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介绍 || 张量 || Autograd || 构建模型 || TensorBoard 支持 || 训练模型 || 模型理解
PyTorch TensorBoard 支持¶
创建于: Nov 30, 2021 | 最后更新: May 29, 2024 | 最后验证: Nov 05, 2024
请跟随下面的视频或在 youtube 上观看。
开始之前¶
要运行本教程，你需要安装 PyTorch、TorchVision、Matplotlib 和 TensorBoard。
使用 conda
conda install pytorch torchvision -c pytorch
conda install matplotlib tensorboard
使用 pip
pip install torch torchvision matplotlib tensorboard
安装完依赖后，请在你安装它们所在的 Python 环境中重新启动此 notebook。
引言¶
在本 notebook 中，我们将针对 Fashion-MNIST 数据集训练一个 LeNet-5 的变体。Fashion-MNIST 是一组描绘各种服装的图像块，带有十个类别标签，指示所描绘服装的类型。
# 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
# Gather datasets and prepare them for consumption
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))])
# Store separate training and validations splits in ./data
training_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 labels
classes = ('T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle Boot')
# 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 # unnormalize
npimg = img.numpy()
if one_channel:
plt.imshow(npimg, cmap="Greys")
else:
plt.imshow(np.transpose(npimg, (1, 2, 0)))
# Extract a batch of 4 images
dataiter = iter(training_loader)
images, labels = next(dataiter)
# Create a grid from the images and show them
img_grid = torchvision.utils.make_grid(images)
matplotlib_imshow(img_grid, one_channel=True)
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上面，我们使用 TorchVision 和 Matplotlib 创建了输入数据 minibatch 的可视网格。下面，我们在 SummaryWriter 上使用 add_image() 调用，将图像记录到 TensorBoard 供其使用，并且我们还调用 flush() 以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 above
writer = SummaryWriter('runs/fashion_mnist_experiment_1')
# Write image data to TensorBoard log dir
writer.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/
如果你在命令行启动 TensorBoard 并在新的浏览器标签页中打开它（通常在 localhost:6006），你应该在 IMAGES 标签页下看到图像网格。
绘制标量以可视化训练过程¶
TensorBoard 对于跟踪训练的进度和效果很有用。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(self.conv2(x)))
x = x.view(-1, 16 * 4 * 4)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
net = Net()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
现在让我们训练一个 epoch，并每隔 1000 个 batch 评估训练集和验证集的损失
print(len(validation_loader))
for epoch in range(1): # loop over the dataset multiple times
running_loss = 0.0
for i, data in enumerate(training_loader, 0):
# basic training loop
inputs, labels = data
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
if i % 1000 == 999: # Every 1000 mini-batches...
print('Batch {}'.format(i + 1))
# Check against the validation set
running_vloss = 0.0
# In evaluation mode some model specific operations can be omitted eg. dropout layer
net.train(False) # Switching to evaluation mode, eg. turning off regularisation
for j, vdata in enumerate(validation_loader, 0):
vinputs, vlabels = vdata
voutputs = net(vinputs)
vloss = criterion(voutputs, vlabels)
running_vloss += vloss.item()
net.train(True) # Switching back to training mode, eg. turning on regularisation
avg_loss = running_loss / 1000
avg_vloss = running_vloss / len(validation_loader)
# Log the running loss averaged per batch
writer.add_scalars('Training vs. Validation Loss',
{ 'Training' : avg_loss, 'Validation' : avg_vloss },
epoch * len(training_loader) + i)
running_loss = 0.0
print('Finished Training')
writer.flush()
2500
Batch 1000
Batch 2000
Batch 3000
Batch 4000
Batch 5000
Batch 6000
Batch 7000
Batch 8000
Batch 9000
Batch 10000
Batch 11000
Batch 12000
Batch 13000
Batch 14000
Batch 15000
Finished Training
切换到你已打开的 TensorBoard，查看 SCALARS 标签页。
可视化你的模型¶
TensorBoard 也可用于检查模型内的数据流。为此，请使用模型和样本输入调用 add_graph() 方法
# Again, grab a single mini-batch of images
dataiter = iter(training_loader)
images, labels = next(dataiter)
# add_graph() will trace the sample input through your model,
# and render it as a graph.
writer.add_graph(net, images)
writer.flush()
当你切换到 TensorBoard 时，你应该会看到一个 GRAPHS 标签页。双击“NET”节点以查看模型内的层和数据流。
使用嵌入可视化你的数据集¶
我们使用的 28x28 图像块可以建模为 784 维向量 (28 * 28 = 784)。将其投影到低维表示是有益的。add_embedding() 方法会将一组数据投影到方差最高的三个维度上，并将其显示为交互式 3D 图表。add_embedding() 方法通过投影到方差最高的三个维度来自动完成此操作。
下面，我们将抽取一部分数据，并生成这样的嵌入
# Select a random subset of data and corresponding labels
def select_n_random(data, labels, n=100):
assert len(data) == len(labels)
perm = torch.randperm(len(data))
return data[perm][:n], labels[perm][:n]
# Extract a random subset of data
images, labels = select_n_random(training_set.data, training_set.targets)
# get the class labels for each image
class_labels = [classes[label] for label in labels]
# log embeddings
features = images.view(-1, 28 * 28)
writer.add_embedding(features,
metadata=class_labels,
label_img=images.unsqueeze(1))
writer.flush()
writer.close()
现在，如果你切换到 TensorBoard 并选择 PROJECTOR 标签页，你应该会看到投影的 3D 表示。你可以旋转和缩放模型。在大尺度和小尺度下检查它，看看能否发现投影数据中的模式和标签的聚类。
为了获得更好的可视化效果，建议
在左侧的“Color by”下拉菜单中选择“label”。
切换顶部的夜间模式图标，将浅色图像放在深色背景上。
其他资源¶
欲了解更多信息，请参阅
关于
torch.utils.tensorboard.SummaryWriter的 PyTorch 文档PyTorch.org 教程 的 Tensorboard 教程内容
欲了解更多关于 TensorBoard 的信息，请参阅 TensorBoard 文档
脚本总运行时间: ( 1 分 51.246 秒)
