machinelearning 12

๋ฐ์ดํ„ฐ ์ œ๋„ˆ๋ ˆ์ดํ„ฐ๋ฅผ ํ†ตํ•ด ์ด๋ฏธ์ง€๋ฅผ ์ฆ๊ฐ•ํ•˜๊ณ  Transfer Learningํ•˜๊ธฐ

BASE MODEL HEAD MODEL - ํŠธ๋žœ์Šคํผ ๋Ÿฌ๋‹์€, ํ•™์Šต์ด ์ž˜ ๋œ ๋ชจ๋ธ์„ ๊ฐ€์ ธ์™€์„œ ์šฐ๋ฆฌ์˜ ๋ฌธ์ œ์— ๋งž๊ฒŒ ํ™œ์šฉํ•˜๋Š” ๊ฒƒ์ด๋ฏ€๋กœ ํ•™์Šต์ด ์ž˜ ๋œ ๋ชจ๋ธ์˜ base model๋งŒ ๊ฐ€์ ธ์˜จ๋‹ค ์ฆ‰, head๋ชจ๋ธ์€ ๋นผ๊ณ  ๊ฐ€์ ธ์™€์„œ ์‚ฌ์šฉ์ž๊ฐ€ ์ง์ ‘ head๋ชจ๋ธ์„ ์˜ˆ์ธกํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ํŠธ๋žœ์Šคํผ ๋Ÿฌ๋‹์˜ ๊ณผ์ •์€ ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค ์„ค์น˜ํ•˜๊ธฐ !pip install tensorflow-gpu==2.0.0.alpha0 !pip install tqdm Dogs vs Cats dataset ๋‹ค์šด๋กœ๋“œ๋ฐ›๊ธฐ !wget --no-check-certificate \ https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtered.zip \ -O ./cats_and_dogs_filtered.zip ์ž„..

๋”ฅ๋Ÿฌ๋‹ : CNN(ํ•ฉ์„ฑ๊ณฑ ์‹ ๊ฒฝ๋ง, Convolutional Neural Network), ์ปฌ๋Ÿฌ์‚ฌ์ง„ ์‹๋ณ„ํ•˜๊ธฐ

CNN์€ ์ปจ๋ณผ๋ฃจ์…˜ ๊ณ„์ธต(convolutional layer)๊ณผ ํ’€๋ง ๊ณ„์ธต(pooling layer)์œผ๋กœ ๊ตฌ์„ฑ๋œ ์‹ ๊ฒฝ๋ง์ด๋‹ค. 1. ์ปจ๋ณผ๋ฃจ์…˜ ๊ณ„์ธต์€ ์ž…๋ ฅ ์ด๋ฏธ์ง€์— ๋Œ€ํ•ด ํ•„ํ„ฐ(๋˜๋Š” ์ปค๋„)๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ์ž…๋ ฅ ๋ฐ์ดํ„ฐ๋ฅผ ๋ณ€ํ™˜ํ•˜๋Š” ๊ณ„์ธต์ด๋‹ค. ์ด ํ•„ํ„ฐ๋Š” ์ž…๋ ฅ ์ด๋ฏธ์ง€์˜ ํŠน์ • ํŒจํ„ด์„ ๊ฐ์ง€ํ•˜๋Š” ์—ญํ• ์„ ํ•ฉ๋‹ˆ๋‹ค. ์˜ˆ๋ฅผ ๋“ค์–ด, ๊ฐ€์žฅ์ž๋ฆฌ, ์งˆ๊ฐ, ์ƒ‰์ƒ ๋“ฑ์„ ๊ฐ์ง€ํ•  ์ˆ˜ ์žˆ๋‹ค. 2. ํ’€๋ง ๊ณ„์ธต์€ ์ถœ๋ ฅ์˜ ๊ณต๊ฐ„ ํฌ๊ธฐ๋ฅผ ์ค„์ด๋Š” ์—ญํ• ์„ ํ•œ๋‹ค. ์ผ๋ฐ˜์ ์œผ๋กœ ์ตœ๋Œ€ ํ’€๋ง(max pooling)์ด๋‚˜ ํ‰๊ท  ํ’€๋ง(average pooling)์„ ์‚ฌ์šฉํ•˜์—ฌ ์ถœ๋ ฅ์„ ๊ฐ ์˜์—ญ์—์„œ ๊ฐ€์žฅ ํฐ ๊ฐ’ ๋˜๋Š” ํ‰๊ท  ๊ฐ’์œผ๋กœ ๋Œ€์ฒดํ•œ๋‹ค. ์ด๋Š” ๊ณต๊ฐ„ ํ•ด์ƒ๋„๋ฅผ ๊ฐ์†Œ์‹œํ‚ค๊ณ , ๊ณ„์‚ฐ๋Ÿ‰์„ ์ค„์ด๋ฉฐ, ๋ถˆํ•„์š”ํ•œ ์ •๋ณด๋ฅผ ์ œ๊ฑฐํ•˜์—ฌ ํŠน์ง•์„ ๋ณด๋‹ค ๊ฐ•์กฐํ•œ๋‹ค. CNN์€ ์ด๋Ÿฌํ•œ ์ปจ๋ณผ๋ฃจ์…˜ ๊ณ„์ธต๊ณผ ํ’€๋ง ๊ณ„์ธต์„ ..

ํ‘๋ฐฑ ์ด๋ฏธ์ง€ ๋ฐ์ดํ„ฐ์…‹์„ AI์—๊ฒŒ ํŒ๋ณ„์‹œ์ผœ, ์นดํ…Œ๊ณ ๋ฆฌ์˜ ์ •๋‹ต์„ ๋งž์ถ”๋Š” ๋จธ์‹ ๋Ÿฌ๋‹ ๊ธฐ๋ฒ•

https://www.tensorflow.org/datasets/catalog/fashion_mnist?hl=ko ํŒจ์…˜_์— ๋‹ˆ์ŠคํŠธ | TensorFlow Datasets ์ด ํŽ˜์ด์ง€๋Š” Cloud Translation API๋ฅผ ํ†ตํ•ด ๋ฒˆ์—ญ๋˜์—ˆ์Šต๋‹ˆ๋‹ค. ํŒจ์…˜_์— ๋‹ˆ์ŠคํŠธ ์ปฌ๋ ‰์…˜์„ ์‚ฌ์šฉํ•ด ์ •๋ฆฌํ•˜๊ธฐ ๋‚ด ํ™˜๊ฒฝ์„ค์ •์„ ๊ธฐ์ค€์œผ๋กœ ์ฝ˜ํ…์ธ ๋ฅผ ์ €์žฅํ•˜๊ณ  ๋ถ„๋ฅ˜ํ•˜์„ธ์š”. Fashion-MNIST๋Š” 60,000๊ฐœ์˜ ์˜ˆ์ œ๋กœ ๊ตฌ์„ฑ๋œ www.tensorflow.org import numpy as np import tensorflow as tf from tensorflow.keras.datasets import fashion_mnist #fashion_mnist ์‚ฌ์šฉ (X_train, y_train), (X_test, y_test) = fashion..

GridSearch ๋ฅผ ์ด์šฉํ•œ ์ตœ์ ์˜ ํ•˜์ดํผ ํŒŒ๋ผ๋ฏธํ„ฐ ์ฐพ๊ธฐ

https://www.tensorflow.org/api_docs/python/tf/keras?_gl=1*7074vd*_up*MQ..*_ga*MjEzMDk5NTY2NC4xNzEzNDg3ODQ0*_ga_W0YLR4190T*MTcxMzQ4Nzg0NC4xLjAuMTcxMzQ4Nzg0NC4wLjAuMA.. Module: tf.keras | TensorFlow v2.16.1 DO NOT EDIT. www.tensorflow.org ! pip install scikeras from scikeras.wrappers import KerasClassifier from sklearn.model_selection import GridSearchCV from keras.models import Sequential from ke..

๋”ฅ๋Ÿฌ๋‹ : Neural Networks ์œผ๋กœ Classification(๋ถ„๋ฅ˜) ํ•˜๊ธฐ

๋”ฅ๋Ÿฌ๋‹์€ ์ด๋ฏธ์ง€ ์ธ์‹, ์ž์—ฐ์–ด ์ฒ˜๋ฆฌ, ์Œ์„ฑ ์ธ์‹ ๋“ฑ ๋‹ค์–‘ํ•œ ์˜์—ญ์—์„œ ๋งค์šฐ ์„ฑ๊ณต์ ์œผ๋กœ ์‚ฌ์šฉ๋˜๊ณ  ์žˆ๋‹ค. ๋”ฅ๋Ÿฌ๋‹์€ ํžˆ๋“ ๋ ˆ์ด์–ด๊ฐ€ ๋งŽ๋‹ค๋Š” ๊ฒƒ์ด ํŠน์ง•์ด๋‹ค - ๋”ฅ๋Ÿฌ๋‹์—์„œ "ํžˆ๋“  ๋ ˆ์ด์–ด"๋Š” ์ž…๋ ฅ์ธต(input layer)๊ณผ ์ถœ๋ ฅ์ธต(output layer) ์‚ฌ์ด์— ์žˆ๋Š” ์ค‘๊ฐ„ ๋ ˆ์ด์–ด๋ฅผ ๊ฐ€๋ฆฌํ‚จ๋‹ค. ํžˆ๋“  ๋ ˆ์ด์–ด๊ฐ€ ๋งŽ์„์ˆ˜๋ก, ๋ชจ๋ธ์€ ๋” ๋ณต์žกํ•œ ํŒจํ„ด์ด๋‚˜ ๊ด€๊ณ„๋ฅผ ํ•™์Šตํ•  ์ˆ˜ ์žˆ๊ฒŒ ๋œ๋‹ค. ์ด๋Š” ์ž…๋ ฅ๊ณผ ์ถœ๋ ฅ ๊ฐ„์˜ ๋ณต์žกํ•œ ๋น„์„ ํ˜• ๊ด€๊ณ„๋ฅผ ๋ชจ๋ธ๋งํ•˜๋Š” ๋ฐ ๋„์›€์ด ๋ฉ๋‹ˆ๋‹ค. - ๋” ๋งŽ์€ ํžˆ๋“  ๋ ˆ์ด์–ด๋ฅผ ๊ฐ€์ง„ ์‹ ๊ฒฝ๋ง์€ ๋” ๋งŽ์€ ํŒŒ๋ผ๋ฏธํ„ฐ๋ฅผ ๊ฐ€์ง€๊ธฐ ๋•Œ๋ฌธ์—, ๋” ๋งŽ์€ ๋ฐ์ดํ„ฐ์™€ ์—ฐ์‚ฐ ๋ฆฌ์†Œ์Šค๋ฅผ ํ•„์š”๋กœ ํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ถฉ๋ถ„ํ•œ ๋ฐ์ดํ„ฐ์™€ ๋ฆฌ์†Œ์Šค๊ฐ€ ์ œ๊ณต๋œ๋‹ค๋ฉด, ๊นŠ์€ ์‹ ๊ฒฝ๋ง์€ ๋งค์šฐ ๋ณต์žกํ•œ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๋Š” ๋ฐ ํšจ๊ณผ์ ์ผ ์ˆ˜ ์žˆ๋‹ค. ๊ธˆ์œต์ƒํ’ˆ ๊ฐฑ์‹ (0 ๋˜๋Š” 1) ์—ฌ๋ถ€ ์˜ˆ์ธกํ•˜๋Š” ..

DTree(Decision Tree) ์•Œ๊ณ ๋ฆฌ์ฆ˜์œผ๋กœ ์ƒˆ๋กœ์šด ๋ฐ์ดํ„ฐ ์นดํ…Œ๊ณ ๋ฆฌ ๋ถ„๋ฅ˜ํ•˜๊ธฐ

๋จธ์‹ ๋Ÿฌ๋‹์˜ ์ง€๋„ํ•™์Šต์— ์†ํ•˜๋Š” Classfication(๋ถ„๋ฅ˜) - Logistic Regression (๋กœ์ง€์Šคํ‹ฑ ํšŒ๊ท€) - KNN(K nearest neighbor) ์•Œ๊ณ ๋ฆฌ์ฆ˜, - SVC(Support Vector Machine) ์•Œ๊ณ ๋ฆฌ์ฆ˜, - DT(Decision Tree) ์•Œ๊ณ ๋ฆฌ์ฆ˜ ๋„ค ๊ฐ€์ง€ ๋ฐฉ๋ฒ• ์ค‘์— ์ •ํ™•๋„๊ฐ€ ๋” ๋†’์€ ๋ฐฉ๋ฒ•์œผ๋กœ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์„ ํƒํ•˜์—ฌ ์‚ฌ์šฉํ•œ๋‹ค import numpy as np import matplotlib.pyplot as plt import pandas as pd DT(Decision Tree) ์˜์‚ฌ๊ฒฐ์ • ๋‚˜๋ฌด๋Š” ํ๋ฆ„๋„์ด๋ฉฐ ์ด์ „ ๊ฒฝํ—˜์„ ๋ฐ”ํƒ•์œผ๋กœ ์˜์‚ฌ๊ฒฐ์ •์„ ๋‚ด๋ฆฌ๋Š” ๋ฐ ๋„์›€์„ ์ฃผ๋Š” ๊ฒƒ์ด๋‹ค. df User ID Gender Age EstimatedSalary Purchased 0 1562451..

SVM(Support Vector Machine) ์•Œ๊ณ ๋ฆฌ์ฆ˜์œผ๋กœ ์ƒˆ๋กœ์šด ๋ฐ์ดํ„ฐ ์นดํ…Œ๊ณ ๋ฆฌ ๋ถ„๋ฅ˜ํ•˜๊ธฐ

๋จธ์‹ ๋Ÿฌ๋‹์˜ ์ง€๋„ํ•™์Šต์— ์†ํ•˜๋Š” Classfication(๋ถ„๋ฅ˜) - Logistic Regression (๋กœ์ง€์Šคํ‹ฑ ํšŒ๊ท€) - KNN(K nearest neighbor) ์•Œ๊ณ ๋ฆฌ์ฆ˜, - SVC(Support Vector Machine) ์•Œ๊ณ ๋ฆฌ์ฆ˜, - DT(Decision Tree) ์•Œ๊ณ ๋ฆฌ์ฆ˜ ๋„ค ๊ฐ€์ง€ ๋ฐฉ๋ฒ• ์ค‘์— ์ •ํ™•๋„๊ฐ€ ๋” ๋†’์€ ๋ฐฉ๋ฒ•์œผ๋กœ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์„ ํƒํ•˜์—ฌ ์‚ฌ์šฉํ•œ๋‹ค SVM(Support Vector Machine) SVC (Support Vector Classifier): SVC๋Š” ๋ถ„๋ฅ˜ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•œ SVM์˜ ๋ณ€ํ˜•์ด๋‹ค ์ด๊ฒƒ์€ ์ฃผ์–ด์ง„ ๋ฐ์ดํ„ฐ๋ฅผ ๋ถ„๋ฅ˜ํ•˜๊ธฐ ์œ„ํ•ด ์ตœ์ ์˜ ๋ถ„๋ฆฌ ์ดˆํ‰๋ฉด์„ ์ฐพ๋Š”๋‹ค SVC๋Š” ํด๋ž˜์Šค ๊ฐ„์˜ ๊ฒฝ๊ณ„๋ฅผ ๋ถ„๋ฆฌํ•˜๊ธฐ ์œ„ํ•ด ์ตœ์ ์˜ ์ดˆํ‰๋ฉด์„ ์ฐพ๋Š” ๊ฒƒ์ด ๋ชฉํ‘œ SVR (Support Vector Regress..

KNN(K nearest neighbor) ์•Œ๊ณ ๋ฆฌ์ฆ˜์œผ๋กœ ์ƒˆ๋กœ์šด ๋ฐ์ดํ„ฐ ์นดํ…Œ๊ณ ๋ฆฌ ๋ถ„๋ฅ˜ํ•˜๊ธฐ

๋จธ์‹ ๋Ÿฌ๋‹์˜ ์ง€๋„ํ•™์Šต์— ์†ํ•˜๋Š” Classfication(๋ถ„๋ฅ˜) - Logistic Regression (๋กœ์ง€์Šคํ‹ฑ ํšŒ๊ท€) - KNN(K nearest neighbor) ์•Œ๊ณ ๋ฆฌ์ฆ˜, - SVC(Support Vector Machine) ์•Œ๊ณ ๋ฆฌ์ฆ˜, - DT(Decision Tree) ์•Œ๊ณ ๋ฆฌ์ฆ˜ ๋„ค ๊ฐ€์ง€ ๋ฐฉ๋ฒ• ์ค‘์— ์ •ํ™•๋„๊ฐ€ ๋” ๋†’์€ ๋ฐฉ๋ฒ•์œผ๋กœ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์„ ํƒํ•˜์—ฌ ์‚ฌ์šฉํ•œ๋‹ค import numpy as np import matplotlib.pyplot as plt import pandas as pd df User ID Gender Age EstimatedSalary Purchased 0 15624510 Male 19 19000 0 1 15810944 Male 35 20000 0 2 15668575 Female 26 430..

๋ฐ์ดํ„ฐ ๋ถˆ๊ท ํ˜•์ด ๋ฐœ์ƒํ•  ๋•Œ, ๋ฐ์ดํ„ฐ ๋ฆฌ์ƒ˜ํ”Œ๋งํ•˜๊ธฐ

import numpy as np import matplotlib.pyplot as plt import pandas as pd import seaborn as sb #๋‹น๋‡จ๋ณ‘์„ ๋ถ„๋ฅ˜ํ•˜๋Š” ๋ชจ๋ธ df Preg Plas Pres skin test mass pedi age class 0 6 148 72 35 0 33.6 0.627 50 1 1 1 85 66 29 0 26.6 0.351 31 0 2 8 183 64 0 0 23.3 0.672 32 1 3 1 89 66 23 94 28.1 0.167 21 0 4 0 137 40 35 168 43.1 2.288 33 1 ... ... ... ... ... ... ... ... ... ... 763 10 101 76 48 180 32.9 0.171 63 0 764 2 1..

Logistic Regression (๋กœ์ง€์Šคํ‹ฑ ํšŒ๊ท€)

๋จธ์‹ ๋Ÿฌ๋‹์˜ ์ง€๋„ํ•™์Šต์— ์†ํ•˜๋Š” Classfication(๋ถ„๋ฅ˜) - Logistic Regression (๋กœ์ง€์Šคํ‹ฑ ํšŒ๊ท€) - KNN(K nearest neighbor) ์•Œ๊ณ ๋ฆฌ์ฆ˜, - SVC(Support Vector Machine) ์•Œ๊ณ ๋ฆฌ์ฆ˜, - DT(Decision Tree) ์•Œ๊ณ ๋ฆฌ์ฆ˜ ๋„ค ๊ฐ€์ง€ ๋ฐฉ๋ฒ• ์ค‘์— ์ •ํ™•๋„๊ฐ€ ๋” ๋†’์€ ๋ฐฉ๋ฒ•์œผ๋กœ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์„ ํƒํ•˜์—ฌ ์‚ฌ์šฉํ•œ๋‹ค ๋กœ์ง€์Šคํ‹ฑ ํšŒ๊ท€๋ž€ ? - ๋ถ„๋ฅ˜ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๋Š”๊ฒƒ์„ ๋ชฉํ‘œ๋กœ ํ•ฉ๋‹ˆ๋‹ค - ์—ฐ์†์ ์ธ ๊ฒฐ๊ณผ๋ฅผ ์˜ˆ์ธกํ•˜๋Š” ์„ ํ˜•ํšŒ๊ท€์™€ ๋‹ค๋ฅด๊ฒŒ ๋ฒ”์ฃผํ˜• ๊ฒฐ๊ณผ๋ฅผ ์˜ˆ์ธกํ•˜์—ฌ ์ด๋ฅผ ์ˆ˜ํ–‰ํ•ฉ๋‹ˆ๋‹ค - ๊ฐ€์žฅ ๊ฐ„๋‹จํ•œ ๋ฐฉ๋ฒ•์ด ์ดํ•ญ์ด๋ผ๊ณ  ๋ถˆ๋ฆฌ๋Š” ๋‘๊ฐ€์ง€ ๊ฒฐ๊ณผ๊ฐ€ ์žˆ๋Š”๋ฐ ๊ทธ ์˜ˆ๋กœ ์•”์ด ์•…์„ฑ์ธ์ง€ ์–‘์„ฑ์ธ์ง€ ์˜ˆ์ธกํ•˜๋Š” ๊ฒƒ์ž…๋‹ˆ๋‹ค Confusion Matrix ๋Œ€๋ถ€๋ถ„์˜ ๊ฒฝ์šฐ, C๊ฐ€ ๋” ์ค‘์š”ํ•˜๋‹ค ์˜คํƒ์ง€์˜ ๊ฒฝ์šฐ ๋งž์€๊ฒƒ์„ ๋ชป์ฐพ..