30살 인공지능 도전기

(2022.07.06) Pytorch Car Object Detection

import cv2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import albumentations as A from albumentations.pytorch.transforms import ToTensorV2 import torch from torch.utils.data import Dataset, DataLoader from torchvision.models.detection.faster_rcnn import FastRCNNPredictor from torchvision.models.detection import FasterRCNN from torchvision.models.detection import faster..

→2022.07.06

(2022.07.04) Rcnn Fruit object detection (pytorch)

import pandas as pd import numpy as np import os import random import matplotlib.pyplot as plt import matplotlib.patches as patches import cv2 import torch import torchvision from torchvision import transforms, datasets from torchvision.models.detection import * from torchvision.models.detection.faster_rcnn import FastRCNNPredictor from torch.utils.data import Dataset # from engine import train_..

→2022.07.04

(2022.07.03) Brain Tumor MRI detection

import numpy as np import matplotlib.pyplot as plt import torch import torch.nn as nn import torchvision.transforms as transforms from PIL import Image import gc from sklearn.model_selection import train_test_split import os dataset_path = './practic_torch/data/brain_tumor_dataset' paths = [] labels = [] # os.walk()는 하위의 폴더들을 for문으로 탐색 for label in ['yes','no']: for dirname, _, filenames in os.w..

→2022.07.03

(2022.07.01) Pytorch Rice Classification

import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim import torch.utils.data as data from torch.optim import Adam import torchvision.transforms as transforms import torchvision.datasets as datasets from torch.utils.data import DataLoader from torch.utils.data import Dataset from random import randint from PIL import Image import matplotlib.pyplot as plt ..

→2022.07.01

(2022.07.01) CNN

import torch import torch.nn as nn import torch.nn.functional as F import torchvision import torchvision.transforms as transforms import matplotlib.pyplot as plt import numpy as np # Device configuration device = torch.device("cuda:0" if torch.cuda.is_available() else 'cpu') # Hyoer-parameters num_epochs = 4 batch_size = 4 learning_rate = 0.001 # dataset has PILImage images of range [0, 1]. # We..

→2022.07.01

(2022.07.01) Feed Forward Net

import torch import torch.nn as nn import torchvision import torchvision.transforms as transforms import matplotlib.pyplot as plt #device config device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') #hyper parmameters input_size = 784 # 28*28 hidden_size = 100 num_classes = 10 num_epochs = 2 batch_size = 100 learning_rate = 0.001 #MNIST train_dataset = torchvision.datasets.MNIS..

→2022.07.01

(2022.06.30) Dataset and Dataloader

''' epoch = 1 forward and backward pass of ALL training samples batch_size = number of training samples in one forward & backward pass number of = iterations = number of passes, each pass using [batch_size] number of sampeles e.g. 100 samples, batch_size = 20 --> 100/20 = 5 iterations for 1 epoch ''' import torch import torchvision from torch.utils.data import Dataset, DataLoader import numpy as..

→2022.06.30

(2022.06.30) pytorch linear_regression

''' 1) Design model(input, output, forward pass) 2) Construct loss and optimizer 3) Traning loop - forward pass : compute prediction and loss - backward pass : gradients - updata weights ''' import torch import torch.nn as nn import numpy as np from sklearn import datasets from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split # prepare data bc = da..

→2022.06.30

인공지능 공부/딥러닝 논문읽기

(Paper review) Do Prompt-Based Models Really Understand the Meaning of Their Prompts?

→2022.06.20

(NLP연구) What makes Good Prompt?

→2022.06.07

(NLP연구) P-tuning 실험

실험 Released results on RoBERTa-large Released results on RoBERTa-large

→2022.05.13

(연구서버) Code-Server 연동하기

Server 접속 server : ip pass : pass ssh 서버 pass : apss path : /폴더/giai #개인 폴더 mkdir 이름 만들기 Docker Pull - run - start - attach Docker server () 참고 apt update apt upgrade apt install curl curl -fsSL | sh apt install vim 입력 : code-server vim ~/.config/code-server/config.yaml bind-addr: 0.0.0.0:8080(포트설정) auth: password password: f****************6e85ed0 -> 비빌번호알아두기(변경가능) cert: false 저장 : ESC - :w - :..

→2022.05.11

(NLP연구) 서버 사용법

docker pull repo/image_name:tag docker run -itd -v local_path:docker_path --ipc=host --network=host \\ --gpus='"device=0,1"' --name=container_name repo/image_name:tag Run Flags -i: 표준 입력(stdin)을 활성화하며 컨테이너와 연결(attach)되어 있지 않더라도 표준 입력을 유지 -t: TTY 모드를 사용, Bash를 사용하려면 이 옵션을 설정 필수 -it : -i와 -t를 동시에 사용한 것으로 터미널 입력을 위한 옵션 (컨테이너의 표준 입력과 로컬 컴퓨터의 키보드 입력을 연결) -d: 백그라운드로 실행 (container에서 detach되더라도, contain..

→2022.05.10

(NLP) Prompt Learning Translation 연구, 코딩(2022.05.06)

P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks Xiao Liu, Kaixuan Ji, Yicheng Fu, Weng Lam Tam, Zhengxiao Du, Zhilin Yang, Jie Tang Search | arXiv e-print repository Showing 1–50 of 622 results for author: Tang, J arXiv:2205.00637 [pdf, other] cs.CV cs.AI Enhancing Adversarial Training with Feature Separability Authors: Yaxin Li, Xiaorui Liu, Han X..

→2022.05.06

(NLP) 2차 Prompt Learning 발표 (2022-05-04)

논문 리스트 정리 Attention Is All You Need https://arxiv.org/pdf/1706.03762.pdf BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding https://arxiv.org/pdf/1810.04805.pdf ALBERT: A Lite BERT for Self-supervised Learning of Language Representations https://arxiv.org/pdf/1909.11942.pdf Improving language understanding by generative pre-training https://s3-us-west-2.amazonaws.co..

→2022.05.04

플러터 다트(dart) - Login 화면 만들기 (2022.04.29)

import 'package:flutter/material.dart'; void main() => runApp(MyApp()); class MyApp extends StatelessWidget { @override Widget build(BuildContext context) { return MaterialApp( debugShowCheckedModeBanner: false, title: 'Dice game', home: LogIn(), ); } } class LogIn extends StatefulWidget { @override State createState() => _LogInState(); } class _LogInState extends State { @override Widget build(..

→2022.04.29

플러터 다트(dart) - Statefull Widget

import 'package:flutter/material.dart'; void main() => runApp(MyApp()); class MyApp extends StatefulWidget { @override State createState() { // TODO: implement createState return MyAppState(); } } class MyAppState extends State{ int counter = 0; @override Widget build(BuildContext context) { return MaterialApp( theme: ThemeData( primarySwatch: Colors.blue, ), home: Scaffold( appBar: AppBar(), bo..

→2022.04.29

플러터 다트(dart) 네비게이터(Navigator) 와 푸시네임드 메소드(pushNamed method)(2022.04.28)

import 'package:flutter/material.dart'; import 'package:contrainer/ScreenA.dart'; import 'package:contrainer/ScreenB.dart'; import 'package:contrainer/ScreenC.dart'; void main() => runApp(MyApp()); class MyApp extends StatelessWidget { const MyApp({Key? key}) : super(key: key); @override Widget build(BuildContext context) { return MaterialApp( initialRoute: "/", routes: { "/" : (context) => Scre..

→2022.04.28

플러터 다트(dart) Navigator(네비게이터)(2022.04.28)

import 'package:flutter/material.dart'; void main() => runApp(MyApp()); class MyApp extends StatelessWidget { const MyApp({Key? key}) : super(key: key); @override Widget build(BuildContext context) { return MaterialApp( title: "MyApp", theme: ThemeData( primaryColor: Colors.blue ), home: MyPage(), ); } } class MyPage extends StatelessWidget { const MyPage({Key? key}) : super(key: key); @override..

→2022.04.28

플러터 다트(dart) Toast (2022.04.28)

import 'package:flutter/material.dart'; import 'package:fluttertoast/fluttertoast.dart'; void main() => runApp(MyApp()); class MyApp extends StatelessWidget { MyApp({Key? key}) : super(key: key); @override Widget build(BuildContext context) { return MaterialApp( title: 'Appbar', theme: ThemeData(primarySwatch: Colors.red), home: MyPage()); } } class MyPage extends StatelessWidget { const MyPage(..

→2022.04.28

플러터 다트(dart) 빌더(Builder widget)위젯 없이 스낵바(Snack bar)(2022.04.28)

import 'package:flutter/material.dart'; void main() => runApp(MyApp()); class MyApp extends StatelessWidget { MyApp({Key? key}) : super(key: key); @override Widget build(BuildContext context) { return MaterialApp( title: 'Appbar', theme: ThemeData(primarySwatch: Colors.red), home: MyPage()); } } class MyPage extends StatelessWidget { const MyPage({Key? key}) : super(key: key); @override Widget b..

→2022.04.28

(NLP) Prompt Learning 발표 (2022-04-27)

Attention Is All You Need https://arxiv.org/pdf/1706.03762.pdf BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding https://arxiv.org/pdf/1810.04805.pdf ALBERT: A Lite BERT for Self-supervised Learning of Language Representations https://arxiv.org/pdf/1909.11942.pdf Improving language understanding by generative pre-training https://s3-us-west-2.amazonaws.com/openai-a..

→2022.04.27

플러터 다트(dart) 메뉴아이콘(2022.04.25)

import 'package:flutter/material.dart'; void main() => runApp(MyApp()); class MyApp extends StatelessWidget { const MyApp({Key? key}) : super(key: key); @override Widget build(BuildContext context) { return MaterialApp( debugShowCheckedModeBanner: false, title: 'Appbar', theme: ThemeData( primarySwatch: Colors.red // ThemeData를 통해서 전체적인 Theme을 설정할 수 있습니다. ), home: MyPage(), ); } } class MyPage e..

→2022.04.25

플러터 다트(dart) 핵심정리(2022.04.25)

// ignore_for_file: prefer_const_constructors, prefer_const_literals_to_create_immutables import 'package:flutter/material.dart'; void main() => runApp(MyApp()); class MyApp extends StatelessWidget { const MyApp({Key? key}) : super(key: key); @override Widget build(BuildContext context) { return MaterialApp( debugShowCheckedModeBanner: false, title: 'BBANTO', home: Grade(), ); } } class Grade ex..

→2022.04.25