torch.nn.MSELoss() 求predict和target之间的loss。 代码示例 单个求其loss: crit =nn.MSELoss()#target = torch.Tensor(1)#target[0] = 10#res = torch.Tensor(1)#res[0] = 5#cost = crit(res,target)#25#print(cost)target = torch.Tensor(2) target[0]= 10target[1] = 6res= torch.Tensor...
loss(xi,yi)=(xi−yi)2 函数需要输入两个tensor,类型统一设置为float,否则会报错,也可以在全局设置torch.set_default_tensor_type(torch.FloatTensor),也可以在计算时转换 loss=torch.nn.MSELoss() c=torch.tensor([[1,2],[3,4]]) d=torch.tensor([[5,6],[7,8]]) loss(c.float(),d.float())...
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deftrain_model(dataloader):importtorchimporttorch.nnasnnfromtorch.optimimportSGD device = torch.device("cuda"iftorch.cuda.is_available()else"cpu") model = SimpleModel().to(device) optimizer = SGD(model.parameters(), lr=0.01) loss_fn = nn.MSELoss()forepochinrange(10):forbatchindataloader:...
MSELoss() optimizer = optim.Adam(model.parameters(), lr=0.001) 结合长短时注意力机制后,模型结构包括以下组件: (1)LSTM层:提取时间序列的初步特征。(2)注意力层:计算全局和局部的时间步重要性。(3)全连接层:进行最终预测。 2.3 模型训练 核心代码: num_epochs = 10000 model.train() for epoch in ...
损失函数:nn.MSELoss,nn.CrossEntropyLoss,nn.NLLLoss 这些类的实例具有一个内置的__call__函数,可以通过图层运行输入。 import torch.nn as nn import torch.nn.functional as F class My_Model(nn.Module): def __init__(self): super(My_Model, self).__init__() ...
torch.nn.Module -- 损失函数: L1Loss(Module)/ MSELoss(Module)/SmoothL1Loss(Module)/CrossEntropyLoss(Module) -- 激活函数:Threshold(Module)/ReLU(Module)/Sigmoid(Module) -- BN类:BatchNorm2d(Module)/InstanceNorm2d(Module)/LayerNorm(Module)/GroupNorm(Module) -- conv类: Conv2d(Module)/ConvTrans...
MSELoss() optimizer = torch.optim.SGD(linear.parameters(), lr=0.01) # 前向计算 pred = linear(x) # 计算loss loss = criterion(pred, y) print('loss: ', loss.item()) loss.backward() # 打印输出梯度 print ('dL/dw: ', linear.weight.grad) print ('dL/db: ', linear.bias.grad) #...
torch.nn.MSELoss()for epoch in range(3000):l = 0for iter in range(10):opt.zero_grad()output = linear(x_train[iter])loss_dropout = loss(output, y_train[iter])loss_dropout.backward()l = loss_dropout.detach() + lopt.step()print(epoch,'loss=%s'%(l))# plt.scatter(epoch,l,s=...
() criterion = nn.MSELoss() # 使用均方误差作为损失函数 optimizer = optim.SGD(model.parameters(), lr=0.01) # 使用SGD优化器 # 准备数据 x_train = torch.randn(100, 1) # 生成100个随机样本作为输入 y_train = 2 * x_train + 1 + torch.randn(100, 1) * 0.1 # 生成对应的输出,加入一些...