Python Deep Learning Cookbook

How to do it... 

1. We start by importing the libraries:

import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_data

2. Next, we load the MNIST data:

mnist = input_data.read_data_sets('Data/mnist', one_hot=True)

3. We define the TensorFlow placeholders as follows:

n_classes = 10input_size = 784x = tf.placeholder(tf.float32, shape=[None, input_size])y = tf.placeholder(tf.float32, shape=[None, n_classes])keep_prob = tf.placeholder(tf.float32)

4. For convenience, we create functions to build our deep learning network:

def weight_variable(shape):    initial = tf.truncated_normal(shape, stddev=0.1)    return tf.Variable(initial)def bias_variable(shape):    initial = tf.constant(0.1, shape=shape)    return tf.Variable(initial)def conv2d(x, ...