Method save

tensorflow/python/keras/engine/network.py:1122–1171

Saves the model to Tensorflow SavedModel or a single HDF5 file. The savefile includes: - The model architecture, allowing to re-instantiate the model. - The model weights. - The state of the optimizer, allowing to resume training exactly where you left off.

(self,
           filepath,
           overwrite=True,
           include_optimizer=True,
           save_format=None,
           signatures=None)

    return model

  def save(self,
           filepath,
           overwrite=True,
           include_optimizer=True,
           save_format=None,
           signatures=None):
    """Saves the model to Tensorflow SavedModel or a single HDF5 file.

    The savefile includes:
        - The model architecture, allowing to re-instantiate the model.
        - The model weights.
        - The state of the optimizer, allowing to resume training
            exactly where you left off.

    This allows you to save the entirety of the state of a model
    in a single file.

    Saved models can be reinstantiated via `keras.models.load_model`.
    The model returned by `load_model`
    is a compiled model ready to be used (unless the saved model
    was never compiled in the first place).

    Arguments:
        filepath: String, path to SavedModel or H5 file to save the model.
        overwrite: Whether to silently overwrite any existing file at the
            target location, or provide the user with a manual prompt.
        include_optimizer: If True, save optimizer's state together.
        save_format: Either 'tf' or 'h5', indicating whether to save the model
          to Tensorflow SavedModel or HDF5. The default is currently 'h5', but
          will switch to 'tf' in TensorFlow 2.0. The 'tf' option is currently
          disabled (use `tf.keras.experimental.export_saved_model` instead).
      signatures: Signatures to save with the SavedModel. Applicable to the 'tf'
        format only. Please see the `signatures` argument in
        `tf.saved_model.save` for details.

    Example:

    ```python
    from keras.models import load_model

    model.save('my_model.h5')  # creates a HDF5 file 'my_model.h5'
    del model  # deletes the existing model

    # returns a compiled model
    # identical to the previous one
    model = load_model('my_model.h5')
    ```
    """
    saving.save_model(self, filepath, overwrite, include_optimizer, save_format,
                      signatures)

  def save_weights(self, filepath, overwrite=True, save_format=None):
    """Saves all layer weights.