This document summarizes machine learning frameworks and libraries, neural network structures, and the process of building and training a neural network model for image classification. It discusses TensorFlow and PyTorch frameworks, describes the structure of a convolutional neural network, and provides code to import datasets, define a model, train the model on GPUs, and test the model's accuracy.

2. Machine Learning library
Framework: Backend engines + library(module, function, …)
library

3. Other services
Colaboratory
Fast ai
AWS public dataset program

4. Tensorflow
Static Computational Graph
Tensorflow Fold: Dynamic Computational Graph
sess.run(hypothesis, feed_dict={X: [5]})
sess = tf.Session()

5. Pytorch
Dynamic Computational Graph

6. Network structure
Conv 2d
image
pooling
Conv 2d
linear
linear
linear
output
Shape: (32, 32, 3)
In_ch: 3, out_ch: 6, kernel: (5, 5), relu
Shape: (28, 28, 6)
kernel: (2, 2)
Shape: (14, 14, 6)
In_ch: 6, out_ch: 16, kernel: (5, 5), relu
Shape: (10, 10, 16)
pooling kernel: (2, 2)
Shape: (5, 5, 16)
Shape: (120)
In_feat: 300, out_feat: 120, relu
In_feat: 120, out_feat: 84, relu
In_feat: 84, out_feat: 10, softmax
flatten
Shape: (400)
Shape: (84)
Shape: (10)

7. Import module
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
import torch.optim as optim

8. Create neural network structure
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

9. Download and import 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')

10. training
net = Net()
# CrossEntropyLoss=logSoftmax+NLLLoss
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
for epoch in range(2):
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
inputs, labels = data
optimizer.zero_grad()
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

11. Confirm network structure
print("network specification")
print(net)
params = list(net.parameters())
print("nnumber of network parmeters")
print(len(params))
print("ntensor shape of each network parameters")
for param in params:
print(param.size())
Parameter shape
Shape of output of each layers
print(x.size())

12. Save and load
torch.save(net.state_dict(),"./model_param")
Save model
Load model
net = Net()
net.load_state_dict(torch.load("./model_param"))
net.eval()
Python code에 한글 쓸 경우
# -*- coding: utf-8 -*-

13. Test
correct = 0
total = 0
with torch.no_grad():
for data in testloader:
images, labels = data
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 10000 test
images: %d %%' % (
100 * correct / total))
Test

14. Training on GPU
device = torch.device("cuda:0" if torch.cuda.is_available() else
"cpu")
Get device
Network upload to device
net.to(device)
Data upload to device
inputs, labels = inputs.to(device), labels.to(device)

15. Show the image and prediction
def imshow(img):
img = img / 2 + 0.5
npimg = img.numpy()
plt.imshow(np.transpose(npimg, (1, 2, 0)))
Image show function
Get image and show
dataiter = iter(testloader)
images, labels = dataiter.next()
imshow(torchvision.utils.make_grid(images))
plt.show()

16. Show the image and prediction
Get prediction result
outputs = net(images)
value, predicted = torch.max(outputs, 1)
print('GroundTruth: ', ' '.join('%5s' % classes[labels[j]]
for j in range(4)))
print('Predicted: ', ' '.join('%5s' % classes[predicted[j]]
for j in range(4)))
print('value: ', ' '.join('%3.3f' % value[j]
for j in range(4)))

17. Schedule
다음 시간: 각자 논문 읽고 발표하기.