这篇教程Pytorch可视化的几种实现方法写得很实用，希望能帮到您。
一，利用 tensorboardX 可视化网络结构
参考 https://github.com/lanpa/tensorboardX
支持scalar, image, figure, histogram, audio, text, graph, onnx_graph, embedding, pr_curve and video summaries.
例子要求tensorboardX>=1.2 and pytorch>=0.4 安装 pip install tensorboardX 或 pip install git+https://github.com/lanpa/tensorboardX
例子 # demo.pyimport torchimport torchvision.utils as vutilsimport numpy as npimport torchvision.models as modelsfrom torchvision import datasetsfrom tensorboardX import SummaryWriterresnet18 = models.resnet18(False)writer = SummaryWriter()sample_rate = 44100freqs = [262, 294, 330, 349, 392, 440, 440, 440, 440, 440, 440]for n_iter in range(100): dummy_s1 = torch.rand(1) dummy_s2 = torch.rand(1) # data grouping by `slash` writer.add_scalar('data/scalar1', dummy_s1[0], n_iter) writer.add_scalar('data/scalar2', dummy_s2[0], n_iter) writer.add_scalars('data/scalar_group', {'xsinx': n_iter * np.sin(n_iter), 'xcosx': n_iter * np.cos(n_iter), 'arctanx': np.arctan(n_iter)}, n_iter) dummy_img = torch.rand(32, 3, 64, 64) # output from network if n_iter % 10 == 0: x = vutils.make_grid(dummy_img, normalize=True, scale_each=True) writer.add_image('Image', x, n_iter) dummy_audio = torch.zeros(sample_rate * 2) for i in range(x.size(0)): # amplitude of sound should in [-1, 1] dummy_audio[i] = np.cos(freqs[n_iter // 10] * np.pi * float(i) / float(sample_rate)) writer.add_audio('myAudio', dummy_audio, n_iter, sample_rate=sample_rate) writer.add_text('Text', 'text logged at step:' + str(n_iter), n_iter) for name, param in resnet18.named_parameters(): writer.add_histogram(name, param.clone().cpu().data.numpy(), n_iter) # needs tensorboard 0.4RC or later writer.add_pr_curve('xoxo', np.random.randint(2, size=100), np.random.rand(100), n_iter)dataset = datasets.MNIST('mnist', train=False, download=True)images = dataset.test_data[:100].float()label = dataset.test_labels[:100]features = images.view(100, 784)writer.add_embedding(features, metadata=label, label_img=images.unsqueeze(1))# export scalar data to JSON for external processingwriter.export_scalars_to_json("./all_scalars.json")writer.close()运行: python demo.py 会出现runs文件夹，然后在cd到工程目录运行tensorboard --logdir runs 结果： 
二，利用 vistom 可视化
参考：https://github.com/facebookresearch/visdom 安装和启动
安装： pip install visdom
启动：python -m visdom.server示例 from visdom import Visdom #单张 viz.image( np.random.rand(3, 512, 256), opts=dict(title=/////'Random!/////', caption=/////'How random./////'), ) #多张 viz.images( np.random.randn(20, 3, 64, 64), opts=dict(title=/////'Random images/////', caption=/////'How random./////') ) 
from visdom import Visdomimage = np.zeros((100,100))vis = Visdom() vis.text("hello world!!!")vis.image(image)vis.line(Y = np.column_stack((np.random.randn(10),np.random.randn(10))), X = np.column_stack((np.arange(10),np.arange(10))), opts = dict(title = "line", legend=["Test","Test1"]))
三，利用pytorchviz可视化网络结构
参考：https://github.com/szagoruyko/pytorchviz 到此这篇关于Pytorch可视化的几种实现方法的文章就介绍到这了,更多相关Pytorch可视化内容请搜索51zixue.net以前的文章或继续浏览下面的相关文章希望大家以后多多支持51zixue.net！
Python爬虫必备之XPath解析库
Python中常见的反爬机制及其破解方法总结 |
