Instructions to use eyescry/PracticheskayaCK with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use eyescry/PracticheskayaCK 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://eyescry/PracticheskayaCK") - Notebooks
- Google Colab
- Kaggle
PracticheskayaCK
Эта модель обучена распознавать рукописные цифры 0, 5 и 9 из набора данных MNIST.
Архитектура
- Входной слой:
Flatten(input_shape=(28, 28, 1)) - Скрытый слой:
Dense(128, activation='relu') - Выходной слой:
Dense(3, activation='softmax')
Использование
import keras
model = keras.saving.load_model("hf://eyescry/PracticheskayaCK")
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