# 如果存在预训练权重则载入if os.path.exists(weights_path):weights_dict = torch.load(weights_path, map_location=device)# 简单对比每层的权重参数个数是否一致load_weights_dict = {k: v for k, v in weights_dict.items()if model.state_dict()[k]...
weights = torch.ones_like(targets).float() for class_idx, weight in enumerate(self.class_weights): weights[targets == class_idx] = weight weighted_loss = ce_loss * weights return torch.mean(weighted_loss) # Assuming you have a model and training data model = YourModel() optimizer = to...
deftrain_ch6(net,train_iter,test_iter,num_epochs,lr,device):"""用GPU训练模型"""definit_weights(m):iftype(m)==nn.Linear ortype(m)==nn.Conv2d:nn.init.xavier_uniform_(m.weight)net.apply(init_weights)print('training on',device)net.to(device)optimizer=torch.optim.SGD(net.parameters()...
batch_size=100mnist=datasets.MNIST('./data/MNIST',download=True,train=True,transform=transform)mnist_loader=DataLoader(dataset=mnist,batch_size=batch_size,shuffle=True)#CPUdefimshow(img,title):img=utils.make_grid(img.cpu().detach())img=(img+1)/2npimg=img.detach().numpy()plt.imshow(np.tr...
for shape in sorted(data['shapes'], key=lambda x: x['label']): label_name = shape['label'] if label_name in label_name_to_value: label_value = label_name_to_value[label_name] else: label_value = len(label_name_to_value) ...
(out, target)# Calculate the losstrain_loss += loss.item()# Keep a running total of loss for each batch# backpropagate adjustments to weights/biasloss.backward() optimizer.step()#Return average loss for all batchesavg_loss = train_loss / (batch+1) print('Training set: Average loss: {...
first_batch=next(iter(train_loader))forbatch_idx,(data,target)inenumerate([first_batch]*50):# training code here 你可以看到我将“first_batch”乘以了50次,以确保我会过拟合。 常见错误 #2: 忘记为网络设置 train/eval 模式 为什么PyTorch关注我们是训练还是评估模型?最大的原因是dropout。这项技术在训...
sess.run(init)foriinrange(epoch): sess.run(train_step, feed_dict={x_: XOR_X, y_: XOR_Y}) 接下来我们开始 TensorFlow 会话。 这是与预先构建的图进行交互的唯一方法。 在会话内部,您可以遍历数据,并使用session.run方法将数据传递到图。 因此,输入的大小应与图中定义的大小相同。
列表12.9 training.py:31,class LunaTrainingApp class LunaTrainingApp: def __init__(self, sys_argv=None): # ... line 52 parser.add_argument('--balanced', help="Balance the training data to half positive, half negative.", action='store_true', default=False, ) 然后我们将该参数传递给Lu...
dataset = MNIST(root=self.hparams.data_root, train=train, download=True) loader = DataLoader(dataset, batch_size=32, shuffle=True) forbatchinloader: x, y = batch model.training_step(x, y) ... 2.DataLoaders中的workers的数量 另一个加速的...