BDNN and tested on MNIST. Structure E was used by the hybrid-BDNN and tested on CIFAR10 (non-binary data). Therefore, for Structure E of hybrid-BDNN, the input layer and the following kernels were real numbers (normal neural network part) and the rest of the network was the BDNN part.

First, the error rates of MNIST are shown in Table \[CNNIST\]. Clearly, by using deeper structure (more layers), the performance of BDNN was improved. Meanwhile, the normal CNN of the same structure still had much better result. In Structure D, when more feature maps were used, the result of BDNN became better. This also shows the potential of BDNN of larger scales.

[c ![width 1.5pt]{} c c|c c]{} & &\
& *Train* & *Test* & *Train* & *Test*\
Struct. C & 11.78 & 9.98 & 0.26 & 1.10\
Struct. D & 7.11 & 6.54 & &\

Second, the error rates of CIFAR10 are shown in Table \[CNNCIFAR\]. CIFAR10 is a database of color images of 10 different kinds of objects. In the experiment, the color images were converted into grayscale and then used. Since this database was very difficult, the error rates of both networks were very high, though the largest network 