Confusion matrix for the selection predictions, compared between random initialization (top) and autoencoder initialization (bottom), for a deep network with 6 hidden layers.

Again, ideally we would like all 1’s down the diagonal, and 0’s in the off-diagonal entries. The largest number in each row is shown in boldface. When the network is initialized randomly, almost every dataset is classified as neutral; the network has not really learned anything meaningful from the input data. The overall percentage of misclassification is 74.8% for random initialization, while it is only 6.1% for autoencoder initialization.

理想状态下，我们期望矩阵的对角线上元素全为1，非对角线上元素全为0。每行中的最大数值以粗体标注。当网络采用随机初始化方式时，几乎所有数据样本均被归类为中性类别，表明该网络尚未从输入数据中学到任何有意义的信息。随机初始化下的整体误分类率为74.8%，而自编码器（autoencoder）初始化下的整体误分类率仅为6.1%。