Tensorflow学习笔记(2)：tf.nn.dropout 与 tf.layers.dropout

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

You want to use the dropout() function in tensorflow.contrib.layers, not the one in tensorflow.nn.

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)."   The first one turns off(no-op) when not training, which is what you want, while the sec‐ond one does not.