neural_combinatorial_rl.py 文件源码

def forward(self, inputs):
        """
        Args:
            inputs: [embedding_dim x batch_size x sourceL] of embedded inputs
        """

        (encoder_hx, encoder_cx) = self.encoder.enc_init_state
        encoder_hx = encoder_hx.unsqueeze(0).repeat(inputs.size(1), 1).unsqueeze(0)
        encoder_cx = encoder_cx.unsqueeze(0).repeat(inputs.size(1), 1).unsqueeze(0)       

        # encoder forward pass
        enc_outputs, (enc_h_t, enc_c_t) = self.encoder(inputs, (encoder_hx, encoder_cx))

        # grab the hidden state and process it via the process block 
        process_block_state = enc_h_t[-1]
        for i in range(self.n_process_block_iters):
            ref, logits = self.process_block(process_block_state, enc_outputs)
            process_block_state = torch.bmm(ref, self.sm(logits).unsqueeze(2)).squeeze(2)
        # produce the final scalar output
        out = self.decoder(process_block_state)
        return out