In [3]:
# -*- coding: utf-8 -*-
"""
Training a classifier
=====================
This is it. You have seen how to define neural networks, compute loss and make
updates to the weights of the network.
Now you might be thinking,
What about data?
----------------
Generally, when you have to deal with image, text, audio or video data,
you can use standard python packages that load data into a numpy array.
Then you can convert this array into a ``torch.*Tensor``.
- For images, packages such as Pillow, OpenCV are useful.
- For audio, packages such as scipy and librosa
- For text, either raw Python or Cython based loading, or NLTK and
SpaCy are useful.
Specifically for ``vision``, we have created a package called
``torchvision``, that has data loaders for common datasets such as
Imagenet, CIFAR10, MNIST, etc. and data transformers for images, viz.,
``torchvision.datasets`` and ``torch.utils.data.DataLoader``.
This provides a huge convenience and avoids writing boilerplate code.
For this tutorial, we will use the CIFAR10 dataset.
It has the classes: ‘airplane’, ‘automobile’, ‘bird’, ‘cat’, ‘deer’,
‘dog’, ‘frog’, ‘horse’, ‘ship’, ‘truck’. The images in CIFAR-10 are of
size 3x32x32, i.e. 3-channel color images of 32x32 pixels in size.
.. figure:: /_static/img/cifar10.png
:alt: cifar10
cifar10
Training an image classifier
----------------------------
We will do the following steps in order:
1. Load and normalizing the CIFAR10 training and test datasets using
``torchvision``
2. Define a Convolution Neural Network
3. Define a loss function
4. Train the network on the training data
5. Test the network on the test data
1. Loading and normalizing CIFAR10
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Using ``torchvision``, it’s extremely easy to load CIFAR10.
"""
import torch
import torchvision
import torchvision.transforms as transforms
########################################################################
# The output of torchvision datasets are PILImage images of range [0, 1].
# We transform them to Tensors of normalized range [-1, 1]
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
shuffle=True, num_workers=2)
testset = torchvision.datasets.CIFAR10(root='./data', train=False,
download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=4,
shuffle=False, num_workers=2)
classes = ('plane', 'car', 'bird', 'cat',
'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
########################################################################
# Let us show some of the training images, for fun.
import matplotlib.pyplot as plt
import numpy as np
# functions to show an image
def imshow(img):
img = img / 2 + 0.5 # unnormalize
npimg = img.numpy()
plt.imshow(np.transpose(npimg, (1, 2, 0)))
# get some random training images
dataiter = iter(trainloader)
images, labels = dataiter.next()
print (type(images))
print (type(labels))
print ("Label:" + str(labels))
# show images
imshow(torchvision.utils.make_grid(images))
# print labels
print(' '.join('%5s' % classes[labels[j]] for j in range(4)))
########################################################################
# 2. Define a Convolution Neural Network
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
# Copy the neural network from the Neural Networks section before and modify it to
# take 3-channel images (instead of 1-channel images as it was defined).
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 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 * 5 * 5)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
net = Net()
########################################################################
# 3. Define a Loss function and optimizer
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
# Let's use a Classification Cross-Entropy loss and SGD with momentum
import torch.optim as optim
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
########################################################################
# 4. Train the network
# ^^^^^^^^^^^^^^^^^^^^
#
# This is when things start to get interesting.
# We simply have to loop over our data iterator, and feed the inputs to the
# network and optimize
for epoch in range(2): # loop over the dataset multiple times
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
# get the inputs
inputs, labels = data
# wrap them in Variable
inputs, labels = Variable(inputs), Variable(labels)
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# print statistics
running_loss += loss.data[0]
if i % 2000 == 1999: # print every 2000 mini-batches
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 2000))
running_loss = 0.0
print('Finished Training')
########################################################################
# 5. Test the network on the test data
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
#
# We have trained the network for 2 passes over the training dataset.
# But we need to check if the network has learnt anything at all.
#
# We will check this by predicting the class label that the neural network
# outputs, and checking it against the ground-truth. If the prediction is
# correct, we add the sample to the list of correct predictions.
#
# Okay, first step. Let us display an image from the test set to get familiar.
dataiter = iter(testloader)
images, labels = dataiter.next()
# print images
imshow(torchvision.utils.make_grid(images))
print('GroundTruth: ', ' '.join('%5s' % classes[labels[j]] for j in range(4)))
########################################################################
# Okay, now let us see what the neural network thinks these examples above are:
outputs = net(Variable(images))
########################################################################
# The outputs are energies for the 10 classes.
# Higher the energy for a class, the more the network
# thinks that the image is of the particular class.
# So, let's get the index of the highest energy:
_, predicted = torch.max(outputs.data, 1)
print('Predicted: ', ' '.join('%5s' % classes[predicted[j]]
for j in range(4)))
########################################################################
# The results seem pretty good.
#
# Let us look at how the network performs on the whole dataset.
correct = 0
total = 0
for data in testloader:
images, labels = data
outputs = net(Variable(images))
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum()
print('Accuracy of the network on the 10000 test images: %d %%' % (
100 * correct / total))
########################################################################
# That looks waaay better than chance, which is 10% accuracy (randomly picking
# a class out of 10 classes).
# Seems like the network learnt something.
#
# Hmmm, what are the classes that performed well, and the classes that did
# not perform well:
class_correct = list(0. for i in range(10))
class_total = list(0. for i in range(10))
for data in testloader:
images, labels = data
outputs = net(Variable(images))
_, predicted = torch.max(outputs.data, 1)
c = (predicted == labels).squeeze()
for i in range(4):
label = labels[i]
class_correct[label] += c[i]
class_total[label] += 1
for i in range(10):
print('Accuracy of %5s : %2d %%' % (
classes[i], 100 * class_correct[i] / class_total[i]))
########################################################################
# Okay, so what next?
#
# How do we run these neural networks on the GPU?
#
# Training on GPU
# ----------------
# Just like how you transfer a Tensor on to the GPU, you transfer the neural
# net onto the GPU.
# This will recursively go over all modules and convert their parameters and
# buffers to CUDA tensors:
#
# .. code:: python
#
# net.cuda()
#
#
# Remember that you will have to send the inputs and targets at every step
# to the GPU too:
#
# ::
#
# inputs, labels = Variable(inputs.cuda()), Variable(labels.cuda())
#
# Why dont I notice MASSIVE speedup compared to CPU? Because your network
# is realllly small.
#
# **Exercise:** Try increasing the width of your network (argument 2 of
# the first ``nn.Conv2d``, and argument 1 of the second ``nn.Conv2d`` –
# they need to be the same number), see what kind of speedup you get.
#
# **Goals achieved**:
#
# - Understanding PyTorch's Tensor library and neural networks at a high level.
# - Train a small neural network to classify images
#
# Where do I go next?
# -------------------
#
# - :doc:`Train neural nets to play video games </intermediate/reinforcement_q_learning>`
# - `Train a state-of-the-art ResNet network on imagenet`_
# - `Train an face generator using Generative Adversarial Networks`_
# - `Train a word-level language model using Recurrent LSTM networks`_
# - `More examples`_
# - `More tutorials`_
# - `Discuss PyTorch on the Forums`_
# - `Chat with other users on Slack`_
#
# .. _Train a state-of-the-art ResNet network on imagenet: https://github.com/pytorch/examples/tree/master/imagenet
# .. _Train an face generator using Generative Adversarial Networks: https://github.com/pytorch/examples/tree/master/dcgan
# .. _Train a word-level language model using Recurrent LSTM networks: https://github.com/pytorch/examples/tree/master/word_language_model
# .. _More examples: https://github.com/pytorch/examples
# .. _More tutorials: https://github.com/pytorch/tutorials
# .. _Discuss PyTorch on the Forums: https://discuss.pytorch.org/
# .. _Chat with other users on Slack: http://pytorch.slack.com/messages/beginner/
In [ ]: