Package evaluation to test Tsunami on Julia 1.10.10 (95f30e51f4*) started at 2026-02-03T00:47:57.665 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Activating project at `~/.julia/environments/v1.10` Set-up completed after 5.31s ################################################################################ # Installation # Installing Tsunami... Resolving package versions... Updating `~/.julia/environments/v1.10/Project.toml` [36e41bbe] + Tsunami v0.3.1 Updating `~/.julia/environments/v1.10/Manifest.toml` [47edcb42] + ADTypes v1.21.0 [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 v0.4.7 [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 v1.0.6 [f151be2c] + EnzymeCore v0.8.18 [cc61a311] + FLoops v0.2.2 [b9860ae5] + FLoopsBase v0.1.1 [5789e2e9] + FileIO v1.18.0 [1a297f60] + FillArrays v1.16.0 [53c48c17] + FixedPointNumbers v0.8.5 [587475ba] + Flux v0.16.9 [f6369f11] + ForwardDiff v1.3.2 [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 v9.4.6 [2ab3a3ac] + LogExpFunctions v0.3.29 [c2834f40] + MLCore v1.0.0 [7e8f7934] + MLDataDevices v1.17.4 [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 [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 v1.1.1 [56f22d72] + Artifacts [2a0f44e3] + Base64 [8bf52ea8] + CRC32c [ade2ca70] + Dates [8ba89e20] + Distributed [f43a241f] + Downloads v1.6.0 [7b1f6079] + FileWatching [9fa8497b] + Future [b77e0a4c] + InteractiveUtils [4af54fe1] + LazyArtifacts [b27032c2] + LibCURL v0.6.4 [76f85450] + LibGit2 [8f399da3] + Libdl [37e2e46d] + LinearAlgebra [56ddb016] + Logging [d6f4376e] + Markdown [a63ad114] + Mmap [ca575930] + NetworkOptions v1.2.0 [44cfe95a] + Pkg v1.10.0 [de0858da] + Printf [3fa0cd96] + REPL [9a3f8284] + Random [ea8e919c] + SHA v0.7.0 [9e88b42a] + Serialization [6462fe0b] + Sockets [2f01184e] + SparseArrays v1.10.0 [10745b16] + Statistics v1.10.0 [4607b0f0] + SuiteSparse [fa267f1f] + TOML v1.0.3 [a4e569a6] + Tar v1.10.0 [8dfed614] + Test [cf7118a7] + UUIDs [4ec0a83e] + Unicode [e66e0078] + CompilerSupportLibraries_jll v1.1.1+0 [deac9b47] + LibCURL_jll v8.4.0+0 [e37daf67] + LibGit2_jll v1.6.4+0 [29816b5a] + LibSSH2_jll v1.11.0+1 [c8ffd9c3] + MbedTLS_jll v2.28.2+1 [14a3606d] + MozillaCACerts_jll v2023.1.10 [4536629a] + OpenBLAS_jll v0.3.23+4 [05823500] + OpenLibm_jll v0.8.5+0 [bea87d4a] + SuiteSparse_jll v7.2.1+1 [83775a58] + Zlib_jll v1.2.13+1 [8e850b90] + libblastrampoline_jll v5.11.0+0 [8e850ede] + nghttp2_jll v1.52.0+1 [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 10.73s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... ┌ Warning: Could not use exact versions of packages in manifest, re-resolving └ @ TestEnv ~/.julia/packages/TestEnv/h9a3r/src/julia-1.9/activate_set.jl:63 Precompiling package dependencies... Precompiling packages... 157223.1 ms ✓ Flux → FluxEnzymeExt 46240.3 ms ✓ Tsunami 85395.1 ms ✓ Tsunami → TsunamiEnzymeExt 3 dependencies successfully precompiled in 304 seconds. 284 already precompiled. Precompilation completed after 320.28s ################################################################################ # Testing # Testing Tsunami ┌ Warning: Could not use exact versions of packages in manifest, re-resolving └ @ Pkg.Operations /opt/julia/share/julia/stdlib/v1.10/Pkg/src/Operations.jl:1829 Status `/tmp/jl_nEbNN9/Project.toml` [a93c6f00] DataFrames v1.8.1 [7da242da] Enzyme v0.13.129 [587475ba] Flux v0.16.9 [d9f16b24] Functors v0.5.2 [7e8f7934] MLDataDevices v1.17.4 [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 [f8b46487] TestItemRunner v1.1.4 [1c621080] TestItems v1.0.0 [36e41bbe] Tsunami v0.3.1 [44cfe95a] Pkg v1.10.0 [9a3f8284] Random [10745b16] Statistics v1.10.0 [8dfed614] Test Status `/tmp/jl_nEbNN9/Manifest.toml` [47edcb42] ADTypes v1.21.0 [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 v0.4.7 [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 v1.0.6 [7da242da] Enzyme v0.13.129 [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 v1.18.0 [48062228] FilePathsBase v0.9.24 [1a297f60] FillArrays v1.16.0 [53c48c17] FixedPointNumbers v0.8.5 [587475ba] Flux v0.16.9 [f6369f11] ForwardDiff v1.3.2 [d9f16b24] Functors v0.5.2 [0c68f7d7] GPUArrays v11.3.4 [46192b85] GPUArraysCore v0.2.0 [61eb1bfa] GPUCompiler v1.8.2 [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] 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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 [82ae8749] StatsAPI v1.8.0 [2913bbd2] StatsBase v0.34.10 [4db3bf67] StridedViews v0.4.3 [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 v1.28.0 [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 v0.0.249+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 v3.5.5+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 v1.1.5+0 [0dad84c5] ArgTools v1.1.1 [56f22d72] Artifacts [2a0f44e3] Base64 [8bf52ea8] CRC32c [ade2ca70] Dates [8ba89e20] Distributed [f43a241f] Downloads v1.6.0 [7b1f6079] FileWatching [9fa8497b] Future [b77e0a4c] InteractiveUtils [4af54fe1] LazyArtifacts [b27032c2] LibCURL v0.6.4 [76f85450] LibGit2 [8f399da3] Libdl [37e2e46d] LinearAlgebra [56ddb016] Logging [d6f4376e] Markdown [a63ad114] Mmap [ca575930] NetworkOptions v1.2.0 [44cfe95a] Pkg v1.10.0 [de0858da] Printf [3fa0cd96] REPL [9a3f8284] Random [ea8e919c] SHA v0.7.0 [9e88b42a] Serialization [6462fe0b] Sockets [2f01184e] SparseArrays v1.10.0 [10745b16] Statistics v1.10.0 [4607b0f0] SuiteSparse [fa267f1f] TOML v1.0.3 [a4e569a6] Tar v1.10.0 [8dfed614] Test [cf7118a7] UUIDs [4ec0a83e] Unicode [e66e0078] CompilerSupportLibraries_jll v1.1.1+0 [deac9b47] LibCURL_jll v8.4.0+0 [e37daf67] LibGit2_jll v1.6.4+0 [29816b5a] LibSSH2_jll v1.11.0+1 [c8ffd9c3] MbedTLS_jll v2.28.2+1 [14a3606d] MozillaCACerts_jll v2023.1.10 [4536629a] OpenBLAS_jll v0.3.23+4 [05823500] OpenLibm_jll v0.8.5+0 [bea87d4a] SuiteSparse_jll v7.2.1+1 [83775a58] Zlib_jll v1.2.13+1 [8e850b90] libblastrampoline_jll v5.11.0+0 [8e850ede] nghttp2_jll v1.52.0+1 [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... ┌ 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/4qHOT/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, 372 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, 372 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, 372 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, 372 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, 372 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, 372 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, 372 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 652 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, 372 bytes. [ Info: Run Directory: /home/pkgeval/.julia/packages/Tsunami/5iz8R/test/tsunami_logs/run_1 Train Epoch 1: 100%|██████████████████████| Time: 0:00:00 ( 0.60 ms/it) Train Epoch 2: 100%|██████████████████████| Time: 0:00:00 ( 0.59 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, 372 bytes. [ Info: Run Directory: /home/pkgeval/.julia/packages/Tsunami/5iz8R/test/tsunami_logs/run_2 Train Epoch 1: 100%|██████████████████████| Time: 0:00:00 ( 0.42 ms/it) Train Epoch 2: 100%|██████████████████████| Time: 0:00:00 ( 0.43 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, 372 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, 372 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, 372 bytes. [ Info: Run Directory: /home/pkgeval/.julia/packages/Tsunami/5iz8R/test/tsunami_logs/run_1 Testing: 67%|██████████████████▋ | ETA: 0:00:00 (52.21 ms/it) a: 1.0 b: 1.5     Testing: 100%|████████████████████████████| Time: 0:00:00 (34.89 ms/it) a: 1.0 b: 2.0 Validation: 100%|█████████████████████████| Time: 0:00:00 (18.83 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, 372 bytes. [ Info: Run Directory: /home/pkgeval/.julia/packages/Tsunami/5iz8R/test/tsunami_logs/run_1 Train Epoch 1: 50%|███████████ | ETA: 0:00:21 (21.70 s/it) Train Epoch 1: 100%|██████████████████████| Time: 0:00:21 (10.86 s/it) Train Epoch 2: 100%|██████████████████████| Time: 0:00:00 ( 0.48 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, 364 bytes. [ Info: Run Directory: /home/pkgeval/.julia/packages/Tsunami/5iz8R/test/tsunami_logs/run_2 Train Epoch 1: 50%|███████████ | ETA: 0:00:02 ( 2.73 s/it) train/batch_idx_step: 1 train/loss_step: 4.37     Train Epoch 1: 100%|██████████████████████| Time: 0:00:02 ( 1.38 s/it) train/batch_idx_step: 2 train/loss_step: 1.52   Train Epoch 2: 100%|██████████████████████| Time: 0:00:00 ( 0.56 ms/it) train/batch_idx_step: 2 train/loss_step: 1.51   Train Epoch 3: 100%|██████████████████████| Time: 0:00:00 ( 0.59 ms/it) train/batch_idx_step: 2 train/loss_step: 1.51   Train Epoch 4: 100%|██████████████████████| Time: 0:00:00 ( 0.56 ms/it) train/batch_idx_step: 2 train/loss_step: 1.51 Test Summary: | Pass Total Time Package | 67 67 8m45.7s [ 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, 397.828 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: TODO forward zero-set of arraycopy of found (false) eltype (nothing) used memset rather than runtime type │ Caused by: │ Stacktrace: │ [1] copy │ @ ./array.jl:411 │ [2] unaliascopy │ @ ./abstractarray.jl:1496 │ [3] unalias │ @ ./abstractarray.jl:1480 │ [4] broadcast_unalias │ @ ./broadcast.jl:977 │ [5] preprocess │ @ ./broadcast.jl:984 │ [6] preprocess_args │ @ ./broadcast.jl:987 │ [7] preprocess_args │ @ ./broadcast.jl:986 │ [8] preprocess │ @ ./broadcast.jl:983 │ [9] override_bc_copyto! │ @ ~/.julia/packages/Enzyme/S3nC6/src/compiler/interpreter.jl:818 │ [10] copyto! │ @ ./broadcast.jl:956 │ [11] materialize! │ @ ./broadcast.jl:914 │ [12] materialize! │ @ ./broadcast.jl:911 │ [13] #logsoftmax!#208 │ @ ~/.julia/packages/NNlib/srXYX/src/softmax.jl:114 └ @ Enzyme.Compiler ~/.julia/packages/Enzyme/S3nC6/src/rules/llvmrules.jl:514 ┌ Warning: TODO forward zero-set of arraycopy of found (false) eltype (nothing) used memset rather than runtime type │ Caused by: │ Stacktrace: │ [1] copy │ @ ./array.jl:411 │ [2] unaliascopy │ @ ./abstractarray.jl:1496 │ [3] unalias │ @ ./abstractarray.jl:1480 │ [4] broadcast_unalias │ @ ./broadcast.jl:977 │ [5] preprocess │ @ ./broadcast.jl:984 │ [6] preprocess_args │ @ ./broadcast.jl:987 │ [7] preprocess_args │ @ ./broadcast.jl:986 │ [8] preprocess │ @ ./broadcast.jl:983 │ [9] preprocess_args │ @ ./broadcast.jl:987 │ [10] preprocess_args (repeats 2 times) │ @ ./broadcast.jl:986 │ [11] preprocess │ @ ./broadcast.jl:983 │ [12] override_bc_copyto! │ @ ~/.julia/packages/Enzyme/S3nC6/src/compiler/interpreter.jl:818 │ [13] copyto! │ @ ./broadcast.jl:956 │ [14] materialize! │ @ ./broadcast.jl:914 │ [15] materialize! │ @ ./broadcast.jl:911 │ [16] #logsoftmax!#208 │ @ ~/.julia/packages/NNlib/srXYX/src/softmax.jl:117 └ @ Enzyme.Compiler ~/.julia/packages/Enzyme/S3nC6/src/rules/llvmrules.jl:514 [ 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, 397.828 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: TODO forward zero-set of arraycopy of found (false) eltype (nothing) used memset rather than runtime type │ Caused by: │ Stacktrace: │ [1] copy │ @ ./array.jl:411 │ [2] unaliascopy │ @ ./abstractarray.jl:1496 │ [3] unalias │ @ ./abstractarray.jl:1480 │ [4] broadcast_unalias │ @ ./broadcast.jl:977 │ [5] preprocess │ @ ./broadcast.jl:984 │ [6] preprocess_args │ @ ./broadcast.jl:987 │ [7] preprocess_args │ @ ./broadcast.jl:986 │ [8] preprocess │ @ ./broadcast.jl:983 │ [9] override_bc_copyto! │ @ ~/.julia/packages/Enzyme/S3nC6/src/compiler/interpreter.jl:818 │ [10] copyto! │ @ ./broadcast.jl:956 │ [11] materialize! │ @ ./broadcast.jl:914 │ [12] materialize! │ @ ./broadcast.jl:911 │ [13] #logsoftmax!#208 │ @ ~/.julia/packages/NNlib/srXYX/src/softmax.jl:114 └ @ Enzyme.Compiler ~/.julia/packages/Enzyme/S3nC6/src/rules/llvmrules.jl:514 ┌ Warning: TODO forward zero-set of arraycopy of found (false) eltype (nothing) used memset rather than runtime type │ Caused by: │ Stacktrace: │ [1] copy │ @ ./array.jl:411 │ [2] unaliascopy │ @ ./abstractarray.jl:1496 │ [3] unalias │ @ ./abstractarray.jl:1480 │ [4] broadcast_unalias │ @ ./broadcast.jl:977 │ [5] preprocess │ @ ./broadcast.jl:984 │ [6] preprocess_args │ @ ./broadcast.jl:987 │ [7] preprocess_args │ @ ./broadcast.jl:986 │ [8] preprocess │ @ ./broadcast.jl:983 │ [9] preprocess_args │ @ ./broadcast.jl:987 │ [10] preprocess_args (repeats 2 times) │ @ ./broadcast.jl:986 │ [11] preprocess │ @ ./broadcast.jl:983 │ [12] override_bc_copyto! │ @ ~/.julia/packages/Enzyme/S3nC6/src/compiler/interpreter.jl:818 │ [13] copyto! │ @ ./broadcast.jl:956 │ [14] materialize! │ @ ./broadcast.jl:914 │ [15] materialize! │ @ ./broadcast.jl:911 │ [16] #logsoftmax!#208 │ @ ~/.julia/packages/NNlib/srXYX/src/softmax.jl:117 └ @ Enzyme.Compiler ~/.julia/packages/Enzyme/S3nC6/src/rules/llvmrules.jl:514 Test Summary: | Pass Total Time Package | 4 4 5m03.9s [ 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 ( 0.12 s/it) accuracy/val: 0.148 loss/val: 2.35     Val Epoch 0: 68%|████████████████▍ | ETA: 0:00:00 (17.67 ms/it) accuracy/val: 0.111 loss/val: 2.35     Val Epoch 0: 100%|████████████████████████| Time: 0:00:00 (13.90 ms/it) accuracy/val: 0.108 loss/val: 2.35   Train Epoch 1: 0%| | ETA: 1:26:41 (12.35 s/it) accuracy/train: 0.125 loss/train: 2.36     Train Epoch 1: 1%|▎ | ETA: 0:17:19 ( 2.49 s/it) accuracy/train: 0.555 loss/train: 2.46     Train Epoch 1: 2%|▌ | ETA: 0:09:37 ( 1.40 s/it) accuracy/train: 0.68 loss/train: 1.32     Train Epoch 1: 3%|▋ | ETA: 0:06:39 ( 0.98 s/it) accuracy/train: 0.766 loss/train: 0.657     Train Epoch 1: 4%|▉ | ETA: 0:05:05 ( 0.75 s/it) accuracy/train: 0.852 loss/train: 0.441     Train Epoch 1: 5%|█ | ETA: 0:04:21 ( 0.65 s/it) accuracy/train: 0.859 loss/train: 0.482     Train Epoch 1: 6%|█▎ | ETA: 0:03:37 ( 0.55 s/it) accuracy/train: 0.867 loss/train: 0.49     Train Epoch 1: 7%|█▌ | ETA: 0:02:59 ( 0.46 s/it) accuracy/train: 0.922 loss/train: 0.372     Train Epoch 1: 8%|█▊ | ETA: 0:02:37 ( 0.40 s/it) accuracy/train: 0.93 loss/train: 0.214     Train Epoch 1: 9%|█▉ | ETA: 0:02:19 ( 0.36 s/it) accuracy/train: 0.914 loss/train: 0.342     Train Epoch 1: 10%|██▏ | ETA: 0:02:05 ( 0.33 s/it) accuracy/train: 0.898 loss/train: 0.387     Train Epoch 1: 11%|██▍ | ETA: 0:01:54 ( 0.30 s/it) accuracy/train: 0.891 loss/train: 0.386     Train Epoch 1: 12%|██▌ | ETA: 0:01:44 ( 0.28 s/it) accuracy/train: 0.945 loss/train: 0.217     Train Epoch 1: 12%|██▋ | ETA: 0:01:41 ( 0.27 s/it) accuracy/train: 0.891 loss/train: 0.41     Train Epoch 1: 13%|██▉ | ETA: 0:01:33 ( 0.25 s/it) accuracy/train: 0.945 loss/train: 0.249     Train Epoch 1: 14%|███▏ | ETA: 0:01:27 ( 0.24 s/it) accuracy/train: 0.938 loss/train: 0.236     Train Epoch 1: 15%|███▎ | ETA: 0:01:21 ( 0.23 s/it) accuracy/train: 0.969 loss/train: 0.127     Train Epoch 1: 16%|███▌ | ETA: 0:01:16 ( 0.21 s/it) accuracy/train: 0.891 loss/train: 0.317     Train Epoch 1: 17%|███▊ | ETA: 0:01:11 ( 0.20 s/it) accuracy/train: 0.945 loss/train: 0.193     Train Epoch 1: 18%|███▉ | ETA: 0:01:07 ( 0.20 s/it) accuracy/train: 0.945 loss/train: 0.166     Train Epoch 1: 19%|████▏ | ETA: 0:01:04 ( 0.19 s/it) accuracy/train: 0.922 loss/train: 0.267     Train Epoch 1: 20%|████▍ | ETA: 0:01:00 ( 0.18 s/it) accuracy/train: 0.914 loss/train: 0.381     Train Epoch 1: 21%|████▋ | ETA: 0:00:57 ( 0.17 s/it) accuracy/train: 0.922 loss/train: 0.244     Train Epoch 1: 22%|████▉ | ETA: 0:00:53 ( 0.16 s/it) accuracy/train: 0.922 loss/train: 0.189     Train Epoch 1: 23%|█████▏ | ETA: 0:00:50 ( 0.16 s/it) accuracy/train: 0.93 loss/train: 0.263     Train Epoch 1: 24%|█████▍ | ETA: 0:00:47 ( 0.15 s/it) accuracy/train: 0.953 loss/train: 0.168     Train Epoch 1: 26%|█████▋ | ETA: 0:00:45 ( 0.14 s/it) accuracy/train: 0.922 loss/train: 0.239     Train Epoch 1: 27%|█████▉ | ETA: 0:00:42 ( 0.14 s/it) accuracy/train: 0.93 loss/train: 0.182     Train Epoch 1: 28%|██████▏ | ETA: 0:00:40 ( 0.13 s/it) accuracy/train: 0.922 loss/train: 0.267     Train Epoch 1: 29%|██████▍ | ETA: 0:00:38 ( 0.13 s/it) accuracy/train: 0.945 loss/train: 0.173     Train Epoch 1: 30%|██████▋ | ETA: 0:00:36 ( 0.13 s/it) accuracy/train: 0.984 loss/train: 0.0631     Train Epoch 1: 32%|██████▉ | ETA: 0:00:35 ( 0.12 s/it) accuracy/train: 0.961 loss/train: 0.164     Train Epoch 1: 33%|███████▎ | ETA: 0:00:33 ( 0.12 s/it) accuracy/train: 0.93 loss/train: 0.274     Train Epoch 1: 34%|███████▍ | ETA: 0:00:32 ( 0.12 s/it) accuracy/train: 0.953 loss/train: 0.175     Train Epoch 1: 35%|███████▋ | ETA: 0:00:30 ( 0.11 s/it) accuracy/train: 0.953 loss/train: 0.139     Train Epoch 1: 36%|███████▉ | ETA: 0:00:29 ( 0.11 s/it) accuracy/train: 0.898 loss/train: 0.312     Train Epoch 1: 37%|████████▏ | ETA: 0:00:28 ( 0.11 s/it) accuracy/train: 0.922 loss/train: 0.294     Train Epoch 1: 38%|████████▌ | ETA: 0:00:27 ( 0.10 s/it) accuracy/train: 0.961 loss/train: 0.194     Train Epoch 1: 39%|████████▋ | ETA: 0:00:26 ( 0.10 s/it) accuracy/train: 0.992 loss/train: 0.0642     Train Epoch 1: 41%|████████▉ | ETA: 0:00:25 ( 0.10 s/it) accuracy/train: 0.953 loss/train: 0.125     Train Epoch 1: 42%|█████████▏ | ETA: 0:00:24 (97.81 ms/it) accuracy/train: 0.938 loss/train: 0.229     Train Epoch 1: 43%|█████████▍ | ETA: 0:00:23 (95.79 ms/it) accuracy/train: 0.969 loss/train: 0.114     Train Epoch 1: 44%|█████████▊ | ETA: 0:00:22 (93.87 ms/it) accuracy/train: 0.922 loss/train: 0.197     Train Epoch 1: 45%|██████████ | ETA: 0:00:21 (92.06 ms/it) accuracy/train: 0.93 loss/train: 0.283     Train Epoch 1: 46%|██████████▎ | ETA: 0:00:20 (90.33 ms/it) accuracy/train: 0.906 loss/train: 0.334     Train Epoch 1: 48%|██████████▌ | ETA: 0:00:19 (88.70 ms/it) accuracy/train: 0.961 loss/train: 0.127     Train Epoch 1: 49%|██████████▊ | ETA: 0:00:18 (87.14 ms/it) accuracy/train: 0.961 loss/train: 0.157     Train Epoch 1: 50%|███████████ | ETA: 0:00:18 (85.66 ms/it) accuracy/train: 0.984 loss/train: 0.0892     Train Epoch 1: 51%|███████████▎ | ETA: 0:00:17 (84.22 ms/it) accuracy/train: 0.93 loss/train: 0.207     Train Epoch 1: 52%|███████████▌ | ETA: 0:00:16 (82.86 ms/it) accuracy/train: 0.945 loss/train: 0.208     Train Epoch 1: 54%|███████████▊ | ETA: 0:00:15 (81.58 ms/it) accuracy/train: 0.93 loss/train: 0.197     Train Epoch 1: 55%|████████████ | ETA: 0:00:15 (80.35 ms/it) accuracy/train: 0.969 loss/train: 0.12     Train Epoch 1: 56%|████████████▎ | ETA: 0:00:14 (79.16 ms/it) accuracy/train: 0.938 loss/train: 0.275     Train Epoch 1: 57%|████████████▋ | ETA: 0:00:14 (78.04 ms/it) accuracy/train: 0.969 loss/train: 0.13     Train Epoch 1: 58%|████████████▉ | ETA: 0:00:13 (76.95 ms/it) accuracy/train: 0.984 loss/train: 0.081     Train Epoch 1: 59%|█████████████▏ | ETA: 0:00:12 (75.90 ms/it) accuracy/train: 0.961 loss/train: 0.156     Train Epoch 1: 61%|█████████████▍ | ETA: 0:00:12 (74.91 ms/it) accuracy/train: 0.969 loss/train: 0.0809     Train Epoch 1: 62%|█████████████▋ | ETA: 0:00:11 (73.95 ms/it) accuracy/train: 0.984 loss/train: 0.0587     Train Epoch 1: 63%|█████████████▉ | ETA: 0:00:11 (73.02 ms/it) accuracy/train: 0.945 loss/train: 0.221     Train Epoch 1: 64%|██████████████▏ | ETA: 0:00:10 (72.12 ms/it) accuracy/train: 0.961 loss/train: 0.149     Train Epoch 1: 65%|██████████████▍ | ETA: 0:00:10 (71.26 ms/it) accuracy/train: 0.984 loss/train: 0.0793     Train Epoch 1: 67%|██████████████▋ | ETA: 0:00:09 (70.43 ms/it) accuracy/train: 0.961 loss/train: 0.131     Train Epoch 1: 68%|██████████████▉ | ETA: 0:00:09 (69.63 ms/it) accuracy/train: 0.977 loss/train: 0.0973     Train Epoch 1: 69%|███████████████▏ | ETA: 0:00:09 (68.86 ms/it) accuracy/train: 0.922 loss/train: 0.188     Train Epoch 1: 70%|███████████████▍ | ETA: 0:00:08 (68.12 ms/it) accuracy/train: 0.945 loss/train: 0.136     Train Epoch 1: 71%|███████████████▊ | ETA: 0:00:08 (67.40 ms/it) accuracy/train: 0.969 loss/train: 0.0725     Train Epoch 1: 73%|████████████████ | ETA: 0:00:07 (66.70 ms/it) accuracy/train: 0.945 loss/train: 0.173     Train Epoch 1: 73%|████████████████▏ | ETA: 0:00:07 (66.20 ms/it) accuracy/train: 0.922 loss/train: 0.212     Train Epoch 1: 75%|████████████████▍ | ETA: 0:00:07 (65.54 ms/it) accuracy/train: 0.953 loss/train: 0.113     Train Epoch 1: 76%|████████████████▋ | ETA: 0:00:06 (65.05 ms/it) accuracy/train: 0.961 loss/train: 0.106     Train Epoch 1: 77%|████████████████▉ | ETA: 0:00:06 (64.43 ms/it) accuracy/train: 0.922 loss/train: 0.293     Train Epoch 1: 78%|█████████████████▏ | ETA: 0:00:05 (63.82 ms/it) accuracy/train: 0.969 loss/train: 0.0893     Train Epoch 1: 79%|█████████████████▍ | ETA: 0:00:05 (63.23 ms/it) accuracy/train: 0.961 loss/train: 0.167     Train Epoch 1: 80%|█████████████████▋ | ETA: 0:00:05 (62.66 ms/it) accuracy/train: 0.977 loss/train: 0.059     Train Epoch 1: 82%|█████████████████▉ | ETA: 0:00:04 (62.10 ms/it) accuracy/train: 0.953 loss/train: 0.139     Train Epoch 1: 83%|██████████████████▎ | ETA: 0:00:04 (61.57 ms/it) accuracy/train: 0.93 loss/train: 0.172     Train Epoch 1: 84%|██████████████████▍ | ETA: 0:00:04 (61.18 ms/it) accuracy/train: 0.977 loss/train: 0.0986     Train Epoch 1: 85%|██████████████████▋ | ETA: 0:00:03 (60.77 ms/it) accuracy/train: 0.961 loss/train: 0.0969     Train Epoch 1: 86%|██████████████████▉ | ETA: 0:00:03 (60.27 ms/it) accuracy/train: 0.969 loss/train: 0.267     Train Epoch 1: 87%|███████████████████▏ | ETA: 0:00:03 (59.78 ms/it) accuracy/train: 0.945 loss/train: 0.219     Train Epoch 1: 88%|███████████████████▍ | ETA: 0:00:02 (59.34 ms/it) accuracy/train: 0.969 loss/train: 0.0827     Train Epoch 1: 89%|███████████████████▋ | ETA: 0:00:02 (58.89 ms/it) accuracy/train: 0.969 loss/train: 0.0897     Train Epoch 1: 90%|███████████████████▉ | ETA: 0:00:02 (58.56 ms/it) accuracy/train: 0.969 loss/train: 0.0745     Train Epoch 1: 91%|████████████████████▏ | ETA: 0:00:02 (58.11 ms/it) accuracy/train: 0.969 loss/train: 0.125     Train Epoch 1: 93%|████████████████████▍ | ETA: 0:00:01 (57.68 ms/it) accuracy/train: 0.969 loss/train: 0.109     Train Epoch 1: 94%|████████████████████▋ | ETA: 0:00:01 (57.26 ms/it) accuracy/train: 0.984 loss/train: 0.0587     Train Epoch 1: 95%|████████████████████▉ | ETA: 0:00:01 (56.86 ms/it) accuracy/train: 0.953 loss/train: 0.151     Train Epoch 1: 96%|█████████████████████▏| ETA: 0:00:00 (56.47 ms/it) accuracy/train: 0.969 loss/train: 0.125     Train Epoch 1: 97%|█████████████████████▍| ETA: 0:00:00 (56.08 ms/it) accuracy/train: 0.953 loss/train: 0.179     Train Epoch 1: 99%|█████████████████████▋| ETA: 0:00:00 (55.69 ms/it) accuracy/train: 0.969 loss/train: 0.0751     Train Epoch 1: 100%|██████████████████████| ETA: 0:00:00 (55.33 ms/it) accuracy/train: 0.984 loss/train: 0.0835     Train Epoch 1: 100%|██████████████████████| Time: 0:00:23 (55.25 ms/it) accuracy/train: 0.955 loss/train: 0.142   Val Epoch 1: 40%|█████████▊ | ETA: 0:00:00 ( 5.44 ms/it) accuracy/val: 0.966 loss/val: 0.096        Val Epoch 1: 85%|████████████████████▍ | ETA: 0:00:00 ( 5.16 ms/it) accuracy/val: 0.968 loss/val: 0.101        Val Epoch 1: 100%|████████████████████████| Time: 0:00:00 ( 5.12 ms/it) accuracy/val: 0.968 loss/val: 0.102      Train Epoch 2: 1%|▎ | ETA: 0:00:10 (24.95 ms/it) accuracy/train: 0.969 loss/train: 0.0847     Train Epoch 2: 2%|▌ | ETA: 0:00:10 (24.89 ms/it) accuracy/train: 0.961 loss/train: 0.109     Train Epoch 2: 4%|▊ | ETA: 0:00:09 (24.56 ms/it) accuracy/train: 0.984 loss/train: 0.0448     Train Epoch 2: 4%|█ | ETA: 0:00:12 (29.86 ms/it) accuracy/train: 0.961 loss/train: 0.0834     Train Epoch 2: 5%|█▎ | ETA: 0:00:11 (28.63 ms/it) accuracy/train: 0.984 loss/train: 0.0387     Train Epoch 2: 7%|█▌ | ETA: 0:00:10 (27.88 ms/it) accuracy/train: 0.977 loss/train: 0.13     Train Epoch 2: 8%|█▊ | ETA: 0:00:10 (27.34 ms/it) accuracy/train: 0.953 loss/train: 0.171     Train Epoch 2: 9%|██ | ETA: 0:00:10 (27.01 ms/it) accuracy/train: 0.945 loss/train: 0.169     Train Epoch 2: 10%|██▎ | ETA: 0:00:10 (26.75 ms/it) accuracy/train: 0.969 loss/train: 0.121     Train Epoch 2: 11%|██▌ | ETA: 0:00:09 (26.50 ms/it) accuracy/train: 0.984 loss/train: 0.0535     Train Epoch 2: 13%|██▊ | ETA: 0:00:09 (26.33 ms/it) accuracy/train: 0.922 loss/train: 0.154     Train Epoch 2: 14%|███ | ETA: 0:00:09 (26.29 ms/it) accuracy/train: 0.953 loss/train: 0.12     Train Epoch 2: 14%|███▏ | ETA: 0:00:09 (26.37 ms/it) accuracy/train: 0.977 loss/train: 0.0772     Train Epoch 2: 15%|███▍ | ETA: 0:00:09 (26.61 ms/it) accuracy/train: 0.969 loss/train: 0.112     Train Epoch 2: 16%|███▋ | ETA: 0:00:09 (26.75 ms/it) accuracy/train: 0.969 loss/train: 0.123     Train Epoch 2: 17%|███▊ | ETA: 0:00:09 (26.83 ms/it) accuracy/train: 0.969 loss/train: 0.114     Train Epoch 2: 18%|████ | ETA: 0:00:09 (27.22 ms/it) accuracy/train: 0.961 loss/train: 0.164     Train Epoch 2: 19%|████▎ | ETA: 0:00:09 (27.15 ms/it) accuracy/train: 0.969 loss/train: 0.091     Train Epoch 2: 20%|████▌ | ETA: 0:00:09 (26.99 ms/it) accuracy/train: 0.977 loss/train: 0.104     Train Epoch 2: 21%|████▊ | ETA: 0:00:08 (26.95 ms/it) accuracy/train: 0.945 loss/train: 0.169     Train Epoch 2: 23%|█████ | ETA: 0:00:08 (26.82 ms/it) accuracy/train: 0.969 loss/train: 0.0614     Train Epoch 2: 24%|█████▎ | ETA: 0:00:08 (26.72 ms/it) accuracy/train: 0.945 loss/train: 0.189     Train Epoch 2: 25%|█████▌ | ETA: 0:00:08 (26.60 ms/it) accuracy/train: 0.977 loss/train: 0.146     Train Epoch 2: 26%|█████▊ | ETA: 0:00:08 (26.49 ms/it) accuracy/train: 0.953 loss/train: 0.145     Train Epoch 2: 27%|██████ | ETA: 0:00:08 (26.47 ms/it) accuracy/train: 0.961 loss/train: 0.194     Train Epoch 2: 28%|██████▎ | ETA: 0:00:07 (26.39 ms/it) accuracy/train: 0.969 loss/train: 0.131     Train Epoch 2: 29%|██████▌ | ETA: 0:00:07 (26.34 ms/it) accuracy/train: 0.977 loss/train: 0.067     Train Epoch 2: 30%|██████▋ | ETA: 0:00:07 (26.32 ms/it) accuracy/train: 0.969 loss/train: 0.14     Train Epoch 2: 31%|██████▉ | ETA: 0:00:07 (26.30 ms/it) accuracy/train: 0.992 loss/train: 0.0384     Train Epoch 2: 32%|███████▏ | ETA: 0:00:07 (26.37 ms/it) accuracy/train: 0.961 loss/train: 0.23     Train Epoch 2: 33%|███████▎ | ETA: 0:00:07 (26.43 ms/it) accuracy/train: 0.969 loss/train: 0.128     Train Epoch 2: 34%|███████▌ | ETA: 0:00:07 (26.49 ms/it) accuracy/train: 0.953 loss/train: 0.161     Train Epoch 2: 35%|███████▊ | ETA: 0:00:07 (26.59 ms/it) accuracy/train: 0.953 loss/train: 0.121     Train Epoch 2: 36%|███████▉ | ETA: 0:00:07 (26.65 ms/it) accuracy/train: 0.938 loss/train: 0.197     Train Epoch 2: 37%|████████▏ | ETA: 0:00:07 (26.57 ms/it) accuracy/train: 0.961 loss/train: 0.104     Train Epoch 2: 38%|████████▌ | ETA: 0:00:06 (26.53 ms/it) accuracy/train: 0.945 loss/train: 0.168     Train Epoch 2: 39%|████████▋ | ETA: 0:00:06 (26.50 ms/it) accuracy/train: 0.961 loss/train: 0.0968     Train Epoch 2: 41%|████████▉ | ETA: 0:00:06 (26.44 ms/it) accuracy/train: 0.961 loss/train: 0.191     Train Epoch 2: 42%|█████████▏ | ETA: 0:00:06 (26.38 ms/it) accuracy/train: 0.945 loss/train: 0.175     Train Epoch 2: 43%|█████████▍ | ETA: 0:00:06 (26.33 ms/it) accuracy/train: 0.984 loss/train: 0.114     Train Epoch 2: 44%|█████████▊ | ETA: 0:00:06 (26.28 ms/it) accuracy/train: 0.953 loss/train: 0.164     Train Epoch 2: 45%|██████████ | ETA: 0:00:06 (26.24 ms/it) accuracy/train: 0.953 loss/train: 0.173     Train Epoch 2: 46%|██████████▏ | ETA: 0:00:05 (26.22 ms/it) accuracy/train: 0.984 loss/train: 0.041     Train Epoch 2: 47%|██████████▍ | ETA: 0:00:05 (26.18 ms/it) accuracy/train: 0.977 loss/train: 0.0629     Train Epoch 2: 49%|██████████▋ | ETA: 0:00:05 (26.14 ms/it) accuracy/train: 0.953 loss/train: 0.126     Train Epoch 2: 50%|███████████ | ETA: 0:00:05 (26.09 ms/it) accuracy/train: 0.961 loss/train: 0.146     Train Epoch 2: 51%|███████████▎ | ETA: 0:00:05 (26.05 ms/it) accuracy/train: 0.984 loss/train: 0.0637     Train Epoch 2: 52%|███████████▌ | ETA: 0:00:05 (26.02 ms/it) accuracy/train: 0.953 loss/train: 0.117     Train Epoch 2: 53%|███████████▊ | ETA: 0:00:05 (25.99 ms/it) accuracy/train: 0.969 loss/train: 0.0827     Train Epoch 2: 55%|████████████ | ETA: 0:00:04 (25.96 ms/it) accuracy/train: 0.992 loss/train: 0.0309     Train Epoch 2: 56%|████████████▎ | ETA: 0:00:04 (25.94 ms/it) accuracy/train: 0.977 loss/train: 0.0547     Train Epoch 2: 57%|████████████▌ | ETA: 0:00:04 (25.91 ms/it) accuracy/train: 0.969 loss/train: 0.183     Train Epoch 2: 58%|████████████▊ | ETA: 0:00:04 (25.88 ms/it) accuracy/train: 0.969 loss/train: 0.154     Train Epoch 2: 59%|█████████████ | ETA: 0:00:04 (25.88 ms/it) accuracy/train: 0.977 loss/train: 0.0852     Train Epoch 2: 60%|█████████████▎ | ETA: 0:00:04 (25.88 ms/it) accuracy/train: 0.977 loss/train: 0.104     Train Epoch 2: 61%|█████████████▌ | ETA: 0:00:04 (25.90 ms/it) accuracy/train: 0.945 loss/train: 0.185     Train Epoch 2: 62%|█████████████▋ | ETA: 0:00:04 (25.89 ms/it) accuracy/train: 0.969 loss/train: 0.0931     Train Epoch 2: 63%|█████████████▉ | ETA: 0:00:04 (25.87 ms/it) accuracy/train: 0.977 loss/train: 0.0911     Train Epoch 2: 64%|██████████████▏ | ETA: 0:00:03 (25.85 ms/it) accuracy/train: 0.945 loss/train: 0.106     Train Epoch 2: 66%|██████████████▌ | ETA: 0:00:03 (25.83 ms/it) accuracy/train: 0.953 loss/train: 0.198     Train Epoch 2: 67%|██████████████▋ | ETA: 0:00:03 (25.82 ms/it) accuracy/train: 0.984 loss/train: 0.0862     Train Epoch 2: 68%|██████████████▉ | ETA: 0:00:03 (25.83 ms/it) accuracy/train: 0.977 loss/train: 0.0652     Train Epoch 2: 68%|███████████████▏ | ETA: 0:00:03 (25.83 ms/it) accuracy/train: 0.961 loss/train: 0.102     Train Epoch 2: 70%|███████████████▍ | ETA: 0:00:03 (25.82 ms/it) accuracy/train: 0.961 loss/train: 0.184     Train Epoch 2: 71%|███████████████▋ | ETA: 0:00:03 (25.79 ms/it) accuracy/train: 0.961 loss/train: 0.137     Train Epoch 2: 72%|███████████████▉ | ETA: 0:00:03 (25.78 ms/it) accuracy/train: 0.945 loss/train: 0.14     Train Epoch 2: 73%|████████████████ | ETA: 0:00:02 (25.78 ms/it) accuracy/train: 0.953 loss/train: 0.114     Train Epoch 2: 74%|████████████████▎ | ETA: 0:00:02 (25.78 ms/it) accuracy/train: 0.977 loss/train: 0.0904     Train Epoch 2: 75%|████████████████▌ | ETA: 0:00:02 (25.80 ms/it) accuracy/train: 0.961 loss/train: 0.169     Train Epoch 2: 76%|████████████████▋ | ETA: 0:00:02 (25.80 ms/it) accuracy/train: 0.984 loss/train: 0.0394     Train Epoch 2: 77%|████████████████▉ | ETA: 0:00:02 (25.84 ms/it) accuracy/train: 0.969 loss/train: 0.168     Train Epoch 2: 78%|█████████████████▏ | ETA: 0:00:02 (25.87 ms/it) accuracy/train: 0.961 loss/train: 0.113     Train Epoch 2: 79%|█████████████████▎ | ETA: 0:00:02 (25.90 ms/it) accuracy/train: 0.945 loss/train: 0.186     Train Epoch 2: 80%|█████████████████▌ | ETA: 0:00:02 (25.92 ms/it) accuracy/train: 0.977 loss/train: 0.0824     Train Epoch 2: 81%|█████████████████▊ | ETA: 0:00:02 (25.95 ms/it) accuracy/train: 0.953 loss/train: 0.14     Train Epoch 2: 82%|█████████████████▉ | ETA: 0:00:02 (25.98 ms/it) accuracy/train: 0.977 loss/train: 0.0619     Train Epoch 2: 82%|██████████████████▏ | ETA: 0:00:01 (25.99 ms/it) accuracy/train: 0.969 loss/train: 0.177     Train Epoch 2: 83%|██████████████████▍ | ETA: 0:00:01 (26.02 ms/it) accuracy/train: 0.945 loss/train: 0.202     Train Epoch 2: 84%|██████████████████▌ | ETA: 0:00:01 (26.04 ms/it) accuracy/train: 0.969 loss/train: 0.0603     Train Epoch 2: 85%|██████████████████▊ | ETA: 0:00:01 (26.07 ms/it) accuracy/train: 0.945 loss/train: 0.261     Train Epoch 2: 86%|███████████████████ | ETA: 0:00:01 (26.10 ms/it) accuracy/train: 0.992 loss/train: 0.0382     Train Epoch 2: 87%|███████████████████▏ | ETA: 0:00:01 (26.12 ms/it) accuracy/train: 0.969 loss/train: 0.144     Train Epoch 2: 88%|███████████████████▍ | ETA: 0:00:01 (26.13 ms/it) accuracy/train: 0.977 loss/train: 0.065     Train Epoch 2: 89%|███████████████████▋ | ETA: 0:00:01 (26.17 ms/it) accuracy/train: 0.961 loss/train: 0.135     Train Epoch 2: 90%|███████████████████▊ | ETA: 0:00:01 (26.21 ms/it) accuracy/train: 0.969 loss/train: 0.147     Train Epoch 2: 91%|████████████████████ | ETA: 0:00:00 (26.27 ms/it) accuracy/train: 0.938 loss/train: 0.194     Train Epoch 2: 92%|████████████████████▎ | ETA: 0:00:00 (26.30 ms/it) accuracy/train: 0.969 loss/train: 0.086     Train Epoch 2: 93%|████████████████████▍ | ETA: 0:00:00 (26.35 ms/it) accuracy/train: 0.969 loss/train: 0.141     Train Epoch 2: 94%|████████████████████▋ | ETA: 0:00:00 (26.39 ms/it) accuracy/train: 0.914 loss/train: 0.186     Train Epoch 2: 95%|████████████████████▉ | ETA: 0:00:00 (26.40 ms/it) accuracy/train: 0.969 loss/train: 0.129     Train Epoch 2: 96%|█████████████████████ | ETA: 0:00:00 (26.42 ms/it) accuracy/train: 0.953 loss/train: 0.0854     Train Epoch 2: 97%|█████████████████████▎| ETA: 0:00:00 (26.42 ms/it) accuracy/train: 0.984 loss/train: 0.0383     Train Epoch 2: 98%|█████████████████████▌| ETA: 0:00:00 (26.41 ms/it) accuracy/train: 0.898 loss/train: 0.393     Train Epoch 2: 99%|█████████████████████▊| ETA: 0:00:00 (26.43 ms/it) accuracy/train: 0.992 loss/train: 0.0141     Train Epoch 2: 100%|██████████████████████| ETA: 0:00:00 (26.44 ms/it) accuracy/train: 0.992 loss/train: 0.0213     Train Epoch 2: 100%|██████████████████████| Time: 0:00:11 (26.44 ms/it) accuracy/train: 0.982 loss/train: 0.0517   Val Epoch 2: 36%|████████▋ | ETA: 0:00:00 ( 5.91 ms/it) accuracy/val: 0.965 loss/val: 0.127        Val Epoch 2: 74%|█████████████████▉ | ETA: 0:00:00 ( 5.85 ms/it) accuracy/val: 0.967 loss/val: 0.116        Val Epoch 2: 100%|████████████████████████| Time: 0:00:00 ( 5.80 ms/it) accuracy/val: 0.967 loss/val: 0.117      Train Epoch 3: 1%|▎ | ETA: 0:00:12 (28.83 ms/it) accuracy/train: 0.977 loss/train: 0.065     Train Epoch 3: 2%|▍ | ETA: 0:00:12 (29.40 ms/it) accuracy/train: 0.953 loss/train: 0.174     Train Epoch 3: 3%|▋ | ETA: 0:00:11 (29.20 ms/it) accuracy/train: 0.969 loss/train: 0.15     Train Epoch 3: 4%|▉ | ETA: 0:00:11 (29.13 ms/it) accuracy/train: 0.984 loss/train: 0.0742     Train Epoch 3: 5%|█ | ETA: 0:00:11 (29.05 ms/it) accuracy/train: 0.977 loss/train: 0.0558     Train Epoch 3: 6%|█▎ | ETA: 0:00:11 (28.99 ms/it) accuracy/train: 0.992 loss/train: 0.03     Train Epoch 3: 7%|█▌ | ETA: 0:00:11 (28.93 ms/it) accuracy/train: 0.984 loss/train: 0.0464     Train Epoch 3: 8%|█▋ | ETA: 0:00:11 (28.91 ms/it) accuracy/train: 0.984 loss/train: 0.086     Train Epoch 3: 9%|█▉ | ETA: 0:00:11 (28.89 ms/it) accuracy/train: 0.969 loss/train: 0.0554     Train Epoch 3: 9%|██▏ | ETA: 0:00:11 (28.85 ms/it) accuracy/train: 0.984 loss/train: 0.0536     Train Epoch 3: 10%|██▎ | ETA: 0:00:10 (29.06 ms/it) accuracy/train: 0.984 loss/train: 0.0657     Train Epoch 3: 11%|██▌ | ETA: 0:00:10 (29.29 ms/it) accuracy/train: 0.992 loss/train: 0.0211     Train Epoch 3: 12%|██▊ | ETA: 0:00:10 (29.40 ms/it) accuracy/train: 1.0 loss/train: 0.0129     Train Epoch 3: 13%|██▉ | ETA: 0:00:10 (29.33 ms/it) accuracy/train: 0.977 loss/train: 0.0523     Train Epoch 3: 14%|███▏ | ETA: 0:00:10 (29.16 ms/it) accuracy/train: 0.977 loss/train: 0.0673     Train Epoch 3: 15%|███▍ | ETA: 0:00:10 (29.14 ms/it) accuracy/train: 0.984 loss/train: 0.0253     Train Epoch 3: 16%|███▌ | ETA: 0:00:10 (29.22 ms/it) accuracy/train: 0.984 loss/train: 0.0533     Train Epoch 3: 17%|███▊ | ETA: 0:00:10 (29.23 ms/it) accuracy/train: 0.984 loss/train: 0.0355     Train Epoch 3: 18%|████ | ETA: 0:00:10 (29.24 ms/it) accuracy/train: 0.977 loss/train: 0.0783     Train Epoch 3: 19%|████▏ | ETA: 0:00:09 (29.23 ms/it) accuracy/train: 0.992 loss/train: 0.0406     Train Epoch 3: 20%|████▍ | ETA: 0:00:09 (29.26 ms/it) accuracy/train: 0.992 loss/train: 0.0167     Train Epoch 3: 21%|████▋ | ETA: 0:00:09 (29.27 ms/it) accuracy/train: 0.969 loss/train: 0.0831     Train Epoch 3: 22%|████▊ | ETA: 0:00:09 (29.27 ms/it) accuracy/train: 0.992 loss/train: 0.0456     Train Epoch 3: 23%|█████ | ETA: 0:00:09 (29.30 ms/it) accuracy/train: 0.984 loss/train: 0.0402     Train Epoch 3: 24%|█████▎ | ETA: 0:00:09 (29.31 ms/it) accuracy/train: 0.984 loss/train: 0.0386     Train Epoch 3: 25%|█████▍ | ETA: 0:00:09 (29.31 ms/it) accuracy/train: 0.977 loss/train: 0.0387     Train Epoch 3: 26%|█████▋ | ETA: 0:00:09 (29.32 ms/it) accuracy/train: 0.984 loss/train: 0.0276     Train Epoch 3: 27%|█████▉ | ETA: 0:00:09 (29.31 ms/it) accuracy/train: 0.977 loss/train: 0.113     Train Epoch 3: 27%|██████ | ETA: 0:00:08 (29.32 ms/it) accuracy/train: 1.0 loss/train: 0.0107     Train Epoch 3: 28%|██████▎ | ETA: 0:00:08 (29.37 ms/it) accuracy/train: 0.984 loss/train: 0.0373     Train Epoch 3: 29%|██████▌ | ETA: 0:00:08 (29.37 ms/it) accuracy/train: 0.984 loss/train: 0.0644     Train Epoch 3: 30%|██████▋ | ETA: 0:00:08 (29.38 ms/it) accuracy/train: 0.969 loss/train: 0.091     Train Epoch 3: 31%|██████▉ | ETA: 0:00:08 (29.38 ms/it) accuracy/train: 0.992 loss/train: 0.0354     Train Epoch 3: 32%|███████▏ | ETA: 0:00:08 (29.41 ms/it) accuracy/train: 0.992 loss/train: 0.0282     Train Epoch 3: 33%|███████▎ | ETA: 0:00:08 (29.41 ms/it) accuracy/train: 0.984 loss/train: 0.0437     Train Epoch 3: 34%|███████▌ | ETA: 0:00:08 (29.41 ms/it) accuracy/train: 0.992 loss/train: 0.0297     Train Epoch 3: 35%|███████▊ | ETA: 0:00:08 (29.43 ms/it) accuracy/train: 0.992 loss/train: 0.0119     Train Epoch 3: 36%|███████▉ | ETA: 0:00:07 (29.39 ms/it) accuracy/train: 0.984 loss/train: 0.0171     Train Epoch 3: 37%|████████▏ | ETA: 0:00:07 (29.39 ms/it) accuracy/train: 0.992 loss/train: 0.0285     Train Epoch 3: 38%|████████▍ | ETA: 0:00:07 (29.39 ms/it) accuracy/train: 0.992 loss/train: 0.0221     Train Epoch 3: 39%|████████▌ | ETA: 0:00:07 (29.37 ms/it) accuracy/train: 0.984 loss/train: 0.105     Train Epoch 3: 40%|████████▊ | ETA: 0:00:07 (29.35 ms/it) accuracy/train: 0.984 loss/train: 0.0409     Train Epoch 3: 41%|█████████ | ETA: 0:00:07 (29.33 ms/it) accuracy/train: 1.0 loss/train: 0.0168     Train Epoch 3: 42%|█████████▏ | ETA: 0:00:07 (29.31 ms/it) accuracy/train: 0.992 loss/train: 0.0192     Train Epoch 3: 43%|█████████▍ | ETA: 0:00:07 (29.30 ms/it) accuracy/train: 0.984 loss/train: 0.056     Train Epoch 3: 44%|█████████▋ | ETA: 0:00:06 (29.29 ms/it) accuracy/train: 0.977 loss/train: 0.0953     Train Epoch 3: 45%|█████████▊ | ETA: 0:00:06 (29.30 ms/it) accuracy/train: 0.977 loss/train: 0.0648     Train Epoch 3: 45%|██████████ | ETA: 0:00:06 (29.27 ms/it) accuracy/train: 0.984 loss/train: 0.0346     Train Epoch 3: 46%|██████████▎ | ETA: 0:00:06 (29.26 ms/it) accuracy/train: 0.961 loss/train: 0.111     Train Epoch 3: 47%|██████████▍ | ETA: 0:00:06 (29.24 ms/it) accuracy/train: 0.977 loss/train: 0.0945     Train Epoch 3: 48%|██████████▋ | ETA: 0:00:06 (29.22 ms/it) accuracy/train: 0.961 loss/train: 0.114     Train Epoch 3: 49%|██████████▉ | ETA: 0:00:06 (29.21 ms/it) accuracy/train: 0.977 loss/train: 0.0758     Train Epoch 3: 50%|███████████ | ETA: 0:00:06 (29.20 ms/it) accuracy/train: 0.984 loss/train: 0.0332     Train Epoch 3: 51%|███████████▎ | ETA: 0:00:06 (29.17 ms/it) accuracy/train: 0.984 loss/train: 0.0319     Train Epoch 3: 52%|███████████▌ | ETA: 0:00:05 (29.16 ms/it) accuracy/train: 0.992 loss/train: 0.0241     Train Epoch 3: 53%|███████████▋ | ETA: 0:00:05 (29.14 ms/it) accuracy/train: 0.984 loss/train: 0.0221     Train Epoch 3: 54%|███████████▉ | ETA: 0:00:05 (29.12 ms/it) accuracy/train: 1.0 loss/train: 0.0212     Train Epoch 3: 55%|████████████▏ | ETA: 0:00:05 (29.11 ms/it) accuracy/train: 0.984 loss/train: 0.0409     Train Epoch 3: 56%|████████████▎ | ETA: 0:00:05 (29.09 ms/it) accuracy/train: 0.984 loss/train: 0.0795     Train Epoch 3: 57%|████████████▌ | ETA: 0:00:05 (29.07 ms/it) accuracy/train: 0.992 loss/train: 0.0303     Train Epoch 3: 58%|████████████▊ | ETA: 0:00:05 (29.05 ms/it) accuracy/train: 1.0 loss/train: 0.0154     Train Epoch 3: 59%|████████████▉ | ETA: 0:00:05 (29.05 ms/it) accuracy/train: 0.992 loss/train: 0.0245     Train Epoch 3: 60%|█████████████▏ | ETA: 0:00:04 (29.05 ms/it) accuracy/train: 0.961 loss/train: 0.0761     Train Epoch 3: 61%|█████████████▍ | ETA: 0:00:04 (29.04 ms/it) accuracy/train: 0.992 loss/train: 0.082     Train Epoch 3: 62%|█████████████▌ | ETA: 0:00:04 (29.03 ms/it) accuracy/train: 0.992 loss/train: 0.0302     Train Epoch 3: 63%|█████████████▊ | ETA: 0:00:04 (29.03 ms/it) accuracy/train: 0.984 loss/train: 0.0547     Train Epoch 3: 64%|██████████████ | ETA: 0:00:04 (29.02 ms/it) accuracy/train: 0.984 loss/train: 0.0222     Train Epoch 3: 64%|██████████████▏ | ETA: 0:00:04 (29.01 ms/it) accuracy/train: 1.0 loss/train: 0.0207     Train Epoch 3: 65%|██████████████▍ | ETA: 0:00:04 (29.00 ms/it) accuracy/train: 0.984 loss/train: 0.0436     Train Epoch 3: 66%|██████████████▋ | ETA: 0:00:04 (28.99 ms/it) accuracy/train: 0.984 loss/train: 0.0788     Train Epoch 3: 67%|██████████████▊ | ETA: 0:00:03 (28.98 ms/it) accuracy/train: 0.992 loss/train: 0.0468     Train Epoch 3: 68%|███████████████ | ETA: 0:00:03 (28.98 ms/it) accuracy/train: 0.992 loss/train: 0.03     Train Epoch 3: 69%|███████████████▎ | ETA: 0:00:03 (28.97 ms/it) accuracy/train: 0.984 loss/train: 0.0481     Train Epoch 3: 70%|███████████████▍ | ETA: 0:00:03 (28.97 ms/it) accuracy/train: 0.977 loss/train: 0.0567     Train Epoch 3: 71%|███████████████▋ | ETA: 0:00:03 (28.96 ms/it) accuracy/train: 0.969 loss/train: 0.0528     Train Epoch 3: 72%|███████████████▉ | ETA: 0:00:03 (28.95 ms/it) accuracy/train: 0.984 loss/train: 0.0583     Train Epoch 3: 73%|████████████████ | ETA: 0:00:03 (28.95 ms/it) accuracy/train: 0.984 loss/train: 0.0391     Train Epoch 3: 74%|████████████████▎ | ETA: 0:00:03 (28.94 ms/it) accuracy/train: 1.0 loss/train: 0.0133     Train Epoch 3: 75%|████████████████▌ | ETA: 0:00:03 (28.93 ms/it) accuracy/train: 0.992 loss/train: 0.0249     Train Epoch 3: 76%|████████████████▋ | ETA: 0:00:02 (28.93 ms/it) accuracy/train: 1.0 loss/train: 0.00206     Train Epoch 3: 77%|████████████████▉ | ETA: 0:00:02 (28.92 ms/it) accuracy/train: 0.984 loss/train: 0.0417     Train Epoch 3: 78%|█████████████████▏ | ETA: 0:00:02 (28.92 ms/it) accuracy/train: 0.969 loss/train: 0.137     Train Epoch 3: 79%|█████████████████▎ | ETA: 0:00:02 (28.91 ms/it) accuracy/train: 0.992 loss/train: 0.03     Train Epoch 3: 80%|█████████████████▌ | ETA: 0:00:02 (28.91 ms/it) accuracy/train: 0.984 loss/train: 0.0528     Train Epoch 3: 81%|█████████████████▊ | ETA: 0:00:02 (28.90 ms/it) accuracy/train: 0.992 loss/train: 0.0231     Train Epoch 3: 82%|█████████████████▉ | ETA: 0:00:02 (28.90 ms/it) accuracy/train: 1.0 loss/train: 0.0142     Train Epoch 3: 82%|██████████████████▏ | ETA: 0:00:02 (28.90 ms/it) accuracy/train: 0.969 loss/train: 0.0957     Train Epoch 3: 83%|██████████████████▍ | ETA: 0:00:02 (28.90 ms/it) accuracy/train: 0.984 loss/train: 0.0313     Train Epoch 3: 84%|██████████████████▌ | ETA: 0:00:01 (28.89 ms/it) accuracy/train: 0.992 loss/train: 0.0264     Train Epoch 3: 85%|██████████████████▊ | ETA: 0:00:01 (28.88 ms/it) accuracy/train: 0.992 loss/train: 0.0281     Train Epoch 3: 86%|███████████████████ | ETA: 0:00:01 (28.88 ms/it) accuracy/train: 1.0 loss/train: 0.0163     Train Epoch 3: 87%|███████████████████▏ | ETA: 0:00:01 (28.88 ms/it) accuracy/train: 1.0 loss/train: 0.0137     Train Epoch 3: 88%|███████████████████▍ | ETA: 0:00:01 (28.88 ms/it) accuracy/train: 1.0 loss/train: 0.0103     Train Epoch 3: 89%|███████████████████▋ | ETA: 0:00:01 (28.88 ms/it) accuracy/train: 0.992 loss/train: 0.0356     Train Epoch 3: 90%|███████████████████▊ | ETA: 0:00:01 (28.87 ms/it) accuracy/train: 0.992 loss/train: 0.0618     Train Epoch 3: 91%|████████████████████ | ETA: 0:00:01 (28.90 ms/it) accuracy/train: 0.992 loss/train: 0.015     Train Epoch 3: 92%|████████████████████▎ | ETA: 0:00:00 (28.89 ms/it) accuracy/train: 1.0 loss/train: 0.00366     Train Epoch 3: 93%|████████████████████▍ | ETA: 0:00:00 (28.88 ms/it) accuracy/train: 1.0 loss/train: 0.00324     Train Epoch 3: 94%|████████████████████▋ | ETA: 0:00:00 (28.88 ms/it) accuracy/train: 1.0 loss/train: 0.0173     Train Epoch 3: 95%|████████████████████▉ | ETA: 0:00:00 (28.88 ms/it) accuracy/train: 0.984 loss/train: 0.0368     Train Epoch 3: 96%|█████████████████████ | ETA: 0:00:00 (28.88 ms/it) accuracy/train: 1.0 loss/train: 0.00543     Train Epoch 3: 97%|█████████████████████▎| ETA: 0:00:00 (28.88 ms/it) accuracy/train: 0.984 loss/train: 0.0428     Train Epoch 3: 98%|█████████████████████▌| ETA: 0:00:00 (28.88 ms/it) accuracy/train: 0.992 loss/train: 0.0117     Train Epoch 3: 99%|█████████████████████▋| ETA: 0:00:00 (28.87 ms/it) accuracy/train: 1.0 loss/train: 0.00629     Train Epoch 3: 100%|█████████████████████▉| ETA: 0:00:00 (28.87 ms/it) accuracy/train: 0.984 loss/train: 0.047     Train Epoch 3: 100%|██████████████████████| Time: 0:00:12 (28.86 ms/it) accuracy/train: 0.982 loss/train: 0.0556 Val Epoch 3: 38%|█████████▎ | ETA: 0:00:00 ( 6.83 ms/it) accuracy/val: 0.977 loss/val: 0.0747     Val Epoch 3: 79%|██████████████████▉ | ETA: 0:00:00 ( 6.22 ms/it) accuracy/val: 0.979 loss/val: 0.0691     Val Epoch 3: 100%|████████████████████████| Time: 0:00:00 ( 6.08 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: 40%|█████████▊ | ETA: 0:00:00 ( 5.51 ms/it) accuracy/val: 0.982 loss/val: 0.0687     Val Epoch 3: 81%|███████████████████▍ | ETA: 0:00:00 ( 7.67 ms/it) accuracy/val: 0.979 loss/val: 0.0752     Val Epoch 3: 100%|████████████████████████| Time: 0:00:00 ( 7.25 ms/it) accuracy/val: 0.98 loss/val: 0.0722   Train Epoch 4: 1%|▎ | ETA: 0:00:11 (28.12 ms/it) accuracy/train: 0.992 loss/train: 0.0266     Train Epoch 4: 2%|▍ | ETA: 0:00:11 (28.05 ms/it) accuracy/train: 0.992 loss/train: 0.0293     Train Epoch 4: 3%|▋ | ETA: 0:00:11 (28.12 ms/it) accuracy/train: 0.984 loss/train: 0.0375     Train Epoch 4: 4%|▉ | ETA: 0:00:11 (28.17 ms/it) accuracy/train: 0.984 loss/train: 0.0247     Train Epoch 4: 5%|█ | ETA: 0:00:11 (28.26 ms/it) accuracy/train: 0.977 loss/train: 0.0433     Train Epoch 4: 6%|█▎ | ETA: 0:00:11 (28.29 ms/it) accuracy/train: 1.0 loss/train: 0.00314     Train Epoch 4: 7%|█▌ | ETA: 0:00:11 (28.25 ms/it) accuracy/train: 1.0 loss/train: 0.00958     Train Epoch 4: 8%|█▋ | ETA: 0:00:11 (28.22 ms/it) accuracy/train: 0.992 loss/train: 0.0352     Train Epoch 4: 9%|█▉ | ETA: 0:00:10 (28.21 ms/it) accuracy/train: 1.0 loss/train: 0.00747     Train Epoch 4: 9%|██▏ | ETA: 0:00:10 (28.18 ms/it) accuracy/train: 1.0 loss/train: 0.0116     Train Epoch 4: 10%|██▎ | ETA: 0:00:10 (28.18 ms/it) accuracy/train: 0.984 loss/train: 0.0294     Train Epoch 4: 11%|██▌ | ETA: 0:00:10 (28.20 ms/it) accuracy/train: 0.977 loss/train: 0.112     Train Epoch 4: 12%|██▊ | ETA: 0:00:10 (28.57 ms/it) accuracy/train: 0.977 loss/train: 0.0451     Train Epoch 4: 13%|██▉ | ETA: 0:00:10 (28.96 ms/it) accuracy/train: 0.984 loss/train: 0.0705     Train Epoch 4: 14%|███▏ | ETA: 0:00:10 (29.27 ms/it) accuracy/train: 0.992 loss/train: 0.0241     Train Epoch 4: 15%|███▎ | ETA: 0:00:10 (29.64 ms/it) accuracy/train: 0.992 loss/train: 0.0231     Train Epoch 4: 16%|███▌ | ETA: 0:00:10 (29.59 ms/it) accuracy/train: 0.992 loss/train: 0.0248     Train Epoch 4: 17%|███▋ | ETA: 0:00:10 (29.52 ms/it) accuracy/train: 0.984 loss/train: 0.06     Train Epoch 4: 18%|███▉ | ETA: 0:00:10 (29.48 ms/it) accuracy/train: 1.0 loss/train: 0.00596     Train Epoch 4: 18%|████▏ | ETA: 0:00:10 (29.38 ms/it) accuracy/train: 0.984 loss/train: 0.0643     Train Epoch 4: 19%|████▎ | ETA: 0:00:09 (29.33 ms/it) accuracy/train: 0.992 loss/train: 0.0367     Train Epoch 4: 20%|████▌ | ETA: 0:00:09 (29.31 ms/it) accuracy/train: 0.984 loss/train: 0.0435     Train Epoch 4: 21%|████▊ | ETA: 0:00:09 (29.26 ms/it) accuracy/train: 0.992 loss/train: 0.0139     Train Epoch 4: 22%|████▉ | ETA: 0:00:09 (29.20 ms/it) accuracy/train: 0.992 loss/train: 0.016     Train Epoch 4: 23%|█████▏ | ETA: 0:00:09 (29.21 ms/it) accuracy/train: 1.0 loss/train: 0.0146     Train Epoch 4: 24%|█████▍ | ETA: 0:00:09 (29.19 ms/it) accuracy/train: 0.992 loss/train: 0.0202     Train Epoch 4: 25%|█████▌ | ETA: 0:00:09 (29.15 ms/it) accuracy/train: 0.992 loss/train: 0.0369     Train Epoch 4: 26%|█████▊ | ETA: 0:00:09 (29.11 ms/it) accuracy/train: 0.984 loss/train: 0.0748     Train Epoch 4: 27%|██████ | ETA: 0:00:08 (29.07 ms/it) accuracy/train: 0.992 loss/train: 0.0208     Train Epoch 4: 28%|██████▏ | ETA: 0:00:08 (29.06 ms/it) accuracy/train: 0.984 loss/train: 0.0291     Train Epoch 4: 29%|██████▍ | ETA: 0:00:08 (29.09 ms/it) accuracy/train: 1.0 loss/train: 0.0113     Train Epoch 4: 30%|██████▋ | ETA: 0:00:08 (29.06 ms/it) accuracy/train: 0.992 loss/train: 0.045     Train Epoch 4: 31%|██████▊ | ETA: 0:00:08 (29.04 ms/it) accuracy/train: 0.984 loss/train: 0.023     Train Epoch 4: 32%|███████ | ETA: 0:00:08 (29.02 ms/it) accuracy/train: 1.0 loss/train: 0.00516     Train Epoch 4: 33%|███████▎ | ETA: 0:00:08 (29.00 ms/it) accuracy/train: 0.984 loss/train: 0.0564     Train Epoch 4: 34%|███████▍ | ETA: 0:00:08 (28.98 ms/it) accuracy/train: 1.0 loss/train: 0.0241     Train Epoch 4: 35%|███████▋ | ETA: 0:00:07 (28.98 ms/it) accuracy/train: 0.992 loss/train: 0.0148     Train Epoch 4: 36%|███████▉ | ETA: 0:00:07 (28.96 ms/it) accuracy/train: 0.992 loss/train: 0.0303     Train Epoch 4: 36%|████████ | ETA: 0:00:07 (28.95 ms/it) accuracy/train: 0.992 loss/train: 0.026     Train Epoch 4: 37%|████████▎ | ETA: 0:00:07 (28.92 ms/it) accuracy/train: 0.984 loss/train: 0.0269     Train Epoch 4: 38%|████████▌ | ETA: 0:00:07 (28.91 ms/it) accuracy/train: 1.0 loss/train: 0.00343     Train Epoch 4: 39%|████████▋ | ETA: 0:00:07 (28.90 ms/it) accuracy/train: 0.984 loss/train: 0.0624     Train Epoch 4: 40%|████████▉ | ETA: 0:00:07 (28.88 ms/it) accuracy/train: 0.984 loss/train: 0.0262     Train Epoch 4: 41%|█████████▏ | ETA: 0:00:07 (28.88 ms/it) accuracy/train: 1.0 loss/train: 0.00831     Train Epoch 4: 42%|█████████▎ | ETA: 0:00:07 (28.87 ms/it) accuracy/train: 0.977 loss/train: 0.13     Train Epoch 4: 43%|█████████▌ | ETA: 0:00:06 (28.90 ms/it) accuracy/train: 0.992 loss/train: 0.0174     Train Epoch 4: 44%|█████████▊ | ETA: 0:00:06 (28.89 ms/it) accuracy/train: 0.992 loss/train: 0.028     Train Epoch 4: 45%|█████████▉ | ETA: 0:00:06 (28.88 ms/it) accuracy/train: 0.984 loss/train: 0.0274     Train Epoch 4: 46%|██████████▏ | ETA: 0:00:06 (28.87 ms/it) accuracy/train: 0.977 loss/train: 0.122     Train Epoch 4: 47%|██████████▍ | ETA: 0:00:06 (28.86 ms/it) accuracy/train: 0.977 loss/train: 0.111     Train Epoch 4: 48%|██████████▌ | ETA: 0:00:06 (28.84 ms/it) accuracy/train: 0.992 loss/train: 0.0199     Train Epoch 4: 49%|██████████▊ | ETA: 0:00:06 (28.83 ms/it) accuracy/train: 0.992 loss/train: 0.0147     Train Epoch 4: 50%|███████████ | ETA: 0:00:06 (28.83 ms/it) accuracy/train: 0.984 loss/train: 0.0634     Train Epoch 4: 51%|███████████▏ | ETA: 0:00:05 (28.82 ms/it) accuracy/train: 1.0 loss/train: 0.011     Train Epoch 4: 52%|███████████▍ | ETA: 0:00:05 (28.81 ms/it) accuracy/train: 0.984 loss/train: 0.0439     Train Epoch 4: 53%|███████████▋ | ETA: 0:00:05 (28.81 ms/it) accuracy/train: 0.992 loss/train: 0.015     Train Epoch 4: 54%|███████████▊ | ETA: 0:00:05 (28.80 ms/it) accuracy/train: 0.969 loss/train: 0.0509     Train Epoch 4: 55%|████████████ | ETA: 0:00:05 (28.79 ms/it) accuracy/train: 0.984 loss/train: 0.0445     Train Epoch 4: 55%|████████████▎ | ETA: 0:00:05 (28.78 ms/it) accuracy/train: 1.0 loss/train: 0.00493     Train Epoch 4: 56%|████████████▍ | ETA: 0:00:05 (28.78 ms/it) accuracy/train: 1.0 loss/train: 0.00865     Train Epoch 4: 57%|████████████▋ | ETA: 0:00:05 (28.86 ms/it) accuracy/train: 1.0 loss/train: 0.00947     Train Epoch 4: 58%|████████████▉ | ETA: 0:00:05 (28.85 ms/it) accuracy/train: 1.0 loss/train: 0.0119     Train Epoch 4: 59%|█████████████ | ETA: 0:00:04 (28.85 ms/it) accuracy/train: 0.992 loss/train: 0.0296     Train Epoch 4: 60%|█████████████▎ | ETA: 0:00:04 (28.85 ms/it) accuracy/train: 1.0 loss/train: 0.0177     Train Epoch 4: 61%|█████████████▌ | ETA: 0:00:04 (28.85 ms/it) accuracy/train: 1.0 loss/train: 0.0122     Train Epoch 4: 62%|█████████████▋ | ETA: 0:00:04 (28.84 ms/it) accuracy/train: 0.992 loss/train: 0.011     Train Epoch 4: 63%|█████████████▉ | ETA: 0:00:04 (28.84 ms/it) accuracy/train: 0.992 loss/train: 0.0186     Train Epoch 4: 64%|██████████████▏ | ETA: 0:00:04 (28.84 ms/it) accuracy/train: 1.0 loss/train: 0.0155     Train Epoch 4: 65%|██████████████▎ | ETA: 0:00:04 (28.83 ms/it) accuracy/train: 1.0 loss/train: 0.0163     Train Epoch 4: 66%|██████████████▌ | ETA: 0:00:04 (28.84 ms/it) accuracy/train: 1.0 loss/train: 0.0168     Train Epoch 4: 67%|██████████████▊ | ETA: 0:00:04 (28.84 ms/it) accuracy/train: 0.977 loss/train: 0.0933     Train Epoch 4: 68%|██████████████▉ | ETA: 0:00:03 (28.84 ms/it) accuracy/train: 0.992 loss/train: 0.0177     Train Epoch 4: 69%|███████████████▏ | ETA: 0:00:03 (28.84 ms/it) accuracy/train: 1.0 loss/train: 0.0114     Train Epoch 4: 70%|███████████████▍ | ETA: 0:00:03 (28.90 ms/it) accuracy/train: 0.992 loss/train: 0.0439     Train Epoch 4: 71%|███████████████▌ | ETA: 0:00:03 (28.96 ms/it) accuracy/train: 1.0 loss/train: 0.0189     Train Epoch 4: 72%|███████████████▊ | ETA: 0:00:03 (29.02 ms/it) accuracy/train: 0.977 loss/train: 0.0363     Train Epoch 4: 73%|████████████████ | ETA: 0:00:03 (29.04 ms/it) accuracy/train: 1.0 loss/train: 0.00584     Train Epoch 4: 73%|████████████████▏ | ETA: 0:00:03 (29.04 ms/it) accuracy/train: 0.984 loss/train: 0.058     Train Epoch 4: 74%|████████████████▍ | ETA: 0:00:03 (29.03 ms/it) accuracy/train: 0.984 loss/train: 0.0408     Train Epoch 4: 75%|████████████████▋ | ETA: 0:00:03 (29.04 ms/it) accuracy/train: 0.984 loss/train: 0.0208     Train Epoch 4: 76%|████████████████▊ | ETA: 0:00:02 (29.03 ms/it) accuracy/train: 0.992 loss/train: 0.0214     Train Epoch 4: 77%|█████████████████ | ETA: 0:00:02 (29.02 ms/it) accuracy/train: 0.992 loss/train: 0.0322     Train Epoch 4: 78%|█████████████████▎ | ETA: 0:00:02 (29.01 ms/it) accuracy/train: 0.992 loss/train: 0.0237     Train Epoch 4: 79%|█████████████████▍ | ETA: 0:00:02 (29.00 ms/it) accuracy/train: 0.984 loss/train: 0.0573     Train Epoch 4: 80%|█████████████████▋ | ETA: 0:00:02 (28.99 ms/it) accuracy/train: 0.992 loss/train: 0.0206     Train Epoch 4: 81%|█████████████████▉ | ETA: 0:00:02 (28.98 ms/it) accuracy/train: 0.992 loss/train: 0.028     Train Epoch 4: 82%|██████████████████ | ETA: 0:00:02 (28.97 ms/it) accuracy/train: 0.984 loss/train: 0.0407     Train Epoch 4: 83%|██████████████████▎ | ETA: 0:00:02 (28.97 ms/it) accuracy/train: 0.984 loss/train: 0.0365     Train Epoch 4: 84%|██████████████████▌ | ETA: 0:00:01 (28.97 ms/it) accuracy/train: 0.984 loss/train: 0.056     Train Epoch 4: 85%|██████████████████▋ | ETA: 0:00:01 (28.96 ms/it) accuracy/train: 0.984 loss/train: 0.024     Train Epoch 4: 86%|██████████████████▉ | ETA: 0:00:01 (28.96 ms/it) accuracy/train: 0.992 loss/train: 0.0342     Train Epoch 4: 87%|███████████████████▏ | ETA: 0:00:01 (29.01 ms/it) accuracy/train: 0.977 loss/train: 0.0503     Train Epoch 4: 88%|███████████████████▎ | ETA: 0:00:01 (29.00 ms/it) accuracy/train: 0.992 loss/train: 0.0204     Train Epoch 4: 89%|███████████████████▌ | ETA: 0:00:01 (28.99 ms/it) accuracy/train: 1.0 loss/train: 0.00782     Train Epoch 4: 90%|███████████████████▊ | ETA: 0:00:01 (28.99 ms/it) accuracy/train: 1.0 loss/train: 0.0108     Train Epoch 4: 91%|███████████████████▉ | ETA: 0:00:01 (28.98 ms/it) accuracy/train: 1.0 loss/train: 0.0156     Train Epoch 4: 91%|████████████████████▏ | ETA: 0:00:01 (28.97 ms/it) accuracy/train: 0.984 loss/train: 0.0279     Train Epoch 4: 92%|████████████████████▍ | ETA: 0:00:00 (28.97 ms/it) accuracy/train: 1.0 loss/train: 0.0102     Train Epoch 4: 93%|████████████████████▌ | ETA: 0:00:00 (28.96 ms/it) accuracy/train: 0.992 loss/train: 0.0226     Train Epoch 4: 94%|████████████████████▊ | ETA: 0:00:00 (28.96 ms/it) accuracy/train: 1.0 loss/train: 0.0133     Train Epoch 4: 95%|█████████████████████ | ETA: 0:00:00 (28.95 ms/it) accuracy/train: 0.984 loss/train: 0.0443     Train Epoch 4: 96%|█████████████████████▏| ETA: 0:00:00 (28.94 ms/it) accuracy/train: 1.0 loss/train: 0.00694     Train Epoch 4: 97%|█████████████████████▍| ETA: 0:00:00 (28.93 ms/it) accuracy/train: 0.992 loss/train: 0.014     Train Epoch 4: 98%|█████████████████████▋| ETA: 0:00:00 (28.96 ms/it) accuracy/train: 0.984 loss/train: 0.0647     Train Epoch 4: 99%|█████████████████████▊| ETA: 0:00:00 (29.00 ms/it) accuracy/train: 1.0 loss/train: 0.00225     Train Epoch 4: 100%|██████████████████████| Time: 0:00:12 (29.04 ms/it) accuracy/train: 0.991 loss/train: 0.0187   Val Epoch 4: 21%|█████▏ | ETA: 0:00:00 (10.28 ms/it) accuracy/val: 0.975 loss/val: 0.0911        Val Epoch 4: 57%|█████████████▊ | ETA: 0:00:00 ( 7.61 ms/it) accuracy/val: 0.978 loss/val: 0.0808        Val Epoch 4: 94%|██████████████████████▌ | ETA: 0:00:00 ( 7.17 ms/it) accuracy/val: 0.98 loss/val: 0.0714        Val Epoch 4: 100%|████████████████████████| Time: 0:00:00 ( 7.17 ms/it) accuracy/val: 0.98 loss/val: 0.0706      Train Epoch 5: 1%|▎ | ETA: 0:00:11 (27.54 ms/it) accuracy/train: 0.992 loss/train: 0.0437     Train Epoch 5: 2%|▍ | ETA: 0:00:11 (27.66 ms/it) accuracy/train: 1.0 loss/train: 0.0169     Train Epoch 5: 3%|▋ | ETA: 0:00:11 (27.84 ms/it) accuracy/train: 1.0 loss/train: 0.00808     Train Epoch 5: 4%|▉ | ETA: 0:00:11 (27.85 ms/it) accuracy/train: 0.992 loss/train: 0.00872     Train Epoch 5: 5%|█ | ETA: 0:00:11 (27.85 ms/it) accuracy/train: 0.992 loss/train: 0.0294     Train Epoch 5: 6%|█▎ | ETA: 0:00:11 (27.90 ms/it) accuracy/train: 1.0 loss/train: 0.011     Train Epoch 5: 7%|█▌ | ETA: 0:00:11 (27.96 ms/it) accuracy/train: 1.0 loss/train: 0.00176     Train Epoch 5: 8%|█▋ | ETA: 0:00:10 (27.98 ms/it) accuracy/train: 1.0 loss/train: 0.0124     Train Epoch 5: 9%|█▉ | ETA: 0:00:10 (28.04 ms/it) accuracy/train: 1.0 loss/train: 0.00404     Train Epoch 5: 9%|██▏ | ETA: 0:00:10 (28.07 ms/it) accuracy/train: 0.992 loss/train: 0.0327     Train Epoch 5: 10%|██▎ | ETA: 0:00:10 (28.08 ms/it) accuracy/train: 0.984 loss/train: 0.0586     Train Epoch 5: 11%|██▌ | ETA: 0:00:10 (28.02 ms/it) accuracy/train: 0.992 loss/train: 0.015     Train Epoch 5: 12%|██▊ | ETA: 0:00:10 (28.00 ms/it) accuracy/train: 0.984 loss/train: 0.055     Train Epoch 5: 13%|██▉ | ETA: 0:00:10 (28.12 ms/it) accuracy/train: 1.0 loss/train: 0.00585     Train Epoch 5: 14%|███▏ | ETA: 0:00:10 (28.12 ms/it) accuracy/train: 1.0 loss/train: 0.0118     Train Epoch 5: 15%|███▍ | ETA: 0:00:10 (28.14 ms/it) accuracy/train: 1.0 loss/train: 0.005     Train Epoch 5: 16%|███▌ | ETA: 0:00:09 (28.15 ms/it) accuracy/train: 0.992 loss/train: 0.0215     Train Epoch 5: 17%|███▊ | ETA: 0:00:09 (28.16 ms/it) accuracy/train: 0.992 loss/train: 0.0314     Train Epoch 5: 18%|████ | ETA: 0:00:09 (28.15 ms/it) accuracy/train: 1.0 loss/train: 0.00413     Train Epoch 5: 19%|████▏ | ETA: 0:00:09 (28.15 ms/it) accuracy/train: 0.984 loss/train: 0.035     Train Epoch 5: 20%|████▍ | ETA: 0:00:09 (28.14 ms/it) accuracy/train: 1.0 loss/train: 0.00355     Train Epoch 5: 21%|████▋ | ETA: 0:00:09 (28.13 ms/it) accuracy/train: 1.0 loss/train: 0.0109     Train Epoch 5: 22%|████▊ | ETA: 0:00:09 (28.15 ms/it) accuracy/train: 1.0 loss/train: 0.0104     Train Epoch 5: 23%|█████ | ETA: 0:00:09 (28.13 ms/it) accuracy/train: 1.0 loss/train: 0.00798     Train Epoch 5: 24%|█████▎ | ETA: 0:00:09 (28.13 ms/it) accuracy/train: 1.0 loss/train: 0.00879     Train Epoch 5: 25%|█████▍ | ETA: 0:00:08 (28.13 ms/it) accuracy/train: 1.0 loss/train: 0.0157     Train Epoch 5: 26%|█████▋ | ETA: 0:00:08 (28.15 ms/it) accuracy/train: 1.0 loss/train: 0.00404     Train Epoch 5: 27%|█████▉ | ETA: 0:00:08 (28.12 ms/it) accuracy/train: 1.0 loss/train: 0.0117     Train Epoch 5: 27%|██████ | ETA: 0:00:08 (28.13 ms/it) accuracy/train: 1.0 loss/train: 0.00875     Train Epoch 5: 28%|██████▎ | ETA: 0:00:08 (28.17 ms/it) accuracy/train: 1.0 loss/train: 0.00932     Train Epoch 5: 29%|██████▌ | ETA: 0:00:08 (28.18 ms/it) accuracy/train: 1.0 loss/train: 0.00752     Train Epoch 5: 30%|██████▋ | ETA: 0:00:08 (28.18 ms/it) accuracy/train: 0.992 loss/train: 0.0476     Train Epoch 5: 31%|██████▉ | ETA: 0:00:08 (28.18 ms/it) accuracy/train: 0.992 loss/train: 0.0185     Train Epoch 5: 32%|███████▏ | ETA: 0:00:08 (28.18 ms/it) accuracy/train: 0.992 loss/train: 0.0122     Train Epoch 5: 33%|███████▎ | ETA: 0:00:07 (28.19 ms/it) accuracy/train: 1.0 loss/train: 0.00305     Train Epoch 5: 34%|███████▌ | ETA: 0:00:07 (28.20 ms/it) accuracy/train: 1.0 loss/train: 0.00942     Train Epoch 5: 35%|███████▊ | ETA: 0:00:07 (28.21 ms/it) accuracy/train: 1.0 loss/train: 0.00971     Train Epoch 5: 36%|███████▉ | ETA: 0:00:07 (28.21 ms/it) accuracy/train: 0.984 loss/train: 0.0425     Train Epoch 5: 37%|████████▏ | ETA: 0:00:07 (28.21 ms/it) accuracy/train: 1.0 loss/train: 0.0077     Train Epoch 5: 38%|████████▍ | ETA: 0:00:07 (28.20 ms/it) accuracy/train: 0.992 loss/train: 0.0259     Train Epoch 5: 39%|████████▌ | ETA: 0:00:07 (28.21 ms/it) accuracy/train: 0.992 loss/train: 0.0106     Train Epoch 5: 40%|████████▊ | ETA: 0:00:07 (28.21 ms/it) accuracy/train: 1.0 loss/train: 0.00532     Train Epoch 5: 41%|█████████ | ETA: 0:00:07 (28.20 ms/it) accuracy/train: 1.0 loss/train: 0.0124     Train Epoch 5: 42%|█████████▏ | ETA: 0:00:06 (28.20 ms/it) accuracy/train: 1.0 loss/train: 0.00901     Train Epoch 5: 43%|█████████▍ | ETA: 0:00:06 (28.32 ms/it) accuracy/train: 0.992 loss/train: 0.0456     Train Epoch 5: 44%|█████████▋ | ETA: 0:00:06 (28.32 ms/it) accuracy/train: 1.0 loss/train: 0.00726     Train Epoch 5: 45%|█████████▊ | ETA: 0:00:06 (28.31 ms/it) accuracy/train: 0.984 loss/train: 0.0477     Train Epoch 5: 45%|██████████ | ETA: 0:00:06 (28.32 ms/it) accuracy/train: 1.0 loss/train: 0.0139     Train Epoch 5: 46%|██████████▎ | ETA: 0:00:06 (28.33 ms/it) accuracy/train: 0.992 loss/train: 0.021     Train Epoch 5: 47%|██████████▍ | ETA: 0:00:06 (28.33 ms/it) accuracy/train: 1.0 loss/train: 0.0136     Train Epoch 5: 48%|██████████▋ | ETA: 0:00:06 (28.32 ms/it) accuracy/train: 0.984 loss/train: 0.0355     Train Epoch 5: 49%|██████████▉ | ETA: 0:00:06 (28.33 ms/it) accuracy/train: 1.0 loss/train: 0.00568     Train Epoch 5: 50%|███████████ | ETA: 0:00:05 (28.34 ms/it) accuracy/train: 0.992 loss/train: 0.0187     Train Epoch 5: 51%|███████████▎ | ETA: 0:00:05 (28.34 ms/it) accuracy/train: 0.984 loss/train: 0.0277     Train Epoch 5: 52%|███████████▌ | ETA: 0:00:05 (28.34 ms/it) accuracy/train: 0.992 loss/train: 0.0151     Train Epoch 5: 53%|███████████▋ | ETA: 0:00:05 (28.33 ms/it) accuracy/train: 1.0 loss/train: 0.0181     Train Epoch 5: 54%|███████████▉ | ETA: 0:00:05 (28.39 ms/it) accuracy/train: 1.0 loss/train: 0.0105     Train Epoch 5: 55%|████████████▏ | ETA: 0:00:05 (28.47 ms/it) accuracy/train: 1.0 loss/train: 0.00911     Train Epoch 5: 56%|████████████▎ | ETA: 0:00:05 (28.56 ms/it) accuracy/train: 0.984 loss/train: 0.0203     Train Epoch 5: 57%|████████████▌ | ETA: 0:00:05 (28.64 ms/it) accuracy/train: 1.0 loss/train: 0.017     Train Epoch 5: 58%|████████████▋ | ETA: 0:00:05 (28.64 ms/it) accuracy/train: 0.984 loss/train: 0.0652     Train Epoch 5: 59%|████████████▉ | ETA: 0:00:05 (28.63 ms/it) accuracy/train: 1.0 loss/train: 0.0132     Train Epoch 5: 59%|█████████████▏ | ETA: 0:00:04 (28.63 ms/it) accuracy/train: 0.992 loss/train: 0.0239     Train Epoch 5: 60%|█████████████▎ | ETA: 0:00:04 (28.63 ms/it) accuracy/train: 1.0 loss/train: 0.00837     Train Epoch 5: 61%|█████████████▌ | ETA: 0:00:04 (28.62 ms/it) accuracy/train: 1.0 loss/train: 0.00889     Train Epoch 5: 62%|█████████████▊ | ETA: 0:00:04 (28.63 ms/it) accuracy/train: 1.0 loss/train: 0.0051     Train Epoch 5: 63%|█████████████▉ | ETA: 0:00:04 (28.62 ms/it) accuracy/train: 0.992 loss/train: 0.0149     Train Epoch 5: 64%|██████████████▏ | ETA: 0:00:04 (28.63 ms/it) accuracy/train: 0.984 loss/train: 0.0534     Train Epoch 5: 65%|██████████████▍ | ETA: 0:00:04 (28.61 ms/it) accuracy/train: 0.992 loss/train: 0.0243     Train Epoch 5: 66%|██████████████▌ | ETA: 0:00:04 (28.60 ms/it) accuracy/train: 0.992 loss/train: 0.0202     Train Epoch 5: 67%|██████████████▊ | ETA: 0:00:03 (28.59 ms/it) accuracy/train: 1.0 loss/train: 0.0128     Train Epoch 5: 68%|███████████████ | ETA: 0:00:03 (28.58 ms/it) accuracy/train: 0.992 loss/train: 0.016     Train Epoch 5: 69%|███████████████▏ | ETA: 0:00:03 (28.57 ms/it) accuracy/train: 0.992 loss/train: 0.019     Train Epoch 5: 70%|███████████████▍ | ETA: 0:00:03 (28.59 ms/it) accuracy/train: 1.0 loss/train: 0.0142     Train Epoch 5: 71%|███████████████▋ | ETA: 0:00:03 (28.58 ms/it) accuracy/train: 0.992 loss/train: 0.0218     Train Epoch 5: 72%|███████████████▊ | ETA: 0:00:03 (28.64 ms/it) accuracy/train: 0.992 loss/train: 0.0452     Train Epoch 5: 73%|████████████████ | ETA: 0:00:03 (28.63 ms/it) accuracy/train: 0.992 loss/train: 0.0215     Train Epoch 5: 74%|████████████████▎ | ETA: 0:00:03 (28.63 ms/it) accuracy/train: 0.992 loss/train: 0.0599     Train Epoch 5: 75%|████████████████▍ | ETA: 0:00:03 (28.62 ms/it) accuracy/train: 1.0 loss/train: 0.012     Train Epoch 5: 76%|████████████████▋ | ETA: 0:00:02 (28.62 ms/it) accuracy/train: 1.0 loss/train: 0.0131     Train Epoch 5: 77%|████████████████▉ | ETA: 0:00:02 (28.61 ms/it) accuracy/train: 0.984 loss/train: 0.0426     Train Epoch 5: 77%|█████████████████ | ETA: 0:00:02 (28.60 ms/it) accuracy/train: 0.992 loss/train: 0.00776     Train Epoch 5: 78%|█████████████████▎ | ETA: 0:00:02 (28.63 ms/it) accuracy/train: 1.0 loss/train: 0.0116     Train Epoch 5: 79%|█████████████████▌ | ETA: 0:00:02 (28.63 ms/it) accuracy/train: 0.992 loss/train: 0.02     Train Epoch 5: 80%|█████████████████▋ | ETA: 0:00:02 (28.62 ms/it) accuracy/train: 0.984 loss/train: 0.0355     Train Epoch 5: 81%|█████████████████▉ | ETA: 0:00:02 (28.61 ms/it) accuracy/train: 0.992 loss/train: 0.0266     Train Epoch 5: 82%|██████████████████▏ | ETA: 0:00:02 (28.61 ms/it) accuracy/train: 1.0 loss/train: 0.00531     Train Epoch 5: 83%|██████████████████▎ | ETA: 0:00:02 (28.66 ms/it) accuracy/train: 1.0 loss/train: 0.0193     Train Epoch 5: 84%|██████████████████▌ | ETA: 0:00:01 (28.71 ms/it) accuracy/train: 1.0 loss/train: 0.011     Train Epoch 5: 85%|██████████████████▊ | ETA: 0:00:01 (28.76 ms/it) accuracy/train: 0.992 loss/train: 0.0165     Train Epoch 5: 86%|██████████████████▉ | ETA: 0:00:01 (28.79 ms/it) accuracy/train: 0.992 loss/train: 0.0258     Train Epoch 5: 87%|███████████████████▏ | ETA: 0:00:01 (28.79 ms/it) accuracy/train: 1.0 loss/train: 0.006     Train Epoch 5: 88%|███████████████████▍ | ETA: 0:00:01 (28.78 ms/it) accuracy/train: 0.992 loss/train: 0.0235     Train Epoch 5: 89%|███████████████████▌ | ETA: 0:00:01 (28.78 ms/it) accuracy/train: 0.984 loss/train: 0.0449     Train Epoch 5: 90%|███████████████████▊ | ETA: 0:00:01 (28.78 ms/it) accuracy/train: 1.0 loss/train: 0.00884     Train Epoch 5: 91%|████████████████████ | ETA: 0:00:01 (28.77 ms/it) accuracy/train: 1.0 loss/train: 0.00825     Train Epoch 5: 92%|████████████████████▏ | ETA: 0:00:01 (28.76 ms/it) accuracy/train: 0.992 loss/train: 0.0114     Train Epoch 5: 93%|████████████████████▍ | ETA: 0:00:00 (28.75 ms/it) accuracy/train: 0.984 loss/train: 0.0281     Train Epoch 5: 94%|████████████████████▋ | ETA: 0:00:00 (28.73 ms/it) accuracy/train: 1.0 loss/train: 0.00737     Train Epoch 5: 95%|████████████████████▊ | ETA: 0:00:00 (28.72 ms/it) accuracy/train: 1.0 loss/train: 0.013     Train Epoch 5: 95%|█████████████████████ | ETA: 0:00:00 (28.72 ms/it) accuracy/train: 0.992 loss/train: 0.0258     Train Epoch 5: 96%|█████████████████████▎| ETA: 0:00:00 (28.71 ms/it) accuracy/train: 1.0 loss/train: 0.00543     Train Epoch 5: 97%|█████████████████████▍| ETA: 0:00:00 (28.71 ms/it) accuracy/train: 0.984 loss/train: 0.0358     Train Epoch 5: 98%|█████████████████████▋| ETA: 0:00:00 (28.71 ms/it) accuracy/train: 0.992 loss/train: 0.0503     Train Epoch 5: 99%|█████████████████████▉| ETA: 0:00:00 (28.71 ms/it) accuracy/train: 0.984 loss/train: 0.0565     Train Epoch 5: 100%|██████████████████████| Time: 0:00:12 (28.74 ms/it) accuracy/train: 0.991 loss/train: 0.0188 Val Epoch 5: 40%|█████████▊ | ETA: 0:00:00 ( 5.55 ms/it) accuracy/val: 0.981 loss/val: 0.0736     Val Epoch 5: 81%|███████████████████▍ | ETA: 0:00:00 ( 5.54 ms/it) accuracy/val: 0.982 loss/val: 0.0716     Val Epoch 5: 100%|████████████████████████| Time: 0:00:00 ( 5.55 ms/it) accuracy/val: 0.982 loss/val: 0.0687 Testing: 4%|█▏ | ETA: 0:00:02 (33.68 ms/it) accuracy/test: 0.984 loss/test: 0.0563     Testing: 28%|███████▊ | ETA: 0:00:00 ( 9.28 ms/it) accuracy/test: 0.974 loss/test: 0.0858     Testing: 52%|██████████████▌ | ETA: 0:00:00 ( 7.57 ms/it) accuracy/test: 0.977 loss/test: 0.0866     Testing: 76%|█████████████████████▎ | ETA: 0:00:00 ( 6.91 ms/it) accuracy/test: 0.981 loss/test: 0.0733     Testing: 100%|████████████████████████████| Time: 0:00:00 ( 6.50 ms/it) accuracy/test: 0.982 loss/test: 0.0671 Test Summary: | Time Examples | None 3m14.9s Testing Tsunami tests passed Testing completed after 1060.7s PkgEval succeeded after 1423.8s