diff --git "a/week3/[MLNovice]\355\231\251\354\247\200\354\233\220_week3-1.ipynb" "b/week3/[MLNovice]\355\231\251\354\247\200\354\233\220_week3-1.ipynb"
new file mode 100644
index 0000000..2c537a3
--- /dev/null
+++ "b/week3/[MLNovice]\355\231\251\354\247\200\354\233\220_week3-1.ipynb"
@@ -0,0 +1 @@
+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyN+NosSh6cOGlVX9GZGUYoV"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"mUPgayDTIuMU"},"outputs":[],"source":["# 럭키백의 확률\n","\n","# 데이터 준비하기\n","import pandas as pd\n","\n","fish = pd.read_csv('https://bit.ly/fish_csv_data')\n","fish.head()  # 테이블로 출력해\n","\n","# input 데이터\n","fish_input = fish[['Weight','Length','Diagonal','Height','Width']].to_numpy()\n","# target 데이터\n","fish_target = fish['Species'].to_numpy()"]},{"cell_type":"code","source":["# 데이터 전처리\n","# train set, test set 나누기\n","from sklearn.model_selection import train_test_split\n","\n","train_input, test_input, train_target, test_target = train_test_split(\n","    fish_input, fish_target, random_state=42)\n","\n","# data scaling\n","from sklearn.preprocessing import StandardScaler\n","\n","ss = StandardScaler()\n","ss.fit(train_input)\n","train_scaled = ss.transform(train_input)\n","test_scaled = ss.transform(test_input)"],"metadata":{"id":"vUaGxPeON4Rb"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# K-최근접 이웃의 다중분류 확률 예측\n","from sklearn.neighbors import KNeighborsClassifier\n","\n","kn = KNeighborsClassifier(n_neighbors=3) # 기본값은 5, 이번엔 3으로 설정\n","kn.fit(train_scaled, train_target)\n","\n","print(kn.classes_)\n","# class가 어떤식으로 나눠졌지 확인\n","\n","import numpy as np\n","\n","# 확률 출력 (predict_proba 메소드 사용)\n","proba = kn.predict_proba(test_scaled[:5])\n","print(np.round(proba, decimals=4))\n","\n","distances, indexes = kn.kneighbors(test_scaled[3:4])\n","print(train_target[indexes])"],"metadata":{"id":"5ZCyyrihN7sA"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 시그모이드 함수 만들기\n","import numpy as np\n","import matplotlib.pyplot as plt\n","\n","z = np.arange(-5, 5, 0.1)\n","phi = 1 / (1 + np.exp(-z))\n","\n","plt.plot(z, phi)\n","plt.xlabel('z')\n","plt.ylabel('phi')\n","plt.show()"],"metadata":{"id":"taWW1UNoOClC"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 로지스틱 회귀로 이진 분류\n","bream_smelt_indexes = (train_target == 'Bream') | (train_target == 'Smelt') # 불린 인덱싱\n","train_bream_smelt = train_scaled[bream_smelt_indexes]\n","target_bream_smelt = train_target[bream_smelt_indexes]\n","\n","from sklearn.linear_model import LogisticRegression\n","\n","# 객체 만들기\n","lr = LogisticRegression()\n","lr.fit(train_bream_smelt, target_bream_smelt)\n","\n","# 샘플 5개 (도미, 빙어)\n","print(lr.predict(train_bream_smelt[:5]))\n","# 샘플에 대한 확률\n","print(lr.predict_proba(train_bream_smelt[:5]))\n","\n","# 가중치\n","print(lr.coef_, lr.intercept_)\n","\n","# z값 계산\n","decisions = lr.decision_function(train_bream_smelt[:5])\n","print(decisions)\n","\n","# scipy에서 시그모이드함수 불러오기\n","from scipy.special import expit\n","print(expit(decisions))"],"metadata":{"id":"tWN1R-diOD7t"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 로지스틱 회귀로 다중 분류\n","lr = LogisticRegression(C=20, max_iter=1000)\n","lr.fit(train_scaled, train_target)\n","\n","# 정확도 예측\n","print(lr.score(train_scaled, train_target))\n","print(lr.score(test_scaled, test_target))\n","\n","# predict_proba 메소드로 확률 출력\n","proba = lr.predict_proba(test_scaled[:5])\n","print(np.round(proba, decimals=3))\n","\n","decision = lr.decision_function(test_scaled[:5])\n","print(np.round(decision, decimals=2))\n","\n","# 소프트맥스 함수\n","from scipy.special import softmax\n","\n","proba = softmax(decision, axis=1)\n","print(np.round(proba, decimals=3))"],"metadata":{"id":"w7q7UdRFOORO"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"FRzXkTPzPqlU"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"ftJkVWXTPqtU"},"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git "a/week3/[MLNovice]\355\231\251\354\247\200\354\233\220_week3-2.ipynb" "b/week3/[MLNovice]\355\231\251\354\247\200\354\233\220_week3-2.ipynb"
new file mode 100644
index 0000000..41f1719
--- /dev/null
+++ "b/week3/[MLNovice]\355\231\251\354\247\200\354\233\220_week3-2.ipynb"
@@ -0,0 +1 @@
+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyPp4SoE1DvNF6xZT3xKSAFg"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"k-tsR6BKTlHO"},"outputs":[],"source":["#SGD Classifier (확률적 경사 하강법 분류기)"]},{"cell_type":"code","source":["# 데이터 전처리\n","import pandas as pd\n","\n","fish = pd.read_csv('https://bit.ly/fish_csv_data')\n","\n","fish_input = fish[['Weight','Length','Diagonal','Height','Width']].to_numpy()\n","fish_target = fish['Species'].to_numpy()\n","\n","from sklearn.model_selection import train_test_split\n","\n","train_input, test_input, train_target, test_target = train_test_split(\n","    fish_input, fish_target, random_state=42)\n","\n","from sklearn.preprocessing import StandardScaler\n","\n","ss = StandardScaler()\n","ss.fit(train_input)\n","train_scaled = ss.transform(train_input)\n","test_scaled = ss.transform(test_input)"],"metadata":{"id":"bHmNSvnpTmxX"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 사이킷런에서 제공하는 SGD 모델\n","from sklearn.linear_model import SGDClassifier\n","\n","# 로지스틱 손실함수 지정\n","sc = SGDClassifier(loss='log_loss', max_iter=10, random_state=42)\n","sc.fit(train_scaled, train_target)\n","\n","# 정확도 출력\n","print(sc.score(train_scaled, train_target))\n","print(sc.score(test_scaled, test_target))\n","\n","# 이전에 훈련한 걸 다시 사용할지\n","sc.partial_fit(train_scaled, train_target)\n","\n","print(sc.score(train_scaled, train_target))\n","print(sc.score(test_scaled, test_target))"],"metadata":{"id":"HCBeluU2Tm2i"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 에포크와 과대/과소적합\n","\n","import numpy as np\n","\n","sc = SGDClassifier(loss='log_loss', random_state=42)\n","\n","train_score = []\n","test_score = []\n","\n","# partial_fit 메소드로 훈련\n","classes = np.unique(train_target)\n","for _ in range(0, 300):\n","    sc.partial_fit(train_scaled, train_target, classes=classes)\n","\n","    train_score.append(sc.score(train_scaled, train_target))\n","    test_score.append(sc.score(test_scaled, test_target))\n","\n","sc = SGDClassifier(loss='log_loss', max_iter=100, tol=None, random_state=42)\n","sc.fit(train_scaled, train_target)\n","\n","print(sc.score(train_scaled, train_target))\n","print(sc.score(test_scaled, test_target))"],"metadata":{"id":"GFBSr9OPe7Uq"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 훈련세트와 테스트셋의 성능 측정 그래프\n","import matplotlib.pyplot as plt\n","\n","plt.plot(train_score)\n","plt.plot(test_score)\n","plt.xlabel('epoch')\n","plt.ylabel('accuracy')\n","plt.show()"],"metadata":{"id":"fomxBb2IgKyN"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 소프트맥스 함수 사용할때는 손실함수에 'hinge'\n","sc = SGDClassifier(loss='hinge', max_iter=100, tol=None, random_state=42)\n","sc.fit(train_scaled, train_target)\n","\n","print(sc.score(train_scaled, train_target))\n","print(sc.score(test_scaled, test_target))"],"metadata":{"id":"5ZFYqBype7ho"},"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git "a/week4/[MLNovice]\355\231\251\354\247\200\354\233\220_week4-1.ipynb" "b/week4/[MLNovice]\355\231\251\354\247\200\354\233\220_week4-1.ipynb"
new file mode 100644
index 0000000..560728c
--- /dev/null
+++ "b/week4/[MLNovice]\355\231\251\354\247\200\354\233\220_week4-1.ipynb"
@@ -0,0 +1 @@
+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyNpugaSWMPn0ue9hSmkzVix"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"5QrsbuJIMTLD"},"outputs":[],"source":["# 데이터준비\n","import pandas as pd\n","\n","wine = pd.read_csv('https://bit.ly/wine_csv_data')\n","\n","# 누락된 데이터 값이 있는지 간단히 확인\n","wine.describe()\n","\n","data = wine[['alcohol', 'sugar', 'pH']].to_numpy()\n","target = wine['class'].to_numpy()"]},{"cell_type":"code","source":["# 데이터 스케일링\n","from sklearn.model_selection import train_test_split\n","\n","train_input, test_input, train_target, test_target = train_test_split(\n","    data, target, test_size=0.2, random_state=42)\n","\n","\n","from sklearn.preprocessing import StandardScaler\n","\n","ss = StandardScaler()\n","ss.fit(train_input)\n","\n","train_scaled = ss.transform(train_input)\n","test_scaled = ss.transform(test_input)"],"metadata":{"id":"z61hBwLES0oq"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 로지스틱 회귀\n","from sklearn.linear_model import LogisticRegression\n","\n","lr = LogisticRegression()\n","lr.fit(train_scaled, train_target)\n","\n","print(lr.score(train_scaled, train_target))\n","print(lr.score(test_scaled, test_target))\n","\n","print(lr.coef_, lr.intercept_)\n","# [[ 0.51268071  1.67335441 -0.68775646]] [1.81773456]"],"metadata":{"id":"gX6Q_8EES0rm"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 결정 트리\n","from sklearn.tree import DecisionTreeClassifier\n","\n","dt = DecisionTreeClassifier(random_state=42) # 사용할 특성을 랜덤하게 선택\n","dt.fit(train_scaled, train_target)\n","\n","# 트리 구조 보기\n","import matplotlib.pyplot as plt\n","from sklearn.tree import plot_tree\n","\n","plt.figure(figsize=(10,7))\n","plot_tree(dt)\n","plt.show()"],"metadata":{"id":"hq3oVCKYS0t4"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 트리 구조 간단히 보기\n","plt.figure(figsize=(10,7))\n","plot_tree(dt, max_depth=1, filled=True, feature_names=['alcohol', 'sugar', 'pH'])\n","plt.show()"],"metadata":{"id":"KR1woCnmS0wM"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 가지치기\n","dt = DecisionTreeClassifier(max_depth=3, random_state=42)\n","# max_depth로 트리의 깊이 조절\n","dt.fit(train_scaled, train_target)\n","\n","plt.figure(figsize=(20,15))\n","plot_tree(dt, filled=True, feature_names=['alcohol', 'sugar', 'pH'])\n","plt.show()\n","\n","dt = DecisionTreeClassifier(max_depth=3, random_state=42)\n","dt.fit(train_input, train_target)\n","\n","plt.figure(figsize=(20,15))\n","plot_tree(dt, filled=True, feature_names=['alcohol', 'sugar', 'pH'])\n","plt.show()"],"metadata":{"id":"Hn-P2w22S8YQ"},"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git "a/week4/[MLNovice]\355\231\251\354\247\200\354\233\220_week4-2.ipynb" "b/week4/[MLNovice]\355\231\251\354\247\200\354\233\220_week4-2.ipynb"
new file mode 100644
index 0000000..288a8a5
--- /dev/null
+++ "b/week4/[MLNovice]\355\231\251\354\247\200\354\233\220_week4-2.ipynb"
@@ -0,0 +1 @@
+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyNy8oz0bYMZiwhj1kBoh0/Z"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"nDI_gzaCTPjJ"},"outputs":[],"source":["# 검증 세트\n","\n","# 데이터\n","import pandas as pd\n","\n","wine = pd.read_csv('https://bit.ly/wine_csv_data')\n","data = wine[['alcohol', 'sugar', 'pH']].to_numpy()\n","target = wine['class'].to_numpy()\n","\n","# train, test 나누기\n","from sklearn.model_selection import train_test_split\n","\n","train_input, test_input, train_target, test_target = train_test_split(\n","    data, target, test_size=0.2, random_state=42)\n","\n","sub_input, val_input, sub_target, val_target = train_test_split(\n","    train_input, train_target, test_size=0.2, random_state=42)\n","\n","from sklearn.tree import DecisionTreeClassifier\n","\n","dt = DecisionTreeClassifier(random_state=42)\n","dt.fit(sub_input, sub_target)"]},{"cell_type":"code","source":["# 교차 검증\n","from sklearn.model_selection import cross_validate\n","\n","# 처음 나눈 세트로 교차검증\n","scores = cross_validate(dt, train_input, train_target)\n","\n","# 검증의 점수 평균 내보기\n","import numpy as np\n","print(np.mean(scores['test_score']))"],"metadata":{"id":"-n5UDAQ7bGsQ"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 분할기를 사용한 교차 검증\n","from sklearn.model_selection import StratifiedKFold\n","\n","scores = cross_validate(dt, train_input, train_target, cv=StratifiedKFold())\n","\n","\n","splitter = StratifiedKFold(n_splits=10, shuffle=True, random_state=42)\n","scores = cross_validate(dt, train_input, train_target, cv=splitter"],"metadata":{"id":"FEygEuZsbGv0"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 하이퍼 파라미터 튜닝\n","from sklearn.model_selection import GridSearchCV\n","\n","# 매개변수를 딕셔너리에 정의\n","params = {'min_impurity_decrease': [0.0001, 0.0002, 0.0003, 0.0004, 0.0005]}\n","\n","gs = GridSearchCV(DecisionTreeClassifier(random_state=42), params, n_jobs=-1)\n","gs.fit(train_input, train_target)\n","\n","# 최적의 값을 찾아서 best_estimator_에 넣어줌\n","dt = gs.best_estimator_\n","print(dt.score(train_input, train_target))\n","\n","print(gs.best_params_)\n","print(gs.cv_results_['mean_test_score'])"],"metadata":{"id":"5pD2hPx0hZt6"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 확률 분포 선택\n","from scipy.stats import uniform, randint  # 균등 분포 샘플링\n","\n","rgen = randint(0, 10)\n","rgen.rvs(10)\n","\n","np.unique(rgen.rvs(1000), return_counts=True)\n","\n","ugen = uniform(0, 1) # 실수값을 샘플링\n","ugen.rvs(10)"],"metadata":{"id":"yX1FcDMzhZwq"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 랜덤 서치\n","params = {'min_impurity_decrease': uniform(0.0001, 0.001),\n","          'max_depth': randint(20, 50),\n","          'min_samples_split': randint(2, 25),\n","          'min_samples_leaf': randint(1, 25),\n","          }\n","\n","from sklearn.model_selection import RandomizedSearchCV # 랜덤 서치 함수\n","\n","gs = RandomizedSearchCV(DecisionTreeClassifier(random_state=42), params,\n","                      n_iter=100, n_jobs=-1, random_state=42)\n","# n_iter : 모델 개수\n","\n","gs.fit(train_input, train_target)m sklearn.model_selection import RandomizedSearchCV\n","\n","gs = RandomizedSearchCV(DecisionTreeClassifier(random_state=42), params,\n","                        n_iter=100, n_jobs=-1, random_state=42)\n","gs.fit(train_input, train_target)"],"metadata":{"id":"QJxx6uh2bGyu"},"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git "a/week4/[MLNovice]\355\231\251\354\247\200\354\233\220_week4-3.ipynb" "b/week4/[MLNovice]\355\231\251\354\247\200\354\233\220_week4-3.ipynb"
new file mode 100644
index 0000000..bc22488
--- /dev/null
+++ "b/week4/[MLNovice]\355\231\251\354\247\200\354\233\220_week4-3.ipynb"
@@ -0,0 +1 @@
+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyM4+2lCH8tm12reo9hz8z2m"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"g-eXJt1LbCY5"},"outputs":[],"source":["# 랜덤 포레스트\n","\n","# 데이터 불러오기\n","import numpy as np\n","import pandas as pd\n","from sklearn.model_selection import train_test_split\n","\n","wine = pd.read_csv('https://bit.ly/wine_csv_data')\n","\n","data = wine[['alcohol', 'sugar', 'pH']].to_numpy()\n","target = wine['class'].to_numpy()\n","\n","train_input, test_input, train_target, test_target = train_test_split(data, target, test_size=0.2, random_state=42)\n","\n","# 앙상블에서 랜덤포레스트 류기\n","from sklearn.model_selection import cross_validate\n","from sklearn.ensemble import RandomForestClassifier\n","\n","rf = RandomForestClassifier(n_jobs=-1, random_state=42)\n","scores = cross_validate(rf, train_input, train_target, return_train_score=True, n_jobs=-1)\n","# 훈련세트, 검증세트 점수 확인\n","print(np.mean(scores['train_score']), np.mean(scores['test_score']))\n","\n","# OOB 샘플 : 남는 샘플데이터를 활용하여 검증\n","rf.fit(train_input, train_target)\n","rf = RandomForestClassifier(oob_score=True, n_jobs=-1, random_state=42)\n","rf.fit(train_input, train_target)"]},{"cell_type":"code","source":["# 엑스트라 트리\n","from sklearn.ensemble import ExtraTreesClassifier\n","\n","et = ExtraTreesClassifier(n_jobs=-1, random_state=42)\n","scores = cross_validate(et, train_input, train_target, return_train_score=True, n_jobs=-1)\n","\n","print(np.mean(scores['train_score']), np.mean(scores['test_score']))\n","\n","et.fit(train_input, train_target)\n","print(et.feature_importances_)"],"metadata":{"id":"gcS6Ifvbqmq5"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 그래디언트 부스팅\n","from sklearn.ensemble import GradientBoostingClassifier\n","\n","gb = GradientBoostingClassifier(random_state=42)\n","scores = cross_validate(gb, train_input, train_target, return_train_score=True, n_jobs=-1)\n","\n","# 트리 개수 늘리기 (500개로 늘림)\n","gb = GradientBoostingClassifier(n_estimators=500, learning_rate=0.2, random_state=42)\n","scores = cross_validate(gb, train_input, train_target, return_train_score=True, n_jobs=-1)\n","\n","gb.fit(train_input, train_target)"],"metadata":{"id":"XuvMPwC9qmtZ"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 히스토그램 기반 부스팅\n","from sklearn.ensemble import HistGradientBoostingClassifier\n","\n","hgb = HistGradientBoostingClassifier(random_state=42)\n","scores = cross_validate(hgb, train_input, train_target, return_train_score=True, n_jobs=-1)\n","\n","from sklearn.inspection import permutation_importance\n","\n","hgb.fit(train_input, train_target)\n","result = permutation_importance(hgb, train_input, train_target, n_repeats=10,random_state=42, n_jobs=-1)\n","\n","result = permutation_importance(hgb, test_input, test_target, n_repeats=10, random_state=42, n_jobs=-1)\n","hgb.score(test_input, test_target)"],"metadata":{"id":"qmHHje1UqqOL"},"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/week4/ch5_TreeAlgorithm b/week4/ch5_TreeAlgorithm
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/week4/ch5_TreeAlgorithm
@@ -0,0 +1 @@
+
diff --git a/week5/Ch6 Clustering b/week5/Ch6 Clustering
new file mode 100644
index 0000000..11dcf5d
--- /dev/null
+++ b/week5/Ch6 Clustering	
@@ -0,0 +1 @@
+Ch6 Clustering
diff --git "a/week5/[MLNovice]\355\231\251\354\247\200\354\233\220_week5.ipynb" "b/week5/[MLNovice]\355\231\251\354\247\200\354\233\220_week5.ipynb"
new file mode 100644
index 0000000..b506c42
--- /dev/null
+++ "b/week5/[MLNovice]\355\231\251\354\247\200\354\233\220_week5.ipynb"
@@ -0,0 +1 @@
+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyMscckwFuxfW+EJASgZe3lf"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","source":["# 군집 알고리즘"],"metadata":{"id":"s60iWWQdJjWH"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"TikC71eZ2kFf"},"outputs":[],"source":["# 과일 데이터 준비\n","\n","# numpy 파일로 준비, 코랩에 데이터 파일로 불러오기\n","!wget https://bit.ly/fruits_300_data -O fruits_300.npy\n","\n","import numpy as np\n","import matplotlib.pyplot as plt\n","\n","fruits = np.load('fruits_300.npy')\n","\n","print(fruits.shape) #총 300개, 100x100 크기의 데이터\n","\n","# numpy 배열의 데이터를 이미지로 보여줌\n","plt.imshow(fruits[0], cmap='gray')\n","plt.show()\n","\n","# 색상 반전을 하여 보기 좋게 만들기\n","plt.imshow(fruits[0], cmap='gray_r')\n","plt.show()\n","\n","fig, axs = plt.subplots(1, 2)\n","axs[0].imshow(fruits[100], cmap='gray_r')  # 파인애플\n","axs[1].imshow(fruits[200], cmap='gray_r')  # 바나나\n","plt.show()"]},{"cell_type":"code","source":["# 샘플 차원 변경하기 (픽셀을 100x100 1차원 배열에 넣기)\n","apple = fruits[0:100].reshape(-1, 100*100)\n","pineapple = fruits[100:200].reshape(-1, 100*100)\n","banana = fruits[200:300].reshape(-1, 100*100)\n","\n","# 샘플 평균의 히스토그램\n","plt.hist(np.mean(apple, axis=1), alpha=0.8)\n","plt.hist(np.mean(pineapple, axis=1), alpha=0.8)\n","plt.hist(np.mean(banana, axis=1), alpha=0.8)\n","plt.legend(['apple', 'pineapple', 'banana'])\n","plt.show()\n","\n","# 픽셀 평균의 히스토그램\n","fig, axs = plt.subplots(1, 3, figsize=(20, 5))\n","axs[0].bar(range(10000), np.mean(apple, axis=0))\n","axs[1].bar(range(10000), np.mean(pineapple, axis=0))\n","axs[2].bar(range(10000), np.mean(banana, axis=0))\n","plt.show()\n","\n","# 평균 이미지 그리기\n","apple_mean = np.mean(apple, axis=0).reshape(100, 100)\n","pineapple_mean = np.mean(pineapple, axis=0).reshape(100, 100)\n","banana_mean = np.mean(banana, axis=0).reshape(100, 100)\n","\n","fig, axs = plt.subplots(1, 3, figsize=(20, 5))\n","axs[0].imshow(apple_mean, cmap='gray_r')\n","axs[1].imshow(pineapple_mean, cmap='gray_r')\n","axs[2].imshow(banana_mean, cmap='gray_r')\n","plt.show()"],"metadata":{"id":"VQyFRn5pJjRM"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 평균값과 가까운 사진 고르기\n","abs_diff = np.abs(fruits - apple_mean)\n","bs_mean = np.mean(abs_diff, axis=(1,2))\n","\n","apple_index = np.argsort(abs_mean)[:100]\n","fig, axs = plt.subplots(10, 10, figsize=(10,10))\n","for i in range(10):\n","    for j in range(10):\n","        axs[i, j].imshow(fruits[apple_index[i*10 + j]], cmap='gray_r')\n","        axs[i, j].axis('off')\n","plt.show()"],"metadata":{"id":"rJrdVi_AJjUA"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# K-평균"],"metadata":{"id":"-ed-hq3jKAnr"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 데이터 준비\n","!wget https://bit.ly/fruits_300_data -O fruits_300.npy\n","\n","import numpy as np\n","\n","fruits = np.load('fruits_300.npy')\n","fruits_2d = fruits.reshape(-1, 100*100)"],"metadata":{"id":"elmjftpzKL2a"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 모델 훈련\n","from sklearn.cluster import KMeans\n","\n","km = KMeans(n_clusters=3, random_state=42)\n","km.fit(fruits_2d)  # 데이터를 2차원 배열로 변경\n","\n","# 잘 분류가 되었는지 확인해보기 (데이터 수가 적어서 가능)\n","print(np.unique(km.labels_, return_counts=True))"],"metadata":{"id":"e98fIQq2KL4w"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 첫번째 클러스터\n","\n","import matplotlib.pyplot as plt\n","\n","def draw_fruits(arr, ratio=1):\n","    n = len(arr)    # n은 샘플 개수\n","    # 한 줄에 10개씩 이미지를 그립니다. 샘플 개수를 10으로 나누어 전체 행 개수를 계산합니다.\n","    rows = int(np.ceil(n/10))\n","    # 행이 1개 이면 열 개수는 샘플 개수입니다. 그렇지 않으면 10개입니다.\n","    cols = n if rows < 2 else 10\n","    fig, axs = plt.subplots(rows, cols,\n","                            figsize=(cols*ratio, rows*ratio), squeeze=False)\n","    for i in range(rows):\n","        for j in range(cols):\n","            if i*10 + j < n:    # n 개까지만 그립니다.\n","                axs[i, j].imshow(arr[i*10 + j], cmap='gray_r')\n","            axs[i, j].axis('off')\n","    plt.show()\n","\n","    draw_fruits(fruits[km.labels_==0])"],"metadata":{"id":"yH-WWn51KL7i"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 클러스터 중심\n","draw_fruits(km.cluster_centers_.reshape(-1, 100, 100), ratio=3)"],"metadata":{"id":"qRcRHRNHKL-I"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 최적의 K 찾기\n","inertia = []\n","for k in range(2, 7):\n","    km = KMeans(n_clusters=k, n_init='auto', random_state=42)\n","    km.fit(fruits_2d)\n","    inertia.append(km.inertia_)\n","\n","plt.plot(range(2, 7), inertia)\n","plt.xlabel('k')\n","plt.ylabel('inertia')\n","plt.show()"],"metadata":{"id":"Ii4uhzJVYMPJ"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 주성분 분석"],"metadata":{"id":"FUoTpNERYMVe"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 데이터 불러오기\n","!wget https://bit.ly/fruits_300_data -O fruits_300.npy\n","\n","import numpy as np\n","\n","fruits = np.load('fruits_300.npy')\n","fruits_2d = fruits.reshape(-1, 100*100)"],"metadata":{"id":"P6M9z9_mYRt4"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# PCA\n","from sklearn.decomposition import PCA\n","\n","pca = PCA(n_components=50)\n","pca.fit(fruits_2d)\n","\n","import matplotlib.pyplot as plt\n","\n","def draw_fruits(arr, ratio=1):\n","    n = len(arr)    # n은 샘플 개수입니다\n","    # 한 줄에 10개씩 이미지를 그립니다. 샘플 개수를 10으로 나누어 전체 행 개수를 계산합니다.\n","    rows = int(np.ceil(n/10))\n","    # 행이 1개 이면 열 개수는 샘플 개수입니다. 그렇지 않으면 10개입니다.\n","    cols = n if rows < 2 else 10\n","    fig, axs = plt.subplots(rows, cols,\n","                            figsize=(cols*ratio, rows*ratio), squeeze=False)\n","    for i in range(rows):\n","        for j in range(cols):\n","            if i*10 + j < n:    # n 개까지만 그립니다.\n","                axs[i, j].imshow(arr[i*10 + j], cmap='gray_r')\n","            axs[i, j].axis('off')\n","    plt.show()\n","\n","draw_fruits(pca.components_.reshape(-1, 100, 100))"],"metadata":{"id":"sWZd0aucYRoP"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 뽑은 주성분으로 원본 데이터 복원\n","\n","fruits_inverse = pca.inverse_transform(fruits_pca)\n","print(fruits_inverse.shape)\n","\n","fruits_reconstruct = fruits_inverse.reshape(-1, 100, 100)\n","\n","for start in [0, 100, 200]:\n","    draw_fruits(fruits_reconstruct[start:start+100])\n","    print(\"\\n\")"],"metadata":{"id":"bjvZuPo4fOFh"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 설명된 분산\n","print(np.sum(pca.explained_variance_ratio_))\n","\n","plt.plot(pca.explained_variance_ratio_)"],"metadata":{"id":"Uf0x2rjIfPud"},"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/week7/Ch8 Convolution b/week7/Ch8 Convolution
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/week7/Ch8 Convolution	
@@ -0,0 +1 @@
+
diff --git "a/week7/[MLNovice]\355\231\251\354\247\200\354\233\220_week7.ipynb" "b/week7/[MLNovice]\355\231\251\354\247\200\354\233\220_week7.ipynb"
new file mode 100644
index 0000000..0bba6e6
--- /dev/null
+++ "b/week7/[MLNovice]\355\231\251\354\247\200\354\233\220_week7.ipynb"
@@ -0,0 +1 @@
+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyPQbvi+FLkxdQa8dUROlnkk"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"usXAq7K9w_Go"},"outputs":[],"source":["# 합성곱 신경망을 사용한 이미지 분류"]},{"cell_type":"code","source":["# 패션 MNIST 데이터 불러오기\n","from tensorflow import keras\n","from sklearn.model_selection import train_test_split\n","\n","(train_input, train_target), (test_input, test_target) = \\\n","    keras.datasets.fashion_mnist.load_data()\n","\n","train_scaled = train_input.reshape(-1, 28, 28, 1) / 255.0  # 마지막 차원만 늘\n","\n","train_scaled, val_scaled, train_target, val_target = train_test_split(\n","    train_scaled, train_target, test_size=0.2, random_state=42)"],"metadata":{"id":"yEqM-KJDxHMT"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 합성곱 신경망 만들기\n","model = keras.Sequential()\n","\n","# 첫번째 층 (필터 사이즈, 활성함수, 패딩)\n","model.add(keras.layers.Conv2D(32, kernel_size=3, activation='relu',padding='same', input_shape=(28,28,1)))\n","# 풀링 레이어 추가\n","model.add(keras.layers.MaxPooling2D(2))\n","\n","model.add(keras.layers.Conv2D(64, kernel_size=(3,3), activation='relu',padding='same'))\n","model.add(keras.layers.MaxPooling2D(2))\n","\n","model.add(keras.layers.Flatten())\n","model.add(keras.layers.Dense(100, activation='relu'))\n","model.add(keras.layers.Dropout(0.4))\n","model.add(keras.layers.Dense(10, activation='softmax'))\n","\n","model.summary()"],"metadata":{"id":"BsPngWqCxHR5"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 컴파일과 훈련\n","model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n","\n","checkpoint_cb = keras.callbacks.ModelCheckpoint('best-model.keras', save_best_only=True)\n","early_stopping_cb = keras.callbacks.EarlyStopping(patience=2, restore_best_weights=True)\n","\n","history = model.fit(train_scaled, train_target, epochs=20, verbose=0,\n","                    validation_data=(val_scaled, val_target),\n","                    callbacks=[checkpoint_cb, early_stopping_cb])"],"metadata":{"id":"DHYHpWrNI2F8"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 합성곱 신경망  시각화"],"metadata":{"id":"qwXQZqzyI2IK"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"XCgVeRnYxHVe"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"gxv2VKQMI9XQ"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"l078ZGmFI9Zu"},"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/week8/Ch9 RNN b/week8/Ch9 RNN
new file mode 100644
index 0000000..e6224a1
--- /dev/null
+++ b/week8/Ch9 RNN	
@@ -0,0 +1 @@
+RNN
diff --git "a/week8/[MLNovice]\355\231\251\354\247\200\354\233\220_week8.ipynb" "b/week8/[MLNovice]\355\231\251\354\247\200\354\233\220_week8.ipynb"
new file mode 100644
index 0000000..2f73ed5
--- /dev/null
+++ "b/week8/[MLNovice]\355\231\251\354\247\200\354\233\220_week8.ipynb"
@@ -0,0 +1 @@
+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyMrIgdGhNXr0VrXkQp6YpAN"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"3aYY73BdpXJE"},"outputs":[],"source":["# 순환 신경망으로 IMDB 리뷰 분류하기"]},{"cell_type":"code","source":["# IMDB 리뷰 데이터셋 불러오기\n","from tensorflow.keras.datasets import imdb\n","(train_input, train_target), (test_input, test_target) = imdb.load_data(num_words=200)\n","\n","# train, test set 설정\n","from sklearn.model_selection import train_test_split\n","train_input, val_input, train_target, val_target = train_test_split(train_input, train_target, test_size=0.2, random_state=42)\n","\n","# 리뷰 길이 시각화\n","import matplotlib.pyplot as plt\n","\n","plt.hist(lengths)\n","plt.xlabel('length')\n","plt.ylabel('frequency')\n","plt.show()"],"metadata":{"id":"skd11nHjpZqi"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 시퀀스 패딩\n","from tensorflow.keras.preprocessing.sequence import pad_sequences\n","train_seq = pad_sequences(train_input, maxlen=100)\n","# 100보다 긴 리뷰는 자르고, 100보다 짧은 리뷰는 100으로 패딩"],"metadata":{"id":"h8bPpFaspZso"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 순환 신경망 만들기\n","from tensorflow import keras\n","\n","model = keras.Sequential()\n","\n","model.add(keras.layers.SimpleRNN(8, input_shape=(100, 200)))\n","model.add(keras.layers.Dense(1, activation='sigmoid'))\n","\n","train_oh = keras.utils.to_categorical(train_seq)\n","\n","val_oh = keras.utils.to_categorical(val_seq)\n","model.summary()"],"metadata":{"id":"07wvb3FV2Vuq"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 모델 훈련\n","rmsprop = keras.optimizers.RMSprop(learning_rate=1e-4)\n","model.compile(optimizer=rmsprop, loss='binary_crossentropy', metrics=['accuracy'])\n","\n","checkpoint_cb = keras.callbacks.ModelCheckpoint('best-simplernn-model.keras', save_best_only=True)\n","early_stopping_cb = keras.callbacks.EarlyStopping(patience=3, restore_best_weights=True)\n","\n","history = model.fit(train_oh, train_target, epochs=100, batch_size=64, validation_data=(val_oh, val_target),\n","callbacks=[checkpoint_cb, early_stopping_cb])\n","\n","# 성능 평가\n","plt.plot(history.history['loss'])\n","plt.plot(history.history['val_loss'])\n","plt.xlabel('epoch')\n","plt.ylabel('loss')\n","plt.legend(['train', 'val'])\n","plt.show()"],"metadata":{"id":"NFxdY1NO2VxQ"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 임베딩 사용 모델\n","model2 = keras.Sequential()\n","\n","model2.add(keras.layers.Embedding(200, 16, input_shape=(100,)))\n","model2.add(keras.layers.SimpleRNN(8))\n","model2.add(keras.layers.Dense(1, activation='sigmoid'))\n","\n","model2.summary()"],"metadata":{"id":"QfqCQ9SW2Vzw"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 모델 훈련\n","rmsprop = keras.optimizers.RMSprop(learning_rate=1e-4)\n","model2.compile(optimizer=rmsprop, loss='binary_crossentropy',\n","               metrics=['accuracy'])\n","\n","checkpoint_cb = keras.callbacks.ModelCheckpoint('best-embedding-model.keras', save_best_only=True)\n","early_stopping_cb = keras.callbacks.EarlyStopping(patience=3, restore_best_weights=True)\n","\n","history = model2.fit(train_seq, train_target, epochs=100, batch_size=64, validation_data=(val_seq, val_target),\n","callbacks=[checkpoint_cb, early_stopping_cb])\n","\n","plt.plot(history.history['loss'])\n","plt.plot(history.history['val_loss'])\n","plt.xlabel('epoch')\n","plt.ylabel('loss')\n","plt.legend(['train', 'val'])\n","plt.show()"],"metadata":{"id":"xB1NEr8RpZu7"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# LSTM과 GRU 셀"],"metadata":{"id":"NJJ1DYp72gN_"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# LSTM 신경망 훈련하기\n","from tensorflow.keras.datasets import imdb\n","from sklearn.model_selection import train_test_split\n","\n","(train_input, train_target), (test_input, test_target) = imdb.load_data(num_words=500)\n","train_input, val_input, train_target, val_target = train_test_split(train_input, train_target, test_size=0.2, random_state=42)\n","\n","\n","from tensorflow.keras.preprocessing.sequence import pad_sequences\n","\n","train_seq = pad_sequences(train_input, maxlen=100)\n","val_seq = pad_sequences(val_input, maxlen=100)"],"metadata":{"id":"3VeHwT612gQ1"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 모델 만들기 (keras에서 RNN을 LSTM으로 바꾸면 됨)\n","from tensorflow import keras\n","\n","model = keras.Sequential()\n","\n","model.add(keras.layers.Embedding(500, 16, input_shape=(100,)))\n","model.add(keras.layers.LSTM(8))\n","model.add(keras.layers.Dense(1, activation='sigmoid'))\n","\n","model.summary()"],"metadata":{"id":"P87IZIAs3j2V"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 모델 훈련\n","rmsprop = keras.optimizers.RMSprop(learning_rate=1e-4)\n","model.compile(optimizer=rmsprop, loss='binary_crossentropy', metrics=['accuracy'])\n","\n","checkpoint_cb = keras.callbacks.ModelCheckpoint('best-lstm-model.keras', save_best_only=True)\n","early_stopping_cb = keras.callbacks.EarlyStopping(patience=3, restore_best_weights=True)\n","\n","history = model.fit(train_seq, train_target, epochs=100, batch_size=64,\n","                    validation_data=(val_seq, val_target),\n","                    callbacks=[checkpoint_cb, early_stopping_cb])"],"metadata":{"id":"PsMwgtkL6QVM"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 성능 평가\n","import matplotlib.pyplot as plt\n","\n","plt.plot(history.history['loss'])\n","plt.plot(history.history['val_loss'])\n","plt.xlabel('epoch')\n","plt.ylabel('loss')\n","plt.legend(['train', 'val'])\n","plt.show()"],"metadata":{"id":"7JgudiBv3j5R"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 순환 층에 드롭아웃 적용하기\n","model2 = keras.Sequential()\n","\n","model2.add(keras.layers.Embedding(500, 16, input_shape=(100,)))\n","model2.add(keras.layers.LSTM(8, dropout=0.3))\n","model2.add(keras.layers.Dense(1, activation='sigmoid'))"],"metadata":{"id":"FjAaqHXp6TgE"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 2개의 층을 연결하기\n","model3 = keras.Sequential()\n","\n","model3.add(keras.layers.Embedding(500, 16, input_shape=(100,)))\n","model3.add(keras.layers.LSTM(8, dropout=0.3, return_sequences=True)) # return_sequences=True 기억하기\n","model3.add(keras.layers.LSTM(8, dropout=0.3))\n","model3.add(keras.layers.Dense(1, activation='sigmoid'))\n","\n","model3.summary()"],"metadata":{"id":"HgpUGpro6Tiz"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# GRU 신경망 훈련하기\n","model4 = keras.Sequential()\n","\n","model4.add(keras.layers.Embedding(500, 16, input_shape=(100,)))\n","model4.add(keras.layers.GRU(8))\n","model4.add(keras.layers.Dense(1, activation='sigmoid'))\n","\n","model4.summary()"],"metadata":{"id":"56W6-nEx6X4r"},"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/week9/CIFAR-10 - Object Recognition in Images b/week9/CIFAR-10 - Object Recognition in Images
new file mode 100644
index 0000000..7e2e2e5
--- /dev/null
+++ b/week9/CIFAR-10 - Object Recognition in Images	
@@ -0,0 +1 @@
+CIFAR-10 - Object Recognition in Images
diff --git "a/week9/[MLNovice]\355\231\251\354\247\200\354\233\220_week9.ipynb" "b/week9/[MLNovice]\355\231\251\354\247\200\354\233\220_week9.ipynb"
new file mode 100644
index 0000000..ca0f0be
--- /dev/null
+++ "b/week9/[MLNovice]\355\231\251\354\247\200\354\233\220_week9.ipynb"
@@ -0,0 +1 @@
+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"gpuType":"T4","authorship_tag":"ABX9TyPCfux6edvQfIJjc2rYVezW"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"code","execution_count":1,"metadata":{"id":"Dg_KLBMMxDPB","executionInfo":{"status":"ok","timestamp":1733275384623,"user_tz":-540,"elapsed":3518,"user":{"displayName":"황지원","userId":"01099698570160424720"}}},"outputs":[],"source":["import keras\n","from keras.datasets import cifar10\n","#from keras.preprocessing.image import ImageDataGenerator\n","from keras.models import Sequential\n","from keras.layers import Dense, Dropout, Activation, Flatten\n","from keras.layers import Conv2D, MaxPooling2D\n","\n","import numpy as np\n","\n","import seaborn as sns\n","import matplotlib\n","import matplotlib.pyplot as plt\n","\n","from sklearn.metrics import confusion_matrix, classification_report\n","import itertools"]},{"cell_type":"code","source":["batch_size = 32  # The default batch size of keras.\n","num_classes = 10  # Number of class for the dataset\n","epochs = 20\n","data_augmentation = False"],"metadata":{"id":"HMlthHjvxE3N","executionInfo":{"status":"ok","timestamp":1733275390231,"user_tz":-540,"elapsed":294,"user":{"displayName":"황지원","userId":"01099698570160424720"}}},"execution_count":2,"outputs":[]},{"cell_type":"code","source":["# The data, split between train and test sets:\n","(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n","print('x_train shape:', x_train.shape)\n","print('y_train shape:', y_train.shape)\n","print(x_train.shape[0], 'train samples')\n","print(x_test.shape[0], 'test samples')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_Sfr5RTNxE7Z","executionInfo":{"status":"ok","timestamp":1733275393261,"user_tz":-540,"elapsed":1520,"user":{"displayName":"황지원","userId":"01099698570160424720"}},"outputId":"cc75bfa2-e76b-4ee4-9189-00a9fe3ec759"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["x_train shape: (50000, 32, 32, 3)\n","y_train shape: (50000, 1)\n","50000 train samples\n","10000 test samples\n"]}]},{"cell_type":"code","source":["# Normalize the data. Before we need to connvert data type to float for computation.\n","x_train = x_train.astype('float32')\n","x_test = x_test.astype('float32')\n","x_train /= 255\n","x_test /= 255\n","\n","# Convert class vectors to binary class matrices. This is called one hot encoding.\n","y_train = keras.utils.to_categorical(y_train, num_classes)\n","y_test = keras.utils.to_categorical(y_test, num_classes)"],"metadata":{"id":"ndjfxvyPxE-A","executionInfo":{"status":"ok","timestamp":1733275394555,"user_tz":-540,"elapsed":285,"user":{"displayName":"황지원","userId":"01099698570160424720"}}},"execution_count":4,"outputs":[]},{"cell_type":"code","source":["#define the convnet\n","model = Sequential()\n","# CONV => RELU => CONV => RELU => POOL => DROPOUT\n","model.add(Conv2D(32, (3, 3), padding='same',input_shape=x_train.shape[1:]))\n","model.add(Activation('relu'))\n","model.add(Conv2D(32, (3, 3)))\n","model.add(Activation('relu'))\n","model.add(MaxPooling2D(pool_size=(2, 2)))\n","model.add(Dropout(0.25))\n","\n","# CONV => RELU => CONV => RELU => POOL => DROPOUT\n","model.add(Conv2D(64, (3, 3), padding='same'))\n","model.add(Activation('relu'))\n","model.add(Conv2D(64, (3, 3)))\n","model.add(Activation('relu'))\n","model.add(MaxPooling2D(pool_size=(2, 2)))\n","model.add(Dropout(0.25))\n","\n","# FLATTERN => DENSE => RELU => DROPOUT\n","model.add(Flatten())\n","model.add(Dense(512))\n","model.add(Activation('relu'))\n","model.add(Dropout(0.5))\n","# a softmax classifier\n","model.add(Dense(num_classes))\n","model.add(Activation('softmax'))\n","\n","model.summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":805},"id":"HTgGR2ylOk3k","executionInfo":{"status":"ok","timestamp":1733275398029,"user_tz":-540,"elapsed":1926,"user":{"displayName":"황지원","userId":"01099698570160424720"}},"outputId":"96d25dcf-72f3-4099-ed6a-9b1d985b3fbb"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/keras/src/layers/convolutional/base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n","  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"display_data","data":{"text/plain":["\u001b[1mModel: \"sequential\"\u001b[0m\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n","┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n","│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │             \u001b[38;5;34m896\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ activation (\u001b[38;5;33mActivation\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │           \u001b[38;5;34m9,248\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ activation_1 (\u001b[38;5;33mActivation\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dropout (\u001b[38;5;33mDropout\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │          \u001b[38;5;34m18,496\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ activation_2 (\u001b[38;5;33mActivation\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ conv2d_3 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │          \u001b[38;5;34m36,928\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ activation_3 (\u001b[38;5;33mActivation\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m64\u001b[0m)            │               \u001b[38;5;34m0\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m64\u001b[0m)            │               \u001b[38;5;34m0\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ flatten (\u001b[38;5;33mFlatten\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2304\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dense (\u001b[38;5;33mDense\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)                 │       \u001b[38;5;34m1,180,160\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ activation_4 (\u001b[38;5;33mActivation\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)                  │           \u001b[38;5;34m5,130\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ activation_5 (\u001b[38;5;33mActivation\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)                  │               \u001b[38;5;34m0\u001b[0m │\n","└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n","┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n","│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">896</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ activation (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │           <span style=\"color: #00af00; text-decoration-color: #00af00\">9,248</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ activation_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ max_pooling2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">15</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">15</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">15</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">15</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ conv2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">15</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">15</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │          <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ activation_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">15</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">15</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ conv2d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │          <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ activation_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ max_pooling2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)            │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dropout_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)            │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2304</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)                 │       <span style=\"color: #00af00; text-decoration-color: #00af00\">1,180,160</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ activation_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dropout_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)                  │           <span style=\"color: #00af00; text-decoration-color: #00af00\">5,130</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ activation_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)                  │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Total params: \u001b[0m\u001b[38;5;34m1,250,858\u001b[0m (4.77 MB)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,250,858</span> (4.77 MB)\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m1,250,858\u001b[0m (4.77 MB)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,250,858</span> (4.77 MB)\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n","</pre>\n"]},"metadata":{}}]},{"cell_type":"code","source":["from tensorflow.keras.optimizers import Adam\n","\n","model.compile(optimizer=Adam(learning_rate=0.001),\n","              loss='categorical_crossentropy',\n","              metrics=['accuracy'])"],"metadata":{"id":"Ouh0oAs2Ok5_","executionInfo":{"status":"ok","timestamp":1733275400369,"user_tz":-540,"elapsed":264,"user":{"displayName":"황지원","userId":"01099698570160424720"}}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":["history = model.fit(x_train, y_train,\n","              batch_size=batch_size,\n","              epochs=epochs,\n","              validation_data=(x_test, y_test),\n","              shuffle=True)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"tFcSRLjwP7Md","executionInfo":{"status":"ok","timestamp":1733275583714,"user_tz":-540,"elapsed":181586,"user":{"displayName":"황지원","userId":"01099698570160424720"}},"outputId":"3cb0b8dc-86cd-4342-831f-aea6880cda3e"},"execution_count":7,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/20\n","\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 8ms/step - accuracy: 0.3391 - loss: 1.7770 - val_accuracy: 0.5835 - val_loss: 1.1544\n","Epoch 2/20\n","\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - accuracy: 0.5852 - loss: 1.1565 - val_accuracy: 0.6666 - val_loss: 0.9254\n","Epoch 3/20\n","\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.6601 - loss: 0.9585 - val_accuracy: 0.6949 - val_loss: 0.8740\n","Epoch 4/20\n","\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.6969 - loss: 0.8537 - val_accuracy: 0.7167 - val_loss: 0.8012\n","Epoch 5/20\n","\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.7166 - loss: 0.8056 - val_accuracy: 0.7430 - val_loss: 0.7391\n","Epoch 6/20\n","\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.7375 - loss: 0.7515 - val_accuracy: 0.7589 - val_loss: 0.6972\n","Epoch 7/20\n","\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 4ms/step - accuracy: 0.7506 - loss: 0.7052 - val_accuracy: 0.7678 - val_loss: 0.6748\n","Epoch 8/20\n","\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 4ms/step - accuracy: 0.7622 - loss: 0.6690 - val_accuracy: 0.7476 - val_loss: 0.7262\n","Epoch 9/20\n","\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - accuracy: 0.7727 - loss: 0.6430 - val_accuracy: 0.7708 - val_loss: 0.6646\n","Epoch 10/20\n","\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 4ms/step - accuracy: 0.7856 - loss: 0.6121 - val_accuracy: 0.7751 - val_loss: 0.6579\n","Epoch 11/20\n","\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.7913 - loss: 0.5902 - val_accuracy: 0.7688 - val_loss: 0.6792\n","Epoch 12/20\n","\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.7928 - loss: 0.5859 - val_accuracy: 0.7846 - val_loss: 0.6281\n","Epoch 13/20\n","\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.8015 - loss: 0.5587 - val_accuracy: 0.7875 - val_loss: 0.6388\n","Epoch 14/20\n","\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 4ms/step - accuracy: 0.8054 - loss: 0.5511 - val_accuracy: 0.7831 - val_loss: 0.6376\n","Epoch 15/20\n","\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 4ms/step - accuracy: 0.8108 - loss: 0.5380 - val_accuracy: 0.7855 - val_loss: 0.6367\n","Epoch 16/20\n","\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 4ms/step - accuracy: 0.8147 - loss: 0.5268 - val_accuracy: 0.7903 - val_loss: 0.6240\n","Epoch 17/20\n","\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 4ms/step - accuracy: 0.8188 - loss: 0.5127 - val_accuracy: 0.7909 - val_loss: 0.6292\n","Epoch 18/20\n","\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 4ms/step - accuracy: 0.8240 - loss: 0.5044 - val_accuracy: 0.7760 - val_loss: 0.6655\n","Epoch 19/20\n","\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 4ms/step - accuracy: 0.8275 - loss: 0.4970 - val_accuracy: 0.7817 - val_loss: 0.6755\n","Epoch 20/20\n","\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 4ms/step - accuracy: 0.8321 - loss: 0.4833 - val_accuracy: 0.7771 - val_loss: 0.6703\n"]}]},{"cell_type":"code","source":["%matplotlib inline\n","\n","fig, axs = plt.subplots(1,2,figsize=(15,5))\n","\n","# summarize history for accuracy\n","axs[0].plot(history.history['accuracy'])\n","axs[0].plot(history.history['val_accuracy'])\n","axs[0].set_title('Model Accuracy')\n","axs[0].set_ylabel('Accuracy')\n","axs[0].set_xlabel('Epoch')\n","axs[0].legend(['train', 'validate'], loc='upper left')\n","\n","# summarize history for loss\n","axs[1].plot(history.history['loss'])\n","axs[1].plot(history.history['val_loss'])\n","axs[1].set_title('Model Loss')\n","axs[1].set_ylabel('Loss')\n","axs[1].set_xlabel('Epoch')\n","axs[1].legend(['train', 'validate'], loc='upper left')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":487},"id":"InwcPZgyQeqJ","executionInfo":{"status":"ok","timestamp":1733275692749,"user_tz":-540,"elapsed":881,"user":{"displayName":"황지원","userId":"01099698570160424720"}},"outputId":"ab6acb4e-866b-4ff0-c021-ace6c8b8b651"},"execution_count":8,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1500x500 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["# Score trained model.\n","scores = model.evaluate(x_test, y_test, verbose=1)\n","print('Test loss:', scores[0])\n","print('Test accuracy:', scores[1])\n","\n","# make prediction.\n","pred = model.predict(x_test)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"YyrP_7vaSGCx","executionInfo":{"status":"ok","timestamp":1733275727675,"user_tz":-540,"elapsed":4363,"user":{"displayName":"황지원","userId":"01099698570160424720"}},"outputId":"c7b0450e-d4a3-4cd5-9f09-36b3309a44cf"},"execution_count":9,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7838 - loss: 0.6489\n","Test loss: 0.6702848076820374\n","Test accuracy: 0.7771000266075134\n","\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step\n"]}]},{"cell_type":"code","source":["labels = ['Airplane', 'Automobile', 'Bird', 'Cat', 'Deer', 'Dog', 'Frog', 'Horse', 'Ship', 'Truck']\n","\n","# Convert predictions classes to one hot vectors\n","Y_pred_classes = np.argmax(pred, axis=1)\n","# Convert validation observations to one hot vectors\n","Y_true = np.argmax(y_test, axis=1)\n","# Errors are difference between predicted labels and true labels\n","errors = (Y_pred_classes - Y_true != 0)\n","\n","Y_pred_classes_errors = Y_pred_classes[errors]\n","Y_pred_errors = pred[errors]\n","Y_true_errors = Y_true[errors]\n","X_test_errors = x_test[errors]"],"metadata":{"id":"UjMkzOq7THfd","executionInfo":{"status":"ok","timestamp":1733275978560,"user_tz":-540,"elapsed":357,"user":{"displayName":"황지원","userId":"01099698570160424720"}}},"execution_count":11,"outputs":[]},{"cell_type":"code","source":["R = 5\n","C = 5\n","fig, axes = plt.subplots(R, C, figsize=(12,12))\n","axes = axes.ravel()\n","\n","for i in np.arange(0, R*C):\n","    axes[i].imshow(x_test[i])\n","    axes[i].set_title(\"True: %s \\nPredict: %s\" % (labels[Y_true[i]], labels[Y_pred_classes[i]]))\n","    axes[i].axis('off')\n","    plt.subplots_adjust(wspace=1)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":948},"id":"uceYKpweSGFN","executionInfo":{"status":"ok","timestamp":1733275982276,"user_tz":-540,"elapsed":1960,"user":{"displayName":"황지원","userId":"01099698570160424720"}},"outputId":"05c71ec8-0d69-47ec-916d-18c032478c06"},"execution_count":12,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x1200 with 25 Axes>"],"image/png":"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