Instructions to use lysandre/test-save-keras-sequential-seconsd-try with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use lysandre/test-save-keras-sequential-seconsd-try 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://lysandre/test-save-keras-sequential-seconsd-try") - Notebooks
- Google Colab
- Kaggle
Download saved_model.pb from lysandre/test-save-keras-sequential-seconsd-try: direct link, hf CLI and curl.
- Browser
- Download file 59.9 kB
-
https://huggingface.co/lysandre/test-save-keras-sequential-seconsd-try/resolve/refs%2Fpr%2F1/saved_model.pb
- Command line
-
hf download hf://lysandre/test-save-keras-sequential-seconsd-try@refs/pr/1/saved_model.pb
-
curl -L -o saved_model.pb https://huggingface.co/lysandre/test-save-keras-sequential-seconsd-try/resolve/refs%2Fpr%2F1/saved_model.pb
59.9 kB
- Xet hash:
- 661c92467c11b53467c8226dc523379c12b26f188978e3ab543ab75816013502
- Size of remote file:
- 59.9 kB
- SHA256:
- d74288b4442d519e30ba8a03a2a33c19f1784dc02f203c9cd4368a284e03a702
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.