def __init__(self,
num,
definition,
limits=default_limits,
internal_rng=False,
name=None):
assert len(limits) == 2
assert limits[1] > limits[0]
self.limits = tuple(float(l) for l in limits)
self.span = limits[1] - limits[0]
if len(definition) != 1:
raise ValueError(
'definition should have 1 parameter (dim), not %d' %
len(definition))
try:
dim = int(definition[0])
except ValueError:
raise ValueError('non-integer dim: %s' % dim)
self.recon_dim = self.sample_dim = dim
self.num = num
self.rangekw = dict(low=self.limits[0], high=self.limits[1])
if internal_rng:
self.placeholders = [
t_rng.uniform(size=(num, dim), **self.rangekw)
]
else:
self.placeholders = [T.matrix()]
self.flat_data = [Output(self.placeholders[0], shape=(self.num, dim))]
def main():
# Parameters
task = 'toy'
name = '25G'
DIM=512
begin_save = 0
loss_type = ['trickLogD','minimax','ls']
nloss = 3
DATASET = '25gaussians'
batchSize = 64
ncandi = 1
kD = 1 # # of discrim updates for each gen update
kG = 1 # # of discrim updates for each gen update
ntf = 256
b1 = 0.5 # momentum term of adam
nz = 2 # # of dim for Z
niter = 4 # # of iter at starting learning rate
lr = 0.0001 # initial learning rate for adam G
lrd = 0.0001 # initial learning rate for adam D
N_up = 100000
save_freq = 10000
show_freq = 10000
test_deterministic = True
beta = 1.
GP_norm = False # if use gradients penalty on discriminator
LAMBDA = 2. # hyperparameter of GP
# Load the dataset
# MODEL D
print("Building model and compiling functions...")
# Prepare Theano variables for inputs and targets
real_imgs = T.matrix('real_imgs')
fake_imgs = T.matrix('fake_imgs')
# Create neural network model
discriminator = models_uncond.build_discriminator_toy(nd=DIM, GP_norm=GP_norm)
# Create expression for passing real data through the discriminator
real_out = lasagne.layers.get_output(discriminator, real_imgs)
# Create expression for passing fake data through the discriminator
fake_out = lasagne.layers.get_output(discriminator, fake_imgs)
# Create loss expressions
discriminator_loss = (lasagne.objectives.binary_crossentropy(real_out, 1)
+ lasagne.objectives.binary_crossentropy(fake_out, 0)).mean()
# Gradients penalty norm
if GP_norm is True:
alpha = t_rng.uniform((batchSize,1), low=0.,high=1.)
differences = fake_imgs - real_imgs
interpolates = real_imgs + (alpha*differences)
gradients = theano.grad(lasagne.layers.get_output(discriminator, interpolates).sum(), wrt=interpolates)
slopes = T.sqrt(T.sum(T.sqr(gradients), axis=(1)))
gradient_penalty = T.mean((slopes-1.)**2)
D_loss = discriminator_loss + LAMBDA*gradient_penalty
b1_d = 0.
else:
D_loss = discriminator_loss
b1_d = 0.
# Create update expressions for training
discriminator_params = lasagne.layers.get_all_params(discriminator, trainable=True)
lrtd = theano.shared(lasagne.utils.floatX(lrd))
updates_d = lasagne.updates.adam(
D_loss, discriminator_params, learning_rate=lrtd, beta1=b1_d)
lrt = theano.shared(lasagne.utils.floatX(lr))
# Fd Socre
Fd = theano.gradient.grad(discriminator_loss, discriminator_params)
Fd_score = beta*T.log(sum(T.sum(T.sqr(x)) for x in Fd))
# Compile a function performing a training step on a mini-batch (by giving
# the updates dictionary) and returning the corresponding training loss:
train_d = theano.function([real_imgs, fake_imgs],
discriminator_loss,
updates=updates_d)
# Compile another function generating some data
dis_fn = theano.function([real_imgs,fake_imgs],[(fake_out).mean(),Fd_score])
disft_fn = theano.function([real_imgs,fake_imgs],
[real_out.mean(),
fake_out.mean(),
(real_out>0.5).mean(),
(fake_out>0.5).mean(),
Fd_score])
# Finally, launch the training loop.
print("Starting training...")
desc = task + '_' + name
print desc
if not os.path.isdir('logs'):
os.mkdir(os.path.join('logs'))
f_log = open('logs/%s.ndjson'%desc, 'wb')
if not os.path.isdir('models'):
os.mkdir(os.path.join('models/'))
if not os.path.isdir('models/'+desc):
os.mkdir(os.path.join('models/',desc))
gen_new_params = []
# We iterate over epochs:
for n_updates in range(N_up):
xmb = toy_dataset(DATASET=DATASET, size=batchSize*kD)
xmb = xmb[0:batchSize*kD]
# initial G cluster
if n_updates == 0:
for can_i in range(0,ncandi):
train_g, gen_fn, generator = create_G(
loss_type=loss_type[can_i%nloss],
discriminator=discriminator, lr=lr, b1=b1, DIM=DIM)
for _ in range(0,kG):
zmb = floatX(np_rng.uniform(-1., 1., size=(batchSize, nz)))
cost = train_g(zmb)
sample_zmb = floatX(np_rng.uniform(-1., 1., size=(ntf, nz)))
gen_imgs = gen_fn(sample_zmb)
gen_new_params.append(lasagne.layers.get_all_param_values(generator))
if can_i == 0:
g_imgs_old=gen_imgs
fmb = gen_imgs[0:batchSize/ncandi*kD,:]
else:
g_imgs_old = np.append(g_imgs_old,gen_imgs,axis=0)
fmb = np.append(fmb,gen_imgs[0:batchSize/ncandi*kD,:],axis=0)
#print gen_new_params
# MODEL G
noise = T.matrix('noise')
generator = models_uncond.build_generator_toy(noise,nd=DIM)
Tgimgs = lasagne.layers.get_output(generator)
Tfake_out = lasagne.layers.get_output(discriminator, Tgimgs)
g_loss_logD = lasagne.objectives.binary_crossentropy(Tfake_out, 1).mean()
g_loss_minimax = -lasagne.objectives.binary_crossentropy(Tfake_out, 0).mean()
g_loss_ls = T.mean(T.sqr((Tfake_out - 1)))
g_params = lasagne.layers.get_all_params(generator, trainable=True)
up_g_logD = lasagne.updates.adam(g_loss_logD, g_params, learning_rate=lrt, beta1=b1)
up_g_minimax = lasagne.updates.adam(g_loss_minimax, g_params, learning_rate=lrt, beta1=b1)
up_g_ls = lasagne.updates.adam(g_loss_ls, g_params, learning_rate=lrt, beta1=b1)
train_g = theano.function([noise],g_loss_logD,updates=up_g_logD)
train_g_minimax = theano.function([noise],g_loss_minimax,updates=up_g_minimax)
train_g_ls = theano.function([noise],g_loss_ls,updates=up_g_ls)
gen_fn = theano.function([noise], lasagne.layers.get_output(
generator,deterministic=True))
else:
gen_old_params = gen_new_params
for can_i in range(0,ncandi):
for type_i in range(0,nloss):
lasagne.layers.set_all_param_values(generator, gen_old_params[can_i])
if loss_type[type_i] == 'trickLogD':
for _ in range(0,kG):
zmb = floatX(np_rng.uniform(-1., 1., size=(batchSize, nz)))
cost = train_g(zmb)
elif loss_type[type_i] == 'minimax':
for _ in range(0,kG):
zmb = floatX(np_rng.uniform(-1., 1., size=(batchSize, nz)))
cost = train_g_minimax(zmb)
elif loss_type[type_i] == 'ls':
for _ in range(0,kG):
zmb = floatX(np_rng.uniform(-1., 1., size=(batchSize, nz)))
cost = train_g_ls(zmb)
sample_zmb = floatX(np_rng.uniform(-1., 1., size=(ntf, nz)))
gen_imgs = gen_fn(sample_zmb)
frr_score, fd_score = dis_fn(xmb[0:ntf],gen_imgs)
#frr = frr[0]
frr = frr_score - fd_score
if can_i*nloss + type_i < ncandi:
idx = can_i*nloss + type_i
gen_new_params[idx]=lasagne.layers.get_all_param_values(generator)
fake_rate[idx]=frr
g_imgs_old[idx*ntf:(idx+1)*ntf,:]=gen_imgs
fmb[idx*batchSize/ncandi*kD:(idx+1)*batchSize/ncandi*kD,:] = \
gen_imgs[0:batchSize/ncandi*kD,:]
else:
fr_com = fake_rate - frr
if min(fr_com) < 0:
ids_replace = np.where(fr_com==min(fr_com))
idr = ids_replace[0][0]
fake_rate[idr]=frr
gen_new_params[idr] = lasagne.layers.get_all_param_values(generator)
g_imgs_old[idr*ntf:(idr+1)*ntf,:]=gen_imgs
fmb[idr*batchSize/ncandi*kD:(idr+1)*batchSize/ncandi*kD,:] = \
gen_imgs[0:batchSize/ncandi*kD,:]
sample_xmb = toy_dataset(DATASET=DATASET, size=ncandi*ntf)
sample_xmb = sample_xmb[0:ncandi*ntf]
for i in range(0, ncandi):
xfake = g_imgs_old[i*ntf:(i+1)*ntf,:]
xreal = sample_xmb[i*ntf:(i+1)*ntf,:]
tr, fr, trp, frp, fdscore = disft_fn(xreal,xfake)
if i == 0:
fake_rate = np.array([fr])
real_rate = np.array([tr])
fake_rate_p = np.array([frp])
real_rate_p = np.array([trp])
FDL = np.array([fdscore])
else:
fake_rate = np.append(fake_rate,fr)
real_rate = np.append(real_rate,tr)
fake_rate_p = np.append(fake_rate_p,frp)
real_rate_p = np.append(real_rate_p,trp)
FDL = np.append(FDL,fdscore)
print fake_rate, fake_rate_p, FDL
print (n_updates, real_rate.mean(), real_rate_p.mean())
f_log.write(str(fake_rate)+' '+str(fake_rate_p)+'\n'+ str(n_updates) + ' ' + str(real_rate.mean())+ ' ' +str(real_rate_p.mean())+'\n')
f_log.flush()
# train D
for xreal,xfake in iter_data(xmb, shuffle(fmb), size=batchSize):
cost = train_d(xreal, xfake)
if n_updates%show_freq == 0:
s_zmb = floatX(np_rng.uniform(-1., 1., size=(512, nz)))
g_imgs = gen_fn(s_zmb)
xmb = toy_dataset(DATASET=DATASET, size=512)
generate_image(xmb,g_imgs,n_updates/save_freq,desc)
def main(
problem,
popsize,
moegan,
freq,
loss_type=['trickLogD', 'minimax', 'ls'],
postfix=None,
nPassD=1, #backpropagation pass for discriminator
inBatchSize=64):
# Parameters
task = 'toy'
name = '{}_{}_{}MMDu2'.format(
problem, "MOEGAN" if moegan else "EGAN",
postfix + "_" if postfix is not None else "") #'8G_MOEGAN_PFq_NFd_t2'
DIM = 512
begin_save = 0
nloss = len(loss_type)
batchSize = inBatchSize
if problem == "8G":
DATASET = '8gaussians'
elif problem == "25G":
DATASET = '25gaussians'
else:
exit(-1)
ncandi = popsize
kD = nPassD # # of discrim updates for each gen update
kG = 1 # # of discrim updates for each gen update
ntf = 256
b1 = 0.5 # momentum term of adam
nz = 2 # # of dim for Z
niter = 4 # # of iter at starting learning rate
lr = 0.0001 # initial learning rate for adam G
lrd = 0.0001 # initial learning rate for adam D
N_up = 100000
save_freq = freq
show_freq = freq
test_deterministic = True
beta = 1.
GP_norm = False # if use gradients penalty on discriminator
LAMBDA = 2. # hyperparameter sudof GP
NSGA2 = moegan
# Load the dataset
# MODEL D
print("Building model and compiling functions...")
# Prepare Theano variables for inputs and targets
real_imgs = T.matrix('real_imgs')
fake_imgs = T.matrix('fake_imgs')
# Create neural network model
discriminator = models_uncond.build_discriminator_toy(nd=DIM,
GP_norm=GP_norm)
# Create expression for passing real data through the discriminator
real_out = lasagne.layers.get_output(discriminator, real_imgs)
# Create expression for passing fake data through the discriminator
fake_out = lasagne.layers.get_output(discriminator, fake_imgs)
# Create loss expressions
discriminator_loss = (
lasagne.objectives.binary_crossentropy(real_out, 1) +
lasagne.objectives.binary_crossentropy(fake_out, 0)).mean()
# Gradients penalty norm
if GP_norm is True:
alpha = t_rng.uniform((batchSize, 1), low=0., high=1.)
differences = fake_imgs - real_imgs
interpolates = real_imgs + (alpha * differences)
gradients = theano.grad(lasagne.layers.get_output(
discriminator, interpolates).sum(),
wrt=interpolates)
slopes = T.sqrt(T.sum(T.sqr(gradients), axis=(1)))
gradient_penalty = T.mean((slopes - 1.)**2)
D_loss = discriminator_loss + LAMBDA * gradient_penalty
b1_d = 0.
else:
D_loss = discriminator_loss
b1_d = 0.
# Create update expressions for training
discriminator_params = lasagne.layers.get_all_params(discriminator,
trainable=True)
lrtd = theano.shared(lasagne.utils.floatX(lrd))
updates_d = lasagne.updates.adam(D_loss,
discriminator_params,
learning_rate=lrtd,
beta1=b1_d)
lrt = theano.shared(lasagne.utils.floatX(lr))
# Fd Socre
Fd = theano.gradient.grad(discriminator_loss, discriminator_params)
Fd_score = beta * T.log(sum(T.sum(T.sqr(x)) for x in Fd))
# Compile a function performing a training step on a mini-batch (by giving
# the updates dictionary) and returning the corresponding training loss:
train_d = theano.function([real_imgs, fake_imgs],
discriminator_loss,
updates=updates_d)
# Compile another function generating some data
dis_fn = theano.function([real_imgs, fake_imgs],
[(fake_out).mean(), Fd_score])
disft_fn = theano.function([real_imgs, fake_imgs], [
real_out.mean(),
fake_out.mean(), (real_out > 0.5).mean(),
(fake_out > 0.5).mean(), Fd_score
])
#main MODEL G
noise = T.matrix('noise')
generator_trainer = create_G(noise=noise,
discriminator=discriminator,
lr=lr,
b1=b1,
DIM=DIM)
# Finally, launch the training loop.
print("Starting training...")
desc = task + '_' + name
print(desc)
if not os.path.isdir('front'):
os.mkdir(os.path.join('front'))
if not os.path.isdir('front/' + desc):
os.mkdir(os.path.join('front/', desc))
if not os.path.isdir('logs'):
os.mkdir(os.path.join('logs'))
f_log = open('logs/%s.ndjson' % desc, 'wb')
if not os.path.isdir('models'):
os.mkdir(os.path.join('models/'))
if not os.path.isdir('models/' + desc):
os.mkdir(os.path.join('models/', desc))
instances = []
class Instance:
def __init__(self, fq, fd, params, img_values):
self.fq = fq
self.fd = fd
self.params = params
self.img = img_values
def f(self):
return self.fq - self.fd
# We iterate over epochs:
for n_updates in range(N_up):
xmb = toy_dataset(DATASET=DATASET, size=batchSize * kD)
xmb = xmb[0:batchSize * kD]
# initial G cluster
if n_updates == 0:
for can_i in range(0, ncandi):
init_generator_trainer = create_G(noise=noise,
discriminator=discriminator,
lr=lr,
b1=b1,
DIM=DIM)
zmb = floatX(np_rng.uniform(-1., 1., size=(batchSize, nz)))
cost = init_generator_trainer.train(loss_type[can_i % nloss],
zmb)
sample_zmb = floatX(np_rng.uniform(-1., 1., size=(ntf, nz)))
gen_imgs = init_generator_trainer.gen(sample_zmb)
frr_score, fd_score = dis_fn(xmb[0:ntf], gen_imgs)
instances.append(
Instance(
frr_score, fd_score,
lasagne.layers.get_all_param_values(
init_generator_trainer.generator), gen_imgs))
else:
instances_old = instances
instances = []
for can_i in range(0, ncandi):
for type_i in range(0, nloss):
generator_trainer.set(instances_old[can_i].params)
#train
zmb = floatX(np_rng.uniform(-1., 1., size=(batchSize, nz)))
generator_trainer.train(loss_type[type_i], zmb)
#score
sample_zmb = floatX(np_rng.uniform(-1., 1.,
size=(ntf, nz)))
gen_imgs = generator_trainer.gen(sample_zmb)
frr_score, fd_score = dis_fn(xmb[0:ntf], gen_imgs)
#save
instances.append(
Instance(frr_score, fd_score, generator_trainer.get(),
gen_imgs))
if ncandi <= (len(instances) + len(instances_old)):
if NSGA2 == True:
#add parents in the pool
for inst in instances_old:
generator_trainer.set(inst.params)
sample_zmb = floatX(
np_rng.uniform(-1., 1., size=(ntf, nz)))
gen_imgs = generator_trainer.gen(sample_zmb)
frr_score, fd_score = dis_fn(xmb[0:ntf], gen_imgs)
instances.append(
Instance(frr_score, fd_score,
generator_trainer.get(), gen_imgs))
#cromos = { idx:[float(inst.fq),-0.5*float(inst.fd)] for idx,inst in enumerate(instances) } # S1
cromos = {
idx: [-float(inst.fq), 0.5 * float(inst.fd)]
for idx, inst in enumerate(instances)
} # S2
cromos_idxs = [idx for idx, _ in enumerate(instances)]
finalpop = nsga_2_pass(ncandi, cromos, cromos_idxs)
instances = [instances[p] for p in finalpop]
with open('front/%s.tsv' % desc, 'wb') as ffront:
for inst in instances:
ffront.write(
(str(inst.fq) + "\t" + str(inst.fd)).encode())
ffront.write("\n".encode())
elif nloss > 1:
#sort new
instances.sort(
key=lambda inst: -inst.f()) #wrong def in the paper
#print([inst.f() for inst in instances])
#cut best ones
instances = instances[len(instances) - ncandi:]
#print([inst.f() for inst in instances])
sample_xmb = toy_dataset(DATASET=DATASET, size=ncandi * ntf)
sample_xmb = sample_xmb[0:ncandi * ntf]
for i in range(0, ncandi):
xfake = instances[i].img[0:ntf, :]
xreal = sample_xmb[i * ntf:(i + 1) * ntf, :]
tr, fr, trp, frp, fdscore = disft_fn(xreal, xfake)
fake_rate = np.array([fr]) if i == 0 else np.append(fake_rate, fr)
real_rate = np.array([tr]) if i == 0 else np.append(real_rate, tr)
fake_rate_p = np.array([frp]) if i == 0 else np.append(
fake_rate_p, frp)
real_rate_p = np.array([trp]) if i == 0 else np.append(
real_rate_p, trp)
FDL = np.array([fdscore]) if i == 0 else np.append(FDL, fdscore)
print(fake_rate, fake_rate_p, FDL)
print(n_updates, real_rate.mean(), real_rate_p.mean())
f_log.write((str(fake_rate) + ' ' + str(fake_rate_p) + '\n' +
str(n_updates) + ' ' + str(real_rate.mean()) + ' ' +
str(real_rate_p.mean()) + '\n').encode())
f_log.flush()
# train D
#for xreal, xfake in iter_data(xmb, shuffle(fmb), size=batchSize):
# cost = train_d(xreal, xfake)
imgs_fakes = instances[0].img[0:int(batchSize / ncandi * kD), :]
for i in range(1, len(instances)):
img = instances[i].img[0:int(batchSize / ncandi * kD), :]
imgs_fakes = np.append(imgs_fakes, img, axis=0)
for xreal, xfake in iter_data(xmb, shuffle(imgs_fakes),
size=batchSize):
cost = train_d(xreal, xfake)
if (n_updates % show_freq == 0 and n_updates != 0) or n_updates == 1:
id_update = int(n_updates / save_freq)
#metric
s_zmb = floatX(np_rng.uniform(-1., 1., size=(512, nz)))
xmb = toy_dataset(DATASET=DATASET, size=512)
#compue mmd for all points
mmd2_all = []
for i in range(0, ncandi):
generator_trainer.set(instances[i].params)
g_imgs = generator_trainer.gen(s_zmb)
mmd2_all.append(abs(compute_metric_mmd2(g_imgs, xmb)))
mmd2_all = np.array(mmd2_all)
#print pareto front
if NSGA2 == True:
front_path = os.path.join('front/', desc)
with open('%s/%d_%s_mmd2u.tsv' % (front_path, id_update, desc),
'wb') as ffront:
for idx in range(0, ncandi):
ffront.write((str(instances[idx].fq) + "\t" +
str(instances[idx].fd) + "\t" +
str(mmd2_all[idx])).encode())
ffront.write("\n".encode())
#mmd2 output
print(n_updates, "mmd2u:", np.min(mmd2_all), "id:",
np.argmin(mmd2_all))
#save best
params = instances[np.argmin(mmd2_all)].params
generator_trainer.set(params)
g_imgs_min = generator_trainer.gen(s_zmb)
generate_image(xmb,
g_imgs_min,
id_update,
desc,
postfix="_mmu2d_best")
np.savez('models/%s/gen_%d.npz' % (desc, id_update),
*lasagne.layers.get_all_param_values(discriminator))
np.savez('models/%s/dis_%d.npz' % (desc, id_update),
*generator_trainer.get())
#worst_debug
params = instances[np.argmax(mmd2_all)].params
generator_trainer.set(params)
g_imgs_max = generator_trainer.gen(s_zmb)
generate_image(xmb,
g_imgs_max,
id_update,
desc,
postfix="_mmu2d_worst")
def main():
# Parameters
data_path = '../datasets/'
task = 'face'
name = '128'
start = 0
stop = 202560
input_nc = 3
loss_type = ['trickLogD','minimax','ls']
nloss = 3
shuffle_ = True
batchSize = 32
fineSize = 128
flip = True
ncandi = 1 # # of survived childern
kD = 3 # # of discrim updates for each gen update
kG = 1 # # of discrim updates for each gen update
ntf = batchSize*kD
b1 = 0.5 # momentum term of adam
nz = 100 # # of dim for Z
ngf = 64 # # of gen filters in first conv layer
ndf = 64 # # of discrim filters in first conv layer
niter = 25 # # of iter at starting learning rate
lr = 0.0002 # initial learning rate for adam G
lrd = 0.0002 # initial learning rate for adam D
beta = 0.001 # the hyperparameter that balance fitness score
GP_norm = False # if use gradients penalty on discriminator
LAMBDA = 2. # hyperparameter of GP
save_freq = 5000
show_freq = 500
begin_save = 0
test_deterministic = True
# Load the dataset
print("Loading data...")
f = h5py.File(data_path+'img_align_celeba_128.hdf5','r')
trX = f['data']
ids = range(start, stop)
################## MODEL D #######################
print("Building model and compiling functions...")
# Prepare Theano variables for inputs and targets
real_imgs = T.tensor4('real_imgs')
fake_imgs = T.tensor4('fake_imgs')
# Create neural network model
discriminator = models_uncond.build_discriminator_128(ndf=ndf)
# Create expression for passing real data through the discriminator
real_out = lasagne.layers.get_output(discriminator, real_imgs)
# Create expression for passing fake data through the discriminator
fake_out = lasagne.layers.get_output(discriminator, fake_imgs)
# Create loss expressions
discriminator_loss = (lasagne.objectives.binary_crossentropy(real_out, 1)
+ lasagne.objectives.binary_crossentropy(fake_out, 0)).mean()
# Gradients penalty norm
if GP_norm is True:
alpha = t_rng.uniform((batchSize,1,1,1), low=0.,high=1.)
differences = fake_imgs - real_imgs
interpolates = real_imgs + (alpha*differences)
gradients = theano.grad(lasagne.layers.get_output(discriminator, interpolates).sum(), wrt=interpolates)
slopes = T.sqrt(T.sum(T.sqr(gradients), axis=(1,2,3)))
gradient_penalty = T.mean((slopes-1.)**2)
D_loss = discriminator_loss + LAMBDA*gradient_penalty
b1_d = 0.
else:
D_loss = discriminator_loss
b1_d = b1
# Create update expressions for training
discriminator_params = lasagne.layers.get_all_params(discriminator, trainable=True)
lrtd = theano.shared(lasagne.utils.floatX(lrd))
updates_d = lasagne.updates.adam(
D_loss, discriminator_params, learning_rate=lrtd, beta1=b1_d)
lrt = theano.shared(lasagne.utils.floatX(lr))
# Diversity fitnees
Fd = theano.gradient.grad(discriminator_loss, discriminator_params)
Fd_score = beta*T.log(sum(T.sum(T.sqr(x)) for x in Fd))
# Compile a function performing a training step on a mini-batch (by giving
# the updates dictionary) and returning the corresponding training loss:
train_d = theano.function([real_imgs, fake_imgs],
discriminator_loss,
updates=updates_d)
# Compile another function generating some data
disft_fn = theano.function([real_imgs,fake_imgs],
[(real_out).mean(),
(fake_out).mean(),
Fd_score])
# Finally, launch the training loop.
print("Starting training...")
desc = task + '_' + name
print desc
if not os.path.isdir('logs'):
os.mkdir(os.path.join('logs'))
f_log = open('logs/%s.ndjson'%desc, 'wb')
if not os.path.isdir('samples'):
os.mkdir(os.path.join('samples/'))
if not os.path.isdir('samples/'+desc):
os.mkdir(os.path.join('samples/',desc))
if not os.path.isdir('models'):
os.mkdir(os.path.join('models/'))
if not os.path.isdir('models/'+desc):
os.mkdir(os.path.join('models/',desc))
gen_new_params = []
n_updates = 0
# We iterate over epochs:
for epoch in range(niter):
t = time()
if shuffle_ is True:
ids = shuffle(ids)
for index_ in iter_data(ids, size=batchSize*kD):
index = sorted(index_)
xmb = trX[index,:,:,:]
xmb = Batch(xmb,fineSize,input_nc,flip=flip)
xmb = processing_img(xmb, center=True, scale=True, convert=False)
rand_idx = random.randint(start,stop-ntf-1)
rand_ids = ids[rand_idx:rand_idx+ntf]
rand_ids = sorted(rand_ids)
sample_xmb = trX[rand_ids,:,:,:]
sample_xmb = Batch(sample_xmb,fineSize,input_nc,flip=flip)
sample_xmb = processing_img(sample_xmb, center=True, scale=True, convert=False)
# initial G cluster
if epoch + n_updates == 0:
for can_i in range(0,ncandi):
train_g, gen_fn, generator = create_G(
loss_type=loss_type[can_i%nloss],
discriminator=discriminator, lr=lr, b1=b1, ngf=ngf)
for _ in range(0,kG):
zmb = floatX(np_rng.uniform(-1., 1., size=(batchSize, nz)))
cost = train_g(zmb)
sample_zmb = floatX(np_rng.uniform(-1., 1., size=(ntf, nz)))
gen_imgs = gen_fn(sample_zmb)
gen_new_params.append(lasagne.layers.get_all_param_values(generator))
if can_i == 0:
g_imgs_old=gen_imgs
fmb = gen_imgs[0:batchSize/ncandi*kD,:,:,:]
else:
g_imgs_old = np.append(g_imgs_old,gen_imgs,axis=0)
fmb = np.append(fmb,gen_imgs[0:batchSize/ncandi*kD,:,:,:],axis=0)
#print gen_new_params
# MODEL G
noise = T.matrix('noise')
generator = models_uncond.build_generator_128(noise,ngf=ngf)
Tgimgs = lasagne.layers.get_output(generator)
Tfake_out = lasagne.layers.get_output(discriminator, Tgimgs)
g_loss_logD = lasagne.objectives.binary_crossentropy(Tfake_out, 1).mean()
g_loss_minimax = -lasagne.objectives.binary_crossentropy(Tfake_out, 0).mean()
g_loss_ls = T.mean(T.sqr((Tfake_out - 1)))
g_params = lasagne.layers.get_all_params(generator, trainable=True)
up_g_logD = lasagne.updates.adam(g_loss_logD, g_params, learning_rate=lrt, beta1=b1)
up_g_minimax = lasagne.updates.adam(g_loss_minimax, g_params, learning_rate=lrt, beta1=b1)
up_g_ls = lasagne.updates.adam(g_loss_ls, g_params, learning_rate=lrt, beta1=b1)
train_g = theano.function([noise],g_loss_logD,updates=up_g_logD)
train_g_minimax = theano.function([noise],g_loss_minimax,updates=up_g_minimax)
train_g_ls = theano.function([noise],g_loss_ls,updates=up_g_ls)
gen_fn = theano.function([noise], lasagne.layers.get_output(
generator,deterministic=True))
else:
gen_old_params = gen_new_params
for can_i in range(0,ncandi):
for type_i in range(0,nloss):
lasagne.layers.set_all_param_values(generator, gen_old_params[can_i])
if loss_type[type_i] == 'trickLogD':
for _ in range(0,kG):
zmb = floatX(np_rng.uniform(-1., 1., size=(batchSize, nz)))
cost = train_g(zmb)
elif loss_type[type_i] == 'minimax':
for _ in range(0,kG):
zmb = floatX(np_rng.uniform(-1., 1., size=(batchSize, nz)))
cost = train_g_minimax(zmb)
elif loss_type[type_i] == 'ls':
for _ in range(0,kG):
zmb = floatX(np_rng.uniform(-1., 1., size=(batchSize, nz)))
cost = train_g_ls(zmb)
sample_zmb = floatX(np_rng.uniform(-1., 1., size=(ntf, nz)))
gen_imgs = gen_fn(sample_zmb)
_, fr_score, fd_score = disft_fn(sample_xmb,gen_imgs)
fit = fr_score - fd_score
if can_i*nloss + type_i < ncandi:
idx = can_i*nloss + type_i
gen_new_params[idx]=lasagne.layers.get_all_param_values(generator)
fitness[idx]=fit
fake_rate[idx]=fr_score
g_imgs_old[idx*ntf:(idx+1)*ntf,:,:,:]=gen_imgs
fmb[idx*batchSize/ncandi*kD:(idx+1)*batchSize/ncandi*kD,:,:,:] = \
gen_imgs[0:batchSize/ncandi*kD,:,:,:]
else:
fit_com = fitness - fit
if min(fit_com) < 0:
ids_replace = np.where(fit_com==min(fit_com))
idr = ids_replace[0][0]
fitness[idr]=fit
fake_rate[idr]=fr_score
gen_new_params[idr] = lasagne.layers.get_all_param_values(generator)
g_imgs_old[idr*ntf:(idr+1)*ntf,:,:,:]=gen_imgs
fmb[idr*batchSize/ncandi*kD:(idr+1)*batchSize/ncandi*kD,:,:,:] = \
gen_imgs[0:batchSize/ncandi*kD,:,:,:]
print fake_rate, fitness
f_log.write(str(fake_rate) + ' '+str(fd_score) +' ' + str(fitness)+ '\n')
# train D
for xreal,xfake in iter_data(xmb, shuffle(fmb), size=batchSize):
cost = train_d(xreal, xfake)
for i in range(0, ncandi):
xfake = g_imgs_old[i*ntf:(i+1)*ntf,:,:,:]
xreal = sample_xmb[0:ntf,:,:,:]
tr, fr, fd = disft_fn(xreal,xfake)
if i == 0:
fake_rate = np.array([fr])
fitness = np.array([0.])
real_rate = np.array([tr])
FDL = np.array([fd])
else:
fake_rate = np.append(fake_rate,fr)
fitness = np.append(fitness,[0.])
real_rate = np.append(real_rate,tr)
FDL = np.append(FDL,fd)
print fake_rate, FDL
print (n_updates, epoch,real_rate.mean())
n_updates += 1
f_log.write(str(fake_rate)+' '+str(FDL)+ '\n'+ str(epoch)+' '+str(n_updates)+' '+str(real_rate.mean())+'\n')
f_log.flush()
if n_updates%show_freq == 0:
blank_image = Image.new("RGB",(fineSize*8+9,fineSize*8+9))
for i in range(8):
for ii in range(8):
img = g_imgs_old[i*8+ii,:,:,:]
img = ImgRescale(img, center=True, scale=True, convert_back=True)
blank_image.paste(Image.fromarray(img),(ii*fineSize+ii+1,i*fineSize+i+1))
blank_image.save('samples/%s/%s_%d.png'%(desc,desc,n_updates/save_freq))
if n_updates%save_freq == 0 and epoch > begin_save - 1:
# Optionally, you could now dump the network weights to a file like this:
np.savez('models/%s/gen_%d.npz'%(desc,n_updates/save_freq), *lasagne.layers.get_all_param_values(generator))
np.savez('models/%s/dis_%d.npz'%(desc,n_updates/save_freq), *lasagne.layers.get_all_param_values(discriminator))
def main(
problem,
popsize,
algorithm,
save_freq,
loss_type=['trickLogD', 'minimax', 'ls'],
postfix=None,
nPassD=1, #backpropagation pass for discriminator
batchSize=64,
metric="default",
output_dir="runs",
gradients_penalty=False):
if not (problem in problem_table.keys()):
exit(-1)
#task
task_args = problem_table[problem][1]
task = problem_table[problem][0](nPassD, popsize, batchSize, metric)
net_otype = task.net_output_type()
# description
description_name = '{}_{}_{}_{}'.format(
str(task),
algorithm,
popsize,
postfix if postfix is not None else "",
)
# share params
nloss = len(loss_type)
lr = task_args['lr'] # initial learning rate for adam G
lrd = task_args['lrd'] # initial learning rate for adam D
b1 = task_args['b1'] # momentum term of adam
beta = task_args['beta'] # momentum term of adam
samples = task_args['metric_samples'] # metric samples
DIM = task_args['dim'] # momentum term of adam
GP_norm = gradients_penalty # if use gradients penalty on discriminator
LAMBDA = 2. # hyperparameter sudof GP
# algorithm params
if algorithm == "egan":
VARIATION = "all"
MULTI_OBJECTIVE_SELECTION = False
elif algorithm == "moegan":
VARIATION = "all"
MULTI_OBJECTIVE_SELECTION = True
elif algorithm == "smoegan":
VARIATION = "deepqlearning"
MULTI_OBJECTIVE_SELECTION = True
else:
exit(-2)
# Load the dataset
def create_generator_trainer(noise=None,
discriminator=None,
lr=0.0002,
b1=0.5,
DIM=64):
return GeneratorTrainer(noise, task.create_geneator(noise, DIM),
discriminator, lr, b1)
# MODEL D
print("Building model and compiling functions...")
# Prepare Theano variables for inputs and targets
real_imgs = net_otype('real_imgs')
fake_imgs = net_otype('fake_imgs')
# Create neural network model
discriminator = task.create_discriminator(DIM, GP_norm)
# Create expression for passing real data through the discriminator
real_out = lasagne.layers.get_output(discriminator, real_imgs)
# Create expression for passing fake data through the discriminator
fake_out = lasagne.layers.get_output(discriminator, fake_imgs)
# Create loss expressions
discriminator_loss = (
lasagne.objectives.binary_crossentropy(real_out, 1) +
lasagne.objectives.binary_crossentropy(fake_out, 0)).mean()
# Gradients penalty norm
if GP_norm is True:
alpha = t_rng.uniform((batchSize, 1), low=0., high=1.)
differences = fake_imgs - real_imgs
interpolates = real_imgs + (alpha * differences)
gradients = theano.grad(lasagne.layers.get_output(
discriminator, interpolates).sum(),
wrt=interpolates)
slopes = T.sqrt(T.sum(T.sqr(gradients), axis=(1)))
gradient_penalty = T.mean((slopes - 1.)**2)
D_loss = discriminator_loss + LAMBDA * gradient_penalty
b1_d = 0.
else:
D_loss = discriminator_loss
b1_d = 0.
# Create update expressions for training
discriminator_params = lasagne.layers.get_all_params(discriminator,
trainable=True)
lrtd = theano.shared(lasagne.utils.floatX(lrd))
updates_d = lasagne.updates.adam(D_loss,
discriminator_params,
learning_rate=lrtd,
beta1=b1_d)
#lrt = theano.shared(lasagne.utils.floatX(lr))
# Fd Socre
Fd = theano.gradient.grad(discriminator_loss, discriminator_params)
Fd_score = beta * T.log(sum(T.sum(T.sqr(x)) for x in Fd))
# max is ~7.5 for toy dataset and ~0.025 for real ones (it will be updated after 1 iteration, which is likely the worst one)
Fd_auto_normalization = AutoNormalization(float(0.1))
# Compile a function performing a training step on a mini-batch (by giving
# the updates dictionary) and returning the corresponding training loss:
train_d = theano.function([real_imgs, fake_imgs],
discriminator_loss,
updates=updates_d)
# Compile another function generating some data
dis_fn = theano.function([real_imgs, fake_imgs],
[fake_out.mean(), Fd_score])
disft_fn = theano.function([real_imgs, fake_imgs], [
real_out.mean(),
fake_out.mean(), (real_out > 0.5).mean(),
(fake_out > 0.5).mean(), Fd_score
])
#main MODEL G
noise = T.matrix('noise')
generator_trainer = create_generator_trainer(noise, discriminator, lr, b1,
DIM)
# Finally, launch the training loop.
print("Starting training...")
print(description_name)
#build dirs
path_front, path_logs, path_models, path_models_last, path_images = build_output_dirs(
output_dir, description_name)
#define a problem instance
instances = []
instances_old = []
#generator of a offspring
def generate_offsptring(xreal, loss_id, pop_id, inst=None):
if inst == None:
newparams = create_generator_trainer(noise=noise,
discriminator=discriminator,
lr=lr,
b1=b1,
DIM=DIM).get()
inst = Instance(-float("inf"), float("inf"), newparams, -1, pop_id,
None)
#init gen
generator_trainer.set(inst.params)
#train
generator_trainer.train(loss_type[loss_id], task.noise_batch())
#score
xfake = generator_trainer.gen(task.noise_batch())
frr_score, fd_score = dis_fn(xreal, xfake)
#new instance
new_instance = Instance(frr_score, fd_score, generator_trainer.get(),
loss_id, pop_id, xfake)
#save
instances.append(new_instance)
#info stuff
return new_instance
#init varation
variation = get_varation(VARIATION)(popsize, nloss, generate_offsptring)
#reval pop with new D
def reval_pupulation(in_instances):
#ret
out_instances = []
#generates new batches of images for each generator, and then eval these sets by means (new) D
for inst in in_instances:
generator_trainer.set(inst.params)
xfake = generator_trainer.gen(task.noise_batch())
frr_score, fd_score = dis_fn(xreal_eval, xfake)
out_instances.append(
Instance(frr_score,
fd_score,
generator_trainer.get(),
inst.loss_id,
inst.pop_id,
xfake,
im_parent=True))
return out_instances
#log stuff
LOG_HEADER, LOG_TEMPLATE = build_log_template(popsize, nloss)
log = Logger(os.path.join(path_logs, 'logs.tsv'),
header=LOG_HEADER.encode())
timer = Timer()
losses_counter = [0] * nloss
# We iterate over epochs:
for n_updates in task.get_range():
#get batch
xmb = task.batch()
#get eval batch
if xmb.shape[0] == batchSize:
xreal_eval = xmb
else:
xreal_eval = shuffle(xmb)[:batchSize]
# initial G cluster
if MULTI_OBJECTIVE_SELECTION:
instances_old = reval_pupulation(instances)
else:
instances_old = instances
#reset
instances = []
variation.update(instances_old, task.is_last())
for pop_id in range(0, popsize):
variation.gen(xreal_eval,
instances_old[pop_id] if n_updates else None, pop_id)
if popsize <= (len(instances) + len(instances_old)):
if MULTI_OBJECTIVE_SELECTION == True:
#add parents in the pool
instances = [*instances_old, *instances]
#from the orginal code, we have to maximize D(G(X)),
#Since in NSGA2 performences a minimization,
#We are going to minimize -D(G(X)),
#also we want maximize the diversity score,
#So, we are going to minimize -diversity score (also we wanna normalize that value)
cromos = {
idx:
[-float(inst.fq), -float(Fd_auto_normalization(inst.fd))]
for idx, inst in enumerate(instances)
} # S2
cromos_idxs = [idx for idx, _ in enumerate(instances)]
finalpop = nsga_2_pass(popsize, cromos, cromos_idxs)
instances = [instances[p] for p in finalpop]
with open(os.path.join(path_front, 'last.tsv'),
'wb') as ffront:
for inst in instances:
ffront.write(
(str(inst.fq) + "\t" + str(inst.fd)).encode())
ffront.write("\n".encode())
elif nloss > 1:
#sort new
instances.sort(key=lambda inst: inst.f()
) #(from the orginal code in github) maximize
#cut best ones
instances = instances[len(instances) - popsize:]
for i in range(0, popsize):
xreal, xfake = task.statistic_datas(instances[i].img)
tr, fr, trp, frp, fdscore = disft_fn(xreal, xfake)
fake_rate = np.array([fr]) if i == 0 else np.append(fake_rate, fr)
real_rate = np.array([tr]) if i == 0 else np.append(real_rate, tr)
fake_rate_p = np.array([frp]) if i == 0 else np.append(
fake_rate_p, frp)
real_rate_p = np.array([trp]) if i == 0 else np.append(
real_rate_p, trp)
FDL = np.array([fdscore]) if i == 0 else np.append(FDL, fdscore)
losses_counter[instances[i].loss_id] += 1
# train D
for xreal, xfake in task.iter_data_discriminator(xmb, instances):
train_d(xreal, xfake)
#show it info
print(n_updates, real_rate.mean(), real_rate_p.mean())
#write logs
log.writeln(
LOG_TEMPLATE.format(n_updates, str(timer), fake_rate.mean(),
real_rate.mean(), *fake_rate, *real_rate, *FDL,
*losses_counter).encode())
#varation logs
variation.logs(path_logs, n_updates, last_iteration=task.is_last())
if (n_updates % save_freq == 0
and n_updates != 0) or n_updates == 1 or task.is_last():
#it same
if task.is_last():
id_name_update = math.ceil(float(n_updates) / save_freq)
else:
id_name_update = math.floor(float(n_updates) / save_freq)
#if is egan, eval only the best one.
if MULTI_OBJECTIVE_SELECTION == True:
instances_to_eval = instances
else:
instances_to_eval = [instances[-1]]
#metric
metric_results = task.compute_metrics(
instances_to_eval,
lambda inst, nz: generator_trainer.set(inst.params).gen(nz),
samples)
#mmd2 output
print(n_updates, "metric:", np.min(metric_results), "id:",
np.argmin(metric_results))
#best
best = np.argmin(metric_results)
worst = np.argmax(metric_results)
np.savez(
os.path.join(path_models, 'dis_%s.npz') % (id_name_update),
*lasagne.layers.get_all_param_values(discriminator))
np.savez(
os.path.join(path_models, 'gen_%s.npz') % (id_name_update),
*instances_to_eval[best].params)
#save best
generator_trainer.set(instances_to_eval[best].params)
xfake_best = generator_trainer.gen(task.noise_batch(samples))
#worst_debug
generator_trainer.set(instances_to_eval[worst].params)
xfake_worst = generator_trainer.gen(task.noise_batch(samples))
#save images
task.save_image(xmb, xfake_best, path_images,
"best_%s" % (id_name_update))
task.save_image(xmb, xfake_worst, path_images,
"worst_%s" % (id_name_update))
#print pareto front
with open(
os.path.join(path_front, '%s.tsv') % (id_name_update),
'wb') as ffront:
for idx in range(len(instances_to_eval)):
ffront.write((str(instances_to_eval[idx].fq) + "\t" +
str(instances_to_eval[idx].fd) + "\t" +
str(metric_results[idx])).encode())
ffront.write("\n".encode())
#save all last models:
if task.is_last():
for key, inst in enumerate(instances_to_eval):
np.savez(
os.path.join(path_models_last, 'gen_%s.npz') % (key),
*inst.params)
def main():
# Parameters
task = 'toy'
name = '8G_MOEGAN_MMDu2' #'8G_MOEGAN_PFq_NFd_t2'
DIM = 512
begin_save = 0
loss_type = ['trickLogD', 'minimax', 'ls'] #['trickLogD', 'minimax', 'ls']
nloss = 3 #2
DATASET = '8gaussians'
batchSize = 64
ncandi = 8
kD = 1 # # of discrim updates for each gen update
kG = 1 # # of discrim updates for each gen update
ntf = 256
b1 = 0.5 # momentum term of adam
nz = 2 # # of dim for Z
niter = 4 # # of iter at starting learning rate
lr = 0.0001 # initial learning rate for adam G
lrd = 0.0001 # initial learning rate for adam D
N_up = 100000
save_freq = 10000 / 10
show_freq = 10000 / 10
test_deterministic = True
beta = 1.
GP_norm = False # if use gradients penalty on discriminator
LAMBDA = 2. # hyperparameter of GP
NSGA2 = True
# Load the dataset
# MODEL D
print("Building model and compiling functions...")
# Prepare Theano variables for inputs and targets
real_imgs = T.matrix('real_imgs')
fake_imgs = T.matrix('fake_imgs')
# Create neural network model
discriminator = models_uncond.build_discriminator_toy(nd=DIM,
GP_norm=GP_norm)
# Create expression for passing real data through the discriminator
real_out = lasagne.layers.get_output(discriminator, real_imgs)
# Create expression for passing fake data through the discriminator
fake_out = lasagne.layers.get_output(discriminator, fake_imgs)
# Create loss expressions
discriminator_loss = (
lasagne.objectives.binary_crossentropy(real_out, 1) +
lasagne.objectives.binary_crossentropy(fake_out, 0)).mean()
# Gradients penalty norm
if GP_norm is True:
alpha = t_rng.uniform((batchSize, 1), low=0., high=1.)
differences = fake_imgs - real_imgs
interpolates = real_imgs + (alpha * differences)
gradients = theano.grad(lasagne.layers.get_output(
discriminator, interpolates).sum(),
wrt=interpolates)
slopes = T.sqrt(T.sum(T.sqr(gradients), axis=(1)))
gradient_penalty = T.mean((slopes - 1.)**2)
D_loss = discriminator_loss + LAMBDA * gradient_penalty
b1_d = 0.
else:
D_loss = discriminator_loss
b1_d = 0.
# Create update expressions for training
discriminator_params = lasagne.layers.get_all_params(discriminator,
trainable=True)
lrtd = theano.shared(lasagne.utils.floatX(lrd))
updates_d = lasagne.updates.adam(D_loss,
discriminator_params,
learning_rate=lrtd,
beta1=b1_d)
lrt = theano.shared(lasagne.utils.floatX(lr))
# Fd Socre
Fd = theano.gradient.grad(discriminator_loss, discriminator_params)
Fd_score = beta * T.log(sum(T.sum(T.sqr(x)) for x in Fd))
# Compile a function performing a training step on a mini-batch (by giving
# the updates dictionary) and returning the corresponding training loss:
train_d = theano.function([real_imgs, fake_imgs],
discriminator_loss,
updates=updates_d)
# Compile another function generating some data
dis_fn = theano.function([real_imgs, fake_imgs],
[(fake_out).mean(), Fd_score])
disft_fn = theano.function([real_imgs, fake_imgs], [
real_out.mean(),
fake_out.mean(), (real_out > 0.5).mean(),
(fake_out > 0.5).mean(), Fd_score
])
# Finally, launch the training loop.
print("Starting training...")
desc = task + '_' + name
print(desc)
if not os.path.isdir('logs'):
os.mkdir(os.path.join('logs'))
f_log = open('logs/%s.ndjson' % desc, 'wb')
if not os.path.isdir('models'):
os.mkdir(os.path.join('models/'))
if not os.path.isdir('models/' + desc):
os.mkdir(os.path.join('models/', desc))
gen_new_params = []
# We iterate over epochs:
for n_updates in range(N_up):
xmb = toy_dataset(DATASET=DATASET, size=batchSize * kD)
xmb = xmb[0:batchSize * kD]
# initial G cluster
if n_updates == 0:
for can_i in range(0, ncandi):
train_g, gen_fn, generator = create_G(
loss_type=loss_type[can_i % nloss],
discriminator=discriminator,
lr=lr,
b1=b1,
DIM=DIM)
for _ in range(0, kG):
zmb = floatX(np_rng.uniform(-1., 1., size=(batchSize, nz)))
cost = train_g(zmb)
sample_zmb = floatX(np_rng.uniform(-1., 1., size=(ntf, nz)))
gen_imgs = gen_fn(sample_zmb)
gen_new_params.append(
lasagne.layers.get_all_param_values(generator))
if can_i == 0:
g_imgs_old = gen_imgs
fmb = gen_imgs[0:int(batchSize / ncandi * kD), :]
else:
g_imgs_old = np.append(g_imgs_old, gen_imgs, axis=0)
newfmb = gen_imgs[0:int(batchSize / ncandi * kD), :]
fmb = np.append(fmb, newfmb, axis=0)
# print gen_new_params
# MODEL G
noise = T.matrix('noise')
generator = models_uncond.build_generator_toy(noise, nd=DIM)
Tgimgs = lasagne.layers.get_output(generator)
Tfake_out = lasagne.layers.get_output(discriminator, Tgimgs)
g_loss_logD = lasagne.objectives.binary_crossentropy(Tfake_out,
1).mean()
g_loss_minimax = - \
lasagne.objectives.binary_crossentropy(Tfake_out, 0).mean()
g_loss_ls = T.mean(T.sqr((Tfake_out - 1)))
g_params = lasagne.layers.get_all_params(generator, trainable=True)
up_g_logD = lasagne.updates.adam(g_loss_logD,
g_params,
learning_rate=lrt,
beta1=b1)
up_g_minimax = lasagne.updates.adam(g_loss_minimax,
g_params,
learning_rate=lrt,
beta1=b1)
up_g_ls = lasagne.updates.adam(g_loss_ls,
g_params,
learning_rate=lrt,
beta1=b1)
train_g = theano.function([noise], g_loss_logD, updates=up_g_logD)
train_g_minimax = theano.function([noise],
g_loss_minimax,
updates=up_g_minimax)
train_g_ls = theano.function([noise], g_loss_ls, updates=up_g_ls)
gen_fn = theano.function([noise],
lasagne.layers.get_output(
generator, deterministic=True))
else:
class Instance:
def __init__(self, fq, fd, params, img_values, image_copy):
self.fq = fq
self.fd = fd
self.params = params
self.vimg = img_values
self.cimg = image_copy
def f(self):
return self.fq - self.fd
instances = []
fq_list = np.zeros(ncandi)
fd_list = np.zeros(ncandi)
gen_old_params = gen_new_params
for can_i in range(0, ncandi):
for type_i in range(0, nloss):
lasagne.layers.set_all_param_values(
generator, gen_old_params[can_i])
if loss_type[type_i] == 'trickLogD':
for _ in range(0, kG):
zmb = floatX(
np_rng.uniform(-1., 1., size=(batchSize, nz)))
cost = train_g(zmb)
elif loss_type[type_i] == 'minimax':
for _ in range(0, kG):
zmb = floatX(
np_rng.uniform(-1., 1., size=(batchSize, nz)))
cost = train_g_minimax(zmb)
elif loss_type[type_i] == 'ls':
for _ in range(0, kG):
zmb = floatX(
np_rng.uniform(-1., 1., size=(batchSize, nz)))
cost = train_g_ls(zmb)
sample_zmb = floatX(np_rng.uniform(-1., 1.,
size=(ntf, nz)))
gen_imgs = gen_fn(sample_zmb)
frr_score, fd_score = dis_fn(xmb[0:ntf], gen_imgs)
instances.append(
Instance(
frr_score, fd_score,
lasagne.layers.get_all_param_values(generator),
gen_imgs,
gen_imgs[0:int(batchSize / ncandi * kD), :]))
if ncandi < len(instances):
if NSGA2 == True:
cromos = {
idx: [float(inst.fq), -float(inst.fd)]
for idx, inst in enumerate(instances)
}
cromos_idxs = [idx for idx, _ in enumerate(instances)]
finalpop = nsga_2_pass(ncandi, cromos, cromos_idxs)
for idx, p in enumerate(finalpop):
inst = instances[p]
gen_new_params[idx] = inst.params
fq_list[idx] = inst.fq
fd_list[idx] = inst.fd
fake_rate[idx] = inst.f()
g_imgs_old[idx * ntf:(idx + 1) * ntf, :] = inst.vimg
fmb[int(idx * batchSize / ncandi *
kD):math.ceil((idx + 1) * batchSize / ncandi *
kD), :] = inst.cimg
with open('front/%s.tsv' % desc, 'wb') as ffront:
for idx, p in enumerate(finalpop):
inst = instances[p]
ffront.write(
(str(inst.fq) + "\t" + str(inst.fd)).encode())
ffront.write("\n".encode())
else:
for idx, inst in enumerate(instances):
if idx < ncandi:
gen_new_params[idx] = inst.params
fake_rate[idx] = inst.f()
fq_list[idx] = inst.fq
fd_list[idx] = inst.fd
g_imgs_old[idx * ntf:(idx + 1) *
ntf, :] = inst.vimg
fmb[int(idx * batchSize / ncandi *
kD):math.ceil((idx + 1) * batchSize /
ncandi * kD), :] = inst.cimg
else:
fr_com = fake_rate - inst.f()
if min(fr_com) < 0:
idr = np.where(fr_com == min(fr_com))[0][0]
gen_new_params[idr] = inst.params
fake_rate[idr] = inst.f()
g_imgs_old[idr * ntf:(idr + 1) *
ntf, :] = inst.vimg
fmb[int(idr * batchSize / ncandi *
kD):math.ceil((idr + 1) * batchSize /
ncandi *
kD), :] = inst.cimg
sample_xmb = toy_dataset(DATASET=DATASET, size=ncandi * ntf)
sample_xmb = sample_xmb[0:ncandi * ntf]
for i in range(0, ncandi):
xfake = g_imgs_old[i * ntf:(i + 1) * ntf, :]
xreal = sample_xmb[i * ntf:(i + 1) * ntf, :]
tr, fr, trp, frp, fdscore = disft_fn(xreal, xfake)
if i == 0:
fake_rate = np.array([fr])
real_rate = np.array([tr])
fake_rate_p = np.array([frp])
real_rate_p = np.array([trp])
FDL = np.array([fdscore])
else:
fake_rate = np.append(fake_rate, fr)
real_rate = np.append(real_rate, tr)
fake_rate_p = np.append(fake_rate_p, frp)
real_rate_p = np.append(real_rate_p, trp)
FDL = np.append(FDL, fdscore)
print(fake_rate, fake_rate_p, FDL)
print(n_updates, real_rate.mean(), real_rate_p.mean())
f_log.write((str(fake_rate) + ' ' + str(fake_rate_p) + '\n' +
str(n_updates) + ' ' + str(real_rate.mean()) + ' ' +
str(real_rate_p.mean()) + '\n').encode())
f_log.flush()
# train D
for xreal, xfake in iter_data(xmb, shuffle(fmb), size=batchSize):
cost = train_d(xreal, xfake)
if n_updates % show_freq == 0:
s_zmb = floatX(np_rng.uniform(-1., 1., size=(512, nz)))
params_max = gen_new_params[np.argmax(fake_rate)]
lasagne.layers.set_all_param_values(generator, params_max)
g_imgs_max = gen_fn(s_zmb)
if n_updates % show_freq == 0 and n_updates != 0:
#metric
s_zmb = floatX(np_rng.uniform(-1., 1., size=(512, nz)))
xmb = toy_dataset(DATASET=DATASET, size=512)
mmd2_all = []
for i in range(0, ncandi):
lasagne.layers.set_all_param_values(generator,
gen_new_params[i])
g_imgs_min = gen_fn(s_zmb)
mmd2_all.append(compute_metric_mmd2(g_imgs_min, xmb))
mmd2_all = np.array(mmd2_all)
if NSGA2:
with open('front/%s_mmd2u.tsv' % desc, 'wb') as ffront:
for idx in range(0, ncandi):
ffront.write(
(str(fq_list[idx]) + "\t" + str(fd_list[idx]) +
"\t" + str(mmd2_all[idx])).encode())
ffront.write("\n".encode())
#save best
params = gen_new_params[np.argmin(mmd2_all)]
lasagne.layers.set_all_param_values(generator, params)
g_imgs_min = gen_fn(s_zmb)
generate_image(xmb,
g_imgs_min,
n_updates / save_freq,
desc,
postfix="_mmu2d")
np.savez('models/%s/gen_%d.npz' % (desc, n_updates / save_freq),
*lasagne.layers.get_all_param_values(discriminator))
np.savez('models/%s/dis_%d.npz' % (desc, n_updates / save_freq),
*lasagne.layers.get_all_param_values(generator))