batch): xb, labels = batch outs = self(xb) loss = F.cross_entropy(outs, labels) return loss # similar to `train_step`, but includes acc calculation & detach def val_step(self, batch): xb, labels = batch outs = self(xb) loss = F.cross...
criterion=nn.CrossEntropyLoss()optimizer1=optim.Adam(model_part1.parameters(),lr=0.001)# 使用Adam优化器optimizer2=optim.Adam(model_part2.parameters(),lr=0.001)forepochinrange(5):# 进行5轮训练forinputs,targetsindataloader:inputs,targets=inputs.to(device1),targets.to(device2)# 将数据移动到对...
model=My_CNN()optimizer=torch.optim.Adam(model.parameters(),lr=0.001)loss_function=nn.CrossEntropyLoss() 最后开始训练,所有 PyTorch 训练循环都将经过每个 epoch 和每个DataPoint(在训练DataLoader 对象中)。 代码语言:javascript 代码运行次数:0 运行 ...
AI检测代码解析 deftrain_model(encoder,decoder,data_loader,epochs=10):optimizer=torch.optim.Adam(list(encoder.parameters())+list(decoder.parameters()))criterion=nn.CrossEntropyLoss()forepochinrange(epochs):forx,yindata_loader:optimizer.zero_grad()encoder_outputs=encoder(x)decoder_outputs=decoder(y,...
CrossEntropyLoss() optimizer = optim.Adam(cnn.parameters(), lr = learning_rate) # define train function that trains the model using a CIFAR10 dataset def train(model, epoch, num_epochs): model.train() total_batch = len(train_dataset) // batch_size for i, (images, labels) in ...
# 交叉熵损失函数，带权重criterion=t.nn.CrossEntropyLoss(weight=t.Tensor([1,3]))input=t.randn(...
loss = F.cross_entropy(outs, labels) return loss # similar to `train_step`, but includes acc calculation & detach def val_step(self, batch): xb, labels = batch outs = self(xb) loss = F.cross_entropy(outs, labels) acc = accuracy(outs, labels) return {'loss': loss.detach(), '...
def categorical_cross_entropy(p, y): return -np.mean(np.sum(y*np.log(p))) 到目前为止,我们已经了解了前馈传播,以及构成前馈传播的各种组件,如权重初始化、与节点相关的偏差、激活和损失函数。在下一节中,我们将学习反向传播,这是一种调整权重的技术,使权重的损失尽可能小。 实现反向传播 在前馈传播中...
criterion = torch.nn.CrossEntropyLoss()optimizer = torch.optim.SGD(model.parameters(), lr=0.001) 训练模型model.to(device)for epoch in range(10): # epochs looprunning_loss = 0 # reset every epoch for the loss calculation purpose only! (not necessary)for i, data in enumerate(trainloader)...
cross_entropy(outs, labels) return loss # similar to `train_step`, but includes acc calculation & detach def val_step(self, batch): xb, labels = batch outs = self(xb) loss = F.cross_entropy(outs, labels) acc = accuracy(outs, labels) return {'loss': loss.detach(), 'acc': acc....