train dataloader has 126 iters

That means the training dataset has been divided into 126 batches, and the dataloader is iterating over each batch during training. Each iteration involves loading a batch of data, processing it through the model, and updating the model parameters based on the loss. This process is repeated for all batches in the dataset until the model has been trained on the entire dataset for several epochs.

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train_dataset = TensorDataset(X_train, y_train) train_dataloader = DataLoader(train_dataset, batch_size=32, shuffle=True)

这段代码是用来构建PyTorch中的数据集和数据加载器，用于训练深度学习模型。具体来说，该代码将训练数据集X_train和y_train打包为一个TensorDataset对象，然后使用DataLoader对象将其转换为批量数据，每个批次大小为32，并且在每个epoch中随机打乱数据集顺序。这样做是为了使训练更加高效，因为深度学习模型需要大量的数据进行训练，而将数据分批次处理可以减少内存占用和提高计算效率。

train_dataloader = DataLoader(train_data, batch_size=64, shuffle=True)

This line of code creates a DataLoader object called "train_dataloader" which takes in "train_data" as the dataset to load. It specifies a batch size of 64, meaning that the data will be loaded in batches of 64 samples at a time. The "shuffle=True" argument indicates that the order of the samples within each batch will be randomized during training, which helps to prevent the model from overfitting to any particular order of the data.