Python tensorflow.keras.optimizers() Examples

The following are 3 code examples of tensorflow.keras.optimizers(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module tensorflow.keras , or try the search function .
Example #1
Source File: utils.py    From MultiPlanarUNet with MIT License 5 votes vote down vote up 
def init_optimizer(optimizer_string, logger=None, **kwargs):
    """
    Same as 'init_losses', but for optimizers.
    Please refer to the 'init_losses' docstring.
    """
    optimizer = _init(
        optimizer_string,
        tf_funcs=[optimizers, addon_optimizers],
        custom_funcs=None,
        logger=logger
    )[0]
    return optimizer(**kwargs)
Example #2
Source File: utils.py    From MultiPlanarUNet with MIT License 5 votes vote down vote up 
def init_activation(activation_string, logger=None, **kwargs):
    """
    Same as 'init_losses', but for optimizers.
    Please refer to the 'init_losses' docstring.
    """
    activation = _init(
        activation_string,
        tf_funcs=[activations, addon_activations],
        custom_funcs=None,
        logger=logger
    )[0]
    return activation
Example #3
Source File: keras_classification_model.py    From DeepPavlov with Apache License 2.0 5 votes vote down vote up 
def compile(self, model: Model, optimizer_name: str, loss_name: str,
                learning_rate: Optional[Union[float, List[float]]],
                learning_rate_decay: Optional[Union[float, str]]) -> Model:
        """
        Compile model with given optimizer and loss

        Args:
            model: keras uncompiled model
            optimizer_name: name of optimizer from keras.optimizers
            loss_name: loss function name (from keras.losses)
            learning_rate: learning rate.
            learning_rate_decay: learning rate decay.

        Returns:

        """
        optimizer_func = getattr(tensorflow.keras.optimizers, optimizer_name, None)
        if callable(optimizer_func):
            if isinstance(learning_rate, float) and isinstance(learning_rate_decay, float):
                # in this case decay will be either given in config or, by default, learning_rate_decay=0.
                self.optimizer = optimizer_func(lr=learning_rate, decay=learning_rate_decay)
            else:
                self.optimizer = optimizer_func()
        else:
            raise AttributeError("Optimizer {} is not defined in `tensorflow.keras.optimizers`".format(optimizer_name))

        loss_func = getattr(tensorflow.keras.losses, loss_name, None)
        if callable(loss_func):
            loss = loss_func
        else:
            raise AttributeError("Loss {} is not defined".format(loss_name))

        model.compile(optimizer=self.optimizer,
                      loss=loss)
        return model