what does arg_scope actually do?

Question

I am a beginner in neural nets and TensorFlow, and I am trying to understand the role of arg_scope.

It seems to me that it is a way to put together a dictionary of "things you want to do" to a certain layer with certain variables. Please correct me if I am wrong. How would you explain exactly what it is for, to a beginner?

Answer 1

When defining convolution layers, you may always use the same padding type and the same initializer, and maybe even the same convolution size. For you pooling, maybe you are also always using the same 2x2 pooling size. And so on.

arg_scope is a way to avoid repeating providing the same arguments over and over again to the same layer types.

Examples from the source documentation:

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')