hanifekaptan commited on
Commit
5e2329b
·
verified ·
1 Parent(s): 3fe0a1b

Update model.py

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  1. model.py +39 -38
model.py CHANGED
@@ -1,38 +1,39 @@
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- import pandas as pd
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- import joblib
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- from keras.api.models import Sequential
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- from keras.api.layers import Dense, Dropout, BatchNormalization
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- from sklearn.preprocessing import StandardScaler
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-
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- # Veriyi yükle
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- data = pd.read_csv("diabetes.csv")
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- data = data[data.BMI >= 5]
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-
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- X = data.drop("Outcome", axis=1)
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- y = data["Outcome"]
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-
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- # Ön işleme adımı
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- sc = StandardScaler()
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- X = sc.fit_transform(X)
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-
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- # Modeli oluşturma
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- model = Sequential()
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- model.add(Dense(120, activation="relu", input_shape=(X.shape[1],))) # input_shape belirtin
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- model.add(BatchNormalization())
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- model.add(Dropout(0.3)) # Dropout oranı
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- model.add(Dense(64, activation="relu"))
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- model.add(BatchNormalization())
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- model.add(Dropout(0.3)) # Dropout oranı
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- model.add(Dense(1, activation="sigmoid")) # Son katmanın çıkış boyutu 1 olmalı
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- model.compile(loss="binary_crossentropy", optimizer="adam", metrics=["accuracy"])
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-
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- # Modeli eğitin
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- model.fit(X, y,
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- epochs=10,
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- batch_size=32,
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- validation_split=0.2,
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- verbose=1)
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-
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- # Modeli kaydetme
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- model.save("diabetes_model.keras")
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- joblib.dump(sc, "diabetes_scaler.pkl")
 
 
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+ import pandas as pd
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+ import pickle
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+ from keras.api.models import Sequential
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+ from keras.api.layers import Dense, Dropout, BatchNormalization
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+ from sklearn.preprocessing import StandardScaler
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+
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+ # Veriyi yükle
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+ data = pd.read_csv("diabetes.csv")
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+ data = data[data.BMI >= 5]
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+
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+ X = data.drop("Outcome", axis=1)
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+ y = data["Outcome"]
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+
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+ # Ön işleme adımı
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+ sc = StandardScaler()
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+ X = sc.fit_transform(X)
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+
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+ # Modeli oluşturma
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+ model = Sequential()
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+ model.add(Dense(120, activation="relu", input_shape=(X.shape[1],))) # input_shape belirtin
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+ model.add(BatchNormalization())
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+ model.add(Dropout(0.3)) # Dropout oranı
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+ model.add(Dense(64, activation="relu"))
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+ model.add(BatchNormalization())
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+ model.add(Dropout(0.3)) # Dropout oranı
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+ model.add(Dense(1, activation="sigmoid")) # Son katmanın çıkış boyutu 1 olmalı
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+ model.compile(loss="binary_crossentropy", optimizer="adam", metrics=["accuracy"])
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+
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+ # Modeli eğitin
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+ model.fit(X, y,
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+ epochs=10,
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+ batch_size=32,
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+ validation_split=0.2,
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+ verbose=1)
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+
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+ # Modeli kaydetme
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+ model.save("model.keras")
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+ with open("dscaler.pkl", "wb") as scaler_file:
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+ pickle.dump(scaler, scaler_file)