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Python Deep Learning Cookbook by Indra den Bakker

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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
  1. Next, we specify the Fashion MNIST dataset and load it:
mnist = input_data.read_data_sets('Data/fashion', one_hot=True)
  1. 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])
  1. 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, ...

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