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1.6.0-DEV-cbd854b0f4.log
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Julia Version 1.6.0-DEV.185
Commit cbd854b0f4 (2020-06-06 13:20 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-9.0.1 (ORCJIT, skylake)
Environment:
JULIA_DEPOT_PATH = ::/usr/local/share/julia
JULIA_NUM_THREADS = 2
Resolving package versions...
Installed Mocking ────────────────────── v0.7.1
Installed SetRounding ────────────────── v0.2.0
Installed Lz4_jll ────────────────────── v1.9.2+0
Installed RecipesBase ────────────────── v1.0.1
Installed BufferedStreams ────────────── v1.0.0
Installed OrderedCollections ─────────── v1.2.0
Installed DocStringExtensions ────────── v0.8.1
Installed MIPVerify ──────────────────── v0.2.2
Installed Compat ─────────────────────── v2.2.0
Installed Nullables ──────────────────── v1.0.0
Installed Blosc_jll ──────────────────── v1.14.3+1
Installed CommonSubexpressions ───────── v0.2.0
Installed TranscodingStreams ─────────── v0.9.5
Installed HDF5 ───────────────────────── v0.13.2
Installed MAT ────────────────────────── v0.8.0
Installed DataFrames ─────────────────── v0.21.2
Installed TimeZones ──────────────────── v1.2.0
Installed DiffRules ──────────────────── v1.0.1
Installed InvertedIndices ────────────── v1.0.0
Installed DataValueInterfaces ────────── v1.0.0
Installed Reexport ───────────────────── v0.2.0
Installed CodecZlib ──────────────────── v0.7.0
Installed Syslogs ────────────────────── v0.3.0
Installed StaticArrays ───────────────── v0.12.3
Installed Zlib_jll ───────────────────── v1.2.11+10
Installed EzXML ──────────────────────── v1.1.0
Installed FilePathsBase ──────────────── v0.8.0
Installed AutoHashEquals ─────────────── v0.2.0
Installed TableTraits ────────────────── v1.0.0
Installed XML2_jll ───────────────────── v2.9.9+4
Installed CRlibm ─────────────────────── v0.8.0
Installed PooledArrays ───────────────── v0.5.3
Installed SortingAlgorithms ──────────── v0.3.1
Installed DataStructures ─────────────── v0.17.17
Installed HDF5_jll ───────────────────── v1.10.5+5
Installed Calculus ───────────────────── v0.5.1
Installed CategoricalArrays ──────────── v0.8.1
Installed JSON ───────────────────────── v0.21.0
Installed ErrorfreeArithmetic ────────── v0.5.0
Installed ForwardDiff ────────────────── v0.10.10
Installed IntervalArithmetic ─────────── v0.17.4
Installed Libiconv_jll ───────────────── v1.16.0+2
Installed NaNMath ────────────────────── v0.3.3
Installed ProgressMeter ──────────────── v1.3.0
Installed Tables ─────────────────────── v1.0.4
Installed Blosc ──────────────────────── v0.7.0
Installed RoundingEmulator ───────────── v0.2.1
Installed MathProgBase ───────────────── v0.7.8
Installed Parsers ────────────────────── v1.0.5
Installed WeakRefStrings ─────────────── v0.6.2
Installed OpenSpecFun_jll ────────────── v0.5.3+3
Installed CompilerSupportLibraries_jll ─ v0.3.3+0
Installed DataAPI ────────────────────── v1.3.0
Installed ReverseDiffSparse ──────────── v0.8.6
Installed FastRounding ───────────────── v0.2.0
Installed DiffResults ────────────────── v1.0.2
Installed Missings ───────────────────── v0.4.3
Installed IteratorInterfaceExtensions ── v1.0.0
Installed JuMP ───────────────────────── v0.18.6
Installed ExprTools ──────────────────── v0.1.1
Installed ConditionalJuMP ────────────── v0.1.0
Installed Zstd_jll ───────────────────── v1.4.5+0
Installed SpecialFunctions ───────────── v0.10.3
Installed Memento ────────────────────── v1.1.0
Installed CSV ────────────────────────── v0.6.2
Updating `~/.julia/environments/v1.6/Project.toml`
[e5e5f8be] + MIPVerify v0.2.2
Updating `~/.julia/environments/v1.6/Manifest.toml`
[15f4f7f2] + AutoHashEquals v0.2.0
[a74b3585] + Blosc v0.7.0
[0b7ba130] + Blosc_jll v1.14.3+1
[e1450e63] + BufferedStreams v1.0.0
[96374032] + CRlibm v0.8.0
[336ed68f] + CSV v0.6.2
[49dc2e85] + Calculus v0.5.1
[324d7699] + CategoricalArrays v0.8.1
[944b1d66] + CodecZlib v0.7.0
[bbf7d656] + CommonSubexpressions v0.2.0
[34da2185] + Compat v2.2.0
[e66e0078] + CompilerSupportLibraries_jll v0.3.3+0
[ae04f764] + ConditionalJuMP v0.1.0
[9a962f9c] + DataAPI v1.3.0
[a93c6f00] + DataFrames v0.21.2
[864edb3b] + DataStructures v0.17.17
[e2d170a0] + DataValueInterfaces v1.0.0
[163ba53b] + DiffResults v1.0.2
[b552c78f] + DiffRules v1.0.1
[ffbed154] + DocStringExtensions v0.8.1
[90fa49ef] + ErrorfreeArithmetic v0.5.0
[e2ba6199] + ExprTools v0.1.1
[8f5d6c58] + EzXML v1.1.0
[fa42c844] + FastRounding v0.2.0
[48062228] + FilePathsBase v0.8.0
[f6369f11] + ForwardDiff v0.10.10
[f67ccb44] + HDF5 v0.13.2
[0234f1f7] + HDF5_jll v1.10.5+5
[d1acc4aa] + IntervalArithmetic v0.17.4
[41ab1584] + InvertedIndices v1.0.0
[82899510] + IteratorInterfaceExtensions v1.0.0
[682c06a0] + JSON v0.21.0
[4076af6c] + JuMP v0.18.6
[94ce4f54] + Libiconv_jll v1.16.0+2
[5ced341a] + Lz4_jll v1.9.2+0
[23992714] + MAT v0.8.0
[e5e5f8be] + MIPVerify v0.2.2
[fdba3010] + MathProgBase v0.7.8
[f28f55f0] + Memento v1.1.0
[e1d29d7a] + Missings v0.4.3
[78c3b35d] + Mocking v0.7.1
[77ba4419] + NaNMath v0.3.3
[4d1e1d77] + Nullables v1.0.0
[efe28fd5] + OpenSpecFun_jll v0.5.3+3
[bac558e1] + OrderedCollections v1.2.0
[69de0a69] + Parsers v1.0.5
[2dfb63ee] + PooledArrays v0.5.3
[92933f4c] + ProgressMeter v1.3.0
[3cdcf5f2] + RecipesBase v1.0.1
[189a3867] + Reexport v0.2.0
[89212889] + ReverseDiffSparse v0.8.6
[5eaf0fd0] + RoundingEmulator v0.2.1
[3cc68bcd] + SetRounding v0.2.0
[a2af1166] + SortingAlgorithms v0.3.1
[276daf66] + SpecialFunctions v0.10.3
[90137ffa] + StaticArrays v0.12.3
[cea106d9] + Syslogs v0.3.0
[3783bdb8] + TableTraits v1.0.0
[bd369af6] + Tables v1.0.4
[f269a46b] + TimeZones v1.2.0
[3bb67fe8] + TranscodingStreams v0.9.5
[ea10d353] + WeakRefStrings v0.6.2
[02c8fc9c] + XML2_jll v2.9.9+4
[83775a58] + Zlib_jll v1.2.11+10
[3161d3a3] + Zstd_jll v1.4.5+0
[2a0f44e3] + Base64
[ade2ca70] + Dates
[8bb1440f] + DelimitedFiles
[8ba89e20] + Distributed
[9fa8497b] + Future
[b77e0a4c] + InteractiveUtils
[76f85450] + LibGit2
[8f399da3] + Libdl
[37e2e46d] + LinearAlgebra
[56ddb016] + Logging
[d6f4376e] + Markdown
[a63ad114] + Mmap
[44cfe95a] + Pkg
[de0858da] + Printf
[3fa0cd96] + REPL
[9a3f8284] + Random
[ea8e919c] + SHA
[9e88b42a] + Serialization
[1a1011a3] + SharedArrays
[6462fe0b] + Sockets
[2f01184e] + SparseArrays
[10745b16] + Statistics
[8dfed614] + Test
[cf7118a7] + UUIDs
[4ec0a83e] + Unicode
Building HDF5 ─────→ `~/.julia/packages/HDF5/pAi1D/deps/build.log`
Building TimeZones → `~/.julia/packages/TimeZones/OjMoF/deps/build.log`
Building CRlibm ───→ `~/.julia/packages/CRlibm/NFCH5/deps/build.log`
Testing MIPVerify
Status `/tmp/jl_5mqd0m/Project.toml`
[15f4f7f2] AutoHashEquals v0.2.0
[336ed68f] CSV v0.6.2
[9961bab8] Cbc v0.6.7
[ae04f764] ConditionalJuMP v0.1.0
[a93c6f00] DataFrames v0.21.2
[ffbed154] DocStringExtensions v0.8.1
[4076af6c] JuMP v0.18.6
[23992714] MAT v0.8.0
[e5e5f8be] MIPVerify v0.2.2
[fdba3010] MathProgBase v0.7.8
[f28f55f0] Memento v1.1.0
[92933f4c] ProgressMeter v1.3.0
[a759f4b9] TimerOutputs v0.5.6
[ade2ca70] Dates
[8bb1440f] DelimitedFiles
[9e88b42a] Serialization
[10745b16] Statistics
[8dfed614] Test
Status `/tmp/jl_5mqd0m/Manifest.toml`
[15f4f7f2] AutoHashEquals v0.2.0
[6e4b80f9] BenchmarkTools v0.5.0
[b99e7846] BinaryProvider v0.5.10
[a74b3585] Blosc v0.7.0
[0b7ba130] Blosc_jll v1.14.3+1
[e1450e63] BufferedStreams v1.0.0
[6e34b625] Bzip2_jll v1.0.6+2
[96374032] CRlibm v0.8.0
[336ed68f] CSV v0.6.2
[49dc2e85] Calculus v0.5.1
[324d7699] CategoricalArrays v0.8.1
[9961bab8] Cbc v0.6.7
[523fee87] CodecBzip2 v0.7.2
[944b1d66] CodecZlib v0.7.0
[bbf7d656] CommonSubexpressions v0.2.0
[34da2185] Compat v2.2.0
[e66e0078] CompilerSupportLibraries_jll v0.3.3+0
[ae04f764] ConditionalJuMP v0.1.0
[9a962f9c] DataAPI v1.3.0
[a93c6f00] DataFrames v0.21.2
[864edb3b] DataStructures v0.17.17
[e2d170a0] DataValueInterfaces v1.0.0
[163ba53b] DiffResults v1.0.2
[b552c78f] DiffRules v1.0.1
[ffbed154] DocStringExtensions v0.8.1
[90fa49ef] ErrorfreeArithmetic v0.5.0
[e2ba6199] ExprTools v0.1.1
[8f5d6c58] EzXML v1.1.0
[fa42c844] FastRounding v0.2.0
[48062228] FilePathsBase v0.8.0
[f6369f11] ForwardDiff v0.10.10
[f67ccb44] HDF5 v0.13.2
[0234f1f7] HDF5_jll v1.10.5+5
[cd3eb016] HTTP v0.8.15
[83e8ac13] IniFile v0.5.0
[d1acc4aa] IntervalArithmetic v0.17.4
[41ab1584] InvertedIndices v1.0.0
[82899510] IteratorInterfaceExtensions v1.0.0
[682c06a0] JSON v0.21.0
[7d188eb4] JSONSchema v0.3.0
[4076af6c] JuMP v0.18.6
[94ce4f54] Libiconv_jll v1.16.0+2
[5ced341a] Lz4_jll v1.9.2+0
[23992714] MAT v0.8.0
[e5e5f8be] MIPVerify v0.2.2
[b8f27783] MathOptInterface v0.9.14
[fdba3010] MathProgBase v0.7.8
[739be429] MbedTLS v1.0.2
[c8ffd9c3] MbedTLS_jll v2.16.0+2
[f28f55f0] Memento v1.1.0
[e1d29d7a] Missings v0.4.3
[78c3b35d] Mocking v0.7.1
[d8a4904e] MutableArithmetics v0.2.9
[77ba4419] NaNMath v0.3.3
[4d1e1d77] Nullables v1.0.0
[efe28fd5] OpenSpecFun_jll v0.5.3+3
[bac558e1] OrderedCollections v1.2.0
[69de0a69] Parsers v1.0.5
[2dfb63ee] PooledArrays v0.5.3
[92933f4c] ProgressMeter v1.3.0
[3cdcf5f2] RecipesBase v1.0.1
[189a3867] Reexport v0.2.0
[89212889] ReverseDiffSparse v0.8.6
[5eaf0fd0] RoundingEmulator v0.2.1
[3cc68bcd] SetRounding v0.2.0
[a2af1166] SortingAlgorithms v0.3.1
[276daf66] SpecialFunctions v0.10.3
[90137ffa] StaticArrays v0.12.3
[cea106d9] Syslogs v0.3.0
[3783bdb8] TableTraits v1.0.0
[bd369af6] Tables v1.0.4
[f269a46b] TimeZones v1.2.0
[a759f4b9] TimerOutputs v0.5.6
[3bb67fe8] TranscodingStreams v0.9.5
[ea10d353] WeakRefStrings v0.6.2
[02c8fc9c] XML2_jll v2.9.9+4
[a5390f91] ZipFile v0.9.2
[83775a58] Zlib_jll v1.2.11+10
[3161d3a3] Zstd_jll v1.4.5+0
[2a0f44e3] Base64
[ade2ca70] Dates
[8bb1440f] DelimitedFiles
[8ba89e20] Distributed
[9fa8497b] Future
[b77e0a4c] InteractiveUtils
[76f85450] LibGit2
[8f399da3] Libdl
[37e2e46d] LinearAlgebra
[56ddb016] Logging
[d6f4376e] Markdown
[a63ad114] Mmap
[44cfe95a] Pkg
[de0858da] Printf
[3fa0cd96] REPL
[9a3f8284] Random
[ea8e919c] SHA
[9e88b42a] Serialization
[1a1011a3] SharedArrays
[6462fe0b] Sockets
[2f01184e] SparseArrays
[10745b16] Statistics
[8dfed614] Test
[cf7118a7] UUIDs
[4ec0a83e] Unicode
[notice | MIPVerify]: Attempting to find adversarial example. Neural net predicted label is 3, target labels are [1]
[notice | MIPVerify]: Rebuilding model from scratch. This may take some time as we determine upper and lower bounds for the input to each non-linear unit.
[info | MIPVerify]: Applying Flatten() ...
[info | MIPVerify]: Applying Linear(32 -> 16) ...
[info | MIPVerify]: Applying MaskedReLU with expected input size (16,). (5 zeroed, 5 as-is, 6 rectified). ...
Calculating upper bounds: 33%|███████▋ | ETA: 0:00:02[K Calculating upper bounds: 50%|███████████▌ | ETA: 0:00:01[K Calculating upper bounds: 100%|███████████████████████| Time: 0:00:01[K
Calculating lower bounds: 17%|███▉ | ETA: 0:00:01[K Calculating lower bounds: 33%|███████▋ | ETA: 0:00:01[K Calculating lower bounds: 100%|███████████████████████| Time: 0:00:00[K
[info | MIPVerify]: Behavior of ReLUs - split: 6, zero_output: 0, linear_in_input: 0, constant_output: 0
Imposing relu constraint: 17%|███▉ | ETA: 0:00:02[K Imposing relu constraint: 100%|███████████████████████| Time: 0:00:00[K
[info | MIPVerify]: Applying Linear(16 -> 8) ...
[info | MIPVerify]: Applying MaskedReLU with expected input size (8,). (3 zeroed, 3 as-is, 2 rectified). ...
[info | MIPVerify]: Behavior of ReLUs - split: 2, zero_output: 0, linear_in_input: 0, constant_output: 0
[info | MIPVerify]: Applying Linear(8 -> 4) ...
[notice | MIPVerify]: The model built will be cached and re-used for future solves, unless you explicitly set rebuild=true.
[notice | MIPVerify]: Attempting to find adversarial example. Neural net predicted label is 3, target labels are [2]
[notice | MIPVerify]: Loading model from cache.
[notice | MIPVerify]: Attempting to find adversarial example. Neural net predicted label is 3, target labels are [3]
[notice | MIPVerify]: Loading model from cache.
[notice | MIPVerify]: Attempting to find adversarial example. Neural net predicted label is 3, target labels are [1]
[notice | MIPVerify]: Rebuilding model from scratch. This may take some time as we determine upper and lower bounds for the input to each non-linear unit.
[info | MIPVerify]: Applying Flatten() ...
[info | MIPVerify]: Applying Linear(32 -> 16) ...
[info | MIPVerify]: Applying MaskedReLU with expected input size (16,). (5 zeroed, 5 as-is, 6 rectified). ...
[info | MIPVerify]: Behavior of ReLUs - split: 3, zero_output: 2, linear_in_input: 1, constant_output: 0
[info | MIPVerify]: Applying Linear(16 -> 8) ...
[info | MIPVerify]: Applying MaskedReLU with expected input size (8,). (3 zeroed, 3 as-is, 2 rectified). ...
[info | MIPVerify]: Behavior of ReLUs - split: 1, zero_output: 0, linear_in_input: 1, constant_output: 0
[info | MIPVerify]: Applying Linear(8 -> 4) ...
[notice | MIPVerify]: The model built will be cached and re-used for future solves, unless you explicitly set rebuild=true.
┌ Warning: Not solved to optimality, status: Infeasible
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/solvers.jl:212
┌ Warning: Infeasibility ray (Farkas proof) not available
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/solvers.jl:223
[notice | MIPVerify]: Attempting to find adversarial example. Neural net predicted label is 3, target labels are [1]
[notice | MIPVerify]: Rebuilding model from scratch. This may take some time as we determine upper and lower bounds for the input to each non-linear unit.
[info | MIPVerify]: Applying Flatten() ...
[info | MIPVerify]: Applying Linear(32 -> 16) ...
[info | MIPVerify]: Applying MaskedReLU with expected input size (16,). (5 zeroed, 5 as-is, 6 rectified). ...
[info | MIPVerify]: Behavior of ReLUs - split: 4, zero_output: 2, linear_in_input: 0, constant_output: 0
[info | MIPVerify]: Applying Linear(16 -> 8) ...
[info | MIPVerify]: Applying MaskedReLU with expected input size (8,). (3 zeroed, 3 as-is, 2 rectified). ...
[info | MIPVerify]: Behavior of ReLUs - split: 1, zero_output: 0, linear_in_input: 1, constant_output: 0
[info | MIPVerify]: Applying Linear(8 -> 4) ...
[notice | MIPVerify]: The model built will be cached and re-used for future solves, unless you explicitly set rebuild=true.
[notice | MIPVerify]: Attempting to find adversarial example. Neural net predicted label is 1, target labels are [2]
[notice | MIPVerify]: Rebuilding model from scratch. This may take some time as we determine upper and lower bounds for the input to each non-linear unit.
[info | MIPVerify]: Applying Conv2d(1, 1, kernel_size=(5, 5), stride=(1, 1), padding=same) ...
[info | MIPVerify]: Applying Conv2d(1, 2, kernel_size=(2, 2), stride=(1, 1), padding=same) ...
[info | MIPVerify]: Specifying max pooling with a 2x2 filter and a stride of (2, 2) ...
Calculating upper bounds: 50%|███████████▌ | ETA: 0:00:00[K Calculating upper bounds: 100%|███████████████████████| Time: 0:00:00[K
Calculating lower bounds: 50%|███████████▌ | ETA: 0:00:00[K Calculating lower bounds: 100%|███████████████████████| Time: 0:00:00[K
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 4
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 4
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 4
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 4
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 4
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 4
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 4
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 4
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 4
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 4
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 4
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 4
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 4
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 4
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 4
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 4
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 4
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 4
[info | MIPVerify]: Applying ReLU() ...
[info | MIPVerify]: Tightening via interval_arithmetic gives a better result than lp; using best bound found.
[info | MIPVerify]: Tightening via interval_arithmetic gives a better result than lp; using best bound found.
Calculating upper bounds: 22%|█████▏ | ETA: 0:00:00[K[info | MIPVerify]: Tightening via interval_arithmetic gives a better result than lp; using best bound found.
[info | MIPVerify]: Tightening via interval_arithmetic gives a better result than lp; using best bound found.
[info | MIPVerify]: Tightening via interval_arithmetic gives a better result than lp; using best bound found.
[info | MIPVerify]: Tightening via interval_arithmetic gives a better result than lp; using best bound found.
[info | MIPVerify]: Tightening via interval_arithmetic gives a better result than lp; using best bound found.
[info | MIPVerify]: Tightening via interval_arithmetic gives a better result than lp; using best bound found.
[info | MIPVerify]: Tightening via interval_arithmetic gives a better result than lp; using best bound found.
[info | MIPVerify]: Tightening via interval_arithmetic gives a better result than lp; using best bound found.
Calculating upper bounds: 83%|███████████████████▏ | ETA: 0:00:00[K Calculating upper bounds: 100%|███████████████████████| Time: 0:00:00[K
Calculating lower bounds: 6%|█▎ | ETA: 0:00:03[K Calculating lower bounds: 83%|███████████████████▏ | ETA: 0:00:00[K Calculating lower bounds: 100%|███████████████████████| Time: 0:00:00[K
[info | MIPVerify]: Behavior of ReLUs - split: 4, zero_output: 0, linear_in_input: 14, constant_output: 0
Imposing relu constraint: 6%|█▎ | ETA: 0:00:05[K Imposing relu constraint: 100%|███████████████████████| Time: 0:00:00[K
[info | MIPVerify]: Applying Flatten() ...
[info | MIPVerify]: Applying Linear(18 -> 4) ...
[info | MIPVerify]: Applying ReLU() ...
[info | MIPVerify]: Behavior of ReLUs - split: 4, zero_output: 0, linear_in_input: 0, constant_output: 0
[info | MIPVerify]: Applying Linear(4 -> 3) ...
[notice | MIPVerify]: The model built will be cached and re-used for future solves, unless you explicitly set rebuild=true.
[notice | MIPVerify]: Attempting to find adversarial example. Neural net predicted label is 1, target labels are [3]
[notice | MIPVerify]: Loading model from cache.
┌ Warning: Not solved to optimality, status: Infeasible
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/solvers.jl:212
┌ Warning: Infeasibility ray (Farkas proof) not available
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/solvers.jl:223
[notice | MIPVerify]: Attempting to find adversarial example. Neural net predicted label is 1, target labels are [1]
[notice | MIPVerify]: Rebuilding model from scratch. This may take some time as we determine upper and lower bounds for the input to each non-linear unit.
[info | MIPVerify]: Applying Conv2d(1, 2, kernel_size=(2, 2), stride=(1, 1), padding=same) ...
[info | MIPVerify]: Specifying max pooling with a 2x2 filter and a stride of (2, 2) ...
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 4
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 4
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 4
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 4
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 4
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 4
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 4
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 4
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 4
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 4
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 4
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 4
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 4
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 4
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 4
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 4
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 4
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 4
[info | MIPVerify]: Applying ReLU() ...
[info | MIPVerify]: Tightening via interval_arithmetic gives a better result than lp; using best bound found.
[info | MIPVerify]: Tightening via interval_arithmetic gives a better result than lp; using best bound found.
[info | MIPVerify]: Tightening via interval_arithmetic gives a better result than lp; using best bound found.
[info | MIPVerify]: Tightening via interval_arithmetic gives a better result than lp; using best bound found.
[info | MIPVerify]: Behavior of ReLUs - split: 4, zero_output: 0, linear_in_input: 14, constant_output: 0
[info | MIPVerify]: Applying Flatten() ...
[info | MIPVerify]: Applying Linear(18 -> 4) ...
[info | MIPVerify]: Applying ReLU() ...
[info | MIPVerify]: Behavior of ReLUs - split: 4, zero_output: 0, linear_in_input: 0, constant_output: 0
[info | MIPVerify]: Applying Linear(4 -> 3) ...
[notice | MIPVerify]: The model built will be cached and re-used for future solves, unless you explicitly set rebuild=true.
[notice | MIPVerify]: Attempting to find adversarial example. Neural net predicted label is 1, target labels are [2]
[notice | MIPVerify]: Loading model from cache.
[notice | MIPVerify]: Attempting to find adversarial example. Neural net predicted label is 1, target labels are [1]
[notice | MIPVerify]: Loading model from cache.
[notice | MIPVerify]: Attempting to find adversarial example. Neural net predicted label is 1, target labels are [2]
[notice | MIPVerify]: Loading model from cache.
[notice | MIPVerify]: Attempting to find adversarial example. Neural net predicted label is 1, target labels are [3]
[notice | MIPVerify]: Loading model from cache.
[notice | MIPVerify]: Attempting to find adversarial example. Neural net predicted label is 1, target labels are [2]
[notice | MIPVerify]: Loading model from cache.
[notice | MIPVerify]: Attempting to find adversarial example. Neural net predicted label is 1, target labels are [2, 3]
[notice | MIPVerify]: Loading model from cache.
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 2
[notice | MIPVerify]: Attempting to find adversarial example. Neural net predicted label is 1, target labels are [2]
[notice | MIPVerify]: Rebuilding model from scratch. This may take some time as we determine upper and lower bounds for the input to each non-linear unit.
[info | MIPVerify]: Applying Conv2d(1, 2, kernel_size=(2, 2), stride=(1, 1), padding=same) ...
[info | MIPVerify]: Specifying max pooling with a 2x2 filter and a stride of (2, 2) ...
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 3
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 2
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 3
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 4
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 1
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 2
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 3
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 4
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 1
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 2
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 1
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 1
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 1
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 1
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 1
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 2
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 3
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 2
[info | MIPVerify]: Applying ReLU() ...
[info | MIPVerify]: Tightening via interval_arithmetic gives a better result than lp; using best bound found.
[info | MIPVerify]: Behavior of ReLUs - split: 0, zero_output: 0, linear_in_input: 18, constant_output: 0
[info | MIPVerify]: Applying Flatten() ...
[info | MIPVerify]: Applying Linear(18 -> 4) ...
[info | MIPVerify]: Applying ReLU() ...
[info | MIPVerify]: Behavior of ReLUs - split: 0, zero_output: 2, linear_in_input: 2, constant_output: 0
[info | MIPVerify]: Applying Linear(4 -> 3) ...
[notice | MIPVerify]: The model built will be cached and re-used for future solves, unless you explicitly set rebuild=true.
┌ Warning: Not solved to optimality, status: Infeasible
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/solvers.jl:212
┌ Warning: Infeasibility ray (Farkas proof) not available
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/solvers.jl:223
[notice | MIPVerify]: Attempting to find adversarial example. Neural net predicted label is 1, target labels are [2]
[notice | MIPVerify]: Rebuilding model from scratch. This may take some time as we determine upper and lower bounds for the input to each non-linear unit.
[info | MIPVerify]: Applying Conv2d(1, 2, kernel_size=(2, 2), stride=(1, 1), padding=same) ...
[info | MIPVerify]: Specifying max pooling with a 2x2 filter and a stride of (2, 2) ...
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 3
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 2
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 3
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 4
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 1
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 2
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 3
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 4
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 1
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 2
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 1
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 1
[info | MIPVerify]: Tightening via interval_arithmetic gives a better result than lp; using best bound found.
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 1
[info | MIPVerify]: Tightening via interval_arithmetic gives a better result than lp; using best bound found.
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 2
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 1
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 2
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 3
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 2
[info | MIPVerify]: Applying ReLU() ...
Calculating upper bounds: 89%|████████████████████▌ | ETA: 0:00:00[K Calculating upper bounds: 100%|███████████████████████| Time: 0:00:00[K
[info | MIPVerify]: Tightening via interval_arithmetic gives a better result than lp; using best bound found.
[info | MIPVerify]: Behavior of ReLUs - split: 0, zero_output: 0, linear_in_input: 18, constant_output: 0
[info | MIPVerify]: Applying Flatten() ...
[info | MIPVerify]: Applying Linear(18 -> 4) ...
[info | MIPVerify]: Applying ReLU() ...
[info | MIPVerify]: Behavior of ReLUs - split: 0, zero_output: 2, linear_in_input: 2, constant_output: 0
[info | MIPVerify]: Applying Linear(4 -> 3) ...
[notice | MIPVerify]: The model built will be cached and re-used for future solves, unless you explicitly set rebuild=true.
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 2
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 2
[info | MIPVerify]: Behavior of ReLUs - split: 1, zero_output: 0, linear_in_input: 0, constant_output: 0
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 2
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 2
┌ Warning: Not solved to optimality, status: Infeasible
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/solvers.jl:212
┌ Warning: Infeasibility ray (Farkas proof) not available
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/solvers.jl:223
┌ Warning: Not solved to optimality, status: Infeasible
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/solvers.jl:212
┌ Warning: Infeasibility ray (Farkas proof) not available
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/solvers.jl:223
[info | MIPVerify]: Applying Linear(2 -> 2) ...
[info | MIPVerify]: Applying ReLU() ...
[info | MIPVerify]: Behavior of ReLUs - split: 2, zero_output: 0, linear_in_input: 0, constant_output: 0
[info | MIPVerify]: Applying Linear(2 -> 2) ...
[info | MIPVerify]: Applying Linear(2 -> 2) ...
[info | MIPVerify]: Applying ReLU() ...
[info | MIPVerify]: Behavior of ReLUs - split: 2, zero_output: 0, linear_in_input: 0, constant_output: 0
[info | MIPVerify]: Applying Linear(2 -> 2) ...
[info | MIPVerify]: Applying Linear(2 -> 2) ...
[info | MIPVerify]: Applying ReLU() ...
[info | MIPVerify]: Behavior of ReLUs - split: 2, zero_output: 0, linear_in_input: 0, constant_output: 0
[info | MIPVerify]: Applying Linear(2 -> 2) ...
[info | MIPVerify]: Applying Conv2d(2, 1, kernel_size=(3, 3), stride=(1, 1), padding=same) ...
[info | MIPVerify]: Applying Conv2d(2, 1, kernel_size=(3, 3), stride=(1, 1), padding=same) ...
[info | MIPVerify]: Applying Conv2d(2, 1, kernel_size=(3, 3), stride=(2, 2), padding=same) ...
[info | MIPVerify]: Applying Conv2d(2, 1, kernel_size=(3, 3), stride=(2, 2), padding=same) ...
[info | MIPVerify]: Applying Conv2d(2, 1, kernel_size=(4, 4), stride=(2, 2), padding=same) ...
[info | MIPVerify]: Applying Conv2d(2, 1, kernel_size=(4, 4), stride=(2, 2), padding=same) ...
[info | MIPVerify]: Applying Conv2d(1, 1, kernel_size=(3, 3), stride=(1, 1), padding=valid) ...
[info | MIPVerify]: Applying Conv2d(1, 1, kernel_size=(3, 3), stride=(1, 1), padding=valid) ...
[info | MIPVerify]: Applying Conv2d(2, 1, kernel_size=(3, 3), stride=(1, 1), padding=valid) ...
[info | MIPVerify]: Applying Conv2d(2, 1, kernel_size=(3, 3), stride=(1, 1), padding=valid) ...
[info | MIPVerify]: Applying Conv2d(1, 1, kernel_size=(3, 3), stride=(1, 1), padding=valid) ...
[info | MIPVerify]: Applying Conv2d(1, 1, kernel_size=(3, 3), stride=(1, 1), padding=valid) ...
[info | MIPVerify]: Applying Conv2d(1, 1, kernel_size=(2, 3), stride=(1, 1), padding=valid) ...
[info | MIPVerify]: Applying Conv2d(1, 1, kernel_size=(2, 3), stride=(1, 1), padding=valid) ...
[info | MIPVerify]: Applying Conv2d(1, 1, kernel_size=(3, 3), stride=(2, 2), padding=valid) ...
[info | MIPVerify]: Applying Conv2d(1, 1, kernel_size=(3, 3), stride=(2, 2), padding=valid) ...
[info | MIPVerify]: Applying Conv2d(1, 1, kernel_size=(2, 2), stride=(1, 1), padding=valid) ...
[info | MIPVerify]: Applying Conv2d(1, 1, kernel_size=(2, 2), stride=(1, 1), padding=valid) ...
[info | MIPVerify]: Applying Conv2d(1, 1, kernel_size=(2, 2), stride=(2, 2), padding=valid) ...
[info | MIPVerify]: Applying Conv2d(1, 1, kernel_size=(2, 2), stride=(2, 2), padding=valid) ...
[info | MIPVerify]: Applying Conv2d(1, 1, kernel_size=(3, 3), stride=(1, 1), padding=valid) ...
[info | MIPVerify]: Applying Conv2d(1, 1, kernel_size=(3, 3), stride=(1, 1), padding=valid) ...
[info | MIPVerify]: Applying Conv2d(1, 1, kernel_size=(3, 3), stride=(2, 2), padding=valid) ...
[info | MIPVerify]: Applying Conv2d(1, 1, kernel_size=(3, 3), stride=(2, 2), padding=valid) ...
[info | MIPVerify]: Applying Conv2d(1, 1, kernel_size=(2, 2), stride=(1, 1), padding=valid) ...
[info | MIPVerify]: Applying Conv2d(1, 1, kernel_size=(2, 2), stride=(1, 1), padding=valid) ...
[info | MIPVerify]: Applying Conv2d(1, 1, kernel_size=(2, 2), stride=(3, 3), padding=valid) ...
[info | MIPVerify]: Applying Conv2d(1, 1, kernel_size=(2, 2), stride=(3, 3), padding=valid) ...
[info | MIPVerify]: Applying Conv2d(1, 1, kernel_size=(3, 3), stride=(1, 1), padding=(0, 0)) ...
[info | MIPVerify]: Applying Conv2d(1, 1, kernel_size=(3, 3), stride=(1, 1), padding=(0, 0)) ...
[info | MIPVerify]: Applying Conv2d(1, 1, kernel_size=(3, 3), stride=(2, 2), padding=(0, 0)) ...
[info | MIPVerify]: Applying Conv2d(1, 1, kernel_size=(3, 3), stride=(2, 2), padding=(0, 0)) ...
[info | MIPVerify]: Applying Conv2d(1, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ...
[info | MIPVerify]: Applying Conv2d(1, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ...
[info | MIPVerify]: Applying Conv2d(1, 1, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) ...
[info | MIPVerify]: Applying Conv2d(1, 1, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) ...
[info | MIPVerify]: Applying Conv2d(1, 1, kernel_size=(3, 3), stride=(1, 1), padding=1) ...
[info | MIPVerify]: Applying Conv2d(1, 1, kernel_size=(3, 3), stride=(1, 1), padding=1) ...
[info | MIPVerify]: Applying Conv2d(1, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ...
[info | MIPVerify]: Applying Conv2d(1, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ...
[info | MIPVerify]: Applying Conv2d(1, 1, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) ...
[info | MIPVerify]: Applying Conv2d(1, 1, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) ...
[info | MIPVerify]: Applying Conv2d(1, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 2)) ...
[info | MIPVerify]: Applying Conv2d(1, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 2)) ...
[info | MIPVerify]: Applying Conv2d(1, 1, kernel_size=(3, 3), stride=(2, 2), padding=(1, 2)) ...
[info | MIPVerify]: Applying Conv2d(1, 1, kernel_size=(3, 3), stride=(2, 2), padding=(1, 2)) ...
[info | MIPVerify]: Applying Conv2d(2, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ...
[info | MIPVerify]: Applying Conv2d(2, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ...
[info | MIPVerify]: Applying Conv2d(2, 1, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) ...
[info | MIPVerify]: Applying Conv2d(2, 1, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) ...
[info | MIPVerify]: Applying Conv2d(1, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1, 1, 1)) ...
[info | MIPVerify]: Applying Conv2d(1, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1, 1, 1)) ...
[info | MIPVerify]: Applying Conv2d(1, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 2, 3, 4)) ...
[info | MIPVerify]: Applying Conv2d(1, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 2, 3, 4)) ...
[info | MIPVerify]: Applying Linear(2 -> 2) ...
Calculating lower bounds: 50%|███████████▌ | ETA: 0:00:00[K Calculating lower bounds: 100%|███████████████████████| Time: 0:00:00[K
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 2
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 2
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 2
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 2
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 2
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 2
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 2
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 2
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 2
Computing fraction correct... 56%|████████████▎ | ETA: 0:00:01[KComputing fraction correct...100%|██████████████████████| Time: 0:00:01[K
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[info | MIPVerify]: Working on index 1
[notice | MIPVerify]: Attempting to find adversarial example. Neural net predicted label is 8, target labels are [1, 2, 3, 4, 5, 6, 7, 9, 10]
[notice | MIPVerify]: Rebuilding model from scratch. This may take some time as we determine upper and lower bounds for the input to each non-linear unit.
[info | MIPVerify]: Applying Conv2d(1, 16, kernel_size=(4, 4), stride=(2, 2), padding=same) ...
[info | MIPVerify]: Applying ReLU() ...
Calculating upper bounds: 15%|███▌ | ETA: 0:00:01[K Calculating upper bounds: 100%|███████████████████████| Time: 0:00:00[K
Calculating lower bounds: 0%| | ETA: 0:04:18[K Calculating lower bounds: 100%|███████████████████████| Time: 0:00:00[K
[info | MIPVerify]: Behavior of ReLUs - split: 37, zero_output: 2249, linear_in_input: 850, constant_output: 0
[info | MIPVerify]: Applying Conv2d(16, 32, kernel_size=(4, 4), stride=(2, 2), padding=same) ...
[info | MIPVerify]: Applying ReLU() ...
Calculating upper bounds: 2%|▍ | ETA: 0:00:06[K Calculating upper bounds: 4%|█ | ETA: 0:00:05[K Calculating upper bounds: 6%|█▌ | ETA: 0:00:05[K Calculating upper bounds: 8%|█▊ | ETA: 0:00:05[K Calculating upper bounds: 8%|██ | ETA: 0:00:07[K Calculating upper bounds: 11%|██▌ | ETA: 0:00:07[K Calculating upper bounds: 12%|██▊ | ETA: 0:00:07[K Calculating upper bounds: 15%|███▍ | ETA: 0:00:06[K Calculating upper bounds: 17%|███▉ | ETA: 0:00:06[K Calculating upper bounds: 18%|████▏ | ETA: 0:00:06[K Calculating upper bounds: 20%|████▌ | ETA: 0:00:05[K Calculating upper bounds: 20%|████▋ | ETA: 0:00:06[K Calculating upper bounds: 21%|████▊ | ETA: 0:00:06[K Calculating upper bounds: 21%|████▉ | ETA: 0:00:06[K Calculating upper bounds: 22%|█████▏ | ETA: 0:00:06[K Calculating upper bounds: 23%|█████▎ | ETA: 0:00:06[K Calculating upper bounds: 24%|█████▌ | ETA: 0:00:06[K Calculating upper bounds: 25%|█████▊ | ETA: 0:00:06[K Calculating upper bounds: 26%|█████▉ | ETA: 0:00:06[K Calculating upper bounds: 26%|██████▏ | ETA: 0:00:06[K Calculating upper bounds: 27%|██████▎ | ETA: 0:00:06[K Calculating upper bounds: 28%|██████▌ | ETA: 0:00:07[K Calculating upper bounds: 29%|██████▋ | ETA: 0:00:07[K Calculating upper bounds: 30%|███████ | ETA: 0:00:06[K Calculating upper bounds: 32%|███████▍ | ETA: 0:00:06[K Calculating upper bounds: 39%|█████████ | ETA: 0:00:05[K Calculating upper bounds: 41%|█████████▍ | ETA: 0:00:04[K Calculating upper bounds: 42%|█████████▊ | ETA: 0:00:04[K Calculating upper bounds: 45%|██████████▍ | ETA: 0:00:04[K Calculating upper bounds: 46%|██████████▋ | ETA: 0:00:04[K Calculating upper bounds: 48%|███████████ | ETA: 0:00:04[K Calculating upper bounds: 49%|███████████▏ | ETA: 0:00:04[K Calculating upper bounds: 50%|███████████▌ | ETA: 0:00:04[K Calculating upper bounds: 52%|███████████▉ | ETA: 0:00:04[K Calculating upper bounds: 54%|████████████▍ | ETA: 0:00:03[K Calculating upper bounds: 55%|████████████▊ | ETA: 0:00:03[K Calculating upper bounds: 61%|██████████████▏ | ETA: 0:00:03[K Calculating upper bounds: 63%|██████████████▌ | ETA: 0:00:03[K Calculating upper bounds: 64%|██████████████▊ | ETA: 0:00:02[K Calculating upper bounds: 65%|███████████████ | ETA: 0:00:02[K Calculating upper bounds: 67%|███████████████▍ | ETA: 0:00:02[K Calculating upper bounds: 67%|███████████████▍ | ETA: 0:00:02[K Calculating upper bounds: 67%|███████████████▌ | ETA: 0:00:02[K Calculating upper bounds: 68%|███████████████▊ | ETA: 0:00:02[K Calculating upper bounds: 69%|███████████████▉ | ETA: 0:00:02[K Calculating upper bounds: 70%|████████████████ | ETA: 0:00:02[K Calculating upper bounds: 71%|████████████████▎ | ETA: 0:00:02[K Calculating upper bounds: 72%|████████████████▌ | ETA: 0:00:02[K Calculating upper bounds: 73%|████████████████▊ | ETA: 0:00:02[K Calculating upper bounds: 74%|█████████████████ | ETA: 0:00:02[K Calculating upper bounds: 76%|█████████████████▍ | ETA: 0:00:02[K Calculating upper bounds: 77%|█████████████████▉ | ETA: 0:00:02[K Calculating upper bounds: 80%|██████████████████▌ | ETA: 0:00:01[K Calculating upper bounds: 82%|███████████████████ | ETA: 0:00:01[K Calculating upper bounds: 84%|███████████████████▎ | ETA: 0:00:01[K Calculating upper bounds: 84%|███████████████████▍ | ETA: 0:00:01[K Calculating upper bounds: 85%|███████████████████▌ | ETA: 0:00:01[K Calculating upper bounds: 86%|███████████████████▋ | ETA: 0:00:01[K Calculating upper bounds: 87%|███████████████████▉ | ETA: 0:00:01[K Calculating upper bounds: 87%|████████████████████ | ETA: 0:00:01[K Calculating upper bounds: 88%|████████████████████▎ | ETA: 0:00:01[K Calculating upper bounds: 89%|████████████████████▌ | ETA: 0:00:01[K Calculating upper bounds: 90%|████████████████████▊ | ETA: 0:00:01[K Calculating upper bounds: 92%|█████████████████████▏ | ETA: 0:00:01[K Calculating upper bounds: 92%|█████████████████████▎ | ETA: 0:00:01[K Calculating upper bounds: 93%|█████████████████████▍ | ETA: 0:00:01[K Calculating upper bounds: 94%|█████████████████████▋ | ETA: 0:00:00[K Calculating upper bounds: 95%|█████████████████████▉ | ETA: 0:00:00[K Calculating upper bounds: 96%|██████████████████████▏| ETA: 0:00:00[K Calculating upper bounds: 97%|██████████████████████▍| ETA: 0:00:00[K Calculating upper bounds: 98%|██████████████████████▌| ETA: 0:00:00[K Calculating upper bounds: 99%|██████████████████████▊| ETA: 0:00:00[K Calculating upper bounds: 100%|███████████████████████| ETA: 0:00:00[K Calculating upper bounds: 100%|███████████████████████| Time: 0:00:08[K
Calculating lower bounds: 5%|█▏ | ETA: 0:00:02[K Calculating lower bounds: 15%|███▍ | ETA: 0:00:01[K Calculating lower bounds: 27%|██████▎ | ETA: 0:00:01[K Calculating lower bounds: 52%|███████████▉ | ETA: 0:00:00[K Calculating lower bounds: 64%|██████████████▊ | ETA: 0:00:00[K Calculating lower bounds: 79%|██████████████████▏ | ETA: 0:00:00[K Calculating lower bounds: 97%|██████████████████████▍| ETA: 0:00:00[K Calculating lower bounds: 100%|███████████████████████| ETA: 0:00:00[K Calculating lower bounds: 100%|███████████████████████| Time: 0:00:00[K
[info | MIPVerify]: Behavior of ReLUs - split: 77, zero_output: 846, linear_in_input: 645, constant_output: 0
[info | MIPVerify]: Applying Flatten() ...
[info | MIPVerify]: Applying Linear(1568 -> 100) ...
[info | MIPVerify]: Applying ReLU() ...
Calculating upper bounds: 11%|██▌ | ETA: 0:00:01[K Calculating upper bounds: 67%|███████████████▍ | ETA: 0:00:00[K Calculating upper bounds: 100%|███████████████████████| Time: 0:00:00[K
[info | MIPVerify]: Behavior of ReLUs - split: 1, zero_output: 91, linear_in_input: 8, constant_output: 0
[info | MIPVerify]: Applying Linear(100 -> 10) ...
Calculating upper bounds: 56%|████████████▊ | ETA: 0:00:00[K Calculating upper bounds: 100%|███████████████████████| Time: 0:00:00[K
Calculating lower bounds: 56%|████████████▊ | ETA: 0:00:00[K Calculating lower bounds: 100%|███████████████████████| Time: 0:00:00[K
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 2
┌ Warning: Not solved to optimality, status: Infeasible
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/solvers.jl:212
┌ Warning: Infeasibility ray (Farkas proof) not available
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/solvers.jl:223
[info | MIPVerify]: Working on index 9
[notice | MIPVerify]: Attempting to find adversarial example. Neural net predicted label is 6, target labels are [1, 2, 3, 4, 5, 7, 8, 9, 10]
[notice | MIPVerify]: Rebuilding model from scratch. This may take some time as we determine upper and lower bounds for the input to each non-linear unit.
[info | MIPVerify]: Applying Conv2d(1, 16, kernel_size=(4, 4), stride=(2, 2), padding=same) ...
[info | MIPVerify]: Applying ReLU() ...
[info | MIPVerify]: Behavior of ReLUs - split: 48, zero_output: 2200, linear_in_input: 888, constant_output: 0
[info | MIPVerify]: Applying Conv2d(16, 32, kernel_size=(4, 4), stride=(2, 2), padding=same) ...
[info | MIPVerify]: Applying ReLU() ...
[info | MIPVerify]: Behavior of ReLUs - split: 94, zero_output: 799, linear_in_input: 675, constant_output: 0
[info | MIPVerify]: Applying Flatten() ...
[info | MIPVerify]: Applying Linear(1568 -> 100) ...
[info | MIPVerify]: Applying ReLU() ...
[info | MIPVerify]: Behavior of ReLUs - split: 0, zero_output: 91, linear_in_input: 9, constant_output: 0
[info | MIPVerify]: Applying Linear(100 -> 10) ...
[info | MIPVerify]: Number of inputs to maximum function possibly taking maximum value: 2
[info | MIPVerify]: Working on index 248
[notice | MIPVerify]: Attempting to find adversarial example. Neural net predicted label is 7, target labels are [1, 2, 3, 4, 6, 7, 8, 9, 10]
[info | MIPVerify]: Working on index 1, with true_label 8 and target_label 1
[notice | MIPVerify]: Attempting to find adversarial example. Neural net predicted label is 8, target labels are [1]
[notice | MIPVerify]: Rebuilding model from scratch. This may take some time as we determine upper and lower bounds for the input to each non-linear unit.
[info | MIPVerify]: Applying Conv2d(1, 16, kernel_size=(4, 4), stride=(2, 2), padding=same) ...
[info | MIPVerify]: Applying ReLU() ...
[info | MIPVerify]: Behavior of ReLUs - split: 37, zero_output: 2249, linear_in_input: 850, constant_output: 0
[info | MIPVerify]: Applying Conv2d(16, 32, kernel_size=(4, 4), stride=(2, 2), padding=same) ...
[info | MIPVerify]: Applying ReLU() ...
[info | MIPVerify]: Behavior of ReLUs - split: 91, zero_output: 839, linear_in_input: 638, constant_output: 0
[info | MIPVerify]: Applying Flatten() ...
[info | MIPVerify]: Applying Linear(1568 -> 100) ...
[info | MIPVerify]: Applying ReLU() ...
[info | MIPVerify]: Behavior of ReLUs - split: 3, zero_output: 91, linear_in_input: 6, constant_output: 0
[info | MIPVerify]: Applying Linear(100 -> 10) ...
┌ Warning: Not solved to optimality, status: Infeasible
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/solvers.jl:212
┌ Warning: Infeasibility ray (Farkas proof) not available
└ @ JuMP ~/.julia/packages/JuMP/I7whV/src/solvers.jl:223
[info | MIPVerify]: Working on index 1
[info | MIPVerify]: Applying Conv2d(1, 16, kernel_size=(4, 4), stride=(2, 2), padding=same) ...
[info | MIPVerify]: Applying ReLU() ...
[info | MIPVerify]: Behavior of ReLUs - split: 37, zero_output: 2249, linear_in_input: 850, constant_output: 0
[info | MIPVerify]: Applying Conv2d(16, 32, kernel_size=(4, 4), stride=(2, 2), padding=same) ...
[info | MIPVerify]: Applying ReLU() ...
Calculating upper bounds: 76%|█████████████████▌ | ETA: 0:00:00[K Calculating upper bounds: 100%|███████████████████████| Time: 0:00:00[K
[info | MIPVerify]: Behavior of ReLUs - split: 91, zero_output: 839, linear_in_input: 638, constant_output: 0
[info | MIPVerify]: Applying Flatten() ...
[info | MIPVerify]: Applying Linear(1568 -> 100) ...
[info | MIPVerify]: Applying ReLU() ...
[info | MIPVerify]: Behavior of ReLUs - split: 3, zero_output: 91, linear_in_input: 6, constant_output: 0
[info | MIPVerify]: Applying Linear(100 -> 10) ...
──────────────────────────────────────────────────────────────────────────────
Time Allocations
────────────────────── ───────────────────────
Tot / % measured: 600s / 98.9% 18.6GiB / 98.7%
Section ncalls time %tot avg alloc %tot avg
──────────────────────────────────────────────────────────────────────────────
utils/ 1 226s 38.1% 226s 6.43GiB 35.0% 6.43GiB
import_example_n... 1 198s 33.3% 198s 3.07GiB 16.7% 3.07GiB
get_example_ne... 1 198s 33.3% 198s 3.07GiB 16.7% 3.07GiB
MNIST.WK17a_... 1 181s 30.5% 181s 1.54GiB 8.39% 1.54GiB
MNIST.RSL18a... 1 12.2s 2.06% 12.2s 788MiB 4.19% 788MiB
MNIST.n1 1 4.47s 0.75% 4.47s 775MiB 4.12% 775MiB
import_datasets.jl 1 27.9s 4.69% 27.9s 3.36GiB 18.3% 3.36GiB
integration/ 1 182s 30.7% 182s 3.33GiB 18.1% 3.33GiB
sequential/ 1 182s 30.7% 182s 3.33GiB 18.1% 3.33GiB
generated_weig... 1 182s 30.7% 182s 3.33GiB 18.1% 3.33GiB
conv+fc+soft... 1 133s 22.4% 133s 838MiB 4.46% 838MiB
BlurringPe... 1 75.0s 12.6% 75.0s 701MiB 3.72% 701MiB
Unrestrict... 1 55.4s 9.33% 55.4s 112MiB 0.60% 112MiB
Minimizi... 1 19.2s 3.23% 19.2s 10.9MiB 0.06% 10.9MiB
With mul... 1 15.9s 2.68% 15.9s 36.9MiB 0.20% 36.9MiB
Increasi... 1 10.6s 1.78% 10.6s 25.7MiB 0.14% 25.7MiB
Minimizi... 1 9.73s 1.64% 9.73s 38.4MiB 0.20% 38.4MiB
LInfNormBo... 1 2.20s 0.37% 2.20s 20.7MiB 0.11% 20.7MiB
mfc+mfc+soft... 1 49.4s 8.33% 49.4s 2.51GiB 13.7% 2.51GiB
batch_processing_h... 1 151s 25.5% 151s 7.32GiB 39.8% 7.32GiB
integration.jl 1 145s 24.4% 145s 6.70GiB 36.5% 6.70GiB
unit.jl 1 5.87s 0.99% 5.87s 635MiB 3.38% 635MiB
net_components/ 1 34.1s 5.74% 34.1s 1.29GiB 7.00% 1.29GiB
layers/ 1 19.3s 3.25% 19.3s 732MiB 3.89% 732MiB
core_ops.jl 1 9.58s 1.61% 9.58s 443MiB 2.36% 443MiB
nets/ 1 186ms 0.03% 186ms 4.47MiB 0.02% 4.47MiB
models.jl 1 33.0ms 0.01% 33.0ms 168KiB 0.00% 168KiB
──────────────────────────────────────────────────────────────────────────────
Test Summary: | Pass Total
MIPVerify | 340 340
Testing MIPVerify tests passed