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VAE.py
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# # Imports
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import numpy as np
import tensorflow as tf
import tensorflow.contrib.distributions as dis
from keras import backend as K
from keras.layers import Input, Dense, Lambda, Layer
from keras.models import Model
from keras import metrics
from keras.datasets import mnist
from scipy.stats import norm
# # Variational autoencoder (VAE)
Normal = tf.contrib.distributions.Normal
t = dis.kl(Normal(3.0, 2.0), Normal(0.0, 1.0)) #mean, st dev
t2 = dis.kl(Normal(3.0, 1.0), Normal(2.9,1.0))
t3 = dis.kl(Normal(3.0, 1.0), Normal(3.0,1.0))
with tf.Session() as session:
t_val = session.run(t)
print ('KLD(N(3,2), N(0,1)) =', t_val, ", value = ", .5*(np.log(2) - 1 + 2.0 + 3**2 ))
t_val = session.run(t2)
print('KLD(N(3,1), N(2.9,1)) =', t_val)
t_val = session.run(t3)
print('KLD(N(3,1), N(3,1)) =', t_val)
# # Implementing the variational auto encoder
#hyper parameters
batch_size = 200
latent_dim = 2
intermediate_dim = 625
epochs = 20 #150
epsilon_std = 1.0
original_dim = 784
x = Input(shape=(original_dim,))
h = Dense(intermediate_dim, activation='relu')(x) #relu
#latent variables
z_mean = Dense(latent_dim)(h)
z_log_var = Dense(latent_dim)(h)
def sampling(args):
z_mean, z_log_var = args
epsilon = K.random_normal(shape=(K.shape(z_mean)[0], latent_dim), mean=0.,
stddev=epsilon_std)
return z_mean + K.exp(z_log_var / 2) * epsilon
z = Lambda(sampling, output_shape=(latent_dim,))([z_mean, z_log_var])
decoder_h = Dense(intermediate_dim, activation='relu')
decoder_mean = Dense(original_dim, activation='sigmoid')
h_decoded = decoder_h(z)
x_decoded_mean = decoder_mean(h_decoded)
# Custom loss layer
class CustomVariationalLayer(Layer):
def __init__(self, **kwargs):
self.is_placeholder = True
super(CustomVariationalLayer, self).__init__(**kwargs)
def vae_loss(self, x, x_decoded_mean):
xent_loss = original_dim * metrics.binary_crossentropy(x, x_decoded_mean)
kl_loss = - 0.5 * K.sum(1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=-1)
return K.mean(xent_loss + kl_loss)
def call(self, inputs):
x = inputs[0]
x_decoded_mean = inputs[1]
loss = self.vae_loss(x, x_decoded_mean)
self.add_loss(loss, inputs=inputs)
# We won't actually use the output.
return x
y = CustomVariationalLayer()([x, x_decoded_mean])
vae = Model(x, y)
vae.compile(optimizer='rmsprop', loss=None)
# train the VAE on MNIST digits
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))
x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))
history = vae.fit(x_train,
shuffle=True,
epochs=epochs,
batch_size=batch_size,
validation_data=(x_test, None))
encoder = Model(x, z_mean)
# plot history for loss
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'validation'], loc='upper right')
# display a 2D plot of the digit classes in the latent space
x_test_encoded = encoder.predict(x_test, batch_size=batch_size)
plt.figure(figsize=(8, 8))
colors = cm.rainbow(np.linspace(0, 1, len(set(y_test))))
colors = colors[np.asarray(y_test)]
plt.scatter(x_test_encoded[:, 0], x_test_encoded[:, 1], c=colors)
plt.xlabel("$\mu$")
plt.ylabel("$\log(\Sigma)$")
m = cm.ScalarMappable(cmap=cm.jet)
m.set_array(colors)
plt.colorbar(m)
# build a digit generator that can sample from the learned distribution
decoder_input = Input(shape=(latent_dim,))
_h_decoded = decoder_h(decoder_input)
_x_decoded_mean = decoder_mean(_h_decoded)
generator = Model(decoder_input, _x_decoded_mean)
# display a 2D manifold of the digits
n = 15 # figure with 15x15 digits
digit_size = 28
figure = np.zeros((digit_size * n, digit_size * n))
grid_x = norm.ppf(np.linspace(0.05, 0.95, n))
grid_y = norm.ppf(np.linspace(0.05, 0.95, n))
for i, yi in enumerate(grid_x):
for j, xi in enumerate(grid_y):
z_sample = np.array([[xi, yi]])
x_decoded = generator.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))
plt.imshow(figure, cmap='Greys_r')