python 之中使用单下划线“_”做变量的目的？

在阅读Google的免费深度学习课程的练习代码的时候，看到下面一段代码：

num_steps = 801

def accuracy(predictions, labels):
  return (100.0 * np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1))
          / predictions.shape[0])

with tf.Session(graph=graph) as session:
  # This is a one-time operation which ensures the parameters get initialized as
  # we described in the graph: random weights for the matrix, zeros for the
  # biases. 
  tf.initialize_all_variables().run()
  print('Initialized')
  for step in range(num_steps):
    # Run the computations. We tell .run() that we want to run the optimizer,
    # and get the loss value and the training predictions returned as numpy
    # arrays.
    _, l, predictions = session.run([optimizer, loss, train_prediction])
    if (step % 100 == 0):
      print('Loss at step %d: %f' % (step, l))
      print('Training accuracy: %.1f%%' % accuracy(
        predictions, train_labels[:train_subset, :]))
      # Calling .eval() on valid_prediction is basically like calling run(), but
      # just to get that one numpy array. Note that it recomputes all its graph
      # dependencies.
      print('Validation accuracy: %.1f%%' % accuracy(
        valid_prediction.eval(), valid_labels))
  print('Test accuracy: %.1f%%' % accuracy(test_prediction.eval(), test_labels))

num_steps = 801

def accuracy(predictions, labels):

return (100.0 * np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1))

/ predictions.shape[0])

with tf.Session(graph=graph) as session:

# This is a one-time operation which ensures the parameters get initialized as

# we described in the graph: random weights for the matrix, zeros for the

# biases.

tf.initialize_all_variables().run()

print('Initialized')

for step in range(num_steps):

# Run the computations. We tell .run() that we want to run the optimizer,

# and get the loss value and the training predictions returned as numpy

# arrays.

_, l, predictions = session.run([optimizer, loss, train_prediction])

if (step % 100 == 0):

print('Loss at step %d: %f' % (step, l))

print('Training accuracy: %.1f%%' % accuracy(

predictions, train_labels[:train_subset, :]))

# Calling .eval() on valid_prediction is basically like calling run(), but

# just to get that one numpy array. Note that it recomputes all its graph

# dependencies.

print('Validation accuracy: %.1f%%' % accuracy(

valid_prediction.eval(), valid_labels))

print('Test accuracy: %.1f%%' % accuracy(test_prediction.eval(), test_labels))

注意第17行，第一个变量居然是一个下划线！

在查阅了一些资料之后，有人是这么描述单个下划线的使用场景：

在交互式的时候，保持上一次执行的结果
1. 没看懂。原文如下：
2. To hold the result of the last executed statement in an interactive interpreter session. This precedent was set by the standard CPython interpreter, and other interpreters have followed suit
i18n的时候，自动加载翻译

forms.ValidationError(_("Please enter a correct username"))

1

forms.ValidationError(_("Please enter a correct username"))
可以直接忽略、丢弃的变量

我们再回原始代码看看：https://github.com/tensorflow/tensorflow/blob/3737ac321e67410bf061257d5f644eae8abbf79b/tensorflow/examples/udacity/2_fullyconnected.ipynb

Ctrl+F 搜索一下，可以看到下面的代码完全没使用这个变量。因此在这里的作用，应该属于第三种：你完全可以忽略。。。

今天又学习了一个小技巧...

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原文链接：http://www.flyml.net/2016/11/09/purpose-of-the-single-underscore-variable-in-python/

本作品采用知识共享署名 4.0 国际许可协议进行许可

python 之中使用单下划线“_”做变量的目的？

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