December 2017
Intermediate to advanced
536 pages
14h 23m
English
Content preview from TensorFlow 1.x Deep Learning Cookbook
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How to do it...
- The first step, as always, is including the necessary modules:
import tensorflow as tfimport numpy as npfrom tensorflow.examples.tutorials.mnist import input_dataimport matplotlib.pyplot as pltimport math%matplotlib inline
- Load the input data:
mnist = input_data.read_data_sets("MNIST_data/")trX, trY, teX, teY = mnist.train.images, mnist.train.labels, mnist.test.images, mnist.test.labels
- Define the network parameters. Here, we also calculate the spatial dimensions of the output of each max-pool layer; we need this information to upsample the image in the decoder network:
# Network Parametersh_in, w_in = 28, 28 # Image size height and widthk = 3 # Kernel sizep = 2 # pools = 2 # Strides in maxpoolfilters = {1:32,2:32,3:16} ...Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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