Learning Transferable Architectures for Scalable Image Recognition
Developing neural network image classification models often requires significant architecture engineering. In this paper, we study a method to learn the model architectures directly on the dataset of interest. As this approach is expensive when the dataset is large, we propose to search for an architectural building block on a small dataset and then transfer the block to a larger dataset. The key contribution of this work is the design of a new search space (which we call the “NASNet search space”) which enables transferability. In our experiments, we search for the best convolutional layer (or “cell”) on the CIFAR-10 dataset and then apply this cell to the ImageNet dataset by stacking together more copies of this cell, each with their own parameters to design a convolutional architecture, which we name a “NASNet architecture”.
from keras.applications.nasnet import NASNetLarge, NASNetMobile NASNetLarge(input_shape=None, include_top=True, weights='imagenet', input_tensor=None, pooling=None, classes=1000) NASNetMobile(input_shape=None, include_top=True, weights='imagenet', input_tensor=None, pooling=None, classes=1000)