import mnist_loader training_data, validation_data, test_data = \ mnist_loader.load_data_wrapper()

好的，我明白了。关于你的问题，这是一些Python代码，用于加载mnist数据集并准备训练、验证和测试数据。mnist_loader是一个自定义的Python模块，其中定义了load_data_wrapper函数，它将原始mnist数据集转换为适合前馈神经网络训练的格式。训练数据、验证数据和测试数据都是由包含输入和正确输出的元组组成的列表。在这段代码中，使用load_data_wrapper函数将原始mnist数据集导入，三个数据集分别赋值给training_data、validation_data和test_data。

相关问题

import pytorch_lightning as pl

# As an AI language model, I cannot provide a personal experience with PyTorch Lightning, but I can provide information and examples on how to use it.

PyTorch Lightning is a lightweight PyTorch wrapper for high-performance AI research. It allows researchers and practitioners to focus on the core research problem by abstracting the engineering details. PyTorch Lightning provides a high-level interface for building complex deep learning models and training pipelines. It also simplifies the process of scaling models to multiple GPUs or TPUs.

Here is an example of how to use PyTorch Lightning to train a simple neural network for image classification:

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision.datasets import MNIST
from torchvision.transforms import ToTensor
import pytorch_lightning as pl

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, 3, 1)
        self.conv2 = nn.Conv2d(32, 64, 3, 1)
        self.dropout1 = nn.Dropout2d(0.25)
        self.dropout2 = nn.Dropout2d(0.5)
        self.fc1 = nn.Linear(9216, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = nn.ReLU()(x)
        x = self.conv2(x)
        x = nn.ReLU()(x)
        x = nn.MaxPool2d(2)(x)
        x = self.dropout1(x)
        x = torch.flatten(x, 1)
        x = self.fc1(x)
        x = nn.ReLU()(x)
        x = self.dropout2(x)
        x = self.fc2(x)
        output = nn.LogSoftmax(dim=1)(x)
        return output

class LitMNIST(pl.LightningModule):
    def __init__(self):
        super().__init__()
        self.net = Net()

    def forward(self, x):
        return self.net(x)

    def training_step(self, batch, batch_idx):
        x, y = batch
        y_hat = self(x)
        loss = nn.NLLLoss()(y_hat, y)
        self.log('train_loss', loss)
        return loss

    def configure_optimizers(self):
        optimizer = optim.Adam(self.parameters(), lr=1e-3)
        return optimizer

train_data = MNIST('.', train=True, download=True, transform=ToTensor())
train_loader = DataLoader(train_data, batch_size=64)

trainer = pl.Trainer(gpus=1, max_epochs=10)
model = LitMNIST()
trainer.fit(model, train_loader)

In this example, we define a simple neural network for image classification using PyTorch. We then wrap the model in a PyTorch Lightning module, which provides hooks for training and validation steps. We define a training step that calculates the loss and logs it to the PyTorch Lightning log, and we configure the optimizer to use the Adam optimizer. Finally, we create a PyTorch DataLoader for the MNIST dataset, create a PyTorch Lightning trainer with one GPU, and fit the model to the training data for 10 epochs.

Overall, PyTorch Lightning simplifies the process of training deep learning models while still allowing for flexibility and customization.

python导入MNIST数据集代码

以下是Python中导入MNIST数据集的代码：

import gzip
import pickle
import numpy as np

def load_data():
    with gzip.open('mnist.pkl.gz', 'rb') as f:
        training_data, validation_data, test_data = pickle.load(f, encoding='bytes')
    return (training_data, validation_data, test_data)

def load_data_wrapper():
    tr_d, va_d, te_d = load_data()
    training_inputs = [np.reshape(x, (784, 1)) for x in tr_d[0]]
    training_results = [vectorized_result(y) for y in tr_d[1]]
    training_data = zip(training_inputs, training_results)
    validation_inputs = [np.reshape(x, (784, 1)) for x in va_d[0]]
    validation_data = zip(validation_inputs, va_d[1])
    test_inputs = [np.reshape(x, (784, 1)) for x in te_d[0]]
    test_data = zip(test_inputs, te_d[1])
    return (training_data, validation_data, test_data)

def vectorized_result(j):
    e = np.zeros((10, 1))
    e[j] = 1.0
    return e

这段代码会读取MNIST数据集并将其分成三个部分：训练数据、验证数据和测试数据。训练数据包括输入和期望输出，验证和测试数据只包括输入。该代码还包括将期望输出向量化的函数。