custom_layers.py 文件源码

def __init__(self, incoming, unchanged_W, unchanged_W_shape,
                 oov_in_train_W, oov_in_train_W_shape,
                 p=0.5, rescale=True, dropout_mask=None,
                 **kwargs):
        super(CustomEmbedding, self).__init__(incoming, **kwargs)
        self.output_size = unchanged_W_shape[1]
        self.unchanged_W = self.add_param(unchanged_W, unchanged_W_shape,
                                          name="unchanged_W",
                                          trainable=False,
                                          regularizable=False)
        self.oov_in_train_W = self.add_param(oov_in_train_W,
                                             oov_in_train_W_shape, name='oov_in_train_W')
        self.W = T.concatenate([self.unchanged_W, self.oov_in_train_W])
        self.p = p
        self.rescale = rescale
        if dropout_mask is None:
            dropout_mask = RandomStreams(_rng.randint(1, 2147462579)).binomial(self.W.shape,
                                                                               p=1 - self.p,
                                                                               dtype=self.W.dtype)
        self.dropout_mask = dropout_mask