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# bai-2.1 (338787 parametre) | ||
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## EEG üzerinden duygu sınıflandırması yapan "bai-2.1" modeli, bir önceki model olan "bai-2.0" modeline göre overfitting ihtimali azaltılmış ve optimize edilmiş versiyonudur. Tüm işlevleri aynıdır. | ||
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#### NOT: Gerçek zamanlı EEG veri takibi uygulamasına modeli entegre ederseniz, gerçek zamanlı olarak duygu tahmini yapabilmektedir. Uygulamaya erişebilmek için: https://github.com/neurazum/Realtime-EEG-Monitoring | ||
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## ----------------------------------------------------------------------------------- | ||
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# bai-2.1 (338787 parameters) | ||
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## The "bai-2.1" model, which performs emotion classification over EEG, is an optimised version of the previous model "bai-2.0" with reduced overfitting probability. All functions are the same. | ||
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#### NOTE: If you integrate the model into a real-time EEG data tracking application, it can predict emotions in real time. To access the application: https://github.com/neurazum/Realtime-EEG-Monitoring | ||
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**Doğruluk/Accuracy: %97.93621013133207** | ||
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## ----------------------------------------------------------------------------------- | ||
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# Kullanım / Usage: | ||
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```python | ||
import numpy as np | ||
import pandas as pd | ||
from sklearn.preprocessing import StandardScaler | ||
from tensorflow.keras.models import load_model | ||
import matplotlib.pyplot as plt | ||
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model_path = 'model-path' | ||
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model = load_model(model_path) | ||
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model_name = model_path.split('/')[-1].split('.')[0] | ||
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plt.figure(figsize=(10, 6)) | ||
plt.title(f'Duygu Tahmini ({model_name}.1)') | ||
plt.xlabel('Zaman') | ||
plt.ylabel('Sınıf') | ||
plt.legend(loc='upper right') | ||
plt.grid(True) | ||
plt.show() | ||
model.summary() | ||
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``` |
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