def lsgan_epoch_train(self): #training LSGAN epoch-wise
save_file_gan_ex = self.gan_save_box.toPlainText() #####input save folder for single experiments
rounds = self.epochs_gan_spinBox.value()
bs = self.bs_gan_spinBox.value()
kd = self.k_d_spinBox.value()
kg = self.k_g_spinBox.value()
n_interval = self.interval_spinBox.value()
channel_n = self.channel_spinBox.value()
z_dim = self.z_dim_spinBox.value()
in_r = self.smooth_real_doubleSpinBox.value() ###
in_f = self.smooth_fake_doubleSpinBox.value() ###
save_file_gan = os.path.join('../lsgan_result/', str(save_file_gan_ex))
lr_g = self.optimizer_g_lr_spinBox.value()
b_g = self.optimizer_g_b_spinBox.value()
lr_d = self.optimizer_d_lr_spinBox.value()
b_d = self.optimizer_d_b_spinBox.value()
real_dim = (96, 96, channel_n)
train_set = np.load(os.path.join('../npy/', str(self.file_raw)))
ndis = self.display_spinBox.value()
textg = self.gen_comboBox.currentText()
if textg == 'gen1':
gen = gan_g1(z_dim, channel_n)
elif textg == 'gen2':
gen = gan_g2(z_dim, channel_n)
elif textg == 'gen3':
gen = gan_g3(z_dim, channel_n)
elif textg == 'gen4':
gen = resnet_gen(z_dim, channel_n)
elif textg == 'gen5':
gen = gan_g5(z_dim, channel_n)
textb = self.dis_comboBox.currentText()
if textb == 'dis4':
dis = wgan_d1(real_dim)
elif textb == 'dis5':
dis = wgan_d2(real_dim)
elif textb == 'dis6':
dis = resnet_dis_w(channel_n)
optimizer_g = Adam(lr_g, b_g)
optimizer_d = Adam(lr_d, b_d)
if not os.path.exists('../lsgan_result'): # create folder to save the experiment result.
os.mkdir('../lsgan_result')
if not os.path.exists(save_file_gan):
os.mkdir(save_file_gan)
train6(X=train_set, G=gen, D=dis, nb_epochs=rounds, batch_size=bs, k_d=kd, k_g=kg,
index_r=in_r, index_f=in_f, opt_g=optimizer_g, opt_d=optimizer_d, z_dim=z_dim,
channel=channel_n, save_file=save_file_gan, n_i=n_interval, n=ndis)
def dcgan_loop_train(self): #training DCGAN loop-wise
save_file_gan_ex = self.gan_save_box.toPlainText() #####input save folder for single experiments
rounds = self.epochs_gan_spinBox.value() #training loops
bs = self.bs_gan_spinBox.value() #batch size
kd = self.k_d_spinBox.value() #training D kd times per loop
kg = self.k_g_spinBox.value() #training G kg times per loop
ndis = self.display_spinBox.value() #row/column of generated display images
n_interval = self.interval_spinBox.value() #sacing interval
channel_n = self.channel_spinBox.value() #image channel
z_dim = self.z_dim_spinBox.value() #latent vector size
in_r = self.smooth_real_doubleSpinBox.value() ###label somooth for real images
in_f = self.smooth_fake_doubleSpinBox.value() ###label smooth for fake images
save_file_gan = os.path.join('../dcgan_result/', str(save_file_gan_ex)) #saving path of the result
lr_g = self.optimizer_g_lr_spinBox.value()
b_g = self.optimizer_g_b_spinBox.value()
lr_d = self.optimizer_d_lr_spinBox.value()
b_d = self.optimizer_d_b_spinBox.value()
real_dim = (96, 96, channel_n) #image size
train_set = np.load(os.path.join('../npy/', str(self.file_raw))) #path of trained original image dataset
textg = self.gen_comboBox.currentText() #select network
if textg == 'gen1':
gen = gan_g1(z_dim, channel_n)
elif textg == 'gen2':
gen = gan_g2(z_dim, channel_n)
elif textg == 'gen3':
gen = gan_g3(z_dim, channel_n)
elif textg == 'gen4':
gen = resnet_gen(z_dim, channel_n)
elif textg == 'gen5':
gen = gan_g5(z_dim, channel_n)
textb = self.dis_comboBox.currentText()
if textb == 'dis1':
dis = dcgan_d1(real_dim)
elif textb == 'dis2':
dis = dcgan_d2(real_dim)
elif textb == 'dis3':
dis = resnet_dis(channel_n)
optimizer_g = Adam(lr_g, b_g)
optimizer_d = Adam(lr_d, b_d)
if not os.path.exists('../dcgan_result'): # create folder to save the experiment result.
os.mkdir('../dcgan_result')
if not os.path.exists(save_file_gan):
os.mkdir(save_file_gan)
train(X = train_set, G = gen, D = dis, loops = rounds, batch_size = bs, k_d = kd, k_g = kg,
index_r = in_r, index_f = in_f, opt_g= optimizer_g, opt_d= optimizer_d, z_dim= z_dim,
channel= channel_n, save_file = save_file_gan, n_i= n_interval, n = ndis)