cnn_hw3.py

import torch
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from torch.optim import Adam, SGD
########################################


#  Loading and normalizing CIFAR10

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')


########################################


# 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)))
    plt.show()


# 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)))


########################################
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.pool = nn.MaxPool2d(2, 2)
        self.conv1 = nn.Conv2d(3, 10, 5)
        self.conv2 = nn.Conv2d(10, 20, 5, padding=1)
        self.conv3 = nn.Conv2d(20, 35, 5)
        self.fc1 = nn.Linear(35, 30)
        self.fc2 = nn.Linear(30, 20)
        self.fc3 = nn.Linear(20, 10)

    def forward(self, x):
        in_size = x.size(0)
        #x = F.relu(self.pool(self.conv1(x)))
        #x = F.relu(self.pool(self.conv2(x)))
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = self.pool(F.relu(self.conv3(x)))
        x = x.view(in_size, -1)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x


########################################
# Define a Loss function and optimizer
# Add code here (use built-in function)


model = Net()
# Cross Entropy Loss Function
lossf = nn.CrossEntropyLoss()
# Stochastic Gradient Descent with Momentum
optimizer = Adam(model.parameters())

n = 20 
########################################
# Train the network

for epoch in range(n):  # loop over the dataset multiple times
    # Add code here, this should include
    # 1- forward pass +otimize + backward pass
    i = 0
    running_loss = 0.0
    for data in trainloader:
        inputs, labels = data
        optimizer.zero_grad()

        outputs = model(inputs)
        # 2- calculate and print loss
        loss = lossf(outputs, labels)
        loss.backward()
        optimizer.step()

        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
        i += 1

########################################
# Save the trained model
# Add code here
PATH='./3cnn.pth'
torch.save(model.state_dict(), PATH) 

########################################
# Print Confusion matrix for the test set
# Add code here

correct = 0
total = 0
with torch.no_grad():
    for data in testloader:
        images, labels = data
        outputs = model(images)

        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

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

########################################
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