计算状态:未找到:张量名称“input

本文介绍了计算状态:未找到:张量名称“input_producer/limit_epochs/epochs"在检查点文件中找不到的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！

问题描述

限时送ChatGPT账号..

我使用的是 CIFAR10 示例.我用提供的代码训练了网络.培训顺利完成.由于我只想在我的数据集上评估每个示例一次，因此我将 cifar10_input.py 中的输入修改为以下内容.

I'm using the CIFAR10 example. I trained the net as it is with the code provided. The training was done successfully. As I wanted to evaluate each example only once on my data set, I have modified inputs in cifar10_input.py to the following.

def inputs(eval_data, data_dir, batch_size):
  filename = os.path.join(data_dir, TEST_FILE)
  filename_queue = tf.train.string_input_producer([filename],num_epochs=1)
  image, label = read_and_decode(filename_queue)
  float_image = tf.image.per_image_whitening(image)
  min_fraction_of_examples_in_queue = 0.4
  min_queue_examples = int(NUM_EXAMPLES_PER_EPOCH_FOR_EVAL *
                           min_fraction_of_examples_in_queue)
  images, label_batch = tf.train.batch(
      [image, label],
      batch_size=batch_size,
      num_threads=1,
      capacity=min_queue_examples + 3 * batch_size)

  tf.image_summary('images', images)
  return images, tf.reshape(label_batch, [batch_size])

我已将问题隔离为以下几点:

I have isolated the problem to the following:

tf.train_string_input_producer([文件名], num_epochs = 1)

tf.train_string_input_producer([filename], num_epochs = 1)

如果我不设置 num_epochs = 1，则一切正常.如果我这样做，我会收到以下错误.

If I don't set num_epochs = 1, everything works fine as it is. If I do, I get the following error.

0x2cf2700 Compute status: Not found: Tensor name "input_producer/limit_epochs/epochs" not found in checkpoint files /home/jkschin/tensorflow/my_code/data/svhn/train/model.ckpt-8000

感谢您的帮助！

编辑 3 @mrry:

EDIT 3 @mrry:

它仍然失败.这是踪迹.

It still fails. Here's the trace.

Traceback (most recent call last):
  File "cnn_eval.py", line 148, in <module>
    tf.app.run()
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/platform/default/_app.py", line 30, in run
    sys.exit(main(sys.argv))
  File "cnn_eval.py", line 144, in main
    evaluate()
  File "cnn_eval.py", line 119, in evaluate
    saver = tf.train.Saver([v for v in variables_to_restore if v.name != "input_producer/limit_epochs/epochs"])
AttributeError: 'unicode' object has no attribute 'name'

编辑 4 @mrry:

EDIT 4 @mrry:

softmax_linear/biases/ExponentialMovingAverage

softmax_linear/biases/ExponentialMovingAverage

conv2/biases/ExponentialMovingAverage
local4/biases/ExponentialMovingAverage
local3/biases/ExponentialMovingAverage
softmax_linear/weights/ExponentialMovingAverage
conv1/biases/ExponentialMovingAverage
local4/weights/ExponentialMovingAverage
conv2/weights/ExponentialMovingAverage
input_producer/limit_epochs/epochs
local3/weights/ExponentialMovingAverage
conv1/weights/ExponentialMovingAverage

Traceback (most recent call last):
  File "cnn_eval.py", line 148, in <module>
    tf.app.run()
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/platform/default/_app.py", line 30, in run
    sys.exit(main(sys.argv))
  File "cnn_eval.py", line 144, in main
    evaluate()
  File "cnn_eval.py", line 119, in evaluate
    saver = tf.train.Saver([v for v in variables_to_restore if v != "input_producer/limit_epochs/epochs"])
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/saver.py", line 784, in __init__
    restore_sequentially=restore_sequentially)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/saver.py", line 437, in build
    vars_to_save = self._ValidateAndSliceInputs(names_to_variables)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/saver.py", line 340, in _ValidateAndSliceInputs
    names_to_variables = self._VarListToDict(names_to_variables)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/saver.py", line 314, in _VarListToDict
    raise TypeError("Variable to save is not a Variable: %s" % var)
TypeError: Variable to save is not a Variable: Tensor("Const:0", shape=(), dtype=string)

编辑 5 @mrry:

EDIT 5 @mrry:

saver = tf.train.Saver([tf.Variable(0.0,validate_shape=False,name=v) for v in variables_to_restore if v != "input_producer/limit_epochs/epochs"])

0x21d0cb0 Compute status: Invalid argument: Assign requires shapes of both tensors to match. lhs shape= [] rhs shape= [10]
     [[Node: save/Assign_8 = Assign[T=DT_FLOAT, use_locking=true, validate_shape=true, _device="/job:localhost/replica:0/task:0/gpu:0"](softmax_linear/biases/ExponentialMovingAverage, save/restore_slice_8/_20)]]

推荐答案

TL;DR: 在 cifar10_eval.py，将saver构造函数改为:

TL;DR: In cifar10_eval.py, change the saver constructor so that it is:

saver = tf.train.Saver([v for v in variables_to_restore
                        if v != "input_producer/limit_epochs/epochs"])

出现这个问题是因为tf.train.string_input_producer() 在其 num_epochs 参数不是 None 时在内部创建一个变量(称为 "input_producer/limit_epochs/epochs").当，在 cifar10_eval.py 中有一个 tf.train.Saver 被创建，它使用 tf.all_variables()，包括从 tf.nn.string_input_producer().这个变量列表决定了 TensorFlow 在检查点文件中查找的名称集.

This problem arises because tf.train.string_input_producer() internally creates a variable (called "input_producer/limit_epochs/epochs") when its num_epochs argument is not None. When, in cifar10_eval.py a tf.train.Saver is created, it uses tf.all_variables(), which includes the implicitly-created variable from the tf.nn.string_input_producer(). This list of variables determines the set of names that TensorFlow looks up in the checkpoint file.

目前没有一个很好的方法来引用隐式创建的变量，除了它们的名字.因此，最好的解决方法是按名称从 Saver 构造函数中排除变量.

Currently there isn't a great way to refer to implicitly created variables, other than by their name. Therefore, the best fix is to exclude the variable from the Saver constructor by name.

这篇关于计算状态:未找到:张量名称“input_producer/limit_epochs/epochs"在检查点文件中找不到的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！