INNER CODE UNIT · Python

train_for_epoch

floriankark/cs224n-win2223 · a3/run.py:71

def train_for_epoch(parser, train_data, dev_data, optimizer, loss_func, batch_size):
    """ Train the neural dependency parser for single epoch.

    Note: In PyTorch we can signify train versus test and automatically have
    the Dropout Layer applied and removed, accordingly, by specifying
    whether we are training, `model.train()`, or evaluating, `model.eval()`

    @param parser (Parser): Neural Dependency Parser
    @param train_data ():
    @param dev_data ():
    @param optimizer (nn.Optimizer): Adam Optimizer
    @param loss_func (nn.CrossEntropyLoss): Cross Entropy Loss Function
    @param batch_size (int): batch size

    @return dev_UAS (float): Unlabeled Attachment Score (UAS) for dev data
    """
    parser.model.train() # Places model in "train" mode, i.e. apply dropout layer
    n_minibatches = math.ceil(len(train_data) / batch_size)

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