Package evaluation of JudiLing on Julia 1.11.4 (a71dd056e0*) started at 2025-04-08T18:37:22.920 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Set-up completed after 9.01s ################################################################################ # Installation # Installing JudiLing... Resolving package versions... Updating `~/.julia/environments/v1.11/Project.toml` [b43a184b] + JudiLing v0.12.0 Updating `~/.julia/environments/v1.11/Manifest.toml` [66dad0bd] + AliasTables v1.1.3 ⌅ [15f4f7f2] + AutoHashEquals v0.2.0 [fbb218c0] + BSON v0.3.9 ⌅ [6e4b80f9] + BenchmarkTools v1.5.0 [d1d4a3ce] + BitFlags v0.1.9 [336ed68f] + CSV v0.10.15 [944b1d66] + CodecZlib v0.7.8 [34da2185] + Compat v4.16.0 [f0e56b4a] + ConcurrentUtilities v2.5.0 [a8cc5b0e] + Crayons v4.1.1 [9a962f9c] + DataAPI v1.16.0 [124859b0] + DataDeps v0.7.13 [a93c6f00] + DataFrames v1.7.0 [2e981812] + DataLoaders v0.1.3 [864edb3b] + DataStructures v0.18.22 [e2d170a0] + DataValueInterfaces v1.0.0 [8bb1440f] + DelimitedFiles v1.9.1 [b4f34e82] + Distances v0.10.12 [31c24e10] + Distributions v0.25.118 ⌅ [ffbed154] + DocStringExtensions v0.8.6 [c5bfea45] + Embeddings v0.4.6 [460bff9d] + ExceptionUnwrapping v0.1.11 [48062228] + FilePathsBase v0.9.24 [1a297f60] + FillArrays v1.13.0 [92fee26a] + GZip v0.6.2 ⌅ [91feb7a0] + GoogleDrive v0.1.3 [cd3eb016] + HTTP v1.10.15 [34004b35] + HypergeometricFunctions v0.3.28 [842dd82b] + InlineStrings v1.4.3 [41ab1584] + InvertedIndices v1.3.1 [92d709cd] + IrrationalConstants v0.2.4 [82899510] + IteratorInterfaceExtensions v1.0.0 [692b3bcd] + JLLWrappers v1.7.0 [682c06a0] + JSON v0.21.4 [b43a184b] + JudiLing v0.12.0 [b964fa9f] + LaTeXStrings v1.4.0 ⌅ [7f8f8fb0] + LearnBase v0.3.0 [2ab3a3ac] + LogExpFunctions v0.3.29 [e6f89c97] + LoggingExtras v1.1.0 ⌃ [9920b226] + MLDataPattern v0.5.4 [66a33bbf] + MLLabelUtils v0.5.7 [dbb5928d] + MappedArrays v0.4.2 [739be429] + MbedTLS v1.1.9 [e1d29d7a] + Missings v1.2.0 [4d8831e6] + OpenSSL v1.4.3 [bac558e1] + OrderedCollections v1.8.0 [90014a1f] + PDMats v0.11.33 [d96e819e] + Parameters v0.12.3 [69de0a69] + Parsers v2.8.1 [2dfb63ee] + PooledArrays v1.4.3 ⌅ [aea7be01] + PrecompileTools v1.2.1 [21216c6a] + Preferences v1.4.3 [08abe8d2] + PrettyTables v2.4.0 [92933f4c] + ProgressMeter v1.10.4 [43287f4e] + PtrArrays v1.3.0 [1fd47b50] + QuadGK v2.11.2 [3cdcf5f2] + RecipesBase v1.3.4 [189a3867] + Reexport v1.2.2 [ae029012] + Requires v1.3.1 [79098fc4] + Rmath v0.8.0 [6c6a2e73] + Scratch v1.2.1 [91c51154] + SentinelArrays v1.4.8 [777ac1f9] + SimpleBufferStream v1.2.0 [a2af1166] + SortingAlgorithms v1.2.1 [276daf66] + SpecialFunctions v2.5.0 [10745b16] + Statistics v1.11.1 [82ae8749] + StatsAPI v1.7.0 ⌅ [2913bbd2] + StatsBase v0.33.21 [4c63d2b9] + StatsFuns v1.4.0 [892a3eda] + StringManipulation v0.4.1 [3783bdb8] + TableTraits v1.0.1 [bd369af6] + Tables v1.12.0 ⌅ [b189fb0b] + ThreadPools v1.2.1 [3bb67fe8] + TranscodingStreams v0.11.3 [5c2747f8] + URIs v1.5.2 [3a884ed6] + UnPack v1.0.2 [ea10d353] + WeakRefStrings v1.4.2 [76eceee3] + WorkerUtilities v1.6.1 [458c3c95] + OpenSSL_jll v3.0.16+0 [efe28fd5] + OpenSpecFun_jll v0.5.6+0 [f50d1b31] + Rmath_jll v0.5.1+0 [0dad84c5] + ArgTools v1.1.2 [56f22d72] + Artifacts v1.11.0 [2a0f44e3] + Base64 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 [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 [de0858da] + Printf v1.11.0 [9abbd945] + Profile 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 [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 ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m` Installation completed after 4.69s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... Precompiling package dependencies... Precompilation completed after 641.45s ################################################################################ # Testing # Testing JudiLing ┌ 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:1920 Status `/tmp/jl_RF6RBD/Project.toml` [336ed68f] CSV v0.10.15 [8f4d0f93] Conda v1.10.2 [a93c6f00] DataFrames v1.7.0 [2e981812] DataLoaders v0.1.3 [587475ba] Flux v0.16.3 [b43a184b] JudiLing v0.12.0 [438e738f] PyCall v1.96.4 [1bc83da4] SafeTestsets v0.1.0 [37e2e46d] LinearAlgebra v1.11.0 [2f01184e] SparseArrays v1.11.0 [8dfed614] Test v1.11.0 Status `/tmp/jl_RF6RBD/Manifest.toml` [621f4979] AbstractFFTs v1.5.0 [7d9f7c33] Accessors v0.1.42 [79e6a3ab] Adapt v4.3.0 [66dad0bd] AliasTables v1.1.3 [dce04be8] ArgCheck v2.5.0 [a9b6321e] Atomix v1.1.1 ⌅ [15f4f7f2] AutoHashEquals v0.2.0 [fbb218c0] BSON v0.3.9 [198e06fe] BangBang v0.4.4 [9718e550] Baselet v0.1.1 ⌅ [6e4b80f9] BenchmarkTools 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FilePathsBase v0.9.24 [1a297f60] FillArrays v1.13.0 [587475ba] Flux v0.16.3 [f6369f11] ForwardDiff v1.0.1 [d9f16b24] Functors v0.5.2 [46192b85] GPUArraysCore v0.2.0 [92fee26a] GZip v0.6.2 ⌅ [91feb7a0] GoogleDrive v0.1.3 [cd3eb016] HTTP v1.10.15 [076d061b] HashArrayMappedTries v0.2.0 [34004b35] HypergeometricFunctions v0.3.28 [7869d1d1] IRTools v0.4.14 [22cec73e] InitialValues v0.3.1 [842dd82b] InlineStrings v1.4.3 [3587e190] InverseFunctions v0.1.17 [41ab1584] InvertedIndices v1.3.1 [92d709cd] IrrationalConstants v0.2.4 [82899510] IteratorInterfaceExtensions v1.0.0 [692b3bcd] JLLWrappers v1.7.0 [682c06a0] JSON v0.21.4 [b43a184b] JudiLing v0.12.0 [b14d175d] JuliaVariables v0.2.4 [63c18a36] KernelAbstractions v0.9.34 [b964fa9f] LaTeXStrings v1.4.0 ⌅ [7f8f8fb0] LearnBase v0.3.0 [2ab3a3ac] LogExpFunctions v0.3.29 [e6f89c97] LoggingExtras v1.1.0 [c2834f40] MLCore v1.0.0 [7e8f7934] MLDataDevices v1.9.1 ⌃ [9920b226] MLDataPattern v0.5.4 [66a33bbf] MLLabelUtils v0.5.7 [d8e11817] MLStyle 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OpenSpecFun_jll v0.5.6+0 [f50d1b31] Rmath_jll v0.5.1+0 [0dad84c5] ArgTools v1.1.2 [56f22d72] Artifacts v1.11.0 [2a0f44e3] Base64 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 [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 [de0858da] Printf v1.11.0 [9abbd945] Profile 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 [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 ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. Testing Running tests... Precompiling JudiLing... 2266.8 ms ✓ LearnBase 11591.7 ms ✓ Distributions 3457.7 ms ✓ MLLabelUtils 4826.6 ms ✓ Distributions → DistributionsTestExt 3054.8 ms ✓ MLDataPattern 4824.5 ms ✓ DataLoaders 33284.6 ms ✓ JudiLing 7 dependencies successfully precompiled in 70 seconds. 117 already precompiled. [ Info: Running `conda config --set pip_interop_enabled true --file /home/pkgeval/.julia/conda/3/x86_64/condarc-julia.yml` in root environment [ Info: Running `pip install -U pyndl` in root environment Collecting pyndl Downloading pyndl-1.2.3.tar.gz (38 kB) Installing build dependencies: started Installing build dependencies: finished with status 'done' Getting requirements to build wheel: started Getting requirements to build wheel: finished with status 'done' Preparing metadata (pyproject.toml): started Preparing metadata (pyproject.toml): finished with status 'done' Collecting Cython>=3.0.0 (from pyndl) Using cached Cython-3.0.12-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (3.3 kB) Collecting netCDF4>=1.6.0 (from pyndl) Downloading netCDF4-1.7.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (1.8 kB) Requirement already satisfied: numpy>=1.24.1 in /home/pkgeval/.julia/conda/3/x86_64/lib/python3.12/site-packages (from pyndl) (2.2.4) 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filename=pyndl-1.2.3-cp312-cp312-manylinux_2_36_x86_64.whl size=393267 sha256=5198aea3f18305306b8209f51724e610d218fa59ae2f07af3e701fc3182a52cf Stored in directory: /home/pkgeval/.cache/pip/wheels/37/dc/89/1716df4b978655ffe855f598b4364aa2b139465240707ecdd2 Successfully built pyndl Installing collected packages: pytz, tzdata, toml, six, scipy, Cython, cftime, python-dateutil, netCDF4, pandas, xarray, pyndl Successfully installed Cython-3.0.12 cftime-1.6.4.post1 netCDF4-1.7.2 pandas-2.2.3 pyndl-1.2.3 python-dateutil-2.9.0.post0 pytz-2025.2 scipy-1.15.2 six-1.17.0 toml-0.10.2 tzdata-2025.2 xarray-2025.3.1 /home/pkgeval/.julia/conda/3/x86_64/lib/python3.12/multiprocessing/popen_fork.py:66: DeprecationWarning: This process (pid=450) is multi-threaded, use of fork() may lead to deadlocks in the child. self.pid = os.fork() /home/pkgeval/.julia/conda/3/x86_64/lib/python3.12/multiprocessing/popen_fork.py:66: DeprecationWarning: This process (pid=450) is multi-threaded, use of fork() may lead to deadlocks in the child. self.pid = os.fork() /home/pkgeval/.julia/conda/3/x86_64/lib/python3.12/multiprocessing/popen_fork.py:66: DeprecationWarning: This process (pid=450) is multi-threaded, use of fork() may lead to deadlocks in the child. self.pid = os.fork() /home/pkgeval/.julia/conda/3/x86_64/lib/python3.12/multiprocessing/popen_fork.py:66: DeprecationWarning: This process (pid=450) is multi-threaded, use of fork() may lead to deadlocks in the child. self.pid = os.fork() /home/pkgeval/.julia/conda/3/x86_64/lib/python3.12/multiprocessing/popen_fork.py:66: DeprecationWarning: This process (pid=450) is multi-threaded, use of fork() may lead to deadlocks in the child. self.pid = os.fork() /home/pkgeval/.julia/conda/3/x86_64/lib/python3.12/multiprocessing/popen_fork.py:66: DeprecationWarning: This process (pid=450) is multi-threaded, use of fork() may lead to deadlocks in the child. self.pid = os.fork() /home/pkgeval/.julia/conda/3/x86_64/lib/python3.12/multiprocessing/popen_fork.py:66: DeprecationWarning: This process (pid=450) is multi-threaded, use of fork() may lead to deadlocks in the child. self.pid = os.fork() /home/pkgeval/.julia/conda/3/x86_64/lib/python3.12/multiprocessing/popen_fork.py:66: DeprecationWarning: This process (pid=450) is multi-threaded, use of fork() may lead to deadlocks in the child. self.pid = os.fork() /home/pkgeval/.julia/conda/3/x86_64/lib/python3.12/multiprocessing/popen_fork.py:66: DeprecationWarning: This process (pid=450) is multi-threaded, use of fork() may lead to deadlocks in the child. self.pid = os.fork() /home/pkgeval/.julia/conda/3/x86_64/lib/python3.12/multiprocessing/popen_fork.py:66: DeprecationWarning: This process (pid=450) is multi-threaded, use of fork() may lead to deadlocks in the child. self.pid = os.fork() /home/pkgeval/.julia/conda/3/x86_64/lib/python3.12/multiprocessing/popen_fork.py:66: DeprecationWarning: This process (pid=450) is multi-threaded, use of fork() may lead to deadlocks in the child. self.pid = os.fork() /home/pkgeval/.julia/conda/3/x86_64/lib/python3.12/multiprocessing/popen_fork.py:66: DeprecationWarning: This process (pid=450) is multi-threaded, use of fork() may lead to deadlocks in the child. self.pid = os.fork() /home/pkgeval/.julia/conda/3/x86_64/lib/python3.12/multiprocessing/popen_fork.py:66: DeprecationWarning: This process (pid=450) is multi-threaded, use of fork() may lead to deadlocks in the child. self.pid = os.fork() /home/pkgeval/.julia/conda/3/x86_64/lib/python3.12/multiprocessing/popen_fork.py:66: DeprecationWarning: This process (pid=450) is multi-threaded, use of fork() may lead to deadlocks in the child. self.pid = os.fork() /home/pkgeval/.julia/conda/3/x86_64/lib/python3.12/multiprocessing/popen_fork.py:66: DeprecationWarning: This process (pid=450) is multi-threaded, use of fork() may lead to deadlocks in the child. self.pid = os.fork() /home/pkgeval/.julia/conda/3/x86_64/lib/python3.12/multiprocessing/popen_fork.py:66: DeprecationWarning: This process (pid=450) is multi-threaded, use of fork() may lead to deadlocks in the child. self.pid = os.fork() making adjacency matrix... Test Summary: | Pass Total Time pyndl tests | 13 13 4m22.4s Test Summary: | Pass Total Time input tests | 27 27 18.8s Test Summary: | Pass Total Time cholesky tests | 10 10 17.2s Test Summary: | Pass Total Time frequency tests | 3 3 9.6s 6×7 DataFrame Row │ Data #vo voc oco coo oo# oca │ String15 Int64 Int64 Int64 Int64 Int64 Int64 ─────┼──────────────────────────────────────────────────── 1 │ vocoo 1 1 1 1 1 0 2 │ vocaas 1 1 0 0 0 1 3 │ vocat 1 1 0 0 0 1 4 │ vocaamus 1 1 0 0 0 1 5 │ vocaatis 1 1 0 0 0 1 6 │ vocant 1 1 0 0 0 1 6×7 DataFrame Row │ Data S1 S2 S3 S4 S5 S6 │ String15 Float64 Float64 Float64 Float64 Float64 Float64 ─────┼──────────────────────────────────────────────────────────────────────── 1 │ vocoo 15.2465 -20.8614 15.5945 -9.9225 0.401251 4.89594 2 │ vocaas 20.8716 -21.4455 19.1184 -8.76425 -5.05156 10.7338 3 │ vocat 22.2822 -29.0044 9.79204 -5.54939 -1.81678 4.56271 4 │ vocaamus 16.8365 -17.9527 8.56939 -8.22765 9.02656 -0.0304184 5 │ vocaatis 19.8971 -19.9789 10.4833 -6.74136 1.33763 3.6688 6 │ vocant 19.9549 -26.7096 7.54822 -4.31259 5.78011 -3.90409 6×7 DataFrame Row │ Data #vo voc oco coo oo# oca │ String Float64 Float64 Float64 Float64 Float64 Float64 ─────┼─────────────────────────────────────────────────────────────────────────────────────── 1 │ S1 -0.00141756 -0.00141756 -0.0236324 -0.0132617 -0.0108323 0.0222148 2 │ S2 -0.00273367 -0.00273367 0.00451568 0.000452137 0.00437199 -0.00724935 3 │ S3 -0.00153557 -0.00153557 -0.0191963 -0.00511315 -0.00986019 0.0176608 4 │ S4 -0.0013362 -0.0013362 0.00183685 0.00286875 0.00883363 -0.00317305 5 │ S5 -0.00294256 -0.00294256 -0.0174473 -0.00299004 0.0797393 0.0145048 6 │ S6 0.00229429 0.00229429 0.0123282 -0.00299352 0.0275983 -0.0100339 6×7 DataFrame Row │ Data #vo voc oco coo oo# oca │ String15 Float64 Float64 Float64 Float64 Float64 Float64 ─────┼───────────────────────────────────────────────────────────────────────────── 1 │ vocoo 0.988964 0.988964 0.862193 0.866111 0.869592 0.126771 2 │ vocaas 1.00243 1.00243 -0.0444658 -0.0342458 -0.0382966 1.0469 3 │ vocat 1.00112 1.00112 0.00442709 -0.0183283 0.0107253 0.996696 4 │ vocaamus 0.998892 0.998892 0.00706063 0.016144 0.0557306 0.991831 5 │ vocaatis 1.01126 1.01126 0.0808647 0.0567661 -0.0188942 0.930395 6 │ vocant 0.995767 0.995767 0.00154401 -0.0189361 -0.0530909 0.994223 6×7 DataFrame Row │ Data S1 S2 S3 S4 S5 S6 │ String Float64 Float64 Float64 Float64 Float64 Float64 ─────┼──────────────────────────────────────────────────────────────────────── 1 │ #vo 5.72851 -7.76108 3.5086 -2.02855 -0.436928 1.58815 2 │ voc 5.72851 -7.76108 3.5086 -2.02855 -0.436928 1.58815 3 │ oco 1.42971 -3.03506 1.62397 -1.31801 -0.234167 0.88979 4 │ coo 0.820988 0.0194814 1.38457 -0.821628 -0.0331436 0.074187 5 │ oo# 1.52235 -2.32403 5.54109 -3.70934 1.54308 0.75419 6 │ oca 4.29879 -4.72601 1.88464 -0.710533 -0.202761 0.698355 6×7 DataFrame Row │ Data S1 S2 S3 S4 S5 S6 │ String15 Float64 Float64 Float64 Float64 Float64 Float64 ─────┼────────────────────────────────────────────────────────────────────── 1 │ vocoo 15.2301 -20.8618 15.5668 -9.90607 0.401914 4.89446 2 │ vocaas 21.1856 -21.497 18.7371 -8.27066 -6.04744 10.5006 3 │ vocat 22.2289 -28.9307 9.7931 -5.54923 -1.80306 4.54148 4 │ vocaamus 16.8829 -18.2816 7.88645 -8.25267 8.51863 -1.95841 5 │ vocaatis 19.7887 -19.1196 10.4815 -6.73917 1.86 3.51972 6 │ vocant 20.6622 -26.5988 5.84837 -5.02826 4.97536 -3.94898 6×7 DataFrame Row │ Data #vo voc oco coo oo# oca │ String Int64 Int64 Int64 Int64 Int64 Int64 ─────┼────────────────────────────────────────────────── 1 │ #vo 0 1 0 0 0 0 2 │ voc 0 0 1 0 0 1 3 │ oco 0 0 0 1 0 0 4 │ coo 0 0 0 0 1 0 5 │ oo# 0 0 0 0 0 0 6 │ oca 0 0 0 0 0 0 6×7 DataFrame Row │ Data vocoo vocaas vocat vocaamus vocaatis vocant │ String15 Float64 Float64 Float64 Float64 Float64 Float64 ─────┼────────────────────────────────────────────────────────────────────── 1 │ vocoo 0.942076 0.383369 0.41285 0.371298 0.404084 0.365981 2 │ vocaas 0.322011 0.951319 0.523442 0.633156 0.57495 0.487577 3 │ vocat 0.381475 0.565054 0.964657 0.484204 0.463868 0.538595 4 │ vocaamus 0.292859 0.525276 0.449704 0.975416 0.494743 0.377306 5 │ vocaatis 0.325014 0.574024 0.476682 0.480271 0.947895 0.405188 6 │ vocant 0.330853 0.509106 0.5232 0.442983 0.400525 0.987605 6×7 DataFrame Row │ Data vocoo vocaas vocat vocaamus vocaatis vocant │ String15 Int64 Int64 Int64 Int64 Int64 Int64 ─────┼──────────────────────────────────────────────────────────── 1 │ vocoo 1 0 0 0 0 0 2 │ vocaas 0 1 0 0 0 0 3 │ vocat 0 0 1 0 0 0 4 │ vocaamus 0 0 0 1 0 0 5 │ vocaatis 0 0 0 0 1 0 6 │ vocant 0 0 0 0 0 1 Test Summary: | Total Time display tests | 0 49.8s ┌ Warning: eval_SC: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:263 ┌ Warning: eval_SC: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:263 ┌ Warning: eval_SC: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:263 ┌ Warning: eval_SC: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:556 ┌ Warning: eval_SC: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:556 ┌ Warning: eval_SC: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:556 ┌ Warning: eval_SC: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:556 ┌ Warning: eval_SC: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:556 ┌ Warning: eval_SC: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:556 ┌ Warning: eval_SC: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:263 ┌ Warning: eval_SC: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:263 ┌ Warning: eval_SC: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:263 ┌ Warning: eval_SC: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:263 ┌ Warning: eval_SC: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:263 ┌ Warning: eval_SC: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:263 ┌ Warning: eval_SC: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:263 ┌ Warning: eval_SC: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:263 ┌ Warning: eval_SC: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:263 ┌ Warning: eval_SC: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:263 ┌ Warning: eval_SC: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:263 ┌ Warning: eval_SC: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:263 ┌ Warning: eval_SC_loose: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:726 ┌ Warning: eval_SC_loose: You set k=1. Note that if there are duplicate vectors in the S/C matrix, it is not guaranteed that eval_SC_loose with k=1 gives the same result as eval_SC. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:728 ┌ Warning: eval_SC_loose: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:726 ┌ Warning: eval_SC_loose: You set k=1. Note that if there are duplicate vectors in the S/C matrix, it is not guaranteed that eval_SC_loose with k=1 gives the same result as eval_SC. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:728 ┌ Warning: eval_SC_loose: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:726 ┌ Warning: eval_SC_loose: You set k=1. Note that if there are duplicate vectors in the S/C matrix, it is not guaranteed that eval_SC_loose with k=1 gives the same result as eval_SC. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:728 ┌ Warning: eval_SC_loose: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:726 ┌ Warning: eval_SC_loose: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:726 ┌ Warning: eval_SC_loose: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:726 ┌ Warning: eval_SC_loose: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:726 ┌ Warning: eval_SC_loose: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:726 ┌ Warning: eval_SC_loose: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:726 ┌ Warning: eval_SC_loose: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:726 ┌ Warning: eval_SC_loose: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:726 ┌ Warning: eval_SC_loose: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:726 ┌ Warning: eval_SC_loose: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:726 ┌ Warning: eval_SC_loose: You set k=1. Note that if there are duplicate vectors in the S/C matrix, it is not guaranteed that eval_SC_loose with k=1 gives the same result as eval_SC. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:728 ┌ Warning: eval_SC_loose: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:726 ┌ Warning: eval_SC_loose: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:726 ┌ Warning: eval_SC_loose: You set k=1. Note that if there are duplicate vectors in the S/C matrix, it is not guaranteed that eval_SC_loose with k=1 gives the same result as eval_SC. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:728 ┌ Warning: eval_SC_loose: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:726 ┌ Warning: eval_SC_loose: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:726 ┌ Warning: eval_SC_loose: You set k=1. Note that if there are duplicate vectors in the S/C matrix, it is not guaranteed that eval_SC_loose with k=1 gives the same result as eval_SC. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:728 ┌ Warning: eval_SC_loose: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:726 ┌ Warning: eval_SC_loose: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:726 ┌ Warning: eval_SC_loose: You set k=1. Note that if there are duplicate vectors in the S/C matrix, it is not guaranteed that eval_SC_loose with k=1 gives the same result as eval_SC. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:728 ┌ Warning: eval_SC_loose: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:726 ┌ Warning: eval_SC_loose: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:726 ┌ Warning: eval_SC_loose: You set k=1. Note that if there are duplicate vectors in the S/C matrix, it is not guaranteed that eval_SC_loose with k=1 gives the same result as eval_SC. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:728 ┌ Warning: eval_SC_loose: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:726 ┌ Warning: eval_SC_loose: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:726 ┌ Warning: eval_SC_loose: You set k=1. Note that if there are duplicate vectors in the S/C matrix, it is not guaranteed that eval_SC_loose with k=1 gives the same result as eval_SC. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:728 ┌ Warning: eval_SC_loose: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:726 ┌ Warning: accuracy_comprehension: This dataset contains homophones/homographs. Note that some of the results on the correctness of comprehended base/inflections may be misleading. See documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:88 ┌ Warning: eval_SC: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:263 ┌ Warning: eval_SC: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:263 Test Summary: | Pass Total Time eval tests | 150 150 1m03.7s Test Summary: | Total Time find_path tests | 0 6.1s Test Summary: | Pass Total Time make_adjacency_matrix tests | 7 7 0.9s true Test Summary: | Pass Total Time make_cue_matrix tests | 21 21 7.4s true Test Summary: | Pass Total Time make_semantic_matrix tests | 72 72 8.0s Test Summary: | Total Time make_yt_matrix tests | 0 0.1s Test Summary: | Pass Total Time output_matrix tests | 10 10 17.1s Test Summary: | Total Time preprocess tests | 0 1.1s ┌ Warning: test_combo: test_combo is deprecated. While it will remain in the package it is no longer actively maintained. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/test_combo.jl:132 ┌ Warning: eval_SC: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:263 ┌ Warning: eval_SC: The C or S matrix contains duplicate vectors (usually because of homophones/homographs). Supplying the dataset and target column is recommended for a realistic evaluation. See the documentation of this function for more information. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/eval.jl:263 ┌ Warning: test_combo: test_combo is deprecated. While it will remain in the package it is no longer actively maintained. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/test_combo.jl:132 ┌ Warning: test_combo: test_combo is deprecated. While it will remain in the package it is no longer actively maintained. └ @ JudiLing ~/.julia/packages/JudiLing/TmEZc/src/test_combo.jl:132 Test Summary: | Total Time test_combo tests | 0 21.4s Test Summary: | Pass Total Time wh tests | 5 5 5.3s Precompiling StatsFunsChainRulesCoreExt... 4324.7 ms ✓ StatsFuns → StatsFunsChainRulesCoreExt 1 dependency successfully precompiled in 5 seconds. 30 already precompiled. Precompiling DistributionsChainRulesCoreExt... 5057.3 ms ✓ Distributions → DistributionsChainRulesCoreExt 1 dependency successfully precompiled in 7 seconds. 56 already precompiled. Precompiling StatsFunsInverseFunctionsExt... 1767.7 ms ✓ StatsFuns → StatsFunsInverseFunctionsExt 1 dependency successfully precompiled in 2 seconds. 27 already precompiled. Precompiling ZygoteDistancesExt... 6918.6 ms ✓ Zygote → ZygoteDistancesExt 1 dependency successfully precompiled in 8 seconds. 74 already precompiled. Setting up model... ┌ 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) └ @ MLDataDevices.Internal ~/.julia/packages/MLDataDevices/lMKtX/src/internal.jl:96 model = Chain(Dense(33 => 1000, relu), Dense(1000 => 200)) Setting up data structures... Setting up optimizer... Training... Progress: 2%|▉ | ETA: 1:38:11 Training loss: 136.2665052702724   Progress: 98%|████████████████████████████████████████▏| ETA: 0:00:03 Training loss: 4.68441067248936   Progress: 100%|█████████████████████████████████████████| Time: 0:02:03 Training loss: 4.482754494799172 Setting up model... model = Chain(Dense(200 => 1000, relu), Dense(1000 => 33)) Setting up data structures... Setting up optimizer... Training... ┌ Warning: Layer with Float32 parameters got Float64 input. │ The input will be converted, but any earlier layers may be very slow. │ layer = Dense(200 => 1000, relu) # 201_000 parameters │ summary(x) = "200×3 Matrix{Float64}" └ @ Flux ~/.julia/packages/Flux/3711C/src/layers/stateless.jl:60 Progress: 2%|▉ | ETA: 0:24:14 Training loss: 7.5811825   Progress: 5%|██ | ETA: 0:09:26 Training loss: 10.196899   Progress: 9%|███▊ | ETA: 0:05:02 Training loss: 7.591371   Progress: 13%|█████▍ | ETA: 0:03:21 Training loss: 0.851448   Progress: 18%|███████▍ | ETA: 0:02:17 Training loss: 2.6577294   Progress: 23%|█████████▍ | ETA: 0:01:41 Training loss: 0.49689454   Progress: 28%|███████████▌ | ETA: 0:01:18 Training loss: 0.5026373   Progress: 33%|█████████████▌ | ETA: 0:01:02 Training loss: 0.43341818   Progress: 38%|███████████████▋ | ETA: 0:00:50 Training loss: 0.048797842   Progress: 43%|█████████████████▋ | ETA: 0:00:41 Training loss: 0.18405329   Progress: 48%|███████████████████▋ | ETA: 0:00:33 Training loss: 0.031148521   Progress: 53%|█████████████████████▊ | ETA: 0:00:27 Training loss: 0.03508255   Progress: 58%|███████████████████████▊ | ETA: 0:00:23 Training loss: 0.036323648   Progress: 62%|█████████████████████████▍ | ETA: 0:00:19 Training loss: 0.0053788233   Progress: 67%|███████████████████████████▌ | ETA: 0:00:15 Training loss: 0.015207298   Progress: 72%|█████████████████████████████▌ | ETA: 0:00:12 Training loss: 0.0049975896   Progress: 77%|███████████████████████████████▋ | ETA: 0:00:09 Training loss: 0.0014226976   Progress: 82%|█████████████████████████████████▋ | ETA: 0:00:07 Training loss: 0.003080451   Progress: 87%|███████████████████████████████████▋ | ETA: 0:00:05 Training loss: 0.001448846   Progress: 92%|█████████████████████████████████████▊ | ETA: 0:00:03 Training loss: 0.00025809451   Progress: 97%|███████████████████████████████████████▊ | ETA: 0:00:01 Training loss: 0.00047823263   Progress: 100%|█████████████████████████████████████████| Time: 0:00:32 Training loss: 0.00021136894 Setting up model... model = Chain(Dense(33 => 1000, relu), Dense(1000 => 200)) Setting up data structures... Setting up optimizer... Training... Progress: 2%|▉ | ETA: 0:02:31 Training loss: 136.2665052702724 Validation loss: 115.66047845446742 Validation accuracy: 0.0       Progress: 5%|██ | ETA: 0:01:01 Training loss: 133.48384257801834 Validation loss: 113.76005568180845 Validation accuracy: 0.0       Progress: 8%|███▎ | ETA: 0:00:38 Training loss: 130.2244481256631 Validation loss: 111.67203162481937 Validation accuracy: 0.0       Progress: 13%|█████▍ | ETA: 0:00:23 Training loss: 122.9682799017793 Validation loss: 107.5220194869742 Validation accuracy: 0.0       Progress: 17%|███████ | ETA: 0:00:17 Training loss: 115.13426013633551 Validation loss: 103.4637427489424 Validation accuracy: 0.0       Progress: 22%|█████████ | ETA: 0:00:13 Training loss: 102.61912377014526 Validation loss: 97.46255928877483 Validation accuracy: 0.0       Progress: 27%|███████████▏ | ETA: 0:00:10 Training loss: 87.46331542853757 Validation loss: 90.75123900751416 Validation accuracy: 0.0       Progress: 32%|█████████████▏ | ETA: 0:00:08 Training loss: 70.73453400930393 Validation loss: 84.01582378985287 Validation accuracy: 0.0       Progress: 37%|███████████████▏ | ETA: 0:00:07 Training loss: 54.091555746467975 Validation loss: 78.16941348420849 Validation accuracy: 0.0       Progress: 42%|█████████████████▎ | ETA: 0:00:06 Training loss: 39.36171454198027 Validation loss: 73.96308061351593 Validation accuracy: 0.0       Progress: 47%|███████████████████▎ | ETA: 0:00:05 Training loss: 27.863266583412248 Validation loss: 71.6447872597769 Validation accuracy: 0.0       Progress: 52%|█████████████████████▍ | ETA: 0:00:04 Training loss: 20.003568121576137 Validation loss: 70.66919529345951 Validation accuracy: 0.0       Progress: 57%|███████████████████████▍ | ETA: 0:00:03 Training loss: 15.186527870881148 Validation loss: 70.11676998784887 Validation accuracy: 0.0       Progress: 61%|█████████████████████████ | ETA: 0:00:03 Training loss: 12.730876500954787 Validation loss: 69.56881700211572 Validation accuracy: 0.0       Progress: 65%|██████████████████████████▋ | ETA: 0:00:03 Training loss: 10.947032814573426 Validation loss: 68.89339316680457 Validation accuracy: 0.0       Progress: 69%|████████████████████████████▎ | ETA: 0:00:02 Training loss: 9.543733087898282 Validation loss: 68.17415693485606 Validation accuracy: 0.0       Progress: 73%|█████████████████████████████▉ | ETA: 0:00:02 Training loss: 8.405613313483148 Validation loss: 67.54859583015724 Validation accuracy: 0.0       Progress: 78%|████████████████████████████████ | ETA: 0:00:01 Training loss: 7.297831295560228 Validation loss: 67.03303518037602 Validation accuracy: 0.0       Progress: 82%|█████████████████████████████████▋ | ETA: 0:00:01 Training loss: 6.617918412801383 Validation loss: 66.81097392553144 Validation accuracy: 0.0       Progress: 86%|███████████████████████████████████▎ | ETA: 0:00:01 Training loss: 6.056915641953044 Validation loss: 66.68839289121047 Validation accuracy: 0.0       Progress: 90%|████████████████████████████████████▉ | ETA: 0:00:01 Training loss: 5.56314002808457 Validation loss: 66.64598189049576 Validation accuracy: 0.0       Progress: 98%|████████████████████████████████████████▏| ETA: 0:00:00 Training loss: 4.68441067248936 Validation loss: 66.66165430107954 Validation accuracy: 0.0       Progress: 100%|█████████████████████████████████████████| Time: 0:00:05 Training loss: 4.482754494799172 Validation loss: 66.65935106439977 Validation accuracy: 0.0 Setting up model... model = Chain(Dense(200 => 1000, relu), Dense(1000 => 33)) Setting up data structures... Setting up optimizer... Training... Progress: 2%|▉ | ETA: 0:03:11 Training loss: 7.5811825 Validation loss: 23.362167 Validation accuracy: 0.0       Progress: 12%|████▉ | ETA: 0:00:30 Training loss: 2.565964 Validation loss: 12.899682 Validation accuracy: 0.1667       Progress: 17%|███████ | ETA: 0:00:21 Training loss: 2.9981523 Validation loss: 11.494986 Validation accuracy: 0.1667       Progress: 21%|████████▋ | ETA: 0:00:16 Training loss: 0.32272664 Validation loss: 10.852688 Validation accuracy: 0.0       Progress: 31%|████████████▊ | ETA: 0:00:10 Training loss: 0.12263489 Validation loss: 10.124907 Validation accuracy: 0.3333       Progress: 38%|███████████████▋ | ETA: 0:00:08 Training loss: 0.048797842 Validation loss: 10.063402 Validation accuracy: 0.0       Progress: 48%|███████████████████▋ | ETA: 0:00:05 Training loss: 0.031148521 Validation loss: 9.826639 Validation accuracy: 0.3333       Progress: 54%|██████████████████████▏ | ETA: 0:00:04 Training loss: 0.014187865 Validation loss: 9.823248 Validation accuracy: 0.0       Progress: 62%|█████████████████████████▍ | ETA: 0:00:03 Training loss: 0.0053788233 Validation loss: 9.807602 Validation accuracy: 0.0       Progress: 69%|████████████████████████████▎ | ETA: 0:00:02 Training loss: 0.0048226444 Validation loss: 9.775978 Validation accuracy: 0.0       Progress: 78%|████████████████████████████████ | ETA: 0:00:01 Training loss: 0.0009678599 Validation loss: 9.743053 Validation accuracy: 0.0       Progress: 87%|███████████████████████████████████▋ | ETA: 0:00:01 Training loss: 0.001448846 Validation loss: 9.770295 Validation accuracy: 0.0       Progress: 97%|███████████████████████████████████████▊ | ETA: 0:00:00 Training loss: 0.00047823263 Validation loss: 9.753482 Validation accuracy: 0.0       Progress: 100%|█████████████████████████████████████████| Time: 0:00:05 Training loss: 0.00021136894 Validation loss: 9.768948 Validation accuracy: 0.0 Setting up model... model = Chain(Dense(33 => 1000, relu), Dense(1000 => 200)) Setting up data structures... Setting up model... model = Chain(Dense(33 => 1000, relu), Dense(1000 => 200)) Setting up data structures... Setting up optimizer... Training... Progress: 2%|▉ | ETA: 0:00:10 Training loss: 136.1213472638287 Training accuracy: 1.0     Progress: 5%|██ | ETA: 0:00:07 Training loss: 133.1541100449666 Training accuracy: 1.0     Progress: 9%|███▊ | ETA: 0:00:05 Training loss: 128.32016255288048 Training accuracy: 0.6667     Progress: 13%|█████▍ | ETA: 0:00:04 Training loss: 121.8900602308897 Training accuracy: 0.6667     Progress: 18%|███████▍ | ETA: 0:00:03 Training loss: 111.14874618344862 Training accuracy: 0.6667     Progress: 23%|█████████▍ | ETA: 0:00:03 Training loss: 97.35413144591028 Training accuracy: 0.6667     Progress: 27%|███████████▏ | ETA: 0:00:03 Training loss: 84.54310677223076 Training accuracy: 0.6667     Progress: 32%|█████████████▏ | ETA: 0:00:02 Training loss: 67.3983204543683 Training accuracy: 0.6667     Progress: 37%|███████████████▏ | ETA: 0:00:02 Training loss: 50.653066791082196 Training accuracy: 0.6667     Progress: 42%|█████████████████▎ | ETA: 0:00:02 Training loss: 36.19274447673215 Training accuracy: 0.6667     Progress: 47%|███████████████████▎ | ETA: 0:00:02 Training loss: 25.30376131179588 Training accuracy: 0.6667     Progress: 52%|█████████████████████▍ | ETA: 0:00:01 Training loss: 18.24684941382116 Training accuracy: 0.6667     Progress: 57%|███████████████████████▍ | ETA: 0:00:01 Training loss: 14.08594341873143 Training accuracy: 1.0     Progress: 62%|█████████████████████████▍ | ETA: 0:00:01 Training loss: 11.477471547898434 Training accuracy: 1.0     Progress: 67%|███████████████████████████▌ | ETA: 0:00:01 Training loss: 9.579723855840518 Training accuracy: 1.0     Progress: 72%|█████████████████████████████▌ | ETA: 0:00:01 Training loss: 8.118820029624434 Training accuracy: 1.0     Progress: 77%|███████████████████████████████▋ | ETA: 0:00:01 Training loss: 7.028107372335075 Training accuracy: 1.0     Progress: 81%|█████████████████████████████████▎ | ETA: 0:00:01 Training loss: 6.361311109400841 Training accuracy: 1.0     Progress: 86%|███████████████████████████████████▎ | ETA: 0:00:00 Training loss: 5.674727730571976 Training accuracy: 1.0     Progress: 91%|█████████████████████████████████████▎ | ETA: 0:00:00 Training loss: 5.069931804442271 Training accuracy: 1.0     Progress: 96%|███████████████████████████████████████▍ | ETA: 0:00:00 Training loss: 4.514483164565182 Training accuracy: 1.0     Progress: 100%|█████████████████████████████████████████| Time: 0:00:02 Training loss: 4.102810598396507 Training accuracy: 1.0 Setting up model... model = Chain(Dense(33 => 1000, relu), Dense(1000 => 200)) Setting up data structures... Setting up optimizer... Training... Progress: 4%|█▋ | ETA: 0:00:03 Training loss: 134.49369142400806 Validation loss: 114.36544288026057 Validation accuracy: 0.0 Training accuracy: 1.0         Progress: 7%|██▉ | ETA: 0:00:03 Training loss: 131.37623764238438 Validation loss: 112.45136414497952 Validation accuracy: 0.0 Training accuracy: 1.0         Progress: 11%|████▌ | ETA: 0:00:03 Training loss: 126.07318760485117 Validation loss: 109.48348625944831 Validation accuracy: 0.0 Training accuracy: 1.0         Progress: 15%|██████▏ | ETA: 0:00:03 Training loss: 119.06156271922441 Validation loss: 105.89486774032108 Validation accuracy: 0.0 Training accuracy: 0.6667         Progress: 19%|███████▊ | ETA: 0:00:02 Training loss: 110.16628513878496 Validation loss: 101.61399929932261 Validation accuracy: 0.0 Training accuracy: 0.6667         Progress: 23%|█████████▍ | ETA: 0:00:02 Training loss: 99.38955491372346 Validation loss: 96.68203225474946 Validation accuracy: 0.0 Training accuracy: 0.6667         Progress: 27%|███████████▏ | ETA: 0:00:02 Training loss: 87.01939707667547 Validation loss: 91.2991951982845 Validation accuracy: 0.0 Training accuracy: 0.3333         Progress: 31%|████████████▊ | ETA: 0:00:02 Training loss: 73.63024920991306 Validation loss: 85.83801400435081 Validation accuracy: 0.0 Training accuracy: 0.3333         Progress: 35%|██████████████▍ | ETA: 0:00:02 Training loss: 60.06523875241784 Validation loss: 80.74363222388646 Validation accuracy: 0.0 Training accuracy: 0.3333         Progress: 40%|████████████████▍ | ETA: 0:00:02 Training loss: 44.280845715881384 Validation loss: 75.54842440512029 Validation accuracy: 0.0 Training accuracy: 0.3333         Progress: 44%|██████████████████ | ETA: 0:00:02 Training loss: 33.62752701453278 Validation loss: 72.70702941214468 Validation accuracy: 0.0 Training accuracy: 0.3333         Progress: 49%|████████████████████▏ | ETA: 0:00:01 Training loss: 23.62062259896562 Validation loss: 70.75583895028232 Validation accuracy: 0.0 Training accuracy: 0.6667         Progress: 53%|█████████████████████▊ | ETA: 0:00:01 Training loss: 18.269171252618744 Validation loss: 70.0281366622392 Validation accuracy: 0.0 Training accuracy: 0.6667         Progress: 58%|███████████████████████▊ | ETA: 0:00:01 Training loss: 14.091848856928761 Validation loss: 69.36585442148973 Validation accuracy: 0.0 Training accuracy: 1.0         Progress: 61%|█████████████████████████ | ETA: 0:00:01 Training loss: 12.412864379241096 Validation loss: 68.92698321559789 Validation accuracy: 0.0 Training accuracy: 1.0         Progress: 65%|██████████████████████████▋ | ETA: 0:00:01 Training loss: 10.68575794389141 Validation loss: 68.28147995006458 Validation accuracy: 0.0 Training accuracy: 1.0         Progress: 70%|████████████████████████████▊ | ETA: 0:00:01 Training loss: 9.005604122470174 Validation loss: 67.48456940772482 Validation accuracy: 0.0 Training accuracy: 1.0         Progress: 74%|██████████████████████████████▍ | ETA: 0:00:01 Training loss: 7.939047137739884 Validation loss: 66.95599078852743 Validation accuracy: 0.0 Training accuracy: 1.0         Progress: 78%|████████████████████████████████ | ETA: 0:00:01 Training loss: 7.094575534648743 Validation loss: 66.60746829173387 Validation accuracy: 0.0 Training accuracy: 1.0         Progress: 82%|█████████████████████████████████▋ | ETA: 0:00:00 Training loss: 6.430353482446876 Validation loss: 66.43183049775206 Validation accuracy: 0.0 Training accuracy: 1.0         Progress: 87%|███████████████████████████████████▋ | ETA: 0:00:00 Training loss: 5.755434239536527 Validation loss: 66.34852118777928 Validation accuracy: 0.0 Training accuracy: 1.0         Progress: 91%|█████████████████████████████████████▎ | ETA: 0:00:00 Training loss: 5.277269638694451 Validation loss: 66.33391116835378 Validation accuracy: 0.0 Training accuracy: 1.0         Progress: 97%|███████████████████████████████████████▊ | ETA: 0:00:00 Training loss: 4.6189414202473085 Validation loss: 66.35687648556386 Validation accuracy: 0.0 Training accuracy: 1.0         Progress: 100%|█████████████████████████████████████████| Time: 0:00:02 Training loss: 4.313025460671513 Validation loss: 66.36638547169328 Validation accuracy: 0.0 Training accuracy: 1.0 Setting up model... model = Chain(Dense(33 => 1000, relu), Dense(1000 => 200)) Setting up data structures... Setting up optimizer... Training... 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Setting up optimizer... Training... 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Setting up optimizer... Training... 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Setting up optimizer... Training... 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Setting up optimizer... Training... Progress: 6%|██▌ | ETA: 0:00:02 Training loss: 126.79411776577801 Validation loss: 114.5556257377752 Validation accuracy: 0.0       Progress: 17%|███████ | ETA: 0:00:01 Training loss: 108.27046507337822 Validation loss: 105.75956923228175 Validation accuracy: 0.0   Setting up model... model = Chain(Dense(32 => 1000, relu), Dense(1000 => 200)) Setting up data structures... Setting up optimizer... Training... Progress: 6%|██▌ | ETA: 0:00:02 Training loss: 126.73313823043847 Validation loss: 114.3920734793101 Validation accuracy: 0.0   Setting up model... model = Chain(Dense(32 => 1000, relu), Dense(1000 => 200)) Setting up data structures... Setting up optimizer... Training... 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Setting up optimizer... Training... Progress: 4%|█▋ | ETA: 0:00:03 Training loss: 4.560597 Validation loss: 17.092367 Validation accuracy: 0.0       Progress: 9%|███▊ | ETA: 0:00:03 Training loss: 3.8349795 Validation loss: 10.339524 Validation accuracy: 0.3333       Progress: 13%|█████▍ | ETA: 0:00:02 Training loss: 0.70521224 Validation loss: 9.476362 Validation accuracy: 0.1667       Progress: 17%|███████ | ETA: 0:00:02 Training loss: 0.19107169 Validation loss: 9.083819 Validation accuracy: 0.1667       Progress: 21%|████████▋ | ETA: 0:00:02 Training loss: 0.12699752 Validation loss: 8.737544 Validation accuracy: 0.1667       Progress: 27%|███████████▏ | ETA: 0:00:02 Training loss: 0.096786916 Validation loss: 8.702395 Validation accuracy: 0.3333       Progress: 33%|█████████████▌ | ETA: 0:00:02 Training loss: 0.039084807 Validation loss: 8.712727 Validation accuracy: 0.1667       Progress: 39%|████████████████ | ETA: 0:00:01 Training loss: 0.012765752 Validation loss: 8.684787 Validation accuracy: 0.1667       Progress: 45%|██████████████████▌ | ETA: 0:00:01 Training loss: 0.005135334 Validation loss: 8.638478 Validation accuracy: 0.1667       Progress: 50%|████████████████████▌ | ETA: 0:00:01 Training loss: 0.0015271627 Validation loss: 8.659349 Validation accuracy: 0.1667       Progress: 56%|███████████████████████ | ETA: 0:00:01 Training loss: 0.001407084 Validation loss: 8.682021 Validation accuracy: 0.1667       Progress: 62%|█████████████████████████▍ | ETA: 0:00:01 Training loss: 0.00038842324 Validation loss: 8.670367 Validation accuracy: 0.1667       Progress: 68%|███████████████████████████▉ | ETA: 0:00:01 Training loss: 0.00016822043 Validation loss: 8.666213 Validation accuracy: 0.1667       Progress: 74%|██████████████████████████████▍ | ETA: 0:00:01 Training loss: 0.00013503656 Validation loss: 8.689452 Validation accuracy: 0.1667       Progress: 80%|████████████████████████████████▊ | ETA: 0:00:00 Training loss: 0.0004773556 Validation loss: 8.659523 Validation accuracy: 0.1667       Progress: 86%|███████████████████████████████████▎ | ETA: 0:00:00 Training loss: 0.000114011134 Validation loss: 8.661791 Validation accuracy: 0.1667       Progress: 92%|█████████████████████████████████████▊ | ETA: 0:00:00 Training loss: 0.0007103628 Validation loss: 8.700376 Validation accuracy: 0.1667       Progress: 98%|████████████████████████████████████████▏| ETA: 0:00:00 Training loss: 0.00022290571 Validation loss: 8.680296 Validation accuracy: 0.1667       Progress: 100%|█████████████████████████████████████████| Time: 0:00:02 Training loss: 0.0001907741 Validation loss: 8.660024 Validation accuracy: 0.1667 Setting up model... model = Chain(Dense(200 => 1000, relu), Dense(1000 => 33)) Setting up data structures... Setting up optimizer... Training... 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Training loss: 2.5166246e-13 Validation loss: 8.431295 Validation accuracy: 0.1667       Progress: 99%|████████████████████████████████████████▋| ETA: 0:00:00 Training loss: 2.976839e-13 Validation loss: 8.431295 Validation accuracy: 0.1667       Progress: 100%|█████████████████████████████████████████| Time: 0:00:05 Training loss: 2.7676104e-13 Validation loss: 8.431295 Validation accuracy: 0.1667 Setting up model... model = Chain(Dense(200 => 1000, relu), Dense(1000 => 33)) Setting up data structures... Setting up optimizer... Training... 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Setting up optimizer... Training... Progress: 6%|██▌ | ETA: 0:00:02 Training loss: 0.19461608 Validation loss: 249.20517 Validation accuracy: 0.0       Progress: 16%|██████▌ | ETA: 0:00:01 Training loss: 0.32895526 Validation loss: 1251.8013 Validation accuracy: 0.0       Progress: 26%|██████████▋ | ETA: 0:00:01 Training loss: 0.40056175 Validation loss: 1983.2764 Validation accuracy: 0.0       Progress: 36%|██████████████▊ | ETA: 0:00:01 Training loss: 0.4314163 Validation loss: 2344.0608 Validation accuracy: 0.0       Progress: 46%|██████████████████▉ | ETA: 0:00:01 Training loss: 0.44331276 Validation loss: 2498.1265 Validation accuracy: 0.0       Progress: 56%|███████████████████████ | ETA: 0:00:00 Training loss: 0.44717836 Validation loss: 2559.7144 Validation accuracy: 0.0       Progress: 66%|███████████████████████████ | ETA: 0:00:00 Training loss: 0.44776955 Validation loss: 2583.5254 Validation accuracy: 0.0       Progress: 76%|███████████████████████████████▏ | ETA: 0:00:00 Training loss: 0.44702828 Validation loss: 2592.5786 Validation accuracy: 0.0       Progress: 86%|███████████████████████████████████▎ | ETA: 0:00:00 Training loss: 0.44572076 Validation loss: 2595.9968 Validation accuracy: 0.0       Progress: 96%|███████████████████████████████████████▍ | ETA: 0:00:00 Training loss: 0.4441453 Validation loss: 2597.3071 Validation accuracy: 0.0       Progress: 100%|█████████████████████████████████████████| Time: 0:00:01 Training loss: 0.44346893 Validation loss: 2597.5679 Validation accuracy: 0.0 Setting up model... model = Chain(Dense(200 => 200, relu), Dense(200 => 33)) Setting up data structures... Setting up optimizer... Training... Progress: 16%|██████▌ | ETA: 0:00:01 Training loss: 3.3205967 Validation loss: 25.572678 Validation accuracy: 0.1667       Progress: 32%|█████████████▏ | ETA: 0:00:00 Training loss: 0.5706232 Validation loss: 23.549036 Validation accuracy: 0.1667       Progress: 59%|████████████████████████▎ | ETA: 0:00:00 Training loss: 0.03677736 Validation loss: 23.187466 Validation accuracy: 0.1667       Progress: 85%|██████████████████████████████████▉ | ETA: 0:00:00 Training loss: 0.0024092456 Validation loss: 23.158611 Validation accuracy: 0.1667       Progress: 100%|█████████████████████████████████████████| Time: 0:00:00 Training loss: 0.0005761981 Validation loss: 23.222952 Validation accuracy: 0.1667 ┌ Warning: `Flux.params(m...)` is deprecated. Use `Flux.trainable(model)` for parameter collection, │ and the explicit `gradient(m -> loss(m, x, y), model)` for gradient computation. └ @ Flux ~/.julia/packages/Flux/3711C/src/deprecations.jl:93 Setting up model... model = Chain(Dense(200 => 500), Dense(500 => 500), Dense(500 => 33)) Setting up data structures... Setting up optimizer... Training... Progress: 2%|▉ | ETA: 0:02:29 Training loss: 299.4347 Validation loss: 91.30579 Validation accuracy: 0.1667       Progress: 7%|██▉ | ETA: 0:00:42 Training loss: 60.78133 Validation loss: 52.540813 Validation accuracy: 0.3333       Progress: 11%|████▌ | ETA: 0:00:26 Training loss: 20.59612 Validation loss: 52.317017 Validation accuracy: 0.3333       Progress: 21%|████████▋ | ETA: 0:00:13 Training loss: 8.922816 Validation loss: 45.81684 Validation accuracy: 0.3333       Progress: 31%|████████████▊ | ETA: 0:00:08 Training loss: 2.887811 Validation loss: 41.80809 Validation accuracy: 0.3333       Progress: 42%|█████████████████▎ | ETA: 0:00:05 Training loss: 0.78921324 Validation loss: 41.214664 Validation accuracy: 0.3333       Progress: 50%|████████████████████▌ | ETA: 0:00:04 Training loss: 0.08028344 Validation loss: 41.358738 Validation accuracy: 0.3333       Progress: 57%|███████████████████████▍ | ETA: 0:00:03 Training loss: 0.13609666 Validation loss: 41.30693 Validation accuracy: 0.3333       Progress: 64%|██████████████████████████▎ | ETA: 0:00:02 Training loss: 0.051198743 Validation loss: 41.336544 Validation accuracy: 0.3333       Progress: 71%|█████████████████████████████▏ | ETA: 0:00:02 Training loss: 0.016220527 Validation loss: 41.239628 Validation accuracy: 0.3333       Progress: 77%|███████████████████████████████▋ | ETA: 0:00:01 Training loss: 0.017973408 Validation loss: 41.35016 Validation accuracy: 0.3333       Progress: 85%|██████████████████████████████████▉ | ETA: 0:00:01 Training loss: 0.0019120533 Validation loss: 41.250668 Validation accuracy: 0.3333       Progress: 92%|█████████████████████████████████████▊ | ETA: 0:00:00 Training loss: 0.0038019957 Validation loss: 41.263264 Validation accuracy: 0.3333       Progress: 99%|████████████████████████████████████████▋| ETA: 0:00:00 Training loss: 0.0014656847 Validation loss: 41.272804 Validation accuracy: 0.3333       Progress: 100%|█████████████████████████████████████████| Time: 0:00:04 Training loss: 0.0018686217 Validation loss: 41.273216 Validation accuracy: 0.3333 Setting up model... model = Chain(Dense(200 => 1000, relu), Dense(1000 => 33), σ) Setting up data structures... Setting up optimizer... Training... Progress: 2%|▉ | ETA: 0:43:51 Training loss: 0.39982933 Validation loss: 1.9066198 Validation accuracy: 0.0       Progress: 12%|████▉ | ETA: 0:06:34 Training loss: 0.0063018384 Validation loss: 3.7745562 Validation accuracy: 0.0       Progress: 22%|█████████ | ETA: 0:03:11 Training loss: 0.0003009053 Validation loss: 3.9533677 Validation accuracy: 0.0       Progress: 31%|████████████▊ | ETA: 0:02:00 Training loss: 4.6380326e-5 Validation loss: 3.987875 Validation accuracy: 0.0       Progress: 39%|████████████████ | ETA: 0:01:25 Training loss: 2.366534e-5 Validation loss: 3.998641 Validation accuracy: 0.0       Progress: 47%|███████████████████▎ | ETA: 0:01:01 Training loss: 1.7305421e-5 Validation loss: 4.0062485 Validation accuracy: 0.0       Progress: 54%|██████████████████████▏ | ETA: 0:00:46 Training loss: 1.3285306e-5 Validation loss: 4.011359 Validation accuracy: 0.0       Progress: 63%|█████████████████████████▉ | ETA: 0:00:32 Training loss: 9.789024e-6 Validation loss: 4.016986 Validation accuracy: 0.0       Progress: 72%|█████████████████████████████▌ | ETA: 0:00:21 Training loss: 7.680028e-6 Validation loss: 4.022082 Validation accuracy: 0.0       Progress: 82%|█████████████████████████████████▋ | ETA: 0:00:12 Training loss: 6.2769373e-6 Validation loss: 4.02531 Validation accuracy: 0.0       Progress: 90%|████████████████████████████████████▉ | ETA: 0:00:06 Training loss: 5.5519367e-6 Validation loss: 4.027434 Validation accuracy: 0.0       Progress: 100%|█████████████████████████████████████████| Time: 0:00:54 Training loss: 4.923299e-6 Validation loss: 4.0296454 Validation accuracy: 0.0 Setting up model... model = Chain(Dense(33 => 1000, relu), Dense(1000 => 200)) Setting up data structures... Setting up optimizer... Training... Progress: 3%|█▎ | ETA: 0:00:04 Training loss: 135.33196816308538   Progress: 8%|███▎ | ETA: 0:00:03 Training loss: 130.13941916840923   Progress: 13%|█████▍ | ETA: 0:00:02 Training loss: 122.78921984408221   Progress: 18%|███████▍ | ETA: 0:00:02 Training loss: 112.60239292227845   Progress: 23%|█████████▍ | ETA: 0:00:02 Training loss: 99.43726102222382   Progress: 28%|███████████▌ | ETA: 0:00:02 Training loss: 83.72956161694948   Progress: 33%|█████████████▌ | ETA: 0:00:02 Training loss: 66.69962191989026   Progress: 38%|███████████████▋ | ETA: 0:00:01 Training loss: 50.095712301721555   Progress: 43%|█████████████████▋ | ETA: 0:00:01 Training loss: 35.774255217729554   Progress: 48%|███████████████████▋ | ETA: 0:00:01 Training loss: 25.008041992360983   Progress: 53%|█████████████████████▊ | ETA: 0:00:01 Training loss: 18.01171160590189   Progress: 57%|███████████████████████▍ | ETA: 0:00:01 Training loss: 14.555358594744506   Progress: 62%|█████████████████████████▍ | ETA: 0:00:01 Training loss: 11.778651340930223   Progress: 67%|███████████████████████████▌ | ETA: 0:00:01 Training loss: 9.821376379338346   Progress: 72%|█████████████████████████████▌ | ETA: 0:00:01 Training loss: 8.312280326570281   Progress: 76%|███████████████████████████████▏ | ETA: 0:00:01 Training loss: 7.369898450781839   Progress: 81%|█████████████████████████████████▎ | ETA: 0:00:00 Training loss: 6.470425358636948   Progress: 86%|███████████████████████████████████▎ | ETA: 0:00:00 Training loss: 5.771572580422031   Progress: 91%|█████████████████████████████████████▎ | ETA: 0:00:00 Training loss: 5.169681280517391   Progress: 96%|███████████████████████████████████████▍ | ETA: 0:00:00 Training loss: 4.620437932541403   Progress: 100%|█████████████████████████████████████████| Time: 0:00:02 Training loss: 4.211690263599279 Setting up model... model = Chain(Dense(200 => 1000, relu), Dense(1000 => 33)) Setting up data structures... Setting up optimizer... Training... Progress: 4%|█▋ | ETA: 0:00:03 Training loss: 20.038164   Progress: 8%|███▎ | ETA: 0:00:03 Training loss: 6.814798   Progress: 13%|█████▍ | ETA: 0:00:02 Training loss: 0.44263393   Progress: 18%|███████▍ | ETA: 0:00:02 Training loss: 2.2443912   Progress: 23%|█████████▍ | ETA: 0:00:02 Training loss: 1.017323   Progress: 28%|███████████▌ | ETA: 0:00:02 Training loss: 0.18575929   Progress: 33%|█████████████▌ | ETA: 0:00:02 Training loss: 0.5374229   Progress: 38%|███████████████▋ | ETA: 0:00:02 Training loss: 0.052996557   Progress: 43%|█████████████████▋ | ETA: 0:00:01 Training loss: 0.12471397   Progress: 48%|███████████████████▋ | ETA: 0:00:01 Training loss: 0.08700554   Progress: 53%|█████████████████████▊ | ETA: 0:00:01 Training loss: 0.007344324   Progress: 58%|███████████████████████▊ | ETA: 0:00:01 Training loss: 0.03567538   Progress: 63%|█████████████████████████▉ | ETA: 0:00:01 Training loss: 0.015671683   Progress: 68%|███████████████████████████▉ | ETA: 0:00:01 Training loss: 0.0021660607   Progress: 73%|█████████████████████████████▉ | ETA: 0:00:01 Training loss: 0.0072784335   Progress: 77%|███████████████████████████████▋ | ETA: 0:00:01 Training loss: 0.0024544592   Progress: 81%|█████████████████████████████████▎ | ETA: 0:00:00 Training loss: 0.002077095   Progress: 86%|███████████████████████████████████▎ | ETA: 0:00:00 Training loss: 0.0022662706   Progress: 91%|█████████████████████████████████████▎ | ETA: 0:00:00 Training loss: 0.0006255018   Progress: 96%|███████████████████████████████████████▍ | ETA: 0:00:00 Training loss: 0.00017898485   Progress: 100%|█████████████████████████████████████████| Time: 0:00:02 Training loss: 0.00052282447 Making fac C ========== Timestep 1 Calculating Yt... Calculating Mt... Auto mode: Sparsity: 0.0101010101010101 Returning a sparse matrix format Calculating Ythat... Sparsity: 0.030303030303030304 Finding paths... ========== Timestep 2 average 1.0 of paths currently Calculating Yt... Calculating Mt... Auto mode: Sparsity: 0.0101010101010101 Returning a sparse matrix format Calculating Ythat... Sparsity: 0.030303030303030304 Finding paths... ========== Timestep 3 average 1.0 of paths currently Calculating Yt... Calculating Mt... Auto mode: Sparsity: 0.0202020202020202 Returning a sparse matrix format Calculating Ythat... Sparsity: 0.06060606060606061 Finding paths... ========== Timestep 4 average 1.0 of paths currently Calculating Yt... Calculating Mt... Auto mode: Sparsity: 0.030303030303030304 Returning a sparse matrix format Calculating Ythat... Sparsity: 0.09090909090909091 Finding paths... ========== Timestep 5 average 1.0 of paths currently Calculating Yt... Calculating Mt... Auto mode: Sparsity: 0.030303030303030304 Returning a sparse matrix format Calculating Ythat... Sparsity: 0.09090909090909091 Finding paths... ========== Timestep 6 average 0.3333333333333333 of paths currently Calculating Yt... Calculating Mt... Auto mode: Sparsity: 0.0101010101010101 Returning a sparse matrix format Calculating Ythat... Sparsity: 0.030303030303030304 Finding paths... ========== Timestep 7 average 0.0 of paths currently Calculating Yt... Calculating Mt... Auto mode: Sparsity: 0.0 Returning a sparse matrix format Calculating Ythat... Sparsity: 0.0 Finding paths... Evaluating paths... average 1.0 of paths to evaluate Progress: 67%|███████████████████████████▍ | ETA: 0:00:01 Progress: 100%|█████████████████████████████████████████| Time: 0:00:01 Setting up model... model = Chain(Dense(33 => 1000, relu), Dense(1000 => 200)) Setting up optimizer... Setting up data for evaluation... Setting up data loader... Training... Progress: 67%|███████████████████████████▍ | ETA: 0:00:05 Step loss: 131.14099893438816 Overall loss: 135.85572493843063 Overall accuracy: 0.3333       Progress: 100%|█████████████████████████████████████████| Time: 0:00:10 Step loss: 146.49485746489026 Overall loss: 135.1610744416433 Overall accuracy: 0.3333 Done! Setting up model... model = Chain(Dense(33 => 1000, relu), Dense(1000 => 200)) Setting up optimizer... Setting up data for evaluation... Setting up data loader... Training... Progress: 50%|████████████████████▌ | ETA: 0:00:00 Step loss: 146.49485746489026 Overall loss: 135.1610744416433 Overall accuracy: 0.3333       Progress: 100%|█████████████████████████████████████████| Time: 0:00:00 Step loss: 144.39176453339 Overall loss: 132.97095160544072 Overall accuracy: 0.6667 Done! Setting up model... model = Chain(Dense(33 => 1000, relu), Dense(1000 => 200)) Setting up optimizer... Setting up data for evaluation... Setting up data loader... Training... Progress: 33%|█████████████▋ | ETA: 0:00:00 Step loss: 145.65858509349266 Overall loss: 134.1443622338372 Overall accuracy: 0.3333       Progress: 75%|██████████████████████████████▊ | ETA: 0:00:00 Step loss: 140.70036382302956 Overall loss: 130.01272937505908 Overall accuracy: 0.6667       Progress: 100%|█████████████████████████████████████████| Time: 0:00:00 Step loss: 136.7197594604057 Overall loss: 126.98830851273253 Overall accuracy: 0.6667 Done! Setting up model... model = Chain(Dense(33 => 1000, relu), Dense(1000 => 200)) Setting up optimizer... Setting up data for evaluation... Setting up data loader... Training... Progress: 33%|█████████████▋ | ETA: 0:00:00 Step loss: 146.40921997833055 Overall loss: 134.97982308094285 Overall accuracy: 1.0       Progress: 89%|████████████████████████████████████▌ | ETA: 0:00:00 Step loss: 126.40757666514506 Overall loss: 131.10847618284353 Overall accuracy: 0.3333       Progress: 100%|█████████████████████████████████████████| Time: 0:00:00 Step loss: 125.3289995899268 Overall loss: 130.24475423530154 Overall accuracy: 0.3333 Done! Setting up model... model = Chain(Dense(33 => 1000, relu), Dense(1000 => 200)) Setting up optimizer... Setting up data for evaluation... Setting up data loader... Training... Progress: 80%|████████████████████████████████▊ | ETA: 0:00:00 Step loss: 127.25452932074396 Overall loss: 131.33763075062595 Overall accuracy: 0.6667   Done! Setting up model... model = Chain(Dense(33 => 1000, relu), Dense(1000 => 200)) Setting up optimizer... Setting up data for evaluation... Setting up data loader... Training... Progress: 67%|███████████████████████████▍ | ETA: 0:00:00 Step loss: 136.2665052695647 Overall loss: 135.36709303725198 Overall accuracy: 1.0       Progress: 100%|█████████████████████████████████████████| Time: 0:00:00 Step loss: 135.36709303725198 Overall loss: 134.44507860114422 Overall accuracy: 1.0 Done! Setting up model... model = Chain(Dense(33 => 1000, relu), Dense(1000 => 200)) Setting up optimizer... Setting up data for evaluation... Setting up data loader... Training... Progress: 22%|█████████▏ | ETA: 0:00:00 Step loss: 131.14099893438816 Overall loss: 135.85572493843063 Overall accuracy: 0.3333       Progress: 44%|██████████████████▎ | ETA: 0:00:00 Step loss: 130.16812457299167 Overall loss: 134.46400128629588 Overall accuracy: 0.3333       Progress: 89%|████████████████████████████████████▌ | ETA: 0:00:00 Step loss: 126.37263619471189 Overall loss: 131.33763075062595 Overall accuracy: 0.6667       Progress: 100%|█████████████████████████████████████████| Time: 0:00:00 Step loss: 142.00239555793692 Overall loss: 130.4477443735128 Overall accuracy: 0.6667 Done! Setting up model... model = Chain(Dense(33 => 1000, relu), Dense(1000 => 200)) Setting up optimizer... Setting up data for evaluation... Setting up data loader... Training... Progress: 44%|██████████████████▎ | ETA: 0:00:00 Step loss: 145.65858509349266 Overall loss: 134.1443622338372 Overall accuracy: 0.3333       Progress: 100%|█████████████████████████████████████████| Time: 0:00:00 Step loss: 140.70036382302956 Overall loss: 130.01272937505908 Overall accuracy: 0.6667 Done! WARNING: Method definition compute_target_corr(Any, Any, Any, Any, Any, Any, Any) in module ##deep learning tests#258 at /home/pkgeval/.julia/packages/JudiLing/TmEZc/test/deep_learning_tests.jl:619 overwritten at /home/pkgeval/.julia/packages/JudiLing/TmEZc/test/deep_learning_tests.jl:666. Setting up model... model = Chain(Dense(33 => 1000, relu), Dense(1000 => 200)) Setting up optimizer... Setting up data for evaluation... Setting up data loader... Training... Progress: 40%|████████████████▍ | ETA: 0:00:00 Step loss: 139.05467735440448 Overall loss: 138.01479947270724 Overall accuracy: 1.0   Done! WARNING: Method definition compute_target_corr(Any, Any, Any, Any, Any, Any, Any) in module ##deep learning tests#258 at /home/pkgeval/.julia/packages/JudiLing/TmEZc/test/deep_learning_tests.jl:666 overwritten at /home/pkgeval/.julia/packages/JudiLing/TmEZc/test/deep_learning_tests.jl:691. Setting up model... model = Chain(Dense(33 => 1000, relu), Dense(1000 => 200)) Setting up optimizer... Setting up data for evaluation... Setting up data loader... Training... Progress: 22%|█████████▏ | ETA: 0:00:00 Step loss: 131.45998225637794 Overall loss: 136.22927057022608 Overall accuracy: 0.6667       Progress: 78%|███████████████████████████████▉ | ETA: 0:00:00 Step loss: 128.24459519503844 Overall loss: 132.74668750892215 Overall accuracy: 0.6667       Progress: 100%|█████████████████████████████████████████| Time: 0:00:00 Step loss: 142.87674765216178 Overall loss: 131.10236280057742 Overall accuracy: 0.6667 Done! Setting up model... model = Chain(Dense(33 => 1000, relu), Dense(1000 => 200)) Setting up optimizer... Setting up data for evaluation... Setting up data loader... Training... Done! Test Summary: | Pass Total Time deep learning tests | 96 96 6m14.8s Testing JudiLing tests passed Testing completed after 861.29s PkgEval succeeded after 1558.39s