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Deep Learning with TensorFlow

You're reading from   Deep Learning with TensorFlow Explore neural networks with Python

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Product type Paperback
Published in Apr 2017
Publisher Packt
ISBN-13 9781786469786
Length 320 pages
Edition 1st Edition
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Authors (4):
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Md. Rezaul Karim Md. Rezaul Karim
Author Profile Icon Md. Rezaul Karim
Md. Rezaul Karim
Ahmed Menshawy Ahmed Menshawy
Author Profile Icon Ahmed Menshawy
Ahmed Menshawy
Giancarlo Zaccone Giancarlo Zaccone
Author Profile Icon Giancarlo Zaccone
Giancarlo Zaccone
Fabrizio Milo Fabrizio Milo
Author Profile Icon Fabrizio Milo
Fabrizio Milo
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Toc

Table of Contents (11) Chapters Close

Preface 1. Getting Started with Deep Learning FREE CHAPTER 2. First Look at TensorFlow 3. Using TensorFlow on a Feed-Forward Neural Network 4. TensorFlow on a Convolutional Neural Network 5. Optimizing TensorFlow Autoencoders 6. Recurrent Neural Networks 7. GPU Computing 8. Advanced TensorFlow Programming 9. Advanced Multimedia Programming with TensorFlow 10. Reinforcement Learning

Digit classifier

In this example, we'll define and train either a two-layer model or a convolutional model in the style of LeNet 5:

from six.moves import xrange   
import tensorflow as tf
import prettytensor as pt
from prettytensor.tutorial import data_utils

tf.app.flags.DEFINE_string(
'save_path', None, 'Where to save the model checkpoints.')
FLAGS = tf.app.flags.FLAGS

BATCH_SIZE = 50
EPOCH_SIZE = 60000 // BATCH_SIZE
TEST_SIZE = 10000 // BATCH_SIZE

Since we are feeding our data as numpy arrays, we need to create placeholders in the graph. These must then be fed using the feed dict.

image_placeholder = tf.placeholder\
(tf.float32, [BATCH_SIZE, 28, 28, 1])
labels_placeholder = tf.placeholder\
(tf.float32, [BATCH_SIZE, 10])

tf.app.flags.DEFINE_string('model', 'full',
'Choose one of the models...
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