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vae.py
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vae.py
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import numpy as np
import tensorflow as tf
class VAE(tf.keras.Model):
def __init__(self, latent_dim, inference_net, generative_net):
super(VAE, self).__init__()
self.latent_dim = latent_dim
self.optimizer = tf.keras.optimizers.Adam(1e-4)
self.inference_net = inference_net
self.generative_net = generative_net
@tf.function
def sample(self, eps=None):
if eps is None:
eps = tf.random.normal(shape=(100, self.latent_dim))
return self.decode(eps, apply_sigmoid=True)
def encode(self, x):
mean, logvar = tf.split(self.inference_net(x), num_or_size_splits=2, axis=1)
return mean, logvar
def reparameterize(self, mean, logvar):
eps = tf.random.normal(shape=mean.shape)
return eps * tf.exp(logvar * .5) + mean
def decode(self, z, apply_sigmoid=False):
logits = self.generative_net(z)
if apply_sigmoid:
probs = tf.sigmoid(logits)
return probs
return logits
@tf.function
def compute_loss(self, x):
mean, logvar = self.encode(x)
z = self.reparameterize(mean, logvar)
x_logit = self.decode(z)
cross_ent = tf.nn.sigmoid_cross_entropy_with_logits(logits=x_logit, labels=x)
logpx_z = -tf.reduce_sum(cross_ent, axis=[1, 2, 3])
logpz = log_normal_pdf(z, 0., 0.)
logqz_x = log_normal_pdf(z, mean, logvar)
return -tf.reduce_mean(logpx_z + logpz - logqz_x)
@tf.function
def compute_apply_gradients(self, x):
with tf.GradientTape() as tape:
loss = self.compute_loss(x)
gradients = tape.gradient(loss, self.trainable_variables)
self.optimizer.apply_gradients(zip(gradients, self.trainable_variables))
def log_normal_pdf(sample, mean, logvar, raxis=1):
log2pi = tf.math.log(2. * np.pi)
return tf.reduce_sum(
-.5 * ((sample - mean) ** 2. * tf.exp(-logvar) + logvar + log2pi),
axis=raxis)
if __name__ == "__main__":
import time
import glob
import matplotlib.pyplot as plt
import PIL
import imageio
(train_images, _), (test_images, _) = tf.keras.datasets.fashion_mnist.load_data()
train_images = train_images.reshape(train_images.shape[0], 28, 28, 1).astype('float32')
test_images = test_images.reshape(test_images.shape[0], 28, 28, 1).astype('float32')
# Normalizing the images to the range of [0., 1.]
train_images /= 255.
test_images /= 255.
TRAIN_BUF = 60000
BATCH_SIZE = 100
TEST_BUF = 10000
train_dataset = tf.data.Dataset.from_tensor_slices(train_images).shuffle(TRAIN_BUF).batch(BATCH_SIZE)
test_dataset = tf.data.Dataset.from_tensor_slices(test_images).shuffle(TEST_BUF).batch(BATCH_SIZE)
epochs = 100
latent_dim = 50
num_examples_to_generate = 16
inference_net = tf.keras.Sequential(
[
tf.keras.layers.InputLayer(input_shape=(28, 28, 1)),
tf.keras.layers.Conv2D(
filters=32, kernel_size=3, strides=(2, 2), activation='relu'),
tf.keras.layers.Conv2D(
filters=64, kernel_size=3, strides=(2, 2), activation='relu'),
tf.keras.layers.Flatten(),
# No activation
tf.keras.layers.Dense(latent_dim + latent_dim),
]
)
generative_net = tf.keras.Sequential(
[
tf.keras.layers.InputLayer(input_shape=(latent_dim,)),
tf.keras.layers.Dense(units=7 * 7 * 32, activation=tf.nn.relu),
tf.keras.layers.Reshape(target_shape=(7, 7, 32)),
tf.keras.layers.Conv2DTranspose(
filters=64,
kernel_size=3,
strides=(2, 2),
padding="SAME",
activation='relu'),
tf.keras.layers.Conv2DTranspose(
filters=32,
kernel_size=3,
strides=(2, 2),
padding="SAME",
activation='relu'),
# No activation
tf.keras.layers.Conv2DTranspose(
filters=1, kernel_size=3, strides=(1, 1), padding="SAME"),
]
)
# keeping the random vector constant for generation (prediction) so
# it will be easier to see the improvement.
random_vector_for_generation = tf.random.normal(
shape=[num_examples_to_generate, latent_dim])
model = VAE(latent_dim, inference_net, generative_net)
def generate_and_save_images(model, epoch, test_input):
predictions = model.sample(test_input)
plt.close()
plt.figure(figsize=(4, 4))
for i in range(predictions.shape[0]):
plt.subplot(4, 4, i + 1)
plt.imshow(predictions[i, :, :, 0], cmap='gray')
plt.axis('off')
plt.savefig('image_at_epoch_{:04d}.png'.format(epoch))
generate_and_save_images(model, 0, random_vector_for_generation)
for epoch in range(1, epochs + 1):
start_time = time.time()
for train_x in train_dataset:
model.compute_apply_gradients(train_x)
end_time = time.time()
if epoch % 1 == 0:
loss = tf.keras.metrics.Mean()
for test_x in test_dataset:
loss(model.compute_loss(test_x))
elbo = -loss.result()
print('Epoch: {}, Test set ELBO: {}, '
'time elapse for current epoch {}'.format(epoch,
elbo,
end_time - start_time))
generate_and_save_images(
model, epoch, random_vector_for_generation)
def display_image(epoch_no):
return PIL.Image.open('image_at_epoch_{:04d}.png'.format(epoch_no))
anim_file = 'vae.gif'
with imageio.get_writer(anim_file, mode='I') as writer:
filenames = glob.glob('image*.png')
filenames = sorted(filenames)
last = -1
for i, filename in enumerate(filenames):
frame = 2 * (i ** 0.5)
if round(frame) > round(last):
last = frame
else:
continue
image = imageio.imread(filename)
writer.append_data(image)
image = imageio.imread(filename)
writer.append_data(image)