gradients1 = autograd.grad(outputs=disc_interpolates, inputs=interpolates, grad_outputs=torch.ones(disc_interpolates.size()).cuda(), create_graph=True, retain_graph=True, only_inputs=True, allow_unused=True)[0] gradients2 = autograd.grad(outputs=s, inputs=interpolates, grad_outputs=torch.ones(s.size()).cuda(), create_graph=True, retain_graph=True, only_inputs=True, allow_unused=True)[0]中，出现错误：AttributeError: 'NoneType' object has no attribute 'norm'。s只是一个分数，好像不可导

def calc_gradient_penalty(self, netD, real_data, fake_data): alpha = torch.rand(1, 1) alpha = alpha.expand(real_data.size()) alpha = alpha.cuda() interpolates = alpha * real_data + ((1 - alpha) * fake_data) interpolates = interpolates.cuda() interpolates = Variable(interpolates, requires_grad=True) disc_interpolates, s = netD.forward(interpolates) s = torch.autograd.Variable(torch.tensor(0.0), requires_grad=True).cuda() gradients1 = autograd.grad(outputs=disc_interpolates, inputs=interpolates, grad_outputs=torch.ones(disc_interpolates.size()).cuda(), create_graph=True, retain_graph=True, only_inputs=True, allow_unused=True)[0] gradients2 = autograd.grad(outputs=s, inputs=interpolates, grad_outputs=torch.ones(s.size()).cuda(), create_graph=True, retain_graph=True, only_inputs=True, allow_unused=True)[0] gradient_penalty = (((gradients1.norm(2, dim=1) - 1) ** 2).mean() * self.LAMBDA) + \ (((gradients2.norm(2, dim=1) - 1) ** 2).mean() * self.LAMBDA) return gradient_penalty，上述代码中(((gradients2.norm(2, dim=1) - 1) ** 2).mean() * self.LAMBDA)提示出现错误：RuntimeError: One of the differentiated Tensors appears to not have been used in the graph. Set allow_unused=True if this is the desired behavior.

grad_outputs=torch.ones(s.size()).cuda(), create_graph=True, retain_graph=True, only_inputs=True, allow_unused=True)[0] 改为： gradients2 = autograd.grad(outputs=s, inputs=interpolates, ...

def compute_gradient_penalty(D, real_samples, fake_samples): alpha = torch.from_numpy(np.random.random((real_samples.size()[0], 1, 1, 1))).float().cuda() interpolates = (alpha * real_samples + ((1 - alpha) * fake_samples)).requires_grad_(True) d_interpolates, _ = D(interpolates) fake = autograd.Variable(torch.ones(real_samples.size()[0]), requires_grad=False).cuda() gradients = autograd.grad( outputs=d_interpolates, inputs=interpolates, grad_outputs=fake, create_graph=True, retain_graph=True, only_inputs=True, )[0] gradients = gradients.view(gradients.size(0), -1) gradient_penalty = ((gradients.norm(2, dim=1) - 1) ** 2).mean() return gradient_penalty解释函数

- 随机生成一个(0,1)之间的随机数alpha，其形状为(batch_size, 1, 1, 1)。 - 根据随机数alpha和真实图像样本、生成图像样本，生成一组插值样本(interpolates)，其形状与真实图像样本一致。 - 将插值样本...

torch.no_grad

torch.no_grad is a context manager that disables gradient calculation. It is used during inference or testing, when we do not need to compute gradients. By disabling gradient calculation, the memory ...

def train(epoch, tloaders, tasks, net, args, optimizer, list_criterion=None): print('\nEpoch: %d' % epoch) # print('...................',tasks) net.train() batch_time = AverageMeter() data_time = AverageMeter() losses = [AverageMeter() for i in tasks] top1 = [AverageMeter() for i in tasks] end = time.time() loaders = [tloaders[i] for i in tasks] min_len_loader = np.min([len(i) for i in loaders]) train_iter = [iter(i) for i in loaders] for batch_idx in range(min_len_loaderlen(tasks)): config_task.first_batch = (batch_idx == 0) # Round robin process of the tasks 任务的轮循进程 current_task_index = batch_idx % len(tasks) inputs, targets = (train_iter[current_task_index]).next() config_task.task = tasks[current_task_index] # measure data loading time data_time.update(time.time() - end) if args.use_cuda: inputs, targets = inputs.cuda(), targets.cuda() optimizer.zero_grad() inputs, targets = Variable(inputs), Variable(targets) outputs = net(inputs) # net_graph = make_dot(outputs) # net_graph.render(filename='net.dot') loss = args.criterion(outputs, targets) # measure accuracy and record loss (losses[current_task_index]).update(loss.data, targets.size(0)) _, predicted = torch.max(outputs.data, 1) correct = predicted.eq(targets.data).cpu().sum() correct = correct.numpy() (top1[current_task_index]).update(correct100./targets.size(0), targets.size(0)) # apply gradients loss.backward() optimizer.step() # measure elapsed time测量运行时间 batch_time.update(time.time() - end) end = time.time() if batch_idx % 5 == 0: print('Epoch: [{0}][{1}/{2}]\t' 'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t' 'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'.format( epoch, batch_idx, min_len_loader*len(tasks), batch_time=batch_time, data_time=data_time)) for i in range(len(tasks)): print('Task {0} : Loss {loss.val:.4f} ({loss.avg:.4f})\t' 'Acc {top1.val:.3f} ({top1.avg:.3f})'.format(tasks[i], loss=losses[i], top1=top1[i])) return [top1[i].avg for i in range(len(tasks))], [losses[i].avg for i in range(len(tasks))]

1. 将模型设置为训练模式。 2. 初始化一些测量指标，如平均损失、准确率和批处理时间。 3. 循环遍历每个任务的数据加载器，并从中获取输入和目标。 4. 将输入和目标转换为张量，并在需要时将其移动到GPU上。 5. 清零...

import torch.optim as optim

perm = torch.randperm(train_inputs.size(0)) train_inputs = train_inputs[perm] train_labels = train_labels[perm] # Loop over the training data in batches for i in range(0, train_inputs.size(0), ...

解释utilize the LSTM model in torch.nn

h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_dim).requires_grad_() c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_dim).requires_grad_() # Forward pass through LSTM layer ...

AttributeError Traceback (most recent call last) Cell In[18], line 21 19 data, target = data.cuda(), target.cuda() 20 # clear the gradients of all optimized variables ---> 21 optimizer.zero_grad() 22 # forward pass: compute predicted outputs by passing inputs to the model 23 output = model(data) AttributeError: 'NoneType' object has no attribute 'zero_grad'

这个错误表明你尝试在一个空对象上调用 zero_grad() ...optimizer = torch.optim.Adam(model.parameters(), lr=0.001) 确保在调用 optimizer.zero_grad() 之前，optimizer 对象已经被正确地定义和初始化。

please explain the code of torch.optim.AdamW

model = torch.nn.Linear(10, 1) # Define the optimizer optimizer = optim.AdamW(model.parameters(), lr=0.001, weight_decay=0.01) # Define the loss function criterion = torch.nn.MSELoss() # Train the ...

Write pytorch-based Python code to implement a neural network that solves binary classification problem with an output layer of a positively weighted sub-network plus a negatively weighted sub-network，where the negative weight is random and take the opposite of the absolute value of the standard normal distribution, the exact value of the two weights are obtained from the objective function by Back propagation. The activation function is set to tanh.

predicted = torch.round(torch.sigmoid(outputs)) total += labels.size(0) correct += (predicted == labels).sum().item() accuracy = correct / total print(f"Test Accuracy: {accuracy}") Make ...

写一段torch深度学习的代码

import torch.nn as nn #定义网络模型 class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(3, 10, kernel_size=5) self.conv2 = nn.Conv2d(10, 20, kernel_size=5...

输入为1111大小数据集用wgan生成1111灰度图代码

gradients = torch.autograd.grad(outputs=d_interpolated, inputs=interpolated, grad_outputs=torch.ones(d_interpolated.size(), device=device), create_graph=True, retain_graph=True)[0] gradients = ...

wgan-gp代码

gradients = torch.autograd.grad(outputs=prob_interpolated, inputs=interpolated, grad_outputs=torch.ones(prob_interpolated.size()).cuda(), create_graph=True, retain_graph=True)[0] # 计算梯度惩罚项...

optimizer.step()

optimizer = torch.optim.SGD(model.parameters(), lr=0.01) criterion = nn.CrossEntropyLoss() # Perform forward pass and compute loss outputs = model(inputs) loss = criterion(outputs, labels) # Compute...

写一个用花分类数据集且添加了注意力机制的网络代码

attention_input = torch.cat((hidden, encoder_outputs[i]), 1) attention_weights[:, i] = self.attention(attention_input).squeeze(1) return self.softmax(attention_weights).unsqueeze(1) class Net(nn...

基于pytorch，写LSTM深度学习基于时间序列A预测序列B的代码

h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size) c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size) out, (hn, cn) = self.lstm(x, (h0, c0)) out = self.fc(out[:, -1, :]) ...

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