trainloader = DataLoader(ImageNet(
train=True,
transform=transforms.Compose([transforms.ToTensor()]),
target_transform=transforms.Compose([transforms.ToTensor()]),
),
batch_size=10,
shuffle=False,
num_workers=8)
model = ResNetMCC()
model.to(device=DEVICE)
criterion = torch.nn.MSELoss(reduction='sum')
# criterion = multi_angular_loss
optimizer = torch.optim.Adam(model.parameters(),
lr=LEARNING_RATE,
weight_decay=WEIGHT_DECAY)
# optimizer = torch.optim.SGD(
# model.parameters(),
# momentum=0.9,
# lr=LEARNING_RATE,
# weight_decay=WEIGHT_DECAY
# )
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.1)
def run(epoch):
statistical_losses = StatisticalValue()
statistical_angular_errors = StatisticalValue()
target_transform=transforms.Compose([
transforms.ToTensor()
]),
),
batch_size=10,
shuffle=False,
num_workers=8
)
model = ResNetMCC(layer_count=152)
model.to(device=DEVICE)
criterion = torch.nn.MSELoss(reduction='sum')
# criterion = multi_angular_loss
optimizer = torch.optim.Adam(
model.parameters(),
lr=LEARNING_RATE,
weight_decay=WEIGHT_DECAY
)
# optimizer = torch.optim.SGD(
# model.parameters(),
# momentum=0.9,
# lr=LEARNING_RATE,
# weight_decay=WEIGHT_DECAY
# )
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.1)
def run(epoch):
statistical_losses = StatisticalValue()
statistical_angular_errors = StatisticalValue()