我想使用NumPy创建一个数据集，然后训练和测试一个简单的模型，如’线性’或’逻辑’模型。

我正在学习Pytorch Lightning。我找到了一篇教程，我们可以使用NumPy数据集，并可以使用均匀分布这里。作为新手，我没有完全理解如何做到这一点！

我的代码如下

import numpy as np import pytorch_lightning as pl from torch.utils.data import random_split, DataLoader, TensorDatasetimport torchfrom torch.autograd import Variablefrom torchvision import transformsnp.random.seed(42)device = 'cuda' if torch.cuda.is_available() else 'cpu'class DataModuleClass(pl.LightningDataModule):    def __init__(self):        super().__init__()        self.constant = 2        self.batch_size = 10        self.transform = transforms.Compose([            transforms.ToTensor()        ])            def prepare_data(self):        a = np.random.uniform(0, 500, 500)        b = np.random.normal(0, self.constant, len(x))        c = a + b        X = np.transpose(np.array([a, b]))        idx = np.arange(500)        np.random.shuffle(idx)                # Uses foirst 400 random indices for training        train_idx = idx[:400]        # Uses the remaining indices for validation        val_idx = idx[400:]                # Generate train and validation dataset        x_train, y_train = X[train_idx], y[train_idx]        x_val, y_val = X[val_idx], y[val_idx]                # Converting numpy array to Tensor        self.x_train_tensor = torch.from_numpy(x_train).float().to(device)        self.y_train_tensor = torch.from_numpy(y_train).float().to(device)                self.x_val_tensor = torch.from_numpy(x_val).float().to(device)        self.y_val_tensor = torch.from_numpy(y_val).float().to(device)                training_dataset = TensorDataset(self.x_train_tensor, self.y_train_tensor)                validation_dataset = TensorDataset(self.x_val_tensor, self.y_val_tensor)        return training_dataset, validation_dataset            def train_dataloader(self):        training_dataloader = prepare_data() # Most probably this is wrong way!!!        return DataLoader(self.training_dataloader)    def val_dataloader(self):        validation_dataloader = prepare_data() # Most probably this is wrong way!!!        return DataLoader(self.validation_dataloader)            # def test_dataloader(self):        obj = DataModuleClass()print(obj.prepare_data())  

这部分是根据给出的答案完成的[在这里，我想将a和b作为特征，c作为标签或目标变量。]

现在，如何将数据集传递到训练和验证方法中？

回答：

您可以使用以下代码从prepare_data()或setup()中获取数据。

def prepare_data(self):    a = np.random.uniform(0, 500, 500)    b = np.random.normal(0, self.constant, len(a))    c = a + b    X = np.transpose(np.array([a, b]))    # Converting numpy array to Tensor    self.x_train_tensor = torch.from_numpy(X).float().to(device)    self.y_train_tensor = torch.from_numpy(c).float().to(device)    training_dataset = TensorDataset(self.x_train_tensor, self.y_train_tensor)    self.training_dataset = training_datasetdef setup(self):    data = self.training_dataset    self.train_data, self.val_data = random_split(data, [400, 100])def train_dataloader(self):    return DataLoader(self.train_data)def val_dataloader(self):    return DataLoader(self.val_data)

您可以使用random_split()来拆分数据集。