第P1周 ：PyTorch 实现MNIST 手写数字识别

• 🍨 本文为🔗365天深度学习训练营 中的学习记录博客
• 🍖 原作者：K同学啊

🚀我的环境：

• 语言环境：python 3.12.6
• 编译器：jupyter lab
• 深度学习环境：TensorFlow 2.17.0

前期准备

pip install torchinfo

Collecting torchinfo
  Downloading torchinfo-1.8.0-py3-none-any.whl.metadata (21 kB)
Downloading torchinfo-1.8.0-py3-none-any.whl (23 kB)
Installing collected packages: torchinfo
Successfully installed torchinfo-1.8.0
Note: you may need to restart the kernel to use updated packages.



[notice] A new release of pip is available: 24.2 -> 24.3.1
[notice] To update, run: python.exe -m pip install --upgrade pip

import torch
import torchvision
import tensorflow as tf
from tensorflow.keras import datasets, layers, models
import matplotlib.pyplot as plt
# 导入mnist数据，依次分别为训练集图片、训练集标签、测试集图片、测试集标签
(train_images, train_labels), (test_images, test_labels) = datasets.mnist.load_data()

train_ds = torchvision.datasets.MNIST('data', 
                                      train=True, 
                                      transform=torchvision.transforms.ToTensor(), # 将数据类型转化为Tensor
                                      download=True)

test_ds  = torchvision.datasets.MNIST('data', 
                                      train=False, 
                                      transform=torchvision.transforms.ToTensor(), # 将数据类型转化为Tensor
                                      download=True)

batch_size = 32

train_dl = torch.utils.data.DataLoader(train_ds, 
                                       batch_size=batch_size, 
                                       shuffle=True)

test_dl  = torch.utils.data.DataLoader(test_ds, 
                                       batch_size=batch_size)

# 取一个批次查看数据格式
# 数据的shape为：[batch_size, channel, height, weight]
# 其中batch_size为自己设定，channel，height和weight分别是图片的通道数，高度和宽度。
imgs, labels = next(iter(train_dl))
imgs.shape

torch.Size([32, 1, 28, 28])

import numpy as np

 # 指定图片大小，图像大小为20宽、5高的绘图(单位为英寸inch)
plt.figure(figsize=(20, 5)) 
for i, imgs in enumerate(imgs[:20]):
    # 维度缩减
    npimg = np.squeeze(imgs.numpy())
    # 将整个figure分成2行10列，绘制第i+1个子图。
    plt.subplot(2,