensor_signature.tensors_compatible(result, signatures))
new_signatures = tensor_signature.create_signatures(result)
self.assertTrue(new_signatures.is_compatible_with(signatures))
def testTensorSignatureExampleParserSingle(self):
examples = tf.placeholder(name='example', shape=[None], dtype=tf.string)
placeholder_a = tf.placeholder(name='test',
shape=[None, 100],
dtype=tf.int32)
signatures = tensor_signature.create_signatures(placeholder_a)
result = tensor_signature.create_example_parser_from_signatures(
signatures, examples)
self.assertTrue(tensor_signature.tensors_compatible(result, signatures))
new_signatures = tensor_signature.create_signatures(result)
self.assertTrue(new_signatures.is_compatible_with(signatures))
def testTensorSignatureExampleParserDict(self):
examples = array_ops.placeholder(
name='example', shape=[None], dtype=dtypes.string)
placeholder_a = array_ops.placeholder(
name='test', shape=[None, 100], dtype=dtypes.int32)
placeholder_b = array_ops.placeholder(
name='bb', shape=[None, 100], dtype=dtypes.float64)
inputs = {'a': placeholder_a, 'b': placeholder_b}
signatures = tensor_signature.create_signatures(inputs)
result = tensor_signature.create_example_parser_from_signatures(signatures,
examples)
self.assertTrue(tensor_signature.tensors_compatible(result, signatures))
new_signatures = tensor_signature.create_signatures(result)
self.assertTrue(new_signatures['a'].is_compatible_with(signatures['a']))
self.assertTrue(new_signatures['b'].is_compatible_with(signatures['b']))
def _get_feature_ops_from_example(self, examples_batch):
"""Returns feature parser for given example batch using features info.
Args:
examples_batch: batch of tf.Example
Returns:
features: `Tensor` or `dict` of `Tensor` objects.
Raises:
ValueError: If `_features_info` attribute is not available.
"""
if self._features_info is None:
raise ValueError('Features information is missing.')
return tensor_signature.create_example_parser_from_signatures(
self._features_info, examples_batch)
def _get_feature_ops_from_example(self, examples_batch):
"""Returns feature parser for given example batch using features info.
Args:
examples_batch: batch of tf.Example
Returns:
features: `Tensor` or `dict` of `Tensor` objects.
Raises:
ValueError: If `_features_info` attribute is not available.
"""
if self._features_info is None:
raise ValueError('Features information is missing.')
return tensor_signature.create_example_parser_from_signatures(
self._features_info, examples_batch)
def testTensorSignatureExampleParserDict(self):
examples = tf.placeholder(name='example', shape=[None], dtype=tf.string)
placeholder_a = tf.placeholder(name='test',
shape=[None, 100],
dtype=tf.int32)
placeholder_b = tf.placeholder(name='bb',
shape=[None, 100],
dtype=tf.float64)
inputs = {'a': placeholder_a, 'b': placeholder_b}
signatures = tensor_signature.create_signatures(inputs)
result = tensor_signature.create_example_parser_from_signatures(
signatures, examples)
self.assertTrue(tensor_signature.tensors_compatible(result, signatures))
new_signatures = tensor_signature.create_signatures(result)
self.assertTrue(new_signatures['a'].is_compatible_with(signatures['a']))
self.assertTrue(new_signatures['b'].is_compatible_with(signatures['b']))
def _get_feature_ops_from_example(self, examples_batch):
"""Returns feature parser for given example batch using features info.
This function requires `fit()` has been called.
Args:
examples_batch: batch of tf.Example
Returns:
features: `Tensor` or `dict` of `Tensor` objects.
Raises:
ValueError: If `_features_info` attribute is not available (usually
because `fit()` has not been called).
"""
if self._features_info is None:
raise ValueError("Features information missing, was fit() ever called?")
return tensor_signature.create_example_parser_from_signatures(self._features_info, examples_batch)
def _get_feature_ops_from_example(self, examples_batch):
"""Returns feature parser for given example batch using features info.
This function requires `fit()` has been called.
Args:
examples_batch: batch of tf.Example
Returns:
features: `Tensor` or `dict` of `Tensor` objects.
Raises:
ValueError: If `_features_info` attribute is not available (usually
because `fit()` has not been called).
"""
if self._features_info is None:
raise ValueError('Features information missing, was fit() ever called?')
return tensor_signature.create_example_parser_from_signatures(
self._features_info, examples_batch)
