NAIAHD

아래 내용은 Udemy에서 Pytorch: Deep Learning and Artificial Intelligence를 보고 정리한 내용이다.

Transfer Learning

Basic Conceps

Recall: Features are hierarchical!

computer vision

ImageNet - Large-scale image dataset

학습 하기 전에것은 좀 힘들다.

Training on ImageNet

2-part CNN

Feature transformer("body")

ANN classifier("head")

"head"를 바꾸고 새로운 해드를 추가 한다.

logistic regression or ANN 등  가능하다.

Freeze the "body"

train only the head (much fuster)

transfer learning 의 장점:

Don't need a lot of data to build a state-of-the-art model

with transfer learniing, this work has been done for us- the earlier features were already trained on lots of data(ImageNet)

Small dataset + a lot less weights helps us train fast

Some pre-trained Models(VGG, ResNet, Inception, MobieNet)

VGG -Visual Geometry Group

VGG16, VGG19

cnn 과 다르지 않고 그냥 bigger.

finally 3 fully-connected 

ResNet 

a cnn with branches(one branch is the identity function, so the other learns the residual)

ResNet50, ResNet101. ResNet152

ResNetv2-50, ResNetv2-101. ResNetv2-152

ResNetXt

layer에 따라 다르다.

Inception

similiar to resnet

Multiple convolutions in parallel branches

filter sizes (1x1, 3x3, 5x5, etc)

어떤것 선택할 지는 데이터 등에 따라 달라서 해야 한다.

MobileNet

Lightweight: makes a tradeoff between speed and accuracy

meant for less powerful machines(mobile, embedded)

Large Datasets pytorch

다양한 datasets

jpg, png

train_dataset = datasets.ImageFolder(

 'data/train',

 transform= train_transform

)

Approches to Transfer learning

2가지 부분

2-part CNN

Imagine the computation in 2 parts;

part 1: z = f(x) # pre-trained CNN - slow

part 2: y_hat = softmax(Wx+b) # logistic regression - fast

for epoch in epochs:

  shuffle(batches)

  for x, y in batches:

    z = vgg_body(x)

    y_hat = softmax(z.dot(w) + b)

   gw = grad(error(y, y_hat) , w)

   gb = grad(error(y, y_hat) , b)

   update w and b using gw and gb

vgg_body  -> 이것은 학습이 안된다.

All data is the same

z = f(x) # use cnn to precompute all z's at once

turn(z,y) into tabular dataset

fit(z,y) to logistic regression , never have to look at X again!

how can we use data augmentation

두가지 접근

1. use data augmentation with ImageFolder Dataset + DataLoader

  loop

2. precompute Z without data augmentation

  (Z,y)

장점과 단점

Transfer learning pytorch code 

pytorch transfer learning with data augmentation

binary classifier

normalizes mean and std are standardized for imagenet

train_dataset = datasets.ImageFolder(
 'data/train',
 transform= train_transform
)

test_dataset = datasets.ImageFolder(
 'data/test',
 transform= test_transform
)

pytorch vgg16 model pre-trained

model = models.vgg16(pretrained=True)

#freeze vgg weights
for param in model.parameters():
  param.requires_grad = False

binary classification

model.classifier = nn.Linear(n_features, 2)

pytorch transfer learning without data augmentation

transform= transforms.Compose([
transforms.Resize(size = 256),
transforms.CenterCrop(size = 224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406] , [0.229, 0.224, 0.225])
])

build the model

vgg = models.vgg16(pretrained=True)
class VGGFeatures(nn.Module):
  def __init__(self, vgg):
    super(VGGFeatures, self).__init__()
    self.vgg = vgg
  def forward(self, x):
    out = vgg.features(x)
    out = vgg.avgpool(out)
    out = out.view(out.size(0), -1) #flatten
    return out
vggf = VGGFeatures(vgg)
out = vggf(torch.rand(1,3, 224, 224))
out.shape