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 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)