SENN.py 文件源码

def loss(self, inf_targets, inf_vads, targets, vads, mtl_fac):
        '''
        Loss definition
        Only speech inference loss is defined and work quite well
        Add VAD cross entropy loss if you want
        '''
        loss_v1 = tf.nn.l2_loss(inf_targets - targets) / self.batch_size
        loss_o = loss_v1
        reg_loss = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
        # ipdb.set_trace()
        loss_v = loss_o + tf.add_n(reg_loss)
        tf.scalar_summary('loss', loss_v)
        # loss_merge = tf.cond(
        #     is_val, lambda: tf.scalar_summary('val_loss_batch', loss_v),
        #     lambda: tf.scalar_summary('loss', loss_v))
        return loss_v, loss_o
        # return tf.reduce_mean(tf.nn.l2_loss(inf_targets - targets))