https://github.com/soulyma/face_emotional_detection
This notebook is designed to train a deep learning model for face emotion recognition. It uses TensorFlow and is run in a Google Colab environment.
https://github.com/soulyma/face_emotional_detection
ai artificial-intelligence artificial-neural-networks colab-notebook emotion-detection nn nural-network nuralnetwork tensorboard testing training validation
Last synced: 2 months ago
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This notebook is designed to train a deep learning model for face emotion recognition. It uses TensorFlow and is run in a Google Colab environment.
- Host: GitHub
- URL: https://github.com/soulyma/face_emotional_detection
- Owner: Soulyma
- Created: 2024-09-08T12:22:23.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2024-09-08T12:29:46.000Z (9 months ago)
- Last Synced: 2025-02-13T09:32:12.919Z (4 months ago)
- Topics: ai, artificial-intelligence, artificial-neural-networks, colab-notebook, emotion-detection, nn, nural-network, nuralnetwork, tensorboard, testing, training, validation
- Language: Jupyter Notebook
- Homepage:
- Size: 4.04 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Face_Emotional_detection
This notebook is designed to train a deep learning model for face emotion recognition. It uses TensorFlow and is run in a Google Colab environment.
Face Emotion Model Training Notebook
This notebook is designed to train a deep learning model for face emotion recognition. It uses TensorFlow and is run in a Google Colab environment. The workflow involves:
- Google Drive Integration: The notebook mounts Google Drive for loading data and saving model checkpoints.
- TensorFlow Setup: Specific versions of TensorFlow and Keras are installed to ensure compatibility with the code.
- TensorBoard Integration: TensorBoard is set up to monitor training metrics during the model training process.
- Kaggle Integration: The notebook connects to Kaggle for accessing datasets, using stored user credentials.
- Data Preparation: The AffectNet dataset is extracted and prepared for training.
The notebook progresses through model definition, training, and evaluation phases, with detailed tracking of performance using TensorBoard.