Let's directly dive in. The thing here is to use Tensorboard to plot your PyTorch trainings. For this, I use TensorboardX which is a nice interface communicating Tensorboard avoiding Tensorflow dependencies.
First install the requirements;
pip install tensorboard pip install tensorboardX
Things thereafter very easy as well, but you need to know how you need to communicate with the board to show your training and it is not that easy, if you don't know Tensorboard hitherto.
... from tensorboardX import SummaryWriter ... writer = SummaryWriter('your/path/to/log_files/') ... # in training loop writer.add_scalar('Train/Loss', loss, num_iteration) writer.add_scalar('Train/Prec@1', top1, num_iteration) writer.add_scalar('Train/Prec@5', top5, num_iteration) ... # in validation loop writer.add_scalar('Val/Loss', loss, epoch) writer.add_scalar('Val/Prec@1', top1, epoch) writer.add_scalar('Val/Pred@5', top5, epoch)
You can also see the embedding of your dataset
from torchvision import datasets from tensorboardX import SummaryWriter dataset = datasets.MNIST('mnist', train=False, download=True) images = dataset.test_data[:100].float() label = dataset.test_labels[:100] features = images.view(100, 784) writer.add_embedding(features, metadata=label, label_img=images.unsqueeze(1))
This is also how you can plot your model graph. The important part is to give the output tensor to writer as well with you model. So that, it computes the tensor shapes in between. I also need to say, it is very slow for large models.
import torch import torch.nn as nn import torchvision.utils as vutils import numpy as np import torch.nn.functional as F import torchvision.models as models from tensorboardX import SummaryWriter class Mnist(nn.Module): def __init__(self): super(Mnist, self).__init__() self.conv1 = nn.Conv2d(1, 10, kernel_size=5) self.conv2 = nn.Conv2d(10, 20, kernel_size=5) self.conv2_drop = nn.Dropout2d() self.fc1 = nn.Linear(320, 50) self.fc2 = nn.Linear(50, 10) self.bn = nn.BatchNorm2d(20) def forward(self, x): x = F.max_pool2d(self.conv1(x), 2) x = F.relu(x)+F.relu(-x) x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2)) x = self.bn(x) x = x.view(-1, 320) x = F.relu(self.fc1(x)) x = F.dropout(x, training=self.training) x = self.fc2(x) x = F.log_softmax(x) return x model = Mnist() # if you want to show the input tensor, set requires_grad=True res = model(torch.autograd.Variable(torch.Tensor(1,1,28,28), requires_grad=True)) writer = SummaryWriter() writer.add_graph(model, res) writer.close()