> For the complete documentation index, see [llms.txt](https://ykkim.gitbook.io/dlip/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://ykkim.gitbook.io/dlip/programming/pytorch/train-convnet-using-cifar10.md).
# Train ConvNet using CIFAR10
### [Source code (Jupyter notebook)](https://github.com/ykkimhgu/gitbook_docs/blob/master/deep-learning-framework/pytorch/neural_networks_tutorial_ykk.ipynb)
## 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 Convolutional Neural Network
3. Define a loss function
4. Train the network on the training data
5. Test the network on the test data
## Data Loading
```
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].
### Download General Dataset
{% embed url="" %}
```python
# Transform: normalize a tensor image wih mead/std
# torchvision.transforms.Normalize(mean, std, inplace=False)
# For 3 channel image
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
# torchvision.datasets.CIFAR10(root, train=True, transform=None, target_transform=None, download=False)
# Train set
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)
# Test set
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')
```
### Use Downloaded General Dataset
Change the option `download=False`, and set the path (`root)`where data is stored.
```python
trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
download=False, transform=transform)
testset = torchvision.datasets.CIFAR10(root='./data', train=False,
download=False, transform=transform)
```
### User Defined Dataset
## Load and Show images(tensor, color)
```python
import matplotlib.pyplot as plt
import numpy as np
# Cannot directly use plt.show() to show Tensor
# Convert to numpy then use plt
def imshow(img):
img = img / 2 + 0.5 # unnormalize
npimg = img.numpy()
plt.imshow(np.transpose(npimg, (1, 2, 0))) # channel goes at last
plt.xticks([])
plt.yticks([])
plt.show()
# get some random training images
dataiter = iter(trainloader)
images, labels = dataiter.next()
# Since batch=4, we get four images at a time
images.size()
# show images
imshow(torchvision.utils.make_grid(images))
# print labels
print(' '.join('%5s' % classes[labels[j]] for j in range(4)))
```
## Define Model
```python
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()
```
## Define Loss function and Optimization
```python
import torch.optim as optim
# loss function
criterion = nn.CrossEntropyLoss()
# Optimization method
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
```
## Train the network
```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; data is a list of [inputs, labels]
inputs, labels = data
# 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.item()
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')
```
## Save model
```python
PATH = './cifar_net.pth'
torch.save(net.state_dict(), PATH)
# To use the saved model
net = Net()
net.load_state_dict(torch.load(PATH))
```
## Test the network
### Show some ground truth of test data
```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)))
```
### Overall accuracy
```python
correct = 0
total = 0
with torch.no_grad():
for data in testloader:
images, labels = data[0].to(device), data[1].to(device)
outputs = net(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Accuracy of the network on the %d test images: %d %%' %(len(testloader.dataset), 100 * correct / total))
```
### Evaluate each class
> numpy.squeeze() function is used when we want to remove single-dimensional entries from the shape of an array.
```python
class_correct = list(0. for i in range(10))
class_total = list(0. for i in range(10))
with torch.no_grad():
for data in testloader:
images, labels = data
outputs = net(images)
_, predicted = torch.max(outputs, 1) # max(outputs, dim=1) returns (values, indices)
c = (predicted == labels).squeeze() # remove dim=1
for i in range(4):
label = labels[i]
class_correct[label] += c[i].item() #c[i] is a tensor either true or false
class_total[label] += 1
for i in range(10):
print('Accuracy of %5s : %2d %%' % (
classes[i], 100 * class_correct[i] / class_total[i]))
```
## 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.
* Build a MNIST Convnet