Neural-Architecture-Search
Neural Architecture Search with Successive Halving.

In this project we use network morphisms and hill climbing for convolutional neural network search; by adding successive halving to the search process. We widen our baseline’s search space by adding separable convolutional layers.Experiments are conducted with CIFAR-10. Additionally, we test different learning rate schedulers, and share the effects of different data augmentation combinations on a small base network.

Requirements:

Name Version Build Channel
blas 1.0 mkl
ca-certificates 2018.03.07 0
certifi 2018.4.16 py36_0
cffi 1.11.5 py36h9745a5d_0
cudatoolkit 8.0 3
freetype 2.9.1 h8a8886c_0
intel-openmp 2018.0.3 0
jpeg 9b h024ee3a_2
libedit 3.1.20170329 h6b74fdf_2
libffi 3.2.1 hd88cf55_4
libgcc-ng 7.2.0 hdf63c60_3
libgfortran-ng 7.2.0 hdf63c60_3
libpng 1.6.34 hb9fc6fc_0
libstdcxx-ng 7.2.0 hdf63c60_3
libtiff 4.0.9 he85c1e1_1
mkl 2018.0.3 1
mkl_fft 1.0.2 py36h651fb7a_0
mkl_random 1.0.1 py36h4414c95_1
ncurses 6.1 hf484d3e_0
ninja 1.8.2 py36h6bb024c_1
numpy 1.14.5 py36h1b885b7_4
numpy-base 1.14.5 py36hdbf6ddf_4
olefile 0.45.1 py36_0
openssl 1.0.2o h20670df_0
pillow 5.1.0 py36heded4f4_0
pip 10.0.1 py36_0
pycparser 2.18 py36_1
python 3.6.6 hc3d631a_0
pytorch 0.4.0 py36_cuda8.0.61_cudnn7.1.2_1 pytorch readline 7.0 ha6073c6_4
setuptools 39.2.0 py36_0
six 1.11.0 py36_1
sqlite 3.24.0 h84994c4_0
tk 8.6.7 hc745277_3
torchvision 0.2.1 py36_1 pytorch wheel 0.31.1 py36_0
xz 5.2.4 h14c3975_4
zlib 1.2.11 ha838bed_2