47 lines
1.5 KiB
Python
47 lines
1.5 KiB
Python
from tensorflow.keras.models import Sequential
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from tensorflow.keras.layers import Dense
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from tensorflow.keras.optimizers import Adam
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from sklearn.metrics import accuracy_score
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from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import StandardScaler, LabelEncoder
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import numpy as np
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import pandas as pd
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x=pd.read_csv('10_x.csv')
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y=pd.read_csv('10_y.csv')
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x_train, x_test, y_train, y_test = train_test_split(x,y , test_size=0.2,train_size=0.8, random_state=21)
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NeuralModel = Sequential([
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Dense(128, activation='relu', input_shape=(14,)),
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Dense(32, activation='relu'),
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Dense(64, activation='relu'),
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Dense(64, activation='relu'),
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Dense(64, activation='relu'),
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Dense(1, activation='sigmoid')
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])
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#https://keras.io/api/losses/
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#https://keras.io/api/optimizers/
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#https://keras.io/api/metrics/
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opt = Adam(lr=0.0003)
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NeuralModel.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy','AUC'])
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NeuralModel.fit(x_train, y_train, batch_size= 16, epochs = 16) #verbose = 1
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y_pred = NeuralModel.predict(x_test)
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y_pred = np.around(y_pred, decimals=0)
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results = accuracy_score(y_test,y_pred)
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text_file = open("sample.txt", "w")
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n = text_file.write(f"accuracy: {results}")
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text_file.close()
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print(f"accuracy: {results}")
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# Accuracy wynosi 1 z powodu banalnego podziału na 2 klasy jakosci Wina: "bad" i "nice". |