Neural networks have recently had a lot of success for many tasks. However, neural
 network architectures that perform well are still typically designed manually
 by experts in a cumbersome trial-and-error process. We propose a new method
 to automatically search for well-performing CNN architectures based on a simple
 hill climbing procedure whose operators apply network morphisms, followed
 by short optimization runs by cosine annealing. Surprisingly, this simple method
 yields competitive results, despite only requiring resources in the same order of
 magnitude as training a single network. E.g., on CIFAR-10, our method designs
 and trains networks with an error rate below 6% in only 12 hours on a single GPU;
 training for one day reduces this error further, to almost 5%.
We propose a simple and efficent method for architecture search for convolutional neural networks. Proposes a neural architecture search method that achieves close to state-of-the-art accuracy on CIFAR10 and takes much less computational resources. Presents a method to search neural network architectures at the same time of training which dramatically saves training time and architecture searching time. Proposes variant of neural architecture search using network morphisms to define a search space using CNN architectures completing the CIFAR image classification task