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# bai-3.0 Emotion (3549313 parametre) |
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## "bai-3.0 Emotion" modeli, EEG üzerine eğitilmiş dünyanın en büyük üçüncü yapay zeka modelidir. Kişinin duygu durum analizini yapmaktadır. |
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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-3.0 Emotion (3549313 parameters) |
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## The "bai-3.0 Emotion" model is the world's third largest artificial intelligence model trained on EEG. It analyzes the person's emotional state. |
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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: %97,79549718574108** |
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[![bai-3.0](https://img.youtube.com/vi/qUkId3S9W94/0.jpg)](https://www.youtube.com/watch?v=qUkId3S9W94) |
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# Kullanım / Usage |
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```python |
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import numpy as np |
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import pandas as pd |
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from sklearn.preprocessing import StandardScaler |
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from tensorflow.keras.models import load_model |
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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)) |
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plt.title(f'Duygu Tahmini ({model_name}.2)') |
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plt.xlabel('Zaman') |
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plt.ylabel('Sınıf') |
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plt.legend(loc='upper right') |
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plt.grid(True) |
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plt.show() |
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model.summary() |
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``` |
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# Tahmin / Prediction |
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```python |
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import numpy as np |
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import pandas as pd |
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from sklearn.preprocessing import StandardScaler |
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from tensorflow.keras.models import load_model |
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model_path = 'model-path' |
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model = load_model(model_path) |
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scaler = StandardScaler() |
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predictions = model.predict(X_new_reshaped) |
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predicted_labels = np.argmax(predictions, axis=1) |
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label_mapping = {'NEGATIVE': 0, 'NEUTRAL': 1, 'POSITIVE': 2} |
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label_mapping_reverse = {v: k for k, v in label_mapping.items()} |
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#new_input = np.array([[23, 465, 12, 9653] * 637]) |
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new_input = np.random.rand(1, 2548) # 1 örnek ve 2548 özellik |
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new_input_scaled = scaler.fit_transform(new_input) |
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new_input_reshaped = new_input_scaled.reshape((new_input_scaled.shape[0], 1, new_input_scaled.shape[1])) |
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new_prediction = model.predict(new_input_reshaped) |
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predicted_label = np.argmax(new_prediction, axis=1)[0] |
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predicted_emotion = label_mapping_reverse[predicted_label] |
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if predicted_emotion == 'NEGATIVE': |
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predicted_emotion = 'Negatif' |
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elif predicted_emotion == 'NEUTRAL': |
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predicted_emotion = 'Nötr' |
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elif predicted_emotion == 'POSITIVE': |
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predicted_emotion = 'Pozitif' |
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print(f'Giriş Verileri: {new_input}') |
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print(f'Tahmin Edilen Duygu: {predicted_emotion}') |
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``` |
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# Python Sürümü / Python Version |
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### 3.9 <=> 3.13 |
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# Modüller / Modules |
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```bash |
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matplotlib==3.8.0 |
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matplotlib-inline==0.1.6 |
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numpy==1.26.4 |
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pandas==2.2.2 |
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scikit-learn==1.3.1 |
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tensorflow==2.15.0 |
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``` |