Practical Convolutional Neural Networks

Applying dropout operations with TensorFlow

If we apply the dropout operation to a sample vector, it will work on transmitting the dropout to all the architecture-dependent units. In order to apply the dropout operation, TensorFlow implements the tf.nn.dropout method, which works as follows:

tf.nn.dropout (x, keep_prob, noise_shape, seed, name)

Where x is the original tensor. The keep_prob means the probability of keeping a neuron and the factor by which the remaining nodes are multiplied. The noise_shape signifies a four-element list that determines whether a dimension will apply zeroing independently or not. Let's have a look at this code segment:

import tensorflow as tf X = [1.5, 0.5, 0.75, 1.0, 0.75, 0.6, 0.4, 0.9] drop_out = tf.nn.dropout(X, ...