Question

（tensorflow版本：'0.12.head'）

TensorArray.close的文档说它关闭当前的TensorArray 。对TensorArray的状态意味着什么？我尝试以下代码

import tensorflow as tf
sess = tf.InteractiveSession()
a1 = tf.TensorArray(tf.int32, 2)
a1.close().run()
a2 = a1.write(0, 0)
a2.close().run()
print(a2.read(0).eval())

并且没有错误。 close的用法是什么？

Learning-to-learn includes TensorArray.close in the reset operations of the network。我无法弄清楚评论Empty array as part of the reset process的含义。

更新

例如，

import tensorflow as tf

sess = tf.InteractiveSession()

N = 3

def cond(i, arr):
    return i < N

def body(i, arr):
    arr = arr.write(i, i)
    i += 1
    return i, arr

arr = tf.TensorArray(tf.int32, N)
_, result_arr = tf.while_loop(cond, body, [0, arr])
reset = arr.close() # corresponds to https://github.com/deepmind/learning-to-learn/blob/6ee52539e83d0452051fe08699b5d8436442f803/meta.py#L370

NUM_EPOCHS = 3
for _ in range(NUM_EPOCHS):
    reset.run() # corresponds to https://github.com/deepmind/learning-to-learn/blob/6ee52539e83d0452051fe08699b5d8436442f803/util.py#L32
    print(result_arr.stack().eval())

为什么arr.close()不会使while循环失败？在每个纪元的开头调用arr.close（）有什么好处？

Answer 1

这是一个包含本机操作系统的Python操作系统，它们都有帮助字符串，但本机操作帮助字符串提供了更多信息。如果您查看inspect.getsourcefile(fx_array.close)，它会指向tensorflow/python/ops/tensor_array_ops.py。在实现中，您会看到它遵循_tensor_array_close_v2。所以你可以这样做

> from tensorflow.python.ops import gen_data_flow_ops
> help(gen_data_flow_ops._tensor_array_close_v2)
Delete the TensorArray from its resource container.  This enables
the user to close and release the resource in the middle of a step/run.

同一个文档字符串也位于TensorArrayCloseV2

下的tensorflow/core/ops/ops.pbtxt中

查看tensorflow/core/kernels/tensor_array_ops.cc您看到TensorArrayCloseOp是TensorArrayCloseV2注册的实施，并且有更多信息

// Delete the TensorArray from its resource container.  This enables
// the user to close and release the resource in the middle of a step/run.
// TODO(ebrevdo): decide whether closing the grad op should happen
// here or on the python side.
class TensorArrayCloseOp : public OpKernel {
 public:
  explicit TensorArrayCloseOp(OpKernelConstruction* context)
      : OpKernel(context) {}

  void Compute(OpKernelContext* ctx) override {
    TensorArray* tensor_array;
    OP_REQUIRES_OK(ctx, GetTensorArray(ctx, &tensor_array));
    core::ScopedUnref unref(tensor_array);
    // Instead of deleting this TA from the ResourceManager, we just
    // clear it away and mark it as closed.  The remaining memory
    // consumed store its mutex and handle Tensor.  This will be
    // cleared out at the end of the step anyway, so it's fine to keep
    // it around until the end of the step.  Further calls to the
    // TensorArray will fail because TensorArray checks internally to
    // see if it is closed or not.

描述似乎与您看到的行为不一致，可能是一个错误。

Answer 2

学习与学习示例中关闭的TensorArray不是传递给while循环的原始TensorArray。

# original array (fx_array) declared here
fx_array = tf.TensorArray(tf.float32, size=len_unroll + 1,
                          clear_after_read=False)
# new array (fx_array) returned here
_, fx_array, x_final, s_final = tf.while_loop(
    cond=lambda t, *_: t < len_unroll,
    body=time_step,
    loop_vars=(0, fx_array, x, state),
    parallel_iterations=1,
    swap_memory=True,
    name="unroll")

从此处对fx_array.close()的任何后续调用都会关闭while循环返回的新数组，而不是在第一次迭代中传递给循环的原始数组。

如果您想了解close的行为方式，请运行：

session.run([reset, loss])

由于TensorArray has already been closed.操作尝试在已关闭的数组上运行loss，因此pack()会失败。

调用TensorArray.close（）有什么影响？

2 个答案: