Once you have trained a model in Keras, you can export it so that it can be used in other applications. There are two main ways to export a Keras model:
- The SavedModel format is the default file format for TensorFlow models. It is a binary format that contains the model's architecture, weights, and training configuration.
- The HDF5 format is a more human-readable format that can be opened in a variety of software applications. It contains the same information as the SavedModel format, but it is stored in a more organized way.
To export a model in the SavedModel format, you can use the `tf.keras.Model.save` method. For example, the following code exports a model named `my_model` to the file `my_model.h5`:
model.save('my_model.h5')
To export a model in the HDF5 format, you can use the `tf.keras.Model.save_weights` method. For example, the following code exports the weights of the model `my_model` to the file `my_model.h5
model.save_weights('my_model.h5')
Once you have exported a model, you can load it into another application using the `tf.keras.models.load_model` method. For example, the following code loads the model `my_model.h5` into a new model object:
model = tf.keras.models.load_model('my_model.h5')
The loaded model will have the same architecture and weights as the original model. You can then use the loaded model to make predictions or to continue training.
Benefits of exporting a model
There are several benefits to exporting a model:
- Portability: Once a model is exported, it can be used in any application that supports the format in which it was exported. This makes it easy to deploy models to different environments, such as production servers or mobile devices.
- Reusability: A model can be exported and reused multiple times. This can save time and effort when developing new applications.
- Scalability: A model can be exported and scaled to handle larger datasets or more complex tasks. This can improve the performance and accuracy of the model.
