( Converts a PIL Image or numpy.ndarray (H x W x C) in the range [0, 255] to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0] ) 3、[0.485, 0.456, 0.406]这一组平均值一般是抽样算出来的。 如何计算数据集的mean和std 数据存放路径: calculate_mead_and_std.py ...
transforms_cifar = transforms.Compose([ transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ]) # Loading the CIFAR-10 dataset: train_dataset = datasets.CIFAR10(root='./data', train=True, download=True, transform=transforms_cifar) test_dataset...
weight, mean=0.0, std=0.01) init.constant_(net[0].bias, val=0.0) # or you can use `net[0].bias.data.fill_(0)` to modify it directly 输出:Parameter containing: tensor([0.], requires_grad=True) for param in net.parameters(): print(param) 输出:Parameter containing: tensor([[-...
( dataset, batch_size=256, shuffle=False, num_workers=0, pin_memory=False, drop_last=False ) # calculate mean and std of cifar10 training set data = list() for x, _ in dataloader: # x: torch tensor # n x c x h x w data.append(x) data = torch.cat(data, dim=0) print(...
# in each epoch, all the samples in dataset will be used once # X is the feature and y is the label of a batch sample for X, y in data_iter(batch_size, features, labels): l = loss(net(X, w, b), y).sum() # calculate the gradient of batch sample loss ...
# Calculate cross-entropy loss between students' and teacher's probabilities.loss = - (teacher_probs * student_probs).sum(dim=1).mean()returnloss 中心化:中心化教师的输出确保学生模型更多地关注教师输出分布中最显著的特征或区别。通过中...
self.gamma = nn.Parameter(torch.ones(1)) self.beta = nn.Parameter(torch.zeros(1)) def forward(self, input): mean = input.mean(dim=-1, keepdim=True) std = input.std(dim=-1, keepdim=True) return self.gamma * ((input - mean)/(std + self.eps)) + self.beta class AddAndNorm...
nonlinearity='leaky_relu') 11 . torch.nn.init.kaiming_normal_(tensor, a=0, mode='fan_in', nonlinearity='leaky_relu') 12 . torch.nn.init.orthogonal_(tensor, gain=1) 13 . torch.nn.init.sparse_(tensor, sparsity, std=0.01) 14 . torch.nn.init.calculate_gain(nonlinearity, param=None)...
# Calculate numberofneighbors n_neighbors = adj_mat.sum(dim=-1, keepdims=True) #Createidentitytensor self.idx_mat = torch.eye( adj_mat.shape[-2], adj_mat.shape[-1], device=n_neighbors.device ) #Addnew(batch) dimensionandexpand
# Calculate MAE prediction = standardizer.restore(nn_prediction.detach().cpu()) mae = mean_absolute_error(output, prediction) avg_mae += mae # Set zero gradients for all tensors optimizer.zero_grad() # Do backward prop loss.backward() ...