From 1c148f2fe4744abc62b3302c382419182d363f00 Mon Sep 17 00:00:00 2001 From: Asim Date: Tue, 5 Jan 2021 20:41:54 +0500 Subject: [PATCH] fixed example broken after gpu refactor (#238) --- examples/mnist_gan.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/examples/mnist_gan.py b/examples/mnist_gan.py index 32270ed58f..7dc463a215 100644 --- a/examples/mnist_gan.py +++ b/examples/mnist_gan.py @@ -62,7 +62,7 @@ if __name__ == "__main__": output_folder = "outputs" os.makedirs(output_folder, exist_ok=True) train_data_size = len(X_train) - ds_noise = Tensor(np.random.randn(64,128).astype(np.float32), gpu=GPU, requires_grad=False) + ds_noise = Tensor(np.random.randn(64,128).astype(np.float32), requires_grad=False) n_steps = int(train_data_size/batch_size) if GPU: [x.gpu_() for x in generator_params+discriminator_params] @@ -78,18 +78,18 @@ if __name__ == "__main__": idx = np.random.randint(0, X_train.shape[0], size=(batch_size)) image_b = X_train[idx].reshape(-1, 28*28).astype(np.float32)/255. image_b = (image_b - 0.5)/0.5 - return Tensor(image_b, gpu=GPU) + return Tensor(image_b) def real_label(bs): y = np.zeros((bs,2), np.float32) y[range(bs), [1]*bs] = -2.0 - real_labels = Tensor(y, gpu=GPU) + real_labels = Tensor(y) return real_labels def fake_label(bs): y = np.zeros((bs,2), np.float32) y[range(bs), [0]*bs] = -2.0 # Can we do label smoothin? i.e -2.0 changed to -1.98789. - fake_labels = Tensor(y, gpu=GPU) + fake_labels = Tensor(y) return fake_labels def train_discriminator(optimizer, data_real, data_fake): @@ -125,12 +125,12 @@ if __name__ == "__main__": for i in tqdm(range(n_steps)): image = generator_batch() for step in range(k): # Try with k = 5 or 7. - noise = Tensor(np.random.randn(batch_size,128), gpu=GPU) + noise = Tensor(np.random.randn(batch_size,128)) data_fake = generator.forward(noise).detach() data_real = image loss_d_step = train_discriminator(optim_d, data_real, data_fake) loss_d += loss_d_step - noise = Tensor(np.random.randn(batch_size,128), gpu=GPU) + noise = Tensor(np.random.randn(batch_size,128)) data_fake = generator.forward(noise) loss_g_step = train_generator(optim_g, data_fake) loss_g += loss_g_step