  Recently, with convolutional neural networks gaining significant achievements
in many challenging machine learning fields, hand-crafted neural networks no
longer satisfy our requirements as designing a network will cost a lot, and
automatically generating architectures has attracted increasingly more
attention and focus. Some research on auto-generated networks has achieved
promising results. However, they mainly aim at picking a series of single
layers such as convolution or pooling layers one by one. There are many elegant
and creative designs in the carefully hand-crafted neural networks, such as
Inception-block in GoogLeNet, residual block in residual network and dense
block in dense convolutional network. Based on reinforcement learning and
taking advantages of the superiority of these networks, we propose a novel
automatic process to design a multi-block neural network, whose architecture
contains multiple types of blocks mentioned above, with the purpose to do
structure learning of deep neural networks and explore the possibility whether
different blocks can be composed together to form a well-behaved neural
network. The optimal network is created by the Q-learning agent who is trained
to sequentially pick different types of blocks. To verify the validity of our
proposed method, we use the auto-generated multi-block neural network to
conduct experiments on image benchmark datasets MNIST, SVHN and CIFAR-10 image
classification task with restricted computational resources. The results
demonstrate that our method is very effective, achieving comparable or better
performance than hand-crafted networks and advanced auto-generated neural
networks.
