theanets/graph.py
def train(self, *args, **kwargs):
        '''Train the network until the trainer converges.

        All arguments are passed to :func:`itertrain`.

        Returns
        -------
        training : dict
            A dictionary of monitor values computed using the training dataset,
            at the conclusion of training. This dictionary will at least contain
            a 'loss' key that indicates the value of the loss function. Other
            keys may be available depending on the trainer being used.
        validation : dict
            A dictionary of monitor values computed using the validation
            dataset, at the conclusion of training.
        '''
        monitors = None
        for monitors in self.itertrain(*args, **kwargs):
            pass
        return monitors
def train(self, *args, **kwargs):
        '''Train the network until the trainer converges.

        All arguments are passed to :func:`itertrain`.

        Returns
        -------
        training : dict
            A dictionary of monitor values computed using the training dataset,
            at the conclusion of training. This dictionary will at least contain
            a 'loss' key that indicates the value of the loss function. Other
            keys may be available depending on the trainer being used.
        validation : dict
            A dictionary of monitor values computed using the validation
            dataset, at the conclusion of training.
        '''
        monitors = None
        for monitors in self.itertrain(*args, **kwargs):
            pass
        return monitors
def train ( self , * args , * * kwargs ) : monitors = None for monitors in self . itertrain ( * args , * * kwargs ) : pass return monitors
Train the network until the trainer converges.

        All arguments are passed to :func:`itertrain`.

        Returns
        -------
        training : dict
            A dictionary of monitor values computed using the training dataset,
            at the conclusion of training. This dictionary will at least contain
            a 'loss' key that indicates the value of the loss function. Other
            keys may be available depending on the trainer being used.
        validation : dict
            A dictionary of monitor values computed using the validation
            dataset, at the conclusion of training.
Train the network until the trainer converges .