NNclassifier.py

# Inspired By Training a Classifier CIFAR10 Tutorial from PyTorch.org
# modified by Andrew Whelan

# Step 1. 
# IMPORT ALL PACKAGES
import torch
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import numpy as np
#Import PyTorch neural network
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim

# Step 1.
# Load and Normalize CIFAR10 Dataset
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')

def imshow(img):
  img = img / 2 + 0.5
  npimg = img.numpy()
  plt.imshow(np.transpose(npimg, (1,2,0)))
  plt.show()

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)))

# Step 2. Define a Convolutional Neural Network
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

# create instance of neural network
net = Net()

# Step 3. Create Loss function and Optimizer
criterion = nn.CrossEntropyLoss() # Why?
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9) #maybe explain a couple different optimizers

# Step 4. Train Neural Network
for epoch in range(2):
  running_loss = 0.0
  for i, data in enumerate(trainloader, 0):
    inputs, labels = data
    optimizer.zero_grad() # Why?

    outputs = net(inputs)
    loss = criterion(outputs, labels)
    loss.backward()
    optimizer.step()

    running_loss += loss.item()
    if i % 2000 == 1999:
      print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 2000))
      running_loss = 0.0

print('Finished Training')

PATH = './cifar_net.pth'
torch.save(net.state_dict(), PATH)



'''
Test your NN.
'''
dataiter = iter(testloader)
images, labels = dataiter.next()

imshow(torchvision.utils.make_grid(images))
print('GroundTrute: ', ' '.join('%5s' % classes[labels[j]] for j in range(4)))

net = Net()
net.load_state_dict(torch.load(PATH))

outputs = net(images)

_, predicted = torch.max(outputs, 1)

print('Predicted: ', ' '.join('%5s' % classes[predicted[j]] for j in range(4)))


correct = 0
total = 0
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.data, 1)
    c = (predicted == labels).squeeze()
    total += labels.size(0)
    correct += (predicted == labels).sum().item()
    for i in range(4):
      label = labels[i]
      class_correct[label] += c[i].item()
      class_total[label] += 1

print('Accuracy of the network on the 10000 test images: %d %%' % (100 * correct / total))

for i in range(10):
  print('Accuracy of %5s : %2d %%' % (classes[i], 100 * class_correct[i] / class_total[i]))