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A cleaned and simplified copy of Neural Architecture Search
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mirayyuce/NAS_updated
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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
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A cleaned and simplified copy of Neural Architecture Search
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