# Understand TensorFlow tf.nn.dropout(): A Beginner Guide – TensorFlow Tutorial

By | December 2, 2020

tf.nn.dropout() allows us to create a dropout layer for tensorflow model. In this tutorial, we will introduce how to use it.

## Syntax

tf.nn.dropout() function is defined as:

tf.nn.dropout(
x,
keep_prob,
noise_shape=None,
seed=None,
name=None
)

It will output the input element x scaled up by 1 / keep_prob, otherwise outputs 0.

We will use an example to help you understand it.

## How to use tf.nn.dropout()?

We will create a 5*5 matrix filled with 1.

import numpy as np
import tensorflow as tf

x = tf.Variable(tf.ones([5, 5]))

Then we will apply a dropout layer on $$x$$ with keep_prob = 0.8

inputs = tf.nn.dropout(x, 0.8)

Finally, we will output all tensor values.

input_sum  = tf.reduce_sum(inputs)

init = tf.initialize_all_variables()
with tf.Session() as sess:
sess.run(init)
print (sess.run(inputs))
print (sess.run(input_sum))

Run this code, we will find $$x$$ is:

[[1.25 1.25 1.25 1.25 1.25]
[1.25 1.25 1.25 0.   1.25]
[0.   1.25 1.25 0.   0.  ]
[1.25 1.25 0.   1.25 1.25]
[1.25 1.25 1.25 1.25 1.25]]

25*(1-0.8) = 5 elements in $$x$$ is set to 0, other 20 elements are 1/0.8 = 1.25

However, you should notice: Applying dropout layer in model can decrease the speed of your model training. Meanwhile, you also can use L2 regularization in tensorflow. Here is an tutorial:

Multi-layer Neural Network Implements L2 Regularization in TensorFlow – TensorFLow Tutorial