.. _sphx_glr_beginner_blitz_cifar10_tutorial.py: 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. .. code-block:: python 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] .. code-block:: python 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. .. code-block:: python 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() # 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). .. code-block:: python 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 .. code-block:: python 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 .. code-block:: python 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. .. code-block:: python 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: .. code-block:: python 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: .. code-block:: python _, 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. .. code-block:: python 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: .. code-block:: python 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 ` - `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/ **Total running time of the script:** ( 0 minutes 0.000 seconds) .. container:: sphx-glr-footer .. container:: sphx-glr-download :download:`Download Python source code: cifar10_tutorial.py ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: cifar10_tutorial.ipynb ` .. rst-class:: sphx-glr-signature `Generated by Sphinx-Gallery `_