target) output2 = torch.nn.functional.smooth_l1_loss(input, target) print('output1: ',output...
define_macros=defines,relative_to=__file__,with_cuda=with_cuda,extra_objects=extra_objects)if__...
In addition, in your snippet, you're not really computing a custom gradient, so you could compute that with Pytorch's autograd, without having to define a special Function. If this is working code and you're going to define the custom loss, here's a quick ...
cuda(gpu) batch_size = 100 # define loss function (criterion) and optimizer criterion = nn.CrossEntropyLoss().cuda(gpu) optimizer = torch.optim.SGD(model.parameters(), 1e-4) # Data loading code train_dataset = torchvision.datasets.MNIST(root='./data', train=True, transform=transforms....
model=model.to(device)# define loss function (criterion) and optimizercriterion=nn.CrossEntropyLoss().to(device)optimizer=torch.optim.SGD(model.parameters(),args.lr,momentum=args.momentum,weight_decay=args.weight_decay)ifargs.apex:model,optimizer=amp.initialize(model,optimizer,opt_level=args.apex_...
# Define the loss function and optimizer loss_function = nn.CrossEntropyLoss() optimizer = torch.optim.Adam(mlp.parameters(), lr=1e-4) # Run the training loop for epoch in range(0, 5): # 5 epochs at maximum # Print epoch print(f'Starting epoch {epoch+1}') ...
# define loss function loss_function = nn.CrossEntropyLoss() # construct an optimizer params = [p for p in net.parameters() if p.requires_grad] optimizer = optim.Adam(params, lr=0.0001) epochs = 3 best_acc = 0.0 save_path = './resNet34.pth' ...
("child ",child_counter," was not frozen")child_counter+=1# Now,let's definenewlayersthat we want to add on top.# Basically,these are just objects we define here.The"adding on top"is defined by theforward()#functionwhich decides the flowofthe input data into the model.#NOTE-Even ...
现在是最终版本,在这里我为MSE实现了我自己的渐变。为此,我在损失函数类中定义了自己的backward方法,显然需要执行mseloss = MyMSELoss.apply。 代码语言:javascript 复制 from torch.autogradimportFunction ###classMyMSELoss(Function):@staticmethod defforward(ctx,y_pred,y):ctx.save_for_backward(y_pred...
# Define the model model = nn.Sequential( nn.Linear(8, 24), nn.ReLU(), nn.Linear(24, 12), nn.ReLU(), nn.Linear(12, 6), nn.ReLU(), nn.Linear(6, 1) ) # loss function and optimizer def loss_fn(output, target): # MAPE loss ...