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<p>Example of VAE on MNIST dataset using CNN</p>
<p>The VAE has a modular design. The encoder, decoder and VAE
are 3 models that share weights. After training the VAE model,
the encoder can be used to generate latent vectors.
The decoder can be used to generate MNIST digits by sampling the
latent vector from a Gaussian distribution with mean=0 and std=1.</p>
<h1 id="reference">Reference</h1>
<p>[1] Kingma, Diederik P., and Max Welling.
"Auto-encoding variational bayes."
https://arxiv.org/abs/1312.6114</p>
<pre><code class="python">from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from keras.layers import Dense, Input
from keras.layers import Conv2D, Flatten, Lambda
from keras.layers import Reshape, Conv2DTranspose
from keras.models import Model
from keras.datasets import mnist
from keras.losses import mse, binary_crossentropy
from keras.utils import plot_model
from keras import backend as K
import numpy as np
import matplotlib.pyplot as plt
import argparse
import os
# reparameterization trick
# instead of sampling from Q(z|X), sample eps = N(0,I)
# then z = z_mean + sqrt(var)*eps
def sampling(args):
"""Reparameterization trick by sampling fr an isotropic unit Gaussian.
# Arguments
args (tensor): mean and log of variance of Q(z|X)
# Returns
z (tensor): sampled latent vector
"""
z_mean, z_log_var = args
batch = K.shape(z_mean)[0]
dim = K.int_shape(z_mean)[1]
# by default, random_normal has mean=0 and std=1.0
epsilon = K.random_normal(shape=(batch, dim))
return z_mean + K.exp(0.5 * z_log_var) * epsilon
def plot_results(models,
data,
batch_size=128,
model_name="vae_mnist"):
"""Plots labels and MNIST digits as function of 2-dim latent vector
# Arguments
models (tuple): encoder and decoder models
data (tuple): test data and label
batch_size (int): prediction batch size
model_name (string): which model is using this function
"""
encoder, decoder = models
x_test, y_test = data
os.makedirs(model_name, exist_ok=True)
filename = os.path.join(model_name, "vae_mean.png")
# display a 2D plot of the digit classes in the latent space
z_mean, _, _ = encoder.predict(x_test,
batch_size=batch_size)
plt.figure(figsize=(12, 10))
plt.scatter(z_mean[:, 0], z_mean[:, 1], c=y_test)
plt.colorbar()
plt.xlabel("z[0]")
plt.ylabel("z[1]")
plt.savefig(filename)
plt.show()
filename = os.path.join(model_name, "digits_over_latent.png")
# display a 30x30 2D manifold of digits
n = 30
digit_size = 28
figure = np.zeros((digit_size * n, digit_size * n))
# linearly spaced coordinates corresponding to the 2D plot
# of digit classes in the latent space
grid_x = np.linspace(-4, 4, n)
grid_y = np.linspace(-4, 4, n)[::-1]
for i, yi in enumerate(grid_y):
for j, xi in enumerate(grid_x):
z_sample = np.array([[xi, yi]])
x_decoded = decoder.predict(z_sample)
digit = x_decoded[0].reshape(digit_size, digit_size)
figure[i * digit_size: (i + 1) * digit_size,
j * digit_size: (j + 1) * digit_size] = digit
plt.figure(figsize=(10, 10))
start_range = digit_size // 2
end_range = n * digit_size + start_range + 1
pixel_range = np.arange(start_range, end_range, digit_size)
sample_range_x = np.round(grid_x, 1)
sample_range_y = np.round(grid_y, 1)
plt.xticks(pixel_range, sample_range_x)
plt.yticks(pixel_range, sample_range_y)
plt.xlabel("z[0]")
plt.ylabel("z[1]")
plt.imshow(figure, cmap='Greys_r')
plt.savefig(filename)
plt.show()
# MNIST dataset
(x_train, y_train), (x_test, y_test) = mnist.load_data()
image_size = x_train.shape[1]
x_train = np.reshape(x_train, [-1, image_size, image_size, 1])
x_test = np.reshape(x_test, [-1, image_size, image_size, 1])
x_train = x_train.astype('float32') / 255
x_test = x_test.astype('float32') / 255
# network parameters
input_shape = (image_size, image_size, 1)
batch_size = 128
kernel_size = 3
filters = 16
latent_dim = 2
epochs = 30
# VAE model = encoder + decoder
# build encoder model
inputs = Input(shape=input_shape, name='encoder_input')
x = inputs
for i in range(2):
filters *= 2
x = Conv2D(filters=filters,
kernel_size=kernel_size,
activation='relu',
strides=2,
padding='same')(x)
# shape info needed to build decoder model
shape = K.int_shape(x)
# generate latent vector Q(z|X)
x = Flatten()(x)
x = Dense(16, activation='relu')(x)
z_mean = Dense(latent_dim, name='z_mean')(x)
z_log_var = Dense(latent_dim, name='z_log_var')(x)
# use reparameterization trick to push the sampling out as input
# note that "output_shape" isn't necessary with the TensorFlow backend
z = Lambda(sampling, output_shape=(latent_dim,), name='z')([z_mean, z_log_var])
# instantiate encoder model
encoder = Model(inputs, [z_mean, z_log_var, z], name='encoder')
encoder.summary()
plot_model(encoder, to_file='vae_cnn_encoder.png', show_shapes=True)
# build decoder model
latent_inputs = Input(shape=(latent_dim,), name='z_sampling')
x = Dense(shape[1] * shape[2] * shape[3], activation='relu')(latent_inputs)
x = Reshape((shape[1], shape[2], shape[3]))(x)
for i in range(2):
x = Conv2DTranspose(filters=filters,
kernel_size=kernel_size,
activation='relu',
strides=2,
padding='same')(x)
filters //= 2
outputs = Conv2DTranspose(filters=1,
kernel_size=kernel_size,
activation='sigmoid',
padding='same',
name='decoder_output')(x)
# instantiate decoder model
decoder = Model(latent_inputs, outputs, name='decoder')
decoder.summary()
plot_model(decoder, to_file='vae_cnn_decoder.png', show_shapes=True)
# instantiate VAE model
outputs = decoder(encoder(inputs)[2])
vae = Model(inputs, outputs, name='vae')
if __name__ == '__main__':
parser = argparse.ArgumentParser()
help_ = "Load h5 model trained weights"
parser.add_argument("-w", "--weights", help=help_)
help_ = "Use mse loss instead of binary cross entropy (default)"
parser.add_argument("-m", "--mse", help=help_, action='store_true')
args = parser.parse_args()
models = (encoder, decoder)
data = (x_test, y_test)
# VAE loss = mse_loss or xent_loss + kl_loss
if args.mse:
reconstruction_loss = mse(K.flatten(inputs), K.flatten(outputs))
else:
reconstruction_loss = binary_crossentropy(K.flatten(inputs),
K.flatten(outputs))
reconstruction_loss *= image_size * image_size
kl_loss = 1 + z_log_var - K.square(z_mean) - K.exp(z_log_var)
kl_loss = K.sum(kl_loss, axis=-1)
kl_loss *= -0.5
vae_loss = K.mean(reconstruction_loss + kl_loss)
vae.add_loss(vae_loss)
vae.compile(optimizer='rmsprop')
vae.summary()
plot_model(vae, to_file='vae_cnn.png', show_shapes=True)
if args.weights:
vae.load_weights(args.weights)
else:
# train the autoencoder
vae.fit(x_train,
epochs=epochs,
batch_size=batch_size,
validation_data=(x_test, None))
vae.save_weights('vae_cnn_mnist.h5')
plot_results(models, data, batch_size=batch_size, model_name="vae_cnn")
</code></pre>
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