Deep Learning Models -- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.
%load_ext watermark
%watermark -a 'Sebastian Raschka' -v -p tensorflow
Sebastian Raschka CPython 3.6.1 IPython 6.0.0 tensorflow 1.2.0
A convolutional autoencoder using deconvolutional layers that compresses 768-pixel MNIST images down to a 7x7x4 (196 pixel) representation.
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
hidden_size = 16
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 => 14x14x8
deconv1 = tf.layers.conv2d_transpose(encode, filters=8,
kernel_size=(3, 3), strides=(2, 2),
padding='same',
activation=tf.nn.relu)
# 14x14x8 => 28x28x8
deconv2 = tf.layers.conv2d_transpose(deconv1, filters=8,
kernel_size=(3, 3), strides=(2, 2),
padding='same',
activation=tf.nn.relu)
# 28x28x8 => 28x28x1
logits = tf.layers.conv2d(deconv2, 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()
Extracting ./train-images-idx3-ubyte.gz Extracting ./train-labels-idx1-ubyte.gz Extracting ./t10k-images-idx3-ubyte.gz Extracting ./t10k-labels-idx1-ubyte.gz
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')
Minibatch: 001 | Cost: 0.693 Minibatch: 201 | Cost: 0.153 Minibatch: 401 | Cost: 0.104 Epoch: 001 | AvgCost: 0.232 Minibatch: 001 | Cost: 0.098 Minibatch: 201 | Cost: 0.096 Minibatch: 401 | Cost: 0.093 Epoch: 002 | AvgCost: 0.093 Minibatch: 001 | Cost: 0.090 Minibatch: 201 | Cost: 0.086 Minibatch: 401 | Cost: 0.089 Epoch: 003 | AvgCost: 0.088 Minibatch: 001 | Cost: 0.086 Minibatch: 201 | Cost: 0.089 Minibatch: 401 | Cost: 0.085 Epoch: 004 | AvgCost: 0.086 Minibatch: 001 | Cost: 0.090 Minibatch: 201 | Cost: 0.083 Minibatch: 401 | Cost: 0.087 Epoch: 005 | AvgCost: 0.084
%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')
INFO:tensorflow:Restoring parameters from ./autoencoder.ckpt