SNAS(Stochastic Neural Architecture Search)

Pytorch implementation of SNAS (Caution : This is not official version and was not written by the author of the paper)

Requirements

Python >= 3.6.5, PyTorch == 0.1

Datasets

Cifar-10 datasets were used, (5000 for training / 5000 for validation).

Note that the authors of the paper used 25000 images for training and validation set, respectively.

Hyperparameters

Overall, I followed hyperparameters that were given in the paper.

However, there are several parameters that were not given in the paper.

Ex) Softmax Temperature ($ \lambda_{0} $) , annealiation rate of the softmax temperature, parameters regarding the levels of resource constraints

Specifically, I found that search validation accuracy is highly influenced by initial softmax temperature (See Figure 1 for detail)

Run the training code

python main.py (can adjust hyperparameters in option/default_option.py) (WITHOUT resource constraint)

python main_constraint.py (WITH resource contraint)

Search Validation Accuracy (without resource constraint)

Figure1 : Search Validation Accuracy with different initial softmax temperature

(Note : the model was not fully trained(<==>converged) due to the limited resources (E.g., GPU and TIME!!)

Network Architecture (without resource constraint at epoch 70)

Figure2 : Network Architecture of normal cell (left) and reduction cell (right)

Network Architecture Comparison (at epoch 30)

Reference

https://github.com/quark0/darts/blob/master/README.md

Since SNAS follows training settings of DARTS, I reused the official pytorch codes of DARTS as a basic framework of this work.

hixiaye/SNAS-Stochastic-Neural-Architecture-Search-