Automated rollback of change 138583261

Change: 139651838

RELEASE.md

@@ -11,8 +11,6 @@ BUS_ANY was used.
 * The C API type `TF_SessionWithGraph` has been renamed to `TF_Session`,
   indicating its preferred use in language bindings for TensorFlow.
   What was previously `TF_Session` has been renamed to `TF_DeprecatedSession`.
-* Renamed Tensor to Output in the Python API. Tensor will be an alias for Output
-  until TensorFlow 2.0 is released.
 * Renamed TF_Port to TF_Output in the C API.
 
 # Release 0.11.0

tensorflow/contrib/labeled_tensor/python/ops/core.py

@@ -279,7 +279,7 @@ class LabeledTensor(object):
     axes: lt.Axes containing axis names and coordinate labels.
   """
 
-  @tc.accepts(object, ops.Output,
+  @tc.accepts(object, ops.Tensor,
               tc.Union(Axes, tc.Collection(tc.Union(string_types, AxisLike))))
   def __init__(self, tensor, axes):
     """Construct a LabeledTenor.
@@ -489,15 +489,15 @@ def __hash__(self):
 # typecheck type abbreviations:
 # abbreviations for third-party types with very long reprs
 tc.register_type_abbreviation(tensor_shape.Dimension, 'tensorflow.Dimension')
-tc.register_type_abbreviation(ops.Output, 'tensorflow.Output')
+tc.register_type_abbreviation(ops.Tensor, 'tensorflow.Tensor')
 tc.register_type_abbreviation(dtypes.DType, 'tensorflow.DType')
 # core LabeledTensor types
 tc.register_type_abbreviation(Axis, 'labeled_tensor.Axis')
 tc.register_type_abbreviation(Axes, 'labeled_tensor.Axes')
 tc.register_type_abbreviation(LabeledTensor, 'labeled_tensor.LabeledTensor')
 
 
-@tc.returns(ops.Output)
+@tc.returns(ops.Tensor)
 @tc.accepts(LabeledTensor)
 def _convert_labeled_tensor_to_tensor(value, *args, **kwargs):
   # call ops.convert_to_tensor to handle optional arguments appropriately
@@ -510,7 +510,7 @@ def _convert_labeled_tensor_to_tensor(value, *args, **kwargs):
 
 # tc class for anything that can be coerced into a LabeledTensor
 # pylint: disable=invalid-name
-LabeledTensorLike = tc.Union(LabeledTensor, ops.Output, np.ndarray, Scalar)
+LabeledTensorLike = tc.Union(LabeledTensor, ops.Tensor, np.ndarray, Scalar)
 # pylint: enable=invalid-name
 
 

tensorflow/contrib/labeled_tensor/python/ops/ops.py

@@ -36,7 +36,7 @@
 
 
 @tc.returns(core.LabeledTensor)
-@tc.accepts(core.LabeledTensor, ops.Output, core.Axis,
+@tc.accepts(core.LabeledTensor, ops.Tensor, core.Axis,
             tc.Optional(string_types))
 def _gather_1d_on_axis(labeled_tensor, indexer, axis, name=None):
   with ops.name_scope(name, 'lt_take', [labeled_tensor]) as scope:

tensorflow/python/framework/framework_lib.py

@@ -20,7 +20,6 @@
 
 @@Graph
 @@Operation
-@@Output
 @@Tensor
 
 ## Tensor types
@@ -74,7 +73,6 @@
 from tensorflow.python.framework.ops import Graph
 from tensorflow.python.framework.ops import Operation
 from tensorflow.python.framework.ops import Tensor
-from tensorflow.python.framework.ops import Output
 from tensorflow.python.framework.ops import IndexedSlices
 
 from tensorflow.python.framework.sparse_tensor import SparseTensor

tensorflow/python/framework/ops.py

@@ -193,28 +193,28 @@ class _TensorLike(object):
   pass
 
 
-class Output(_TensorLike):
+class Tensor(_TensorLike):
   """Represents one of the outputs of an `Operation`.
 
-  An `Output` is a symbolic handle to one of the outputs of an
+  A `Tensor` is a symbolic handle to one of the outputs of an
   `Operation`. It does not hold the values of that operation's output,
   but instead provides a means of computing those values in a
   TensorFlow [`Session`](../../api_docs/python/client.md#Session).
 
   This class has two primary purposes:
 
-  1. An `Output` can be passed as an input to another `Operation`.
+  1. A `Tensor` can be passed as an input to another `Operation`.
      This builds a dataflow connection between operations, which
      enables TensorFlow to execute an entire `Graph` that represents a
      large, multi-step computation.
 
   2. After the graph has been launched in a session, the value of the
-     `Output` can be computed by passing it to
+     `Tensor` can be computed by passing it to
      [`Session.run()`](../../api_docs/python/client.md#Session.run).
      `t.eval()` is a shortcut for calling
      `tf.get_default_session().run(t)`.
 
-  In the following example, `c`, `d`, and `e` are symbolic `Output`
+  In the following example, `c`, `d`, and `e` are symbolic `Tensor`
   objects, whereas `result` is a numpy array that stores a concrete
   value:
 
@@ -282,7 +282,7 @@ class Output(_TensorLike):
   }
 
   def __init__(self, op, value_index, dtype):
-    """Creates a new `Output`.
+    """Creates a new `Tensor`.
 
     Args:
       op: An `Operation`. `Operation` that computes this tensor.
@@ -299,7 +299,7 @@ def __init__(self, op, value_index, dtype):
     self._value_index = value_index
     self._dtype = dtypes.as_dtype(dtype)
     self._shape = tensor_shape.unknown_shape()
-    # List of operations that use this Output as input.  We maintain this list
+    # List of operations that use this Tensor as input.  We maintain this list
     # to easily navigate a computation graph.
     self._consumers = []
 
@@ -376,7 +376,7 @@ def get_shape(self):
 
     In some cases, the inferred shape may have unknown dimensions. If
     the caller has additional information about the values of these
-    dimensions, `Output.set_shape()` can be used to augment the
+    dimensions, `Tensor.set_shape()` can be used to augment the
     inferred shape.
 
     Returns:
@@ -448,10 +448,10 @@ def _as_node_def_input(self):
     """Return a value to use for the NodeDef "input" attribute.
 
     The returned string can be used in a NodeDef "input" attribute
-    to indicate that the NodeDef uses this Output as input.
+    to indicate that the NodeDef uses this Tensor as input.
 
     Raises:
-      ValueError: if this Output's Operation does not have a name.
+      ValueError: if this Tensor's Operation does not have a name.
 
     Returns:
       a string.
@@ -464,15 +464,15 @@ def _as_node_def_input(self):
       return "%s:%d" % (self._op.name, self._value_index)
 
   def __str__(self):
-    return "Output(\"%s\"%s%s%s)" % (
+    return "Tensor(\"%s\"%s%s%s)" % (
         self.name,
         (", shape=%s" % self.get_shape())
         if self.get_shape().ndims is not None else "",
         (", dtype=%s" % self._dtype.name) if self._dtype else "",
         (", device=%s" % self.device) if self.device else "")
 
   def __repr__(self):
-    return "<tf.Output '%s' shape=%s dtype=%s>" % (
+    return "<tf.Tensor '%s' shape=%s dtype=%s>" % (
         self.name, self.get_shape(), self._dtype.name)
 
   def __hash__(self):
@@ -483,37 +483,37 @@ def __eq__(self, other):
     # Necessary to support Python's collection membership operators
     return id(self) == id(other)
 
-  # NOTE(mrry): This enables the Output's overloaded "right" binary
+  # NOTE(mrry): This enables the Tensor's overloaded "right" binary
   # operators to run when the left operand is an ndarray, because it
-  # accords the Output class higher priority than an ndarray, or a
+  # accords the Tensor class higher priority than an ndarray, or a
   # numpy matrix.
   # TODO(mrry): Convert this to using numpy's __numpy_ufunc__
-  # mechanism, which allows more control over how Outputs interact
+  # mechanism, which allows more control over how Tensors interact
   # with ndarrays.
   __array_priority__ = 100
 
   @staticmethod
   def _override_operator(operator, func):
-    _override_helper(Output, operator, func)
+    _override_helper(Tensor, operator, func)
 
   def __iter__(self):
     """Dummy method to prevent iteration. Do not call.
 
     NOTE(mrry): If we register __getitem__ as an overloaded operator,
-    Python will valiantly attempt to iterate over the Output from 0 to
+    Python will valiantly attempt to iterate over the Tensor from 0 to
     infinity.  Declaring this method prevents this unintended
     behavior.
 
     Raises:
       TypeError: when invoked.
     """
-    raise TypeError("'Output' object is not iterable.")
+    raise TypeError("'Tensor' object is not iterable.")
 
   def __bool__(self):
     """Dummy method to prevent a tensor from being used as a Python `bool`.
 
     This overload raises a `TypeError` when the user inadvertently
-    treats an `Output` as a boolean (e.g. in an `if` statement). For
+    treats a `Tensor` as a boolean (e.g. in an `if` statement). For
     example:
 
     ```python
@@ -525,12 +525,12 @@ def __bool__(self):
     ```
 
     This disallows ambiguities between testing the Python value vs testing the
-    dynamic condition of the `Output`.
+    dynamic condition of the `Tensor`.
 
     Raises:
       `TypeError`.
     """
-    raise TypeError("Using a `tf.Output` as a Python `bool` is not allowed. "
+    raise TypeError("Using a `tf.Tensor` as a Python `bool` is not allowed. "
                     "Use `if t is not None:` instead of `if t:` to test if a "
                     "tensor is defined, and use TensorFlow ops such as "
                     "tf.cond to execute subgraphs conditioned on the value of "
@@ -544,7 +544,7 @@ def __nonzero__(self):
     Raises:
       `TypeError`.
     """
-    raise TypeError("Using a `tf.Output` as a Python `bool` is not allowed. "
+    raise TypeError("Using a `tf.Tensor` as a Python `bool` is not allowed. "
                     "Use `if t is not None:` instead of `if t:` to test if a "
                     "tensor is defined, and use TensorFlow ops such as "
                     "tf.cond to execute subgraphs conditioned on the value of "
@@ -557,12 +557,12 @@ def eval(self, feed_dict=None, session=None):
     produce the inputs needed for the operation that produces this
     tensor.
 
-    *N.B.* Before invoking `Output.eval()`, its graph must have been
+    *N.B.* Before invoking `Tensor.eval()`, its graph must have been
     launched in a session, and either a default session must be
     available, or `session` must be specified explicitly.
 
     Args:
-      feed_dict: A dictionary that maps `Output` objects to feed values.
+      feed_dict: A dictionary that maps `Tensor` objects to feed values.
         See [`Session.run()`](../../api_docs/python/client.md#Session.run) for a
         description of the valid feed values.
       session: (Optional.) The `Session` to be used to evaluate this tensor. If
@@ -575,9 +575,6 @@ def eval(self, feed_dict=None, session=None):
     return _eval_using_default_session(self, feed_dict, self.graph, session)
 
 
-Tensor = Output
-
-
 def _TensorTensorConversionFunction(t, dtype=None, name=None, as_ref=False):
   _ = name, as_ref
   if dtype and not dtype.is_compatible_with(t.dtype):

tensorflow/python/kernel_tests/constant_op_test.py

@@ -596,16 +596,16 @@ def testBadShape(self):
 
   def testTensorStr(self):
     a = tf.placeholder(tf.float32, name="a")
-    self.assertEqual("<tf.Output 'a:0' shape=<unknown> dtype=float32>", repr(a))
+    self.assertEqual("<tf.Tensor 'a:0' shape=<unknown> dtype=float32>", repr(a))
 
     b = tf.placeholder(tf.int32, shape=(32, 40), name="b")
     self.assertEqual(
-        "<tf.Output 'b:0' shape=(32, 40) dtype=int32>",
+        "<tf.Tensor 'b:0' shape=(32, 40) dtype=int32>",
         repr(b))
 
     c = tf.placeholder(tf.qint32, shape=(32, None, 2), name="c")
     self.assertEqual(
-        "<tf.Output 'c:0' shape=(32, ?, 2) dtype=qint32>",
+        "<tf.Tensor 'c:0' shape=(32, ?, 2) dtype=qint32>",
         repr(c))
 
 
@@ -695,13 +695,13 @@ def testBadShape(self):
 
   def testTensorStr(self):
     a = array_ops.placeholder_v2(tf.float32, shape=None, name="a")
-    self.assertEqual("<tf.Output 'a:0' shape=<unknown> dtype=float32>", repr(a))
+    self.assertEqual("<tf.Tensor 'a:0' shape=<unknown> dtype=float32>", repr(a))
 
     b = array_ops.placeholder_v2(tf.int32, shape=(32, 40), name="b")
-    self.assertEqual("<tf.Output 'b:0' shape=(32, 40) dtype=int32>", repr(b))
+    self.assertEqual("<tf.Tensor 'b:0' shape=(32, 40) dtype=int32>", repr(b))
 
     c = array_ops.placeholder_v2(tf.qint32, shape=(32, None, 2), name="c")
-    self.assertEqual("<tf.Output 'c:0' shape=(32, ?, 2) dtype=qint32>", repr(c))
+    self.assertEqual("<tf.Tensor 'c:0' shape=(32, ?, 2) dtype=qint32>", repr(c))
 
 
 class PlaceholderWithDefaultTest(tf.test.TestCase):

tensorflow/python/kernel_tests/slice_op_test.py

@@ -242,7 +242,7 @@ def testNotIterable(self):
     c = tf.constant(5.0)
     with self.assertRaisesWithPredicateMatch(
         TypeError,
-        lambda e: "'Output' object is not iterable" in str(e)):
+        lambda e: "'Tensor' object is not iterable" in str(e)):
       for _ in c:
         pass