Instructions to use Jabka/Neue_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use Jabka/Neue_Model with Keras:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://Jabka/Neue_Model") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Jabka/Neue_Model: direct link, hf CLI and curl.
- Browser
- Download file 712 Bytes
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https://huggingface.co/Jabka/Neue_Model/resolve/main/README.md
- Command line
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hf download hf://Jabka/Neue_Model/README.md
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curl -L -o README.md https://huggingface.co/Jabka/Neue_Model/resolve/main/README.md
712 Bytes
| language: | |
| - ru | |
| library_name: keras | |
| # Модель для распознования цифр | |
| Обучена на выборке mnist | |
| # Послойная архитектура НС | |
|  | |
| # Общее количество обучаемых параметров НС | |
|  | |
| # Используемый алгоритмы оптимизации и функция ошибки: | |
| Алгоритм оптимизации - Adam; | |
| Функция ошибки - бинарная кроссэнтропия; | |
| # Размеры тренировочного и тестового датасета: | |
| 60000; 10000 | |
|  | |
| # Результаты обучения модели: | |
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