由于在之前的实验中，通过观察发现Loss和Accuracy不稳定，所以想画个Loss曲线出来，通过Google发现可以使用tensorboard进行可视化，所以进行了相关配置。首先安装tensorboardX和tensorflow命令如下:
参考：https://www.jianshu.com/p/e07299311a36
pip3 install tensorboardX
pip3 install tensorflow (for tensorboard web server)
测试代码：
import torch
import torchvision.utils as vutils
import numpy as npimport torchvision.models as models
from torchvision import datasets
from tensorboardX import SummaryWriter
resnet18 = models.resnet18(False)
writer = SummaryWriter()
sample_rate = 44100
freqs = [262, 294, 330, 349, 392, 440, 440, 440, 440, 440, 440]
for n_iter in range(100):
s1 = torch.rand(1) # value to keep
s2 = torch.rand(1)
writer.add_scalar('data/scalar1', s1[0], n_iter)
writer.add_scalar('data/scalar2', 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)
x = torch.rand(32, 3, 64, 64)
if n_iter%10==0:
x = vutils.make_grid(x, normalize=True, scale_each=True)
writer.add_image('Image', x, n_iter)
x = torch.zeros(sample_rate*2)
for i in range(x.size(0)):
x[i] = np.cos(freqs[n_iter//10]*np.pi*float(i)/float(sample_rate))
writer.add_audio('myAudio', x, 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)
writer.add_pr_curve('xoxo', np.random.randint(2, size=100),
np.random.rand(100), n_iter) #needs tensorboard 0.4RC or later
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()
最后在工程目录下打开terminal运行
tensorboard --logdir runs
结果为：
