cnn_rel_adv_syn.py 文件源码

def get_recon_loss(self, idxs, sent_output):
        len_sent, len_doc_batch, n_d = sent_output.shape
        recon_layer = self.recon_layer
        padding_id = self.padding_id
        dropout = self.dropout

        # (len(sent)*len(doc)*batch)*n_e
        input_flat = idxs.ravel()
        true_recon = self.embedding_layer.recon_forward(input_flat)
        sent_output = apply_dropout(sent_output, dropout)
        pred_recon = recon_layer.forward(sent_output.reshape((len_sent*len_doc_batch, n_d)))

        # (len(sent)*len(doc)*batch)
        mask = T.cast(T.neq(input_flat, padding_id), theano.config.floatX)
        n = T.sum(mask)
        loss = T.sum((true_recon - pred_recon) ** 2, axis=1) * mask
        loss = T.sum(loss) / n
        return loss