本文整理匯總了Python中tensorflow.python.ops.control_flow_ops.IsLoopSwitch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.IsLoopSwitch方法的具體用法？Python control_flow_ops.IsLoopSwitch怎麽用？Python control_flow_ops.IsLoopSwitch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.IsLoopSwitch方法的2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: _UpdatePendingAndEnqueueReady
# 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 IsLoopSwitch [as 別名]
def _UpdatePendingAndEnqueueReady(grads, op, queue, pending_count, loop_state):
"""Update pending count for the inputs of op and enqueue ready ops."""
for x in op.inputs:
# pylint: disable=protected-access
pending_count[x.op._id] -= 1
ready = (pending_count[x.op._id] == 0)
if loop_state and not ready:
ready = (pending_count[x.op._id] > 0 and
control_flow_ops.IsLoopSwitch(x.op))
# pylint: enable=protected-access
if ready:
if control_flow_ops.IsLoopExit(x.op):
# if x is an exit without real gradient, defer processing them.
grad_state = loop_state.GetGradState(x.op, before=False)
grad_state.deferred_exits.append(x)
grad_state.pending_exits_count -= 1
if grad_state.pending_exits_count == 0:
# We now have all the exits so process them.
has_real_grad = False
for y in grad_state.deferred_exits:
if _HasAnyNotNoneGrads(grads, y.op):
has_real_grad = True
queue.append(y.op)
else:
grad_state.unused_exits.append(y)
if has_real_grad:
# For an unused exit, if it has floating-point outputs, backprop
# a zero gradient. Otherwise, just ignore it.
for y in grad_state.unused_exits:
if _IsTrainable(y):
_SetGrad(grads, y, loop_state.ZerosLikeForExit(y))
queue.append(y.op)
else:
# All exits are "unused" so use None as gradient.
for y in grad_state.unused_exits:
queue.append(y.op)
else:
queue.append(x.op) 示例2: _SetGrad
# 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 IsLoopSwitch [as 別名]
def _SetGrad(grads, t, grad):
"""Sets gradient "grad" in "grads" for tensor "t"."""
op = t.op
op_grads = grads.get(op)
if not op_grads:
op_grads = [[] for _ in xrange(len(op.outputs))]
grads[op] = op_grads
t_grads = op_grads[t.value_index]
if isinstance(t_grads, list):
t_grads.append(grad)
else:
assert control_flow_ops.IsLoopSwitch(op)
op_grads[t.value_index] = grad