def main(args):
chainer.config.user_gpu = args.g
if args.g >= 0:
chainer.backends.cuda.get_device_from_id(args.g).use()
print("GPU mode")
mnist_train = chainer.datasets.get_mnist()[0] # MNISTデータ取得
mnist_train = chainer.dataset.concat_examples(mnist_train)[
0] # 画像だけ（ラベルは不要）
mnist_train = mnist_train.reshape((-1, 1, 28, 28)) # 画像形式(N,C,H,W)に整形
mnist_iter = iterators.SerialIterator(mnist_train,
args.batchsize,
shuffle=True,
repeat=True) # iteratorを作成
generator = Generator(Z_DIM)
critic = Critic()
if args.g >= 0:
generator.to_gpu()
critic.to_gpu()
opt_g = optimizers.Adam(args.alpha, args.beta1, args.beta2)
opt_g.setup(generator)
opt_c = optimizers.Adam(args.alpha, args.beta1, args.beta2)
opt_c.setup(critic)
updater = WGANUpdater(mnist_iter, opt_g, opt_c, args.n_cri, args.gp_lam)
trainer = Trainer(updater, (args.epoch, "epoch"), out=args.result_dir)
trainer.extend(extensions.LogReport())
trainer.extend(
extensions.PrintReport(["epoch", "generator/loss", "critic/loss"]))
trainer.extend(extensions.ProgressBar())
trainer.extend(
extensions.PlotReport(("generator/loss", "main/wdist"),
"epoch",
file_name="loss_plot.eps"))
trainer.extend(extensions.snapshot_object(
generator, "model_epoch_{.updater.epoch}.model"),
trigger=(10, "epoch"))
trainer.extend(ext_save_img(generator, args.result_dir + "/out_images"))
trainer.run()
def main(args):
chainer.config.user_gpu = args.g
if args.g >= 0:
chainer.backends.cuda.get_device_from_id(args.g).use()
print("GPU mode")
mnist_3 = get_mnist_num(args.neg_numbers)
mnist_8 = get_mnist_num(args.pos_numbers)
# iteratorを作成
kwds = {"batch_size": args.batchsize, "shuffle": True, "repeat": True}
mnist_3_iter = iterators.SerialIterator(mnist_3, **kwds)
mnist_8_iter = iterators.SerialIterator(mnist_8, **kwds)
generator = Generator()
critic = Critic()
if args.g >= 0:
generator.to_gpu()
critic.to_gpu()
adam_args = args.alpha, args.beta1, args.beta2
opt_g = optimizers.Adam(*adam_args)
opt_g.setup(generator)
opt_c = optimizers.Adam(*adam_args)
opt_c.setup(critic)
updater = WGANUpdater(mnist_3_iter, mnist_8_iter, opt_g, opt_c,
args.n_cri1, args.n_cri2, args.gp_lam, args.l1_lam)
trainer = Trainer(updater, (args.epoch, "epoch"), out=args.result_dir)
trainer.extend(extensions.LogReport())
trainer.extend(
extensions.PrintReport(["epoch", "generator/loss", "critic/loss"]))
trainer.extend(extensions.ProgressBar())
trainer.extend(
extensions.PlotReport(("generator/loss", "critic/loss"),
"epoch",
file_name="loss_plot.eps"))
trainer.extend(
ext_save_img(generator, mnist_8, args.result_dir + "/out_images"))
trainer.run()
