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tensorboard简介

TensorBoard 是一组用于数据可视化的工具。它包含在流行的开源机器学习库 Tensorflow 中.但是也可以独立安装，服务Pytorch等其他的框架
可以常常用来观察训练过程中每一阶段如何输出的

• 安装

  pip install tensorboard
  

• 启动

  tensorboard --logdir=<directory_name>
  

  会默认在6006端口打开，也可以自行制定窗口，如:

  tensorboard --logdir=logs --port=6007
  

用法

1. 所在类：

   from torch.utils.tensorboard import SummaryWriter
   

   介绍：

   class SummaryWriter:"""Writes entries directly to event files in the log_dir to beconsumed by TensorBoard.The `SummaryWriter` class provides a high-level API to create an event filein a given directory and add summaries and events to it. The class updates thefile contents asynchronously. This allows a training program to call methodsto add data to the file directly from the training loop, without slowing downtraining."""
   

2. 创建对象

   writer = SummaryWriter('logs') # 说明写入哪个文件夹
   

3. 常用方法

   writer.add_image()   # 图像方式
   writer.add_scalar()  # 坐标方式writer.close()  # 使用完之后需要close
   

add_scalar()

    def add_scalar(self,tag,scalar_value,global_step=None,walltime=None,new_style=False,double_precision=False,):"""Add scalar data to summary.添加标量数据到summary中Args:tag (str): Data identifier 图表标题scalar_value (float or string/blobname): Value to save 数值（y轴）global_step (int): Global step value to record 训练到多少步（x轴）walltime (float): Optional override default walltime (time.time())with seconds after epoch of eventnew_style (boolean): Whether to use new style (tensor field) or oldstyle (simple_value field). New style could lead to faster data loading.Examples::from torch.utils.tensorboard import SummaryWriterwriter = SummaryWriter()x = range(100)for i in x:writer.add_scalar('y=2x', i * 2, i)writer.close()Expected result:.. image:: _static/img/tensorboard/add_scalar.png:scale: 50 %"""

注意：向writer中写入新事件的同时她也会保留上一个事件，这就会导致一些拟合出现问题
解决：删除之前的log文件，重新生成

add_image()

def add_image(self, tag, img_tensor, global_step=None, walltime=None, dataformats="CHW"):"""Add image data to summary.Note that this requires the ``pillow`` package.Args:tag (str): Data identifierimg_tensor (torch.Tensor, numpy.ndarray, or string/blobname): Image data 注意数据的类型global_step (int): Global step value to record后面不用管walltime (float): Optional override default walltime (time.time())seconds after epoch of eventdataformats (str): Image data format specification of the formCHW, HWC, HW, WH, etc.Shape:img_tensor: Default is :math:`(3, H, W)`. You can use ``torchvision.utils.make_grid()`` toconvert a batch of tensor into 3xHxW format or call ``add_images`` and let us do the job.Tensor with :math:`(1, H, W)`, :math:`(H, W)`, :math:`(H, W, 3)` is also suitable as long ascorresponding ``dataformats`` argument is passed, e.g. ``CHW``, ``HWC``, ``HW``."""

实践

如在tensorboard中展示图片：

from torch.utils.tensorboard import SummaryWriter
import numpy as np
from PIL import Imagewriter = SummaryWriter('logs')
image_path = './dataset2/train/ants_image/0013035.jpg'
img_PIL = Image.open(image_path)
img_array = np.array(img_PIL)
print(type(img_array))
print(img_array.shape)writer.add_image("test",img_array,1,dataformats='HWC') # 展示读取的图片for i in range(100):writer.add_scalar('y=2x', 3*i, i)     # 绘图writer.close()

• writer.add_image中的参数

  def add_image(self, tag, img_tensor, global_step=None, walltime=None, dataformats="CHW"):
  

  名称、图形向量（ndarray类型），第几步（是滑动翻页那种的，这里相当于设定是第几页，每次向后设定时不会清除原来的数据）

当前代码效果如图：

修改图片后：

from torch.utils.tensorboard import SummaryWriter
import numpy as np
from PIL import Imagewriter = SummaryWriter('logs')
image_path = './dataset2/train/ants_image/5650366_e22b7e1065.jpg'
img_PIL = Image.open(image_path)
img_array = np.array(img_PIL)
print(type(img_array))
print(img_array.shape)# 这里更新，说明为第二步
writer.add_image("test",img_array,2,dataformats='HWC')for i in range(100):writer.add_scalar('y=2x', 3*i, i)writer.close()

拖拉就会发现有两张图