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Python Deep Learning Cookbook by Indra den Bakker

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How to do it...

  1. We start by importing TensorFlow, as follows:
import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_data
  1. Now, we will be able to load the Fashion-MNIST dataset with just one line of code:
mnist = input_data.read_data_sets('Data/fashion', one_hot=True)
  1. Before proceeding, we need to set the placeholders for our model:
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)
  1. We define four functions that will help us build our network architecture:
def weight_variable(shape):    initial = tf.truncated_normal(shape, stddev=0.1)    return tf.Variable(initial)def bias_variable(shape): ...

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