python - arg_scope 实际上是做什么的？

Question

我是神经网络和 TensorFlow 的初学者，我正在尝试了解 arg_scope 的作用.

在我看来，这是一种将“你想做的事情”字典放在具有特定变量的特定层的方法。如果我错了，请纠正我。您如何准确地向初学者解释它的用途？

Answer 1

在定义卷积层时，您可能总是使用相同的填充类型和相同的初始化器，甚至可能使用相同的卷积大小。对于你的池，也许你也总是使用相同的 2x2 池大小。以此类推。

arg_scope 是一种避免向相同层类型反复提供相同参数的方法。

source documentation 中的示例:

Example of how to use tf.contrib.framework.arg_scope:

from third_party.tensorflow.contrib.layers.python import layers
  arg_scope = tf.contrib.framework.arg_scope
  with arg_scope([layers.conv2d], padding='SAME',
                 initializer=layers.variance_scaling_initializer(),
                 regularizer=layers.l2_regularizer(0.05)):
    net = layers.conv2d(inputs, 64, [11, 11], 4, padding='VALID', scope='conv1')
    net = layers.conv2d(net, 256, [5, 5], scope='conv2')

The first call to conv2d will behave as follows:

   layers.conv2d(inputs, 64, [11, 11], 4, padding='VALID',
                  initializer=layers.variance_scaling_initializer(),
                  regularizer=layers.l2_regularizer(0.05), scope='conv1')

The second call to conv2d will also use the arg_scope's default for padding:

  layers.conv2d(inputs, 256, [5, 5], padding='SAME',
                  initializer=layers.variance_scaling_initializer(),
                  regularizer=layers.l2_regularizer(0.05), scope='conv2')

Example of how to reuse an arg_scope:

with arg_scope([layers.conv2d], padding='SAME',
                 initializer=layers.variance_scaling_initializer(),
                 regularizer=layers.l2_regularizer(0.05)) as sc:
    net = layers.conv2d(net, 256, [5, 5], scope='conv1')
    ....
  with arg_scope(sc):
    net = layers.conv2d(net, 256, [5, 5], scope='conv2')