prog.add_loss_critic(loss_critic.data.cpu().numpy()[0])
dis_optim.zero_grad()
loss_critic.backward()
dis_optim.step()
prog.add_loss_dis()
z = sampler.sample_z(config['dim_z'],
batchsize,
gaussian=config['gaussian'])
z = Variable(torch.from_numpy(z).cuda())
samples_fake = gen_net(z)
samples_fake /= config['scale']
f_fake = dis_net(samples_fake)
loss_gen = -f_fake.mean()
prog.add_loss_gen(loss_gen.data.cpu().numpy()[0])
gen_optim.zero_grad()
loss_gen.backward()
gen_optim.step()
if (i + 1) % config['num_plot'] == 0:
print(i + 1)
z = sampler.sample_z(config['dim_z'],
10000,
gaussian=config['gaussian'])
z = Variable(torch.from_numpy(z).cuda())
samples_fake = gen_net(z).data.cpu().numpy()
plot.plot_scatter(samples_fake,
filename='{}_scatter'.format(i + 1),
show=True)
dis_optim.zero_grad()
loss_critic.backward()
dis_optim.step()
dis_net.clip()
prog.add_loss_dis()
z = sampler.sample_z(config['dim_z'],
batchsize,
gaussian=config['gaussian'])
z = Variable(torch.from_numpy(z))
samples_fake = gen_net(z)
samples_fake /= config['scale']
f_fake = dis_net(samples_fake)
loss_gen = -f_fake.mean()
prog.add_loss_gen(loss_gen)
gen_optim.zero_grad()
loss_gen.backward()
gen_optim.step()
if (i + 1) % config['num_plot'] == 0:
print(i + 1)
z = sampler.sample_z(config['dim_z'],
10000,
gaussian=config['gaussian'])
z = Variable(torch.from_numpy(z))
samples_fake = gen_net(z).data.numpy()
plot.plot_scatter(samples_fake,
dir='plot',
filename='{}_scatter'.format(i + 1))
f_fake = dis_net(samples_fake)
loss_critic = f_fake.mean() - f_true.mean() + \
(config['lda']*diff).mean()
prog.add_loss_critic(cpu(loss_critic.data).numpy()[0])
dis_optim.zero_grad()
loss_critic.backward()
dis_optim.step()
prog.add_loss_dis()
z = sampler.sample_z(batchsize,config['dim_z'])
z = Variable(cuda(torch.from_numpy(z)))
samples_fake = gen_net(z)
f_fake = dis_net(samples_fake)
loss_gen = -f_fake.mean()
prog.add_loss_gen(cpu(loss_gen.data).numpy()[0])
gen_optim.zero_grad()
loss_gen.backward()
gen_optim.step()
if (i+1)%config['num_plot'] == 0:
print(i+1,prog.duration())
z = sampler.sample_z(500,config['dim_z'])
z = Variable(cuda(torch.from_numpy(z)))
samples_fake = cpu(gen_net(z).data).numpy()
plot.plot_corr(samples_fake,dir='plot',
filename='{}_corr'.format(i+1),show=False)
plot.plot_scatter(sampler.mat[:,:200].T,samples_fake[:200,:],
dir='plot',filename='{}_scatter'.format(i+1),show=False)
prog.start()
