Accompanying code examples of the book "Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python" by Sebastian Raschka. All code examples are released under the MIT license. If you find this content useful, please consider supporting the work by buying a copy of the book.
Other code examples and content are available on GitHub. The PDF and ebook versions of the book are available through Leanpub.
In [1]:
%load_ext watermark
%watermark -a 'Sebastian Raschka' -v -p tensorflow
A convolutional autoencoder using nearest neighbor upscaling layers that compresses 768-pixel MNIST images down to a 7x7x4 (196 pixel) representation.
In [2]:
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
##########################
### DATASET
##########################
mnist = input_data.read_data_sets("./", validation_size=0)
##########################
### SETTINGS
##########################
# Hyperparameters
learning_rate = 0.001
training_epochs = 5
batch_size = 128
# Architecture
input_size = 784
image_width = 28
# Other
print_interval = 200
random_seed = 123
##########################
### GRAPH DEFINITION
##########################
g = tf.Graph()
with g.as_default():
tf.set_random_seed(random_seed)
# Input data
tf_x = tf.placeholder(tf.float32, [None, input_size], name='inputs')
input_layer = tf.reshape(tf_x, shape=[-1, image_width, image_width, 1])
###########
# Encoder
###########
# 28x28x1 => 28x28x8
conv1 = tf.layers.conv2d(input_layer, filters=8, kernel_size=(3, 3),
strides=(1, 1), padding='same',
activation=tf.nn.relu)
# 28x28x8 => 14x14x8
maxpool1 = tf.layers.max_pooling2d(conv1, pool_size=(2, 2),
strides=(2, 2), padding='same')
# 14x14x8 => 14x14x4
conv2 = tf.layers.conv2d(maxpool1, filters=4, kernel_size=(3, 3),
strides=(1, 1), padding='same',
activation=tf.nn.relu)
# 14x14x4 => 7x7x4
encode = tf.layers.max_pooling2d(conv2, pool_size=(2, 2),
strides=(2, 2), padding='same',
name='encoding')
###########
# Decoder
###########
# 7x7x4 => 14x14x4
deconv1 = tf.image.resize_nearest_neighbor(encode,
size=(14, 14))
# 14x14x4 => 14x14x8
conv3 = tf.layers.conv2d(deconv1, filters=8, kernel_size=(3, 3),
strides=(1, 1), padding='same',
activation=tf.nn.relu)
# 14x14x8 => 28x28x8
deconv2 = tf.image.resize_nearest_neighbor(conv3,
size=(28, 28))
# 28x28x8 => 28x28x8
conv4 = tf.layers.conv2d(deconv2, filters=8, kernel_size=(3, 3),
strides=(1, 1), padding='same',
activation=tf.nn.relu)
# 28x28x8 => 28x28x1
logits = tf.layers.conv2d(conv4, filters=1, kernel_size=(3,3),
strides=(1, 1), padding='same',
activation=None)
decode = tf.nn.sigmoid(logits, name='decoding')
##################
# Loss & Optimizer
##################
loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=input_layer,
logits=logits)
cost = tf.reduce_mean(loss, name='cost')
optimizer = tf.train.AdamOptimizer(learning_rate)
train = optimizer.minimize(cost, name='train')
# Saver to save session for reuse
saver = tf.train.Saver()
In [3]:
import numpy as np
##########################
### TRAINING & EVALUATION
##########################
with tf.Session(graph=g) as sess:
sess.run(tf.global_variables_initializer())
np.random.seed(random_seed) # random seed for mnist iterator
for epoch in range(training_epochs):
avg_cost = 0.
total_batch = mnist.train.num_examples // batch_size
for i in range(total_batch):
batch_x, batch_y = mnist.train.next_batch(batch_size)
_, c = sess.run(['train', 'cost:0'], feed_dict={'inputs:0': batch_x})
avg_cost += c
if not i % print_interval:
print("Minibatch: %03d | Cost: %.3f" % (i + 1, c))
print("Epoch: %03d | AvgCost: %.3f" % (epoch + 1, avg_cost / (i + 1)))
saver.save(sess, save_path='./autoencoder.ckpt')
In [5]:
%matplotlib inline
import matplotlib.pyplot as plt
##########################
### VISUALIZATION
##########################
n_images = 15
fig, axes = plt.subplots(nrows=2, ncols=n_images, sharex=True,
sharey=True, figsize=(20, 2.5))
test_images = mnist.test.images[:n_images]
with tf.Session(graph=g) as sess:
saver.restore(sess, save_path='./autoencoder.ckpt')
decoded = sess.run('decoding:0', feed_dict={'inputs:0': test_images})
for i in range(n_images):
for ax, img in zip(axes, [test_images, decoded]):
ax[i].imshow(img[i].reshape((image_width, image_width)), cmap='binary')