Neural Architecture Search (NAS) has become a popular subject in the area of machine-learning science. Commercial services such as Google’s AutoML and open-source libraries such as Auto-Keras  make NAS accessible to the broader machine learning environment. We explore the ideas and approaches of NAS in this blog post to help readers to understand the field better and find possibilities of real-time applications.
Modern deep neural networks sometimes contain several layers of numerous types . Skip connections  and sub-modules  are also being used to promote model convergence. There is no limit to the space of possible model architectures. Most of the deep neural network structures are currently created based on human experience, require a long and tedious trial and error process. NAS tries to detect effective architectures for a specific deep learning problem without human intervention.
Generally, NAS can be categorized into three dimensions- search space, _a _search strategy, and _a _performance estimation strategy.
Figure 1: The fundamental of neural architecture search
The search space determines which neural architectures to be assessed. Better search space may reduce the complexity of searching for suitable neural architectures. In general, not only a constrained but also flexible search space is needed. Constraints eliminate non-intuitive neural architecture to create a finite space for searching. The search space contains every architecture design (often an infinite number) that can be originated from the NAS approaches. It may involve all sets of layer configurations stacked on each other (Figure 2a) or more complicated architectures that include skipping connections (Figure 2b). To reduce the search space dimension, it may also involve sub-modules design. Later sub-modules are stacked together to generate model architecture (Figure 2c).
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Companies need to be thinking long-term before even starting a software development project. These needs are solved at the level of architecture: business owners want to assure agility, scalability, and performance.
The top contenders for scalable solutions are serverless and microservices. Both architectures prioritize security but approach it in their own ways. Let’s take a look at how businesses can benefit from the adoption of serverless architecture vs microservices, examine their differences, advantages, and use cases.
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Neural architecture search (NAS) is a difficult challenge in deep learning. Many of us have experienced that for a given dataset, a network may initially struggle to learn. But with a simple change of a hyper-parameter, the learning can become very effective. Manually tweaking hyper-parameters including architecture is time-consuming and challenging even though to some it can also be a lot of fun. Recently, automatic hyper-parameter tuning has become more and more popular, as it provides an efficient mechanism to solve NAS at scale.
In this post, we will show how to perform hyper-parameter search using an automated machine learning (AutoML) tool — NNI (for Neural Network Intelligence) open-sourced by Microsoft. I just got started to play with NNI and I have liked it so far. Here I want to share how I use NNI to search for optimal hyper-parameters and architectures.
We will build a neural network to approximate math functions, as an example. Neural networks with one or more hidden layers are known as function approximators for continuous functions , assuming certain non-linearity conditions of activation functions are met. Shallow networks such as the ones with a single hidden layer are not as efficient as width-bound deep networks (for example ). Michael Nielsen has given a nice, accessible explanation of how neural nets can approximate functions.
We begin with a simple nonlinear target function y = x². We use a fully connected network with a few hidden layers to learn this function within the range [0, 1].
Below is code to implement this function as a PyTorch dataset. Input data and ground-truth labels are represented by tensors of shape (1,1), where the two components are channels and data dimension, respectively. They are both 1 since x and y hold only scalar values. If you have experience in image-based neural networks, you can think of the data as a single-channel, single-pixel image.
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