tagger.py 文件源码

def get_loss_function(self):
    """ We should feed in non-dimshuffled inputs x0, mask0 and y0.
    """
    loss = CrossEntropyLoss().connect(self.scores, self.mask, self.y)
    grads = gradient_clipping(tensor.grad(loss, self.params),
                  self.max_grad_norm)
    updates = adadelta(self.params, grads)

    return theano.function([self.x0, self.mask0, self.y0], loss,
                 name='f_loss',
                 updates=updates,
                 on_unused_input='warn',
                 givens=({self.is_train: numpy.cast['int8'](1)}))