标签：scalar 机器 log SummaryWriter writer step pytorch tensorboard dir

创建输出文件夹：

write = SummaryWriter("log")

def __init__(self, log_dir=None, comment='', purge_step=None, max_queue=10,
                 flush_secs=120, filename_suffix=''):
"""
　　Creates a `SummaryWriter` that will write out events and summaries
        to the event file.

        Args:
            log_dir (string): Save directory location. Default is
              runs/**CURRENT_DATETIME_HOSTNAME**, which changes after each run.
              Use hierarchical folder structure to compare
              between runs easily. e.g. pass in 'runs/exp1', 'runs/exp2', etc.
              for each new experiment to compare across them.
            comment (string): Comment log_dir suffix appended to the default
              ``log_dir``. If ``log_dir`` is assigned, this argument has no effect.
            purge_step (int):
              When logging crashes at step :math:`T+X` and restarts at step :math:`T`,
              any events whose global_step larger or equal to :math:`T` will be
              purged and hidden from TensorBoard.
              Note that crashed and resumed experiments should have the same ``log_dir``.
            max_queue (int): Size of the queue for pending events and
              summaries before one of the 'add' calls forces a flush to disk.
              Default is ten items.
            flush_secs (int): How often, in seconds, to flush the
              pending events and summaries to disk. Default is every two minutes.
            filename_suffix (string): Suffix added to all event filenames in
              the log_dir directory. More details on filename construction in
              tensorboard.summary.writer.event_file_writer.EventFileWriter.

        Examples::

            from torch.utils.tensorboard import SummaryWriter

            # create a summary writer with automatically generated folder name.
            writer = SummaryWriter()
            # folder location: runs/May04_22-14-54_s-MacBook-Pro.local/

            # create a summary writer using the specified folder name.
            writer = SummaryWriter("my_experiment")
            # folder location: my_experiment

            # create a summary writer with comment appended.
            writer = SummaryWriter(comment="LR_0.1_BATCH_16")
            # folder location: runs/May04_22-14-54_s-MacBook-Pro.localLR_0.1_BATCH_16/

        """

生成图表

write.add_scalar("x=y",i,i)

def add_scalar(
        self, tag, scalar_value, global_step=None, walltime=None, new_style=False
    ):
        """
　　　　Add scalar data to summary.

        Args:
            tag (string): Data identifier
            scalar_value (float or string/blobname): Value to save
            global_step (int): Global step value to record
            walltime (float): Optional override default walltime (time.time())
              with seconds after epoch of event
            new_style (boolean): Whether to use new style (tensor field) or old
              style (simple_value field). New style could lead to faster data loading.
        Examples::

            from torch.utils.tensorboard import SummaryWriter
            writer = 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 %

        """

生成html文件

控制台->tensorboard --logdir=(输出文件夹名称) --port=(端口号，默认为6006)

标签：scalar,机器,log,SummaryWriter,writer,step,pytorch,tensorboard,dir
来源： https://www.cnblogs.com/sk-lqbzblogs/p/15130123.html

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