def testSparseTensorSignaturePlaceholders(self): tensor = tf.SparseTensor(values=[1.0, 2.0], indices=[[0, 2], [0, 3]], shape=[5, 5]) signature = tensor_signature.create_signatures(tensor) placeholder = tensor_signature.create_placeholders_from_signatures( signature) self.assertTrue(isinstance(placeholder, tf.SparseTensor)) self.assertEqual(placeholder.values.dtype, tensor.values.dtype)
def testTensorSignaturePlaceholders(self): placeholder_a = array_ops.placeholder( name='test', shape=[None, 100], dtype=dtypes.int32) signatures = tensor_signature.create_signatures(placeholder_a) placeholder_out = tensor_signature.create_placeholders_from_signatures( signatures) self.assertEqual(placeholder_out.dtype, placeholder_a.dtype) self.assertTrue(placeholder_out.get_shape().is_compatible_with( placeholder_a.get_shape())) self.assertTrue( tensor_signature.tensors_compatible(placeholder_out, signatures)) inputs = {'a': placeholder_a} signatures = tensor_signature.create_signatures(inputs) placeholders_out = tensor_signature.create_placeholders_from_signatures( signatures) self.assertEqual(placeholders_out['a'].dtype, placeholder_a.dtype) self.assertTrue(placeholders_out['a'].get_shape().is_compatible_with( placeholder_a.get_shape())) self.assertTrue( tensor_signature.tensors_compatible(placeholders_out, signatures))
def testTensorSignaturePlaceholders(self): placeholder_a = tf.placeholder(name='test', shape=[None, 100], dtype=tf.int32) signatures = tensor_signature.create_signatures(placeholder_a) placeholder_out = tensor_signature.create_placeholders_from_signatures( signatures) self.assertEqual(placeholder_out.dtype, placeholder_a.dtype) self.assertEqual(placeholder_out.get_shape(), placeholder_a.get_shape()) self.assertTrue(tensor_signature.tensors_compatible(placeholder_out, signatures)) inputs = {'a': placeholder_a} signatures = tensor_signature.create_signatures(inputs) placeholders_out = tensor_signature.create_placeholders_from_signatures( signatures) self.assertEqual(placeholders_out['a'].dtype, placeholder_a.dtype) self.assertEqual(placeholders_out['a'].get_shape(), placeholder_a.get_shape()) self.assertTrue(tensor_signature.tensors_compatible(placeholders_out, signatures))
def _get_predict_ops(self, features): """Method that builds model graph and returns prediction ops. Expected to be overriden by sub-classes that require custom support. This implementation uses `model_fn` passed as parameter to constructor to build model. Args: features: `Tensor` or `dict` of `Tensor` objects. Returns: predictions: `Tensor` or `dict` of `Tensor` objects. """ targets = tensor_signature.create_placeholders_from_signatures(self._targets_info) predictions, _, _ = self._call_model_fn(features, targets, ModeKeys.INFER) return predictions
def _get_predict_ops(self, features): """Method that builds model graph and returns prediction ops. Expected to be overriden by sub-classes that require custom support. This implementation uses `model_fn` passed as parameter to constructor to build model. Args: features: `Tensor` or `dict` of `Tensor` objects. Returns: `ModelFnOps` object. """ labels = tensor_signature.create_placeholders_from_signatures( self._labels_info) return self._call_model_fn(features, labels, model_fn_lib.ModeKeys.INFER)
def _get_predict_ops(self, features): """Method that builds model graph and returns prediction ops. Expected to be overriden by sub-classes that require custom support. This implementation uses `model_fn` passed as parameter to constructor to build model. Args: features: `Tensor` or `dict` of `Tensor` objects. Returns: `ModelFnOps` object. """ self._set_infer_mode_feature_signature(features) labels = tensor_signature.create_placeholders_from_signatures( self._labels_info[model_fn_lib.ModeKeys.INFER]) return self._call_model_fn(features, labels, model_fn_lib.ModeKeys.INFER)
def testTensorPlaceholderNone(self): self.assertEqual( None, tensor_signature.create_placeholders_from_signatures(None))