在Stack Overflow中看到了类似的问题 Custom loss function in PyTorch ，回答中说自定义的Loss Func…学...
# Define the loss function - For classification problem loss_function = nn.CrossEntropyLoss() # Define the loss function - For regression problem loss_function = nn.MSELoss() # Mean Squared Error loss 另请注意,关于损失函数的选择和处理,可以应用一些额外的考虑因素和技术。 其中一些例子是: 自定义...
Pytorch 变量只是一个 Pytorch 张量,但 Pytorch 正在跟踪对其进行的操作,以便它可以反向传播以获得梯度。 这里我展示了一个名为 Regress_Loss 的自定义损失,它将 2 种输入 x 和 y 作为输入。然后将 x 重塑为与 y 相似,最后通过计算重塑后的 x 和 y 之间的 L2 差来返回损失。这是你在训练网络中经常遇到的...
#define triplet loss functionclasstriplet_loss(nn.Module):def__init__(self):super(triplet_loss,self).__init__()self.margin=0.2defforward(self,anchor,positive,negative):pos_dist=(anchor-positive).pow(2).sum(1)neg_dist=(anchor-negative).pow(2).sum(1)loss=F.relu(pos_dist-neg_dist+self...
# Define the loss function and optimizercriterion = nn.CrossEntropyLoss()optimizer = optim.AdamW(model.parameters(), lr=5e-6) # Training loopnum_epochs = 25 # Number of epochs to train for for epoch in tqdm(range(num_epochs)): # loop...
fromtorch.optimimportAdam# Define the loss function with Classification Cross-Entropy loss and an optimizer with Adam optimizerloss_fn = nn.CrossEntropyLoss() optimizer = Adam(model.parameters(), lr=0.001, weight_decay=0.0001) 使用训练数据训练模型。
[1]learning_rate =0.001n_epochs =100# Initialize the model, loss function, and optimizermodel = SimpleNN(input_dim)criterion = nn.MSELoss()optimizer = optim.Adam(model.parameters(), lr=learning_rate)# Define custom compilerdefmy_com...
batch_size =100# define loss function (criterion) and optimizercriterion = nn.CrossEntropyLoss().cuda(gpu) optimizer = torch.optim.SGD(model.parameters(),1e-4)### Wrap the modelmodel = nn.parallel.DistributedDataParallel(model, device_ids=[gpu])### Data loading codetrain_dataset = torchvis...
#define triplet loss functionclass triplet_loss(nn.Module): def __init__(self): super(triplet_loss, self).__init__() self.margin = 0.2def forward(self, anchor, positive, negative): pos_dist = (anchor - positive).pow(2).sum(1) ...
# Define the loss function with Classification Cross-Entropy loss and an optimizer with Adam optimizerloss_fn = nn.CrossEntropyLoss() optimizer = Adam(model.parameters(), lr=0.001, weight_decay=0.0001) 在定型資料上定型模型。 若要定型模型,您必須迴圈處理我們的資料反覆運算器、將輸入饋送至網路,...