def train(args):
"""
Main script.
"""
# Get context.
from nnabla.contrib.context import extension_context
extension_module = args.context
if args.context is None:
extension_module = 'cpu'
logger.info("Running in %s" % extension_module)
ctx = extension_context(extension_module, device_id=args.device_id)
nn.set_default_context(ctx)
# Create CNN network for both training and testing.
# TRAIN
# Fake path
#nn.load_parameters("/home/mizuochi/programing/font/dcgan_model_0220/generator_param_522000.h5")
#z.d = np.random.randn(*z.shape)
#gen.forward()
#for i in range(40):
# Image.fromarray(np.uint8((gen.d[i][0]+1)*255/2.0)).save("./test/"+str(i)+".png")
# Real path
x = nn.Variable([args.batch_size, 1, 28, 28])
x_ref = nn.Variable([args.batch_size, 1, 28, 28])
#vec = nn.Variable([args.batch_size, 100])
pred_vec = vectorizer(x,test = False)
print pred_vec.shape
#z = pred_vec.reshape((args.batch_size, 100, 1, 1))
#gen = generator(z,test=True)
gen = generator(pred_vec,test=False)
gen.persistent = True
with nn.parameter_scope("gen"):
nn.load_parameters("/home/mizuochi/programing/font/dcgan_model_0220/generator_param_290000.h5")
#loss_dis = F.mean(F.sigmoid_cross_entropy(pred_vec, vec))
print "x_ref shape",x_ref.shape
print "gen shape",gen.shape
#loss_dis = F.mean(F.squared_error(x_ref.reshape((64,28*28)), gen.reshape((64,28*28))))
loss_dis = F.mean(F.squared_error(x_ref, gen))
print loss_dis.d
# Create Solver.
solver_dis = S.Adam(args.learning_rate, beta1=0.5)
with nn.parameter_scope("dis"):
solver_dis.set_parameters(nn.get_parameters())
# Create monitor.
import nnabla.monitor as M
monitor = M.Monitor(args.monitor_path)
monitor_loss_dis = M.MonitorSeries(
"Discriminator loss", monitor, interval=10)
monitor_time = M.MonitorTimeElapsed("Time", monitor, interval=100)
#data = data_iterator_mnist(args.batch_size, True)
data = iterator.simple_data_iterator(load_kanji_data(),args.batch_size,True)
# Training loop.
for i in range(args.max_iter):
if i % args.model_save_interval == 0:
with nn.parameter_scope("dis"):
nn.save_parameters(os.path.join(
args.model_save_path, "vectorizer_param_%06d.h5" % i))
# Training forward
buf,buf2 = data.next()
x.d = buf*2.0/255. - 1.0
x_ref.d = buf*2.0/255. - 1.0
# Discriminator update.
solver_dis.zero_grad()
loss_dis.forward(clear_no_need_grad=True)
loss_dis.backward(clear_buffer=True)
solver_dis.weight_decay(args.weight_decay)
solver_dis.update()
monitor_loss_dis.add(i, loss_dis.d.copy())
monitor_time.add(i)
with nn.parameter_scope("dis"):
nn.save_parameters(os.path.join(
args.model_save_path, "vectorizer_param_%06d.h5" % i))
def train(args):
"""
Main script.
"""
# Get context.
from nnabla.contrib.context import extension_context
extension_module = args.context
if args.context is None:
extension_module = 'cpu'
logger.info("Running in %s" % extension_module)
ctx = extension_context(extension_module, device_id=args.device_id)
nn.set_default_context(ctx)
# Create CNN network for both training and testing.
# TRAIN
# Fake path
x1 = nn.Variable([args.batch_size, 1, 28, 28])
#z = nn.Variable([args.batch_size, VEC_SIZE, 1, 1])
#z = vectorizer(x1,maxh = 1024)
#fake = generator(z,maxh= 1024)
z = vectorizer(x1)
fake = generator(z)
fake.persistent = True # Not to clear at backward
pred_fake = discriminator(fake)
loss_gen = F.mean(
F.sigmoid_cross_entropy(pred_fake, F.constant(1, pred_fake.shape)))
loss_vec = F.mean(F.squared_error(fake, x1))
fake_dis = fake.unlinked()
pred_fake_dis = discriminator(fake_dis)
loss_dis = F.mean(
F.sigmoid_cross_entropy(pred_fake_dis,
F.constant(0, pred_fake_dis.shape)))
# Real path
x = nn.Variable([args.batch_size, 1, 28, 28])
pred_real = discriminator(x)
loss_dis += F.mean(
F.sigmoid_cross_entropy(pred_real, F.constant(1, pred_real.shape)))
# Create Solver.
solver_gen = S.Adam(args.learning_rate, beta1=0.5)
solver_dis = S.Adam(args.learning_rate, beta1=0.5)
solver_vec = S.Adam(args.learning_rate, beta1=0.5)
with nn.parameter_scope("vec"):
solver_vec.set_parameters(nn.get_parameters())
with nn.parameter_scope("gen"):
solver_vec.set_parameters(nn.get_parameters())
with nn.parameter_scope("gen"):
solver_gen.set_parameters(nn.get_parameters())
with nn.parameter_scope("dis"):
solver_dis.set_parameters(nn.get_parameters())
# Create monitor.
import nnabla.monitor as M
monitor = M.Monitor(args.monitor_path)
monitor_loss_gen = M.MonitorSeries("Generator loss", monitor, interval=10)
monitor_loss_dis = M.MonitorSeries("Discriminator loss",
monitor,
interval=10)
monitor_loss_vec = M.MonitorSeries("Vectorizer loss", monitor, interval=10)
monitor_time = M.MonitorTimeElapsed("Time", monitor, interval=100)
monitor_fake = M.MonitorImageTile("Fake images",
monitor,
normalize_method=lambda x: x + 1 / 2.)
monitor_vec1 = M.MonitorImageTile("vec images1",
monitor,
normalize_method=lambda x: x + 1 / 2.)
monitor_vec2 = M.MonitorImageTile("vec images2",
monitor,
normalize_method=lambda x: x + 1 / 2.)
#data = data_iterator_mnist(args.batch_size, True)
data = iterator.simple_data_iterator(load_kanji_data(), args.batch_size,
True)
# Training loop.
for i in range(args.max_iter):
if i % args.model_save_interval == 0:
with nn.parameter_scope("gen"):
nn.save_parameters(
os.path.join(args.model_save_path,
"generator_param_%06d.h5" % i))
with nn.parameter_scope("dis"):
nn.save_parameters(
os.path.join(args.model_save_path,
"discriminator_param_%06d.h5" % i))
# Training forward
image, _ = data.next()
x1.d = image / 255. - 0.5
# Generator update.
solver_vec.zero_grad()
loss_vec.forward(clear_no_need_grad=True)
loss_vec.backward(clear_buffer=True)
solver_vec.weight_decay(args.weight_decay)
solver_vec.update()
monitor_vec1.add(i, fake)
monitor_vec2.add(i, x1)
monitor_loss_vec.add(i, loss_vec.d.copy())
x.d = image / 255. - 0.5 # [0, 255] to [-1, 1]
z.d = np.random.randn(*z.shape)
# Generator update.
solver_gen.zero_grad()
loss_gen.forward(clear_no_need_grad=True)
loss_gen.backward(clear_buffer=True)
solver_gen.weight_decay(args.weight_decay)
solver_gen.update()
monitor_fake.add(i, fake)
monitor_loss_gen.add(i, loss_gen.d.copy())
# Discriminator update.
solver_dis.zero_grad()
loss_dis.forward(clear_no_need_grad=True)
loss_dis.backward(clear_buffer=True)
solver_dis.weight_decay(args.weight_decay)
solver_dis.update()
monitor_loss_dis.add(i, loss_dis.d.copy())
monitor_time.add(i)
with nn.parameter_scope("gen"):
nn.save_parameters(
os.path.join(args.model_save_path, "generator_param_%06d.h5" % i))
with nn.parameter_scope("dis"):
nn.save_parameters(
os.path.join(args.model_save_path,
"discriminator_param_%06d.h5" % i))
def train(args):
x1 = nn.Variable([args.batch_size, 1, 28, 28])
z_vec = vectorizer(x1)
z = z_vec.unlinked()
fake2 = generator(z_vec)
fake = generator(z)
fake.persistent = True # Not to clear at backward
pred_fake = discriminator(fake)
loss_gen = F.mean(
F.sigmoid_cross_entropy(pred_fake, F.constant(1, pred_fake.shape)))
loss_vec = F.mean(F.squared_error(fake2, x1))
fake_dis = fake.unlinked()
pred_fake_dis = discriminator(fake_dis)
loss_dis = F.mean(
F.sigmoid_cross_entropy(pred_fake_dis,
F.constant(0, pred_fake_dis.shape)))
# Real path
x = nn.Variable([args.batch_size, 1, 28, 28])
pred_real = discriminator(x)
loss_dis += F.mean(
F.sigmoid_cross_entropy(pred_real, F.constant(1, pred_real.shape)))
# Create Solver.
solver_gen = S.Adam(args.learning_rate, beta1=0.5)
solver_dis = S.Adam(args.learning_rate, beta1=0.5)
solver_vec = S.Adam(args.learning_rate, beta1=0.5)
with nn.parameter_scope("vec"):
solver_vec.set_parameters(nn.get_parameters())
with nn.parameter_scope("gen"):
solver_vec.set_parameters(nn.get_parameters())
with nn.parameter_scope("gen"):
solver_gen.set_parameters(nn.get_parameters())
with nn.parameter_scope("dis"):
solver_dis.set_parameters(nn.get_parameters())
# Create monitor.
import nnabla.monitor as M
monitor = M.Monitor(args.monitor_path)
monitor_loss_gen = M.MonitorSeries("Generator loss", monitor, interval=10)
monitor_loss_dis = M.MonitorSeries("Discriminator loss",
monitor,
interval=10)
monitor_loss_vec = M.MonitorSeries("Vectorizer loss", monitor, interval=10)
monitor_time = M.MonitorTimeElapsed("Time", monitor, interval=100)
monitor_fake = M.MonitorImageTile("Fake images",
monitor,
normalize_method=lambda x: x + 1 / 2.)
monitor_vec1 = M.MonitorImageTile("vec images1",
monitor,
normalize_method=lambda x: x + 1 / 2.)
monitor_vec2 = M.MonitorImageTile("vec images2",
monitor,
normalize_method=lambda x: x + 1 / 2.)
#data = data_iterator_mnist(args.batch_size, True)
data = iterator.simple_data_iterator(load_kanji_data(), args.batch_size,
True)
# Training loop.
for i in range(args.max_iter):
if i % args.model_save_interval == 0:
with nn.parameter_scope("gen"):
nn.save_parameters(
os.path.join(args.model_save_path,
"generator_param_%06d.h5" % i))
with nn.parameter_scope("dis"):
nn.save_parameters(
os.path.join(args.model_save_path,
"discriminator_param_%06d.h5" % i))
with nn.parameter_scope("vec"):
nn.save_parameters(
os.path.join(args.model_save_path,
"vectorizer_param_%06d.h5" % i))
# Training forward
image, _ = data.next()
x1.d = image / 255. * 2 - 1.0
# Generator update.
solver_vec.zero_grad()
loss_vec.forward(clear_no_need_grad=True)
loss_vec.backward(clear_buffer=True)
solver_vec.weight_decay(args.weight_decay)
solver_vec.update()
fake2.forward()
monitor_vec1.add(i, fake2)
monitor_vec2.add(i, x1)
monitor_loss_vec.add(i, loss_vec.d.copy())
x.d = image / 255. * 2 - 1.0 # [0, 255] to [-1, 1]
z.d = np.random.randn(*z.shape)
# Generator update.
solver_gen.zero_grad()
loss_gen.forward(clear_no_need_grad=True)
loss_gen.backward(clear_buffer=True)
solver_gen.weight_decay(args.weight_decay)
solver_gen.update()
monitor_fake.add(i, fake)
monitor_loss_gen.add(i, loss_gen.d.copy())
# Discriminator update.
solver_dis.zero_grad()
loss_dis.forward(clear_no_need_grad=True)
loss_dis.backward(clear_buffer=True)
solver_dis.weight_decay(args.weight_decay)
solver_dis.update()
monitor_loss_dis.add(i, loss_dis.d.copy())
monitor_time.add(i)
