deepcrayon
/
tinygrab
1
0
Fork 0
Code

fixed example broken after gpu refactor (#238)

pull/243/head
Asim 2021-01-05 20:41:54 +05:00 committed by GitHub
parent 8a38e0d207
commit 1c148f2fe4
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 6 additions and 6 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

12
examples/mnist_gan.py
View File

@ -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