model.py 文件源码

def encode(self, X, skip_mask=None):
        batchsize = X.shape[0]
        seq_length = X.shape[1]
        enmbedding = self.encoder_embed(X)
        enmbedding = F.swapaxes(enmbedding, 1, 2)

        out_data = self._forward_encoder_layer(0, enmbedding, skip_mask=skip_mask)
        in_data = [out_data]

        for layer_index in range(1, self.num_layers):
            out_data = self._forward_encoder_layer(layer_index, F.concat(in_data) if self.densely_connected else in_data[-1], skip_mask=skip_mask)
            in_data.append(out_data)

        out_data = F.concat(in_data) if self.densely_connected else in_data[-1] # dense conv

        if self.using_dropout:
            out_data = F.dropout(out_data, ratio=self.dropout)

        last_hidden_states = []
        for layer_index in range(0, self.num_layers):
            encoder = self.get_encoder(layer_index)
            last_hidden_states.append(encoder.get_last_hidden_state())

        return last_hidden_states