原文: https://www.emperinter.info/2020/07/30/tensorboard-in-pytorch/缘由
用法自己上次安装好PyTorch以及训练了一下官方的数据,今天看到了这个TensorBoard来可视化的用法,感觉不错就尝试玩了一下!自己只是尝试了一下追踪模型训练的过程,其他自己去看官网教程吧!
具体详细说明请参考https://pytorch.org/tutorials/intermediate/tensorboard_tutorial.html
简单说就是:
设置TensorBoard。
写入TensorBoard。
运行
打开http://localhost:6006/
步骤我把训练的图片分类的loss用tensorboard给可视化出来了:
- 设置TensorBoard。
简单说是设置基本tensorboard运行需要的东西,我这代码中的imshow(img)和matplotlib_imshow(img, one_channel=False)都是显示图片的函数,可以统一替换,我自己测试就没改了!
# helper function to show an image# (used in the `plot_classes_preds` function below)def matplotlib_imshow(img, one_channel=False): if one_channel: img = img.mean(dim=0) img = img / 2 + 0.5 # unnormalize npimg = img.cpu().numpy() if one_channel: plt.imshow(npimg, cmap="Greys") else: plt.imshow(np.transpose(npimg, (1, 2, 0))) # 设置tensorBoard# default `log_dir` is "runs" - we'll be more specific herewriter = SummaryWriter('runs/image_classify_tensorboard')# get some random training imagesdataiter = iter(trainloader)images, labels = dataiter.next()# create grid of imagesimg_grid = torchvision.utils.make_grid(images)# show images# matplotlib_imshow(img_grid, one_channel=True)imshow(img_grid)# write to tensorboardwriter.add_image('imag_classify', img_grid)# Tracking model training with TensorBoard# helper functionsdef images_to_probs(net, images): ''' Generates predictions and corresponding probabilities from a trained network and a list of images ''' output = net(images) # convert output probabilities to predicted class _, preds_tensor = torch.max(output, 1) # preds = np.squeeze(preds_tensor.numpy()) preds = np.squeeze(preds_tensor.cpu().numpy()) return preds, [F.softmax(el, dim=0)[i].item() for i, el in zip(preds, output)]def plot_classes_preds(net, images, labels): ''' Gener����,Ը��ates matplotlib Figure using a trained network, along with images and labels from a batch, that shows the network's top prediction along with its probability, alongside the actual label, coloring this information based on whether the prediction was correct or not. Uses the "images_to_probs" function. ''' preds, probs = images_to_probs(net, images) # plot the images in the batch, along with predicted and true labels fig = plt.figure(figsize=(12, 48)) for idx in np.arange(4): ax = fig.add_subplot(1, 4, idx+1, xticks=[], yticks=[]) matplotlib_imshow(images[idx], one_channel=True) ax.set_title("{0}, {1:.1f}%\n(label: {2})".format( classes[preds[idx]], probs[idx] * 100.0, classes[labels[idx]]), color=("green" if preds[idx]==labels[idx].item() else "red")) return fig
- 写入TensorBoard。
这个在训练的每一阶段写入tensorboard
if i % 2000 == 1999: # print every 2000 mini-batches print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 2000)) # 把数据写入tensorflow # ...log the running loss writer.add_scalar('image training loss', running_loss / 2000, epoch * len(trainloader) + i) # ...log a Matplotlib Figure showing the model's predictions on a # random mini-batch writer.add_figure('predictions vs. actuals', plot_classes_preds(net, inputs, labels), global_step=epoch * len(trainloader) + i)
- 运行
tensorboard --logdir=runs
- 打开http://localhost:6006/ 即可查看
如需了解完整代码请访问:https://www.emperinter.info/2020/07/30/tensorboard-in-pytorch/
import torchimport torchvisionimport torchvision.transforms as transformsimport matplotlib.pyplot as pltimport numpy as npimport torch.nn as nnimport torch.nn.functional as Fimport torch.optim as optimfrom datetime import datetimefrom torch.utils.tensorboard import SummaryWriter..........................................