Further refine KerasTensors to match behavior in head, and fix several docstring code snippets so they will be valid when we enable KerasTensors

PiperOrigin-RevId: 320491351
Change-Id: I9b91727cd719c92e10171708b25341888bab4d98

tensorflow/python/framework/constant_op.py

@@ -170,10 +170,9 @@ def constant(value, dtype=None, shape=None, name="Const"):
   Note: All eager `tf.Tensor` values are immutable (in contrast to
   `tf.Variable`). There is nothing especially _constant_ about the value
   returned from `tf.constant`. This function it is not fundamentally different
-  from `tf.convert_to_tensor`. The name `tf.constant` comes from the symbolic
-  APIs (like `tf.data` or keras functional models) where the `value` is embeded
-  in a `Const` node in the `tf.Graph`. `tf.constant` is useful for asserting
-  that the value can be embedded that way.
+  from `tf.convert_to_tensor`. The name `tf.constant` comes from the `value`
+  being embeded in a `Const` node in the `tf.Graph`. `tf.constant` is useful
+  for asserting that the value can be embedded that way.
 
   If the argument `dtype` is not specified, then the type is inferred from
   the type of `value`.
@@ -220,11 +219,12 @@ def constant(value, dtype=None, shape=None, name="Const"):
   But, since `tf.constant` embeds the value in the `tf.Graph` this fails for
   symbolic tensors:
 
-  >>> i = tf.keras.layers.Input(shape=[None, None])
-  >>> t = tf.constant(i)
+  >>> with tf.compat.v1.Graph().as_default():
+  ...   i = tf.compat.v1.placeholder(shape=[None, None], dtype=tf.float32)
+  ...   t = tf.constant(i)
   Traceback (most recent call last):
   ...
-  NotImplementedError: ...
+  TypeError: ...
 
   `tf.constant` will _always_ create CPU (host) tensors. In order to create
   tensors on other devices, use `tf.identity`. (If the `value` is an eager
@@ -236,8 +236,9 @@ def constant(value, dtype=None, shape=None, name="Const"):
     * It has no `shape` argument.
     * Symbolic tensors are allowed to pass through.
 
-      >>> i = tf.keras.layers.Input(shape=[None, None])
-      >>> t = tf.convert_to_tensor(i)
+    >>> with tf.compat.v1.Graph().as_default():
+    ...   i = tf.compat.v1.placeholder(shape=[None, None], dtype=tf.float32)
+    ...   t = tf.convert_to_tensor(i)
 
   * `tf.fill`: differs in a few ways:
     *   `tf.constant` supports arbitrary constants, not just uniform scalar

tensorflow/python/keras/backend.py

@@ -853,6 +853,9 @@ def is_sparse(tensor):
   True
 
   """
+  spec = getattr(tensor, '_type_spec', None)
+  if spec is not None:
+    return isinstance(spec, sparse_tensor.SparseTensorSpec)
   return isinstance(tensor, sparse_tensor.SparseTensor)
 
 
@@ -1128,13 +1131,14 @@ def is_keras_tensor(x):
   True
 
   """
-  if keras_tensor.keras_tensors_enabled():
-    return isinstance(x, keras_tensor.KerasTensor)
   if not isinstance(x,
                     (ops.Tensor, variables_module.Variable,
-                     sparse_tensor.SparseTensor, ragged_tensor.RaggedTensor)):
+                     sparse_tensor.SparseTensor, ragged_tensor.RaggedTensor,
+                     keras_tensor.KerasTensor)):
     raise ValueError('Unexpectedly found an instance of type `' + str(type(x)) +
                      '`. Expected a symbolic tensor instance.')
+  if keras_tensor.keras_tensors_enabled():
+    return isinstance(x, keras_tensor.KerasTensor)
   return hasattr(x, '_keras_history')
 
 

tensorflow/python/keras/engine/base_layer.py

@@ -1400,9 +1400,11 @@ class Layer(module.Module, version_utils.LayerVersionSelector):
     >>> outputs = tf.keras.layers.Dense(1)(x)
     >>> model = tf.keras.Model(inputs, outputs)
     >>> # Activity regularization.
+    >>> len(model.losses)
+    0
     >>> model.add_loss(tf.abs(tf.reduce_mean(x)))
-    >>> model.losses
-    [<tf.Tensor 'Abs:0' shape=() dtype=float32>]
+    >>> len(model.losses)
+    1
 
     >>> inputs = tf.keras.Input(shape=(10,))
     >>> d = tf.keras.layers.Dense(10, kernel_initializer='ones')

tensorflow/python/keras/engine/keras_tensor.py

@@ -102,8 +102,6 @@ class KerasTensor(object):
 
     self._type_spec = type_spec
     self._inferred_shape_value = inferred_shape_value
-    if name is None and hasattr(type_spec, 'name'):
-      name = type_spec.name
     self._name = name
 
   @property
@@ -150,7 +148,7 @@ class KerasTensor(object):
           "compatible with supplied shape %s" %
           (self.shape, shape))
     else:
-      self._internal_type_spec._shape = shape  # pylint: disable=protected-access
+      self._type_spec._shape = shape  # pylint: disable=protected-access
 
   @property
   def dtype(self):