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 simple, single-layer autoencoder that compresses 768-pixel MNIST images into 32-pixel vectors (32-times smaller representations).
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
##########################
### WRAPPER FUNCTIONS
##########################
def fully_connected(input_tensor, output_nodes,
activation=None, seed=None,
name='fully_connected'):
with tf.name_scope(name):
input_nodes = input_tensor.get_shape().as_list()[1]
weights = tf.Variable(tf.truncated_normal(shape=(input_nodes,
output_nodes),
mean=0.0,
stddev=0.1,
dtype=tf.float32,
seed=seed),
name='weights')
biases = tf.Variable(tf.zeros(shape=[output_nodes]), name='biases')
act = tf.matmul(input_tensor, weights) + biases
if activation is not None:
act = activation(act)
return act
##########################
### DATASET
##########################
mnist = input_data.read_data_sets("./", validation_size=0)
##########################
### SETTINGS
##########################
# Hyperparameters
learning_rate = 0.01
training_epochs = 5
batch_size = 128
# Architecture
hidden_size = 32
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
input_layer = tf.placeholder(tf.float32, [None, input_size],
name='input')
###########
# Encoder
###########
hidden_layer = fully_connected(input_layer, hidden_size,
activation=tf.nn.relu,
name='encoding')
###########
# Decoder
###########
logits = fully_connected(hidden_layer, input_size,
activation=None, name='logits')
# note MNIST pixels are normalized to 0-1 range
out_layer = tf.nn.sigmoid(logits, name='decoding')
##################
# Loss & Optimizer
##################
cost = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
labels=input_layer, logits=logits), 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={'input: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: 0.702 Minibatch: 201 | Cost: 0.124 Minibatch: 401 | Cost: 0.111 Epoch: 001 | AvgCost: 0.144 Minibatch: 001 | Cost: 0.110 Minibatch: 201 | Cost: 0.107 Minibatch: 401 | Cost: 0.113 Epoch: 002 | AvgCost: 0.108 Minibatch: 001 | Cost: 0.108 Minibatch: 201 | Cost: 0.108 Minibatch: 401 | Cost: 0.112 Epoch: 003 | AvgCost: 0.107 Minibatch: 001 | Cost: 0.105 Minibatch: 201 | Cost: 0.102 Minibatch: 401 | Cost: 0.110 Epoch: 004 | AvgCost: 0.107 Minibatch: 001 | Cost: 0.101 Minibatch: 201 | Cost: 0.106 Minibatch: 401 | Cost: 0.106 Epoch: 005 | AvgCost: 0.107
%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={'input: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