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SmiloAI_model_v1-alpha.h5

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@nimo94 nimo94 released this 03 Nov 16:10
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SmiloAI-ALPHA
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🧠 SmiloAI_model_v1-alpha.h5

Version

v1-alpha — Experimental / Early Prototype

📘 Overview

SmiloAI_model_v1-alpha.h5 is the first experimental model in the SmiloAI series. It represents the initial proof-of-concept stage for the SmiloAI system — a project aimed at combining artificial intelligence with human-centered insight to analyze, interpret, or assist in emotional or behavioral contexts.

As the “alpha” version suggests, this model is in its early development phase, mainly intended for testing, experimentation, and internal validation, not for deployment or production use. Its performance is not yet stable or accurate enough for real-world predictions.

⚙️ Technical Details

  • Framework: TensorFlow / Keras
  • File Type: .h5 (serialized Keras model)
  • Architecture: Early prototype neural network (lightweight layers for quick experimentation)
  • Stage: Alpha — baseline for structural and accuracy improvements
  • Model Purpose: Foundation model for future SmiloAI releases (e.g. v1-beta, v2, etc.)

🎯 Intended Use

  • Purpose: Experimental research, conceptual testing, and early validation of SmiloAI features
  • Users: Developers, students, and researchers analyzing or extending the SmiloAI pipeline
  • Not for: Clinical, commercial, or production environments

📊 Accuracy and Limitations

This version of the model was trained primarily for experimental validation, not optimized accuracy.
Expected results reflect low to moderate accuracy (typically <70% depending on dataset and task).

Reasons for limited accuracy include:

  1. Small or unbalanced dataset during training
  2. Minimal hyperparameter tuning
  3. Early stopping before convergence
  4. Simplified architecture for quick iteration
  5. Possible overfitting or underfitting behavior

Thus, SmiloAI_model_v1-alpha.h5 should not be considered reliable for real-world inference.
Its main role is to serve as a structural and functional baseline for improvement.

“Let's Improvise it together... Feel free to discuss some recommendations.”

🧪 Evaluation Notes

To check the current performance, run an evaluation script such as:

from tensorflow.keras.models import load_model
from sklearn.metrics import classification_report
import numpy as np

model = load_model('SmiloAI_model_v1-alpha.h5')

# Example placeholders – replace with your real data
X_test = np.load('X_test.npy')
y_test = np.load('y_test.npy')

y_pred_probs = model.predict(X_test)
y_pred = np.argmax(y_pred_probs, axis=1) if y_pred_probs.shape[1] > 1 else (y_pred_probs > 0.5).astype(int).squeeze()

print(classification_report(y_test, y_pred))

**Full Changelog**: https://github.com/nimo94/SmiloAI/compare/SmiloAI-ALPHA...SmiloAI-ALPHA
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