Python tensorflow.python.ops.control_flow_ops.no_op() Examples

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()] 

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) 

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] 

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) 

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()] 

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') 

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) 

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) 

def _decay_weights_op(self, var):
    if not self._decay_var_list or var in self._decay_var_list:
      return var.assign_sub(self._weight_decay * var, self._use_locking)
    return control_flow_ops.no_op() 

def _decay_weights_sparse_op(self, var, indices, scatter_add):
		if not self._decay_var_list or var in self._decay_var_list:
			return scatter_add(var, indices, -self._weight_decay * var,
												 self._use_locking)
		return control_flow_ops.no_op()

	# Here, we overwrite the apply functions that the base optimizer calls.
	# super().apply_x resolves to the apply_x function of the BaseOptimizer. 

def get_init_tokens_op(self, num_tokens=-1):
    """Returns the op to fill the sync_token_queue with the tokens.

    This is supposed to be executed in the beginning of the chief/sync thread
    so that even if the total_num_replicas is less than replicas_to_aggregate,
    the model can still proceed as the replicas can compute multiple steps per
    variable update. Make sure:
    `num_tokens >= replicas_to_aggregate - total_num_replicas`.

    Args:
      num_tokens: Number of tokens to add to the queue.

    Returns:
      An op for the chief/sync replica to fill the token queue.

    Raises:
      ValueError: If this is called before apply_gradients().
      ValueError: If num_tokens are smaller than replicas_to_aggregate -
        total_num_replicas.
    """
    if self._gradients_applied is False:
      raise ValueError(
          "get_init_tokens_op() should be called after apply_gradients().")

    tokens_needed = self._replicas_to_aggregate - self._total_num_replicas
    if num_tokens == -1:
      num_tokens = self._replicas_to_aggregate
    elif num_tokens < tokens_needed:
      raise ValueError(
          "Too few tokens to finish the first step: %d (given) vs %d (needed)" %
          (num_tokens, tokens_needed))

    if num_tokens > 0:
      with ops.device(self._global_step.device), ops.name_scope(""):
        tokens = array_ops.fill([num_tokens],
                                self._global_step.ref())
        init_tokens = self._sync_token_queue.enqueue_many((tokens,))
    else:
      init_tokens = control_flow_ops.no_op(name="no_init_tokens")

    return init_tokens 

def tree_initialization(self):
    def _init_tree():
      return state_ops.scatter_update(self.variables.tree, [0], [[-1, -1]]).op

    def _nothing():
      return control_flow_ops.no_op()

    return control_flow_ops.cond(
        math_ops.equal(array_ops.squeeze(array_ops.slice(
            self.variables.tree, [0, 0], [1, 1])), -2),
        _init_tree, _nothing) 

def _decay_weights_sparse_op(self, var, indices, scatter_add):
    if not self._decay_var_list or var in self._decay_var_list:
      return scatter_add(var, indices, -self._weight_decay * var,
                         self._use_locking)
    return control_flow_ops.no_op()

  # Here, we overwrite the apply functions that the base optimizer calls.
  # super().apply_x resolves to the apply_x function of the BaseOptimizer. 

def _decay_weights_op(self, var):
    def apply_decay_fn(v, decay):
      return state_ops.assign_sub(v, decay * v, self._use_locking)
    def merge_fn(strategy, v, value):
      value = strategy.reduce(
          variable_scope.VariableAggregation.MEAN, value, v)
      return strategy.update(v, apply_decay_fn, value)

    if not self._decay_var_list or var in self._decay_var_list:
      replica_context = distribute_lib.get_tower_context()
      return replica_context.merge_call(merge_fn, var, self._weight_decay)
    return control_flow_ops.no_op() 

def _decay_weights_sparse_op(self, var, indices, scatter_add):
    if not self._decay_var_list or var in self._decay_var_list:
      return scatter_add(var, indices, -self._weight_decay * var,
                         self._use_locking)
    return control_flow_ops.no_op()

  # Here, we overwrite the apply functions that the base optimizer calls.
  # super().apply_x resolves to the apply_x function of the BaseOptimizer. 

def _decay_weights_op(self, var):
    if not self._decay_var_list or var in self._decay_var_list:
      return var.assign_sub(self._weight_decay * var, self._use_locking)
    return control_flow_ops.no_op() 

def _get_test_attrs(self):
    x = control_flow_ops.no_op()
    try:
      a = compat.as_text(x.get_attr("_A"))
    except ValueError:
      a = None
    try:
      b = compat.as_text(x.get_attr("_B"))
    except ValueError:
      b = None
    print(a, b)
    return (a, b) 

def testNoConvert(self):
    # Operation cannot be converted to Tensor.
    op = control_flow_ops.no_op()
    with self.assertRaisesRegexp(TypeError,
                                 r"Can't convert Operation '.*' to Tensor"):
      ops.convert_to_tensor(op) 

def get_init_tokens_op(self, num_tokens=-1):
    """Returns the op to fill the sync_token_queue with the tokens.

    This is supposed to be executed in the beginning of the chief/sync thread
    so that even if the total_num_replicas is less than replicas_to_aggregate,
    the model can still proceed as the replicas can compute multiple steps per
    variable update. Make sure:
    `num_tokens >= replicas_to_aggregate - total_num_replicas`.

    Args:
      num_tokens: Number of tokens to add to the queue.

    Returns:
      An op for the chief/sync replica to fill the token queue.

    Raises:
      ValueError: If this is called before apply_gradients().
      ValueError: If num_tokens are smaller than replicas_to_aggregate -
        total_num_replicas.
    """
    if self._gradients_applied is False:
      raise ValueError(
          "get_init_tokens_op() should be called after apply_gradients().")

    tokens_needed = self._replicas_to_aggregate - self._total_num_replicas
    if num_tokens == -1:
      num_tokens = self._replicas_to_aggregate
    elif num_tokens < tokens_needed:
      raise ValueError(
          "Too few tokens to finish the first step: %d (given) vs %d (needed)" %
          (num_tokens, tokens_needed))

    if num_tokens > 0:
      with ops.device(self._global_step.device), ops.name_scope(""):
        tokens = array_ops.fill([num_tokens], self._global_step)
        init_tokens = self._sync_token_queue.enqueue_many((tokens,))
    else:
      init_tokens = control_flow_ops.no_op(name="no_init_tokens")

    return init_tokens 

def _decay_weights_op(self, var):
		if not self._decay_var_list or var in self._decay_var_list:
			return var.assign_sub(self._weight_decay * var, self._use_locking)
		return control_flow_ops.no_op() 

def _decay_weights_sparse_op(self, var, indices, scatter_add):
    if not self._decay_var_list or var in self._decay_var_list:
      update = -self._weight_decay * array_ops.gather(var, indices)
      return scatter_add(var, indices, update, self._use_locking)
    return control_flow_ops.no_op()

  # Here, we overwrite the apply functions that the base optimizer calls.
  # super().apply_x resolves to the apply_x function of the BaseOptimizer. 

def initialize_all_tables(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) 

def _assert_self_adjoint(self):
    return control_flow_ops.no_op("assert_self_adjoint") 

def _assert_positive_definite(self):
    return control_flow_ops.no_op("assert_positive_definite") 

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() 

def get_best(self, n):
    """Return the indices and values of the n highest scores in the TopN."""

    def refresh_shortlist():
      """Update the shortlist with the highest scores in id_to_score."""
      new_scores, new_ids = nn_ops.top_k(self.id_to_score, self.shortlist_size)
      smallest_new_score = math_ops.reduce_min(new_scores)
      new_length = math_ops.reduce_sum(
          math_ops.to_int32(math_ops.greater(new_scores, dtypes.float32.min)))
      u1 = self.sl_ids.assign(
          math_ops.to_int64(array_ops.concat([[new_length], new_ids], 0)))
      u2 = self.sl_scores.assign(
          array_ops.concat([[smallest_new_score], new_scores], 0))
      self.last_ops = [u1, u2]
      return control_flow_ops.group(u1, u2)

    # We only need to refresh the shortlist if n is greater than the
    # current shortlist size (which is stored in sl_ids[0]).
    with ops.control_dependencies(self.last_ops):
      cond_op = control_flow_ops.cond(n > self.sl_ids[0], refresh_shortlist,
                                      control_flow_ops.no_op)
      with ops.control_dependencies([cond_op]):
        topk_values, topk_indices = nn_ops.top_k(
            self.sl_scores,
            math_ops.minimum(n, math_ops.to_int32(self.sl_ids[0])))
        # topk_indices are the indices into the shortlist, we want to return
        # the indices into id_to_score
        gathered_indices = array_ops.gather(self.sl_ids, topk_indices)
        return gathered_indices, topk_values 

def tree_initialization(self):
    def _init_tree():
      return state_ops.scatter_update(self.variables.tree, [0], [[-1, -1]]).op

    def _nothing():
      return control_flow_ops.no_op()

    return control_flow_ops.cond(
        math_ops.equal(
            array_ops.squeeze(
                array_ops.strided_slice(self.variables.tree, [0, 0], [1, 1])),
            -2), _init_tree, _nothing) 

def _resource_apply_sparse(self, grad_values, var, grad_indices):
    return control_flow_ops.no_op() 

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 is None:
    enqueue_ops = [enqueue_fn(tensor_list)] * threads
  else:
    enqueue_ops = [control_flow_ops.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)) 

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 is None:
    enqueue_ops = [enqueue_fn(tl) for tl in tensor_list_list]
  else:
    enqueue_ops = [control_flow_ops.cond(
        keep_input,
        lambda: enqueue_fn(tl),
        control_flow_ops.no_op) for tl in tensor_list_list]
  queue_runner.add_queue_runner(queue_runner.QueueRunner(queue, enqueue_ops))