推荐直接看我写的两个链接，
GitHub地址
Pytorch使用tensorboardX可视化。超详细！！！
Tensorboard的Github
安装
pip install tensorboardX
示例
建议直接将Github下载到本地，然后运行examples文件夹中的示例
import 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 networkif 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 laterwriter.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()
详解
from tensorboardX import SummaryWriterwriter = SummaryWriter()
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