models.py 文件源码

def f_log_probs(self, probs, x, x_mask, y, y_mask,
                    src_selector, trg_selector, cg=None):
        y_flat = y.flatten()
        y_flat_idx = tensor.arange(y_flat.shape[0]) * self.vocab_size + y_flat
        cost = -tensor.log(probs.flatten()[y_flat_idx])
        cost = cost.reshape([y.shape[0], y.shape[1]])
        cost = (cost * y_mask).sum(0)
        func_inps = [x, x_mask, y, y_mask, src_selector, trg_selector]
        return theano.function(
            inputs=func_inps,
            outputs=cost, on_unused_input='warn')