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)