Python Deep Learning Cookbook

How to do it..

1. Let's start by importing TensorFlow and a tool to load mnist datasets, as follows:

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

2. Next, we specify the Fashion MNIST dataset and load it:

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

3. Let's create the placeholders for the input data:

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

4. Before we specify our network architecture, we will define a couple of functions we will be using multiple times in our model. We start with a function that creates and initializes the weights:

def weight_variable(shape): initial = tf.truncated_normal(shape, ...