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Auto-Encoder
import os
import tensorflow as tf
import numpy as np
from tensorflow import keras
from tensorflow.keras import Sequential, layers
from PIL import Image
from matplotlib import pyplot as plt
tf.random.set_seed(22)
np.random.seed(22)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
assert tf.__version__.startswith('2.')
def save_images(imgs, name):
new_im = Image.new('L', (280, 280))
index = 0
for i in range(0, 280, 28):
for j in range(0, 280, 28):
im = imgs[index]
im = Image.fromarray(im, mode='L')
new_im.paste(im, (i, j))
index += 1
new_im.save(name)
h_dim = 20
batchsz = 512
lr = 1e-3
(x_train, y_train), (x_test, y_test) = keras.datasets.fashion_mnist.load_data()
x_train, x_test = x_train.astype(np.float32) / 255., x_test.astype(np.float32) / 255.
# we do not need label
train_db = tf.data.Dataset.from_tensor_slices(x_train)
train_db = train_db.shuffle(batchsz * 5).batch(batchsz)
test_db = tf.data.Dataset.from_tensor_slices(x_test)
test_db = test_db.batch(batchsz)
print(x_train.shape, y_train.shape)
print(x_test.shape, y_test.shape)
class AE(keras.Model):
def __init__(self):
super(AE, self).__init__()
# Encoders
self.encoder = Sequential([
layers.Dense(256, activation=tf.nn.relu),
layers.Dense(128, activation=tf.nn.relu),
layers.Dense(h_dim)
])
# Decoders
self.decoder = Sequential([
layers.Dense(128, activation=tf.nn.relu),
layers.Dense(256, activation=tf.nn.relu),
layers.Dense(784)
])
def call(self, inputs, training=None):
# [b, 784] => [b, 10]
h = self.encoder(inputs)
# [b, 10] => [b, 784]
x_hat = self.decoder(h)
return x_hat
model = AE()
model.build(input_shape=(None, 784))
model.summary()
optimizer = tf.optimizers.Adam(lr=lr)
for epoch in range(100):
for step, x in enumerate(train_db):
#[b, 28, 28] => [b, 784]
x = tf.reshape(x, [-1, 784])
with tf.GradientTape() as tape:
x_rec_logits = model(x)
rec_loss = tf.losses.binary_crossentropy(x, x_rec_logits, from_logits=True)
rec_loss = tf.reduce_mean(rec_loss)
grads = tape.gradient(rec_loss, model.trainable_variables)
optimizer.apply_gradients(zip(grads, model.trainable_variables))
if step % 100 ==0:
print(epoch, step, float(rec_loss))
# evaluation
x = next(iter(test_db))
logits = model(tf.reshape(x, [-1, 784]))
x_hat = tf.sigmoid(logits)
# [b, 784] => [b, 28, 28]
x_hat = tf.reshape(x_hat, [-1, 28, 28])
# [b, 28, 28] => [2b, 28, 28]
x_concat = tf.concat([x, x_hat], axis=0)
x_concat = x_hat
x_concat = x_concat.numpy() * 255.
x_concat = x_concat.astype(np.uint8)
save_images(x_concat, 'ae_images/rec_epoch_%d.png'%epoch)
2. Variational Auto-Encoder
import os
import tensorflow as tf
import numpy as np
from tensorflow import keras
from tensorflow.keras import Sequential, layers
from PIL import Image
from matplotlib import pyplot as plt
tf.random.set_seed(22)
np.random.seed(22)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
assert tf.__version__.startswith('2.')
def save_images(imgs, name):
new_im = Image.new('L', (280, 280))
index = 0
for i in range(0, 280, 28):
for j in range(0, 280, 28):
im = imgs[index]
im = Image.fromarray(im, mode='L')
new_im.paste(im, (i, j))
index += 1
new_im.save(name)
h_dim = 20
batchsz = 512
lr = 1e-3
(x_train, y_train), (x_test, y_test) = keras.datasets.fashion_mnist.load_data()
x_train, x_test = x_train.astype(np.float32) / 255., x_test.astype(np.float32) / 255.
# we do not need label
train_db = tf.data.Dataset.from_tensor_slices(x_train)
train_db = train_db.shuffle(batchsz * 5).batch(batchsz)
test_db = tf.data.Dataset.from_tensor_slices(x_test)
test_db = test_db.batch(batchsz)
print(x_train.shape, y_train.shape)
print(x_test.shape, y_test.shape)
z_dim = 10
class VAE(keras.Model):
def __init__(self):
super(VAE, self).__init__()
# Encoder
self.fc1 = layers.Dense(128)
self.fc2 = layers.Dense(z_dim) # get mean prediction
self.fc3 = layers.Dense(z_dim)
# Decoder
self.fc4 = layers.Dense(128)
self.fc5 = layers.Dense(784)
def encoder(self, x):
h = tf.nn.relu(self.fc1(x))
# get mean
mu = self.fc2(h)
# get variance
log_var = self.fc3(h)
return mu, log_var
def decoder(self, z):
out = tf.nn.relu(self.fc4(z))
out = self.fc5(out)
return out
def reparameterize(self, mu, log_var):
eps = tf.random.normal(log_var.shape)
std = tf.exp(log_var*0.5)
z = mu + std * eps
return z
def call(self, inputs, training=None):
# [b, 784] => [b, z_dim], [b, z_dim]
mu, log_var = self.encoder(inputs)
# reparameterization trick
z = self.reparameterize(mu, log_var)
x_hat = self.decoder(z)
return x_hat, mu, log_var
model = VAE()
model.build(input_shape=(4, 784))
optimizer = tf.optimizers.Adam(lr)
for epoch in range(1000):
for step, x in enumerate(train_db):
x = tf.reshape(x, [-1, 784])
with tf.GradientTape() as tape:
x_rec_logits, mu, log_var = model(x)
rec_loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=x, logits=x_rec_logits)
rec_loss = tf.reduce_sum(rec_loss) / x.shape[0]
# compute kl divergence (mu, var) ~ N (0, 1)
# https://stats.stackexchange.com/questions/7440/kl-divergence-between-two-univariate-gaussians
kl_div = -0.5 * (log_var + 1 - mu**2 - tf.exp(log_var))
kl_div = tf.reduce_sum(kl_div) / x.shape[0]
loss = rec_loss + 1. * kl_div
grads = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(grads, model.trainable_variables))
if step % 100 == 0:
print(epoch, step, 'kl div:', float(kl_div), 'rec loss:', float(rec_loss))
# evaluation
z = tf.random.normal((batchsz, z_dim))
logits = model.decoder(z)
x_hat = tf.sigmoid(logits)
x_hat = tf.reshape(x_hat, [-1, 28, 28]).numpy() *255.
x_hat = x_hat.astype(np.uint8)
save_images(x_hat, './vae_images/sampled_epoch%d.png'%epoch)
x = next(iter(test_db))
x = tf.reshape(x, [-1, 784])
x_hat_logits, _, _ = model(x)
x_hat = tf.sigmoid(x_hat_logits)
x_hat = tf.reshape(x_hat, [-1, 28, 28]).numpy() *255.
x_hat = x_hat.astype(np.uint8)
save_images(x_hat, './vae_images/rec_epoch%d.png'%epoch)