seq2seq_example.py 文件源码

def sequence_loss(self, y_pred, y_true):
        '''
        Loss function for the seq2seq RNN.  Reshape predicted and true (label) tensors, generate dummy weights,
        then use seq2seq.sequence_loss to actually compute the loss function.
        '''
        if self.verbose > 2: print ("my_sequence_loss y_pred=%s, y_true=%s" % (y_pred, y_true))
        logits = tf.unstack(y_pred, axis=1)     # list of [-1, num_decoder_synbols] elements
        targets = tf.unstack(y_true, axis=1)        # y_true has shape [-1, self.out_seq_len]; unpack to list of self.out_seq_len [-1] elements
        if self.verbose > 2:
            print ("my_sequence_loss logits=%s" % (logits,))
            print ("my_sequence_loss targets=%s" % (targets,))
        weights = [tf.ones_like(yp, dtype=tf.float32) for yp in targets]
        if self.verbose > 4: print ("my_sequence_loss weights=%s" % (weights,))
        sl = seq2seq.sequence_loss(logits, targets, weights)
        if self.verbose > 2: print ("my_sequence_loss return = %s" % sl)
        return sl