"""
`Introduction `_ ||
`Tensors `_ ||
`Autograd `_ ||
`Building Models `_ ||
**TensorBoard Support** ||
`Training Models `_ ||
`Model Understanding `_
PyTorch TensorBoard Support
===========================
Follow along with the video below or on `youtube `__.
.. raw:: html
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)
########################################################################
# 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.
#
# 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
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()
#########################################################################
# 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
#
# 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 `__