问当我使用torch.nn.function.mse_loss定义的损失函数时，损失将是NanEN损失函数（loss function）是用来...
Loss = nn.MSELoss(reduction='mean') sgd = SGD([mod.w, mod.b], lr=0.001) 然后就是和之前设计梯度下降法类似的步骤: for i in range(5000): sgd.zero_grad() loss = Loss(mod(X), Y) loss.backward() sgd.step() print(f"[epoch : {i}] loss: {loss}") print(f"w: {mod.w}, ...
loss = nn.MSELoss() input = torch.randn(3, 5, requires_grad=True) target = torch.randn(3, 5) output = loss(input, target) output.backward() 1. 2. 3. 4. 5. print('MSE损失函数的计算结果为',output) 1. MSE损失函数的计算结果为 tensor(3.4806, grad_fn=<MseLossBackward0>) 1. 5...
若遇到torch.nn.MSELoss(reduction='sum'),很多的 loss 函数都有 size_average 和 reduce 两个布尔类型的参数。因为一般损失函数都是直接计算 batch 的数据,因此返回的 loss 结果都是维度为 (batch_size, ) 的向量。 tensor.masked_fill() 将tensor中等于某个值的位置用另一个值填充 掩码操作 tensor.masked_...
isnan(loss_1_update): print(loss_1_update, y_1[ind_2_update], t[ind_2_update], len(loss_1_sorted), remember_rate) return torch.sum(loss_1_update)/num_remember,torch.sum(loss_2_update)/num_remember 大致的思想就是,我认为比较准确的东西,你要多考虑考虑;你认为比较准确的东西,我也多...
See MSELoss for details.margin_ranking_losstorch.nn.functional.margin_ranking_loss(input1, input2, target, margin=0, size_average=None, reduce=None, reduction='mean') → Tensor [source] See MarginRankingLoss for details.multilabel_margin_losstorch...
mse pytorch_torch和pytorch 在pytorch中,经常使用nn.MSELoss作为损失函数,例如 loss=nn.MSELoss() input=torch.randn(3,5,requires_grad=True) target=...torch.randn(3,5) error=loss(input,target) error.backward() 这个地方有一个巨坑,就是一定要小心input和target的位置,说的更具体一些,target...另外...
batch_size, 4, 64, 64, device=device) out = model(x) with amp_context: loss = F.mse_loss(x, out) loss_test = loss.clone() # Ensure local loss is not changed by allreduce torch.distributed.all_reduce(loss_test) # Check if any gpu has NaN loss if rank == 0: iterator.set_...
loss_fn = nn.MSELoss() if adam_config is None: # Set a stupid high LR to move far. adam_config = AdamConfig(max_lr=0.1, weight_decay=0.1) pytorch_opt_map = { AdamWDMode.ORIGINAL: torch.optim.Adam, AdamWDMode.ADAMW: torch.optim.AdamW, AdamWDMode.DECOUPLED: torch.optim.Adam, #...
mse_loss(x, y, reduction='mean') psnr = 10 * torch.log10(self.max_val ** 2 / mse) return psnr Example #21Source File: pairwise_loss.py From sigmanet with MIT License 5 votes def psnr(gt, pred, data_range=None, batch=True, reduce=True): """ Compute the peak signal to ...