tensorflow: what's the difference between tf.nn.dropout and tf.layers.dropout

Question

I'm quite confused about whether to use tf.nn.dropout or tf.layers.dropout.

many MNIST CNN examples seems to use tf.nn.droput, with keep_prop as one of params.

but how is it different with tf.layers.dropout? is the "rate" params in tf.layers.dropout similar to tf.nn.dropout?

Or generally speaking, is the difference between tf.nn.dropout and tf.layers.dropout applies to all other similar situations, like similar functions in tf.nn and tf.layers.

Answer 1

A quick glance through tensorflow/python/layers/core.py and tensorflow/python/ops/nn_ops.py reveals that tf.layers.dropout is a wrapper for tf.nn.dropout.

The only differences in the two functions are:

1. The tf.nn.dropout has parameter keep_prob: "Probability that each element is kept"
   tf.layers.dropout has parameter rate: "The dropout rate"
   Thus, keep_prob = 1 - rate as defined here
2. The tf.layers.dropout has training parameter: "Whether to return the output in training mode (apply dropout) or in inference mode (return the input untouched)."

Answer 2

The idea is the same, the parameters are slightly different. In nn.dropout, keep_prob is the probability that each element is kept. In layers.dropout rate=0.1 would drop out 10% of input units.

So keep_prob = 1 - rate. Also layers.dropout allows training parameter.

In general, just read carefully documentation about the functions you care about and you will see the differences.