def __init__(self, hidden_size, batch_size, learning_rate):
self.input_tensor = tf.placeholder(tf.float32, [None, 28 * 28])
self.is_training = tf.placeholder_with_default(True, [])
with arg_scope([layers.conv2d, layers.conv2d_transpose],
activation_fn=concat_elu,
normalizer_fn=layers.batch_norm,
normalizer_params={
'scale': True,
'is_training': self.is_training
}):
with tf.variable_scope("model"):
D1 = discriminator(self.input_tensor) # positive examples
D_params_num = len(tf.trainable_variables())
G = decoder(tf.random_normal([batch_size, hidden_size]))
self.sampled_tensor = G
with tf.variable_scope("model", reuse=True):
D2 = discriminator(G) # generated examples
D_loss = self.__get_discrinator_loss(D1, D2)
G_loss = self.__get_generator_loss(D2)
params = tf.trainable_variables()
D_params = params[:D_params_num]
G_params = params[D_params_num:]
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
g_update_ops = [
op for op in update_ops if op.name.startswith('model_1/')
]
d_update_ops = [op for op in update_ops if op not in g_update_ops]
# train_discrimator = optimizer.minimize(loss=D_loss, var_list=D_params)
# train_generator = optimizer.minimize(loss=G_loss, var_list=G_params)
global_step = tf.contrib.framework.get_or_create_global_step()
with tf.control_dependencies(d_update_ops):
self.train_discrimator = layers.optimize_loss(D_loss,
global_step,
learning_rate / 10,
'Adam',
variables=D_params,
update_ops=[])
with tf.control_dependencies(g_update_ops):
self.train_generator = layers.optimize_loss(G_loss,
global_step,
learning_rate,
'Adam',
variables=G_params,
update_ops=[])
self.sess = tf.Session()
self.sess.run(tf.global_variables_initializer())
def train_step(video_real, video_wrong, text):
num_clips, t, h, w, c = video_real.shape
word, sentence = bert([text])
word, sentence = tf.convert_to_tensor(word), tf.convert_to_tensor(sentence)
word = tf.squeeze(word)
z = tf.random.normal(shape=(1, 100))
with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
video_fake = generator(video_real, word, z)
# All frames put together with bs = 1
video_real = tf.reshape(video_real, (1, num_clips * t, h, w, c))
video_wrong = tf.reshape(video_wrong, (1, num_clips * t, h, w, c))
video_fake = tf.reshape(video_fake, (1, num_clips * t, h, w, c))
# Discriminator out
disc_video_real, disc_frame_real, disc_motion_real = discriminator(
video_real, sentence)
disc_video_wrong, disc_frame_wrong, disc_motion_wrong = discriminator(
video_wrong, sentence)
disc_video_fake, disc_frame_fake, disc_motion_fake = discriminator(
video_fake, sentence)
# Losses
total_video_loss = video_loss(disc_video_real, disc_video_wrong,
disc_video_fake)
total_frame_loss = frame_loss(disc_frame_real, disc_frame_wrong,
disc_frame_fake)
total_motion_loss = motion_loss(disc_motion_real, disc_motion_wrong,
disc_motion_fake)
disc_loss = discriminator_loss(total_video_loss, total_frame_loss,
total_motion_loss)
gen_loss = generator_loss(disc_video_fake, disc_frame_fake,
disc_motion_fake)
gradients_of_generator = gen_tape.gradient(gen_loss,
generator.trainable_variables)
gradients_of_discriminator = disc_tape.gradient(
disc_loss, discriminator.trainable_variables)
generator_optimizer.apply_gradients(
zip(gradients_of_generator, generator.trainable_variables))
discriminator_optimizer.apply_gradients(
zip(gradients_of_discriminator, discriminator.trainable_variables))
def __init__(self, hidden_size, batch_size, learning_rate, generate_size):
self.input_tensor = tf.placeholder(tf.float32, [None, 28 * 28])
with arg_scope([layers.conv2d, layers.conv2d_transpose],
activation_fn=concat_elu,
normalizer_fn=layers.batch_norm,
normalizer_params={'scale': True}):
with tf.variable_scope("model"):
D1 = discriminator(self.input_tensor) # positive examples
D_params_num = len(tf.trainable_variables())
G = decoder(tf.random_normal([batch_size, hidden_size]))
self.sampled_tensor = G
with tf.variable_scope("model", reuse=True):
D2 = discriminator(G) # generated examples
with tf.variable_scope("model", reuse=True):
self.sampled_tensor_gener = decoder(
tf.random_normal([generate_size, hidden_size]))
D_loss = self.__get_discrinator_loss(D1, D2)
G_loss = self.__get_generator_loss(D2)
params = tf.trainable_variables()
D_params = params[:D_params_num]
G_params = params[D_params_num:]
# train_discrimator = optimizer.minimize(loss=D_loss, var_list=D_params)
# train_generator = optimizer.minimize(loss=G_loss, var_list=G_params)
global_step = tf.contrib.framework.get_or_create_global_step()
self.train_discrimator = layers.optimize_loss(D_loss,
global_step,
learning_rate / 10,
'Adam',
variables=D_params,
update_ops=[])
self.train_generator = layers.optimize_loss(G_loss,
global_step,
learning_rate,
'Adam',
variables=G_params,
update_ops=[])
self.sess = tf.Session()
self.sess.run(tf.global_variables_initializer())
def gans(ginputs, dinputs, choice, freeze_D): gout = generator(ginputs).get_G() gout = tf.cond( freeze_D, lambda: gout, lambda: tf.stop_gradient(gout)) din = tf.cond( choice, lambda: tf.concat([gout, ginputs], axis = -1), lambda: tf.concat([dinputs, ginputs], axis = -1) ) dout = discriminator(din, freeze_D).get_D() return gout, dout
def define_network(self):
# Generators
# This one is used to generate fake data
self.gen_b_fake = generator(self.X_shoes,
self.initializer,
scope_name="generator_sb")
self.gen_s_fake = generator(self.X_bags,
self.initializer,
scope_name="generator_bs")
# Reconstruction Generators
# Note that parameters are being used from previous layers
self.gen_recon_s = generator(self.gen_b_fake,
self.initializer,
scope_name="generator_sb",
reuse=True)
self.gen_recon_b = generator(self.gen_s_fake,
self.initializer,
scope_name="generator_bs",
reuse=True)
# Discriminator for Shoes
self.disc_s_real = discriminator(self.X_shoes,
self.initializer,
scope_name="discriminator_s")
self.disc_s_fake = discriminator(self.gen_s_fake,
self.initializer,
scope_name="discriminator_s",
reuse=True)
# Discriminator for Bags
self.disc_b_real = discriminator(self.X_bags,
self.initializer,
scope_name="discriminator_b")
self.disc_b_fake = discriminator(self.gen_b_fake,
self.initializer,
reuse=True,
scope_name="discriminator_b")
def build_model(self):
noise_dim = self.config.noise_dim
image_dim = self.config.image_dim
learning_rate = self.config.learning_rate
#create placeholders
self.gen_input = tf.placeholder(tf.float32, shape=[None, noise_dim], name="input_noise")
self.disc_input = tf.placeholder(tf.float32, shape=[None, image_dim], name="disc_input")
self.parameters = give_me_parameters(self.config)
#build generator network
with tf.name_scope('generator') as scope:
self.gen_sample = generator(self.gen_input, self.parameters)
with tf.name_scope('discriminator') as scope:
disc_real = discriminator(self.disc_input, self.parameters)
disc_fake = discriminator(self.gen_sample, self.parameters)
with tf.name_scope('loss') as scope:
#make losses
self.gen_loss = -tf.reduce_mean(tf.log(disc_fake))
self.disc_loss = -tf.reduce_mean(tf.log(disc_real) + tf.log(1. - disc_fake))
with tf.name_scope('summaries') as scope:
gen_images = tf.reshape(self.gen_sample, [-1, 28, 28, 1])
tf.summary.scalar('Generative Loss', self.gen_loss)
tf.summary.scalar('Discriminator Loss', self.disc_loss)
#tf.summary.image("Generated image", gen_images)
tf.summary.image("Generated image", gen_images, 100, family='generated_images')
self.merged_summary = tf.summary.merge_all()
with tf.name_scope('optimizers') as scope:
#create Optimizers
optimizer_gen = tf.train.AdamOptimizer(learning_rate=learning_rate)
optimizer_disc = tf.train.AdamOptimizer(learning_rate=learning_rate)
gen_vars = give_gen_vars(self.parameters)
disc_vars = give_disc_vars(self.parameters)
self.train_gen = optimizer_gen.minimize(self.gen_loss, var_list=gen_vars)
self.train_disc = optimizer_disc.minimize(self.disc_loss, var_list=disc_vars)
import tensorflow as tf import os import utils batch_size = 100 iterations = 10000 im_size = 64 layer = 3 z_size = 100 z_in = tf.placeholder(shape=[batch_size,z_size], dtype=tf.float32) real_in = tf.placeholder(shape=[batch_size,im_size,im_size,layers], g = utils.generator(z_in,im_size, layers,batch_size) d = utils.discriminator(real_in,batch_size d_fake = utils.discriminator(g, batch_size, reuse=True) d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(d, tf.ones_like(d))) d_loss = d_loss_fake
def __init__(self,batch_size=10,im_size=64,channels=3,dtype=tf.float32,analytics=True): self.analytics = analytics self.batch_size = batch_size self.x_a = tf.placeholder(dtype,[None,im_size,im_size,channels],name='xa') self.x_b = tf.placeholder(dtype,[None,im_size,im_size,channels],name='xb') #Generator Networks self.g_ab = utils.generator(self.x_a,name="gen_AB",im_size=im_size) self.g_ba = utils.generator(self.x_b,name="gen_BA",im_size=im_size) #Secondary generator networks, reusing params of previous two self.g_aba = utils.generator(self.g_ab,name="gen_BA",im_size=im_size,reuse=True) self.g_bab = utils.generator(self.g_ba,name="gen_AB",im_size=im_size,reuse=True) #Discriminator for input a self.disc_a_real = utils.discriminator(self.x_a,name="disc_a",im_size=im_size) self.disc_a_fake = utils.discriminator(self.g_ba,name="disc_a",im_size=im_size,reuse=True) #Discriminator for input b self.disc_b_real = utils.discriminator(self.x_b,name="disc_b") self.disc_b_fake = utils.discriminator(self.g_ab,name="disc_b",reuse=True) #Reconstruction loss for generators self.l_const_a = tf.reduce_mean(utils.huber_loss(self.g_aba,self.x_a)) self.l_const_b = tf.reduce_mean(utils.huber_loss(self.g_bab,self.x_b)) #Generation loss for generators self.l_gan_a = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=self.disc_a_fake,labels=tf.ones_like(self.disc_a_fake))) self.l_gan_b = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=self.disc_b_fake,labels=tf.ones_like(self.disc_b_fake))) #Real example loss for discriminators self.l_disc_a_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=self.disc_a_real,labels=tf.ones_like(self.disc_a_real))) self.l_disc_b_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=self.disc_b_real,labels=tf.ones_like(self.disc_b_real))) #Fake example loss for discriminators self.l_disc_a_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=self.disc_a_fake,labels=tf.zeros_like(self.disc_a_fake))) self.l_disc_b_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=self.disc_b_fake,labels=tf.zeros_like(self.disc_b_fake))) #Combined loss for individual discriminators self.l_disc_a = self.l_disc_a_real + self.l_disc_a_fake self.l_disc_b = self.l_disc_b_real + self.l_disc_b_fake #Total discriminator loss self.l_disc = self.l_disc_a + self.l_disc_b #Combined loss for individual generators self.l_ga = self.l_gan_a + self.l_const_b self.l_gb = self.l_gan_b + self.l_const_a #Total GAN loss self.l_g = self.l_ga + self.l_gb #Parameter Lists self.disc_params = [] self.gen_params = [] for v in tf.trainable_variables(): if 'disc' in v.name: self.disc_params.append(v) if 'gen' in v.name: self.gen_params.append(v) if self.analytics: self.init_analytics() self.gen_a_dir = 'generator a->b' self.gen_b_dir = 'generator b->a' self.rec_a_dir = 'reconstruct a' self.rec_b_dir = 'reconstruct b' self.model_directory = "models" if not os.path.exists(self.gen_a_dir): os.makedirs(self.gen_a_dir) if not os.path.exists(self.gen_b_dir): os.makedirs(self.gen_b_dir) if not os.path.exists(self.rec_b_dir): os.makedirs(self.rec_b_dir) if not os.path.exists(self.rec_a_dir): os.makedirs(self.rec_a_dir) self.sess = tf.Session() self.saver = tf.train.Saver()
import tensorflow as tf
import os
import utils
tf.reset_default_graph()
batch_size = 1
iterations = 100000
sample_directory = './figs'
model_directory = './models'
im_size = 64
layers = 3
z_size = 100
z_in = tf.placeholder(shape=[batch_size, z_size], dtype=tf.float32)
real_in = tf.placeholder(shape=[batch_size, im_size, im_size, layers],
dtype=tf.float32)
g = utils.generator(z_in, im_size, layers, batch_size)
d = utils.discriminator(real_in, batch_size)
d2 = utils.discriminator(g, batch_size, reuse=True)
d_loss_real = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d, labels=tf.ones_like(d)))
d_loss_fake = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d2,
labels=tf.zeros_like(d2)))
d_loss = d_loss_real + d_loss_fake
g_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d2,
labels=tf.ones_like(d2)))
tvars = tf.trainable_variables()
g_params = []
d_params = []
for var in tvars:
domainB = "EDGES" #Input placeholders x_a = tf.placeholder(tf.float32,shape=[BATCH_SIZE,64,64,3],name="xa") x_b = tf.placeholder(tf.float32,shape=[BATCH_SIZE,64,64,3],name="xb") #Generator Networks g_ab = utils.generator(x_a,BATCH_SIZE,name="gen_AB") g_ba = utils.generator(x_b,BATCH_SIZE,name="gen_BA") #Secondary generator networks, reusing params of previous two g_aba = utils.generator(g_ab,BATCH_SIZE,name="gen_BA",reuse=True) g_bab = utils.generator(g_ba,BATCH_SIZE,name="gen_AB",reuse=True) #Discriminator for input a disc_a_real = utils.discriminator(x_a,name="disc_a") disc_a_fake = utils.discriminator(g_ba,name="disc_a",reuse=True) #Discriminator for input b disc_b_real = utils.discriminator(x_b,name="disc_b") disc_b_fake = utils.discriminator(g_ab,name="disc_b",reuse=True) #Reconstruction loss for generators l_const_a = tf.reduce_mean(utils.huber_loss(g_aba,x_a)) l_const_b = tf.reduce_mean(utils.huber_loss(g_bab,x_b)) #Generation loss for generators l_gan_a = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=disc_a_fake,labels=tf.ones_like(disc_a_fake))) l_gan_b = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=disc_b_fake,labels=tf.ones_like(disc_b_fake))) #Real example loss for discriminators
x = (x - 0.5) / 0.5
n = len(x)
nz = params.nz
ngf = params.uncon_ngf
ndf = params.uncon_ndf
assert (
params.uncon_shape[0] == params.uncon_shape[1]), "Images must be square!"
sz = params.uncon_shape[0]
nc = params.uncon_shape[2]
batchSize = params.uncon_batch_size
lr = params.uncon_lr
niter = params.uncon_niter
netG = utils.generator(sz, nz, ngf, nc)
netD = utils.discriminator(sz, nz, ndf, nc)
criterion = nn.BCELoss()
input = torch.FloatTensor(batchSize, nc, sz, sz)
noise = torch.FloatTensor(batchSize, nz, 1, 1)
fixed_noise = torch.FloatTensor(batchSize, nz, 1, 1).normal_(0, 1)
label = torch.FloatTensor(batchSize)
real_label = 1
fake_label = 0
netD.cuda()
netG.cuda()
criterion.cuda()
input, label = input.cuda(), label.cuda()
noise, fixed_noise = noise.cuda(), fixed_noise.cuda()
NOISE_DIM = 100
batch_size = 128
mnist = datasets.MNIST('./MNIST_data',
train=True,
download=True,
transform=transforms.ToTensor())
loader_train = DataLoader(mnist, batch_size=batch_size, drop_last=True)
imgs = loader_train.__iter__().next()[0].view(batch_size,
784).numpy().squeeze()
show_images(imgs)
#Training original GAN
D = discriminator().to(device)
G = generator().to(device)
D_optimizer = torch.optim.Adam(D.parameters(), lr=1e-3, betas=(0.5, 0.999))
G_optimizer = torch.optim.Adam(G.parameters(), lr=1e-3, betas=(0.5, 0.999))
train(D,
G,
D_optimizer,
G_optimizer,
discriminator_loss,
generator_loss,
train_loader=loader_train,
num_epochs=10,
device=device)
def __init__(self,
hidden_size,
batch_size,
learning_rate,
alpha,
beta,
gamma,
sum_dir,
attri_num,
add_gan=1,
GAN_model="V",
similarity_layer=4):
print("\nInitializing model with following parameters:")
print("batch_size:", batch_size, " learning_rate:", learning_rate,
" alpha:", alpha, " beta:", beta, " gamma:", gamma)
print("GAN_model:", GAN_model, " similarity_layer:", similarity_layer,
"\n")
self.input_tensor = tf.placeholder(tf.float32, [batch_size, 64, 64, 3])
#self.input_label = tf.placeholder(tf.int, [batch_size, attri_num])
self.visual_attri = tf.placeholder(tf.float32, [hidden_size])
with arg_scope([layers.conv2d, layers.conv2d_transpose],
activation_fn=tf.nn.relu,
normalizer_fn=layers.batch_norm,
normalizer_params={'scale': True},
padding='SAME'):
with tf.variable_scope("model") as scope: #Full VAEGAN structure
# Encoder
ENC = encoder(self.input_tensor, hidden_size * 2)
Enc_params_num = len(tf.trainable_variables())
# Add noise
self.mean, stddev = tf.split(1, 2, ENC)
stddev = tf.sqrt(tf.exp(stddev))
epsilon = tf.random_normal(
[tf.shape(self.mean)[0], hidden_size])
ENC_w_noise = self.mean + epsilon * stddev
# Decoder / Generator
self.DEC_of_ENC = decoder(ENC_w_noise)
Enc_n_Dec_params_num = len(tf.trainable_variables())
# Discriminator
if add_gan == 1:
DIS_of_DEC_of_ENC = discriminator(self.DEC_of_ENC,
GAN_model)
Gen_dis_sum = tf.scalar_summary(
'Gen_dis_mean', tf.reduce_mean(DIS_of_DEC_of_ENC))
with tf.variable_scope(
"model", reuse=True) as scope: #Computation for Recon_Loss
if add_gan == 1:
Real_Similarity = discriminator(self.input_tensor,
GAN_model,
extract=similarity_layer)
Gen_Similarity = discriminator(
self.DEC_of_ENC, GAN_model, extract=similarity_layer
) #+ tf.random_normal([batch_size, 8, 8, 256])
with tf.variable_scope(
"model", reuse=True) as scope: #Computation for GAN_Loss
if add_gan == 1:
Real_in_Dis = discriminator(self.input_tensor, GAN_model)
Real_dis_sum = tf.scalar_summary(
'Real_dis_mean', tf.reduce_mean(Real_in_Dis))
Prior_in_Dis = discriminator(
decoder(tf.random_normal([batch_size, hidden_size])),
GAN_model)
Prior_dis_sum = tf.scalar_summary(
'Prior_dis_mean', tf.reduce_mean(Prior_in_Dis))
with tf.variable_scope(
"model", reuse=True) as scope: #Sample from latent space
self.sampled_tensor = decoder(
tf.random_normal([batch_size, hidden_size]))
with tf.variable_scope(
"model", reuse=True) as scope: #Add visual attributes
#expand_mean = tf.expand_dims(self.mean, -1)
print("shape of mean:", np.shape(self.mean),
" shape of visual attri:", np.shape(self.visual_attri))
add_attri = self.mean + np.ones(
[batch_size, 1]
) * self.visual_attri #[batch size, hidden size] (broadcasting)
print("shape of add attri:", tf.shape(add_attri))
self.with_attri_tensor = decoder(add_attri)
self.params = tf.trainable_variables()
self.Enc_params = self.params[:Enc_params_num]
'''
print ('Encoder Param:')
for var in Enc_params:
print (var.name)
'''
self.Dec_params = self.params[Enc_params_num:Enc_n_Dec_params_num]
'''
print ('Decoder Param:')
for var in Dec_params:
print (var.name)
'''
if add_gan == 1:
self.Dis_params = self.params[Enc_n_Dec_params_num:]
'''
print ('Discriminator Param:')
for var in Dis_params:
print (var.name)
'''
self.Prior_loss = self.__get_prior_loss(self.mean, stddev)
Prior_loss_sum = tf.scalar_summary('Prior_loss', self.Prior_loss)
if add_gan == 1:
self.Recon_loss = self.__get_reconstruction_loss(
Gen_Similarity, Real_Similarity)
Recon_loss_sum = tf.scalar_summary('Recon_loss', self.Recon_loss)
self.GAN_loss = self.__get_GAN_loss(Real_in_Dis, Prior_in_Dis,
DIS_of_DEC_of_ENC, GAN_model)
GAN_loss_sum = tf.scalar_summary('GAN_loss', self.GAN_loss)
else:
self.Recon_loss = self.__get_reconstruction_loss(
self.DEC_of_ENC, self.input_tensor)
Recon_loss_sum = tf.scalar_summary('Recon_loss', self.Recon_loss)
# merge summary for Tensorboard
if add_gan == 1:
self.detached_loss_summary_merged = tf.merge_summary([
Prior_loss_sum, Recon_loss_sum, GAN_loss_sum, Real_dis_sum,
Prior_dis_sum, Gen_dis_sum
])
#self.dis_mean_value_summary_merged = tf.merge_summary([Real_dis_sum,Prior_dis_sum,Gen_dis_sum])
else:
self.detached_loss_summary_merged = tf.merge_summary(
[Prior_loss_sum, Recon_loss_sum])
if add_gan == 1:
enc_loss = self.Prior_loss + beta * self.Recon_loss
dec_loss = gamma * self.Recon_loss + self.GAN_loss
dis_loss = (-1) * self.GAN_loss
else:
total_loss = self.Prior_loss + beta * self.Recon_loss
#self.combined_loss_summary_merged = tf.merge_summary([self.prior_loss_sum,self.recon_loss_sum,self.GAN_loss_sum])
if add_gan == 1:
self.train_enc = layers.optimize_loss(enc_loss, tf.contrib.framework.get_or_create_global_step(\
), learning_rate=learning_rate, variables = self.Enc_params, optimizer='RMSProp', update_ops=[])
self.train_dec = layers.optimize_loss(dec_loss, tf.contrib.framework.get_or_create_global_step(\
), learning_rate=learning_rate, variables = self.Dec_params, optimizer='RMSProp', update_ops=[])
self.train_dis = layers.optimize_loss(dis_loss, tf.contrib.framework.get_or_create_global_step(\
), learning_rate=learning_rate * alpha, variables = self.Dis_params, optimizer='RMSProp', update_ops=[])
else:
self.train = layers.optimize_loss(total_loss, tf.contrib.framework.get_or_create_global_step(\
), learning_rate=learning_rate, variables = self.params, optimizer='RMSProp', update_ops=[])
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = tf.Session(config=config)
self.sess.run(tf.initialize_all_variables())
self.train_writer = tf.train.SummaryWriter(sum_dir + '/train',
self.sess.graph)
def __init__(self, hidden_size, batch_size, learning_rate):
self.input_tensor = tf.placeholder(tf.float32, [None, 28 * 28])
self.xs2 = tf.placeholder(tf.float32, [None, 28 * 28])
self.dis = tf.placeholder(tf.float32, [1, None])
self.flag = tf.placeholder(tf.float32, [1, None])
with arg_scope([layers.conv2d, layers.conv2d_transpose],
activation_fn=concat_elu,
normalizer_fn=layers.batch_norm,
normalizer_params={'scale': True}):
with tf.variable_scope("model"):
D1 = discriminator(self.input_tensor) # positive examples
D_params_num = len(tf.trainable_variables())
encoded = encoder(self.input_tensor, hidden_size * 2)
mean = encoded[:, :hidden_size]
stddev = tf.sqrt(tf.square(encoded[:, hidden_size:]))
epsilon = tf.random_normal([tf.shape(mean)[0], hidden_size])
input_sample = mean + epsilon * stddev
# G = decoder(tf.random_normal([batch_size, hidden_size]))
G_params_num = len(tf.trainable_variables())
G = decoder(input_sample)
self.sampled_tensor = G
with tf.variable_scope("model", reuse=True):
D2 = discriminator(G) # generated examples
encoded1 = encoder(self.xs2, hidden_size * 2)
mean1 = encoded1[:, :hidden_size]
stddev1 = tf.sqrt(tf.square(encoded1[:, hidden_size:]))
epsilon1 = tf.random_normal([tf.shape(mean1)[0], hidden_size])
input_sample1 = mean1 + epsilon1 * stddev1
output_tensor1 = decoder(input_sample1)
D_loss = self.__get_discrinator_loss(D1, D2)
G_loss = self.__get_generator_loss(D2, mean, stddev, mean1)
params = tf.trainable_variables()
D_params = params[:D_params_num]
G_params = params[G_params_num:]
# train_discrimator = optimizer.minimize(loss=D_loss, var_list=D_params)
# train_generator = optimizer.minimize(loss=G_loss, var_list=G_params)
global_step = tf.contrib.framework.get_or_create_global_step()
self.train_discrimator = layers.optimize_loss(D_loss,
global_step,
learning_rate / 10,
'Adam',
variables=D_params,
update_ops=[])
self.train_generator = layers.optimize_loss(G_loss,
global_step,
learning_rate,
'Adam',
variables=G_params,
update_ops=[])
self.sess = tf.Session()
self.sess.run(tf.global_variables_initializer())
def __init__(self, input_size, hidden_size, batch_size, learning_rate,
log_dir):
self.input_tensor = tf.placeholder(tf.float32, [None, 3 * input_size])
self.s_t_p_placeholder = tf.placeholder(tf.float32,
[None, hidden_size])
'''
##################################
with open('params.txt') as f:
first = f.readline()
first = first.strip('\n')
temp = first.split(' ')
o_p_dim = int(temp[3]);
s_p_dim = int(temp[4]);
ln = f.readline()
for i in range(s_p_dim):
temp = f.readline()
self.sig_2_init = np.zeros((s_p_dim, s_p_dim), np.float32)
for i in range(s_p_dim):
temp = f.readline().strip('\n').split(' ')
for j in range(s_p_dim):
self.sig_2_init[i,j] = float(temp[j])
eig_val , eig_vec = np.linalg.eig(self.sig_2_init)
cf = np.sqrt(np.repeat(eig_val,s_p_dim).reshape(s_p_dim,s_p_dim).transpose())
self.r_2_init = np.multiply(cf,eig_vec)
self.sig_3_init = np.zeros((o_p_dim, o_p_dim), np.float32)
for i in range(o_p_dim):
temp = f.readline().strip('\n').split(' ')
for j in range(o_p_dim):
self.sig_3_init[i,j] = float(temp[j])
eig_val , eig_vec = np.linalg.eig(self.sig_3_init)
cf = np.sqrt(np.repeat(eig_val,o_p_dim).reshape(o_p_dim,o_p_dim).transpose())
self.r_3_init = np.multiply(cf,eig_vec)
self.a_2_init = np.zeros((s_p_dim, s_p_dim), np.float32)
for i in range(s_p_dim):
temp = f.readline().strip('\n').split(' ')
for j in range(s_p_dim):
self.a_2_init[i,j] = float(temp[j])
self.a_3_init = np.zeros((s_p_dim, o_p_dim), np.float32)
for i in range(s_p_dim):
temp = f.readline().strip('\n').split(' ')
for j in range(o_p_dim):
self.a_3_init[i,j] = float(temp[j])
###################################
'''
with arg_scope([layers.fully_connected], activation_fn=tf.nn.relu):
with tf.variable_scope("encoder"):
with tf.variable_scope("encoder_s_t"):
self.s_t_minus_1_p = encoder(self.input_tensor[:, :input_size],\
hidden_size)
with tf.variable_scope("encoder_s_t", reuse=True):
self.s_t_p = encoder(self.input_tensor[:, input_size:2 * input_size],\
hidden_size)
with tf.variable_scope("encoder_o_t"):
self.o_t_p = encoder(self.input_tensor[:, 2 * input_size:],\
hidden_size)
with tf.variable_scope("decoder"):
with tf.variable_scope("decoder_s_t"):
self.output_s_t_minus_1 = decoder(self.s_t_minus_1_p,
input_size)
with tf.variable_scope("decoder_s_t", reuse=True):
self.output_s_t = decoder(self.s_t_p, input_size)
with tf.variable_scope("decoder_s_t", reuse=True):
self.s_t_decoded = decoder(self.s_t_p_placeholder,
input_size)
with tf.variable_scope("decoder_o_t"):
self.output_o_t = decoder(self.o_t_p, input_size)
self.output_tensor = tf.concat(
[self.output_s_t_minus_1, self.output_s_t, self.output_o_t],
axis=1)
#self.a_2, self.b_2, self.sigma_2, self.a_3, self.b_3, self.sigma_3 = self._MLE_Gaussian_params()
self.a_2, self.b_2, self.sigma_2, self.a_3, self.b_3, self.sigma_3 = self._simple_Gaussian_params(
)
#self.a_2, self.b_2, self.sigma_2, self.a_3, self.b_3, self.sigma_3 = self._simple_Gaussian_plus_offset_params()
self.r_2 = tf.cholesky(self.sigma_2)
self.r_3 = tf.cholesky(self.sigma_3)
#define reconstruction loss
reconstruction_loss = tf.reduce_mean(tf.norm(self.output_tensor - \
self.input_tensor, axis=1))
# define classification loss
y_1 = self.s_t_p - tf.matmul(self.s_t_minus_1_p, self.a_2)
mvn_1 = tf.contrib.distributions.MultivariateNormalFull(
self.b_2, self.sigma_2)
#mvn_1 = tf.contrib.distributions.MultivariateNormalTrill(self.b_2, scale_tril=self.r_2)
pos_samples_1 = mvn_1.sample(batch_size)
y_2 = self.o_t_p - tf.matmul(self.s_t_p, self.a_3)
#mvn_2 = tf.contrib.distributions.MultivariateNormalTriL(self.b_3, scale_tril=self.r_3)
mvn_2 = tf.contrib.distributions.MultivariateNormalFull(
self.b_3, self.sigma_3)
pos_samples_2 = mvn_2.sample(batch_size)
with tf.variable_scope('discriminator'):
with tf.variable_scope('d1'):
pos_samples_1_pred = discriminator(pos_samples_1)
with tf.variable_scope('d1', reuse=True):
neg_samples_1_pred = discriminator(y_1)
with tf.variable_scope('d2'):
pos_samples_2_pred = discriminator(pos_samples_2)
with tf.variable_scope('d2', reuse=True):
neg_samples_2_pred = discriminator(y_2)
classification_loss_1 = compute_classification_loss(
pos_samples_1_pred, neg_samples_1_pred)
classification_loss_2 = compute_classification_loss(
pos_samples_2_pred, neg_samples_2_pred)
classification_loss = classification_loss_1 + classification_loss_2
# define s_t likelihood
s_diff = self.s_t_p - tf.matmul(self.s_t_minus_1_p, self.a_2)
s_t_likelihood = tf.reduce_sum(mvn_1.log_prob(s_diff))
# define o_t likelihood
o_diff = self.o_t_p - tf.matmul(self.s_t_p, self.a_3)
o_t_likelihood = tf.reduce_sum(mvn_2.log_prob(o_diff))
self.likelihood = s_t_likelihood + o_t_likelihood
# add summary ops
tf.summary.scalar('likelihood', self.likelihood)
tf.summary.scalar('s_t_likelihood', s_t_likelihood)
tf.summary.scalar('o_t_likelihood', o_t_likelihood)
tf.summary.scalar('classification_loss', classification_loss)
tf.summary.scalar('classification_loss_1', classification_loss_1)
tf.summary.scalar('classification_loss_2', classification_loss_2)
tf.summary.scalar('reconstruction_loss', reconstruction_loss)
# define references to params
encoder_params = tf.get_collection(
tf.GraphKeys.TRAINABLE_VARIABLES, scope='encoder')
decoder_params = tf.get_collection(
tf.GraphKeys.TRAINABLE_VARIABLES, scope='decoder')
autoencoder_params = encoder_params + decoder_params
gaussian_params = [self.a_2, self.a_3, self.r_2, self.r_3]
discriminator_params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, \
scope='discriminator')
global_step = tf.contrib.framework.get_or_create_global_step()
# define training steps
self.learn_rate = self._get_learn_rate(global_step, learning_rate)
# update autoencoder params to minimise reconstruction loss
self.train_autoencoder = layers.optimize_loss(reconstruction_loss, \
global_step, self.learn_rate * 0.1, optimizer=lambda lr: \
tf.train.AdamOptimizer(lr), variables=\
#tf.train.MomentumOptimizer(lr, 0.9), variables=\
autoencoder_params, update_ops=[])
# update discriminator
self.train_discriminator = layers.optimize_loss(classification_loss, \
global_step, self.learn_rate * 10, optimizer=lambda lr: \
tf.train.MomentumOptimizer(lr, 0.1), variables=\
#tf.train.AdamOptimizer(lr), variables=\
discriminator_params, update_ops=[])
# update encoder params to fool the discriminator
self.train_encoder = layers.optimize_loss(-classification_loss, \
global_step, self.learn_rate , optimizer=lambda lr: \
#tf.train.MomentumOptimizer(lr, 0.9), variables=\
tf.train.AdamOptimizer(lr), variables=\
encoder_params, update_ops=[])
self.sess = tf.Session()
self.merged = tf.summary.merge_all()
self.train_writer = tf.summary.FileWriter(log_dir, \
self.sess.graph)
self.sess.run(tf.global_variables_initializer())
def __init__(self, hidden_size, batch_size, learning_rate, log_dir):
self.input_tensor = tf.placeholder(tf.float32, [None, 28 * 28])
# add gaussian noise to the input
input_with_noise = gaussian_noise_layer(self.input_tensor, 0.3)
with arg_scope([layers.fully_connected], activation_fn=tf.nn.relu):
with tf.variable_scope("encoder"):
self.latent_representation = encoder(input_with_noise,
hidden_size)
encoder_params_num = len(tf.trainable_variables())
with tf.variable_scope('encoder', reuse=True):
self.true_latent_representation = encoder(self.input_tensor,
hidden_size)
with tf.variable_scope('decoder'):
self.recons = decoder(self.latent_representation)
autoencoder_params_num = len(tf.trainable_variables())
with tf.variable_scope('decoder', reuse=True):
self.sampled_imgs = decoder(tf.random_normal([batch_size,
hidden_size]))
pos_samples = tf.random_normal([batch_size, hidden_size],
stddev=5.)
neg_samples = self.latent_representation
with tf.variable_scope('discriminator'):
pos_samples_pred = discriminator(pos_samples)
with tf.variable_scope('discriminator', reuse=True):
neg_samples_pred = discriminator(neg_samples)
#define losses
reconstruction_loss = tf.reduce_mean(tf.square(self.recons -
self.input_tensor)) #* 28 * 28 scale recons loss
classification_loss = tf.losses.sigmoid_cross_entropy(\
tf.ones(tf.shape(pos_samples_pred)), pos_samples_pred) +\
tf.losses.sigmoid_cross_entropy(tf.zeros(
tf.shape(neg_samples_pred)), neg_samples_pred)
tf.summary.scalar('reconstruction_loss', reconstruction_loss)
tf.summary.scalar('classification_loss', classification_loss)
# define references to params
params = tf.trainable_variables()
encoder_params = params[:encoder_params_num]
decoder_params = params[encoder_params_num:autoencoder_params_num]
autoencoder_params = encoder_params + decoder_params
discriminator_params = params[autoencoder_params_num:]
# record true positive rate and true negative rate
correct_pred_pos = tf.equal(tf.cast(pos_samples_pred>0, tf.float32),
tf.ones(tf.shape(pos_samples_pred)))
self.true_pos_rate = tf.reduce_mean(tf.cast(correct_pred_pos,
tf.float32))
correct_pred_neg = tf.equal(tf.cast(neg_samples_pred<0, tf.float32),
tf.ones(tf.shape(pos_samples_pred)))
self.true_neg_rate = tf.reduce_mean(tf.cast(correct_pred_neg,
tf.float32))
tf.summary.scalar('true_pos_rate', self.true_pos_rate)
tf.summary.scalar('true_neg_rate', self.true_neg_rate)
global_step = tf.contrib.framework.get_or_create_global_step()
self.learn_rate = self._get_learn_rate(global_step, learning_rate)
self.train_autoencoder = layers.optimize_loss(reconstruction_loss,
global_step, self.learn_rate/10, optimizer=lambda lr: \
tf.train.MomentumOptimizer(lr, momentum=0.9), variables=
autoencoder_params, update_ops=[])
self.train_discriminator = layers.optimize_loss(classification_loss,
global_step, self.learn_rate, optimizer=lambda lr: \
tf.train.MomentumOptimizer(lr, momentum=0.1), variables=
discriminator_params, update_ops=[])
self.train_encoder = layers.optimize_loss(-classification_loss,
global_step, self.learn_rate/10, optimizer=lambda lr: \
tf.train.MomentumOptimizer(lr, momentum=0.1), variables=
encoder_params, update_ops=[])
self.sess = tf.Session()
self.merged = tf.summary.merge_all()
self.train_writer = tf.summary.FileWriter(log_dir,
self.sess.graph)
self.sess.run(tf.global_variables_initializer())
