PyTorch中TensorBoard如何使用，很多新手对此不是很清楚，为了帮助大家解决这个难题，下面小编将为大家详细讲解，有这方面需求的人可以来学习下，希望你能有所收获。
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设置TensorBoard。
简单说是设置基本tensorboard运行需要的东西，我这代码中的imshow(img)和matplotlib_imshow(img, one_channel=False)都是显示图片的函数，可以统一替换，我自己测试就没改了！
# helper function to show an image
# (used in the `plot_classes_preds` function below)
def matplotlib_imshow(img, one_channel=False):
if one_channel:
img = img.mean(dim=0)
img = img / 2 + 0.5 # unnormalize
npimg = img.cpu().numpy()
if one_channel:
plt.imshow(npimg, cmap="Greys")
else:
plt.imshow(np.transpose(npimg, (1, 2, 0)))
# 设置tensorBoard
# default `log_dir` is "runs" - we'll be more specific here
writer = SummaryWriter('runs/image_classify_tensorboard')
# get some random training images
dataiter = iter(trainloader)
images, labels = dataiter.next()
# create grid of images
img_grid = torchvision.utils.make_grid(images)
# show images
# matplotlib_imshow(img_grid, one_channel=True)
imshow(img_grid)
# write to tensorboard
writer.add_image('imag_classify', img_grid)
# Tracking model training with TensorBoard
# helper functions
def images_to_probs(net, images):
'''
Generates predictions and corresponding probabilities from a trained
network and a list of images
'''
output = net(images)
# convert output probabilities to predicted class
_, preds_tensor = torch.max(output, 1)
# preds = np.squeeze(preds_tensor.numpy())
preds = np.squeeze(preds_tensor.cpu().numpy())
return preds, [F.softmax(el, dim=0)[i].item() for i, el in zip(preds, output)]
def plot_classes_preds(net, images, labels):
'''
Generates matplotlib Figure using a trained network, along with images
and labels from a batch, that shows the network's top prediction along
with its probability, alongside the actual label, coloring this
information based on whether the prediction was correct or not.
Uses the "images_to_probs" function.
'''
preds, probs = images_to_probs(net, images)
# plot the images in the batch, along with predicted and true labels
fig = plt.figure(figsize=(12, 48))
for idx in np.arange(4):
ax = fig.add_subplot(1, 4, idx+1, xticks=[], yticks=[])
matplotlib_imshow(images[idx], one_channel=True)
ax.set_title("{0}, {1:.1f}%\n(label: {2})".format(
classes[preds[idx]],
probs[idx] * 100.0,
classes[labels[idx]]),
color=("green" if preds[idx]==labels[idx].item() else "red"))
return fig写入TensorBoard。
这个在训练的每一阶段写入tensorboard
if i % 2000 == 1999: # print every 2000 mini-batches
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 2000))
# 把数据写入tensorflow
# ...log the running loss
writer.add_scalar('image training loss',
running_loss / 2000,
epoch * len(trainloader) + i)
# ...log a Matplotlib Figure showing the model's predictions on a
# random mini-batch
writer.add_figure('predictions vs. actuals',
plot_classes_preds(net, inputs, labels),
global_step=epoch * len(trainloader) + i)运行
tensorboard --logdir=runs
打开http://localhost:6006/ 即可查看
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