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11_variational_autoencoder.py
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11_variational_autoencoder.py
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"""Training a variational autoencoder with 2 layer fully-connected
encoder/decoders and gaussian noise distribution.
Parag K. Mital, Jan 2016
"""
import tensorflow as tf
import numpy as np
from libs.utils import weight_variable, bias_variable, montage_batch
# %%
def VAE(input_shape=[None, 784],
n_components_encoder=2048,
n_components_decoder=2048,
n_hidden=2,
debug=False):
# %%
# Input placeholder
if debug:
input_shape = [50, 784]
x = tf.Variable(np.zeros((input_shape), dtype=np.float32))
else:
x = tf.placeholder(tf.float32, input_shape)
activation = tf.nn.softplus
dims = x.get_shape().as_list()
n_features = dims[1]
W_enc1 = weight_variable([n_features, n_components_encoder])
b_enc1 = bias_variable([n_components_encoder])
h_enc1 = activation(tf.matmul(x, W_enc1) + b_enc1)
W_enc2 = weight_variable([n_components_encoder, n_components_encoder])
b_enc2 = bias_variable([n_components_encoder])
h_enc2 = activation(tf.matmul(h_enc1, W_enc2) + b_enc2)
W_enc3 = weight_variable([n_components_encoder, n_components_encoder])
b_enc3 = bias_variable([n_components_encoder])
h_enc3 = activation(tf.matmul(h_enc2, W_enc3) + b_enc3)
W_mu = weight_variable([n_components_encoder, n_hidden])
b_mu = bias_variable([n_hidden])
W_log_sigma = weight_variable([n_components_encoder, n_hidden])
b_log_sigma = bias_variable([n_hidden])
z_mu = tf.matmul(h_enc3, W_mu) + b_mu
z_log_sigma = 0.5 * (tf.matmul(h_enc3, W_log_sigma) + b_log_sigma)
# %%
# Sample from noise distribution p(eps) ~ N(0, 1)
if debug:
epsilon = tf.random_normal(
[dims[0], n_hidden])
else:
epsilon = tf.random_normal(
tf.stack([tf.shape(x)[0], n_hidden]))
# Sample from posterior
z = z_mu + tf.exp(z_log_sigma) * epsilon
W_dec1 = weight_variable([n_hidden, n_components_decoder])
b_dec1 = bias_variable([n_components_decoder])
h_dec1 = activation(tf.matmul(z, W_dec1) + b_dec1)
W_dec2 = weight_variable([n_components_decoder, n_components_decoder])
b_dec2 = bias_variable([n_components_decoder])
h_dec2 = activation(tf.matmul(h_dec1, W_dec2) + b_dec2)
W_dec3 = weight_variable([n_components_decoder, n_components_decoder])
b_dec3 = bias_variable([n_components_decoder])
h_dec3 = activation(tf.matmul(h_dec2, W_dec3) + b_dec3)
W_mu_dec = weight_variable([n_components_decoder, n_features])
b_mu_dec = bias_variable([n_features])
y = tf.nn.sigmoid(tf.matmul(h_dec3, W_mu_dec) + b_mu_dec)
# p(x|z)
log_px_given_z = -tf.reduce_sum(
x * tf.log(y + 1e-10) +
(1 - x) * tf.log(1 - y + 1e-10), 1)
# d_kl(q(z|x)||p(z))
# Appendix B: 0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2)
kl_div = -0.5 * tf.reduce_sum(
1.0 + 2.0 * z_log_sigma - tf.square(z_mu) - tf.exp(2.0 * z_log_sigma),
1)
loss = tf.reduce_mean(log_px_given_z + kl_div)
return {'cost': loss, 'x': x, 'z': z, 'y': y}
# %%
def test_mnist():
"""Summary
Returns
-------
name : TYPE
Description
"""
# %%
import tensorflow as tf
import tensorflow.examples.tutorials.mnist.input_data as input_data
import matplotlib.pyplot as plt
# %%
# load MNIST as before
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
ae = VAE()
# %%
learning_rate = 0.001
optimizer = tf.train.AdamOptimizer(learning_rate).minimize(ae['cost'])
# %%
# We create a session to use the graph
sess = tf.Session()
sess.run(tf.global_variables_initializer())
# %%
# Fit all training data
t_i = 0
batch_size = 100
n_epochs = 50
n_examples = 20
test_xs, _ = mnist.test.next_batch(n_examples)
xs, ys = mnist.test.images, mnist.test.labels
fig_manifold, ax_manifold = plt.subplots(1, 1)
fig_reconstruction, axs_reconstruction = plt.subplots(2, n_examples, figsize=(10, 2))
fig_image_manifold, ax_image_manifold = plt.subplots(1, 1)
for epoch_i in range(n_epochs):
print('--- Epoch', epoch_i)
train_cost = 0
for batch_i in range(mnist.train.num_examples // batch_size):
batch_xs, _ = mnist.train.next_batch(batch_size)
train_cost += sess.run([ae['cost'], optimizer],
feed_dict={ae['x']: batch_xs})[0]
if batch_i % 2 == 0:
# %%
# Plot example reconstructions from latent layer
imgs = []
for img_i in np.linspace(-3, 3, n_examples):
for img_j in np.linspace(-3, 3, n_examples):
z = np.array([[img_i, img_j]], dtype=np.float32)
recon = sess.run(ae['y'], feed_dict={ae['z']: z})
imgs.append(np.reshape(recon, (1, 28, 28, 1)))
imgs_cat = np.concatenate(imgs)
ax_manifold.imshow(montage_batch(imgs_cat))
fig_manifold.savefig('manifold_%08d.png' % t_i)
# %%
# Plot example reconstructions
recon = sess.run(ae['y'], feed_dict={ae['x']: test_xs})
print(recon.shape)
for example_i in range(n_examples):
axs_reconstruction[0][example_i].imshow(
np.reshape(test_xs[example_i, :], (28, 28)),
cmap='gray')
axs_reconstruction[1][example_i].imshow(
np.reshape(
np.reshape(recon[example_i, ...], (784,)),
(28, 28)),
cmap='gray')
axs_reconstruction[0][example_i].axis('off')
axs_reconstruction[1][example_i].axis('off')
fig_reconstruction.savefig('reconstruction_%08d.png' % t_i)
# %%
# Plot manifold of latent layer
zs = sess.run(ae['z'], feed_dict={ae['x']: xs})
ax_image_manifold.clear()
ax_image_manifold.scatter(zs[:, 0], zs[:, 1],
c=np.argmax(ys, 1), alpha=0.2)
ax_image_manifold.set_xlim([-6, 6])
ax_image_manifold.set_ylim([-6, 6])
ax_image_manifold.axis('off')
fig_image_manifold.savefig('image_manifold_%08d.png' % t_i)
t_i += 1
print('Train cost:', train_cost /
(mnist.train.num_examples // batch_size))
valid_cost = 0
for batch_i in range(mnist.validation.num_examples // batch_size):
batch_xs, _ = mnist.validation.next_batch(batch_size)
valid_cost += sess.run([ae['cost']],
feed_dict={ae['x']: batch_xs})[0]
print('Validation cost:', valid_cost /
(mnist.validation.num_examples // batch_size))
if __name__ == '__main__':
test_mnist()