# encoder network
with tf.sg_context(name='encoder', size=4, stride=2, act='relu'):
z = (x
.sg_conv(dim=64)
.sg_conv(dim=128)
.sg_flatten()
.sg_dense(dim=1024)
.sg_dense(dim=num_dim, act='linear'))
# decoder network
with tf.sg_context(name='decoder', size=4, stride=2, act='relu'):
xx = (z
.sg_dense(dim=1024)
.sg_dense(dim=7*7*128)
.sg_reshape(shape=(-1, 7, 7, 128))
.sg_upconv(dim=64)
.sg_upconv(dim=1, act='sigmoid'))
# add image summary
tf.sg_summary_image(x, name='origin')
tf.sg_summary_image(xx, name='recon')
# loss
loss = xx.sg_mse(target=x)
# do training
tf.sg_train(loss=loss, log_interval=10, ep_size=data.train.num_batch, save_dir='asset/train/sae')
# continuous latent variable z_con = tf.random_normal((batch_size, con_dim)) # random latent variable dimension z_rand = tf.random_normal((batch_size, rand_dim)) # latent variable z = tf.concat(1, [z_cat.sg_one_hot(depth=cat_dim), z_con, z_rand]) # # Computational graph # # generator gen = generator(z) # add image summary tf.sg_summary_image(x, name='real') tf.sg_summary_image(gen, name='fake') # discriminator disc_real, cat_real, _ = discriminator(x) disc_fake, cat_fake, con_fake = discriminator(gen) # # loss # # discriminator loss loss_d_r = disc_real.sg_bce(target=y_real, name='disc_real') loss_d_f = disc_fake.sg_bce(target=y_fake, name='disc_fake') loss_d = (loss_d_r + loss_d_f) / 2
x_n = x * snr + tf.random_uniform(x.get_shape()) * (1 - snr)
#
# Computational graph
#
# encoder network
with tf.sg_context(name='encoder', size=4, stride=2, act='relu'):
z = (x_n.sg_conv(dim=64).sg_conv(dim=128).sg_flatten().sg_dense(
dim=1024).sg_dense(dim=num_dim, act='linear'))
# decoder network
with tf.sg_context(name='decoder', size=4, stride=2, act='relu'):
xx = (z.sg_dense(dim=1024).sg_dense(dim=7 * 7 * 128).sg_reshape(
shape=(-1, 7, 7, 128)).sg_upconv(dim=64).sg_upconv(dim=1,
act='sigmoid'))
# add image summary
tf.sg_summary_image(x, name='origin')
tf.sg_summary_image(x_n, name='noised')
tf.sg_summary_image(xx, name='recon')
# loss
loss = xx.sg_mse(target=x)
# do training
tf.sg_train(loss=loss,
log_interval=10,
ep_size=data.train.num_batch,
save_dir='asset/train/dae')
y_disc = tf.concat([y, y * 0], 0)
#
# create generator
#
# I've used ESPCN scheme
# http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Shi_Real-Time_Single_Image_CVPR_2016_paper.pdf
#
# generator network
with tf.sg_context(name='generator', act='relu', bn=True):
gen = (x_small.sg_conv(dim=32).sg_conv().sg_conv(
dim=4, act='sigmoid', bn=False).sg_periodic_shuffle(factor=2))
# add image summary
tf.sg_summary_image(gen)
#
# input image pairs
#
x_real_pair = tf.concat([x_nearest, x], 3)
x_fake_pair = tf.concat([x_nearest, gen], 3)
#
# create discriminator & recognizer
#
# create real + fake image input
xx = tf.concat([x_real_pair, x_fake_pair], 0)
with tf.sg_context(name='discriminator', size=4, stride=2, act='leaky_relu'):
# create generator
#
# I've used ESPCN scheme
# http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Shi_Real-Time_Single_Image_CVPR_2016_paper.pdf
#
# generator network
with tf.sg_context(name='generator', act='relu', bn=True):
gen = (x_small
.sg_conv(dim=32)
.sg_conv()
.sg_conv(dim=4, act='sigmoid', bn=False)
.sg_periodic_shuffle(factor=2))
# add image summary
tf.sg_summary_image(gen)
#
# input image pairs
#
x_real_pair = tf.concat(3, [x_nearest, x])
x_fake_pair = tf.concat(3, [x_nearest, gen])
#
# create discriminator & recognizer
#
# create real + fake image input
xx = tf.concat(0, [x_real_pair, x_fake_pair])
with tf.sg_context(name='discriminator', size=4, stride=2, act='leaky_relu'):
