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.. _sphx_glr_beginner_introyt_tensorboardyt_tutorial.py:
`Introduction <introyt1_tutorial.html>`_ ||
`Tensors <tensors_deeper_tutorial.html>`_ ||
`Autograd <autogradyt_tutorial.html>`_ ||
`Building Models <modelsyt_tutorial.html>`_ ||
**TensorBoard Support** ||
`Training Models <trainingyt.html>`_ ||
`Model Understanding <captumyt.html>`_
PyTorch TensorBoard Support
===========================
Follow along with the video below or on `youtube <https://www.youtube.com/watch?v=6CEld3hZgqc>`__.
.. raw:: html
<div style="margin-top:10px; margin-bottom:10px;">
<iframe width="560" height="315" src="https://www.youtube.com/embed/6CEld3hZgqc" frameborder="0" allow="accelerometer; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
</div>
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.
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.. code-block:: default
# 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
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Showing Images in TensorBoard
-----------------------------
Let’s start by adding sample images from our dataset to TensorBoard:
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.. code-block:: default
# 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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Above, we used TorchVision and Matplotlib to create a visual grid of a
minibatch of our input data. Below, we use the ``add_image()`` call on
``SummaryWriter`` to log the image for consumption by TensorBoard, and
we also call ``flush()`` to make sure it’s written to disk right away.
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.. code-block:: default
# 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/
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If you start TensorBoard at the command line and open it in a new
browser tab (usually at `localhost:6006 <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:
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.. code-block:: default
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)
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Now let’s train a single epoch, and evaluate the training vs. validation
set losses every 1000 batches:
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.. code-block:: default
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
net.train(False) # Don't need to track gradents for validation
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) # Turn gradients back on for training
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()
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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. When you open
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.. code-block:: default
# 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()
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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:
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.. code-block:: default
# 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()
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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 <https://pytorch.org/docs/stable/tensorboard.html?highlight=summarywriter>`__
- Tensorboard tutorial content in the `PyTorch.org Tutorials <https://pytorch.org/tutorials/>`__
- For more information about TensorBoard, see the `TensorBoard
documentation <https://www.tensorflow.org/tensorboard>`__
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