  Training a neural network (NN) depends on multiple factors, including but not
limited to the initial weights. In this paper, we focus on initializing deep NN
parameters such that it performs better, comparing to random or zero
initialization. We do this by reducing the process of initialization into an
SMT solver. Previous works consider certain activation functions on small NNs,
however the studied NN is a deep network with different activation functions.
Our experiments show that the proposed approach for parameter initialization
achieves better performance comparing to randomly initialized networks.
