38 lines
971 B
Python
38 lines
971 B
Python
import pandas as pd
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import numpy as np
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from os import path
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from tensorflow import keras
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import sys
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import matplotlib.pyplot as plt
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from sklearn.metrics import accuracy_score, classification_report
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model = keras.models.load_model('wine_model.h5')
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print('evaluating')
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test_df =pd.read_csv('test.csv')
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y_test = test_df.quality
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x_test = test_df.drop(['quality'], axis= 1)
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x_test=((x_test-x_test.min())/(x_test.max()-x_test.min())) #Normalizacja
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y_pred = model.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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with open('results.txt', 'a+', encoding="UTF-8") as f:
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f.write(str(results) +"\n")
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with open('results.txt', 'r', encoding="UTF-8") as f:
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lines = f.readlines()
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fig = plt.figure(figsize=(10,10))
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chart = fig.add_subplot()
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chart.set_ylabel("Accuracy")
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chart.set_xlabel("Number of build")
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x = np.arange(0, len(lines), 1)
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y = [float(x) for x in lines]
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print(y)
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plt.plot(x,y,"ro")
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plt.savefig("evaluation.png") |