parameters(), lr = 0.1) def train_model(iter): for epoch in range(iter): y1 = model(x) loss = criterion(y1, y) writer.add_scalar("Loss/train", loss, epoch) optimizer.zero_grad() loss.backward() optimizer.step() train_model(10) writer.flush() writer.close() 3.3 wandb 可视化 ...
train_iter = data.DataLoader(train_data, batch_size=batch, shuffle=True, num_workers=get_dataloader_worker()) 这里学到了可以使用多个线程对数据进行读取。 num_workers(int,optional) – how many subprocesses to use for data loading.0means that the data will be loaded in the main process. (de...
batch_size, n_train =16,600# 只有前n_train个样本用于训练train_iter = d2l.load_array((features[:n_train], labels[:n_train]), batch_size, is_train=True)# 初始化网络权重的函数definit_weights(m):iftype(m) == nn.Linear: nn.init.xavier_uniform_(m.weight)# 一个简单的多层感知机defge...
train_iter = torch.utils.data.DataLoader(mnist_train, batch_size=batch_size, shuffle=True, num_workers=num_workers) test_iter = torch.utils.data.DataLoader(mnist_test, batch_size=batch_size, shuffle=False, num_workers=num_workers) #loss函数 loss = torch.nn.CrossEntropyLoss() 1. 2. 3. ...
d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, updater) num_hiddens 越大训练效果越好 (2)尝试添加更多的隐藏层,并查看对结果有何影响。 defnet2(X): X = X.reshape((-1, num_inputs)) H1 = relu(X@W1 + b1)# 输入层运算+激活 这里“@”代表矩阵乘法H2 = relu(H1@W2 + b2...
上面所说的生成器的基类叫做 _BaseDataLoaderIter,在它的初始化函数中唯一调用了一次随机数函数,用以确定全局随机数种子。 class_BaseDataLoaderIter(object): def__init__(self, loader: DataLoader)->None: ... self._base_seed = torch.empty((),...
train_size=int(ratio * len(all_datasets)) test_size=len(all_datasets) - train_size train_datasets, test_datasets = torch.utils.data.random_split(all_datasets, [train_size, test_size]) train_iter = torch.utils.data.DataLoader(train_datasets, batch_size=batch_size, shuffle=True) ...
defimshow(img):img=img/2+0.5# unnormalize npimg=img.numpy()# numpy objects plt.imshow(np.transpose(npimg,(1,2,0)))plt.show()# 利用ITER函数获取随机训练图像 dataiter=iter(trainloader)images,labels=dataiter.next()imshow(torchvision.utils.make_grid(images))# print theclassofthe imageprint...
inputs,classes=next(iter(dataloaders['train']))# 创建网格,注意之前的batch_size=4out=torchvision.utils.make_grid(inputs)imshow(out,title=[class_names[x]forxinclasses]) 接下来定义一个训练函数实现以下功能: 1、可以对学习率进行调控; 2、寻找并保存最佳的模型。
28split = int(np.floor(valid_size * num_train)) 29train_idx, valid_idx = indices[split:], indices[:split] 30 31# define samplers for obtaining training and validation batches 32train_sampler = SubsetRandomSampler(train_idx) 33valid_sampl...