官方總結 tensorboardX 使用教程

tensorboardx api手冊& 簡單教程（看著像官方發布的）：

這個東西非常簡單，很多網友的部落格對初學者不利，加了自己的東西，致使簡單上手的tensorboardx變得困難。

下面搬運github readme 部分（請務必多看幾遍，官方說明，沒有比它更合適的學習途徑）

用簡單的函式呼叫寫tensorboard事件。

在anaconda2 / anaconda3上測試，

pytorch 1.0.0 / torchvision 0.2.1 / tensorboard 1.12.0

pip install tensorboardx

或者從源**構建：

git clone && cd tensorboardx && python setup.py install

# demo.py

import torch
import torchvision.utils as vutils
import numpy as np
import torchvision.models as models
from torchvision import datasets
from tensorboardx import summarywriter
resnet18 = models.resnet18(false)
# 注意，在這儲存路徑
writer = summarywriter("./data")
sample_rate = 44100
freqs = [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', , 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 processing
writer.export_scalars_to_json("./all_scalars.json")
writer.close()

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