Write tensorboard events with simple command.
see demo (result of demo.py
and some images generated by BEGAN)
#tested on anaconda3, tensorflow 1.2.1, pytorch 0.1.12, torchvision 0.1.8
pip install tensorboard-pytorch
pip install tensorflow
or pip install tensorflow-gpu
import torch
import torchvision.utils as vutils
import numpy as np
import torchvision.models as models
from datetime import datetime
from tensorboard import SummaryWriter
resnet18 = models.resnet18(True)
writer = SummaryWriter('runs/'+datetime.now().strftime('%B%d %H:%M:%S'))
sample_rate = 44100
freqs = [262, 294, 330, 349, 392, 440, 440, 440, 440, 440, 440]
for n_iter in range(100):
M_global = torch.rand(1) # value to keep
writer.add_scalar('M_global', M_global[0], n_iter)
x = torch.rand(32, 3, 64, 64) # output from network
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)) # sound amplitude should in [-1, 1]
writer.add_audio('Audio', x, n_iter)
writer.add_text('Text', 'testtext', n_iter)
for name, param in resnet18.named_parameters():
writer.add_histogram(name, param.clone().cpu().data.numpy(), n_iter)
writer.close()
python demo.py
tensorboard --logdir runs
To show more images in tensorboard's image tab, just
modify the hardcoded event_accumulator
in
~/anaconda3/lib/python3.6/site-packages/tensorflow/tensorboard/backend/application.py
as you wish.
class SummaryWriter(str log_dir)
log_dir
(str) - the location of the log folder.
add_scalar(str tag, float value, int global_step=None)
value
(float) - the value to keep.
add_image(str tag, torch.Tensor t, int global_step=None)
t
(torch.Tensor) - torch tensor of size (3,H,W). I suggest usetorchvision.utils.make_grid()
to prepare it.
add_audio(str tag, torch.Tensor t, int global_step=None)
t
(torch.Tensor) - one dimensional torch tensor. The value should between [-1, 1]. The sample rate is currently fixed at 44100 KHz.
add_histogram(str tag, numpy.ndarray values, int global_step=None, bins='tensorflow')
values
(numpy.ndarray) - one dimensional numpy array.bins
(str) - one of {'tensorflow', numpy_arguments}, determines how the bins are made.
add_text(str tag, str text_to_save, int global_step=None)
text_to_save
(str) - the string to keep.
tag
(str) - values with same tag group together.global_step
(int) - logs the training step on which the value is saved.
audio sample rate histogram binning method embedding