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小白的进阶之路系列之十二----人工智能从初步到精通pytorch综合运用的讲解第五部分

news 2025/6/7 0:00:37

在本笔记本中，我们将针对Fashion-MNIST数据集训练LeNet-5的变体。Fashion-MNIST是一组描绘各种服装的图像瓦片，有十个类别标签表明所描绘的服装类型。

# PyTorch model and training necessities
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim# Image datasets and image manipulation
import torchvision
import torchvision.transforms as transforms# Image display
import matplotlib.pyplot as plt
import numpy as np# PyTorch TensorBoard support
from torch.utils.tensorboard import SummaryWriter# In case you are using an environment that has TensorFlow installed,
# such as Google Colab, uncomment the following code to avoid
# a bug with saving embeddings to your TensorBoard directory# import tensorflow as tf
# import tensorboard as tb
# tf.io.gfile = tb.compat.tensorflow_stub.io.gfile

在TensorBoard中显示图像

让我们首先将数据集中的样本图像添加到TensorBoard：

# Helper function for inline image display
def matplotlib_imshow(img, one_channel=False):if one_channel:img = img.mean(dim=0)img = img / 2 + 0.5     # unnormalizenpimg = img.numpy()if one_channel:plt.imshow(npimg, cmap="Greys")else:plt.imshow(np.transpose(npimg, (1, 2, 0)))if __name__ == '__main__':transform = transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.5,), (0.5,))])# Store separate training and validations splits in ./datatraining_set = torchvision.datasets.FashionMNIST('./data',download=True,train=True,transform=transform)validation_set = torchvision.datasets.FashionMNIST('./data',download=True,train=False,transform=transform)training_loader = torch.utils.data.DataLoader(training_set,batch_size=4,shuffle=True,num_workers=2)validation_loader = torch.utils.data.DataLoader(validation_set,batch_size=4,shuffle=False,num_workers=2)# Class labelsclasses = ('T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat','Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle Boot')# Extract a batch of 4 imagesdataiter = iter(training_loader)images, labels = next(dataiter)# Create a grid from the images and show themimg_grid = torchvision.utils.make_grid(images)matplotlib_imshow(img_grid, one_channel=True)plt.show()

输出为：

在这里插入图片描述

上面，我们使用TorchVision和Matplotlib创建了一个小批量输入数据的视觉网格。下面，我们在SummaryWriter上使用add_image()调用来记录TensorBoard使用的图像，并且我们还调用flush())来确保它立即写入磁盘。

    # Default log_dir argument is "runs" - but it's good to be specific# torch.utils.tensorboard.SummaryWriter is imported abovewriter = SummaryWriter('runs/fashion_mnist_experiment_1')# Write image data to TensorBoard log dirwriter.add_image('Four Fashion-MNIST Images', img_grid)writer.flush()# To view, start TensorBoard on the command line with:#   tensorboard --logdir=runs# ...and open a browser tab to http://localhost:6006/

如果您在命令行启动TensorBoard并在新的浏览器选项卡中打开它（通常在localhost:6006），您应该在IMAGES选项卡下看到图像网格。

绘制标量以可视化训练

TensorBoard对于跟踪您的训练进度和效果非常有用。下面，我们将运行一个训练循环，跟踪一些指标，并保存数据供TensorBoard使用。

让我们定义一个模型来对图像块进行分类，以及一个用于训练的优化器和损失函数：

    class Net(nn.Module):def __init__(self):super(Net, self).__init__()self.conv1 = nn.Conv2d(1, 6, 5)self.pool = nn.MaxPool2d(2, 2)self.conv2 = nn.Conv2d(6, 16, 5)self.fc1 = nn.Linear(16 * 4 * 4, 120)self.fc2 = nn.Linear(120, 84)self.fc3 = nn.Linear(84, 10)def forward(self, x):x = self.pool(F.relu(self.conv1(x)))x = self.pool(F.relu(sel