Weighted loss: When dealing with imbalanced classes, using weighted BCE loss can help address class imbalance by giving more emphasis to the minority class. Loss combinations: In some cases, combining multiple loss functions can improve model performance. For instance, using a combination of cross e...
import torch.nn.functional as F class CustomLoss(torch.nn.Module): def __init__(self, class_weights): super(CustomLoss, self).__init__() self.class_weights = class_weights def forward(self, inputs, targets): ce_loss = F.cross_entropy(inputs, targets, reduction='none') weights = ...
我们考虑总的损失函数 l 对参数 w 的导数: ∂ Loss∂w=∂[1n∑i=1nl(xi,yi)]∂w=1n∑i=1n∂l(xi,yi)∂w=∑j=1kmjn∂[1mj∑i=mj−1mj−1+mjl(xi,yi)]∂w=∑j=1kmjn∂ lossj∂w=1k∑j=1k∂ lossj∂w 那么接下来我们看一下 PyTorch 究竟是怎么实现数据并行的。
print("The model will be running on", device,"device")# Convert model parameters and buffers to CPU or Cudamodel.to(device)forepochinrange(num_epochs):# loop over the dataset multiple timesrunning_loss =0.0running_acc =0.0fori, (images, labels)inenumerate(train_loader,0):# get the ...
start=torch.cuda.Event(enable_timing=True)end=torch.cuda.Event(enable_timing=True)start.record()forepochinrange(2):# loop over the dataset multiple times running_loss=0.0fori,datainenumerate(trainloader,0):#getthe inputs;data is a listof[inputs,labels]inputs,labels=data # zero the parame...
# define a timer class to record timeclassTimer(object):"""Record multiple running times."""def__init__(self): self.times = [] self.start()defstart(self):# start the timerself.start_time = time.time()defstop(self):# stop the timer and record time into a listself.times.append(tim...
Vector of dimensionality batch_size loss = criterion(outputs, labels) loss.backward() optimizer.step() running_loss += loss.item() print(f"Epoch {epoch+1}/{epochs}, Loss: {running_loss / len(train_loader)}") def test(model, test_loader, device): model.to(device) model.eval() ...
criterion = nn.CrossEntropyLoss() optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9) 1. 2. 3. 4. 4.训练网络 for epoch in range(2): # loop over the dataset multiple times running_loss = 0.0 for i, data in enumerate(trainloader, 0): ...
All functions and classes under torch.jit.quantized will now raise an error if called/instantiated. This API has long been deprecated in favor of torch.ao.nn.quantized.2.2 2.3 # torch.jit.quantized APIs torch.jit.quantized.quantize_rnn_cell_modules torch.jit.quantized.quantize_rnn_modules ...
5.3 构建loss函数 5.4 训练模型:梯度下降法迭代式 5.6 拟合效果评估:图形法 第1章 Autograd库简介 1.1 Autograd库的简介与作用 autograd是所有神经网络的核心内容,为Tensor所有操作提供自动求导方法。 Pytorch Autograd库 (自动求导机制) 是训练神经网络时,反向误差传播(...