def _construct_encoder(self):
"""
CNN encoder.
"""
img = Input(shape=self.img_dim)
conv_block = convnet(img, self.enc_param, bias=False)
flat_1 = Flatten()(conv_block)
fc_1 = bn_dense(flat_1, self.hidden_dim)
z_mu = Dense(self.latent_dim)(fc_1)
z_log_sigma = Dense(self.latent_dim)(fc_1)
def sample_z(args):
mu, log_sigma = args
epsilon = K.random_normal(shape=K.shape(mu),
mean=self.mu,
stddev=self.std_dev)
return mu + K.exp(log_sigma / 2) * epsilon
def vae_loss_fn(x, x_decoded_mean):
x = K.flatten(x)
x_decoded_mean = K.flatten(x_decoded_mean)
flat_dim = np.product(self.img_dim)
reconst_loss = flat_dim * binary_crossentropy(x, x_decoded_mean)
kl_loss = -0.5 * K.sum(
1 + z_log_sigma - K.square(z_mu) - K.exp(z_log_sigma), axis=-1)
return reconst_loss + (self.beta * kl_loss)
z = Lambda(sample_z)([z_mu, z_log_sigma])
encoder = Model(img, z)
return encoder, vae_loss_fn
def _construct_encoder(self):
"""
CNN encoder with mmd loss.
"""
img = Input(shape=self.img_dim)
conv_block = convnet(img, self.enc_param)
flat_1 = Flatten()(conv_block)
fc_1 = bn_dense(flat_1, self.hidden_dim)
z = Dense(self.latent_dim)(fc_1)
def mmd_loss_fn(sample_qz, sample_pz):
"""
Taken mmd loss implementation from
https://github.com/tolstikhin/wae/blob/master/wae.py
"""
sigma2_p = 1.**2
C_base = 2. * self.latent_dim * self.std_dev
n = K.shape(sample_pz)[0]
n = K.cast(n, 'int32')
nf = K.cast(n, 'float32')
norms_pz = K.sum(K.square(sample_pz), axis=1, keepdims=True)
dotprods_pz = K.dot(sample_pz, K.transpose(sample_pz))
distances_pz = norms_pz + K.transpose(norms_pz) - 2. * dotprods_pz
norms_qz = K.sum(K.square(sample_qz), axis=1, keepdims=True)
dotprods_qz = K.dot(sample_qz, K.transpose(sample_qz))
distances_qz = norms_qz + K.transpose(norms_qz) - 2. * dotprods_qz
dotprods = K.dot(sample_qz, K.transpose(sample_pz))
distances = norms_qz + K.transpose(norms_pz) - 2. * dotprods
stat = 0.
for scale in [.1, .2, .5, 1., 2., 5., 10.]:
C = C_base * scale
res1 = C / (C + distances_qz)
res1 += C / (C + distances_pz)
res1 = tf.multiply(res1, 1. - tf.eye(n))
res1 = K.sum(res1) / (nf * nf - nf)
res2 = C / (C + distances)
res2 = K.sum(res2) * 2. / (nf * nf)
stat += res1 - res2
return stat
def vae_loss_fn(x, x_decoded):
reconst_loss = binary_crossentropy(K.flatten(x),
K.flatten(x_decoded))
mmd_loss = mmd_loss_fn(z, K.random_normal(K.shape(z)))
return reconst_loss + (self.mmd_weight * mmd_loss)
encoder = Model(img, z)
return encoder, vae_loss_fn
def _construct_critic(self):
img = Input(shape=self.img_dim)
conv_block = convnet(img, self.d_hidden, bias=False)
d_flat = Flatten()(conv_block)
d_dense = bn_dense(d_flat, 1024)
disc_out = bn_dense(d_dense, 1, activation='linear', use_bias=False)
critic = Model(img, disc_out)
critic.compile(optimizer=self.critic_opt(lr=self.critic_lr),
loss=wasserstein_loss)
return critic
def _construct_encoder(self):
"""
CNN encoder.
"""
img = Input(shape=self.img_dim)
conv_block = convnet(img, self.enc_param)
flat_1 = Flatten()(conv_block)
fc_1 = bn_dense(flat_1, self.hidden_dim)
z = Dense(self.latent_dim)(fc_1)
encoder = Model(img, z)
return encoder
def _construct_encoder(self):
"""
CNN encoder.
"""
img = Input(shape=self.img_dim)
conv_block = convnet(img, self.enc_param)
flat_1 = Flatten()(conv_block)
fc_1 = bn_dense(flat_1, self.hidden_dim)
z = Dense(self.latent_dim)(fc_1)
def vae_loss_fn(x, x_decoded):
reconst_loss = binary_crossentropy(K.flatten(x),
K.flatten(x_decoded))
mmd_loss = mmd_loss_fn(z, K.random_normal(K.shape(z)))
return reconst_loss + (self.mmd_weight * mmd_loss)
encoder = Model(img, z)
return encoder, vae_loss_fn
def _construct_critic(self):
img = Input(shape=self.img_dim)
d = convnet(img,
self.d_hidden,
batchnorm=False,
activation='selu',
bias=False)
d_flat = Flatten()(d)
d_fc = bn_dense(d_flat,
1024,
batchnorm=False,
activation='selu',
use_bias=False)
disc_out = Dense(1, use_bias=False)(d_fc)
critic = Model(img, disc_out)
# critic.compile(optimizer=self.critic_opt(lr=self.critic_lr),
# loss=wasserstein_loss)
return critic