├── Layer2_Projections_and_Images ├── Layer2_Filter111_Top1.JPEG ├── Layer2_Filter111_Top1_Activations.JPEG ├── Layer2_Filter111_Top2.JPEG ├── Layer2_Filter111_Top2_Activations.JPEG ├── Layer2_Filter111_Top3.JPEG ├── Layer2_Filter111_Top3_Activations.JPEG ├── Layer2_Filter111_Top4.JPEG ├── Layer2_Filter111_Top4_Activations.JPEG ├── Layer2_Filter111_Top5.JPEG ├── Layer2_Filter111_Top5_Activations.JPEG ├── Layer2_Filter111_Top6.JPEG ├── Layer2_Filter111_Top6_Activations.JPEG ├── Layer2_Filter111_Top7.JPEG ├── Layer2_Filter111_Top7_Activations.JPEG ├── Layer2_Filter111_Top8.JPEG ├── Layer2_Filter111_Top8_Activations.JPEG ├── Layer2_Filter111_Top9.JPEG ├── Layer2_Filter111_Top9_Activations.JPEG ├── Layer2_Filter112_Top1.JPEG ├── Layer2_Filter112_Top1_Activations.JPEG ├── Layer2_Filter112_Top2.JPEG ├── Layer2_Filter112_Top2_Activations.JPEG ├── Layer2_Filter112_Top3.JPEG ├── Layer2_Filter112_Top3_Activations.JPEG ├── Layer2_Filter112_Top4.JPEG ├── Layer2_Filter112_Top4_Activations.JPEG ├── Layer2_Filter112_Top5.JPEG ├── Layer2_Filter112_Top5_Activations.JPEG ├── Layer2_Filter112_Top6.JPEG ├── Layer2_Filter112_Top6_Activations.JPEG ├── Layer2_Filter112_Top7.JPEG ├── Layer2_Filter112_Top7_Activations.JPEG ├── Layer2_Filter112_Top8.JPEG ├── Layer2_Filter112_Top8_Activations.JPEG ├── Layer2_Filter112_Top9.JPEG ├── Layer2_Filter112_Top9_Activations.JPEG ├── Layer2_Filter14_Top1.JPEG ├── Layer2_Filter14_Top1_Activations.JPEG ├── Layer2_Filter14_Top2.JPEG ├── Layer2_Filter14_Top2_Activations.JPEG ├── Layer2_Filter14_Top3.JPEG ├── Layer2_Filter14_Top3_Activations.JPEG ├── Layer2_Filter14_Top4.JPEG ├── Layer2_Filter14_Top4_Activations.JPEG ├── Layer2_Filter14_Top5.JPEG ├── Layer2_Filter14_Top5_Activations.JPEG ├── Layer2_Filter14_Top6.JPEG ├── Layer2_Filter14_Top6_Activations.JPEG ├── Layer2_Filter14_Top7.JPEG ├── Layer2_Filter14_Top7_Activations.JPEG ├── Layer2_Filter14_Top8.JPEG ├── Layer2_Filter14_Top8_Activations.JPEG ├── Layer2_Filter14_Top9.JPEG ├── Layer2_Filter14_Top9_Activations.JPEG ├── Layer2_Filter194_Top1.JPEG ├── Layer2_Filter194_Top1_Activations.JPEG ├── Layer2_Filter194_Top2.JPEG ├── Layer2_Filter194_Top2_Activations.JPEG ├── Layer2_Filter194_Top3.JPEG ├── Layer2_Filter194_Top3_Activations.JPEG ├── Layer2_Filter194_Top4.JPEG ├── Layer2_Filter194_Top4_Activations.JPEG ├── Layer2_Filter194_Top5.JPEG ├── Layer2_Filter194_Top5_Activations.JPEG ├── Layer2_Filter194_Top6.JPEG ├── Layer2_Filter194_Top6_Activations.JPEG ├── Layer2_Filter194_Top7.JPEG ├── Layer2_Filter194_Top7_Activations.JPEG ├── Layer2_Filter194_Top8.JPEG ├── Layer2_Filter194_Top8_Activations.JPEG ├── Layer2_Filter194_Top9.JPEG ├── Layer2_Filter194_Top9_Activations.JPEG ├── Layer2_Filter1_Top1.JPEG ├── Layer2_Filter1_Top1_Activations.JPEG ├── Layer2_Filter1_Top2.JPEG ├── Layer2_Filter1_Top2_Activations.JPEG ├── Layer2_Filter1_Top3.JPEG ├── Layer2_Filter1_Top3_Activations.JPEG ├── Layer2_Filter1_Top4.JPEG ├── Layer2_Filter1_Top4_Activations.JPEG ├── Layer2_Filter1_Top5.JPEG ├── Layer2_Filter1_Top5_Activations.JPEG ├── Layer2_Filter1_Top6.JPEG ├── Layer2_Filter1_Top6_Activations.JPEG ├── Layer2_Filter1_Top7.JPEG ├── Layer2_Filter1_Top7_Activations.JPEG ├── Layer2_Filter1_Top8.JPEG ├── Layer2_Filter1_Top8_Activations.JPEG ├── Layer2_Filter1_Top9.JPEG ├── Layer2_Filter1_Top9_Activations.JPEG ├── Layer2_Filter230_Top1.JPEG ├── Layer2_Filter230_Top1_Activations.JPEG ├── Layer2_Filter230_Top2.JPEG ├── Layer2_Filter230_Top2_Activations.JPEG ├── Layer2_Filter230_Top3.JPEG ├── Layer2_Filter230_Top3_Activations.JPEG ├── Layer2_Filter230_Top4.JPEG ├── Layer2_Filter230_Top4_Activations.JPEG ├── Layer2_Filter230_Top5.JPEG ├── Layer2_Filter230_Top5_Activations.JPEG ├── Layer2_Filter230_Top6.JPEG ├── Layer2_Filter230_Top6_Activations.JPEG ├── Layer2_Filter230_Top7.JPEG ├── Layer2_Filter230_Top7_Activations.JPEG ├── Layer2_Filter230_Top8.JPEG ├── Layer2_Filter230_Top8_Activations.JPEG ├── Layer2_Filter230_Top9.JPEG ├── Layer2_Filter230_Top9_Activations.JPEG ├── Layer2_Filter249_Top1.JPEG ├── Layer2_Filter249_Top1_Activations.JPEG ├── Layer2_Filter249_Top2.JPEG ├── Layer2_Filter249_Top2_Activations.JPEG ├── Layer2_Filter249_Top3.JPEG ├── Layer2_Filter249_Top3_Activations.JPEG ├── Layer2_Filter249_Top4.JPEG ├── Layer2_Filter249_Top4_Activations.JPEG ├── Layer2_Filter249_Top5.JPEG ├── Layer2_Filter249_Top5_Activations.JPEG ├── Layer2_Filter249_Top6.JPEG ├── Layer2_Filter249_Top6_Activations.JPEG ├── Layer2_Filter249_Top7.JPEG ├── Layer2_Filter249_Top7_Activations.JPEG ├── Layer2_Filter249_Top8.JPEG ├── Layer2_Filter249_Top8_Activations.JPEG ├── Layer2_Filter249_Top9.JPEG ├── Layer2_Filter249_Top9_Activations.JPEG ├── Layer2_Filter39_Top1.JPEG ├── Layer2_Filter39_Top1_Activations.JPEG ├── Layer2_Filter39_Top2.JPEG ├── Layer2_Filter39_Top2_Activations.JPEG ├── Layer2_Filter39_Top3.JPEG ├── Layer2_Filter39_Top3_Activations.JPEG ├── Layer2_Filter39_Top4.JPEG ├── Layer2_Filter39_Top4_Activations.JPEG ├── Layer2_Filter39_Top5.JPEG ├── Layer2_Filter39_Top5_Activations.JPEG ├── Layer2_Filter39_Top6.JPEG ├── Layer2_Filter39_Top6_Activations.JPEG ├── Layer2_Filter39_Top7.JPEG ├── Layer2_Filter39_Top7_Activations.JPEG ├── Layer2_Filter39_Top8.JPEG ├── Layer2_Filter39_Top8_Activations.JPEG ├── Layer2_Filter39_Top9.JPEG ├── Layer2_Filter39_Top9_Activations.JPEG ├── Layer2_Filter78_Top1.JPEG ├── Layer2_Filter78_Top1_Activations.JPEG ├── Layer2_Filter78_Top2.JPEG ├── Layer2_Filter78_Top2_Activations.JPEG ├── Layer2_Filter78_Top3.JPEG ├── Layer2_Filter78_Top3_Activations.JPEG ├── Layer2_Filter78_Top4.JPEG ├── Layer2_Filter78_Top4_Activations.JPEG ├── Layer2_Filter78_Top5.JPEG ├── Layer2_Filter78_Top5_Activations.JPEG ├── Layer2_Filter78_Top6.JPEG ├── Layer2_Filter78_Top6_Activations.JPEG ├── Layer2_Filter78_Top7.JPEG ├── Layer2_Filter78_Top7_Activations.JPEG ├── Layer2_Filter78_Top8.JPEG ├── Layer2_Filter78_Top8_Activations.JPEG ├── Layer2_Filter78_Top9.JPEG └── Layer2_Filter78_Top9_Activations.JPEG ├── Layer3_Projections_and_Images ├── Layer3_Filter10_Top1.JPEG ├── Layer3_Filter10_Top1_Activations.JPEG ├── Layer3_Filter10_Top2.JPEG ├── Layer3_Filter10_Top2_Activations.JPEG ├── Layer3_Filter10_Top3.JPEG ├── Layer3_Filter10_Top3_Activations.JPEG ├── Layer3_Filter10_Top4.JPEG ├── Layer3_Filter10_Top4_Activations.JPEG ├── Layer3_Filter10_Top5.JPEG ├── Layer3_Filter10_Top5_Activations.JPEG ├── Layer3_Filter10_Top6.JPEG ├── Layer3_Filter10_Top6_Activations.JPEG ├── Layer3_Filter10_Top7.JPEG ├── Layer3_Filter10_Top7_Activations.JPEG ├── Layer3_Filter10_Top8.JPEG ├── Layer3_Filter10_Top8_Activations.JPEG ├── Layer3_Filter10_Top9.JPEG ├── Layer3_Filter10_Top9_Activations.JPEG ├── Layer3_Filter191_Top1.JPEG ├── Layer3_Filter191_Top1_Activations.JPEG ├── Layer3_Filter191_Top2.JPEG ├── Layer3_Filter191_Top2_Activations.JPEG ├── Layer3_Filter191_Top3.JPEG ├── Layer3_Filter191_Top3_Activations.JPEG ├── Layer3_Filter191_Top4.JPEG ├── Layer3_Filter191_Top4_Activations.JPEG ├── Layer3_Filter191_Top5.JPEG ├── Layer3_Filter191_Top5_Activations.JPEG ├── Layer3_Filter191_Top6.JPEG ├── Layer3_Filter191_Top6_Activations.JPEG ├── Layer3_Filter191_Top7.JPEG ├── Layer3_Filter191_Top7_Activations.JPEG ├── Layer3_Filter191_Top8.JPEG ├── Layer3_Filter191_Top8_Activations.JPEG ├── Layer3_Filter191_Top9.JPEG ├── Layer3_Filter191_Top9_Activations.JPEG ├── Layer3_Filter210_Top1.JPEG ├── Layer3_Filter210_Top1_Activations.JPEG ├── Layer3_Filter210_Top2.JPEG ├── Layer3_Filter210_Top2_Activations.JPEG ├── Layer3_Filter210_Top3.JPEG ├── Layer3_Filter210_Top3_Activations.JPEG ├── Layer3_Filter210_Top4.JPEG ├── Layer3_Filter210_Top4_Activations.JPEG ├── Layer3_Filter210_Top5.JPEG ├── Layer3_Filter210_Top5_Activations.JPEG ├── Layer3_Filter210_Top6.JPEG ├── Layer3_Filter210_Top6_Activations.JPEG ├── Layer3_Filter210_Top7.JPEG ├── Layer3_Filter210_Top7_Activations.JPEG ├── Layer3_Filter210_Top8.JPEG ├── Layer3_Filter210_Top8_Activations.JPEG ├── Layer3_Filter210_Top9.JPEG ├── Layer3_Filter210_Top9_Activations.JPEG ├── Layer3_Filter299_Top1.JPEG ├── Layer3_Filter299_Top1_Activations.JPEG ├── Layer3_Filter299_Top2.JPEG ├── Layer3_Filter299_Top2_Activations.JPEG ├── Layer3_Filter299_Top3.JPEG ├── Layer3_Filter299_Top3_Activations.JPEG ├── Layer3_Filter299_Top4.JPEG ├── Layer3_Filter299_Top4_Activations.JPEG ├── Layer3_Filter299_Top5.JPEG ├── Layer3_Filter299_Top5_Activations.JPEG ├── Layer3_Filter299_Top6.JPEG ├── Layer3_Filter299_Top6_Activations.JPEG ├── Layer3_Filter299_Top7.JPEG ├── Layer3_Filter299_Top7_Activations.JPEG ├── Layer3_Filter299_Top8.JPEG ├── Layer3_Filter299_Top8_Activations.JPEG ├── Layer3_Filter299_Top9.JPEG ├── Layer3_Filter299_Top9_Activations.JPEG ├── Layer3_Filter307_Top1.JPEG ├── Layer3_Filter307_Top1_Activations.JPEG ├── Layer3_Filter307_Top2.JPEG ├── Layer3_Filter307_Top2_Activations.JPEG ├── Layer3_Filter307_Top3.JPEG ├── Layer3_Filter307_Top3_Activations.JPEG ├── Layer3_Filter307_Top4.JPEG ├── Layer3_Filter307_Top4_Activations.JPEG ├── Layer3_Filter307_Top5.JPEG ├── Layer3_Filter307_Top5_Activations.JPEG ├── Layer3_Filter307_Top6.JPEG ├── Layer3_Filter307_Top6_Activations.JPEG ├── Layer3_Filter307_Top7.JPEG ├── Layer3_Filter307_Top7_Activations.JPEG ├── Layer3_Filter307_Top8.JPEG ├── Layer3_Filter307_Top8_Activations.JPEG ├── Layer3_Filter307_Top9.JPEG ├── Layer3_Filter307_Top9_Activations.JPEG ├── Layer3_Filter365_Top1.JPEG ├── Layer3_Filter365_Top1_Activations.JPEG ├── Layer3_Filter365_Top2.JPEG ├── Layer3_Filter365_Top2_Activations.JPEG ├── Layer3_Filter365_Top3.JPEG ├── Layer3_Filter365_Top3_Activations.JPEG ├── Layer3_Filter365_Top4.JPEG ├── Layer3_Filter365_Top4_Activations.JPEG ├── Layer3_Filter365_Top5.JPEG ├── Layer3_Filter365_Top5_Activations.JPEG ├── Layer3_Filter365_Top6.JPEG ├── Layer3_Filter365_Top6_Activations.JPEG ├── Layer3_Filter365_Top7.JPEG ├── Layer3_Filter365_Top7_Activations.JPEG ├── Layer3_Filter365_Top8.JPEG ├── Layer3_Filter365_Top8_Activations.JPEG ├── Layer3_Filter365_Top9.JPEG ├── Layer3_Filter365_Top9_Activations.JPEG ├── Layer3_Filter378_Top1.JPEG ├── Layer3_Filter378_Top1_Activations.JPEG ├── Layer3_Filter378_Top2.JPEG ├── Layer3_Filter378_Top2_Activations.JPEG ├── Layer3_Filter378_Top3.JPEG ├── Layer3_Filter378_Top3_Activations.JPEG ├── Layer3_Filter378_Top4.JPEG ├── Layer3_Filter378_Top4_Activations.JPEG ├── Layer3_Filter378_Top5.JPEG ├── Layer3_Filter378_Top5_Activations.JPEG ├── Layer3_Filter378_Top6.JPEG ├── Layer3_Filter378_Top6_Activations.JPEG ├── Layer3_Filter378_Top7.JPEG ├── Layer3_Filter378_Top7_Activations.JPEG ├── Layer3_Filter378_Top8.JPEG ├── Layer3_Filter378_Top8_Activations.JPEG ├── Layer3_Filter378_Top9.JPEG ├── Layer3_Filter378_Top9_Activations.JPEG ├── Layer3_Filter382_Top1.JPEG ├── Layer3_Filter382_Top1_Activations.JPEG ├── Layer3_Filter382_Top2.JPEG ├── Layer3_Filter382_Top2_Activations.JPEG ├── Layer3_Filter382_Top3.JPEG ├── Layer3_Filter382_Top3_Activations.JPEG ├── Layer3_Filter382_Top4.JPEG ├── Layer3_Filter382_Top4_Activations.JPEG ├── Layer3_Filter382_Top5.JPEG ├── Layer3_Filter382_Top5_Activations.JPEG ├── Layer3_Filter382_Top6.JPEG ├── Layer3_Filter382_Top6_Activations.JPEG ├── Layer3_Filter382_Top7.JPEG ├── Layer3_Filter382_Top7_Activations.JPEG ├── Layer3_Filter382_Top8.JPEG ├── Layer3_Filter382_Top8_Activations.JPEG ├── Layer3_Filter382_Top9.JPEG ├── Layer3_Filter382_Top9_Activations.JPEG ├── Layer3_Filter9_Top1.JPEG ├── Layer3_Filter9_Top1_Activations.JPEG ├── Layer3_Filter9_Top2.JPEG ├── Layer3_Filter9_Top2_Activations.JPEG ├── Layer3_Filter9_Top3.JPEG ├── Layer3_Filter9_Top3_Activations.JPEG ├── Layer3_Filter9_Top4.JPEG ├── Layer3_Filter9_Top4_Activations.JPEG ├── Layer3_Filter9_Top5.JPEG ├── Layer3_Filter9_Top5_Activations.JPEG ├── Layer3_Filter9_Top6.JPEG ├── Layer3_Filter9_Top6_Activations.JPEG ├── Layer3_Filter9_Top7.JPEG ├── Layer3_Filter9_Top7_Activations.JPEG ├── Layer3_Filter9_Top8.JPEG ├── Layer3_Filter9_Top8_Activations.JPEG ├── Layer3_Filter9_Top9.JPEG └── Layer3_Filter9_Top9_Activations.JPEG ├── Layer4_Projections_and_Images ├── Layer4_Filter121_Top1.JPEG ├── Layer4_Filter121_Top1_Activations.JPEG ├── Layer4_Filter121_Top2.JPEG ├── Layer4_Filter121_Top2_Activations.JPEG ├── Layer4_Filter121_Top3.JPEG ├── Layer4_Filter121_Top3_Activations.JPEG ├── Layer4_Filter121_Top4.JPEG ├── Layer4_Filter121_Top4_Activations.JPEG ├── Layer4_Filter121_Top5.JPEG ├── Layer4_Filter121_Top5_Activations.JPEG ├── Layer4_Filter121_Top6.JPEG ├── Layer4_Filter121_Top6_Activations.JPEG ├── Layer4_Filter121_Top7.JPEG ├── Layer4_Filter121_Top7_Activations.JPEG ├── Layer4_Filter121_Top8.JPEG ├── Layer4_Filter121_Top8_Activations.JPEG ├── Layer4_Filter121_Top9.JPEG ├── Layer4_Filter121_Top9_Activations.JPEG ├── Layer4_Filter131_Top1.JPEG ├── Layer4_Filter131_Top1_Activations.JPEG ├── Layer4_Filter131_Top2.JPEG ├── Layer4_Filter131_Top2_Activations.JPEG ├── Layer4_Filter131_Top3.JPEG ├── Layer4_Filter131_Top3_Activations.JPEG ├── Layer4_Filter131_Top4.JPEG ├── Layer4_Filter131_Top4_Activations.JPEG ├── Layer4_Filter131_Top5.JPEG ├── Layer4_Filter131_Top5_Activations.JPEG ├── Layer4_Filter131_Top6.JPEG ├── Layer4_Filter131_Top6_Activations.JPEG ├── Layer4_Filter131_Top7.JPEG ├── Layer4_Filter131_Top7_Activations.JPEG ├── Layer4_Filter131_Top8.JPEG ├── Layer4_Filter131_Top8_Activations.JPEG ├── Layer4_Filter131_Top9.JPEG ├── Layer4_Filter131_Top9_Activations.JPEG ├── Layer4_Filter144_Top1.JPEG ├── Layer4_Filter144_Top1_Activations.JPEG ├── Layer4_Filter144_Top2.JPEG ├── Layer4_Filter144_Top2_Activations.JPEG ├── Layer4_Filter144_Top3.JPEG ├── Layer4_Filter144_Top3_Activations.JPEG ├── Layer4_Filter144_Top4.JPEG ├── Layer4_Filter144_Top4_Activations.JPEG ├── Layer4_Filter144_Top5.JPEG ├── Layer4_Filter144_Top5_Activations.JPEG ├── Layer4_Filter144_Top6.JPEG ├── Layer4_Filter144_Top6_Activations.JPEG ├── Layer4_Filter144_Top7.JPEG ├── Layer4_Filter144_Top7_Activations.JPEG ├── Layer4_Filter144_Top8.JPEG ├── Layer4_Filter144_Top8_Activations.JPEG ├── Layer4_Filter144_Top9.JPEG ├── Layer4_Filter144_Top9_Activations.JPEG ├── Layer4_Filter218_Top1.JPEG ├── Layer4_Filter218_Top1_Activations.JPEG ├── Layer4_Filter218_Top2.JPEG ├── Layer4_Filter218_Top2_Activations.JPEG ├── Layer4_Filter218_Top3.JPEG ├── Layer4_Filter218_Top3_Activations.JPEG ├── Layer4_Filter218_Top4.JPEG ├── Layer4_Filter218_Top4_Activations.JPEG ├── Layer4_Filter218_Top5.JPEG ├── Layer4_Filter218_Top5_Activations.JPEG ├── Layer4_Filter218_Top6.JPEG ├── Layer4_Filter218_Top6_Activations.JPEG ├── Layer4_Filter218_Top7.JPEG ├── Layer4_Filter218_Top7_Activations.JPEG ├── Layer4_Filter218_Top8.JPEG ├── Layer4_Filter218_Top8_Activations.JPEG ├── Layer4_Filter218_Top9.JPEG ├── Layer4_Filter218_Top9_Activations.JPEG ├── Layer4_Filter370_Top1.JPEG ├── Layer4_Filter370_Top1_Activations.JPEG ├── Layer4_Filter370_Top2.JPEG ├── Layer4_Filter370_Top2_Activations.JPEG ├── Layer4_Filter370_Top3.JPEG ├── Layer4_Filter370_Top3_Activations.JPEG ├── Layer4_Filter370_Top4.JPEG ├── Layer4_Filter370_Top4_Activations.JPEG ├── Layer4_Filter370_Top5.JPEG ├── Layer4_Filter370_Top5_Activations.JPEG ├── Layer4_Filter370_Top6.JPEG ├── Layer4_Filter370_Top6_Activations.JPEG ├── Layer4_Filter370_Top7.JPEG ├── Layer4_Filter370_Top7_Activations.JPEG ├── Layer4_Filter370_Top8.JPEG ├── Layer4_Filter370_Top8_Activations.JPEG ├── Layer4_Filter370_Top9.JPEG └── Layer4_Filter370_Top9_Activations.JPEG ├── Layer5_Projections_and_Images ├── Layer5_Filter120_Top1.JPEG ├── Layer5_Filter120_Top1_Activations.JPEG ├── Layer5_Filter120_Top2.JPEG ├── Layer5_Filter120_Top2_Activations.JPEG ├── Layer5_Filter120_Top3.JPEG ├── Layer5_Filter120_Top3_Activations.JPEG ├── Layer5_Filter120_Top4.JPEG ├── Layer5_Filter120_Top4_Activations.JPEG ├── Layer5_Filter120_Top5.JPEG ├── Layer5_Filter120_Top5_Activations.JPEG ├── Layer5_Filter120_Top6.JPEG ├── Layer5_Filter120_Top6_Activations.JPEG ├── Layer5_Filter120_Top7.JPEG ├── Layer5_Filter120_Top7_Activations.JPEG ├── Layer5_Filter120_Top8.JPEG ├── Layer5_Filter120_Top8_Activations.JPEG ├── Layer5_Filter120_Top9.JPEG ├── Layer5_Filter120_Top9_Activations.JPEG ├── Layer5_Filter128_Top1.JPEG ├── Layer5_Filter128_Top1_Activations.JPEG ├── Layer5_Filter128_Top2.JPEG ├── Layer5_Filter128_Top2_Activations.JPEG ├── Layer5_Filter128_Top3.JPEG ├── Layer5_Filter128_Top3_Activations.JPEG ├── Layer5_Filter128_Top4.JPEG ├── Layer5_Filter128_Top4_Activations.JPEG ├── Layer5_Filter128_Top5.JPEG ├── Layer5_Filter128_Top5_Activations.JPEG ├── Layer5_Filter128_Top6.JPEG ├── Layer5_Filter128_Top6_Activations.JPEG ├── Layer5_Filter128_Top7.JPEG ├── Layer5_Filter128_Top7_Activations.JPEG ├── Layer5_Filter128_Top8.JPEG ├── Layer5_Filter128_Top8_Activations.JPEG ├── Layer5_Filter128_Top9.JPEG ├── Layer5_Filter128_Top9_Activations.JPEG ├── Layer5_Filter143_Top1.JPEG ├── Layer5_Filter143_Top1_Activations.JPEG ├── Layer5_Filter143_Top2.JPEG ├── Layer5_Filter143_Top2_Activations.JPEG ├── Layer5_Filter143_Top3.JPEG ├── Layer5_Filter143_Top3_Activations.JPEG ├── Layer5_Filter143_Top4.JPEG ├── Layer5_Filter143_Top4_Activations.JPEG ├── Layer5_Filter143_Top5.JPEG ├── Layer5_Filter143_Top5_Activations.JPEG ├── Layer5_Filter143_Top6.JPEG ├── Layer5_Filter143_Top6_Activations.JPEG ├── Layer5_Filter143_Top7.JPEG ├── Layer5_Filter143_Top7_Activations.JPEG ├── Layer5_Filter143_Top8.JPEG ├── Layer5_Filter143_Top8_Activations.JPEG ├── Layer5_Filter143_Top9.JPEG ├── Layer5_Filter143_Top9_Activations.JPEG ├── Layer5_Filter194_Top1.JPEG ├── Layer5_Filter194_Top1_Activations.JPEG ├── Layer5_Filter194_Top2.JPEG ├── Layer5_Filter194_Top2_Activations.JPEG ├── Layer5_Filter194_Top3.JPEG ├── Layer5_Filter194_Top3_Activations.JPEG ├── Layer5_Filter194_Top4.JPEG ├── Layer5_Filter194_Top4_Activations.JPEG ├── Layer5_Filter194_Top5.JPEG ├── Layer5_Filter194_Top5_Activations.JPEG ├── Layer5_Filter194_Top6.JPEG ├── Layer5_Filter194_Top6_Activations.JPEG ├── Layer5_Filter194_Top7.JPEG ├── Layer5_Filter194_Top7_Activations.JPEG ├── Layer5_Filter194_Top8.JPEG ├── Layer5_Filter194_Top8_Activations.JPEG ├── Layer5_Filter194_Top9.JPEG ├── Layer5_Filter194_Top9_Activations.JPEG ├── Layer5_Filter211_Top1.JPEG ├── Layer5_Filter211_Top1_Activations.JPEG ├── Layer5_Filter211_Top2.JPEG ├── Layer5_Filter211_Top2_Activations.JPEG ├── Layer5_Filter211_Top3.JPEG ├── Layer5_Filter211_Top3_Activations.JPEG ├── Layer5_Filter211_Top4.JPEG ├── Layer5_Filter211_Top4_Activations.JPEG ├── Layer5_Filter211_Top5.JPEG ├── Layer5_Filter211_Top5_Activations.JPEG ├── Layer5_Filter211_Top6.JPEG ├── Layer5_Filter211_Top6_Activations.JPEG ├── Layer5_Filter211_Top7.JPEG ├── Layer5_Filter211_Top7_Activations.JPEG ├── Layer5_Filter211_Top8.JPEG ├── Layer5_Filter211_Top8_Activations.JPEG ├── Layer5_Filter211_Top9.JPEG ├── Layer5_Filter211_Top9_Activations.JPEG ├── Layer5_Filter222_Top1.JPEG ├── Layer5_Filter222_Top1_Activations.JPEG ├── Layer5_Filter222_Top2.JPEG ├── Layer5_Filter222_Top2_Activations.JPEG ├── Layer5_Filter222_Top3.JPEG ├── Layer5_Filter222_Top3_Activations.JPEG ├── Layer5_Filter222_Top4.JPEG ├── Layer5_Filter222_Top4_Activations.JPEG ├── Layer5_Filter222_Top5.JPEG ├── Layer5_Filter222_Top5_Activations.JPEG ├── Layer5_Filter222_Top6.JPEG ├── Layer5_Filter222_Top6_Activations.JPEG ├── Layer5_Filter222_Top7.JPEG ├── Layer5_Filter222_Top7_Activations.JPEG ├── Layer5_Filter222_Top8.JPEG ├── Layer5_Filter222_Top8_Activations.JPEG ├── Layer5_Filter222_Top9.JPEG ├── Layer5_Filter222_Top9_Activations.JPEG ├── Layer5_Filter227_Top1.JPEG ├── Layer5_Filter227_Top1_Activations.JPEG ├── Layer5_Filter227_Top2.JPEG ├── Layer5_Filter227_Top2_Activations.JPEG ├── Layer5_Filter227_Top3.JPEG ├── Layer5_Filter227_Top3_Activations.JPEG ├── Layer5_Filter227_Top4.JPEG ├── Layer5_Filter227_Top4_Activations.JPEG ├── Layer5_Filter227_Top5.JPEG ├── Layer5_Filter227_Top5_Activations.JPEG ├── Layer5_Filter227_Top6.JPEG ├── Layer5_Filter227_Top6_Activations.JPEG ├── Layer5_Filter227_Top7.JPEG ├── Layer5_Filter227_Top7_Activations.JPEG ├── Layer5_Filter227_Top8.JPEG ├── Layer5_Filter227_Top8_Activations.JPEG ├── Layer5_Filter227_Top9.JPEG └── Layer5_Filter227_Top9_Activations.JPEG ├── Poster_VisualizeCNN.pdf ├── README.md ├── activations.py ├── alexnet.py ├── alexnet_additional_layers.py ├── alexnet_model.png ├── decode_predictions.py ├── deconvolution.py ├── deconvolution_additional_layers.py ├── occlusion.py └── validation.py /Layer2_Projections_and_Images/Layer2_Filter111_Top1.JPEG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/FHainzl/Visualizing_Understanding_CNN_Implementation/4e438c76eb93c4e9758198ddbc6cbe0c220c1586/Layer2_Projections_and_Images/Layer2_Filter111_Top1.JPEG -------------------------------------------------------------------------------- /Layer2_Projections_and_Images/Layer2_Filter111_Top1_Activations.JPEG: -------------------------------------------------------------------------------- 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Zeiler and Rob Fergus [https://www.cs.nyu.edu/~fergus/papers/zeilerECCV2014.pdf] in Keras/Tensorflow. 4 | 5 | The pre-trained AlexNet is from: 6 | https://github.com/heuritech/convnets-keras 7 | 8 | To understand: Check out Poster of this project or the original paper. 9 | 10 | To run: Downloatd the ILSVRC2012 validation set from http://www.image-net.org/challenges/LSVRC/2012/nonpub-downloads (6.3GB) in folder "ILSVRC2012_img_val", run activations.py (this may take a considerable amount of time) and then run deconvolution.py with a certain layer and filter as arguments to project the maxmial activation of a filter back to image space. 11 | 12 | -------------------------------------------------------------------------------- /alexnet_additional_layers.py: -------------------------------------------------------------------------------- 1 | from keras.layers.core import Lambda 2 | from keras import backend as K 3 | 4 | """ 5 | Both layers from 6 | https://github.com/heuritech/convnets-keras/blob/master/convnetskeras/convnets.py 7 | and only slightly modified to work with TF backend as suggested by GitHub user 8 | """ 9 | 10 | def cross_channel_normalization(alpha=1e-4, k=2, beta=0.75, n=5, **kwargs): 11 | # This is the function used for cross channel normalization in the original Alexnet 12 | 13 | def f(X): 14 | K.set_image_dim_ordering('th') 15 | b, ch, r, c = X.get_shape() 16 | half = n // 2 17 | square = K.square(X) 18 | 19 | # Rearrange to make spatial_2d_padding work, then re-rearrange 20 | square = K.permute_dimensions(square, (0, 2, 3, 1)) 21 | extra_channels = K.spatial_2d_padding(square, ((0, 0), (half, half))) 22 | extra_channels = K.permute_dimensions(extra_channels, (0, 3, 1, 2)) 23 | 24 | scale = k 25 | for i in range(n): 26 | ch = int(ch) 27 | scale += alpha * extra_channels[:, i:i + ch, :, :] 28 | scale = scale ** beta 29 | return X / scale 30 | 31 | return Lambda(f, output_shape=lambda input_shape: input_shape, **kwargs) 32 | 33 | def split_tensor(axis=1, ratio_split=1, id_split=0, **kwargs): 34 | def f(X): 35 | div = int(X.get_shape()[axis]) // ratio_split 36 | 37 | if axis == 0: 38 | output = X[id_split * div:(id_split + 1) * div, :, :, :] 39 | elif axis == 1: 40 | output = X[:, id_split * div:(id_split + 1) * div, :, :] 41 | elif axis == 2: 42 | output = X[:, :, id_split * div:(id_split + 1) * div, :] 43 | elif axis == 3: 44 | output = X[:, :, :, id_split * div:(id_split + 1) * div] 45 | else: 46 | raise ValueError("This axis is not possible") 47 | 48 | return output 49 | 50 | def g(input_shape): 51 | output_shape = list(input_shape) 52 | output_shape[axis] = output_shape[axis] // ratio_split 53 | return tuple(output_shape) 54 | 55 | return Lambda(f, output_shape=lambda input_shape: g(input_shape), **kwargs) 56 | -------------------------------------------------------------------------------- /alexnet_model.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/FHainzl/Visualizing_Understanding_CNN_Implementation/4e438c76eb93c4e9758198ddbc6cbe0c220c1586/alexnet_model.png -------------------------------------------------------------------------------- /decode_predictions.py: -------------------------------------------------------------------------------- 1 | import json 2 | 3 | """ 4 | Helper functions to convert 1000dim vector with class probabilities (or batches thereof) to list of top classes 5 | 6 | Based on 7 | decode_predictions from keras.applications.imagenet_utils 8 | """ 9 | 10 | 11 | def decode_classnames_json(preds, top=5): 12 | """ 13 | Returns class code, class name and probability for each class amongst top=5 for each prediction in preds 14 | 15 | e.g. 16 | [[('n01871265', 'tusker', 0.69987053), ('n02504458', 'African_elephant', 0.18252705), ... ]] 17 | """ 18 | 19 | if len(preds.shape) != 2 or preds.shape[1] != 1000: 20 | raise ValueError('`decode_classnames_json` expects ' 21 | 'a batch of predictions ' 22 | '(i.e. a 2D array of shape (samples, 1000)). ' 23 | 'Found array with shape: ' + str(preds.shape)) 24 | 25 | with open('Data/imagenet_class_index.json') as data_file: 26 | data = json.load(data_file) 27 | results = [] 28 | for pred in preds: 29 | top_indices = pred.argsort()[-top:][::-1] 30 | result = [tuple(data[str(i)]) + (pred[i],) for i in top_indices] 31 | results.append(result) 32 | return results 33 | 34 | 35 | def decode_classnumber(preds, top=5): 36 | """ 37 | Return class number between 0 and 999 for each class amongst top=5 for each prediction in preds 38 | 39 | e.g. 40 | [[101, 386, 385, 149, 343]] 41 | """ 42 | 43 | if len(preds.shape) != 2 or preds.shape[1] != 1000: 44 | raise ValueError('`decode_classnumber` expects ' 45 | 'a batch of predictions ' 46 | '(i.e. a 2D array of shape (samples, 1000)). ' 47 | 'Found array with shape: ' + str(preds.shape)) 48 | results = [] 49 | for pred in preds: 50 | top_indices = pred.argsort()[-top:][::-1] 51 | result = list(top_indices) 52 | results.append(result) 53 | return results 54 | -------------------------------------------------------------------------------- /deconvolution_additional_layers.py: -------------------------------------------------------------------------------- 1 | from keras.models import Model 2 | from keras.layers import Input, Conv2DTranspose 3 | from keras.utils.layer_utils import convert_all_kernels_in_model 4 | from keras import backend as K 5 | from keras.optimizers import SGD 6 | 7 | from alexnet import AlexNet 8 | 9 | import numpy as np 10 | 11 | 12 | class DeconvLayers: 13 | """ 14 | Create deconvolutional layers with parameters extracted from alexnet 15 | """ 16 | 17 | conv_layer_names = AlexNet.conv_layer_names 18 | deconv_layer_names = AlexNet.deconv_layer_names 19 | 20 | def __init__(self, conv_base_model): 21 | 22 | # Full AlexNet 23 | self.conv_base_model = conv_base_model 24 | self.conv_layers = self.init_conv_layers() 25 | 26 | self.weights = self.init_weights_dict() 27 | self.bias3D = self.init_bias3D_dict() 28 | 29 | self.deconv_layers = self.init_deconv_layers() 30 | 31 | def deconv_layer_model(self, layer_name): 32 | K.set_image_dim_ordering('th') 33 | 34 | # Get local variables from dictionaries 35 | conv_layer = self.conv_layers[layer_name] 36 | w, b = self.weights[layer_name].tuple 37 | 38 | # Get deconvolutional shapes from convolutional shapes 39 | deconv_from_shape = conv_layer.output_shape[1:] 40 | deconv_to_shape = conv_layer.input_shape[1:] 41 | de_layer_name = 'de' + layer_name 42 | 43 | # Build layer 44 | inputs = Input(shape=deconv_from_shape) 45 | prediction = Conv2DTranspose(filters=deconv_to_shape[0], # N_Filters equals N_channels of convolution input 46 | kernel_size=conv_layer.kernel_size, 47 | strides=conv_layer.strides, 48 | activation='relu', 49 | use_bias=False, 50 | name=de_layer_name)(inputs) 51 | 52 | m = Model(input=inputs, output=prediction) 53 | 54 | assert m.get_layer(de_layer_name).get_weights()[0].shape == w.shape 55 | m.get_layer(de_layer_name).set_weights([w]) 56 | 57 | return m 58 | 59 | def init_conv_layers(self): 60 | conv_layer_dict = {} 61 | for layer_name in self.conv_layer_names: 62 | conv_layer_dict[layer_name] = self.conv_base_model.get_layer(layer_name) 63 | return conv_layer_dict 64 | 65 | def init_weights_dict(self): 66 | weights_dict = {} 67 | for layer_name, layer in self.conv_layers.items(): 68 | w = layer.get_weights()[0] 69 | b = layer.get_weights()[1] 70 | weights_dict[layer_name] = Weights(w, b) 71 | return weights_dict 72 | 73 | def init_bias3D_dict(self): 74 | """ 75 | Arrange bias in 3D shape, such that it can be subtracted from features tensor 76 | """ 77 | 78 | bias_dict = {} 79 | for layer_name, layer in self.conv_layers.items(): 80 | b3D = np.zeros(layer.output_shape[1:]) 81 | for f in range(b3D.shape[0]): 82 | b3D[f] = np.full_like(b3D[0], self.weights[layer_name].b[f]) 83 | b3D = np.expand_dims(b3D, axis=0) 84 | bias_dict[layer_name] = b3D 85 | return bias_dict 86 | 87 | def init_deconv_layers(self): 88 | deconv_layers = {} 89 | for deconv_layer_name in self.deconv_layer_names: 90 | deconv_layers[deconv_layer_name] = self.deconv_layer_model(deconv_layer_name[2:]) 91 | return deconv_layers 92 | 93 | 94 | class Weights: 95 | def __init__(self, w, b): 96 | assert type(w) == np.ndarray 97 | assert type(b) == np.ndarray 98 | self.w = w 99 | self.b = b 100 | self.tuple = (w, b) 101 | -------------------------------------------------------------------------------- /occlusion.py: -------------------------------------------------------------------------------- 1 | from alexnet import AlexNet 2 | from validation import get_labels, get_path_from_id 3 | from activations import get_strongest_filter 4 | from decode_predictions import decode_classnames_json, decode_classnumber 5 | import matplotlib.pyplot as plt 6 | from deconvolution import Deconvolution, DeconvOutput 7 | 8 | import numpy as np 9 | from scipy.misc import imread, imresize 10 | 11 | 12 | def preprocess_image_batch_grey_square(image_paths, square_x, square_y, img_size=(256, 256), crop_size=(227, 13 | 227), 14 | color_mode="rgb", out=None 15 | 16 | ): # Make function callable with single image instead of list 17 | 18 | 19 | if type(image_paths) is str: 20 | image_paths = [image_paths] 21 | 22 | img_list = [] 23 | for im_path in image_paths: 24 | img = imread(im_path, mode='RGB') 25 | img = imresize(img, img_size) 26 | img = img.astype('float32') 27 | 28 | # Normalize the colors (in RGB space) with the empirical means on the training set 29 | img[:, :, 0] -= 123.68 30 | img[:, :, 1] -= 116.779 31 | img[:, :, 2] -= 103.939 32 | # We permute the colors to get them in the BGR order 33 | if color_mode == "bgr": 34 | img[:, :, [0, 1, 2]] = img[:, :, [2, 1, 0]] 35 | img = img.transpose((2, 0, 1)) 36 | 37 | if crop_size: 38 | img = img[:, (img_size[0] - crop_size[0]) // 2:(img_size[0] + crop_size[0]) // 2 39 | , (img_size[1] - crop_size[1]) // 2:(img_size[1] + crop_size[1]) // 2] 40 | 41 | # Add grey square 42 | assert 12 < square_x < 243 43 | assert 12 < square_y < 243 44 | img[:, square_x - 13:square_x + 14, square_y - 13:square_y + 14] = 0 45 | 46 | img_list.append(img) 47 | 48 | try: 49 | img_batch = np.stack(img_list, axis=0) 50 | except: 51 | raise ValueError('when img_size and crop_size are None, images' 52 | ' in image_paths must have the same shapes.') 53 | 54 | if out is not None and hasattr(out, 'append'): 55 | out.append(img_batch) 56 | else: 57 | return img_batch 58 | 59 | 60 | def get_summed_activation_of_feature_map(top_layer_model, f, preprocd_img): 61 | # Get activations for shortened model 62 | activation_img = top_layer_model.predict(preprocd_img) 63 | activation_img = activation_img[0, f - 1, :, :] 64 | activation_img = activation_img.sum(1) 65 | activation_img = activation_img.sum(0) 66 | return activation_img 67 | 68 | 69 | def visualize_one_image(conv_base, path, layer, f, contrast, output_name): 70 | save_to_folder = 'Occlusion/' 71 | 72 | projection = Deconvolution(conv_base).project_down(path, layer, f) 73 | 74 | activation_filename = save_to_folder + output_name 75 | DeconvOutput(projection, contrast).save_as(filename=activation_filename + '.JPEG') 76 | 77 | 78 | def get_heatmaps(img_id, alexnet, title): 79 | base_model = alexnet.base_model 80 | top_layer_model = alexnet.model 81 | labels = get_labels() 82 | 83 | path = get_path_from_id(img_id) 84 | 85 | strongest_filter = get_strongest_filter(img_id, layer=5) 86 | true_label = labels[img_id] 87 | print(strongest_filter, true_label) 88 | 89 | predictions = AlexNet(base_model=base_model).predict(path) 90 | print(decode_classnames_json(predictions)) 91 | print(decode_classnumber(predictions)) 92 | print(true_label) 93 | 94 | # DeconvOutput(preprocess_image_batch_grey_square(image_paths=path, square_x=50, square_y=50)).save_as('Occlusion', 95 | # title + '.JPEG') 96 | activations = np.zeros((30, 30)) 97 | class_prop = np.zeros((30, 30)) 98 | 99 | for x in range(0, 30): 100 | print(x) 101 | for y in range(0, 30): 102 | prep_image = preprocess_image_batch_grey_square(path, 13 + x * 7, 13 + y * 7) 103 | activation = get_summed_activation_of_feature_map(top_layer_model, strongest_filter, prep_image) 104 | prediction = base_model.predict(prep_image) 105 | activations[x, y] = activation 106 | class_prop[x, y] = prediction[0][true_label] 107 | print('done') 108 | 109 | fig, ax = plt.subplots() 110 | cax = ax.imshow(activations, interpolation='nearest', cmap='plasma') 111 | plt.axis('off') 112 | # Add colorbar, make sure to specify tick locations to match desired ticklabels 113 | cbar = fig.colorbar(cax) 114 | plt.show() 115 | 116 | fig, ax = plt.subplots() 117 | cax = ax.imshow(class_prop, interpolation='nearest', cmap='plasma') 118 | plt.axis('off') 119 | # Add colorbar, make sure to specify tick locations to match desired ticklabels 120 | cbar = fig.colorbar(cax) 121 | plt.show() 122 | 123 | 124 | if __name__ == '__main__': 125 | img_id = 9997 126 | alexnet = AlexNet(5) 127 | base_model = alexnet.base_model 128 | top_layer_model = alexnet.model 129 | 130 | get_heatmaps(img_id, alexnet, 'Tractor') 131 | 132 | # path = get_path_from_id(img_id) 133 | # visualize_one_image(base_model, path, 5, get_strongest_feature_map(img_id), contrast=22, output_name='Berg_Deconv_1') 134 | -------------------------------------------------------------------------------- /validation.py: -------------------------------------------------------------------------------- 1 | from alexnet import AlexNet 2 | import os 3 | import time 4 | 5 | 6 | 7 | 8 | 9 | def predict_validation_set(model, batch_size=500, break_after_batches=None, folder='ILSVRC2012_img_val', 10 | filename='Data/ILSVRC2012_img_val_predictions.txt'): 11 | """ 12 | Predict Top5 most likely classes for images in Validation Set and save them to text file 13 | """ 14 | val_set_size = 50000 15 | 16 | # Delete file if exists: 17 | try: 18 | os.remove(filename) 19 | except OSError: 20 | pass 21 | 22 | with open(filename, 'a') as results_file: 23 | start = [] 24 | finish = [] 25 | for batch in range(val_set_size // batch_size): 26 | 27 | start.append(time.time()) 28 | 29 | # Early break for testing 30 | if break_after_batches is not None and batch == break_after_batches: 31 | break 32 | 33 | img_batch_locations = [] 34 | 35 | for i in range(1 + batch * batch_size, 1 + (batch + 1) * batch_size): 36 | img_id = str(i) 37 | while len(img_id) < 5: 38 | img_id = '0' + img_id 39 | img_batch_locations.append(folder + '/ILSVRC2012_val_000' + img_id + '.JPEG') 40 | 41 | batch_predictions = AlexNet(model).top_classes(img_batch_locations) 42 | 43 | for i in range(batch_size): 44 | results_file.write( 45 | img_batch_locations[i][len(folder) + 1:] + ' ' + str(batch_predictions[i])[1:-1] + '\n') 46 | 47 | finish.append(time.time()) 48 | 49 | last_execution = finish[-1] - start[-1] 50 | total_execution = finish[-1] - start[0] 51 | expected_remaining_time = total_execution / (batch + 1) * (val_set_size // batch_size - batch) 52 | 53 | print("The execution of batch #{} took {} s".format(batch + 1, last_execution / 60)) 54 | print("This corresponds to a time of {} s per 100 images".format(100 * last_execution / batch_size)) 55 | print("Total execution time so far: {} min".format(total_execution / 60)) 56 | print("The extrapolated remaining time is {} min".format(expected_remaining_time / 60)) 57 | print('\n') 58 | 59 | def get_path_from_id(img_id): 60 | img_id_str = str(img_id) 61 | while len(img_id_str) < 5: 62 | img_id_str = '0' + img_id_str 63 | 64 | folder = 'ILSVRC2012_IMG_VAL/' 65 | file = 'ILSVRC2012_val_000' + img_id_str + '.JPEG' 66 | 67 | path = folder + file 68 | 69 | return path 70 | 71 | 72 | def get_labels(): 73 | """ 74 | Imports labels for ILSVRC2012 Validation set and stores them in list 75 | Label is stored at array index corresponding to each image's ID 76 | """ 77 | result = [None] * (AlexNet.val_set_size+1) 78 | with open('Data/ILSVRC2012_img_val_labels.txt', 'r') as data: 79 | for line in data: 80 | filename = line.split(' ')[0] 81 | im_id = int(filename[-11:-5]) 82 | label = int(line.split(' ')[1]) 83 | result[im_id] = label 84 | return result 85 | 86 | 87 | 88 | 89 | 90 | def accuracy_validation_set(): 91 | """ 92 | Determine Accuracy of Top5 predictions on Validation set. 93 | This is NOT an honest test accuracy, because validation can be used in training. Labels of test set are not available. 94 | """ 95 | 96 | labels = get_labels() 97 | filename = 'Data/ILSVRC2012_img_val_predictions.txt' 98 | total = 0 99 | correct = 0 100 | with open(filename, 'r') as data: 101 | for line in data: 102 | filename = line.split(' ')[0] 103 | im_id = int(filename[-11:-5]) 104 | predictions = [int(num.strip()) for num in line.split()[1:]] 105 | if labels[im_id] in predictions: 106 | correct += 1 107 | total += 1 108 | return correct / total 109 | 110 | 111 | if __name__ == '__main__': 112 | # Generate prediction and save to text file 113 | if False: 114 | model = AlexNet().model 115 | predict_validation_set(model) 116 | # Print accuracy 117 | print('Accuracy at Validation Set: {}%'.format(accuracy_validation_set())) 118 | --------------------------------------------------------------------------------