Python keras.callbacks.ProgbarLogger() Examples

The following are 4 code examples of keras.callbacks.ProgbarLogger(). 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 keras.callbacks , or try the search function .
Example #1
Source File: callbacks.py    From asr-study with MIT License 5 votes vote down vote up 
def __init__(self, show_metrics=None):
        super(ProgbarLogger, self).__init__()

        self.show_metrics = show_metrics
Example #2
Source File: callbacks.py    From asr-study with MIT License 5 votes vote down vote up 
def on_train_begin(self, logs=None):
        super(ProgbarLogger, self).on_train_begin(logs)

        if self.show_metrics:
            self.params['metrics'] = self.show_metrics
Example #3
Source File: trainer.py    From deephar with MIT License 4 votes vote down vote up 
def train(self, epochs, steps_per_epoch, initial_epoch=0,
            end_of_epoch_callback=None, verbose=1):

        epoch = initial_epoch

        logger = ProgbarLogger(count_mode='steps')
        logger.set_params({
            'epochs': epochs,
            'steps': steps_per_epoch,
            'verbose': verbose,
            'metrics': self.metric_names})
        logger.on_train_begin()

        while epoch < epochs:
            step = 0
            batch = 0

            logger.on_epoch_begin(epoch)

            while step < steps_per_epoch:

                self.batch_logs['batch'] = batch
                logger.on_batch_begin(batch, self.batch_logs)

                for i in range(len(self.models)):
                    x, y = next(self.output_generators[i])
                    outs = self.models[i].train_on_batch(x, y)

                    if not isinstance(outs, list):
                        outs = [outs]
                    if self.print_full_losses:
                        for l, o in zip(self.metric_names, outs):
                            self.batch_logs[l] = o
                    else:
                        self.batch_logs[self.metric_names[i]] = outs[0]

                logger.on_batch_end(batch, self.batch_logs)

                step += 1
                batch += 1

            logger.on_epoch_end(epoch)
            if end_of_epoch_callback is not None:
                end_of_epoch_callback(epoch)

            epoch += 1
Example #4
Source File: models.py    From deep_qa with Apache License 2.0 4 votes vote down vote up 
def _prepare_callbacks(self,
                           callbacks: List[Callback],
                           val_ins: List[numpy.array],
                           epochs: int,
                           batch_size: int,
                           num_train_samples: int,
                           callback_metrics: List[str],
                           do_validation: bool,
                           verbose: int):

        """
        Sets up Keras callbacks to perform various monitoring functions during training.
        """

        self.history = History()  # pylint: disable=attribute-defined-outside-init
        callbacks = [BaseLogger()] + (callbacks or []) + [self.history]
        if verbose:
            callbacks += [ProgbarLogger()]
        callbacks = CallbackList(callbacks)

        # it's possible to callback a different model than self
        # (used by Sequential models).
        if hasattr(self, 'callback_model') and self.callback_model:
            callback_model = self.callback_model
        else:
            callback_model = self  # pylint: disable=redefined-variable-type

        callbacks.set_model(callback_model)
        callbacks.set_params({
                'batch_size': batch_size,
                'epochs': epochs,
                'samples': num_train_samples,
                'verbose': verbose,
                'do_validation': do_validation,
                'metrics': callback_metrics or [],
        })
        callbacks.on_train_begin()
        callback_model.stop_training = False
        for cbk in callbacks:
            cbk.validation_data = val_ins

        return callbacks, callback_model