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Evgeniy Fominov's changes that avoid compiler bugs in Visual Studio.
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@ -47,15 +47,19 @@ using block = BN<con<N,3,3,1,1,relu<BN<con<N,3,3,stride,stride,SUBNET>>>>>;
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template <int N, typename SUBNET> using ares = relu<residual<block,N,affine,SUBNET>>;
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template <int N, typename SUBNET> using ares = relu<residual<block,N,affine,SUBNET>>;
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template <int N, typename SUBNET> using ares_down = relu<residual_down<block,N,affine,SUBNET>>;
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template <int N, typename SUBNET> using ares_down = relu<residual_down<block,N,affine,SUBNET>>;
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template <typename SUBNET> using level1 = ares<512,ares<512,ares_down<512,SUBNET>>>;
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template <typename SUBNET> using level2 = ares<256,ares<256,ares<256,ares<256,ares<256,ares_down<256,SUBNET>>>>>>;
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template <typename SUBNET> using level3 = ares<128,ares<128,ares<128,ares_down<128,SUBNET>>>>;
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template <typename SUBNET> using level4 = ares<64,ares<64,ares<64,SUBNET>>>;
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using anet_type = loss_multiclass_log<fc<1000,avg_pool_everything<
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using anet_type = loss_multiclass_log<fc<1000,avg_pool_everything<
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ares<512,ares<512,ares_down<512,
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level1<
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ares<256,ares<256,ares<256,ares<256,ares<256,ares_down<256,
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level2<
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ares<128,ares<128,ares<128,ares_down<128,
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level3<
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ares<64,ares<64,ares<64,
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level4<
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max_pool<3,3,2,2,relu<affine<con<64,7,7,2,2,
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max_pool<3,3,2,2,relu<affine<con<64,7,7,2,2,
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input_rgb_image_sized<227>
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input_rgb_image_sized<227>
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>>>>>>>>>>>>>>>>>>>>>>>;
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>>>>>>>>>>>;
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// ----------------------------------------------------------------------------------------
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// ----------------------------------------------------------------------------------------
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@ -40,25 +40,35 @@ template <int N, typename SUBNET> using ares_down = relu<residual_down<block,N,a
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// ----------------------------------------------------------------------------------------
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// ----------------------------------------------------------------------------------------
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template <typename SUBNET> using level1 = res<512,res<512,res_down<512,SUBNET>>>;
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template <typename SUBNET> using level2 = res<256,res<256,res<256,res<256,res<256,res_down<256,SUBNET>>>>>>;
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template <typename SUBNET> using level3 = res<128,res<128,res<128,res_down<128,SUBNET>>>>;
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template <typename SUBNET> using level4 = res<64,res<64,res<64,SUBNET>>>;
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template <typename SUBNET> using alevel1 = ares<512,ares<512,ares_down<512,SUBNET>>>;
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template <typename SUBNET> using alevel2 = ares<256,ares<256,ares<256,ares<256,ares<256,ares_down<256,SUBNET>>>>>>;
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template <typename SUBNET> using alevel3 = ares<128,ares<128,ares<128,ares_down<128,SUBNET>>>>;
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template <typename SUBNET> using alevel4 = ares<64,ares<64,ares<64,SUBNET>>>;
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// training network type
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// training network type
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using net_type = loss_multiclass_log<fc<1000,avg_pool_everything<
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using net_type = loss_multiclass_log<fc<1000,avg_pool_everything<
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res<512,res<512,res_down<512,
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level1<
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res<256,res<256,res<256,res<256,res<256,res_down<256,
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level2<
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res<128,res<128,res<128,res_down<128,
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level3<
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res<64,res<64,res<64,
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level4<
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max_pool<3,3,2,2,relu<bn_con<con<64,7,7,2,2,
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max_pool<3,3,2,2,relu<bn_con<con<64,7,7,2,2,
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input_rgb_image_sized<227>
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input_rgb_image_sized<227>
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>>>>>>>>>>>>>>>>>>>>>>>;
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>>>>>>>>>>>;
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// testing network type (replaced batch normalization with fixed affine transforms)
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// testing network type (replaced batch normalization with fixed affine transforms)
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using anet_type = loss_multiclass_log<fc<1000,avg_pool_everything<
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using anet_type = loss_multiclass_log<fc<1000,avg_pool_everything<
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ares<512,ares<512,ares_down<512,
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alevel1<
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ares<256,ares<256,ares<256,ares<256,ares<256,ares_down<256,
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alevel2<
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ares<128,ares<128,ares<128,ares_down<128,
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alevel3<
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ares<64,ares<64,ares<64,
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alevel4<
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max_pool<3,3,2,2,relu<affine<con<64,7,7,2,2,
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max_pool<3,3,2,2,relu<affine<con<64,7,7,2,2,
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input_rgb_image_sized<227>
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input_rgb_image_sized<227>
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>>>>>>>>>>>>>>>>>>>>>>>;
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>>>>>>>>>>>;
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// ----------------------------------------------------------------------------------------
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// ----------------------------------------------------------------------------------------
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