Python control_flow_ops.no_op方法代碼示例

# 需要導入模塊: from tensorflow.python.ops import control_flow_ops [as 別名]
# 或者: from tensorflow.python.ops.control_flow_ops import no_op [as 別名]
def variables_initializer(var_list, name="init"):
  """Returns an Op that initializes a list of variables.

  After you launch the graph in a session, you can run the returned Op to
  initialize all the variables in `var_list`. This Op runs all the
  initializers of the variables in `var_list` in parallel.

  Calling `initialize_variables()` is equivalent to passing the list of
  initializers to `Group()`.

  If `var_list` is empty, however, the function still returns an Op that can
  be run. That Op just has no effect.

  Args:
    var_list: List of `Variable` objects to initialize.
    name: Optional name for the returned operation.

  Returns:
    An Op that run the initializers of all the specified variables.
  """
  if var_list:
    return control_flow_ops.group(*[v.initializer for v in var_list], name=name)
  return control_flow_ops.no_op(name=name) 

# 需要導入模塊: from tensorflow.python.ops import control_flow_ops [as 別名]
# 或者: from tensorflow.python.ops.control_flow_ops import no_op [as 別名]
def assert_zero_imag_part(x, message=None, name="assert_zero_imag_part"):
  """Returns `Op` that asserts Tensor `x` has no non-zero imaginary parts.

  Args:
    x:  Numeric `Tensor`, real, integer, or complex.
    message:  A string message to prepend to failure message.
    name:  A name to give this `Op`.

  Returns:
    An `Op` that asserts `x` has no entries with modulus zero.
  """
  with ops.name_scope(name, values=[x]):
    x = ops.convert_to_tensor(x, name="x")
    dtype = x.dtype.base_dtype

    if dtype.is_floating:
      return control_flow_ops.no_op()

    zero = ops.convert_to_tensor(0, dtype=dtype.real_dtype)
    return check_ops.assert_equal(zero, math_ops.imag(x), message=message) 

# 需要導入模塊: from tensorflow.python.ops import control_flow_ops [as 別名]
# 或者: from tensorflow.python.ops.control_flow_ops import no_op [as 別名]
def insert(self, ids, scores):
    """Insert the ids and scores into the TopN."""
    with ops.control_dependencies(self.last_ops):
      scatter_op = state_ops.scatter_update(self.id_to_score, ids, scores)
      larger_scores = math_ops.greater(scores, self.sl_scores[0])

      def shortlist_insert():
        larger_ids = array_ops.boolean_mask(
            math_ops.to_int64(ids), larger_scores)
        larger_score_values = array_ops.boolean_mask(scores, larger_scores)
        shortlist_ids, new_ids, new_scores = tensor_forest_ops.top_n_insert(
            self.sl_ids, self.sl_scores, larger_ids, larger_score_values)
        u1 = state_ops.scatter_update(self.sl_ids, shortlist_ids, new_ids)
        u2 = state_ops.scatter_update(self.sl_scores, shortlist_ids, new_scores)
        return control_flow_ops.group(u1, u2)

      # We only need to insert into the shortlist if there are any
      # scores larger than the threshold.
      cond_op = control_flow_ops.cond(
          math_ops.reduce_any(larger_scores), shortlist_insert,
          control_flow_ops.no_op)
      with ops.control_dependencies([cond_op]):
        self.last_ops = [scatter_op, cond_op] 

# 需要導入模塊: from tensorflow.python.ops import control_flow_ops [as 別名]
# 或者: from tensorflow.python.ops.control_flow_ops import no_op [as 別名]
def _sync_variables_ops(ctx):
  """Create varriables synchronization ops.

  Gets the variables back from TPU nodes. This means the variables updated
  by TPU will now be *synced* to host memory.
  In BROADCAST mode, we skip this sync since the variables are ususally too
  big to transmit via RPC.

  Args:
    ctx: A `_InternalTPUContext` instance with mode.

  Returns:
    A list of sync ops.
  """

  if not ctx.is_input_broadcast_with_iterators():
    return [
        array_ops.check_numerics(v.read_value(),
                                 'Gradient for %s is NaN' % v.name).op
        for v in variables.trainable_variables()
    ]
  else:
    return [control_flow_ops.no_op()] 

# 需要導入模塊: from tensorflow.python.ops import control_flow_ops [as 別名]
# 或者: from tensorflow.python.ops.control_flow_ops import no_op [as 別名]
def assert_type(tensor, tf_type, message=None, name=None):
  """Statically asserts that the given `Tensor` is of the specified type.

  Args:
    tensor: A tensorflow `Tensor`.
    tf_type: A tensorflow type (`dtypes.float32`, `tf.int64`, `dtypes.bool`,
      etc).
    message: A string to prefix to the default message.
    name:  A name to give this `Op`.  Defaults to "assert_type"

  Raises:
    TypeError: If the tensors data type doesn't match `tf_type`.

  Returns:
    A `no_op` that does nothing.  Type can be determined statically.
  """
  message = message or ''
  with ops.name_scope(name, 'assert_type', [tensor]):
    tensor = ops.convert_to_tensor(tensor, name='tensor')
    if tensor.dtype != tf_type:
      raise TypeError(
          '%s  %s must be of type %s' % (message, tensor.op.name, tf_type))

    return control_flow_ops.no_op('statically_determined_correct_type') 

# 需要導入模塊: from tensorflow.python.ops import control_flow_ops [as 別名]
# 或者: from tensorflow.python.ops.control_flow_ops import no_op [as 別名]
def _resource_apply_sparse(self, grad_values, var, grad_indices):
    return control_flow_ops.no_op() 

# 需要導入模塊: from tensorflow.python.ops import control_flow_ops [as 別名]
# 或者: from tensorflow.python.ops.control_flow_ops import no_op [as 別名]
def _apply_sparse(self, grad, var):
    return control_flow_ops.no_op() 

# 需要導入模塊: from tensorflow.python.ops import control_flow_ops [as 別名]
# 或者: from tensorflow.python.ops.control_flow_ops import no_op [as 別名]
def testOutputSlotWithoutOutgoingEdgeCanBeWatched(self):
    """Test watching output slots not attached to any outgoing edges."""

    with session.Session() as sess:
      u_init_val = np.array([[5.0, 3.0], [-1.0, 0.0]])
      u = constant_op.constant(u_init_val, shape=[2, 2], name="u")

      # Create a control edge from a node with an output: From u to z.
      # Node u will get executed only because of the control edge. The output
      # tensor u:0 is not attached to any outgoing edge in the graph. This test
      # checks that the debugger can watch such a tensor.
      with ops.control_dependencies([u]):
        z = control_flow_ops.no_op(name="z")

      run_options = config_pb2.RunOptions(output_partition_graphs=True)
      debug_utils.watch_graph(
          run_options,
          sess.graph,
          debug_ops=["DebugIdentity"],
          debug_urls=self._debug_urls())

      run_metadata = config_pb2.RunMetadata()
      sess.run(z, options=run_options, run_metadata=run_metadata)

      dump = debug_data.DebugDumpDir(
          self._dump_root, partition_graphs=run_metadata.partition_graphs)

      # Assert that the DebugIdentity watch on u works properly.
      self.assertEqual(1, len(dump.dumped_tensor_data))
      datum = dump.dumped_tensor_data[0]
      self.assertEqual("u", datum.node_name)
      self.assertEqual(0, datum.output_slot)
      self.assertEqual("DebugIdentity", datum.debug_op)
      self.assertAllClose([[5.0, 3.0], [-1.0, 0.0]], datum.get_tensor()) 

# 需要導入模塊: from tensorflow.python.ops import control_flow_ops [as 別名]
# 或者: from tensorflow.python.ops.control_flow_ops import no_op [as 別名]
def get(self, name=None):
    """Gets one element from this staging area.

    If the staging area is empty when this operation executes, it will block
    until there is an element to dequeue.

    Note that unlike others ops that can block, like the queue Dequeue
    operations, this can stop other work from happening.  To avoid this, the
    intended use is for this to be called only when there will be an element
    already available.  One method for doing this in a training loop would be to
    run a `put()` call during a warmup session.run call, and then call both
    `get()` and `put()` in each subsequent step.

    The placement of the returned tensor will be determined by the current
    device scope when this function is called.

    Args:
      name: A name for the operation (optional).

    Returns:
      The tuple of tensors that was gotten.
    """
    if name is None:
      name = "%s_get" % self._name

    with ops.colocate_with(self._coloc_op):
      ret = gen_data_flow_ops.unstage(dtypes=self._dtypes,
                                      shared_name=self._name, name=name)

    curr_device_scope = control_flow_ops.no_op().device
    if curr_device_scope != self._coloc_op.device:
      for i in range(len(ret)):
        ret[i] = array_ops.identity(ret[i])

    for output, shape in zip(ret, self._shapes):
      output.set_shape(shape)

    return self._get_return_value(ret) 

# 需要導入模塊: from tensorflow.python.ops import control_flow_ops [as 別名]
# 或者: from tensorflow.python.ops.control_flow_ops import no_op [as 別名]
def initialize_resources(resource_list, name="init"):
  """Initializes the resources in the given list.

  Args:
   resource_list: list of resources to initialize.
   name: name of the initialization op.

  Returns:
   op responsible for initializing all resources.
  """
  if resource_list:
    return control_flow_ops.group(*[r.create for r in resource_list], name=name)
  return control_flow_ops.no_op(name=name) 

# 需要導入模塊: from tensorflow.python.ops import control_flow_ops [as 別名]
# 或者: from tensorflow.python.ops.control_flow_ops import no_op [as 別名]
def assert_integer(x, message=None, name=None):
  """Assert that `x` is of integer dtype.

  Example of adding a dependency to an operation:

  ```python
  with tf.control_dependencies([tf.assert_integer(x)]):
    output = tf.reduce_sum(x)
  ```

  Args:
    x: `Tensor` whose basetype is integer and is not quantized.
    message: A string to prefix to the default message.
    name: A name for this operation (optional).  Defaults to "assert_integer".

  Raises:
    TypeError:  If `x.dtype` is anything other than non-quantized integer.

  Returns:
    A `no_op` that does nothing.  Type can be determined statically.
  """
  message = message or ''
  with ops.name_scope(name, 'assert_integer', [x]):
    x = ops.convert_to_tensor(x, name='x')
    if not x.dtype.is_integer:
      err_msg = (
          '%s  Expected "x" to be integer type.  Found: %s of dtype %s'
          % (message, x.name, x.dtype))
      raise TypeError(err_msg)

    return control_flow_ops.no_op('statically_determined_was_integer') 

# 需要導入模塊: from tensorflow.python.ops import control_flow_ops [as 別名]
# 或者: from tensorflow.python.ops.control_flow_ops import no_op [as 別名]
def tables_initializer(name="init_all_tables"):
  """Returns an Op that initializes all tables of the default graph.

  Args:
    name: Optional name for the initialization op.

  Returns:
    An Op that initializes all tables.  Note that if there are
    not tables the returned Op is a NoOp.
  """
  initializers = ops.get_collection(ops.GraphKeys.TABLE_INITIALIZERS)
  if initializers:
    return control_flow_ops.group(*initializers, name=name)
  return control_flow_ops.no_op(name=name) 

# 需要導入模塊: from tensorflow.python.ops import control_flow_ops [as 別名]
# 或者: from tensorflow.python.ops.control_flow_ops import no_op [as 別名]
def init(self):
    """The table initialization op."""
    if self._table:
      return self._table.init
    with ops.name_scope(None, "init"):
      return control_flow_ops.no_op() 

# 需要導入模塊: from tensorflow.python.ops import control_flow_ops [as 別名]
# 或者: from tensorflow.python.ops.control_flow_ops import no_op [as 別名]
def _enqueue_join(queue, tensor_list_list, enqueue_many, keep_input):
  """Enqueue `tensor_list_list` in `queue`."""
  if enqueue_many:
    enqueue_fn = queue.enqueue_many
  else:
    enqueue_fn = queue.enqueue
  if keep_input.get_shape().ndims == 1:
    enqueue_ops = [enqueue_fn(_select_which_to_enqueue(x, keep_input))
                   for x in tensor_list_list]
  else:
    enqueue_ops = [_smart_cond(
        keep_input,
        lambda: enqueue_fn(tl),  # pylint:disable=cell-var-from-loop
        control_flow_ops.no_op) for tl in tensor_list_list]
  queue_runner.add_queue_runner(queue_runner.QueueRunner(queue, enqueue_ops)) 

# 需要導入模塊: from tensorflow.python.ops import control_flow_ops [as 別名]
# 或者: from tensorflow.python.ops.control_flow_ops import no_op [as 別名]
def _enqueue(queue, tensor_list, threads, enqueue_many, keep_input):
  """Enqueue `tensor_list` in `queue`."""
  if enqueue_many:
    enqueue_fn = queue.enqueue_many
  else:
    enqueue_fn = queue.enqueue
  if keep_input.get_shape().ndims == 1:
    enqueue_ops = [
        enqueue_fn(_select_which_to_enqueue(tensor_list, keep_input))] * threads
  else:
    enqueue_ops = [_smart_cond(
        keep_input,
        lambda: enqueue_fn(tensor_list),
        control_flow_ops.no_op)] * threads
  queue_runner.add_queue_runner(queue_runner.QueueRunner(queue, enqueue_ops))