Python control_flow_ops._AddNextAndBackEdge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 _AddNextAndBackEdge [as 別名]
def _SwitchGrad(op, *grad):
  """Gradients for a Switch op is calculated using a Merge op.

  If the switch is a loop switch, it will be visited twice. We create
  the merge on the first visit, and update the other input of the merge
  on the second visit. A next_iteration is also added on second visit.
  """
  graph = ops.get_default_graph()
  # pylint: disable=protected-access
  op_ctxt = op._get_control_flow_context()
  grad_ctxt = graph._get_control_flow_context()
  # pylint: enable=protected-access
  if isinstance(op_ctxt, WhileContext):
    merge_grad = grad_ctxt.grad_state.switch_map.get(op)
    if merge_grad is not None:
      # This is the second time this Switch is visited. It comes from
      # the non-exit branch of the Switch, so update the second input
      # to the Merge.
      # TODO: Perform shape inference with this new input.
      if grad[1] is not None:
        # pylint: disable=protected-access
        control_flow_ops._AddNextAndBackEdge(merge_grad, grad[1])
        # pylint: enable=protected-access
      return None, None
    else:
      # This is the first time this Switch is visited. It always comes from
      # the Exit branch, which is grad[0]. grad[1] is empty at this point.
      # Use grad[0] for both inputs to merge for now, but update the second
      # input of merge when we see this Switch the second time.
      merge_grad = merge([grad[0], grad[0]], name="b_switch")[0]
      grad_ctxt.grad_state.switch_map[op] = merge_grad
      return merge_grad, None
  elif isinstance(op_ctxt, CondContext):
    good_grad = grad[op_ctxt.branch]
    zero_grad = grad[1 - op_ctxt.branch]
    # At this point, we have created zero_grad guarded by the right switch.
    return merge([good_grad, zero_grad], name="cond_grad")[0], None
  else:
    false_grad = switch(grad[0], op.inputs[1])[0]
    true_grad = switch(grad[1], op.inputs[1])[1]
    return merge([false_grad, true_grad])[0], None 

# 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 _AddNextAndBackEdge [as 別名]
def _SwitchGrad(op, *grad):
  """Gradients for a Switch op is calculated using a Merge op.

  If the switch is a loop switch, it will be visited twice. We create
  the merge on the first visit, and update the other input of the merge
  on the second visit. A next_iteration is also added on second visit.
  """
  graph = ops.get_default_graph()
  # pylint: disable=protected-access
  op_ctxt = op._get_control_flow_context()
  grad_ctxt = graph._get_control_flow_context()
  # pylint: enable=protected-access
  if isinstance(op_ctxt, WhileContext):
    merge_grad = grad_ctxt.grad_state.switch_map.get(op)
    if merge_grad is not None:
      # This is the second time this Switch is visited. It comes from
      # the non-exit branch of the Switch, so update the second input
      # to the Merge.
      # TODO(yuanbyu): Perform shape inference with this new input.
      if grad[1] is not None:
        # pylint: disable=protected-access
        control_flow_ops._AddNextAndBackEdge(merge_grad, grad[1])
        # pylint: enable=protected-access
      return None, None
    elif grad[0] is not None:
      # This is the first time this Switch is visited. It comes from
      # the Exit branch, which is grad[0]. grad[1] is empty at this point.
      # Use grad[0] for both inputs to merge for now, but update the second
      # input of merge when we see this Switch the second time.
      merge_grad = merge([grad[0], grad[0]], name="b_switch")[0]
      grad_ctxt.grad_state.switch_map[op] = merge_grad
      return merge_grad, None
    else:
      # This is the first time this Switch is visited. It comes from the
      # Identity branch. Such a Switch has `None` gradient for the Exit branch,
      # meaning the output is not differentiable.
      return None, None
  elif isinstance(op_ctxt, CondContext):
    good_grad = grad[op_ctxt.branch]
    zero_grad = grad[1 - op_ctxt.branch]
    # At this point, we have created zero_grad guarded by the right switch.
    return merge([good_grad, zero_grad], name="cond_grad")[0], None
  else:
    false_grad = switch(grad[0], op.inputs[1])[0]
    true_grad = switch(grad[1], op.inputs[1])[1]
    return merge([false_grad, true_grad])[0], None 

# 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 _AddNextAndBackEdge [as 別名]
def _SwitchGrad(op, *grad):
  """Gradients for a Switch op is calculated using a Merge op.

  If the switch is a loop switch, it will be visited twice. We create
  the merge on the first visit, and update the other input of the merge
  on the second visit. A next_iteration is also added on second visit.
  """
  graph = ops.get_default_graph()
  # pylint: disable=protected-access
  op_ctxt = op._get_control_flow_context()
  grad_ctxt = graph._get_control_flow_context()
  # pylint: enable=protected-access
  if isinstance(op_ctxt, WhileContext):
    merge_grad = grad_ctxt.grad_state.switch_map.get(op)
    if merge_grad is not None:
      # This is the second time this Switch is visited. It comes from
      # the non-exit branch of the Switch, so update the second input
      # to the Merge.
      # TODO: Perform shape inference with this new input.
      if grad[1] is not None:
        # pylint: disable=protected-access
        control_flow_ops._AddNextAndBackEdge(merge_grad, grad[1])
        # pylint: enable=protected-access
      return None, None
    elif grad[0] is not None:
      # This is the first time this Switch is visited. It comes from
      # the Exit branch, which is grad[0]. grad[1] is empty at this point.
      # Use grad[0] for both inputs to merge for now, but update the second
      # input of merge when we see this Switch the second time.
      merge_grad = merge([grad[0], grad[0]], name="b_switch")[0]
      grad_ctxt.grad_state.switch_map[op] = merge_grad
      return merge_grad, None
    else:
      # This is the first time this Switch is visited. It comes from the
      # Identity branch. Such a Switch has `None` gradient for the Exit branch,
      # meaning the output is not differentiable.
      return None, None
  elif isinstance(op_ctxt, CondContext):
    good_grad = grad[op_ctxt.branch]
    zero_grad = grad[1 - op_ctxt.branch]
    # At this point, we have created zero_grad guarded by the right switch.
    return merge([good_grad, zero_grad], name="cond_grad")[0], None
  else:
    false_grad = switch(grad[0], op.inputs[1])[0]
    true_grad = switch(grad[1], op.inputs[1])[1]
    return merge([false_grad, true_grad])[0], None 

# 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 _AddNextAndBackEdge [as 別名]
def _SwitchGrad(op, *grad):
  """Gradients for a Switch op is calculated using a Merge op.

  If the switch is a loop switch, it will be visited twice. We create
  the merge on the first visit, and update the other input of the merge
  on the second visit. A next_iteration is also added on second visit.
  """
  graph = ops.get_default_graph()
  # pylint: disable=protected-access
  op_ctxt = op._get_control_flow_context()
  grad_ctxt = graph._get_control_flow_context()
  # pylint: enable=protected-access
  if isinstance(op_ctxt, WhileContext):
    merge_grad = grad_ctxt.grad_state.switch_map.get(op)
    if merge_grad is not None:
      # This is the second time this Switch is visited. It comes from
      # the non-exit branch of the Switch, so update the second input
      # to the Merge.
      # TODO(yuanbyu): Perform shape inference with this new input.
      if grad[1] is not None:
        # pylint: disable=protected-access
        control_flow_ops._AddNextAndBackEdge(merge_grad, grad[1])
        # pylint: enable=protected-access
      return None, None
    elif grad[0] is not None:
      # This is the first time this Switch is visited. It comes from
      # the Exit branch, which is grad[0]. grad[1] is empty at this point.
      # Use grad[0] for both inputs to merge for now, but update the second
      # input of merge when we see this Switch the second time.
      merge_grad = merge([grad[0], grad[0]], name="b_switch")[0]
      grad_ctxt.grad_state.switch_map[op] = merge_grad
      return merge_grad, None
    else:
      # This is the first time this Switch is visited. It comes from the
      # Identity branch. Such a Switch has `None` gradient for the Exit branch,
      # meaning the output is not differentiable.
      return None, None
  elif isinstance(op_ctxt, CondContext):
    good_grad = grad[op_ctxt.branch]
    zero_grad = grad[1 - op_ctxt.branch]
    # At this point, we have created zero_grad guarded by the right switch.
    # Unfortunately, we may still get None here for not trainable data types.
    if zero_grad is None:
      return None, None
    return merge([good_grad, zero_grad], name="cond_grad")[0], None
  else:
    false_grad = switch(grad[0], op.inputs[1])[0]
    true_grad = switch(grad[1], op.inputs[1])[1]
    return merge([false_grad, true_grad])[0], None