Python control_flow_ops._SwitchRefOrTensor方法代碼示例

本文整理匯總了Python中tensorflow.python.ops.control_flow_ops._SwitchRefOrTensor方法的典型用法代碼示例。如果您正苦於以下問題：Python control_flow_ops._SwitchRefOrTensor方法的具體用法？Python control_flow_ops._SwitchRefOrTensor怎麽用？Python control_flow_ops._SwitchRefOrTensor使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類tensorflow.python.ops.control_flow_ops的用法示例。

在下文中一共展示了control_flow_ops._SwitchRefOrTensor方法的4個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: testRefSwitch

# 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 _SwitchRefOrTensor [as 別名]
def testRefSwitch(self):
    with self.test_session():
      v = tf.Variable(7)

      p = tf.constant(True)
      v1 = control_flow_ops._SwitchRefOrTensor(v.ref(), p)
      v2 = tf.assign(v1[1], 9)
      tf.global_variables_initializer().run()
      self.assertEqual(9, v2.eval())

開發者ID:tobegit3hub，項目名稱:deep_image_model，代碼行數:11，代碼來源:control_flow_ops_py_test.py

示例2: _MergeGrad

# 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 _SwitchRefOrTensor [as 別名]
def _MergeGrad(op, grad, _):
  """Gradients for a Merge op are calculated using a Switch op."""
  input_op = op.inputs[0].op
  graph = ops.get_default_graph()
  # pylint: disable=protected-access
  op_ctxt = control_flow_ops._GetOutputContext(input_op)
  grad_ctxt = graph._get_control_flow_context()
  # pylint: enable=protected-access
  if isinstance(op_ctxt, WhileContext):
    # pylint: disable=protected-access
    return control_flow_ops._SwitchRefOrTensor(grad, grad_ctxt.pivot)
    # pylint: enable=protected-access
  elif isinstance(op_ctxt, CondContext):
    pred = op_ctxt.pred
    if grad_ctxt and grad_ctxt.grad_state:
      # This Merge node is part of a cond within a loop.
      # The backprop needs to have the value of this predicate for every
      # iteration. So we must have its values accumulated in the forward, and
      # use the accumulated values as the predicate for this backprop switch.
      grad_state = grad_ctxt.grad_state
      real_pred = grad_state.history_map.get(pred.name)
      if real_pred is None:
        # Remember the value of pred for every iteration.
        grad_ctxt = grad_state.grad_context
        grad_ctxt.Exit()
        history_pred = grad_state.AddForwardAccumulator(pred)
        grad_ctxt.Enter()

        # Add the stack pop op. If pred.op is in a (outer) CondContext,
        # the stack pop will be guarded with a switch.
        real_pred = grad_state.AddBackPropAccumulatedValue(history_pred, pred)
        grad_state.history_map[pred.name] = real_pred
      pred = real_pred
    # pylint: disable=protected-access
    return control_flow_ops._SwitchRefOrTensor(grad, pred, name="cond_grad")
    # pylint: enable=protected-access
  else:
    num_inputs = len(op.inputs)
    cond = [math_ops.equal(op.outputs[1], i) for i in xrange(num_inputs)]
    # pylint: disable=protected-access
    return [control_flow_ops._SwitchRefOrTensor(grad, cond[i])[1]
            for i in xrange(num_inputs)]
    # pylint: enable=protected-access

開發者ID:ryfeus，項目名稱:lambda-packs，代碼行數:45，代碼來源:control_flow_grad.py

示例3: _MergeGrad

# 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 _SwitchRefOrTensor [as 別名]
def _MergeGrad(op, grad, _):
  """Gradients for a Merge op are calculated using a Switch op."""
  input_op = op.inputs[0].op
  graph = ops.get_default_graph()
  # pylint: disable=protected-access
  op_ctxt = input_op._get_control_flow_context()
  grad_ctxt = graph._get_control_flow_context()
  # pylint: enable=protected-access
  if isinstance(op_ctxt, WhileContext):
    # pylint: disable=protected-access
    return control_flow_ops._SwitchRefOrTensor(grad, grad_ctxt.pivot)
    # pylint: enable=protected-access
  elif isinstance(op_ctxt, CondContext):
    pred = op_ctxt.pred
    if grad_ctxt and grad_ctxt.grad_state:
      # This Merge node is part of a cond within a loop.
      # The backprop needs to have the value of this predicate for every
      # iteration. So we must have its values accumulated in the forward, and
      # use the accumulated values as the predicate for this backprop switch.
      grad_state = grad_ctxt.grad_state
      real_pred = grad_state.history_map.get(pred.name)
      if real_pred is None:
        # Remember the value of pred for every iteration.
        grad_ctxt = grad_state.grad_context
        grad_ctxt.Exit()
        history_pred = grad_state.AddForwardAccumulator(pred)
        grad_ctxt.Enter()

        # Add the stack pop op. If pred.op is in a (outer) CondContext,
        # the stack pop will be guarded with a switch.
        real_pred = grad_state.AddBackPropAccumulatedValue(history_pred, pred)
        grad_state.history_map[pred.name] = real_pred
      pred = real_pred
    # pylint: disable=protected-access
    return control_flow_ops._SwitchRefOrTensor(grad, pred, name="cond_grad")
    # pylint: enable=protected-access
  else:
    num_inputs = len(op.inputs)
    cond = [math_ops.equal(op.outputs[1], i) for i in xrange(num_inputs)]
    # pylint: disable=protected-access
    return [control_flow_ops._SwitchRefOrTensor(grad, cond[i])[1]
            for i in xrange(num_inputs)]
    # pylint: enable=protected-access

開發者ID:tobegit3hub，項目名稱:deep_image_model，代碼行數:45，代碼來源:control_flow_grad.py

示例4: _MergeGrad

# 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 _SwitchRefOrTensor [as 別名]
def _MergeGrad(op, grad, _):
  """Gradients for a Merge op are calculated using a Switch op."""
  input_op = op.inputs[0].op
  graph = ops.get_default_graph()
  # pylint: disable=protected-access
  op_ctxt = control_flow_ops._GetOutputContext(input_op)
  grad_ctxt = graph._get_control_flow_context()
  # pylint: enable=protected-access
  if isinstance(op_ctxt, WhileContext):
    # pylint: disable=protected-access
    return control_flow_ops._SwitchRefOrTensor(grad, grad_ctxt.pivot)
    # pylint: enable=protected-access
  elif isinstance(op_ctxt, CondContext):
    pred = op_ctxt.pred
    if grad_ctxt and grad_ctxt.grad_state:
      # This Merge node is part of a cond within a loop.
      # The backprop needs to have the value of this predicate for every
      # iteration. So we must have its values accumulated in the forward, and
      # use the accumulated values as the predicate for this backprop switch.
      grad_state = grad_ctxt.grad_state
      real_pred = grad_state.history_map.get(pred.name)
      if real_pred is None:
        # Remember the value of pred for every iteration.
        grad_ctxt = grad_state.grad_context
        grad_ctxt.Exit()
        history_pred = grad_state.AddForwardAccumulator(pred)
        grad_ctxt.Enter()

        # Add the stack pop op. If pred.op is in a (outer) CondContext,
        # the stack pop will be guarded with a switch.
        real_pred = grad_state.AddBackpropAccumulatedValue(history_pred, pred)
        grad_state.history_map[pred.name] = real_pred
      pred = real_pred
    # pylint: disable=protected-access
    return control_flow_ops._SwitchRefOrTensor(grad, pred, name="cond_grad")
    # pylint: enable=protected-access
  else:
    num_inputs = len(op.inputs)
    cond = [math_ops.equal(op.outputs[1], i) for i in xrange(num_inputs)]
    # pylint: disable=protected-access
    return [control_flow_ops._SwitchRefOrTensor(grad, cond[i])[1]
            for i in xrange(num_inputs)]
    # pylint: enable=protected-access

開發者ID:PacktPublishing，項目名稱:Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda，代碼行數:45，代碼來源:control_flow_grad.py

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