Package evaluation to test Tsunami on Julia 1.11.8 (29b3528cce*) started at 2026-01-20T14:40:11.950 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Activating project at `~/.julia/environments/v1.11` Set-up completed after 8.59s ################################################################################ # Installation # Installing Tsunami... Resolving package versions... Updating `~/.julia/environments/v1.11/Project.toml` [36e41bbe] + Tsunami v0.3.1 Updating `~/.julia/environments/v1.11/Manifest.toml` [621f4979] + AbstractFFTs v1.5.0 [7d9f7c33] + Accessors v0.1.43 [79e6a3ab] + Adapt v4.4.0 [66dad0bd] + AliasTables v1.1.3 [dce04be8] + ArgCheck v2.5.0 [a9b6321e] + Atomix v1.1.2 [ab4f0b2a] + BFloat16s v0.6.1 [fbb218c0] + BSON v0.3.9 [198e06fe] + BangBang v0.4.6 [9718e550] + Baselet v0.1.1 [e1450e63] + BufferedStreams v1.2.2 [fa961155] + CEnum v0.5.0 [082447d4] + ChainRules v1.72.6 [d360d2e6] + ChainRulesCore v1.26.0 [0b6fb165] + ChunkCodecCore v1.0.1 [4c0bbee4] + ChunkCodecLibZlib v1.0.0 [55437552] + ChunkCodecLibZstd v1.0.0 [3da002f7] + ColorTypes v0.12.1 [c3611d14] + ColorVectorSpace v0.11.0 [5ae59095] + Colors v0.13.1 [bbf7d656] + CommonSubexpressions v0.3.1 [34da2185] + Compat v4.18.1 [a33af91c] + CompositionsBase v0.1.2 [187b0558] + ConstructionBase v1.6.0 [6add18c4] + ContextVariablesX v0.1.3 [a8cc5b0e] + Crayons v4.1.1 [9a962f9c] + DataAPI v1.16.0 [864edb3b] + DataStructures v0.19.3 [e2d170a0] + DataValueInterfaces v1.0.0 [244e2a9f] + DefineSingletons v0.1.2 [8bb1440f] + DelimitedFiles v1.9.1 [163ba53b] + DiffResults v1.1.0 [b552c78f] + DiffRules v1.15.1 [ffbed154] + DocStringExtensions v0.9.5 [4e289a0a] + EnumX v1.0.5 [f151be2c] + EnzymeCore v0.8.18 [cc61a311] + FLoops v0.2.2 [b9860ae5] + FLoopsBase v0.1.1 [5789e2e9] + FileIO v1.17.1 [1a297f60] + FillArrays v1.16.0 [53c48c17] + FixedPointNumbers v0.8.5 [587475ba] + Flux v0.16.7 [f6369f11] + ForwardDiff v1.3.1 [d9f16b24] + Functors v0.5.2 [0c68f7d7] + GPUArrays v11.3.4 [46192b85] + GPUArraysCore v0.2.0 [076d061b] + HashArrayMappedTries v0.2.0 [7869d1d1] + IRTools v0.4.15 [a09fc81d] + ImageCore v0.10.5 [22cec73e] + InitialValues v0.3.1 [3587e190] + InverseFunctions v0.1.17 [92d709cd] + IrrationalConstants v0.2.6 [82899510] + IteratorInterfaceExtensions v1.0.0 [033835bb] + JLD2 v0.6.3 [692b3bcd] + JLLWrappers v1.7.1 [b14d175d] + JuliaVariables v0.2.4 [63c18a36] + KernelAbstractions v0.9.39 [929cbde3] + LLVM v9.4.4 [2ab3a3ac] + LogExpFunctions v0.3.29 [c2834f40] + MLCore v1.0.0 [7e8f7934] + MLDataDevices v1.17.1 [d8e11817] + MLStyle v0.4.17 [f1d291b0] + MLUtils v0.4.8 [1914dd2f] + MacroTools v0.5.16 [dbb5928d] + MappedArrays v0.4.3 [128add7d] + MicroCollections v0.2.0 [e1d29d7a] + Missings v1.2.0 [e94cdb99] + MosaicViews v0.3.4 [872c559c] + NNlib v0.9.33 [77ba4419] + NaNMath v1.1.3 [71a1bf82] + NameResolution v0.1.5 [6fe1bfb0] + OffsetArrays v1.17.0 [0b1bfda6] + OneHotArrays v0.2.10 [3bd65402] + Optimisers v0.4.7 [bac558e1] + OrderedCollections v1.8.1 [5432bcbf] + PaddedViews v0.5.12 ⌅ [aea7be01] + PrecompileTools v1.2.1 [21216c6a] + Preferences v1.5.1 [8162dcfd] + PrettyPrint v0.2.0 [33c8b6b6] + ProgressLogging v0.1.6 [3349acd9] + ProtoBuf v1.2.0 [43287f4e] + PtrArrays v1.3.0 [c1ae055f] + RealDot v0.1.0 [189a3867] + Reexport v1.2.2 [ae029012] + Requires v1.3.1 [431bcebd] + SciMLPublic v1.0.1 [7e506255] + ScopedValues v1.5.0 [efcf1570] + Setfield v1.1.2 [605ecd9f] + ShowCases v0.1.0 [699a6c99] + SimpleTraits v0.9.5 [a2af1166] + SortingAlgorithms v1.2.2 [dc90abb0] + SparseInverseSubset v0.1.2 [276daf66] + SpecialFunctions v2.6.1 [171d559e] + SplittablesBase v0.1.15 [cae243ae] + StackViews v0.1.2 [90137ffa] + StaticArrays v1.9.16 [1e83bf80] + StaticArraysCore v1.4.4 [10745b16] + Statistics v1.11.1 [82ae8749] + StatsAPI v1.8.0 [2913bbd2] + StatsBase v0.34.10 [09ab397b] + StructArrays v0.7.2 [3783bdb8] + TableTraits v1.0.1 [bd369af6] + Tables v1.12.1 [899adc3e] + TensorBoardLogger v0.1.26 [62fd8b95] + TensorCore v0.1.1 [28d57a85] + Transducers v0.4.85 [36e41bbe] + Tsunami v0.3.1 [3a884ed6] + UnPack v1.0.2 [013be700] + UnsafeAtomics v0.3.0 [e88e6eb3] + Zygote v0.7.10 [700de1a5] + ZygoteRules v0.2.7 [dad2f222] + LLVMExtra_jll v0.0.38+0 [efe28fd5] + OpenSpecFun_jll v0.5.6+0 [3161d3a3] + Zstd_jll v1.5.7+1 [0dad84c5] + ArgTools v1.1.2 [56f22d72] + Artifacts v1.11.0 [2a0f44e3] + Base64 v1.11.0 [8bf52ea8] + CRC32c v1.11.0 [ade2ca70] + Dates v1.11.0 [8ba89e20] + Distributed v1.11.0 [f43a241f] + Downloads v1.6.0 [7b1f6079] + FileWatching v1.11.0 [9fa8497b] + Future v1.11.0 [b77e0a4c] + InteractiveUtils v1.11.0 [4af54fe1] + LazyArtifacts v1.11.0 [b27032c2] + LibCURL v0.6.4 [76f85450] + LibGit2 v1.11.0 [8f399da3] + Libdl v1.11.0 [37e2e46d] + LinearAlgebra v1.11.0 [56ddb016] + Logging v1.11.0 [d6f4376e] + Markdown v1.11.0 [a63ad114] + Mmap v1.11.0 [ca575930] + NetworkOptions v1.2.0 [44cfe95a] + Pkg v1.11.0 [de0858da] + Printf v1.11.0 [9a3f8284] + Random v1.11.0 [ea8e919c] + SHA v0.7.0 [9e88b42a] + Serialization v1.11.0 [6462fe0b] + Sockets v1.11.0 [2f01184e] + SparseArrays v1.11.0 [4607b0f0] + SuiteSparse [fa267f1f] + TOML v1.0.3 [a4e569a6] + Tar v1.10.0 [8dfed614] + Test v1.11.0 [cf7118a7] + UUIDs v1.11.0 [4ec0a83e] + Unicode v1.11.0 [e66e0078] + CompilerSupportLibraries_jll v1.1.1+0 [deac9b47] + LibCURL_jll v8.6.0+0 [e37daf67] + LibGit2_jll v1.7.2+0 [29816b5a] + LibSSH2_jll v1.11.0+1 [c8ffd9c3] + MbedTLS_jll v2.28.6+0 [14a3606d] + MozillaCACerts_jll v2023.12.12 [4536629a] + OpenBLAS_jll v0.3.27+1 [05823500] + OpenLibm_jll v0.8.5+0 [bea87d4a] + SuiteSparse_jll v7.7.0+0 [83775a58] + Zlib_jll v1.2.13+1 [8e850b90] + libblastrampoline_jll v5.11.0+0 [8e850ede] + nghttp2_jll v1.59.0+0 [3f19e933] + p7zip_jll v17.4.0+2 Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. To see why use `status --outdated -m` Installation completed after 6.6s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... ┌ Warning: Could not use exact versions of packages in manifest, re-resolving └ @ TestEnv ~/.julia/packages/TestEnv/h9a3r/src/julia-1.11/activate_set.jl:78 Precompiling package dependencies... Precompiling project... 61113.5 ms ✓ Tsunami 76099.9 ms ✓ Tsunami → TsunamiEnzymeExt 2 dependencies successfully precompiled in 148 seconds. 314 already precompiled. Precompilation completed after 167.82s ################################################################################ # Testing # Testing Tsunami ┌ Warning: Could not use exact versions of packages in manifest, re-resolving └ @ Pkg.Operations /opt/julia/share/julia/stdlib/v1.11/Pkg/src/Operations.jl:1924 Status `/tmp/jl_DU77S4/Project.toml` [a93c6f00] DataFrames v1.8.1 [7da242da] Enzyme v0.13.118 [587475ba] Flux v0.16.7 [d9f16b24] Functors v0.5.2 [7e8f7934] MLDataDevices v1.17.1 [eb30cadb] MLDatasets v0.7.20 [f1d291b0] MLUtils v0.4.8 [3bd65402] Optimisers v0.4.7 [d7d3b36b] ParameterSchedulers v0.4.3 [189a3867] Reexport v1.2.2 [10745b16] Statistics v1.11.1 [f8b46487] TestItemRunner v1.1.4 [1c621080] TestItems v1.0.0 [36e41bbe] Tsunami v0.3.1 [44cfe95a] Pkg v1.11.0 [9a3f8284] Random v1.11.0 [8dfed614] Test v1.11.0 Status `/tmp/jl_DU77S4/Manifest.toml` [621f4979] AbstractFFTs v1.5.0 [7d9f7c33] Accessors v0.1.43 [79e6a3ab] Adapt v4.4.0 [66dad0bd] AliasTables v1.1.3 [dce04be8] ArgCheck v2.5.0 [4c555306] ArrayLayouts v1.12.2 [a9b6321e] Atomix v1.1.2 [a963bdd2] AtomsBase v0.5.2 ⌅ [ab4f0b2a] BFloat16s v0.5.1 [fbb218c0] BSON v0.3.9 [198e06fe] BangBang v0.4.6 [9718e550] Baselet v0.1.1 [d1d4a3ce] BitFlags v0.1.9 [e1450e63] BufferedStreams v1.2.2 [fa961155] CEnum v0.5.0 [336ed68f] CSV v0.10.15 [082447d4] ChainRules v1.72.6 [d360d2e6] ChainRulesCore v1.26.0 [46823bd8] Chemfiles v0.10.43 [0b6fb165] ChunkCodecCore v1.0.1 [4c0bbee4] ChunkCodecLibZlib v1.0.0 [55437552] ChunkCodecLibZstd v1.0.0 [944b1d66] CodecZlib v0.7.8 [35d6a980] ColorSchemes v3.31.0 [3da002f7] ColorTypes v0.12.1 [c3611d14] ColorVectorSpace v0.11.0 [5ae59095] Colors v0.13.1 [bbf7d656] CommonSubexpressions v0.3.1 [34da2185] Compat v4.18.1 [a33af91c] CompositionsBase v0.1.2 [f0e56b4a] ConcurrentUtilities v2.5.0 [187b0558] ConstructionBase v1.6.0 [6add18c4] ContextVariablesX v0.1.3 [a8cc5b0e] Crayons v4.1.1 [9a962f9c] DataAPI v1.16.0 [124859b0] DataDeps v0.7.13 [a93c6f00] DataFrames v1.8.1 ⌅ [864edb3b] DataStructures v0.18.22 [e2d170a0] DataValueInterfaces v1.0.0 [244e2a9f] DefineSingletons v0.1.2 [8bb1440f] DelimitedFiles v1.9.1 [163ba53b] DiffResults v1.1.0 [b552c78f] DiffRules v1.15.1 [ffbed154] DocStringExtensions v0.9.5 [4e289a0a] EnumX v1.0.5 [7da242da] Enzyme v0.13.118 [f151be2c] EnzymeCore v0.8.18 [460bff9d] ExceptionUnwrapping v0.1.11 [e2ba6199] ExprTools v0.1.10 [cc61a311] FLoops v0.2.2 [b9860ae5] FLoopsBase v0.1.1 [5789e2e9] FileIO v1.17.1 [48062228] FilePathsBase v0.9.24 [1a297f60] FillArrays v1.16.0 [53c48c17] FixedPointNumbers v0.8.5 [587475ba] Flux v0.16.7 [f6369f11] ForwardDiff v1.3.1 [d9f16b24] Functors v0.5.2 [0c68f7d7] GPUArrays v11.3.4 [46192b85] GPUArraysCore v0.2.0 [61eb1bfa] GPUCompiler v1.8.0 [92fee26a] GZip v0.6.2 [c27321d9] Glob v1.4.0 [f67ccb44] HDF5 v0.17.2 [cd3eb016] HTTP v1.10.19 [076d061b] HashArrayMappedTries v0.2.0 [7869d1d1] IRTools v0.4.15 [c817782e] ImageBase v0.1.7 [a09fc81d] ImageCore v0.10.5 [4e3cecfd] ImageShow v0.3.8 ⌅ [4858937d] InfiniteArrays v0.14.4 [e1ba4f0e] Infinities v0.1.12 [22cec73e] InitialValues v0.3.1 [842dd82b] InlineStrings v1.4.5 [7d512f48] InternedStrings v0.7.0 [3587e190] InverseFunctions v0.1.17 [41ab1584] InvertedIndices v1.3.1 [92d709cd] IrrationalConstants v0.2.6 [82899510] IteratorInterfaceExtensions v1.0.0 [033835bb] JLD2 v0.6.3 [692b3bcd] JLLWrappers v1.7.1 [0f8b85d8] JSON3 v1.14.3 [b14d175d] JuliaVariables v0.2.4 [63c18a36] KernelAbstractions v0.9.39 [929cbde3] LLVM v9.4.4 [b964fa9f] LaTeXStrings v1.4.0 [5078a376] LazyArrays v2.9.4 [8cdb02fc] LazyModules v0.3.1 [2ab3a3ac] LogExpFunctions v0.3.29 [e6f89c97] LoggingExtras v1.2.0 [23992714] MAT v0.11.4 [c2834f40] MLCore v1.0.0 [7e8f7934] MLDataDevices v1.17.1 [eb30cadb] MLDatasets v0.7.20 [d8e11817] MLStyle v0.4.17 [f1d291b0] MLUtils v0.4.8 [3da0fdf6] MPIPreferences v0.1.11 [1914dd2f] MacroTools v0.5.16 [dbb5928d] MappedArrays v0.4.3 [739be429] MbedTLS v1.1.9 [128add7d] MicroCollections v0.2.0 [e1d29d7a] Missings v1.2.0 [e94cdb99] MosaicViews v0.3.4 [872c559c] NNlib v0.9.33 [15e1cf62] NPZ v0.4.3 [77ba4419] NaNMath v1.1.3 [71a1bf82] NameResolution v0.1.5 [d8793406] ObjectFile v0.5.0 [6fe1bfb0] OffsetArrays v1.17.0 [0b1bfda6] OneHotArrays v0.2.10 [4d8831e6] OpenSSL v1.6.1 [3bd65402] Optimisers v0.4.7 [bac558e1] OrderedCollections v1.8.1 [65ce6f38] PackageExtensionCompat v1.0.2 [5432bcbf] PaddedViews v0.5.12 [d7d3b36b] ParameterSchedulers v0.4.3 [69de0a69] Parsers v2.8.3 [7b2266bf] PeriodicTable v1.2.1 [fbb45041] Pickle v0.3.6 [2dfb63ee] PooledArrays v1.4.3 ⌅ [aea7be01] PrecompileTools v1.2.1 [21216c6a] Preferences v1.5.1 [8162dcfd] PrettyPrint v0.2.0 [08abe8d2] PrettyTables v3.1.2 [33c8b6b6] ProgressLogging v0.1.6 [3349acd9] ProtoBuf v1.2.0 [43287f4e] PtrArrays v1.3.0 [c1ae055f] RealDot v0.1.0 [189a3867] Reexport v1.2.2 [ae029012] Requires v1.3.1 [431bcebd] SciMLPublic v1.0.1 [7e506255] ScopedValues v1.5.0 [6c6a2e73] Scratch v1.3.0 [91c51154] SentinelArrays v1.4.9 [efcf1570] Setfield v1.1.2 [605ecd9f] ShowCases v0.1.0 [777ac1f9] SimpleBufferStream v1.2.0 [699a6c99] SimpleTraits v0.9.5 [a2af1166] SortingAlgorithms v1.2.2 [dc90abb0] SparseInverseSubset v0.1.2 [276daf66] SpecialFunctions v2.6.1 [171d559e] SplittablesBase v0.1.15 [cae243ae] StackViews v0.1.2 [90137ffa] StaticArrays v1.9.16 [1e83bf80] StaticArraysCore v1.4.4 [10745b16] Statistics v1.11.1 [82ae8749] StatsAPI v1.8.0 [2913bbd2] StatsBase v0.34.10 [4db3bf67] StridedViews v0.4.2 [69024149] StringEncodings v0.3.7 [892a3eda] StringManipulation v0.4.2 [09ab397b] StructArrays v0.7.2 [53d494c1] StructIO v0.3.1 [856f2bd8] StructTypes v1.11.0 [3783bdb8] TableTraits v1.0.1 [bd369af6] Tables v1.12.1 [899adc3e] TensorBoardLogger v0.1.26 [62fd8b95] TensorCore v0.1.1 [f8b46487] TestItemRunner v1.1.4 [1c621080] TestItems v1.0.0 [e689c965] Tracy v0.1.6 [3bb67fe8] TranscodingStreams v0.11.3 [28d57a85] Transducers v0.4.85 [36e41bbe] Tsunami v0.3.1 [5c2747f8] URIs v1.6.1 [3a884ed6] UnPack v1.0.2 [1986cc42] Unitful v1.27.0 [a7773ee8] UnitfulAtomic v1.0.0 [013be700] UnsafeAtomics v0.3.0 [ea10d353] WeakRefStrings v1.4.2 [76eceee3] WorkerUtilities v1.6.1 [a5390f91] ZipFile v0.10.1 [e88e6eb3] Zygote v0.7.10 [700de1a5] ZygoteRules v0.2.7 [78a364fa] Chemfiles_jll v0.10.4+0 [7cc45869] Enzyme_jll v0.0.238+0 ⌅ [0234f1f7] HDF5_jll v1.14.6+0 [e33a78d0] Hwloc_jll v2.12.2+0 [dad2f222] LLVMExtra_jll v0.0.38+0 [ad6e5548] LibTracyClient_jll v0.13.1+0 [94ce4f54] Libiconv_jll v1.18.0+0 [7cb0a576] MPICH_jll v4.3.2+0 [f1f71cc9] MPItrampoline_jll v5.5.4+0 [9237b28f] MicrosoftMPI_jll v10.1.4+3 [fe0851c0] OpenMPI_jll v5.0.9+0 [458c3c95] OpenSSL_jll v3.5.4+0 [efe28fd5] OpenSpecFun_jll v0.5.6+0 ⌅ [02c8fc9c] XML2_jll v2.13.9+0 [a65dc6b1] Xorg_libpciaccess_jll v0.18.1+0 [3161d3a3] Zstd_jll v1.5.7+1 [477f73a3] libaec_jll v1.1.4+0 [0dad84c5] ArgTools v1.1.2 [56f22d72] Artifacts v1.11.0 [2a0f44e3] Base64 v1.11.0 [8bf52ea8] CRC32c v1.11.0 [ade2ca70] Dates v1.11.0 [8ba89e20] Distributed v1.11.0 [f43a241f] Downloads v1.6.0 [7b1f6079] FileWatching v1.11.0 [9fa8497b] Future v1.11.0 [b77e0a4c] InteractiveUtils v1.11.0 [4af54fe1] LazyArtifacts v1.11.0 [b27032c2] LibCURL v0.6.4 [76f85450] LibGit2 v1.11.0 [8f399da3] Libdl v1.11.0 [37e2e46d] LinearAlgebra v1.11.0 [56ddb016] Logging v1.11.0 [d6f4376e] Markdown v1.11.0 [a63ad114] Mmap v1.11.0 [ca575930] NetworkOptions v1.2.0 [44cfe95a] Pkg v1.11.0 [de0858da] Printf v1.11.0 [3fa0cd96] REPL v1.11.0 [9a3f8284] Random v1.11.0 [ea8e919c] SHA v0.7.0 [9e88b42a] Serialization v1.11.0 [6462fe0b] Sockets v1.11.0 [2f01184e] SparseArrays v1.11.0 [f489334b] StyledStrings v1.11.0 [4607b0f0] SuiteSparse [fa267f1f] TOML v1.0.3 [a4e569a6] Tar v1.10.0 [8dfed614] Test v1.11.0 [cf7118a7] UUIDs v1.11.0 [4ec0a83e] Unicode v1.11.0 [e66e0078] CompilerSupportLibraries_jll v1.1.1+0 [deac9b47] LibCURL_jll v8.6.0+0 [e37daf67] LibGit2_jll v1.7.2+0 [29816b5a] LibSSH2_jll v1.11.0+1 [c8ffd9c3] MbedTLS_jll v2.28.6+0 [14a3606d] MozillaCACerts_jll v2023.12.12 [4536629a] OpenBLAS_jll v0.3.27+1 [05823500] OpenLibm_jll v0.8.5+0 [bea87d4a] SuiteSparse_jll v7.7.0+0 [83775a58] Zlib_jll v1.2.13+1 [8e850b90] libblastrampoline_jll v5.11.0+0 [8e850ede] nghttp2_jll v1.59.0+0 [3f19e933] p7zip_jll v17.4.0+2 Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. Testing Running tests... Precompiling BangBangDataFramesExt... 6937.2 ms ✓ BangBang → BangBangDataFramesExt 1 dependency successfully precompiled in 9 seconds. 47 already precompiled. Precompiling TransducersDataFramesExt... 6815.6 ms ✓ Transducers → TransducersDataFramesExt 1 dependency successfully precompiled in 10 seconds. 62 already precompiled. Precompiling FluxEnzymeExt... 55797.9 ms ✓ Enzyme → EnzymeSpecialFunctionsExt 54598.3 ms ✓ Enzyme → EnzymeChainRulesCoreExt 55620.1 ms ✓ Enzyme → EnzymeLogExpFunctionsExt 51471.9 ms ✓ Enzyme → EnzymeGPUArraysCoreExt 66320.9 ms ✓ Flux → FluxEnzymeExt 5 dependencies successfully precompiled in 287 seconds. 185 already precompiled. Precompiling EnzymeBFloat16sExt... 51845.1 ms ✓ Enzyme → EnzymeBFloat16sExt 1 dependency successfully precompiled in 52 seconds. 47 already precompiled. ┌ Warning: No functional GPU backend found! Defaulting to CPU. │ │ 1. If no GPU is available, nothing needs to be done. Set `MLDATADEVICES_SILENCE_WARN_NO_GPU=1` to silence this warning. │ 2. If GPU is available, load the corresponding trigger package. │ a. `CUDA.jl` and `cuDNN.jl` (or just `LuxCUDA.jl`) for NVIDIA CUDA Support. │ b. `AMDGPU.jl` for AMD GPU ROCM Support. │ c. `Metal.jl` for Apple Metal GPU Support. (Experimental) │ d. `oneAPI.jl` for Intel oneAPI GPU Support. (Experimental) │ e. `OpenCL.jl` for OpenCL support. (Experimental) └ @ MLDataDevices.Internal ~/.julia/packages/MLDataDevices/7bVRF/src/internal.jl:114 [ Info: GPUs available: false, used: false [ Info: Model Summary: LinearModel() # 1_000 parameters, plus 1_000 non-trainable [ Info: Run Directory: /home/pkgeval/.julia/packages/Tsunami/5iz8R/test/tsunami_logs/run_1 [ Info: GPUs available: false, used: false [ Info: Model Summary: TestModule1( Chain( Dense(4 => 3, relu), # 15 parameters Dense(3 => 2), # 8 parameters ), 1, 2, ) # Total: 4 arrays, 23 parameters, 324 bytes. [ Info: Run Directory: /home/pkgeval/.julia/packages/Tsunami/5iz8R/test/tsunami_logs/run_1 [ Info: GPUs available: false, used: false [ Info: Model Summary: TestModule1( Chain( Dense(4 => 3, relu), # 15 parameters Dense(3 => 2), # 8 parameters ), 1, 2, ) # Total: 4 arrays, 23 parameters, 324 bytes. [ Info: Run Directory: /home/pkgeval/.julia/packages/Tsunami/5iz8R/test/tsunami_logs/run_1 [ Info: GPUs available: false, used: false [ Info: Model Summary: TestModule1( Chain( Dense(4 => 3, relu), # 15 parameters Dense(3 => 2), # 8 parameters ), 1, 2, ) # Total: 4 arrays, 23 parameters, 324 bytes. [ Info: Run Directory: /home/pkgeval/.julia/packages/Tsunami/5iz8R/test/tsunami_logs/run_1 [ Info: GPUs available: false, used: false [ Info: Model Summary: TestModule1( Chain( Dense(4 => 3, relu), # 15 parameters Dense(3 => 2), # 8 parameters ), 1, 2, ) # Total: 4 arrays, 23 parameters, 324 bytes. [ Info: Run Directory: /home/pkgeval/.julia/packages/Tsunami/5iz8R/test/tsunami_logs/run_1 [ Info: GPUs available: false, used: false [ Info: Model Summary: TestModule1( Chain( Dense(4 => 3, relu), # 15 parameters Dense(3 => 2), # 8 parameters ), 1, 2, ) # Total: 4 arrays, 23 parameters, 324 bytes. [ Info: Run Directory: /home/pkgeval/.julia/packages/Tsunami/5iz8R/test/tsunami_logs/run_1 [ Info: GPUs available: false, used: false [ Info: Model Summary: TestModule1( Chain( Dense(4 => 3, relu), # 15 parameters Dense(3 => 2), # 8 parameters ), 1, 2, ) # Total: 4 arrays, 23 parameters, 324 bytes. [ Info: Run Directory: /home/pkgeval/.julia/packages/Tsunami/5iz8R/test/tsunami_logs/run_1 [ Info: GPUs available: false, used: false [ Info: Model Summary: TestModule1( Chain( Dense(4 => 3, relu), # 15 parameters Dense(3 => 2), # 8 parameters ), 1, 2, ) # Total: 4 arrays, 23 parameters, 324 bytes. [ Info: Run Directory: /home/pkgeval/.julia/packages/Tsunami/5iz8R/test/tsunami_logs/run_1 [ Info: GPUs available: false, used: false [ Info: Model Summary: TestModule1( Chain( Dense(4 => 3, relu), # 15 parameters BatchNorm(3), # 6 parameters, plus 6 Dense(3 => 2), # 8 parameters ), 1, 2, ) # Total: 6 trainable arrays, 29 parameters, # plus 2 non-trainable, 6 parameters, summarysize 596 bytes. [ Info: Run Directory: /home/pkgeval/.julia/packages/Tsunami/5iz8R/test/tsunami_logs/run_1 [ Info: GPUs available: false, used: false [ Info: Model Summary: TestModule1( Chain( Dense(4 => 3, relu), # 15 parameters Dense(3 => 2), # 8 parameters ), 1, 2, ) # Total: 4 arrays, 23 parameters, 324 bytes. [ Info: Run Directory: /home/pkgeval/.julia/packages/Tsunami/5iz8R/test/tsunami_logs/run_1 Train Epoch 1: 100%|██████████████████████| Time: 0:00:00 ( 0.57 ms/it) Train Epoch 2: 100%|██████████████████████| Time: 0:00:00 ( 0.76 ms/it) [ Info: GPUs available: false, used: false [ Info: Model Summary: TestModule1( Chain( Dense(4 => 3, relu), # 15 parameters Dense(3 => 2), # 8 parameters ), 1, 2, ) # Total: 4 arrays, 23 parameters, 324 bytes. [ Info: Run Directory: /home/pkgeval/.julia/packages/Tsunami/5iz8R/test/tsunami_logs/run_2 Train Epoch 1: 100%|██████████████████████| Time: 0:00:00 ( 0.38 ms/it) Train Epoch 2: 100%|██████████████████████| Time: 0:00:00 ( 0.41 ms/it) [ Info: GPUs available: false, used: false [ Info: Model Summary: TestModule1( Chain( Dense(4 => 3, relu), # 15 parameters Dense(3 => 2), # 8 parameters ), 1, 2, ) # Total: 4 arrays, 23 parameters, 324 bytes. [ Info: Run Directory: /home/pkgeval/.julia/packages/Tsunami/5iz8R/test/tsunami_logs/run_1 [ Info: GPUs available: false, used: false [ Info: Model Summary: TestModule1( Chain( Dense(4 => 3, relu), # 15 parameters Dense(3 => 2), # 8 parameters ), 1, 2, ) # Total: 4 arrays, 23 parameters, 324 bytes. [ Info: Run Directory: /home/pkgeval/.julia/packages/Tsunami/5iz8R/test/tsunami_logs/run_1 [ Info: GPUs available: false, used: false [ Info: Model Summary: TestModule1( Chain( Dense(4 => 3, relu), # 15 parameters Dense(3 => 2), # 8 parameters ), 1, 2, ) # Total: 4 arrays, 23 parameters, 324 bytes. [ Info: Run Directory: /home/pkgeval/.julia/packages/Tsunami/5iz8R/test/tsunami_logs/run_1 Testing: 100%|████████████████████████████| Time: 0:00:00 (14.56 ms/it) a: 1.0 b: 2.0 Validation: 100%|█████████████████████████| Time: 0:00:00 (13.35 ms/it) a: 1.0 b: 2.0 [ Info: GPUs available: false, used: false [ Info: Model Summary: TestModule1( Chain( Dense(4 => 3, relu), # 15 parameters Dense(3 => 2), # 8 parameters ), 1, 2, ) # Total: 4 arrays, 23 parameters, 324 bytes. [ Info: Run Directory: /home/pkgeval/.julia/packages/Tsunami/5iz8R/test/tsunami_logs/run_1 Train Epoch 1: 50%|███████████ | ETA: 0:00:31 (31.51 s/it) Train Epoch 1: 100%|██████████████████████| Time: 0:00:31 (15.76 s/it) Train Epoch 2: 100%|██████████████████████| Time: 0:00:00 ( 0.49 ms/it) [ Info: GPUs available: false, used: false [ Info: Model Summary: TBLoggingModule( Chain( Dense(4 => 3, relu), # 15 parameters Dense(3 => 2), # 8 parameters ), true, true, true, true, ) # Total: 4 arrays, 23 parameters, 316 bytes. [ Info: Run Directory: /home/pkgeval/.julia/packages/Tsunami/5iz8R/test/tsunami_logs/run_2 Train Epoch 1: 50%|███████████ | ETA: 0:00:03 ( 3.14 s/it) train/batch_idx_step: 1 train/loss_step: 4.37     Train Epoch 1: 100%|██████████████████████| Time: 0:00:03 ( 1.59 s/it) train/batch_idx_step: 2 train/loss_step: 1.52   Train Epoch 2: 100%|██████████████████████| Time: 0:00:00 ( 0.53 ms/it) train/batch_idx_step: 2 train/loss_step: 1.51   Train Epoch 3: 100%|██████████████████████| Time: 0:00:00 ( 0.49 ms/it) train/batch_idx_step: 2 train/loss_step: 1.51   Train Epoch 4: 100%|██████████████████████| Time: 0:00:00 ( 0.47 ms/it) train/batch_idx_step: 2 train/loss_step: 1.51 Test Summary: | Pass Total Time Package | 67 67 16m48.9s [ Info: GPUs available: false, used: false [ Info: Model Summary: LinearModel() # 1_000 parameters, plus 1_000 non-trainable [ Info: Run Directory: /home/pkgeval/.julia/packages/Tsunami/5iz8R/test/tsunami_logs/run_3 [ Info: GPUs available: false, used: false [ Info: Model Summary: MLP( Chain( Dense(784 => 128, relu), # 100_480 parameters Dense(128 => 10), # 1_290 parameters ), :classification, ) # Total: 4 arrays, 101_770 parameters, 397.750 KiB. [ Info: Run Directory: /home/pkgeval/.julia/packages/Tsunami/5iz8R/test/tsunami_logs/run_3 ┌ Warning: TODO forward zero-set of memorycopy used memset rather than runtime type │ Caused by: │ Stacktrace: │ [1] copy │ @ ./array.jl:350 │ [2] unaliascopy │ @ ./abstractarray.jl:1516 │ [3] unalias │ @ ./abstractarray.jl:1500 │ [4] broadcast_unalias │ @ ./broadcast.jl:946 │ [5] preprocess │ @ ./broadcast.jl:953 │ [6] preprocess_args │ @ ./broadcast.jl:956 │ [7] preprocess_args │ @ ./broadcast.jl:955 │ [8] preprocess │ @ ./broadcast.jl:952 │ [9] override_bc_copyto! │ @ ~/.julia/packages/Enzyme/2lHFw/src/compiler/interpreter.jl:818 │ [10] copyto! │ @ ./broadcast.jl:925 │ [11] materialize! │ @ ./broadcast.jl:883 │ [12] materialize! │ @ ./broadcast.jl:880 │ [13] #logsoftmax!#208 │ @ ~/.julia/packages/NNlib/srXYX/src/softmax.jl:114 └ @ Enzyme.Compiler ~/.julia/packages/Enzyme/2lHFw/src/rules/llvmrules.jl:814 ┌ Warning: TODO forward zero-set of memorycopy used memset rather than runtime type │ Caused by: │ Stacktrace: │ [1] copy │ @ ./array.jl:350 │ [2] unaliascopy │ @ ./abstractarray.jl:1516 │ [3] unalias │ @ ./abstractarray.jl:1500 │ [4] broadcast_unalias │ @ ./broadcast.jl:946 │ [5] preprocess │ @ ./broadcast.jl:953 │ [6] preprocess_args │ @ ./broadcast.jl:956 │ [7] preprocess_args │ @ ./broadcast.jl:955 │ [8] preprocess │ @ ./broadcast.jl:952 │ [9] preprocess_args │ @ ./broadcast.jl:956 │ [10] preprocess_args (repeats 2 times) │ @ ./broadcast.jl:955 │ [11] preprocess │ @ ./broadcast.jl:952 │ [12] override_bc_copyto! │ @ ~/.julia/packages/Enzyme/2lHFw/src/compiler/interpreter.jl:818 │ [13] copyto! │ @ ./broadcast.jl:925 │ [14] materialize! │ @ ./broadcast.jl:883 │ [15] materialize! │ @ ./broadcast.jl:880 │ [16] #logsoftmax!#208 │ @ ~/.julia/packages/NNlib/srXYX/src/softmax.jl:117 └ @ Enzyme.Compiler ~/.julia/packages/Enzyme/2lHFw/src/rules/llvmrules.jl:814 Precompiling MLDatasets... 5449.1 ms ✓ AtomsBase 6319.9 ms ✓ ImageShow 5214.6 ms ✓ Pickle 7406.1 ms ✓ MAT 5849.4 ms ✓ Chemfiles 25877.7 ms ✓ MLDatasets 6 dependencies successfully precompiled in 60 seconds. 207 already precompiled. [ Info: GPUs available: false, used: false [ Info: Model Summary: MLP( Chain( Dense(784 => 128, relu), # 100_480 parameters Dense(128 => 10), # 1_290 parameters ), :classification, ) # Total: 4 arrays, 101_770 parameters, 397.750 KiB. [ Info: Run Directory: /home/pkgeval/.julia/packages/Tsunami/5iz8R/test/tsunami_logs/run_3 WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. ┌ Warning: TODO forward zero-set of memorycopy used memset rather than runtime type │ Caused by: │ Stacktrace: │ [1] copy │ @ ./array.jl:350 │ [2] unaliascopy │ @ ./abstractarray.jl:1516 │ [3] unalias │ @ ./abstractarray.jl:1500 │ [4] broadcast_unalias │ @ ./broadcast.jl:946 │ [5] preprocess │ @ ./broadcast.jl:953 │ [6] preprocess_args │ @ ./broadcast.jl:956 │ [7] preprocess_args │ @ ./broadcast.jl:955 │ [8] preprocess │ @ ./broadcast.jl:952 │ [9] override_bc_copyto! │ @ ~/.julia/packages/Enzyme/2lHFw/src/compiler/interpreter.jl:818 │ [10] copyto! │ @ ./broadcast.jl:925 │ [11] materialize! │ @ ./broadcast.jl:883 │ [12] materialize! │ @ ./broadcast.jl:880 │ [13] #logsoftmax!#208 │ @ ~/.julia/packages/NNlib/srXYX/src/softmax.jl:114 └ @ Enzyme.Compiler ~/.julia/packages/Enzyme/2lHFw/src/rules/llvmrules.jl:814 ┌ Warning: TODO forward zero-set of memorycopy used memset rather than runtime type │ Caused by: │ Stacktrace: │ [1] copy │ @ ./array.jl:350 │ [2] unaliascopy │ @ ./abstractarray.jl:1516 │ [3] unalias │ @ ./abstractarray.jl:1500 │ [4] broadcast_unalias │ @ ./broadcast.jl:946 │ [5] preprocess │ @ ./broadcast.jl:953 │ [6] preprocess_args │ @ ./broadcast.jl:956 │ [7] preprocess_args │ @ ./broadcast.jl:955 │ [8] preprocess │ @ ./broadcast.jl:952 │ [9] preprocess_args │ @ ./broadcast.jl:956 │ [10] preprocess_args (repeats 2 times) │ @ ./broadcast.jl:955 │ [11] preprocess │ @ ./broadcast.jl:952 │ [12] override_bc_copyto! │ @ ~/.julia/packages/Enzyme/2lHFw/src/compiler/interpreter.jl:818 │ [13] copyto! │ @ ./broadcast.jl:925 │ [14] materialize! │ @ ./broadcast.jl:883 │ [15] materialize! │ @ ./broadcast.jl:880 │ [16] #logsoftmax!#208 │ @ ~/.julia/packages/NNlib/srXYX/src/softmax.jl:117 └ @ Enzyme.Compiler ~/.julia/packages/Enzyme/2lHFw/src/rules/llvmrules.jl:814 Test Summary: | Pass Total Time Package | 4 4 8m38.6s Precompiling ParameterSchedulers... 4081.8 ms ✓ InfiniteArrays 3269.9 ms ✓ InfiniteArrays → InfiniteArraysStatisticsExt 3690.0 ms ✓ ParameterSchedulers 3 dependencies successfully precompiled in 11 seconds. 35 already precompiled. Precompiling TransducersLazyArraysExt... 3794.1 ms ✓ Transducers → TransducersLazyArraysExt 1 dependency successfully precompiled in 4 seconds. 55 already precompiled. [ Info: GPUs available: false, used: false [ Info: Model Summary: MLP( Chain( MLUtils.flatten, Dense(784 => 1024, relu), # 803_840 parameters Dense(1024 => 10), # 10_250 parameters ), ) # Total: 4 arrays, 814_090 parameters, 3.106 MiB. [ Info: Run Directory: /home/pkgeval/.julia/packages/Tsunami/5iz8R/test/tsunami_logs/run_3 [ Info: GPUs available: false, used: false [ Info: Model Summary: MLP( Chain( MLUtils.flatten, Dense(784 => 1024, relu), # 803_840 parameters Dense(1024 => 10), # 10_250 parameters ), ) # Total: 4 arrays, 814_090 parameters, 3.106 MiB. [ Info: Run Directory: /home/pkgeval/.julia/packages/Tsunami/5iz8R/examples/MLP_MNIST/tsunami_logs/run_1 Val Epoch 0: 2%|▌ | ETA: 0:00:05 ( 0.11 s/it) accuracy/val: 0.148 loss/val: 2.35     Val Epoch 0: 100%|████████████████████████| Time: 0:00:01 (21.46 ms/it) accuracy/val: 0.108 loss/val: 2.35   Train Epoch 1: 0%| | ETA: 2:51:53 (24.50 s/it) accuracy/train: 0.125 loss/train: 2.36     Train Epoch 1: 1%|▎ | ETA: 0:42:54 ( 6.16 s/it) accuracy/train: 0.602 loss/train: 2.02     Train Epoch 1: 2%|▍ | ETA: 0:25:32 ( 3.69 s/it) accuracy/train: 0.703 loss/train: 1.39     Train Epoch 1: 2%|▌ | ETA: 0:17:50 ( 2.60 s/it) accuracy/train: 0.773 loss/train: 0.766     Train Epoch 1: 3%|▋ | ETA: 0:13:41 ( 2.01 s/it) accuracy/train: 0.766 loss/train: 0.657     Train Epoch 1: 4%|▉ | ETA: 0:11:05 ( 1.64 s/it) accuracy/train: 0.844 loss/train: 0.476     Train Epoch 1: 5%|█ | ETA: 0:09:19 ( 1.39 s/it) accuracy/train: 0.859 loss/train: 0.382     Train Epoch 1: 5%|█▏ | ETA: 0:08:01 ( 1.20 s/it) accuracy/train: 0.883 loss/train: 0.472     Train Epoch 1: 6%|█▎ | ETA: 0:07:02 ( 1.07 s/it) accuracy/train: 0.875 loss/train: 0.55     Train Epoch 1: 7%|█▌ | ETA: 0:06:16 ( 0.96 s/it) accuracy/train: 0.906 loss/train: 0.27     Train Epoch 1: 7%|█▋ | ETA: 0:05:39 ( 0.87 s/it) accuracy/train: 0.914 loss/train: 0.258     Train Epoch 1: 8%|█▊ | ETA: 0:05:18 ( 0.82 s/it) accuracy/train: 0.93 loss/train: 0.214     Train Epoch 1: 9%|█▉ | ETA: 0:04:51 ( 0.75 s/it) accuracy/train: 0.93 loss/train: 0.313     Train Epoch 1: 9%|██ | ETA: 0:04:28 ( 0.70 s/it) accuracy/train: 0.875 loss/train: 0.26     Train Epoch 1: 10%|██▎ | ETA: 0:04:08 ( 0.65 s/it) accuracy/train: 0.961 loss/train: 0.224     Train Epoch 1: 11%|██▍ | ETA: 0:03:51 ( 0.61 s/it) accuracy/train: 0.891 loss/train: 0.386     Train Epoch 1: 11%|██▌ | ETA: 0:03:41 ( 0.59 s/it) accuracy/train: 0.883 loss/train: 0.359     Train Epoch 1: 12%|██▋ | ETA: 0:03:27 ( 0.56 s/it) accuracy/train: 0.914 loss/train: 0.258     Train Epoch 1: 13%|██▊ | ETA: 0:03:15 ( 0.53 s/it) accuracy/train: 0.977 loss/train: 0.181     Train Epoch 1: 13%|██▉ | ETA: 0:03:04 ( 0.50 s/it) accuracy/train: 0.945 loss/train: 0.185     Train Epoch 1: 14%|███▏ | ETA: 0:02:54 ( 0.48 s/it) accuracy/train: 0.938 loss/train: 0.236     Train Epoch 1: 15%|███▎ | ETA: 0:02:45 ( 0.46 s/it) accuracy/train: 0.898 loss/train: 0.296     Train Epoch 1: 15%|███▍ | ETA: 0:02:37 ( 0.44 s/it) accuracy/train: 0.906 loss/train: 0.31     Train Epoch 1: 16%|███▌ | ETA: 0:02:29 ( 0.42 s/it) accuracy/train: 0.938 loss/train: 0.235     Train Epoch 1: 17%|███▋ | ETA: 0:02:25 ( 0.41 s/it) accuracy/train: 0.891 loss/train: 0.342     Train Epoch 1: 17%|███▊ | ETA: 0:02:18 ( 0.40 s/it) accuracy/train: 0.953 loss/train: 0.238     Train Epoch 1: 18%|████ | ETA: 0:02:12 ( 0.38 s/it) accuracy/train: 0.93 loss/train: 0.417     Train Epoch 1: 19%|████▏ | ETA: 0:02:06 ( 0.37 s/it) accuracy/train: 0.922 loss/train: 0.267     Train Epoch 1: 19%|████▎ | ETA: 0:02:01 ( 0.36 s/it) accuracy/train: 0.953 loss/train: 0.193     Train Epoch 1: 20%|████▍ | ETA: 0:01:56 ( 0.35 s/it) accuracy/train: 0.914 loss/train: 0.216     Train Epoch 1: 21%|████▋ | ETA: 0:01:52 ( 0.34 s/it) accuracy/train: 0.922 loss/train: 0.244     Train Epoch 1: 21%|████▊ | ETA: 0:01:49 ( 0.33 s/it) accuracy/train: 0.914 loss/train: 0.238     Train Epoch 1: 22%|████▊ | ETA: 0:01:47 ( 0.32 s/it) accuracy/train: 0.906 loss/train: 0.258     Train Epoch 1: 23%|█████ | ETA: 0:01:43 ( 0.32 s/it) accuracy/train: 0.938 loss/train: 0.204     Train Epoch 1: 23%|█████▏ | ETA: 0:01:39 ( 0.31 s/it) accuracy/train: 0.93 loss/train: 0.263     Train Epoch 1: 24%|█████▎ | ETA: 0:01:36 ( 0.30 s/it) accuracy/train: 0.914 loss/train: 0.294     Train Epoch 1: 25%|█████▍ | ETA: 0:01:32 ( 0.29 s/it) accuracy/train: 0.891 loss/train: 0.337     Train Epoch 1: 25%|█████▋ | ETA: 0:01:29 ( 0.29 s/it) accuracy/train: 0.961 loss/train: 0.136     Train Epoch 1: 26%|█████▊ | ETA: 0:01:26 ( 0.28 s/it) accuracy/train: 0.969 loss/train: 0.169     Train Epoch 1: 27%|█████▉ | ETA: 0:01:24 ( 0.27 s/it) accuracy/train: 0.93 loss/train: 0.182     Train Epoch 1: 27%|██████ | ETA: 0:01:22 ( 0.27 s/it) accuracy/train: 0.961 loss/train: 0.108     Train Epoch 1: 28%|██████▏ | ETA: 0:01:19 ( 0.26 s/it) accuracy/train: 0.922 loss/train: 0.267     Train Epoch 1: 29%|██████▎ | ETA: 0:01:17 ( 0.26 s/it) accuracy/train: 0.938 loss/train: 0.259     Train Epoch 1: 29%|██████▌ | ETA: 0:01:15 ( 0.25 s/it) accuracy/train: 0.969 loss/train: 0.173     Train Epoch 1: 30%|██████▋ | ETA: 0:01:13 ( 0.25 s/it) accuracy/train: 0.961 loss/train: 0.194     Train Epoch 1: 31%|██████▊ | ETA: 0:01:11 ( 0.24 s/it) accuracy/train: 0.945 loss/train: 0.179     Train Epoch 1: 31%|██████▉ | ETA: 0:01:09 ( 0.24 s/it) accuracy/train: 0.938 loss/train: 0.196     Train Epoch 1: 32%|███████ | ETA: 0:01:07 ( 0.24 s/it) accuracy/train: 0.953 loss/train: 0.126     Train Epoch 1: 33%|███████▎ | ETA: 0:01:05 ( 0.23 s/it) accuracy/train: 0.93 loss/train: 0.274     Train Epoch 1: 33%|███████▍ | ETA: 0:01:03 ( 0.23 s/it) accuracy/train: 0.953 loss/train: 0.122     Train Epoch 1: 34%|███████▌ | ETA: 0:01:02 ( 0.22 s/it) accuracy/train: 0.953 loss/train: 0.128     Train Epoch 1: 35%|███████▋ | ETA: 0:01:00 ( 0.22 s/it) accuracy/train: 0.953 loss/train: 0.139     Train Epoch 1: 36%|███████▉ | ETA: 0:00:58 ( 0.22 s/it) accuracy/train: 0.953 loss/train: 0.153     Train Epoch 1: 36%|███████▉ | ETA: 0:00:57 ( 0.21 s/it) accuracy/train: 0.898 loss/train: 0.312     Train Epoch 1: 37%|████████▏ | ETA: 0:00:56 ( 0.21 s/it) accuracy/train: 0.977 loss/train: 0.0763     Train Epoch 1: 37%|████████▎ | ETA: 0:00:54 ( 0.21 s/it) accuracy/train: 0.945 loss/train: 0.168     Train Epoch 1: 38%|████████▍ | ETA: 0:00:53 ( 0.20 s/it) accuracy/train: 0.93 loss/train: 0.124     Train Epoch 1: 39%|████████▌ | ETA: 0:00:52 ( 0.20 s/it) accuracy/train: 0.945 loss/train: 0.137     Train Epoch 1: 40%|████████▊ | ETA: 0:00:50 ( 0.20 s/it) accuracy/train: 0.938 loss/train: 0.155     Train Epoch 1: 40%|████████▉ | ETA: 0:00:49 ( 0.20 s/it) accuracy/train: 0.977 loss/train: 0.0942     Train Epoch 1: 41%|█████████ | ETA: 0:00:48 ( 0.19 s/it) accuracy/train: 0.969 loss/train: 0.112     Train Epoch 1: 42%|█████████▏ | ETA: 0:00:46 ( 0.19 s/it) accuracy/train: 0.938 loss/train: 0.229     Train Epoch 1: 42%|█████████▍ | ETA: 0:00:45 ( 0.19 s/it) accuracy/train: 0.945 loss/train: 0.25     Train Epoch 1: 43%|█████████▌ | ETA: 0:00:44 ( 0.19 s/it) accuracy/train: 0.945 loss/train: 0.178     Train Epoch 1: 44%|█████████▋ | ETA: 0:00:43 ( 0.18 s/it) accuracy/train: 0.969 loss/train: 0.125     Train Epoch 1: 45%|█████████▊ | ETA: 0:00:42 ( 0.18 s/it) accuracy/train: 0.969 loss/train: 0.135     Train Epoch 1: 45%|██████████ | ETA: 0:00:41 ( 0.18 s/it) accuracy/train: 0.93 loss/train: 0.283     Train Epoch 1: 46%|██████████▏ | ETA: 0:00:40 ( 0.18 s/it) accuracy/train: 0.938 loss/train: 0.168     Train Epoch 1: 47%|██████████▎ | ETA: 0:00:39 ( 0.18 s/it) accuracy/train: 0.969 loss/train: 0.142     Train Epoch 1: 47%|██████████▍ | ETA: 0:00:38 ( 0.17 s/it) accuracy/train: 0.945 loss/train: 0.284     Train Epoch 1: 48%|██████████▋ | ETA: 0:00:37 ( 0.17 s/it) accuracy/train: 0.953 loss/train: 0.138     Train Epoch 1: 49%|██████████▊ | ETA: 0:00:36 ( 0.17 s/it) accuracy/train: 0.961 loss/train: 0.157     Train Epoch 1: 50%|██████████▉ | ETA: 0:00:35 ( 0.17 s/it) accuracy/train: 0.953 loss/train: 0.119     Train Epoch 1: 50%|███████████ | ETA: 0:00:34 ( 0.17 s/it) accuracy/train: 0.945 loss/train: 0.142     Train Epoch 1: 51%|███████████▎ | ETA: 0:00:33 ( 0.16 s/it) accuracy/train: 0.984 loss/train: 0.0935     Train Epoch 1: 52%|███████████▍ | ETA: 0:00:33 ( 0.16 s/it) accuracy/train: 0.938 loss/train: 0.263     Train Epoch 1: 52%|███████████▌ | ETA: 0:00:32 ( 0.16 s/it) accuracy/train: 0.945 loss/train: 0.208     Train Epoch 1: 53%|███████████▋ | ETA: 0:00:31 ( 0.16 s/it) accuracy/train: 0.961 loss/train: 0.101     Train Epoch 1: 53%|███████████▊ | ETA: 0:00:31 ( 0.16 s/it) accuracy/train: 0.93 loss/train: 0.151     Train Epoch 1: 54%|███████████▉ | ETA: 0:00:30 ( 0.16 s/it) accuracy/train: 0.961 loss/train: 0.149     Train Epoch 1: 55%|████████████ | ETA: 0:00:30 ( 0.16 s/it) accuracy/train: 0.953 loss/train: 0.127     Train Epoch 1: 55%|████████████▏ | ETA: 0:00:29 ( 0.16 s/it) accuracy/train: 0.945 loss/train: 0.163     Train Epoch 1: 56%|████████████▎ | ETA: 0:00:28 ( 0.15 s/it) accuracy/train: 0.938 loss/train: 0.275     Train Epoch 1: 57%|████████████▌ | ETA: 0:00:27 ( 0.15 s/it) accuracy/train: 0.953 loss/train: 0.158     Train Epoch 1: 57%|████████████▋ | ETA: 0:00:27 ( 0.15 s/it) accuracy/train: 0.945 loss/train: 0.261     Train Epoch 1: 58%|████████████▊ | ETA: 0:00:26 ( 0.15 s/it) accuracy/train: 0.938 loss/train: 0.208     Train Epoch 1: 59%|████████████▉ | ETA: 0:00:25 ( 0.15 s/it) accuracy/train: 0.961 loss/train: 0.166     Train Epoch 1: 59%|█████████████▏ | ETA: 0:00:25 ( 0.15 s/it) accuracy/train: 0.961 loss/train: 0.156     Train Epoch 1: 60%|█████████████▎ | ETA: 0:00:24 ( 0.15 s/it) accuracy/train: 0.938 loss/train: 0.218     Train Epoch 1: 61%|█████████████▍ | ETA: 0:00:24 ( 0.15 s/it) accuracy/train: 0.969 loss/train: 0.0809     Train Epoch 1: 61%|█████████████▌ | ETA: 0:00:23 ( 0.14 s/it) accuracy/train: 0.945 loss/train: 0.216     Train Epoch 1: 62%|█████████████▌ | ETA: 0:00:23 ( 0.14 s/it) accuracy/train: 0.945 loss/train: 0.14     Train Epoch 1: 62%|█████████████▋ | ETA: 0:00:22 ( 0.14 s/it) accuracy/train: 0.953 loss/train: 0.212     Train Epoch 1: 63%|█████████████▉ | ETA: 0:00:22 ( 0.14 s/it) accuracy/train: 0.969 loss/train: 0.12     Train Epoch 1: 64%|██████████████ | ETA: 0:00:21 ( 0.14 s/it) accuracy/train: 0.938 loss/train: 0.256     Train Epoch 1: 64%|██████████████▏ | ETA: 0:00:21 ( 0.14 s/it) accuracy/train: 0.961 loss/train: 0.149     Train Epoch 1: 65%|██████████████▎ | ETA: 0:00:20 ( 0.14 s/it) accuracy/train: 0.945 loss/train: 0.137     Train Epoch 1: 66%|██████████████▌ | ETA: 0:00:20 ( 0.14 s/it) accuracy/train: 0.961 loss/train: 0.146     Train Epoch 1: 66%|██████████████▋ | ETA: 0:00:19 ( 0.14 s/it) accuracy/train: 0.93 loss/train: 0.153     Train Epoch 1: 67%|██████████████▊ | ETA: 0:00:18 ( 0.14 s/it) accuracy/train: 0.969 loss/train: 0.152     Train Epoch 1: 68%|██████████████▉ | ETA: 0:00:18 ( 0.14 s/it) accuracy/train: 0.977 loss/train: 0.0973     Train Epoch 1: 68%|███████████████▏ | ETA: 0:00:17 ( 0.13 s/it) accuracy/train: 0.922 loss/train: 0.179     Train Epoch 1: 69%|███████████████▎ | ETA: 0:00:17 ( 0.13 s/it) accuracy/train: 0.953 loss/train: 0.137     Train Epoch 1: 70%|███████████████▍ | ETA: 0:00:16 ( 0.13 s/it) accuracy/train: 0.938 loss/train: 0.255     Train Epoch 1: 71%|███████████████▌ | ETA: 0:00:16 ( 0.13 s/it) accuracy/train: 0.938 loss/train: 0.275     Train Epoch 1: 71%|███████████████▊ | ETA: 0:00:15 ( 0.13 s/it) accuracy/train: 0.969 loss/train: 0.0725     Train Epoch 1: 72%|███████████████▉ | ETA: 0:00:15 ( 0.13 s/it) accuracy/train: 0.945 loss/train: 0.133     Train Epoch 1: 73%|████████████████ | ETA: 0:00:14 ( 0.13 s/it) accuracy/train: 0.977 loss/train: 0.092     Train Epoch 1: 73%|████████████████▏ | ETA: 0:00:14 ( 0.13 s/it) accuracy/train: 0.922 loss/train: 0.212     Train Epoch 1: 74%|████████████████▎ | ETA: 0:00:14 ( 0.13 s/it) accuracy/train: 0.938 loss/train: 0.176     Train Epoch 1: 75%|████████████████▍ | ETA: 0:00:13 ( 0.13 s/it) accuracy/train: 0.953 loss/train: 0.113     Train Epoch 1: 75%|████████████████▋ | ETA: 0:00:13 ( 0.13 s/it) accuracy/train: 0.938 loss/train: 0.182     Train Epoch 1: 76%|████████████████▊ | ETA: 0:00:12 ( 0.13 s/it) accuracy/train: 0.953 loss/train: 0.108     Train Epoch 1: 77%|████████████████▉ | ETA: 0:00:12 ( 0.12 s/it) accuracy/train: 0.922 loss/train: 0.293     Train Epoch 1: 77%|█████████████████ | ETA: 0:00:11 ( 0.12 s/it) accuracy/train: 0.992 loss/train: 0.0921     Train Epoch 1: 78%|█████████████████▎ | ETA: 0:00:11 ( 0.12 s/it) accuracy/train: 0.961 loss/train: 0.119     Train Epoch 1: 79%|█████████████████▍ | ETA: 0:00:10 ( 0.12 s/it) accuracy/train: 0.961 loss/train: 0.158     Train Epoch 1: 80%|█████████████████▌ | ETA: 0:00:10 ( 0.12 s/it) accuracy/train: 0.969 loss/train: 0.177     Train Epoch 1: 80%|█████████████████▋ | ETA: 0:00:10 ( 0.12 s/it) accuracy/train: 0.977 loss/train: 0.059     Train Epoch 1: 81%|█████████████████▉ | ETA: 0:00:09 ( 0.12 s/it) accuracy/train: 0.953 loss/train: 0.153     Train Epoch 1: 82%|██████████████████ | ETA: 0:00:09 ( 0.12 s/it) accuracy/train: 0.906 loss/train: 0.221     Train Epoch 1: 82%|██████████████████▏ | ETA: 0:00:08 ( 0.12 s/it) accuracy/train: 0.945 loss/train: 0.172     Train Epoch 1: 83%|██████████████████▎ | ETA: 0:00:08 ( 0.12 s/it) accuracy/train: 0.961 loss/train: 0.222     Train Epoch 1: 84%|██████████████████▌ | ETA: 0:00:07 ( 0.12 s/it) accuracy/train: 0.953 loss/train: 0.111     Train Epoch 1: 85%|██████████████████▋ | ETA: 0:00:07 ( 0.12 s/it) accuracy/train: 0.961 loss/train: 0.0969     Train Epoch 1: 85%|██████████████████▊ | ETA: 0:00:07 ( 0.12 s/it) accuracy/train: 0.938 loss/train: 0.202     Train Epoch 1: 86%|██████████████████▉ | ETA: 0:00:06 ( 0.12 s/it) accuracy/train: 0.953 loss/train: 0.154     Train Epoch 1: 87%|███████████████████▏ | ETA: 0:00:06 ( 0.11 s/it) accuracy/train: 0.945 loss/train: 0.0799     Train Epoch 1: 87%|███████████████████▎ | ETA: 0:00:06 ( 0.11 s/it) accuracy/train: 0.945 loss/train: 0.176     Train Epoch 1: 88%|███████████████████▍ | ETA: 0:00:05 ( 0.11 s/it) accuracy/train: 0.969 loss/train: 0.0827     Train Epoch 1: 89%|███████████████████▌ | ETA: 0:00:05 ( 0.11 s/it) accuracy/train: 0.953 loss/train: 0.139     Train Epoch 1: 90%|███████████████████▊ | ETA: 0:00:04 ( 0.11 s/it) accuracy/train: 0.953 loss/train: 0.139     Train Epoch 1: 90%|███████████████████▉ | ETA: 0:00:04 ( 0.11 s/it) accuracy/train: 0.969 loss/train: 0.0745     Train Epoch 1: 91%|████████████████████ | ETA: 0:00:04 ( 0.11 s/it) accuracy/train: 0.93 loss/train: 0.208     Train Epoch 1: 92%|████████████████████▏ | ETA: 0:00:03 ( 0.11 s/it) accuracy/train: 0.922 loss/train: 0.296     Train Epoch 1: 92%|████████████████████▍ | ETA: 0:00:03 ( 0.11 s/it) accuracy/train: 0.969 loss/train: 0.133     Train Epoch 1: 93%|████████████████████▌ | ETA: 0:00:03 ( 0.11 s/it) accuracy/train: 0.938 loss/train: 0.226     Train Epoch 1: 94%|████████████████████▋ | ETA: 0:00:02 ( 0.11 s/it) accuracy/train: 0.984 loss/train: 0.0587     Train Epoch 1: 95%|████████████████████▊ | ETA: 0:00:02 ( 0.11 s/it) accuracy/train: 0.969 loss/train: 0.0863     Train Epoch 1: 95%|█████████████████████ | ETA: 0:00:02 ( 0.11 s/it) accuracy/train: 0.953 loss/train: 0.189     Train Epoch 1: 96%|█████████████████████▏| ETA: 0:00:01 ( 0.11 s/it) accuracy/train: 0.977 loss/train: 0.0641     Train Epoch 1: 97%|█████████████████████▎| ETA: 0:00:01 ( 0.11 s/it) accuracy/train: 0.969 loss/train: 0.159     Train Epoch 1: 97%|█████████████████████▍| ETA: 0:00:01 ( 0.11 s/it) accuracy/train: 0.953 loss/train: 0.179     Train Epoch 1: 98%|█████████████████████▋| ETA: 0:00:00 ( 0.11 s/it) accuracy/train: 0.961 loss/train: 0.114     Train Epoch 1: 99%|█████████████████████▊| ETA: 0:00:00 ( 0.11 s/it) accuracy/train: 0.953 loss/train: 0.132     Train Epoch 1: 100%|█████████████████████▉| ETA: 0:00:00 ( 0.11 s/it) accuracy/train: 0.961 loss/train: 0.13     Train Epoch 1: 100%|██████████████████████| Time: 0:00:44 ( 0.10 s/it) accuracy/train: 0.955 loss/train: 0.142   Val Epoch 1: 32%|███████▋ | ETA: 0:00:00 ( 6.99 ms/it) accuracy/val: 0.967 loss/val: 0.0932        Val Epoch 1: 66%|███████████████▉ | ETA: 0:00:00 ( 6.86 ms/it) accuracy/val: 0.965 loss/val: 0.107        Val Epoch 1: 96%|███████████████████████ | ETA: 0:00:00 (10.04 ms/it) accuracy/val: 0.968 loss/val: 0.104        Val Epoch 1: 100%|████████████████████████| Time: 0:00:00 ( 9.91 ms/it) accuracy/val: 0.968 loss/val: 0.102      Train Epoch 2: 1%|▏ | ETA: 0:00:18 (45.08 ms/it) accuracy/train: 1.0 loss/train: 0.0323     Train Epoch 2: 1%|▍ | ETA: 0:00:18 (43.63 ms/it) accuracy/train: 0.945 loss/train: 0.11     Train Epoch 2: 2%|▌ | ETA: 0:00:17 (42.64 ms/it) accuracy/train: 0.969 loss/train: 0.1     Train Epoch 2: 3%|▋ | ETA: 0:00:18 (44.36 ms/it) accuracy/train: 0.984 loss/train: 0.0467     Train Epoch 2: 3%|▊ | ETA: 0:00:18 (44.64 ms/it) accuracy/train: 0.969 loss/train: 0.0858     Train Epoch 2: 4%|▉ | ETA: 0:00:18 (44.69 ms/it) accuracy/train: 0.953 loss/train: 0.118     Train Epoch 2: 5%|█ | ETA: 0:00:17 (44.76 ms/it) accuracy/train: 0.969 loss/train: 0.0687     Train Epoch 2: 5%|█▎ | ETA: 0:00:17 (44.94 ms/it) accuracy/train: 0.984 loss/train: 0.0387     Train Epoch 2: 6%|█▍ | ETA: 0:00:17 (44.89 ms/it) accuracy/train: 0.977 loss/train: 0.0537     Train Epoch 2: 7%|█▌ | ETA: 0:00:17 (44.78 ms/it) accuracy/train: 0.977 loss/train: 0.108     Train Epoch 2: 8%|█▋ | ETA: 0:00:17 (44.75 ms/it) accuracy/train: 0.977 loss/train: 0.0793     Train Epoch 2: 8%|█▉ | ETA: 0:00:17 (44.53 ms/it) accuracy/train: 0.969 loss/train: 0.0899     Train Epoch 2: 9%|██ | ETA: 0:00:17 (44.40 ms/it) accuracy/train: 0.945 loss/train: 0.169     Train Epoch 2: 10%|██▏ | ETA: 0:00:16 (44.29 ms/it) accuracy/train: 0.977 loss/train: 0.0573     Train Epoch 2: 10%|██▎ | ETA: 0:00:16 (44.35 ms/it) accuracy/train: 0.945 loss/train: 0.162     Train Epoch 2: 11%|██▌ | ETA: 0:00:16 (44.30 ms/it) accuracy/train: 0.953 loss/train: 0.0998     Train Epoch 2: 12%|██▋ | ETA: 0:00:16 (44.29 ms/it) accuracy/train: 0.953 loss/train: 0.129     Train Epoch 2: 13%|██▊ | ETA: 0:00:16 (44.12 ms/it) accuracy/train: 0.922 loss/train: 0.154     Train Epoch 2: 13%|██▉ | ETA: 0:00:16 (44.04 ms/it) accuracy/train: 0.938 loss/train: 0.203     Train Epoch 2: 14%|███▏ | ETA: 0:00:15 (43.98 ms/it) accuracy/train: 0.969 loss/train: 0.128     Train Epoch 2: 15%|███▎ | ETA: 0:00:15 (43.95 ms/it) accuracy/train: 0.984 loss/train: 0.0702     Train Epoch 2: 15%|███▍ | ETA: 0:00:15 (43.81 ms/it) accuracy/train: 0.969 loss/train: 0.112     Train Epoch 2: 16%|███▌ | ETA: 0:00:15 (43.76 ms/it) accuracy/train: 0.945 loss/train: 0.152     Train Epoch 2: 17%|███▊ | ETA: 0:00:15 (43.72 ms/it) accuracy/train: 0.977 loss/train: 0.0555     Train Epoch 2: 18%|███▉ | ETA: 0:00:15 (43.61 ms/it) accuracy/train: 0.93 loss/train: 0.248     Train Epoch 2: 18%|████ | ETA: 0:00:15 (43.54 ms/it) accuracy/train: 0.961 loss/train: 0.164     Train Epoch 2: 19%|████▏ | ETA: 0:00:14 (43.42 ms/it) accuracy/train: 0.969 loss/train: 0.0773     Train Epoch 2: 20%|████▍ | ETA: 0:00:14 (43.34 ms/it) accuracy/train: 0.977 loss/train: 0.0872     Train Epoch 2: 20%|████▌ | ETA: 0:00:14 (43.28 ms/it) accuracy/train: 0.977 loss/train: 0.104     Train Epoch 2: 21%|████▋ | ETA: 0:00:14 (43.51 ms/it) accuracy/train: 0.984 loss/train: 0.0871     Train Epoch 2: 22%|████▊ | ETA: 0:00:14 (43.47 ms/it) accuracy/train: 0.969 loss/train: 0.128     Train Epoch 2: 22%|████▉ | ETA: 0:00:14 (43.38 ms/it) accuracy/train: 0.984 loss/train: 0.0572     Train Epoch 2: 23%|█████ | ETA: 0:00:14 (43.37 ms/it) accuracy/train: 0.969 loss/train: 0.0992     Train Epoch 2: 24%|█████▎ | ETA: 0:00:13 (43.33 ms/it) accuracy/train: 0.945 loss/train: 0.189     Train Epoch 2: 24%|█████▍ | ETA: 0:00:13 (43.33 ms/it) accuracy/train: 0.961 loss/train: 0.098     Train Epoch 2: 25%|█████▌ | ETA: 0:00:13 (43.30 ms/it) accuracy/train: 0.953 loss/train: 0.168     Train Epoch 2: 26%|█████▋ | ETA: 0:00:13 (43.25 ms/it) accuracy/train: 0.977 loss/train: 0.0854     Train Epoch 2: 27%|█████▉ | ETA: 0:00:13 (43.19 ms/it) accuracy/train: 0.945 loss/train: 0.146     Train Epoch 2: 27%|██████ | ETA: 0:00:13 (43.17 ms/it) accuracy/train: 0.977 loss/train: 0.0634     Train Epoch 2: 28%|██████▏ | ETA: 0:00:13 (43.09 ms/it) accuracy/train: 0.961 loss/train: 0.232     Train Epoch 2: 29%|██████▎ | ETA: 0:00:12 (43.06 ms/it) accuracy/train: 0.977 loss/train: 0.0581     Train Epoch 2: 29%|██████▌ | ETA: 0:00:12 (43.05 ms/it) accuracy/train: 0.977 loss/train: 0.067     Train Epoch 2: 30%|██████▋ | ETA: 0:00:12 (43.18 ms/it) accuracy/train: 0.984 loss/train: 0.0424     Train Epoch 2: 31%|██████▊ | ETA: 0:00:12 (43.34 ms/it) accuracy/train: 0.992 loss/train: 0.0592     Train Epoch 2: 31%|██████▉ | ETA: 0:00:12 (43.52 ms/it) accuracy/train: 0.992 loss/train: 0.0384     Train Epoch 2: 32%|███████ | ETA: 0:00:12 (43.67 ms/it) accuracy/train: 0.969 loss/train: 0.0621     Train Epoch 2: 32%|███████▏ | ETA: 0:00:12 (43.64 ms/it) accuracy/train: 0.953 loss/train: 0.243     Train Epoch 2: 33%|███████▎ | ETA: 0:00:12 (43.67 ms/it) accuracy/train: 0.969 loss/train: 0.128     Train Epoch 2: 34%|███████▌ | ETA: 0:00:12 (43.70 ms/it) accuracy/train: 0.969 loss/train: 0.058     Train Epoch 2: 35%|███████▋ | ETA: 0:00:12 (43.59 ms/it) accuracy/train: 0.953 loss/train: 0.129     Train Epoch 2: 36%|███████▉ | ETA: 0:00:11 (43.18 ms/it) accuracy/train: 0.977 loss/train: 0.0549     Train Epoch 2: 36%|████████ | ETA: 0:00:11 (42.82 ms/it) accuracy/train: 0.945 loss/train: 0.228     Train Epoch 2: 37%|████████▎ | ETA: 0:00:11 (42.45 ms/it) accuracy/train: 0.922 loss/train: 0.166     Train Epoch 2: 38%|████████▌ | ETA: 0:00:10 (42.11 ms/it) accuracy/train: 0.945 loss/train: 0.168     Train Epoch 2: 39%|████████▋ | ETA: 0:00:10 (41.93 ms/it) accuracy/train: 0.961 loss/train: 0.0968     Train Epoch 2: 40%|████████▉ | ETA: 0:00:10 (41.66 ms/it) accuracy/train: 0.977 loss/train: 0.0745     Train Epoch 2: 41%|█████████▏ | ETA: 0:00:10 (41.36 ms/it) accuracy/train: 0.953 loss/train: 0.146     Train Epoch 2: 42%|█████████▎ | ETA: 0:00:10 (41.33 ms/it) accuracy/train: 0.969 loss/train: 0.125     Train Epoch 2: 43%|█████████▍ | ETA: 0:00:10 (41.36 ms/it) accuracy/train: 0.969 loss/train: 0.113     Train Epoch 2: 43%|█████████▌ | ETA: 0:00:09 (41.31 ms/it) accuracy/train: 0.977 loss/train: 0.0685     Train Epoch 2: 44%|█████████▊ | ETA: 0:00:09 (41.04 ms/it) accuracy/train: 0.977 loss/train: 0.0522     Train Epoch 2: 45%|██████████ | ETA: 0:00:09 (40.85 ms/it) accuracy/train: 0.953 loss/train: 0.173     Train Epoch 2: 46%|██████████▏ | ETA: 0:00:09 (40.65 ms/it) accuracy/train: 0.984 loss/train: 0.041     Train Epoch 2: 47%|██████████▍ | ETA: 0:00:09 (40.58 ms/it) accuracy/train: 0.984 loss/train: 0.0564     Train Epoch 2: 48%|██████████▌ | ETA: 0:00:08 (40.34 ms/it) accuracy/train: 0.992 loss/train: 0.0241     Train Epoch 2: 49%|██████████▊ | ETA: 0:00:08 (40.13 ms/it) accuracy/train: 0.984 loss/train: 0.0553     Train Epoch 2: 50%|███████████ | ETA: 0:00:08 (39.92 ms/it) accuracy/train: 0.961 loss/train: 0.146     Train Epoch 2: 50%|███████████▏ | ETA: 0:00:08 (39.96 ms/it) accuracy/train: 0.961 loss/train: 0.16     Train Epoch 2: 51%|███████████▎ | ETA: 0:00:08 (40.01 ms/it) accuracy/train: 0.953 loss/train: 0.0865     Train Epoch 2: 52%|███████████▍ | ETA: 0:00:08 (40.06 ms/it) accuracy/train: 0.953 loss/train: 0.237     Train Epoch 2: 53%|███████████▋ | ETA: 0:00:08 (40.14 ms/it) accuracy/train: 0.961 loss/train: 0.159     Train Epoch 2: 53%|███████████▊ | ETA: 0:00:07 (40.23 ms/it) accuracy/train: 0.969 loss/train: 0.0827     Train Epoch 2: 54%|███████████▉ | ETA: 0:00:07 (40.27 ms/it) accuracy/train: 0.977 loss/train: 0.0713     Train Epoch 2: 55%|████████████ | ETA: 0:00:07 (40.32 ms/it) accuracy/train: 0.992 loss/train: 0.0281     Train Epoch 2: 55%|████████████▎ | ETA: 0:00:07 (40.36 ms/it) accuracy/train: 0.992 loss/train: 0.0216     Train Epoch 2: 56%|████████████▍ | ETA: 0:00:07 (40.41 ms/it) accuracy/train: 0.977 loss/train: 0.091     Train Epoch 2: 57%|████████████▌ | ETA: 0:00:07 (40.42 ms/it) accuracy/train: 0.969 loss/train: 0.183     Train Epoch 2: 58%|████████████▋ | ETA: 0:00:07 (40.43 ms/it) accuracy/train: 0.977 loss/train: 0.0914     Train Epoch 2: 58%|████████████▉ | ETA: 0:00:07 (40.46 ms/it) accuracy/train: 0.984 loss/train: 0.0561     Train Epoch 2: 59%|█████████████ | ETA: 0:00:07 (40.49 ms/it) accuracy/train: 0.984 loss/train: 0.0443     Train Epoch 2: 60%|█████████████▏ | ETA: 0:00:06 (40.56 ms/it) accuracy/train: 0.977 loss/train: 0.0479     Train Epoch 2: 60%|█████████████▎ | ETA: 0:00:06 (40.56 ms/it) accuracy/train: 0.961 loss/train: 0.098     Train Epoch 2: 61%|█████████████▌ | ETA: 0:00:06 (40.60 ms/it) accuracy/train: 0.945 loss/train: 0.185     Train Epoch 2: 62%|█████████████▋ | ETA: 0:00:06 (40.61 ms/it) accuracy/train: 0.945 loss/train: 0.225     Train Epoch 2: 63%|█████████████▊ | ETA: 0:00:06 (40.66 ms/it) accuracy/train: 0.945 loss/train: 0.174     Train Epoch 2: 63%|█████████████▉ | ETA: 0:00:06 (40.69 ms/it) accuracy/train: 0.977 loss/train: 0.0911     Train Epoch 2: 64%|██████████████▏ | ETA: 0:00:06 (40.75 ms/it) accuracy/train: 0.969 loss/train: 0.129     Train Epoch 2: 65%|██████████████▎ | ETA: 0:00:06 (40.79 ms/it) accuracy/train: 0.945 loss/train: 0.111     Train Epoch 2: 65%|██████████████▍ | ETA: 0:00:05 (40.82 ms/it) accuracy/train: 0.969 loss/train: 0.224     Train Epoch 2: 66%|██████████████▌ | ETA: 0:00:05 (40.85 ms/it) accuracy/train: 0.953 loss/train: 0.172     Train Epoch 2: 67%|██████████████▊ | ETA: 0:00:05 (40.89 ms/it) accuracy/train: 0.969 loss/train: 0.157     Train Epoch 2: 68%|██████████████▉ | ETA: 0:00:05 (40.94 ms/it) accuracy/train: 0.977 loss/train: 0.0652     Train Epoch 2: 68%|███████████████ | ETA: 0:00:05 (40.97 ms/it) accuracy/train: 0.977 loss/train: 0.0658     Train Epoch 2: 69%|███████████████▏ | ETA: 0:00:05 (41.01 ms/it) accuracy/train: 0.961 loss/train: 0.149     Train Epoch 2: 70%|███████████████▍ | ETA: 0:00:05 (41.07 ms/it) accuracy/train: 0.961 loss/train: 0.184     Train Epoch 2: 70%|███████████████▌ | ETA: 0:00:05 (41.13 ms/it) accuracy/train: 0.977 loss/train: 0.069     Train Epoch 2: 71%|███████████████▋ | ETA: 0:00:05 (41.17 ms/it) accuracy/train: 0.945 loss/train: 0.203     Train Epoch 2: 72%|███████████████▊ | ETA: 0:00:04 (41.22 ms/it) accuracy/train: 0.992 loss/train: 0.0436     Train Epoch 2: 73%|████████████████ | ETA: 0:00:04 (41.27 ms/it) accuracy/train: 0.984 loss/train: 0.0487     Train Epoch 2: 73%|████████████████▏ | ETA: 0:00:04 (41.32 ms/it) accuracy/train: 0.961 loss/train: 0.0887     Train Epoch 2: 74%|████████████████▎ | ETA: 0:00:04 (41.37 ms/it) accuracy/train: 0.977 loss/train: 0.0904     Train Epoch 2: 75%|████████████████▍ | ETA: 0:00:04 (41.43 ms/it) accuracy/train: 0.969 loss/train: 0.11     Train Epoch 2: 75%|████████████████▋ | ETA: 0:00:04 (41.47 ms/it) accuracy/train: 0.969 loss/train: 0.1     Train Epoch 2: 76%|████████████████▊ | ETA: 0:00:04 (41.53 ms/it) accuracy/train: 0.992 loss/train: 0.0339     Train Epoch 2: 77%|████████████████▉ | ETA: 0:00:04 (41.62 ms/it) accuracy/train: 0.984 loss/train: 0.0292     Train Epoch 2: 77%|█████████████████ | ETA: 0:00:04 (41.67 ms/it) accuracy/train: 0.977 loss/train: 0.0536     Train Epoch 2: 78%|█████████████████▏ | ETA: 0:00:03 (41.72 ms/it) accuracy/train: 0.984 loss/train: 0.0483     Train Epoch 2: 79%|█████████████████▎ | ETA: 0:00:03 (41.76 ms/it) accuracy/train: 0.945 loss/train: 0.186     Train Epoch 2: 79%|█████████████████▌ | ETA: 0:00:03 (41.82 ms/it) accuracy/train: 0.984 loss/train: 0.0494     Train Epoch 2: 80%|█████████████████▋ | ETA: 0:00:03 (41.86 ms/it) accuracy/train: 0.969 loss/train: 0.0829     Train Epoch 2: 81%|█████████████████▊ | ETA: 0:00:03 (41.91 ms/it) accuracy/train: 0.969 loss/train: 0.0974     Train Epoch 2: 82%|█████████████████▉ | ETA: 0:00:03 (41.93 ms/it) accuracy/train: 0.977 loss/train: 0.0619     Train Epoch 2: 82%|██████████████████▏ | ETA: 0:00:03 (41.95 ms/it) accuracy/train: 0.984 loss/train: 0.0411     Train Epoch 2: 83%|██████████████████▎ | ETA: 0:00:03 (41.98 ms/it) accuracy/train: 0.984 loss/train: 0.0566     Train Epoch 2: 83%|██████████████████▍ | ETA: 0:00:02 (42.03 ms/it) accuracy/train: 0.945 loss/train: 0.202     Train Epoch 2: 84%|██████████████████▌ | ETA: 0:00:02 (42.05 ms/it) accuracy/train: 0.961 loss/train: 0.114     Train Epoch 2: 85%|██████████████████▋ | ETA: 0:00:02 (42.07 ms/it) accuracy/train: 0.961 loss/train: 0.204     Train Epoch 2: 86%|██████████████████▉ | ETA: 0:00:02 (42.09 ms/it) accuracy/train: 0.953 loss/train: 0.107     Train Epoch 2: 86%|███████████████████ | ETA: 0:00:02 (42.10 ms/it) accuracy/train: 0.992 loss/train: 0.0382     Train Epoch 2: 87%|███████████████████▏ | ETA: 0:00:02 (42.11 ms/it) accuracy/train: 0.969 loss/train: 0.259     Train Epoch 2: 88%|███████████████████▎ | ETA: 0:00:02 (42.12 ms/it) accuracy/train: 0.969 loss/train: 0.0559     Train Epoch 2: 88%|███████████████████▌ | ETA: 0:00:02 (42.13 ms/it) accuracy/train: 0.945 loss/train: 0.171     Train Epoch 2: 89%|███████████████████▋ | ETA: 0:00:01 (42.15 ms/it) accuracy/train: 0.961 loss/train: 0.135     Train Epoch 2: 90%|███████████████████▊ | ETA: 0:00:01 (42.16 ms/it) accuracy/train: 0.977 loss/train: 0.21     Train Epoch 2: 91%|███████████████████▉ | ETA: 0:00:01 (42.17 ms/it) accuracy/train: 0.953 loss/train: 0.133     Train Epoch 2: 91%|████████████████████▏ | ETA: 0:00:01 (42.20 ms/it) accuracy/train: 0.961 loss/train: 0.114     Train Epoch 2: 92%|████████████████████▎ | ETA: 0:00:01 (42.23 ms/it) accuracy/train: 0.969 loss/train: 0.086     Train Epoch 2: 93%|████████████████████▍ | ETA: 0:00:01 (42.24 ms/it) accuracy/train: 0.953 loss/train: 0.161     Train Epoch 2: 93%|████████████████████▌ | ETA: 0:00:01 (42.29 ms/it) accuracy/train: 0.945 loss/train: 0.145     Train Epoch 2: 94%|████████████████████▋ | ETA: 0:00:01 (42.31 ms/it) accuracy/train: 0.914 loss/train: 0.186     Train Epoch 2: 95%|████████████████████▊ | ETA: 0:00:00 (42.32 ms/it) accuracy/train: 0.984 loss/train: 0.0532     Train Epoch 2: 95%|█████████████████████ | ETA: 0:00:00 (42.36 ms/it) accuracy/train: 0.977 loss/train: 0.0417     Train Epoch 2: 96%|█████████████████████▏| ETA: 0:00:00 (42.40 ms/it) accuracy/train: 0.977 loss/train: 0.0658     Train Epoch 2: 97%|█████████████████████▎| ETA: 0:00:00 (42.44 ms/it) accuracy/train: 0.984 loss/train: 0.0383     Train Epoch 2: 97%|█████████████████████▍| ETA: 0:00:00 (42.48 ms/it) accuracy/train: 0.984 loss/train: 0.0409     Train Epoch 2: 98%|█████████████████████▌| ETA: 0:00:00 (42.52 ms/it) accuracy/train: 0.898 loss/train: 0.393     Train Epoch 2: 99%|█████████████████████▋| ETA: 0:00:00 (42.56 ms/it) accuracy/train: 0.953 loss/train: 0.19     Train Epoch 2: 99%|█████████████████████▉| ETA: 0:00:00 (42.60 ms/it) accuracy/train: 0.977 loss/train: 0.114     Train Epoch 2: 100%|██████████████████████| Time: 0:00:17 (42.63 ms/it) accuracy/train: 0.982 loss/train: 0.0517   Val Epoch 2: 32%|███████▋ | ETA: 0:00:00 ( 7.08 ms/it) accuracy/val: 0.965 loss/val: 0.131        Val Epoch 2: 64%|███████████████▍ | ETA: 0:00:00 ( 7.06 ms/it) accuracy/val: 0.966 loss/val: 0.123        Val Epoch 2: 98%|███████████████████████▌| ETA: 0:00:00 ( 6.84 ms/it) accuracy/val: 0.966 loss/val: 0.118        Val Epoch 2: 100%|████████████████████████| Time: 0:00:00 ( 6.84 ms/it) accuracy/val: 0.967 loss/val: 0.117      Train Epoch 3: 1%|▏ | ETA: 0:00:19 (47.52 ms/it) accuracy/train: 0.977 loss/train: 0.0387     Train Epoch 3: 1%|▍ | ETA: 0:00:20 (48.61 ms/it) accuracy/train: 0.969 loss/train: 0.108     Train Epoch 3: 2%|▌ | ETA: 0:00:19 (48.38 ms/it) accuracy/train: 0.984 loss/train: 0.0265     Train Epoch 3: 3%|▋ | ETA: 0:00:19 (47.90 ms/it) accuracy/train: 0.969 loss/train: 0.15     Train Epoch 3: 4%|▊ | ETA: 0:00:19 (47.40 ms/it) accuracy/train: 0.977 loss/train: 0.0722     Train Epoch 3: 4%|█ | ETA: 0:00:19 (47.38 ms/it) accuracy/train: 0.969 loss/train: 0.0928     Train Epoch 3: 5%|█▏ | ETA: 0:00:19 (47.66 ms/it) accuracy/train: 0.977 loss/train: 0.0497     Train Epoch 3: 6%|█▎ | ETA: 0:00:18 (47.72 ms/it) accuracy/train: 0.992 loss/train: 0.03     Train Epoch 3: 6%|█▍ | ETA: 0:00:18 (47.47 ms/it) accuracy/train: 0.992 loss/train: 0.0329     Train Epoch 3: 7%|█▋ | ETA: 0:00:18 (47.33 ms/it) accuracy/train: 0.969 loss/train: 0.0809     Train Epoch 3: 8%|█▊ | ETA: 0:00:18 (47.17 ms/it) accuracy/train: 0.977 loss/train: 0.0376     Train Epoch 3: 9%|█▉ | ETA: 0:00:18 (47.13 ms/it) accuracy/train: 0.969 loss/train: 0.0554     Train Epoch 3: 9%|██ | ETA: 0:00:18 (47.22 ms/it) accuracy/train: 0.984 loss/train: 0.0858     Train Epoch 3: 10%|██▎ | ETA: 0:00:17 (47.31 ms/it) accuracy/train: 0.992 loss/train: 0.0462     Train Epoch 3: 11%|██▍ | ETA: 0:00:17 (47.35 ms/it) accuracy/train: 0.969 loss/train: 0.0967     Train Epoch 3: 11%|██▌ | ETA: 0:00:17 (47.51 ms/it) accuracy/train: 0.992 loss/train: 0.0211     Train Epoch 3: 12%|██▋ | ETA: 0:00:17 (47.53 ms/it) accuracy/train: 0.992 loss/train: 0.0497     Train Epoch 3: 13%|██▉ | ETA: 0:00:17 (47.53 ms/it) accuracy/train: 0.992 loss/train: 0.0276     Train Epoch 3: 14%|███ | ETA: 0:00:17 (47.57 ms/it) accuracy/train: 0.992 loss/train: 0.00967     Train Epoch 3: 14%|███▏ | ETA: 0:00:17 (47.55 ms/it) accuracy/train: 0.977 loss/train: 0.0673     Train Epoch 3: 15%|███▎ | ETA: 0:00:17 (47.73 ms/it) accuracy/train: 0.992 loss/train: 0.0542     Train Epoch 3: 15%|███▍ | ETA: 0:00:17 (47.71 ms/it) accuracy/train: 0.992 loss/train: 0.0203     Train Epoch 3: 16%|███▌ | ETA: 0:00:16 (47.69 ms/it) accuracy/train: 0.984 loss/train: 0.0533     Train Epoch 3: 17%|███▊ | ETA: 0:00:16 (47.70 ms/it) accuracy/train: 0.977 loss/train: 0.0739     Train Epoch 3: 18%|███▉ | ETA: 0:00:16 (47.68 ms/it) accuracy/train: 0.984 loss/train: 0.0259     Train Epoch 3: 18%|████ | ETA: 0:00:16 (47.61 ms/it) accuracy/train: 0.992 loss/train: 0.0633     Train Epoch 3: 19%|████▏ | ETA: 0:00:16 (47.52 ms/it) accuracy/train: 0.992 loss/train: 0.0406     Train Epoch 3: 20%|████▍ | ETA: 0:00:16 (47.50 ms/it) accuracy/train: 0.992 loss/train: 0.0524     Train Epoch 3: 20%|████▌ | ETA: 0:00:15 (47.43 ms/it) accuracy/train: 0.977 loss/train: 0.068     Train Epoch 3: 21%|████▋ | ETA: 0:00:15 (47.40 ms/it) accuracy/train: 0.984 loss/train: 0.0521     Train Epoch 3: 22%|████▊ | ETA: 0:00:15 (47.35 ms/it) accuracy/train: 0.992 loss/train: 0.0456     Train Epoch 3: 23%|█████ | ETA: 0:00:15 (47.29 ms/it) accuracy/train: 0.992 loss/train: 0.018     Train Epoch 3: 23%|█████▏ | ETA: 0:00:15 (47.23 ms/it) accuracy/train: 0.984 loss/train: 0.0592     Train Epoch 3: 24%|█████▎ | ETA: 0:00:15 (47.22 ms/it) accuracy/train: 0.969 loss/train: 0.047     Train Epoch 3: 25%|█████▍ | ETA: 0:00:15 (47.18 ms/it) accuracy/train: 0.977 loss/train: 0.0387     Train Epoch 3: 25%|█████▋ | ETA: 0:00:14 (47.12 ms/it) accuracy/train: 0.992 loss/train: 0.0916     Train Epoch 3: 26%|█████▊ | ETA: 0:00:14 (47.12 ms/it) accuracy/train: 0.977 loss/train: 0.0861     Train Epoch 3: 27%|█████▉ | ETA: 0:00:14 (47.09 ms/it) accuracy/train: 0.977 loss/train: 0.0654     Train Epoch 3: 27%|██████ | ETA: 0:00:14 (47.05 ms/it) accuracy/train: 1.0 loss/train: 0.0107     Train Epoch 3: 28%|██████▎ | ETA: 0:00:14 (47.04 ms/it) accuracy/train: 0.961 loss/train: 0.173     Train Epoch 3: 29%|██████▍ | ETA: 0:00:14 (47.04 ms/it) accuracy/train: 0.984 loss/train: 0.034     Train Epoch 3: 30%|██████▌ | ETA: 0:00:13 (46.99 ms/it) accuracy/train: 0.961 loss/train: 0.0928     Train Epoch 3: 30%|██████▋ | ETA: 0:00:13 (47.06 ms/it) accuracy/train: 0.969 loss/train: 0.091     Train Epoch 3: 31%|██████▊ | ETA: 0:00:13 (47.11 ms/it) accuracy/train: 1.0 loss/train: 0.0124     Train Epoch 3: 32%|██████▉ | ETA: 0:00:13 (47.07 ms/it) accuracy/train: 1.0 loss/train: 0.00753     Train Epoch 3: 32%|███████▏ | ETA: 0:00:13 (47.07 ms/it) accuracy/train: 0.992 loss/train: 0.0282     Train Epoch 3: 33%|███████▎ | ETA: 0:00:13 (47.09 ms/it) accuracy/train: 0.977 loss/train: 0.0215     Train Epoch 3: 34%|███████▍ | ETA: 0:00:13 (47.07 ms/it) accuracy/train: 0.969 loss/train: 0.0866     Train Epoch 3: 34%|███████▌ | ETA: 0:00:13 (47.10 ms/it) accuracy/train: 0.992 loss/train: 0.041     Train Epoch 3: 35%|███████▊ | ETA: 0:00:12 (47.09 ms/it) accuracy/train: 0.992 loss/train: 0.0119     Train Epoch 3: 36%|███████▉ | ETA: 0:00:12 (47.08 ms/it) accuracy/train: 0.984 loss/train: 0.0451     Train Epoch 3: 36%|████████ | ETA: 0:00:12 (47.04 ms/it) accuracy/train: 1.0 loss/train: 0.0172     Train Epoch 3: 37%|████████▏ | ETA: 0:00:12 (47.00 ms/it) accuracy/train: 0.984 loss/train: 0.135     Train Epoch 3: 38%|████████▍ | ETA: 0:00:12 (46.95 ms/it) accuracy/train: 0.992 loss/train: 0.0221     Train Epoch 3: 39%|████████▌ | ETA: 0:00:12 (46.90 ms/it) accuracy/train: 0.953 loss/train: 0.122     Train Epoch 3: 39%|████████▋ | ETA: 0:00:11 (46.87 ms/it) accuracy/train: 0.992 loss/train: 0.0146     Train Epoch 3: 40%|████████▊ | ETA: 0:00:11 (46.83 ms/it) accuracy/train: 0.992 loss/train: 0.0264     Train Epoch 3: 41%|█████████ | ETA: 0:00:11 (46.84 ms/it) accuracy/train: 1.0 loss/train: 0.0168     Train Epoch 3: 41%|█████████▏ | ETA: 0:00:11 (46.85 ms/it) accuracy/train: 0.992 loss/train: 0.0228     Train Epoch 3: 42%|█████████▎ | ETA: 0:00:11 (46.91 ms/it) accuracy/train: 0.969 loss/train: 0.0817     Train Epoch 3: 43%|█████████▍ | ETA: 0:00:11 (47.11 ms/it) accuracy/train: 0.977 loss/train: 0.0983     Train Epoch 3: 43%|█████████▌ | ETA: 0:00:11 (47.24 ms/it) accuracy/train: 0.992 loss/train: 0.0352     Train Epoch 3: 44%|█████████▊ | ETA: 0:00:11 (47.26 ms/it) accuracy/train: 0.984 loss/train: 0.0594     Train Epoch 3: 45%|█████████▉ | ETA: 0:00:11 (47.26 ms/it) accuracy/train: 1.0 loss/train: 0.0138     Train Epoch 3: 45%|██████████ | ETA: 0:00:10 (47.30 ms/it) accuracy/train: 0.984 loss/train: 0.0346     Train Epoch 3: 46%|██████████▏ | ETA: 0:00:10 (47.30 ms/it) accuracy/train: 0.992 loss/train: 0.0143     Train Epoch 3: 47%|██████████▍ | ETA: 0:00:10 (47.31 ms/it) accuracy/train: 0.969 loss/train: 0.0761     Train Epoch 3: 48%|██████████▌ | ETA: 0:00:10 (47.31 ms/it) accuracy/train: 0.992 loss/train: 0.0199     Train Epoch 3: 48%|██████████▋ | ETA: 0:00:10 (47.34 ms/it) accuracy/train: 0.961 loss/train: 0.114     Train Epoch 3: 49%|██████████▊ | ETA: 0:00:10 (47.34 ms/it) accuracy/train: 0.992 loss/train: 0.0245     Train Epoch 3: 50%|███████████ | ETA: 0:00:10 (47.35 ms/it) accuracy/train: 1.0 loss/train: 0.0161     Train Epoch 3: 50%|███████████▏ | ETA: 0:00:09 (47.36 ms/it) accuracy/train: 1.0 loss/train: 0.00467     Train Epoch 3: 51%|███████████▎ | ETA: 0:00:09 (47.38 ms/it) accuracy/train: 0.984 loss/train: 0.0319     Train Epoch 3: 52%|███████████▍ | ETA: 0:00:09 (47.42 ms/it) accuracy/train: 0.984 loss/train: 0.0596     Train Epoch 3: 53%|███████████▋ | ETA: 0:00:09 (47.44 ms/it) accuracy/train: 0.992 loss/train: 0.0451     Train Epoch 3: 53%|███████████▋ | ETA: 0:00:09 (47.52 ms/it) accuracy/train: 0.984 loss/train: 0.0221     Train Epoch 3: 54%|███████████▉ | ETA: 0:00:09 (47.53 ms/it) accuracy/train: 0.969 loss/train: 0.147     Train Epoch 3: 55%|████████████ | ETA: 0:00:09 (47.57 ms/it) accuracy/train: 0.977 loss/train: 0.0808     Train Epoch 3: 55%|████████████▏ | ETA: 0:00:09 (47.64 ms/it) accuracy/train: 0.984 loss/train: 0.0409     Train Epoch 3: 56%|████████████▎ | ETA: 0:00:08 (47.64 ms/it) accuracy/train: 0.992 loss/train: 0.0284     Train Epoch 3: 56%|████████████▍ | ETA: 0:00:08 (47.63 ms/it) accuracy/train: 0.953 loss/train: 0.136     Train Epoch 3: 57%|████████████▋ | ETA: 0:00:08 (47.61 ms/it) accuracy/train: 0.992 loss/train: 0.0294     Train Epoch 3: 58%|████████████▊ | ETA: 0:00:08 (47.62 ms/it) accuracy/train: 1.0 loss/train: 0.0154     Train Epoch 3: 59%|████████████▉ | ETA: 0:00:08 (47.62 ms/it) accuracy/train: 0.992 loss/train: 0.0568     Train Epoch 3: 59%|█████████████ | ETA: 0:00:08 (47.62 ms/it) accuracy/train: 0.977 loss/train: 0.0482     Train Epoch 3: 60%|█████████████▎ | ETA: 0:00:08 (47.58 ms/it) accuracy/train: 0.984 loss/train: 0.0528     Train Epoch 3: 61%|█████████████▍ | ETA: 0:00:07 (47.56 ms/it) accuracy/train: 0.992 loss/train: 0.082     Train Epoch 3: 61%|█████████████▌ | ETA: 0:00:07 (47.52 ms/it) accuracy/train: 0.969 loss/train: 0.0935     Train Epoch 3: 62%|█████████████▋ | ETA: 0:00:07 (47.47 ms/it) accuracy/train: 0.984 loss/train: 0.0382     Train Epoch 3: 63%|█████████████▉ | ETA: 0:00:07 (47.43 ms/it) accuracy/train: 0.992 loss/train: 0.0266     Train Epoch 3: 64%|██████████████ | ETA: 0:00:07 (47.38 ms/it) accuracy/train: 0.984 loss/train: 0.0222     Train Epoch 3: 64%|██████████████▏ | ETA: 0:00:07 (47.35 ms/it) accuracy/train: 1.0 loss/train: 0.0106     Train Epoch 3: 65%|██████████████▎ | ETA: 0:00:07 (47.31 ms/it) accuracy/train: 0.992 loss/train: 0.0454     Train Epoch 3: 65%|██████████████▍ | ETA: 0:00:06 (47.34 ms/it) accuracy/train: 0.984 loss/train: 0.0436     Train Epoch 3: 66%|██████████████▌ | ETA: 0:00:06 (47.30 ms/it) accuracy/train: 0.992 loss/train: 0.0422     Train Epoch 3: 67%|██████████████▊ | ETA: 0:00:06 (47.28 ms/it) accuracy/train: 0.984 loss/train: 0.0597     Train Epoch 3: 68%|██████████████▉ | ETA: 0:00:06 (47.24 ms/it) accuracy/train: 0.984 loss/train: 0.048     Train Epoch 3: 68%|███████████████ | ETA: 0:00:06 (47.21 ms/it) accuracy/train: 0.992 loss/train: 0.03     Train Epoch 3: 69%|███████████████▏ | ETA: 0:00:06 (47.18 ms/it) accuracy/train: 0.984 loss/train: 0.0437     Train Epoch 3: 70%|███████████████▍ | ETA: 0:00:06 (47.15 ms/it) accuracy/train: 1.0 loss/train: 0.0127     Train Epoch 3: 70%|███████████████▌ | ETA: 0:00:05 (47.16 ms/it) accuracy/train: 0.992 loss/train: 0.0141     Train Epoch 3: 71%|███████████████▋ | ETA: 0:00:05 (47.17 ms/it) accuracy/train: 0.969 loss/train: 0.0528     Train Epoch 3: 72%|███████████████▊ | ETA: 0:00:05 (47.18 ms/it) accuracy/train: 0.984 loss/train: 0.0628     Train Epoch 3: 73%|████████████████ | ETA: 0:00:05 (47.20 ms/it) accuracy/train: 0.992 loss/train: 0.0344     Train Epoch 3: 73%|████████████████▏ | ETA: 0:00:05 (47.20 ms/it) accuracy/train: 0.984 loss/train: 0.0229     Train Epoch 3: 74%|████████████████▎ | ETA: 0:00:05 (47.19 ms/it) accuracy/train: 1.0 loss/train: 0.0133     Train Epoch 3: 75%|████████████████▍ | ETA: 0:00:05 (47.17 ms/it) accuracy/train: 0.992 loss/train: 0.05     Train Epoch 3: 75%|████████████████▋ | ETA: 0:00:04 (47.16 ms/it) accuracy/train: 0.992 loss/train: 0.0448     Train Epoch 3: 76%|████████████████▊ | ETA: 0:00:04 (47.19 ms/it) accuracy/train: 0.984 loss/train: 0.035     Train Epoch 3: 77%|████████████████▉ | ETA: 0:00:04 (47.20 ms/it) accuracy/train: 0.984 loss/train: 0.0417     Train Epoch 3: 77%|█████████████████ | ETA: 0:00:04 (47.19 ms/it) accuracy/train: 1.0 loss/train: 0.0254     Train Epoch 3: 78%|█████████████████▎ | ETA: 0:00:04 (47.17 ms/it) accuracy/train: 0.984 loss/train: 0.0468     Train Epoch 3: 79%|█████████████████▍ | ETA: 0:00:04 (47.18 ms/it) accuracy/train: 0.984 loss/train: 0.0685     Train Epoch 3: 80%|█████████████████▌ | ETA: 0:00:04 (47.20 ms/it) accuracy/train: 0.984 loss/train: 0.0528     Train Epoch 3: 80%|█████████████████▋ | ETA: 0:00:03 (47.23 ms/it) accuracy/train: 0.984 loss/train: 0.0776     Train Epoch 3: 81%|█████████████████▊ | ETA: 0:00:03 (47.21 ms/it) accuracy/train: 1.0 loss/train: 0.0176     Train Epoch 3: 82%|█████████████████▉ | ETA: 0:00:03 (47.19 ms/it) accuracy/train: 1.0 loss/train: 0.0142     Train Epoch 3: 82%|██████████████████▏ | ETA: 0:00:03 (47.21 ms/it) accuracy/train: 0.961 loss/train: 0.129     Train Epoch 3: 83%|██████████████████▎ | ETA: 0:00:03 (47.19 ms/it) accuracy/train: 0.992 loss/train: 0.0216     Train Epoch 3: 84%|██████████████████▍ | ETA: 0:00:03 (47.19 ms/it) accuracy/train: 0.992 loss/train: 0.0262     Train Epoch 3: 84%|██████████████████▌ | ETA: 0:00:03 (47.20 ms/it) accuracy/train: 0.992 loss/train: 0.0264     Train Epoch 3: 85%|██████████████████▊ | ETA: 0:00:02 (47.21 ms/it) accuracy/train: 0.992 loss/train: 0.0647     Train Epoch 3: 86%|██████████████████▉ | ETA: 0:00:02 (47.25 ms/it) accuracy/train: 0.992 loss/train: 0.0424     Train Epoch 3: 86%|███████████████████ | ETA: 0:00:02 (47.25 ms/it) accuracy/train: 1.0 loss/train: 0.0163     Train Epoch 3: 87%|███████████████████▏ | ETA: 0:00:02 (47.24 ms/it) accuracy/train: 0.992 loss/train: 0.0629     Train Epoch 3: 88%|███████████████████▎ | ETA: 0:00:02 (47.23 ms/it) accuracy/train: 0.977 loss/train: 0.0662     Train Epoch 3: 88%|███████████████████▌ | ETA: 0:00:02 (47.23 ms/it) accuracy/train: 0.984 loss/train: 0.06     Train Epoch 3: 89%|███████████████████▋ | ETA: 0:00:02 (47.25 ms/it) accuracy/train: 0.992 loss/train: 0.0356     Train Epoch 3: 90%|███████████████████▊ | ETA: 0:00:02 (47.23 ms/it) accuracy/train: 1.0 loss/train: 0.0153     Train Epoch 3: 91%|███████████████████▉ | ETA: 0:00:01 (47.21 ms/it) accuracy/train: 0.992 loss/train: 0.0136     Train Epoch 3: 91%|████████████████████ | ETA: 0:00:01 (47.25 ms/it) accuracy/train: 0.992 loss/train: 0.015     Train Epoch 3: 92%|████████████████████▏ | ETA: 0:00:01 (47.23 ms/it) accuracy/train: 0.984 loss/train: 0.0251     Train Epoch 3: 92%|████████████████████▍ | ETA: 0:00:01 (47.21 ms/it) accuracy/train: 0.984 loss/train: 0.101     Train Epoch 3: 93%|████████████████████▌ | ETA: 0:00:01 (47.19 ms/it) accuracy/train: 0.984 loss/train: 0.0433     Train Epoch 3: 94%|████████████████████▋ | ETA: 0:00:01 (47.18 ms/it) accuracy/train: 1.0 loss/train: 0.0173     Train Epoch 3: 95%|████████████████████▊ | ETA: 0:00:01 (47.17 ms/it) accuracy/train: 0.984 loss/train: 0.0451     Train Epoch 3: 95%|█████████████████████ | ETA: 0:00:00 (47.14 ms/it) accuracy/train: 1.0 loss/train: 0.0158     Train Epoch 3: 96%|█████████████████████▏| ETA: 0:00:00 (47.12 ms/it) accuracy/train: 0.984 loss/train: 0.0788     Train Epoch 3: 97%|█████████████████████▎| ETA: 0:00:00 (47.11 ms/it) accuracy/train: 0.984 loss/train: 0.0428     Train Epoch 3: 97%|█████████████████████▍| ETA: 0:00:00 (47.09 ms/it) accuracy/train: 1.0 loss/train: 0.0131     Train Epoch 3: 98%|█████████████████████▋| ETA: 0:00:00 (47.08 ms/it) accuracy/train: 1.0 loss/train: 0.00542     Train Epoch 3: 99%|█████████████████████▊| ETA: 0:00:00 (47.07 ms/it) accuracy/train: 0.984 loss/train: 0.0455     Train Epoch 3: 100%|█████████████████████▉| ETA: 0:00:00 (47.05 ms/it) accuracy/train: 0.984 loss/train: 0.047     Train Epoch 3: 100%|██████████████████████| Time: 0:00:19 (47.03 ms/it) accuracy/train: 0.982 loss/train: 0.0556 Val Epoch 3: 36%|████████▋ | ETA: 0:00:00 ( 6.16 ms/it) accuracy/val: 0.977 loss/val: 0.0733     Val Epoch 3: 74%|█████████████████▉ | ETA: 0:00:00 ( 6.00 ms/it) accuracy/val: 0.979 loss/val: 0.0713     Val Epoch 3: 100%|████████████████████████| Time: 0:00:00 ( 5.94 ms/it) accuracy/val: 0.98 loss/val: 0.0722 [ Info: GPUs available: false, used: false [ Info: Model Summary: MLP( Chain( MLUtils.flatten, Dense(784 => 1024, relu), # 803_840 parameters Dense(1024 => 10), # 10_250 parameters ), ) # Total: 4 arrays, 814_090 parameters, 3.106 MiB. [ Info: Run Directory: /home/pkgeval/.julia/packages/Tsunami/5iz8R/examples/MLP_MNIST/tsunami_logs/run_2 Val Epoch 3: 34%|████████▏ | ETA: 0:00:00 ( 6.53 ms/it) accuracy/val: 0.981 loss/val: 0.0757     Val Epoch 3: 68%|████████████████▍ | ETA: 0:00:00 ( 6.46 ms/it) accuracy/val: 0.979 loss/val: 0.0773     Val Epoch 3: 100%|████████████████████████| Time: 0:00:00 ( 6.36 ms/it) accuracy/val: 0.98 loss/val: 0.0722   Train Epoch 4: 1%|▏ | ETA: 0:00:19 (46.72 ms/it) accuracy/train: 0.992 loss/train: 0.056     Train Epoch 4: 1%|▎ | ETA: 0:00:22 (53.24 ms/it) accuracy/train: 0.992 loss/train: 0.0414     Train Epoch 4: 2%|▍ | ETA: 0:00:20 (49.85 ms/it) accuracy/train: 0.992 loss/train: 0.0293     Train Epoch 4: 3%|▋ | ETA: 0:00:19 (48.24 ms/it) accuracy/train: 1.0 loss/train: 0.00374     Train Epoch 4: 3%|▊ | ETA: 0:00:19 (47.67 ms/it) accuracy/train: 1.0 loss/train: 0.00654     Train Epoch 4: 4%|▉ | ETA: 0:00:19 (48.32 ms/it) accuracy/train: 0.984 loss/train: 0.0353     Train Epoch 4: 5%|█ | ETA: 0:00:19 (47.95 ms/it) accuracy/train: 0.977 loss/train: 0.0433     Train Epoch 4: 5%|█▎ | ETA: 0:00:19 (47.66 ms/it) accuracy/train: 0.992 loss/train: 0.025     Train Epoch 4: 6%|█▍ | ETA: 0:00:18 (47.50 ms/it) accuracy/train: 1.0 loss/train: 0.026     Train Epoch 4: 7%|█▌ | ETA: 0:00:18 (47.29 ms/it) accuracy/train: 0.984 loss/train: 0.0542     Train Epoch 4: 7%|█▋ | ETA: 0:00:18 (47.60 ms/it) accuracy/train: 0.992 loss/train: 0.0438     Train Epoch 4: 8%|█▊ | ETA: 0:00:18 (47.46 ms/it) accuracy/train: 0.992 loss/train: 0.0409     Train Epoch 4: 9%|█▉ | ETA: 0:00:18 (47.34 ms/it) accuracy/train: 1.0 loss/train: 0.00526     Train Epoch 4: 9%|██▏ | ETA: 0:00:18 (47.21 ms/it) accuracy/train: 1.0 loss/train: 0.0116     Train Epoch 4: 10%|██▎ | ETA: 0:00:17 (47.33 ms/it) accuracy/train: 1.0 loss/train: 0.0142     Train Epoch 4: 11%|██▍ | ETA: 0:00:17 (47.28 ms/it) accuracy/train: 0.992 loss/train: 0.0238     Train Epoch 4: 12%|██▌ | ETA: 0:00:17 (47.29 ms/it) accuracy/train: 1.0 loss/train: 0.0115     Train Epoch 4: 12%|██▊ | ETA: 0:00:17 (47.34 ms/it) accuracy/train: 0.977 loss/train: 0.0451     Train Epoch 4: 13%|██▉ | ETA: 0:00:17 (47.37 ms/it) accuracy/train: 1.0 loss/train: 0.0121     Train Epoch 4: 14%|███ | ETA: 0:00:17 (47.37 ms/it) accuracy/train: 0.977 loss/train: 0.0345     Train Epoch 4: 14%|███▏ | ETA: 0:00:17 (47.35 ms/it) accuracy/train: 0.977 loss/train: 0.0627     Train Epoch 4: 15%|███▍ | ETA: 0:00:16 (47.29 ms/it) accuracy/train: 0.984 loss/train: 0.0311     Train Epoch 4: 16%|███▌ | ETA: 0:00:16 (47.24 ms/it) accuracy/train: 1.0 loss/train: 0.014     Train Epoch 4: 17%|███▋ | ETA: 0:00:16 (47.40 ms/it) accuracy/train: 0.984 loss/train: 0.06     Train Epoch 4: 17%|███▊ | ETA: 0:00:16 (47.39 ms/it) accuracy/train: 0.992 loss/train: 0.0248     Train Epoch 4: 18%|████ | ETA: 0:00:16 (47.40 ms/it) accuracy/train: 0.984 loss/train: 0.0374     Train Epoch 4: 19%|████▏ | ETA: 0:00:16 (47.41 ms/it) accuracy/train: 1.0 loss/train: 0.0114     Train Epoch 4: 19%|████▎ | ETA: 0:00:16 (47.47 ms/it) accuracy/train: 0.992 loss/train: 0.0367     Train Epoch 4: 20%|████▍ | ETA: 0:00:15 (47.47 ms/it) accuracy/train: 0.984 loss/train: 0.0216     Train Epoch 4: 21%|████▋ | ETA: 0:00:15 (47.45 ms/it) accuracy/train: 0.984 loss/train: 0.0223     Train Epoch 4: 21%|████▊ | ETA: 0:00:15 (47.52 ms/it) accuracy/train: 0.992 loss/train: 0.0139     Train Epoch 4: 22%|████▉ | ETA: 0:00:15 (47.50 ms/it) accuracy/train: 1.0 loss/train: 0.0142     Train Epoch 4: 23%|█████ | ETA: 0:00:15 (47.54 ms/it) accuracy/train: 0.992 loss/train: 0.0165     Train Epoch 4: 23%|█████▏ | ETA: 0:00:15 (47.60 ms/it) accuracy/train: 0.992 loss/train: 0.0189     Train Epoch 4: 24%|█████▍ | ETA: 0:00:15 (47.59 ms/it) accuracy/train: 0.992 loss/train: 0.0202     Train Epoch 4: 25%|█████▌ | ETA: 0:00:15 (47.58 ms/it) accuracy/train: 0.984 loss/train: 0.0277     Train Epoch 4: 26%|█████▋ | ETA: 0:00:14 (47.59 ms/it) accuracy/train: 0.992 loss/train: 0.0492     Train Epoch 4: 26%|█████▊ | ETA: 0:00:14 (47.58 ms/it) accuracy/train: 1.0 loss/train: 0.0082     Train Epoch 4: 27%|██████ | ETA: 0:00:14 (47.62 ms/it) accuracy/train: 0.992 loss/train: 0.0208     Train Epoch 4: 28%|██████▏ | ETA: 0:00:14 (47.60 ms/it) accuracy/train: 1.0 loss/train: 0.00862     Train Epoch 4: 28%|██████▎ | ETA: 0:00:14 (47.59 ms/it) accuracy/train: 0.992 loss/train: 0.0297     Train Epoch 4: 29%|██████▍ | ETA: 0:00:14 (47.65 ms/it) accuracy/train: 1.0 loss/train: 0.0113     Train Epoch 4: 30%|██████▌ | ETA: 0:00:14 (47.66 ms/it) accuracy/train: 0.961 loss/train: 0.134     Train Epoch 4: 30%|██████▋ | ETA: 0:00:14 (47.66 ms/it) accuracy/train: 1.0 loss/train: 0.0134     Train Epoch 4: 31%|██████▉ | ETA: 0:00:13 (47.64 ms/it) accuracy/train: 1.0 loss/train: 0.00866     Train Epoch 4: 32%|███████ | ETA: 0:00:13 (47.69 ms/it) accuracy/train: 1.0 loss/train: 0.00516     Train Epoch 4: 32%|███████▏ | ETA: 0:00:13 (47.68 ms/it) accuracy/train: 0.969 loss/train: 0.138     Train Epoch 4: 33%|███████▎ | ETA: 0:00:13 (47.69 ms/it) accuracy/train: 0.961 loss/train: 0.13     Train Epoch 4: 34%|███████▌ | ETA: 0:00:13 (47.69 ms/it) accuracy/train: 1.0 loss/train: 0.0064     Train Epoch 4: 35%|███████▋ | ETA: 0:00:13 (47.70 ms/it) accuracy/train: 0.992 loss/train: 0.0148     Train Epoch 4: 35%|███████▊ | ETA: 0:00:13 (47.71 ms/it) accuracy/train: 1.0 loss/train: 0.011     Train Epoch 4: 36%|███████▉ | ETA: 0:00:12 (47.71 ms/it) accuracy/train: 1.0 loss/train: 0.0154     Train Epoch 4: 37%|████████▏ | ETA: 0:00:12 (47.72 ms/it) accuracy/train: 1.0 loss/train: 0.00856     Train Epoch 4: 37%|████████▎ | ETA: 0:00:12 (47.72 ms/it) accuracy/train: 0.984 loss/train: 0.0269     Train Epoch 4: 38%|████████▍ | ETA: 0:00:12 (47.72 ms/it) accuracy/train: 0.992 loss/train: 0.0235     Train Epoch 4: 39%|████████▌ | ETA: 0:00:12 (47.82 ms/it) accuracy/train: 1.0 loss/train: 0.0131     Train Epoch 4: 39%|████████▋ | ETA: 0:00:12 (47.92 ms/it) accuracy/train: 0.992 loss/train: 0.0364     Train Epoch 4: 40%|████████▊ | ETA: 0:00:12 (47.90 ms/it) accuracy/train: 0.992 loss/train: 0.0382     Train Epoch 4: 41%|████████▉ | ETA: 0:00:12 (47.88 ms/it) accuracy/train: 1.0 loss/train: 0.0127     Train Epoch 4: 41%|█████████▏ | ETA: 0:00:11 (47.87 ms/it) accuracy/train: 1.0 loss/train: 0.00831     Train Epoch 4: 42%|█████████▎ | ETA: 0:00:11 (47.88 ms/it) accuracy/train: 0.992 loss/train: 0.0189     Train Epoch 4: 43%|█████████▍ | ETA: 0:00:11 (47.86 ms/it) accuracy/train: 0.984 loss/train: 0.0222     Train Epoch 4: 43%|█████████▌ | ETA: 0:00:11 (47.84 ms/it) accuracy/train: 0.992 loss/train: 0.0194     Train Epoch 4: 44%|█████████▊ | ETA: 0:00:11 (47.88 ms/it) accuracy/train: 0.992 loss/train: 0.028     Train Epoch 4: 45%|█████████▉ | ETA: 0:00:11 (47.89 ms/it) accuracy/train: 0.977 loss/train: 0.0588     Train Epoch 4: 45%|██████████ | ETA: 0:00:11 (47.91 ms/it) accuracy/train: 0.992 loss/train: 0.0408     Train Epoch 4: 46%|██████████▏ | ETA: 0:00:10 (47.90 ms/it) accuracy/train: 0.984 loss/train: 0.0535     Train Epoch 4: 47%|██████████▍ | ETA: 0:00:10 (47.91 ms/it) accuracy/train: 0.977 loss/train: 0.111     Train Epoch 4: 48%|██████████▌ | ETA: 0:00:10 (47.91 ms/it) accuracy/train: 0.992 loss/train: 0.057     Train Epoch 4: 48%|██████████▋ | ETA: 0:00:10 (47.91 ms/it) accuracy/train: 1.0 loss/train: 0.00278     Train Epoch 4: 49%|██████████▊ | ETA: 0:00:10 (47.90 ms/it) accuracy/train: 0.984 loss/train: 0.0487     Train Epoch 4: 50%|███████████ | ETA: 0:00:10 (47.91 ms/it) accuracy/train: 0.984 loss/train: 0.0634     Train Epoch 4: 50%|███████████▏ | ETA: 0:00:10 (47.92 ms/it) accuracy/train: 0.984 loss/train: 0.0294     Train Epoch 4: 51%|███████████▎ | ETA: 0:00:09 (47.90 ms/it) accuracy/train: 0.992 loss/train: 0.028     Train Epoch 4: 52%|███████████▍ | ETA: 0:00:09 (47.91 ms/it) accuracy/train: 0.992 loss/train: 0.0234     Train Epoch 4: 53%|███████████▋ | ETA: 0:00:09 (47.90 ms/it) accuracy/train: 0.992 loss/train: 0.015     Train Epoch 4: 53%|███████████▊ | ETA: 0:00:09 (47.92 ms/it) accuracy/train: 0.992 loss/train: 0.0212     Train Epoch 4: 54%|███████████▉ | ETA: 0:00:09 (47.91 ms/it) accuracy/train: 0.992 loss/train: 0.0142     Train Epoch 4: 55%|████████████ | ETA: 0:00:09 (47.90 ms/it) accuracy/train: 0.992 loss/train: 0.0273     Train Epoch 4: 55%|████████████▏ | ETA: 0:00:09 (47.93 ms/it) accuracy/train: 1.0 loss/train: 0.00725     Train Epoch 4: 56%|████████████▎ | ETA: 0:00:08 (47.93 ms/it) accuracy/train: 1.0 loss/train: 0.0153     Train Epoch 4: 57%|████████████▌ | ETA: 0:00:08 (47.93 ms/it) accuracy/train: 0.984 loss/train: 0.0739     Train Epoch 4: 57%|████████████▋ | ETA: 0:00:08 (47.92 ms/it) accuracy/train: 1.0 loss/train: 0.00947     Train Epoch 4: 58%|████████████▊ | ETA: 0:00:08 (47.92 ms/it) accuracy/train: 0.992 loss/train: 0.0164     Train Epoch 4: 59%|████████████▉ | ETA: 0:00:08 (47.92 ms/it) accuracy/train: 0.992 loss/train: 0.0408     Train Epoch 4: 59%|█████████████▏ | ETA: 0:00:08 (47.92 ms/it) accuracy/train: 0.984 loss/train: 0.0317     Train Epoch 4: 60%|█████████████▎ | ETA: 0:00:08 (48.00 ms/it) accuracy/train: 0.984 loss/train: 0.0348     Train Epoch 4: 61%|█████████████▍ | ETA: 0:00:07 (48.02 ms/it) accuracy/train: 0.992 loss/train: 0.0338     Train Epoch 4: 61%|█████████████▌ | ETA: 0:00:07 (48.04 ms/it) accuracy/train: 1.0 loss/train: 0.00803     Train Epoch 4: 62%|█████████████▋ | ETA: 0:00:07 (48.06 ms/it) accuracy/train: 0.992 loss/train: 0.011     Train Epoch 4: 63%|█████████████▊ | ETA: 0:00:07 (48.08 ms/it) accuracy/train: 0.984 loss/train: 0.0277     Train Epoch 4: 63%|█████████████▉ | ETA: 0:00:07 (48.07 ms/it) accuracy/train: 0.992 loss/train: 0.0196     Train Epoch 4: 64%|██████████████▏ | ETA: 0:00:07 (48.04 ms/it) accuracy/train: 1.0 loss/train: 0.0155     Train Epoch 4: 65%|██████████████▎ | ETA: 0:00:07 (48.02 ms/it) accuracy/train: 1.0 loss/train: 0.00991     Train Epoch 4: 65%|██████████████▍ | ETA: 0:00:07 (48.04 ms/it) accuracy/train: 1.0 loss/train: 0.011     Train Epoch 4: 66%|██████████████▌ | ETA: 0:00:06 (48.03 ms/it) accuracy/train: 0.984 loss/train: 0.0297     Train Epoch 4: 67%|██████████████▊ | ETA: 0:00:06 (48.01 ms/it) accuracy/train: 0.977 loss/train: 0.0933     Train Epoch 4: 68%|██████████████▉ | ETA: 0:00:06 (47.99 ms/it) accuracy/train: 0.992 loss/train: 0.0153     Train Epoch 4: 68%|███████████████ | ETA: 0:00:06 (47.97 ms/it) accuracy/train: 0.984 loss/train: 0.0607     Train Epoch 4: 69%|███████████████▏ | ETA: 0:00:06 (47.97 ms/it) accuracy/train: 0.992 loss/train: 0.0128     Train Epoch 4: 70%|███████████████▍ | ETA: 0:00:06 (47.94 ms/it) accuracy/train: 0.992 loss/train: 0.0439     Train Epoch 4: 70%|███████████████▌ | ETA: 0:00:05 (47.91 ms/it) accuracy/train: 1.0 loss/train: 0.00328     Train Epoch 4: 71%|███████████████▋ | ETA: 0:00:05 (47.89 ms/it) accuracy/train: 1.0 loss/train: 0.00757     Train Epoch 4: 72%|███████████████▊ | ETA: 0:00:05 (47.87 ms/it) accuracy/train: 0.992 loss/train: 0.00945     Train Epoch 4: 73%|████████████████ | ETA: 0:00:05 (47.85 ms/it) accuracy/train: 1.0 loss/train: 0.00584     Train Epoch 4: 73%|████████████████▏ | ETA: 0:00:05 (47.82 ms/it) accuracy/train: 0.992 loss/train: 0.0432     Train Epoch 4: 74%|████████████████▎ | ETA: 0:00:05 (47.80 ms/it) accuracy/train: 0.977 loss/train: 0.0836     Train Epoch 4: 75%|████████████████▍ | ETA: 0:00:05 (47.79 ms/it) accuracy/train: 0.992 loss/train: 0.0169     Train Epoch 4: 75%|████████████████▋ | ETA: 0:00:04 (47.76 ms/it) accuracy/train: 0.984 loss/train: 0.0208     Train Epoch 4: 76%|████████████████▊ | ETA: 0:00:04 (47.74 ms/it) accuracy/train: 0.977 loss/train: 0.0291     Train Epoch 4: 77%|████████████████▉ | ETA: 0:00:04 (47.72 ms/it) accuracy/train: 0.984 loss/train: 0.064     Train Epoch 4: 77%|█████████████████ | ETA: 0:00:04 (47.72 ms/it) accuracy/train: 1.0 loss/train: 0.00758     Train Epoch 4: 78%|█████████████████▎ | ETA: 0:00:04 (47.70 ms/it) accuracy/train: 0.992 loss/train: 0.0237     Train Epoch 4: 79%|█████████████████▍ | ETA: 0:00:04 (47.68 ms/it) accuracy/train: 1.0 loss/train: 0.0136     Train Epoch 4: 80%|█████████████████▌ | ETA: 0:00:04 (47.65 ms/it) accuracy/train: 1.0 loss/train: 0.00773     Train Epoch 4: 80%|█████████████████▋ | ETA: 0:00:03 (47.63 ms/it) accuracy/train: 1.0 loss/train: 0.00786     Train Epoch 4: 81%|█████████████████▉ | ETA: 0:00:03 (47.62 ms/it) accuracy/train: 0.992 loss/train: 0.028     Train Epoch 4: 82%|██████████████████ | ETA: 0:00:03 (47.60 ms/it) accuracy/train: 0.992 loss/train: 0.0143     Train Epoch 4: 82%|██████████████████▏ | ETA: 0:00:03 (47.58 ms/it) accuracy/train: 0.977 loss/train: 0.0399     Train Epoch 4: 83%|██████████████████▎ | ETA: 0:00:03 (47.55 ms/it) accuracy/train: 0.984 loss/train: 0.0376     Train Epoch 4: 84%|██████████████████▌ | ETA: 0:00:03 (47.54 ms/it) accuracy/train: 0.984 loss/train: 0.056     Train Epoch 4: 85%|██████████████████▋ | ETA: 0:00:03 (47.52 ms/it) accuracy/train: 1.0 loss/train: 0.0067     Train Epoch 4: 85%|██████████████████▊ | ETA: 0:00:02 (47.49 ms/it) accuracy/train: 1.0 loss/train: 0.0136     Train Epoch 4: 86%|██████████████████▉ | ETA: 0:00:02 (47.47 ms/it) accuracy/train: 0.984 loss/train: 0.0257     Train Epoch 4: 87%|███████████████████▏ | ETA: 0:00:02 (47.44 ms/it) accuracy/train: 0.977 loss/train: 0.0503     Train Epoch 4: 87%|███████████████████▎ | ETA: 0:00:02 (47.43 ms/it) accuracy/train: 0.992 loss/train: 0.046     Train Epoch 4: 88%|███████████████████▍ | ETA: 0:00:02 (47.42 ms/it) accuracy/train: 1.0 loss/train: 0.0085     Train Epoch 4: 89%|███████████████████▌ | ETA: 0:00:02 (47.41 ms/it) accuracy/train: 0.992 loss/train: 0.0376     Train Epoch 4: 90%|███████████████████▊ | ETA: 0:00:02 (47.39 ms/it) accuracy/train: 1.0 loss/train: 0.0108     Train Epoch 4: 90%|███████████████████▉ | ETA: 0:00:01 (47.39 ms/it) accuracy/train: 0.992 loss/train: 0.0237     Train Epoch 4: 91%|████████████████████ | ETA: 0:00:01 (47.37 ms/it) accuracy/train: 1.0 loss/train: 0.0114     Train Epoch 4: 92%|████████████████████▏ | ETA: 0:00:01 (47.36 ms/it) accuracy/train: 0.977 loss/train: 0.06     Train Epoch 4: 92%|████████████████████▍ | ETA: 0:00:01 (47.34 ms/it) accuracy/train: 1.0 loss/train: 0.0102     Train Epoch 4: 93%|████████████████████▌ | ETA: 0:00:01 (47.36 ms/it) accuracy/train: 1.0 loss/train: 0.00668     Train Epoch 4: 94%|████████████████████▋ | ETA: 0:00:01 (47.34 ms/it) accuracy/train: 0.984 loss/train: 0.0286     Train Epoch 4: 95%|████████████████████▊ | ETA: 0:00:01 (47.33 ms/it) accuracy/train: 0.984 loss/train: 0.0555     Train Epoch 4: 95%|█████████████████████ | ETA: 0:00:00 (47.31 ms/it) accuracy/train: 0.984 loss/train: 0.0443     Train Epoch 4: 96%|█████████████████████▏| ETA: 0:00:00 (47.29 ms/it) accuracy/train: 0.992 loss/train: 0.0274     Train Epoch 4: 97%|█████████████████████▎| ETA: 0:00:00 (47.29 ms/it) accuracy/train: 1.0 loss/train: 0.00735     Train Epoch 4: 97%|█████████████████████▍| ETA: 0:00:00 (47.28 ms/it) accuracy/train: 1.0 loss/train: 0.0147     Train Epoch 4: 98%|█████████████████████▋| ETA: 0:00:00 (47.26 ms/it) accuracy/train: 0.984 loss/train: 0.0647     Train Epoch 4: 100%|██████████████████████| Time: 0:00:20 (48.61 ms/it) accuracy/train: 0.991 loss/train: 0.0187   Val Epoch 4: 36%|████████▋ | ETA: 0:00:00 ( 6.20 ms/it) accuracy/val: 0.978 loss/val: 0.0839        Val Epoch 4: 72%|█████████████████▍ | ETA: 0:00:00 ( 6.25 ms/it) accuracy/val: 0.977 loss/val: 0.0822        Val Epoch 4: 100%|████████████████████████| Time: 0:00:00 ( 6.36 ms/it) accuracy/val: 0.98 loss/val: 0.0706      Train Epoch 5: 1%|▏ | ETA: 0:00:20 (47.85 ms/it) accuracy/train: 0.977 loss/train: 0.0462     Train Epoch 5: 1%|▍ | ETA: 0:00:19 (46.14 ms/it) accuracy/train: 1.0 loss/train: 0.00192     Train Epoch 5: 2%|▌ | ETA: 0:00:18 (45.37 ms/it) accuracy/train: 1.0 loss/train: 0.00874     Train Epoch 5: 3%|▋ | ETA: 0:00:18 (45.01 ms/it) accuracy/train: 1.0 loss/train: 0.00808     Train Epoch 5: 4%|▊ | ETA: 0:00:18 (45.06 ms/it) accuracy/train: 1.0 loss/train: 0.00181     Train Epoch 5: 4%|█ | ETA: 0:00:18 (45.01 ms/it) accuracy/train: 0.984 loss/train: 0.0293     Train Epoch 5: 5%|█▏ | ETA: 0:00:18 (44.96 ms/it) accuracy/train: 0.992 loss/train: 0.0405     Train Epoch 5: 6%|█▎ | ETA: 0:00:18 (45.45 ms/it) accuracy/train: 1.0 loss/train: 0.011     Train Epoch 5: 6%|█▍ | ETA: 0:00:17 (45.57 ms/it) accuracy/train: 0.984 loss/train: 0.0945     Train Epoch 5: 7%|█▋ | ETA: 0:00:17 (45.75 ms/it) accuracy/train: 1.0 loss/train: 0.00932     Train Epoch 5: 8%|█▊ | ETA: 0:00:17 (45.94 ms/it) accuracy/train: 1.0 loss/train: 0.0118     Train Epoch 5: 9%|█▉ | ETA: 0:00:17 (46.10 ms/it) accuracy/train: 1.0 loss/train: 0.00404     Train Epoch 5: 9%|██ | ETA: 0:00:17 (46.17 ms/it) accuracy/train: 1.0 loss/train: 0.00921     Train Epoch 5: 10%|██▎ | ETA: 0:00:17 (46.23 ms/it) accuracy/train: 0.992 loss/train: 0.0352     Train Epoch 5: 11%|██▍ | ETA: 0:00:17 (46.21 ms/it) accuracy/train: 0.984 loss/train: 0.0422     Train Epoch 5: 11%|██▌ | ETA: 0:00:17 (46.21 ms/it) accuracy/train: 0.992 loss/train: 0.015     Train Epoch 5: 12%|██▋ | ETA: 0:00:17 (46.20 ms/it) accuracy/train: 1.0 loss/train: 0.018     Train Epoch 5: 13%|██▉ | ETA: 0:00:17 (46.28 ms/it) accuracy/train: 0.992 loss/train: 0.0201     Train Epoch 5: 14%|███ | ETA: 0:00:16 (46.31 ms/it) accuracy/train: 1.0 loss/train: 0.00936     Train Epoch 5: 14%|███▏ | ETA: 0:00:16 (46.28 ms/it) accuracy/train: 1.0 loss/train: 0.0118     Train Epoch 5: 15%|███▎ | ETA: 0:00:16 (46.26 ms/it) accuracy/train: 1.0 loss/train: 0.0173     Train Epoch 5: 16%|███▌ | ETA: 0:00:16 (46.26 ms/it) accuracy/train: 1.0 loss/train: 0.0125     Train Epoch 5: 16%|███▋ | ETA: 0:00:16 (46.28 ms/it) accuracy/train: 0.992 loss/train: 0.0306     Train Epoch 5: 17%|███▊ | ETA: 0:00:16 (46.27 ms/it) accuracy/train: 0.992 loss/train: 0.0314     Train Epoch 5: 18%|███▉ | ETA: 0:00:16 (46.26 ms/it) accuracy/train: 0.984 loss/train: 0.0371     Train Epoch 5: 18%|████▏ | ETA: 0:00:15 (46.26 ms/it) accuracy/train: 0.992 loss/train: 0.0485     Train Epoch 5: 19%|████▎ | ETA: 0:00:15 (46.34 ms/it) accuracy/train: 0.992 loss/train: 0.0136     Train Epoch 5: 20%|████▍ | ETA: 0:00:15 (46.36 ms/it) accuracy/train: 1.0 loss/train: 0.00355     Train Epoch 5: 21%|████▌ | ETA: 0:00:15 (46.34 ms/it) accuracy/train: 0.992 loss/train: 0.0213     Train Epoch 5: 21%|████▊ | ETA: 0:00:15 (46.31 ms/it) accuracy/train: 1.0 loss/train: 0.00131     Train Epoch 5: 22%|████▉ | ETA: 0:00:15 (46.33 ms/it) accuracy/train: 1.0 loss/train: 0.0133     Train Epoch 5: 23%|█████ | ETA: 0:00:15 (46.32 ms/it) accuracy/train: 1.0 loss/train: 0.00798     Train Epoch 5: 23%|█████▏ | ETA: 0:00:15 (46.48 ms/it) accuracy/train: 0.992 loss/train: 0.0173     Train Epoch 5: 24%|█████▎ | ETA: 0:00:14 (46.47 ms/it) accuracy/train: 0.992 loss/train: 0.0208     Train Epoch 5: 25%|█████▍ | ETA: 0:00:14 (46.45 ms/it) accuracy/train: 1.0 loss/train: 0.0157     Train Epoch 5: 25%|█████▌ | ETA: 0:00:14 (46.62 ms/it) accuracy/train: 1.0 loss/train: 0.00835     Train Epoch 5: 26%|█████▋ | ETA: 0:00:14 (46.68 ms/it) accuracy/train: 0.984 loss/train: 0.0738     Train Epoch 5: 27%|█████▉ | ETA: 0:00:14 (46.64 ms/it) accuracy/train: 1.0 loss/train: 0.0117     Train Epoch 5: 27%|██████ | ETA: 0:00:14 (46.61 ms/it) accuracy/train: 1.0 loss/train: 0.0048     Train Epoch 5: 28%|██████▏ | ETA: 0:00:14 (46.59 ms/it) accuracy/train: 1.0 loss/train: 0.0101     Train Epoch 5: 29%|██████▎ | ETA: 0:00:14 (46.60 ms/it) accuracy/train: 0.992 loss/train: 0.0224     Train Epoch 5: 29%|██████▌ | ETA: 0:00:13 (46.61 ms/it) accuracy/train: 1.0 loss/train: 0.00752     Train Epoch 5: 30%|██████▋ | ETA: 0:00:13 (46.59 ms/it) accuracy/train: 0.984 loss/train: 0.049     Train Epoch 5: 31%|██████▊ | ETA: 0:00:13 (46.58 ms/it) accuracy/train: 0.984 loss/train: 0.0375     Train Epoch 5: 32%|██████▉ | ETA: 0:00:13 (46.58 ms/it) accuracy/train: 0.977 loss/train: 0.031     Train Epoch 5: 32%|███████▏ | ETA: 0:00:13 (46.59 ms/it) accuracy/train: 0.992 loss/train: 0.0122     Train Epoch 5: 33%|███████▎ | ETA: 0:00:13 (46.60 ms/it) accuracy/train: 1.0 loss/train: 0.00364     Train Epoch 5: 34%|███████▍ | ETA: 0:00:13 (46.58 ms/it) accuracy/train: 1.0 loss/train: 0.00943     Train Epoch 5: 34%|███████▌ | ETA: 0:00:12 (46.59 ms/it) accuracy/train: 1.0 loss/train: 0.0164     Train Epoch 5: 35%|███████▊ | ETA: 0:00:12 (46.57 ms/it) accuracy/train: 1.0 loss/train: 0.00971     Train Epoch 5: 36%|███████▉ | ETA: 0:00:12 (46.56 ms/it) accuracy/train: 1.0 loss/train: 0.00812     Train Epoch 5: 36%|████████ | ETA: 0:00:12 (46.54 ms/it) accuracy/train: 0.984 loss/train: 0.027     Train Epoch 5: 37%|████████▏ | ETA: 0:00:12 (46.52 ms/it) accuracy/train: 0.992 loss/train: 0.0225     Train Epoch 5: 38%|████████▍ | ETA: 0:00:12 (46.55 ms/it) accuracy/train: 0.992 loss/train: 0.0259     Train Epoch 5: 39%|████████▌ | ETA: 0:00:12 (46.58 ms/it) accuracy/train: 1.0 loss/train: 0.00334     Train Epoch 5: 39%|████████▋ | ETA: 0:00:12 (46.74 ms/it) accuracy/train: 0.992 loss/train: 0.0126     Train Epoch 5: 40%|████████▊ | ETA: 0:00:11 (46.88 ms/it) accuracy/train: 1.0 loss/train: 0.00823     Train Epoch 5: 40%|████████▊ | ETA: 0:00:11 (46.99 ms/it) accuracy/train: 0.992 loss/train: 0.0316     Train Epoch 5: 41%|████████▉ | ETA: 0:00:11 (47.15 ms/it) accuracy/train: 0.992 loss/train: 0.0264     Train Epoch 5: 41%|█████████▏ | ETA: 0:00:11 (47.17 ms/it) accuracy/train: 1.0 loss/train: 0.00431     Train Epoch 5: 42%|█████████▎ | ETA: 0:00:11 (47.18 ms/it) accuracy/train: 1.0 loss/train: 0.00543     Train Epoch 5: 43%|█████████▍ | ETA: 0:00:11 (47.21 ms/it) accuracy/train: 0.992 loss/train: 0.0456     Train Epoch 5: 43%|█████████▌ | ETA: 0:00:11 (47.20 ms/it) accuracy/train: 1.0 loss/train: 0.00897     Train Epoch 5: 44%|█████████▋ | ETA: 0:00:11 (47.26 ms/it) accuracy/train: 0.984 loss/train: 0.0573     Train Epoch 5: 45%|█████████▊ | ETA: 0:00:11 (47.29 ms/it) accuracy/train: 0.984 loss/train: 0.0477     Train Epoch 5: 45%|██████████ | ETA: 0:00:10 (47.31 ms/it) accuracy/train: 0.992 loss/train: 0.0173     Train Epoch 5: 46%|██████████ | ETA: 0:00:10 (47.36 ms/it) accuracy/train: 1.0 loss/train: 0.0141     Train Epoch 5: 46%|██████████▏ | ETA: 0:00:10 (47.40 ms/it) accuracy/train: 1.0 loss/train: 0.0175     Train Epoch 5: 47%|██████████▎ | ETA: 0:00:10 (47.48 ms/it) accuracy/train: 1.0 loss/train: 0.00875     Train Epoch 5: 47%|██████████▍ | ETA: 0:00:10 (47.47 ms/it) accuracy/train: 1.0 loss/train: 0.0136     Train Epoch 5: 48%|██████████▋ | ETA: 0:00:10 (47.46 ms/it) accuracy/train: 1.0 loss/train: 0.0136     Train Epoch 5: 49%|██████████▊ | ETA: 0:00:10 (47.45 ms/it) accuracy/train: 1.0 loss/train: 0.0217     Train Epoch 5: 50%|██████████▉ | ETA: 0:00:10 (47.43 ms/it) accuracy/train: 1.0 loss/train: 0.00697     Train Epoch 5: 50%|███████████ | ETA: 0:00:09 (47.45 ms/it) accuracy/train: 0.992 loss/train: 0.0187     Train Epoch 5: 51%|███████████▎ | ETA: 0:00:09 (47.42 ms/it) accuracy/train: 1.0 loss/train: 0.0114     Train Epoch 5: 52%|███████████▍ | ETA: 0:00:09 (47.39 ms/it) accuracy/train: 1.0 loss/train: 0.00561     Train Epoch 5: 52%|███████████▌ | ETA: 0:00:09 (47.35 ms/it) accuracy/train: 0.992 loss/train: 0.0231     Train Epoch 5: 53%|███████████▋ | ETA: 0:00:09 (47.37 ms/it) accuracy/train: 1.0 loss/train: 0.0181     Train Epoch 5: 54%|███████████▉ | ETA: 0:00:09 (47.35 ms/it) accuracy/train: 1.0 loss/train: 0.00429     Train Epoch 5: 54%|████████████ | ETA: 0:00:09 (47.39 ms/it) accuracy/train: 1.0 loss/train: 0.00143     Train Epoch 5: 55%|████████████ | ETA: 0:00:09 (47.44 ms/it) accuracy/train: 1.0 loss/train: 0.00313     Train Epoch 5: 55%|████████████▎ | ETA: 0:00:08 (47.45 ms/it) accuracy/train: 1.0 loss/train: 0.00707     Train Epoch 5: 56%|████████████▎ | ETA: 0:00:08 (47.50 ms/it) accuracy/train: 0.984 loss/train: 0.0203     Train Epoch 5: 57%|████████████▌ | ETA: 0:00:08 (47.48 ms/it) accuracy/train: 1.0 loss/train: 0.017     Train Epoch 5: 57%|████████████▋ | ETA: 0:00:08 (47.52 ms/it) accuracy/train: 1.0 loss/train: 0.00934     Train Epoch 5: 58%|████████████▊ | ETA: 0:00:08 (47.49 ms/it) accuracy/train: 0.992 loss/train: 0.018     Train Epoch 5: 59%|████████████▉ | ETA: 0:00:08 (47.50 ms/it) accuracy/train: 1.0 loss/train: 0.00629     Train Epoch 5: 59%|█████████████▏ | ETA: 0:00:08 (47.49 ms/it) accuracy/train: 0.992 loss/train: 0.0239     Train Epoch 5: 60%|█████████████▎ | ETA: 0:00:07 (47.50 ms/it) accuracy/train: 1.0 loss/train: 0.0148     Train Epoch 5: 61%|█████████████▍ | ETA: 0:00:07 (47.48 ms/it) accuracy/train: 0.992 loss/train: 0.0436     Train Epoch 5: 62%|█████████████▌ | ETA: 0:00:07 (47.49 ms/it) accuracy/train: 1.0 loss/train: 0.00786     Train Epoch 5: 62%|█████████████▊ | ETA: 0:00:07 (47.50 ms/it) accuracy/train: 1.0 loss/train: 0.0051     Train Epoch 5: 63%|█████████████▉ | ETA: 0:00:07 (47.51 ms/it) accuracy/train: 1.0 loss/train: 0.00537     Train Epoch 5: 64%|██████████████ | ETA: 0:00:07 (47.51 ms/it) accuracy/train: 1.0 loss/train: 0.00919     Train Epoch 5: 64%|██████████████▏ | ETA: 0:00:07 (47.52 ms/it) accuracy/train: 1.0 loss/train: 0.00207     Train Epoch 5: 65%|██████████████▎ | ETA: 0:00:07 (47.54 ms/it) accuracy/train: 1.0 loss/train: 0.0113     Train Epoch 5: 66%|██████████████▌ | ETA: 0:00:06 (47.55 ms/it) accuracy/train: 0.984 loss/train: 0.0882     Train Epoch 5: 66%|██████████████▋ | ETA: 0:00:06 (47.54 ms/it) accuracy/train: 1.0 loss/train: 0.00464     Train Epoch 5: 67%|██████████████▊ | ETA: 0:00:06 (47.54 ms/it) accuracy/train: 1.0 loss/train: 0.0128     Train Epoch 5: 68%|██████████████▉ | ETA: 0:00:06 (47.54 ms/it) accuracy/train: 1.0 loss/train: 0.00948     Train Epoch 5: 68%|███████████████▏ | ETA: 0:00:06 (47.53 ms/it) accuracy/train: 1.0 loss/train: 0.0141     Train Epoch 5: 69%|███████████████▎ | ETA: 0:00:06 (47.53 ms/it) accuracy/train: 0.992 loss/train: 0.021     Train Epoch 5: 70%|███████████████▍ | ETA: 0:00:06 (47.58 ms/it) accuracy/train: 1.0 loss/train: 0.00994     Train Epoch 5: 70%|███████████████▍ | ETA: 0:00:06 (47.64 ms/it) accuracy/train: 0.992 loss/train: 0.024     Train Epoch 5: 71%|███████████████▌ | ETA: 0:00:05 (47.72 ms/it) accuracy/train: 1.0 loss/train: 0.00945     Train Epoch 5: 71%|███████████████▋ | ETA: 0:00:05 (47.75 ms/it) accuracy/train: 1.0 loss/train: 0.0149     Train Epoch 5: 72%|███████████████▊ | ETA: 0:00:05 (47.75 ms/it) accuracy/train: 0.992 loss/train: 0.0452     Train Epoch 5: 73%|████████████████ | ETA: 0:00:05 (47.73 ms/it) accuracy/train: 0.984 loss/train: 0.0209     Train Epoch 5: 73%|████████████████▏ | ETA: 0:00:05 (47.72 ms/it) accuracy/train: 1.0 loss/train: 0.0121     Train Epoch 5: 74%|████████████████▎ | ETA: 0:00:05 (47.72 ms/it) accuracy/train: 0.992 loss/train: 0.0287     Train Epoch 5: 75%|████████████████▍ | ETA: 0:00:05 (47.73 ms/it) accuracy/train: 1.0 loss/train: 0.012     Train Epoch 5: 75%|████████████████▋ | ETA: 0:00:04 (47.75 ms/it) accuracy/train: 1.0 loss/train: 0.0115     Train Epoch 5: 76%|████████████████▊ | ETA: 0:00:04 (47.75 ms/it) accuracy/train: 1.0 loss/train: 0.00229     Train Epoch 5: 77%|████████████████▉ | ETA: 0:00:04 (47.74 ms/it) accuracy/train: 0.977 loss/train: 0.0835     Train Epoch 5: 77%|█████████████████ | ETA: 0:00:04 (47.73 ms/it) accuracy/train: 0.992 loss/train: 0.00776     Train Epoch 5: 78%|█████████████████▏ | ETA: 0:00:04 (47.75 ms/it) accuracy/train: 1.0 loss/train: 0.00334     Train Epoch 5: 79%|█████████████████▎ | ETA: 0:00:04 (47.76 ms/it) accuracy/train: 1.0 loss/train: 0.00578     Train Epoch 5: 79%|█████████████████▌ | ETA: 0:00:04 (47.78 ms/it) accuracy/train: 0.992 loss/train: 0.02     Train Epoch 5: 80%|█████████████████▋ | ETA: 0:00:04 (47.75 ms/it) accuracy/train: 0.992 loss/train: 0.0177     Train Epoch 5: 81%|█████████████████▊ | ETA: 0:00:03 (47.73 ms/it) accuracy/train: 1.0 loss/train: 0.00488     Train Epoch 5: 82%|█████████████████▉ | ETA: 0:00:03 (47.70 ms/it) accuracy/train: 0.992 loss/train: 0.0302     Train Epoch 5: 82%|██████████████████▏ | ETA: 0:00:03 (47.67 ms/it) accuracy/train: 1.0 loss/train: 0.00531     Train Epoch 5: 83%|██████████████████▎ | ETA: 0:00:03 (47.66 ms/it) accuracy/train: 0.992 loss/train: 0.0359     Train Epoch 5: 84%|██████████████████▍ | ETA: 0:00:03 (47.64 ms/it) accuracy/train: 0.992 loss/train: 0.0175     Train Epoch 5: 84%|██████████████████▌ | ETA: 0:00:03 (47.61 ms/it) accuracy/train: 0.992 loss/train: 0.017     Train Epoch 5: 89%|███████████████████▌ | ETA: 0:00:02 (49.89 ms/it) accuracy/train: 1.0 loss/train: 0.00593     Train Epoch 5: 90%|███████████████████▊ | ETA: 0:00:02 (49.86 ms/it) accuracy/train: 1.0 loss/train: 0.0152     Train Epoch 5: 90%|███████████████████▉ | ETA: 0:00:02 (49.86 ms/it) accuracy/train: 0.992 loss/train: 0.027     Train Epoch 5: 91%|████████████████████ | ETA: 0:00:01 (49.85 ms/it) accuracy/train: 1.0 loss/train: 0.00959     Train Epoch 5: 92%|████████████████████▏ | ETA: 0:00:01 (49.84 ms/it) accuracy/train: 0.992 loss/train: 0.0114     Train Epoch 5: 92%|████████████████████▍ | ETA: 0:00:01 (49.81 ms/it) accuracy/train: 0.984 loss/train: 0.0577     Train Epoch 5: 93%|████████████████████▍ | ETA: 0:00:01 (49.82 ms/it) accuracy/train: 0.992 loss/train: 0.048     Train Epoch 5: 94%|████████████████████▋ | ETA: 0:00:01 (49.81 ms/it) accuracy/train: 1.0 loss/train: 0.00737     Train Epoch 5: 94%|████████████████████▊ | ETA: 0:00:01 (49.80 ms/it) accuracy/train: 0.984 loss/train: 0.0306     Train Epoch 5: 95%|████████████████████▉ | ETA: 0:00:01 (49.79 ms/it) accuracy/train: 0.992 loss/train: 0.0257     Train Epoch 5: 96%|█████████████████████ | ETA: 0:00:00 (49.78 ms/it) accuracy/train: 1.0 loss/train: 0.0151     Train Epoch 5: 96%|█████████████████████▎| ETA: 0:00:00 (49.76 ms/it) accuracy/train: 1.0 loss/train: 0.00543     Train Epoch 5: 97%|█████████████████████▍| ETA: 0:00:00 (49.78 ms/it) accuracy/train: 0.984 loss/train: 0.0491     Train Epoch 5: 98%|█████████████████████▌| ETA: 0:00:00 (49.76 ms/it) accuracy/train: 1.0 loss/train: 0.00532     Train Epoch 5: 98%|█████████████████████▋| ETA: 0:00:00 (49.73 ms/it) accuracy/train: 0.992 loss/train: 0.0503     Train Epoch 5: 99%|█████████████████████▊| ETA: 0:00:00 (49.71 ms/it) accuracy/train: 1.0 loss/train: 0.00987     Train Epoch 5: 100%|██████████████████████| ETA: 0:00:00 (49.67 ms/it) accuracy/train: 0.984 loss/train: 0.0563     Train Epoch 5: 100%|██████████████████████| Time: 0:00:20 (49.66 ms/it) accuracy/train: 0.991 loss/train: 0.0188 Val Epoch 5: 32%|███████▋ | ETA: 0:00:00 ( 7.02 ms/it) accuracy/val: 0.981 loss/val: 0.0766     Val Epoch 5: 66%|███████████████▉ | ETA: 0:00:00 ( 6.74 ms/it) accuracy/val: 0.982 loss/val: 0.0743     Val Epoch 5: 100%|████████████████████████| Time: 0:00:00 ( 6.60 ms/it) accuracy/val: 0.982 loss/val: 0.0687 Testing: 3%|▊ | ETA: 0:00:05 (68.80 ms/it) accuracy/test: 0.988 loss/test: 0.0451     Testing: 16%|████▋ | ETA: 0:00:01 (18.88 ms/it) accuracy/test: 0.975 loss/test: 0.0876     Testing: 37%|██████████▎ | ETA: 0:00:00 (12.10 ms/it) accuracy/test: 0.976 loss/test: 0.0869     Testing: 57%|████████████████ | ETA: 0:00:00 (10.16 ms/it) accuracy/test: 0.977 loss/test: 0.0841     Testing: 78%|██████████████████████ | ETA: 0:00:00 ( 9.10 ms/it) accuracy/test: 0.981 loss/test: 0.0717     Testing: 100%|████████████████████████████| Time: 0:00:00 ( 8.39 ms/it) accuracy/test: 0.982 loss/test: 0.0671 Test Summary: | Total Time Examples | 0 4m34.0s Testing Tsunami tests passed Testing completed after 1868.36s PkgEval succeeded after 2087.07s