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Introduction || Tensors || Autograd || Building Models || TensorBoard Support || Training Models || Model Understanding
PyTorch TensorBoard Support#
Created On: Nov 30, 2021 | Last Updated: May 29, 2024 | Last Verified: Nov 05, 2024
Follow along with the video below or on youtube.
Before You Start#
To run this tutorial, you’ll need to install PyTorch, TorchVision, Matplotlib, and TensorBoard.
With conda:
conda install pytorch torchvision -c pytorch conda install matplotlib tensorboard With pip:
pip install torch torchvision matplotlib tensorboard Once the dependencies are installed, restart this notebook in the Python environment where you installed them.
Introduction#
In this notebook, we’ll be training a variant of LeNet-5 against the Fashion-MNIST dataset. Fashion-MNIST is a set of image tiles depicting various garments, with ten class labels indicating the type of garment depicted.
# PyTorch model and training necessities import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim # Image datasets and image manipulation import torchvision import torchvision.transforms as transforms # Image display import matplotlib.pyplot as plt import numpy as np # PyTorch TensorBoard support from torch.utils.tensorboard import SummaryWriter # In case you are using an environment that has TensorFlow installed, # such as Google Colab, uncomment the following code to avoid # a bug with saving embeddings to your TensorBoard directory # import tensorflow as tf # import tensorboard as tb # tf.io.gfile = tb.compat.tensorflow_stub.io.gfile Showing Images in TensorBoard#
Let’s start by adding sample images from our dataset to TensorBoard:
# Gather datasets and prepare them for consumption transform = transforms.Compose( [transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))]) # Store separate training and validations splits in ./data training_set = torchvision.datasets.FashionMNIST('./data', download=True, train=True, transform=transform) validation_set = torchvision.datasets.FashionMNIST('./data', download=True, train=False, transform=transform) training_loader = torch.utils.data.DataLoader(training_set, batch_size=4, shuffle=True, num_workers=2) validation_loader = torch.utils.data.DataLoader(validation_set, batch_size=4, shuffle=False, num_workers=2) # Class labels classes = ('T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle Boot') # Helper function for inline image display def matplotlib_imshow(img, one_channel=False): if one_channel: img = img.mean(dim=0) img = img / 2 + 0.5 # unnormalize npimg = img.numpy() if one_channel: plt.imshow(npimg, cmap="Greys") else: plt.imshow(np.transpose(npimg, (1, 2, 0))) # Extract a batch of 4 images dataiter = iter(training_loader) images, labels = next(dataiter) # Create a grid from the images and show them img_grid = torchvision.utils.make_grid(images) matplotlib_imshow(img_grid, one_channel=True) 
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# Default log_dir argument is "runs" - but it's good to be specific # torch.utils.tensorboard.SummaryWriter is imported above writer = SummaryWriter('runs/fashion_mnist_experiment_1') # Write image data to TensorBoard log dir writer.add_image('Four Fashion-MNIST Images', img_grid) writer.flush() # To view, start TensorBoard on the command line with: # tensorboard --logdir=runs # ...and open a browser tab to http://localhost:6006/ If you start TensorBoard at the command line and open it in a new browser tab (usually at localhost:6006), you should see the image grid under the IMAGES tab.
Graphing Scalars to Visualize Training#
TensorBoard is useful for tracking the progress and efficacy of your training. Below, we’ll run a training loop, track some metrics, and save the data for TensorBoard’s consumption.
Let’s define a model to categorize our image tiles, and an optimizer and loss function for training:
class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(1, 6, 5) self.pool = nn.MaxPool2d(2, 2) self.conv2 = nn.Conv2d(6, 16, 5) self.fc1 = nn.Linear(16 * 4 * 4, 120) self.fc2 = nn.Linear(120, 84) self.fc3 = nn.Linear(84, 10) def forward(self, x): x = self.pool(F.relu(self.conv1(x))) x = self.pool(F.relu(self.conv2(x))) x = x.view(-1, 16 * 4 * 4) x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = self.fc3(x) return x net = Net() criterion = nn.CrossEntropyLoss() optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9) Now let’s train a single epoch, and evaluate the training vs. validation set losses every 1000 batches:
print(len(validation_loader)) for epoch in range(1): # loop over the dataset multiple times running_loss = 0.0 for i, data in enumerate(training_loader, 0): # basic training loop inputs, labels = data optimizer.zero_grad() outputs = net(inputs) loss = criterion(outputs, labels) loss.backward() optimizer.step() running_loss += loss.item() if i % 1000 == 999: # Every 1000 mini-batches... print('Batch {}'.format(i + 1)) # Check against the validation set running_vloss = 0.0 # In evaluation mode some model specific operations can be omitted eg. dropout layer net.train(False) # Switching to evaluation mode, eg. turning off regularisation for j, vdata in enumerate(validation_loader, 0): vinputs, vlabels = vdata voutputs = net(vinputs) vloss = criterion(voutputs, vlabels) running_vloss += vloss.item() net.train(True) # Switching back to training mode, eg. turning on regularisation avg_loss = running_loss / 1000 avg_vloss = running_vloss / len(validation_loader) # Log the running loss averaged per batch writer.add_scalars('Training vs. Validation Loss', { 'Training' : avg_loss, 'Validation' : avg_vloss }, epoch * len(training_loader) + i) running_loss = 0.0 print('Finished Training') writer.flush() 2500 Batch 1000 Batch 2000 Batch 3000 Batch 4000 Batch 5000 Batch 6000 Batch 7000 Batch 8000 Batch 9000 Batch 10000 Batch 11000 Batch 12000 Batch 13000 Batch 14000 Batch 15000 Finished Training Switch to your open TensorBoard and have a look at the SCALARS tab.
Visualizing Your Model#
TensorBoard can also be used to examine the data flow within your model. To do this, call the add_graph() method with a model and sample input:
# Again, grab a single mini-batch of images dataiter = iter(training_loader) images, labels = next(dataiter) # add_graph() will trace the sample input through your model, # and render it as a graph. writer.add_graph(net, images) writer.flush() When you switch over to TensorBoard, you should see a GRAPHS tab. Double-click the “NET” node to see the layers and data flow within your model.
Visualizing Your Dataset with Embeddings#
The 28-by-28 image tiles we’re using can be modeled as 784-dimensional vectors (28 * 28 = 784). It can be instructive to project this to a lower-dimensional representation. The add_embedding() method will project a set of data onto the three dimensions with highest variance, and display them as an interactive 3D chart. The add_embedding() method does this automatically by projecting to the three dimensions with highest variance.
Below, we’ll take a sample of our data, and generate such an embedding:
# Select a random subset of data and corresponding labels def select_n_random(data, labels, n=100): assert len(data) == len(labels) perm = torch.randperm(len(data)) return data[perm][:n], labels[perm][:n] # Extract a random subset of data images, labels = select_n_random(training_set.data, training_set.targets) # get the class labels for each image class_labels = [classes[label] for label in labels] # log embeddings features = images.view(-1, 28 * 28) writer.add_embedding(features, metadata=class_labels, label_img=images.unsqueeze(1)) writer.flush() writer.close() Now if you switch to TensorBoard and select the PROJECTOR tab, you should see a 3D representation of the projection. You can rotate and zoom the model. Examine it at large and small scales, and see whether you can spot patterns in the projected data and the clustering of labels.
For better visibility, it’s recommended to:
Select “label” from the “Color by” drop-down on the left.
Toggle the Night Mode icon along the top to place the light-colored images on a dark background.
Other Resources#
For more information, have a look at:
PyTorch documentation on torch.utils.tensorboard.SummaryWriter
Tensorboard tutorial content in the PyTorch.org Tutorials
For more information about TensorBoard, see the TensorBoard documentation
Total running time of the script: (1 minutes 52.877 seconds)
