Deep Learning with TensorFlow

Source code for dropout optimization

Dropout optimization completes our journey in feed-forward networks. Here is the source code for further analysis and implementations:

import mnist_data import tensorflow as tf import math   logs_path = 'log_simple_stats_5_lyers_dropout' batch_size = 100 learning_rate = 0.5 training_epochs = 10  mnist = mnist_data.read_data_sets("data")  X = tf.placeholder(tf.float32, [None, 28, 28, 1]) Y_ = tf.placeholder(tf.float32, [None, 10]) lr = tf.placeholder(tf.float32) pkeep = tf.placeholder(tf.float32)  L = 200 M = 100 N = 60 O = 30  W1 = tf.Variable(tf.truncated_normal([784, L], stddev=0.1))   B1 = tf.Variable(tf.ones([L])/10) W2 = tf.Variable(tf.truncated_normal([L, M], stddev=0.1)) B2 = tf.Variable(tf.ones([M])/10) ...