Package evaluation of JudiLing on Julia 1.11.5 (ca6e7bd5b0*) started at 2025-07-02T00:26:07.971 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Set-up completed after 5.71s ################################################################################ # 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 v4.17.0 [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 v0.25.120 ⌅ [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 v1.10.17 [34004b35] + HypergeometricFunctions v0.3.28 [842dd82b] + InlineStrings v1.4.4 [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 v1.5.0 [bac558e1] + OrderedCollections v1.8.1 [90014a1f] + PDMats v0.11.35 [d96e819e] + Parameters v0.12.3 [69de0a69] + Parsers v2.8.3 [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 v1.3.0 [91c51154] + SentinelArrays v1.4.8 [777ac1f9] + SimpleBufferStream v1.2.0 [a2af1166] + SortingAlgorithms v1.2.1 [276daf66] + SpecialFunctions v2.5.1 [10745b16] + Statistics v1.11.1 [82ae8749] + StatsAPI v1.7.1 ⌅ [2913bbd2] + StatsBase v0.33.21 [4c63d2b9] + StatsFuns v1.5.0 [892a3eda] + StringManipulation v0.4.1 [3783bdb8] + TableTraits v1.0.1 [bd369af6] + Tables v1.12.1 ⌅ [b189fb0b] + ThreadPools v1.2.1 [3bb67fe8] + TranscodingStreams v0.11.3 [5c2747f8] + URIs v1.6.1 [3a884ed6] + UnPack v1.0.2 [ea10d353] + WeakRefStrings v1.4.2 [76eceee3] + WorkerUtilities v1.6.1 [458c3c95] + OpenSSL_jll v3.5.0+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 3.03s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... Precompiling project... 2080.2 ms ✓ TestEnv 1 dependency successfully precompiled in 2 seconds. 23 already precompiled. Precompiling package dependencies... Precompilation completed after 552.05s ################################################################################ # 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:1924 Status `/tmp/jl_xq9ZdU/Project.toml` [336ed68f] CSV v0.10.15 [8f4d0f93] Conda v1.10.2 [a93c6f00] DataFrames v1.7.0 [2e981812] DataLoaders v0.1.3 [587475ba] Flux v0.16.4 [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_xq9ZdU/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 v1.5.0 [d1d4a3ce] BitFlags v0.1.9 [336ed68f] CSV v0.10.15 [082447d4] ChainRules v1.72.5 [d360d2e6] ChainRulesCore v1.25.2 [944b1d66] CodecZlib v0.7.8 [bbf7d656] CommonSubexpressions v0.3.1 [34da2185] Compat v4.17.0 [a33af91c] CompositionsBase v0.1.2 [f0e56b4a] ConcurrentUtilities v2.5.0 [8f4d0f93] Conda v1.10.2 [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 v1.7.0 [2e981812] DataLoaders v0.1.3 [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 [b4f34e82] Distances v0.10.12 [31c24e10] Distributions v0.25.120 ⌅ [ffbed154] DocStringExtensions v0.8.6 [c5bfea45] Embeddings v0.4.6 [f151be2c] EnzymeCore v0.8.12 [460bff9d] ExceptionUnwrapping v0.1.11 [cc61a311] FLoops v0.2.2 [b9860ae5] FLoopsBase v0.1.1 [48062228] FilePathsBase v0.9.24 [1a297f60] FillArrays v1.13.0 [587475ba] Flux v0.16.4 [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 v1.10.17 [076d061b] HashArrayMappedTries v0.2.0 [34004b35] HypergeometricFunctions v0.3.28 [7869d1d1] IRTools v0.4.15 [22cec73e] InitialValues v0.3.1 [842dd82b] InlineStrings v1.4.4 [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 v0.9.36 [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 v1.10.1 ⌃ [9920b226] MLDataPattern v0.5.4 [66a33bbf] MLLabelUtils v0.5.7 [d8e11817] MLStyle v0.4.17 [f1d291b0] MLUtils v0.4.8 [1914dd2f] MacroTools v0.5.16 [dbb5928d] MappedArrays v0.4.2 [739be429] MbedTLS v1.1.9 [128add7d] MicroCollections v0.2.0 [e1d29d7a] Missings v1.2.0 [872c559c] NNlib v0.9.30 [77ba4419] NaNMath v1.1.3 [71a1bf82] NameResolution v0.1.5 [0b1bfda6] OneHotArrays v0.2.10 [4d8831e6] OpenSSL v1.5.0 [3bd65402] Optimisers v0.4.6 [bac558e1] OrderedCollections v1.8.1 [90014a1f] PDMats v0.11.35 [d96e819e] Parameters v0.12.3 [69de0a69] Parsers v2.8.3 [2dfb63ee] PooledArrays v1.4.3 ⌅ [aea7be01] PrecompileTools v1.2.1 [21216c6a] Preferences v1.4.3 [8162dcfd] PrettyPrint v0.2.0 [08abe8d2] PrettyTables v2.4.0 [33c8b6b6] ProgressLogging v0.1.5 [92933f4c] ProgressMeter v1.10.4 [43287f4e] PtrArrays v1.3.0 [438e738f] PyCall v1.96.4 [1fd47b50] QuadGK v2.11.2 [c1ae055f] RealDot v0.1.0 [3cdcf5f2] RecipesBase v1.3.4 [189a3867] Reexport v1.2.2 [ae029012] Requires v1.3.1 [79098fc4] Rmath v0.8.0 [1bc83da4] SafeTestsets v0.1.0 [7e506255] ScopedValues v1.3.0 [6c6a2e73] Scratch v1.3.0 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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... [ 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-3.1.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (5.9 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) (2.3.1) 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filename=pyndl-1.2.3-cp312-cp312-manylinux_2_36_x86_64.whl size=412041 sha256=95af6dc3b9fafc53cc63546c08a993e4a63e6e28c2bae099221e723ada514948 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-3.1.2 cftime-1.6.4.post1 netCDF4-1.7.2 pandas-2.3.0 pyndl-1.2.3 python-dateutil-2.9.0.post0 pytz-2025.2 scipy-1.16.0 six-1.17.0 toml-0.10.2 tzdata-2025.2 xarray-2025.6.1 /home/pkgeval/.julia/conda/3/x86_64/lib/python3.12/multiprocessing/popen_fork.py:66: DeprecationWarning: This process (pid=1113) 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=1113) 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=1113) 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=1113) 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=1113) 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=1113) 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=1113) 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=1113) 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=1113) 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=1113) 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=1113) 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=1113) 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=1113) 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=1113) 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=1113) 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=1113) 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 1m56.8s Test Summary: | Pass Total Time input tests | 27 27 11.0s Test Summary: | Pass Total Time cholesky tests | 10 10 9.6s Test Summary: | Pass Total Time frequency tests | 3 3 5.4s 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 28.8s ┌ 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 35.5s Test Summary: | Total Time find_path tests | 0 3.3s Test Summary: | Pass Total Time make_adjacency_matrix tests | 7 7 0.5s true Test Summary: | Pass Total Time make_cue_matrix tests | 21 21 4.3s true Test Summary: | Pass Total Time make_semantic_matrix tests | 72 72 4.7s Test Summary: | Total Time make_yt_matrix tests | 0 0.0s Test Summary: | Pass Total Time output_matrix tests | 10 10 9.7s Test Summary: | Total Time preprocess tests | 0 0.6s ┌ 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 12.1s Test Summary: | Pass Total Time wh tests | 5 5 2.8s 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/YTfNv/src/internal.jl:112 model = Chain(Dense(33 => 1000, relu), Dense(1000 => 200)) Setting up data structures... Setting up optimizer... Training... Progress: 2%|▉ | ETA: 0:53:39 Training loss: 136.2665052702724   Progress: 66%|███████████████████████████ | ETA: 0:00:35 Training loss: 10.567456292792862   Progress: 94%|██████████████████████████████████████▌ | ETA: 0:00:04 Training loss: 5.108680811532662   Progress: 100%|█████████████████████████████████████████| Time: 0:01:07 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/9PibT/src/layers/stateless.jl:60 Progress: 2%|▉ | ETA: 0:12:32 Training loss: 7.5811825   Progress: 6%|██▌ | ETA: 0:04:07 Training loss: 3.181571   Progress: 12%|████▉ | ETA: 0:01:56 Training loss: 2.565964   Progress: 19%|███████▊ | ETA: 0:01:08 Training loss: 1.79281   Progress: 26%|██████████▋ | ETA: 0:00:46 Training loss: 1.1272478   Progress: 33%|█████████████▌ | ETA: 0:00:33 Training loss: 0.43341818   Progress: 41%|████████████████▊ | ETA: 0:00:23 Training loss: 0.15989771   Progress: 49%|████████████████████▏ | ETA: 0:00:17 Training loss: 0.061197184   Progress: 57%|███████████████████████▍ | ETA: 0:00:12 Training loss: 0.027322868   Progress: 64%|██████████████████████████▎ | ETA: 0:00:09 Training loss: 0.008427495   Progress: 72%|█████████████████████████████▌ | ETA: 0:00:07 Training loss: 0.0049975896   Progress: 79%|████████████████████████████████▍ | ETA: 0:00:04 Training loss: 0.001863128   Progress: 87%|███████████████████████████████████▋ | ETA: 0:00:03 Training loss: 0.001448846   Progress: 94%|██████████████████████████████████████▌ | ETA: 0:00:01 Training loss: 0.00068362546   Progress: 100%|█████████████████████████████████████████| Time: 0:00:17 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: 0:01:20 Training loss: 136.2665052702724 Validation loss: 115.66047845446742 Validation accuracy: 0.0       Progress: 7%|██▉ | ETA: 0:00:23 Training loss: 131.38567017181032 Validation loss: 112.39658445418706 Validation accuracy: 0.0       Progress: 14%|█████▊ | ETA: 0:00:11 Training loss: 121.18877927654745 Validation loss: 106.57253643149627 Validation accuracy: 0.0       Progress: 21%|████████▋ | ETA: 0:00:07 Training loss: 105.3576064632318 Validation loss: 98.73688876505463 Validation accuracy: 0.0       Progress: 29%|███████████▉ | ETA: 0:00:05 Training loss: 80.87493713900922 Validation loss: 88.0079306038181 Validation accuracy: 0.0       Progress: 37%|███████████████▏ | ETA: 0:00:04 Training loss: 54.091555746467975 Validation loss: 78.16941348420849 Validation accuracy: 0.0       Progress: 45%|██████████████████▌ | ETA: 0:00:03 Training loss: 32.02662253311591 Validation loss: 72.36203997988899 Validation accuracy: 0.0       Progress: 53%|█████████████████████▊ | ETA: 0:00:02 Training loss: 18.83126268919119 Validation loss: 70.55058179525705 Validation accuracy: 0.0       Progress: 60%|████████████████████████▋ | ETA: 0:00:02 Training loss: 13.26558732911634 Validation loss: 69.72084599937997 Validation accuracy: 0.0       Progress: 68%|███████████████████████████▉ | ETA: 0:00:01 Training loss: 9.867303707987764 Validation loss: 68.35172756912476 Validation accuracy: 0.0       Progress: 75%|██████████████████████████████▊ | ETA: 0:00:01 Training loss: 7.922924271112943 Validation loss: 67.30576914473906 Validation accuracy: 0.0       Progress: 82%|█████████████████████████████████▋ | ETA: 0:00:01 Training loss: 6.617918412801383 Validation loss: 66.81097392553144 Validation accuracy: 0.0       Progress: 89%|████████████████████████████████████▌ | ETA: 0:00:00 Training loss: 5.682227675346057 Validation loss: 66.65039689168398 Validation accuracy: 0.0       Progress: 100%|█████████████████████████████████████████| Time: 0:00:03 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: 0:01:39 Training loss: 7.5811825 Validation loss: 23.362167 Validation accuracy: 0.0       Progress: 12%|████▉ | ETA: 0:00:16 Training loss: 2.565964 Validation loss: 12.899682 Validation accuracy: 0.1667       Progress: 21%|████████▋ | ETA: 0:00:09 Training loss: 0.32272664 Validation loss: 10.852688 Validation accuracy: 0.0       Progress: 40%|████████████████▍ | ETA: 0:00:04 Training loss: 0.09780932 Validation loss: 10.145142 Validation accuracy: 0.0       Progress: 50%|████████████████████▌ | ETA: 0:00:03 Training loss: 0.083399534 Validation loss: 9.827591 Validation accuracy: 0.3333       Progress: 66%|███████████████████████████ | ETA: 0:00:01 Training loss: 0.017089667 Validation loss: 9.750951 Validation accuracy: 0.3333       Progress: 81%|█████████████████████████████████▎ | ETA: 0:00:01 Training loss: 0.0036683287 Validation loss: 9.754569 Validation accuracy: 0.0       Progress: 100%|█████████████████████████████████████████| Time: 0:00:02 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: 0:00:06 Training loss: 136.1213472638287 Training accuracy: 1.0     Progress: 8%|███▎ | ETA: 0:00:03 Training loss: 129.65975187408176 Training accuracy: 0.6667     Progress: 15%|██████▏ | ETA: 0:00:02 Training loss: 117.97306201708268 Training accuracy: 0.6667     Progress: 23%|█████████▍ | ETA: 0:00:02 Training loss: 97.35413144591028 Training accuracy: 0.6667     Progress: 30%|████████████▎ | ETA: 0:00:01 Training loss: 74.31560057358351 Training accuracy: 0.6667     Progress: 37%|███████████████▏ | ETA: 0:00:01 Training loss: 50.653066791082196 Training accuracy: 0.6667     Progress: 44%|██████████████████ | ETA: 0:00:01 Training loss: 31.3661387750515 Training accuracy: 0.6667     Progress: 52%|█████████████████████▍ | ETA: 0:00:01 Training loss: 18.24684941382116 Training accuracy: 0.6667     Progress: 59%|████████████████████████▎ | ETA: 0:00:01 Training loss: 12.9205195096771 Training accuracy: 1.0     Progress: 66%|███████████████████████████ | ETA: 0:00:01 Training loss: 9.92047314752541 Training accuracy: 1.0     Progress: 74%|██████████████████████████████▍ | ETA: 0:00:00 Training loss: 7.641714199993556 Training accuracy: 1.0     Progress: 81%|█████████████████████████████████▎ | ETA: 0:00:00 Training loss: 6.361311109400841 Training accuracy: 1.0     Progress: 89%|████████████████████████████████████▌ | ETA: 0:00:00 Training loss: 5.305060046203967 Training accuracy: 1.0     Progress: 96%|███████████████████████████████████████▍ | ETA: 0:00:00 Training loss: 4.514483164565182 Training accuracy: 1.0     Progress: 100%|█████████████████████████████████████████| Time: 0:00:01 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: 6%|██▌ | ETA: 0:00:02 Training loss: 132.4804776530344 Validation loss: 113.11286407646799 Validation accuracy: 0.0 Training accuracy: 1.0         Progress: 12%|████▉ | ETA: 0:00:02 Training loss: 124.49235092973407 Validation loss: 108.64858091130895 Validation accuracy: 0.0 Training accuracy: 1.0         Progress: 19%|███████▊ | ETA: 0:00:01 Training loss: 110.16628513878496 Validation loss: 101.61399929932261 Validation accuracy: 0.0 Training accuracy: 0.6667         Progress: 27%|███████████▏ | ETA: 0:00:01 Training loss: 87.01939707667547 Validation loss: 91.2991951982845 Validation accuracy: 0.0 Training accuracy: 0.3333         Progress: 33%|█████████████▌ | ETA: 0:00:01 Training loss: 66.81382182203734 Validation loss: 83.21601062477458 Validation accuracy: 0.0 Training accuracy: 0.3333         Progress: 41%|████████████████▊ | ETA: 0:00:01 Training loss: 41.41996016563803 Validation loss: 74.7213665522012 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: 57%|███████████████████████▍ | ETA: 0:00:01 Training loss: 14.762318412369119 Validation loss: 69.50257721108675 Validation accuracy: 0.0 Training accuracy: 1.0         Progress: 64%|██████████████████████████▎ | ETA: 0:00:01 Training loss: 11.078013861661095 Validation loss: 68.4463206653876 Validation accuracy: 0.0 Training accuracy: 1.0         Progress: 72%|█████████████████████████████▌ | ETA: 0:00:00 Training loss: 8.44376518843828 Validation loss: 67.20040177020788 Validation accuracy: 0.0 Training accuracy: 1.0         Progress: 78%|████████████████████████████████ | ETA: 0:00:00 Training loss: 7.094575534648743 Validation loss: 66.60746829173387 Validation accuracy: 0.0 Training accuracy: 1.0         Progress: 84%|██████████████████████████████████▌ | ETA: 0:00:00 Training loss: 6.145555867099783 Validation loss: 66.38593802321336 Validation accuracy: 0.0 Training accuracy: 1.0         Progress: 89%|████████████████████████████████████▌ | ETA: 0:00:00 Training loss: 5.511636071363488 Validation loss: 66.33747718401284 Validation accuracy: 0.0 Training accuracy: 1.0         Progress: 98%|████████████████████████████████████████▏| ETA: 0:00:00 Training loss: 4.515216292263363 Validation loss: 66.361169520615 Validation accuracy: 0.0 Training accuracy: 1.0         Progress: 100%|█████████████████████████████████████████| Time: 0:00:01 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... Progress: 3%|█▎ | ETA: 0:00:03 Training loss: 135.36709303400983 Training accuracy: 1.0     Progress: 7%|██▉ | ETA: 0:00:03 Training loss: 131.38567017181032 Training accuracy: 1.0     Progress: 13%|█████▍ | ETA: 0:00:02 Training loss: 122.9682799017793 Training accuracy: 1.0     Progress: 19%|███████▊ | ETA: 0:00:02 Training loss: 110.48681725334359 Training accuracy: 0.6667     Progress: 27%|███████████▏ | ETA: 0:00:02 Training loss: 87.46331542853757 Training accuracy: 0.6667     Progress: 35%|██████████████▍ | ETA: 0:00:01 Training loss: 60.61754172096076 Training accuracy: 0.6667     Progress: 43%|█████████████████▋ | ETA: 0:00:01 Training loss: 36.77646524993327 Training accuracy: 0.6667     Progress: 51%|████████████████████▉ | ETA: 0:00:01 Training loss: 21.299648690478033 Training accuracy: 0.6667     Progress: 59%|████████████████████████▎ | ETA: 0:00:01 Training loss: 13.847665068473782 Training accuracy: 1.0     Progress: 67%|███████████████████████████▌ | ETA: 0:00:01 Training loss: 10.208132753016475 Training accuracy: 1.0     Progress: 75%|██████████████████████████████▊ | ETA: 0:00:00 Training loss: 7.922924271112943 Training accuracy: 1.0     Progress: 83%|██████████████████████████████████ | ETA: 0:00:00 Training loss: 6.468972390216974 Training accuracy: 1.0     Progress: 91%|█████████████████████████████████████▎ | ETA: 0:00:00 Training loss: 5.446414653872179 Training accuracy: 1.0     Progress: 99%|████████████████████████████████████████▋| ETA: 0:00:00 Training loss: 4.5827585391573 Training accuracy: 1.0     Progress: 100%|█████████████████████████████████████████| Time: 0:00:01 Training loss: 4.482754494799172 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: 3%|█▎ | ETA: 0:00:03 Training loss: 135.36709303400983 Training accuracy: 1.0     Progress: 8%|███▎ | ETA: 0:00:03 Training loss: 130.2244481256631 Training accuracy: 1.0     Progress: 15%|██████▏ | ETA: 0:00:02 Training loss: 119.29161748500255 Training accuracy: 0.6667     Progress: 22%|█████████ | ETA: 0:00:02 Training loss: 102.61912377014526 Training accuracy: 0.6667     Progress: 29%|███████████▉ | ETA: 0:00:01 Training loss: 80.87493713900922 Training accuracy: 0.6667     Progress: 36%|██████████████▊ | ETA: 0:00:01 Training loss: 57.32190033082655 Training accuracy: 0.6667     Progress: 43%|█████████████████▋ | ETA: 0:00:01 Training loss: 36.77646524993327 Training accuracy: 0.6667     Progress: 50%|████████████████████▌ | ETA: 0:00:01 Training loss: 22.727475472414458 Training accuracy: 0.6667     Progress: 58%|███████████████████████▊ | ETA: 0:00:01 Training loss: 14.485045419158766 Training accuracy: 1.0     Progress: 66%|███████████████████████████ | ETA: 0:00:01 Training loss: 10.567456292792862 Training accuracy: 1.0     Progress: 73%|█████████████████████████████▉ | ETA: 0:00:00 Training loss: 8.405613313483148 Training accuracy: 1.0     Progress: 81%|█████████████████████████████████▎ | ETA: 0:00:00 Training loss: 6.774134752560341 Training accuracy: 1.0     Progress: 88%|████████████████████████████████████▏ | ETA: 0:00:00 Training loss: 5.8038710635679776 Training accuracy: 1.0     Progress: 95%|███████████████████████████████████████ | ETA: 0:00:00 Training loss: 4.999848752617834 Training accuracy: 1.0     Progress: 100%|█████████████████████████████████████████| Time: 0:00:01 Training loss: 4.482754494799172 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... Progress: 1%|▎ | ETA: 0:00:18 Training loss: 127.32384728776519 Validation loss: 114.78662815822663 Validation accuracy: 0.0       Progress: 1%|▌ | ETA: 0:00:17 Training loss: 119.34018995053748 Validation loss: 110.65295882435232 Validation accuracy: 0.0       Progress: 2%|▊ | ETA: 0:00:16 Training loss: 105.25037785058935 Validation loss: 104.25249364291807 Validation accuracy: 0.0       Progress: 3%|█▏ | ETA: 0:00:15 Training loss: 82.86487038120121 Validation loss: 94.99889606791834 Validation accuracy: 0.0       Progress: 4%|█▍ | ETA: 0:00:15 Training loss: 56.81386539126286 Validation loss: 85.3650864871393 Validation accuracy: 0.0       Progress: 4%|█▊ | ETA: 0:00:14 Training loss: 33.36478578198269 Validation loss: 77.95835170621417 Validation accuracy: 0.0       Progress: 5%|██▏ | ETA: 0:00:14 Training loss: 17.847286042647635 Validation loss: 73.81862916687002 Validation accuracy: 0.0       Progress: 6%|██▍ | ETA: 0:00:14 Training loss: 10.131823264335916 Validation loss: 71.91646633348797 Validation accuracy: 0.0       Progress: 7%|██▊ | ETA: 0:00:14 Training loss: 6.370467068572309 Validation loss: 70.60670808342952 Validation accuracy: 0.0       Progress: 8%|███▏ | ETA: 0:00:13 Training loss: 4.2403765055856475 Validation loss: 69.66568806627595 Validation accuracy: 0.0       Progress: 8%|███▍ | ETA: 0:00:13 Training loss: 3.0061798183302373 Validation loss: 69.38943530164148 Validation accuracy: 0.0       Progress: 10%|████ | ETA: 0:00:12 Training loss: 1.7903420308945135 Validation loss: 69.5623197454607 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: 16%|██████▌ | ETA: 0:00:01 Training loss: 110.49517529382871 Validation loss: 106.73543157365351 Validation accuracy: 0.0   Setting up model... model = Chain(Dense(32 => 1000, relu), Dense(1000 => 200)) Setting up data structures... Setting up optimizer... Training... Setting up model... model = Chain(Dense(32 => 1000, relu), Dense(1000 => 200)) Setting up data structures... Setting up optimizer... Training... Progress: 0%|▎ | ETA: 0:00:21 Training loss: 127.94594780431525 Validation loss: 115.12967448211786 Validation accuracy: 0.0       Progress: 1%|▌ | ETA: 0:00:19 Training loss: 120.04672186538338 Validation loss: 110.96859348228752 Validation accuracy: 0.0       Progress: 2%|▊ | ETA: 0:00:17 Training loss: 106.064033017065 Validation loss: 104.55989523479973 Validation accuracy: 0.0       Progress: 3%|█▏ | ETA: 0:00:16 Training loss: 83.60205361748329 Validation loss: 95.23345983075262 Validation accuracy: 0.0       Progress: 3%|█▍ | ETA: 0:00:15 Training loss: 60.555084609600065 Validation loss: 86.6224521439384 Validation accuracy: 0.0       Progress: 4%|█▋ | ETA: 0:00:15 Training loss: 38.78991110535883 Validation loss: 79.53418364372035 Validation accuracy: 0.0       Progress: 5%|██ | ETA: 0:00:15 Training loss: 20.91550732574291 Validation loss: 74.63942810318065 Validation accuracy: 0.0       Progress: 6%|██▎ | ETA: 0:00:14 Training loss: 11.687910752848975 Validation loss: 72.36072846761625 Validation accuracy: 0.0       Progress: 6%|██▋ | ETA: 0:00:14 Training loss: 7.3860064424702525 Validation loss: 70.99117784470441 Validation accuracy: 0.0       Progress: 7%|███ | ETA: 0:00:14 Training loss: 4.967146184739027 Validation loss: 69.92557620647317 Validation accuracy: 0.0       Progress: 8%|███▎ | ETA: 0:00:14 Training loss: 3.550158684045246 Validation loss: 69.51077367029211 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: 8%|███▎ | ETA: 0:00:01 Training loss: 1.9755344 Validation loss: 9.8415785 Validation accuracy: 0.3333       Progress: 15%|██████▏ | ETA: 0:00:01 Training loss: 1.061908 Validation loss: 8.923413 Validation accuracy: 0.1667       Progress: 23%|█████████▍ | ETA: 0:00:01 Training loss: 0.27806348 Validation loss: 9.119209 Validation accuracy: 0.1667       Progress: 32%|█████████████▏ | ETA: 0:00:01 Training loss: 0.05359718 Validation loss: 8.737388 Validation accuracy: 0.1667       Progress: 43%|█████████████████▋ | ETA: 0:00:01 Training loss: 0.00647992 Validation loss: 8.708759 Validation accuracy: 0.1667       Progress: 54%|██████████████████████▏ | ETA: 0:00:01 Training loss: 0.0010970138 Validation loss: 8.665216 Validation accuracy: 0.1667       Progress: 65%|██████████████████████████▋ | ETA: 0:00:00 Training loss: 0.00028831122 Validation loss: 8.662797 Validation accuracy: 0.1667       Progress: 76%|███████████████████████████████▏ | ETA: 0:00:00 Training loss: 0.00014594999 Validation loss: 8.653325 Validation accuracy: 0.1667       Progress: 87%|███████████████████████████████████▋ | ETA: 0:00:00 Training loss: 0.00021264788 Validation loss: 8.670529 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: 0:00:01 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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Setting up optimizer... Training... Progress: 7%|██▉ | ETA: 0:00:02 Training loss: 50.57888 Validation loss: 43.449013 Validation accuracy: 0.0       Progress: 14%|█████▊ | ETA: 0:00:01 Training loss: 43.1337 Validation loss: 39.704792 Validation accuracy: 0.0       Progress: 21%|████████▋ | ETA: 0:00:01 Training loss: 36.516342 Validation loss: 36.317276 Validation accuracy: 0.0       Progress: 28%|███████████▌ | ETA: 0:00:01 Training loss: 30.694498 Validation loss: 33.29029 Validation accuracy: 0.0       Progress: 35%|██████████████▍ | ETA: 0:00:01 Training loss: 25.641188 Validation loss: 30.616184 Validation accuracy: 0.0       Progress: 42%|█████████████████▎ | ETA: 0:00:01 Training loss: 21.312489 Validation loss: 28.271175 Validation accuracy: 0.0       Progress: 49%|████████████████████▏ | ETA: 0:00:01 Training loss: 17.63769 Validation loss: 26.240067 Validation accuracy: 0.0       Progress: 56%|███████████████████████ | ETA: 0:00:01 Training loss: 14.535844 Validation loss: 24.486229 Validation accuracy: 0.0       Progress: 63%|█████████████████████████▉ | ETA: 0:00:01 Training loss: 11.939568 Validation loss: 22.975124 Validation accuracy: 0.0       Progress: 70%|████████████████████████████▊ | ETA: 0:00:00 Training loss: 9.790781 Validation loss: 21.681162 Validation accuracy: 0.0       Progress: 77%|███████████████████████████████▋ | ETA: 0:00:00 Training loss: 8.016506 Validation loss: 20.575783 Validation accuracy: 0.0       Progress: 84%|██████████████████████████████████▌ | ETA: 0:00:00 Training loss: 6.5606503 Validation loss: 19.634026 Validation accuracy: 0.0       Progress: 91%|█████████████████████████████████████▎ | ETA: 0:00:00 Training loss: 5.3748693 Validation loss: 18.830145 Validation accuracy: 0.0       Progress: 98%|████████████████████████████████████████▏| ETA: 0:00:00 Training loss: 4.415696 Validation loss: 18.143456 Validation accuracy: 0.0       Progress: 100%|█████████████████████████████████████████| Time: 0:00:01 Training loss: 4.177827 Validation loss: 17.968048 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: 14%|█████▊ | ETA: 0:00:01 Training loss: 0.30711794 Validation loss: 1055.5469 Validation accuracy: 0.0       Progress: 33%|█████████████▌ | ETA: 0:00:00 Training loss: 0.42489377 Validation loss: 2264.6963 Validation accuracy: 0.0       Progress: 52%|█████████████████████▍ | ETA: 0:00:00 Training loss: 0.44620222 Validation loss: 2541.6965 Validation accuracy: 0.0       Progress: 71%|█████████████████████████████▏ | ETA: 0:00:00 Training loss: 0.4475012 Validation loss: 2589.1326 Validation accuracy: 0.0       Progress: 90%|████████████████████████████████████▉ | ETA: 0:00:00 Training loss: 0.44511387 Validation loss: 2596.6729 Validation accuracy: 0.0       Progress: 100%|█████████████████████████████████████████| Time: 0:00:00 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: 27%|███████████▏ | ETA: 0:00:00 Training loss: 1.3378574 Validation loss: 24.66427 Validation accuracy: 0.1667       Progress: 58%|███████████████████████▊ | ETA: 0:00:00 Training loss: 0.042151716 Validation loss: 23.202656 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/9PibT/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: 0:01:50 Training loss: 299.4347 Validation loss: 91.30579 Validation accuracy: 0.1667       Progress: 4%|█▋ | ETA: 0:00:57 Training loss: 32.32986 Validation loss: 69.95957 Validation accuracy: 0.3333       Progress: 14%|█████▊ | ETA: 0:00:15 Training loss: 16.798212 Validation loss: 42.87909 Validation accuracy: 0.1667       Progress: 22%|█████████ | ETA: 0:00:09 Training loss: 6.347103 Validation loss: 43.937798 Validation accuracy: 0.3333       Progress: 35%|██████████████▍ | ETA: 0:00:05 Training loss: 0.49793452 Validation loss: 41.87196 Validation accuracy: 0.3333       Progress: 48%|███████████████████▋ | ETA: 0:00:03 Training loss: 0.060960595 Validation loss: 41.35208 Validation accuracy: 0.3333       Progress: 61%|█████████████████████████ | ETA: 0:00:02 Training loss: 0.0113826115 Validation loss: 41.180946 Validation accuracy: 0.3333       Progress: 74%|██████████████████████████████▍ | ETA: 0:00:01 Training loss: 0.005717308 Validation loss: 41.16881 Validation accuracy: 0.3333       Progress: 87%|███████████████████████████████████▋ | ETA: 0:00:00 Training loss: 0.003135302 Validation loss: 41.27034 Validation accuracy: 0.3333       Progress: 100%|█████████████████████████████████████████| Time: 0:00:03 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: 0:22:26 Training loss: 0.39982933 Validation loss: 1.9066198 Validation accuracy: 0.0       Progress: 21%|████████▋ | ETA: 0:01:44 Training loss: 0.00034356967 Validation loss: 3.9455621 Validation accuracy: 0.0       Progress: 39%|████████████████ | ETA: 0:00:43 Training loss: 2.366534e-5 Validation loss: 3.998641 Validation accuracy: 0.0       Progress: 59%|████████████████████████▎ | ETA: 0:00:19 Training loss: 1.1130268e-5 Validation loss: 4.01508 Validation accuracy: 0.0       Progress: 77%|███████████████████████████████▋ | ETA: 0:00:08 Training loss: 6.8863396e-6 Validation loss: 4.0237885 Validation accuracy: 0.0       Progress: 96%|███████████████████████████████████████▍ | ETA: 0:00:01 Training loss: 5.1448856e-6 Validation loss: 4.028838 Validation accuracy: 0.0       Progress: 100%|█████████████████████████████████████████| Time: 0:00:28 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: 5%|██ | ETA: 0:00:02 Training loss: 133.4418769758786   Progress: 13%|█████▍ | ETA: 0:00:01 Training loss: 122.78921984408221   Progress: 21%|████████▋ | ETA: 0:00:01 Training loss: 105.05066797010393   Progress: 29%|███████████▉ | ETA: 0:00:01 Training loss: 80.377782038016   Progress: 37%|███████████████▏ | ETA: 0:00:01 Training loss: 53.2906638169727   Progress: 45%|██████████████████▌ | ETA: 0:00:01 Training loss: 31.002321216109486   Progress: 53%|█████████████████████▊ | ETA: 0:00:01 Training loss: 18.01171160590189   Progress: 61%|█████████████████████████ | ETA: 0:00:01 Training loss: 12.246790718661403   Progress: 69%|████████████████████████████▎ | ETA: 0:00:00 Training loss: 9.171644282295716   Progress: 77%|███████████████████████████████▋ | ETA: 0:00:00 Training loss: 7.167844719213046   Progress: 85%|██████████████████████████████████▉ | ETA: 0:00:00 Training loss: 5.90107184363141   Progress: 93%|██████████████████████████████████████▏ | ETA: 0:00:00 Training loss: 4.944699663371199   Progress: 100%|█████████████████████████████████████████| Time: 0:00:01 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: 7%|██▉ | ETA: 0:00:01 Training loss: 2.7209384   Progress: 14%|█████▊ | ETA: 0:00:01 Training loss: 1.1006185   Progress: 21%|████████▋ | ETA: 0:00:01 Training loss: 0.15059157   Progress: 28%|███████████▌ | ETA: 0:00:01 Training loss: 0.18575929   Progress: 36%|██████████████▊ | ETA: 0:00:01 Training loss: 0.10888151   Progress: 44%|██████████████████ | ETA: 0:00:01 Training loss: 0.055727486   Progress: 52%|█████████████████████▍ | ETA: 0:00:01 Training loss: 0.022523986   Progress: 60%|████████████████████████▋ | ETA: 0:00:01 Training loss: 0.007130475   Progress: 67%|███████████████████████████▌ | ETA: 0:00:00 Training loss: 0.006295667   Progress: 74%|██████████████████████████████▍ | ETA: 0:00:00 Training loss: 0.003814346   Progress: 82%|█████████████████████████████████▋ | ETA: 0:00:00 Training loss: 0.0007453682   Progress: 90%|████████████████████████████████████▉ | ETA: 0:00:00 Training loss: 0.00029201558   Progress: 98%|████████████████████████████████████████▏| ETA: 0:00:00 Training loss: 0.00035068596   Progress: 100%|█████████████████████████████████████████| Time: 0:00:01 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: 0:00:00 Progress: 100%|█████████████████████████████████████████| Time: 0:00:00 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: 0:00:03 Step loss: 131.14099893438816 Overall loss: 135.85572493843063 Overall accuracy: 0.3333       Progress: 100%|█████████████████████████████████████████| Time: 0:00:06 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... 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: 58%|███████████████████████▉ | ETA: 0:00:00 Step loss: 142.90495012238247 Overall loss: 131.7731644551819 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: 67%|███████████████████████████▍ | ETA: 0:00:00 Step loss: 128.3223637100985 Overall loss: 132.7297644801879 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... 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: 67%|███████████████████████████▍ | ETA: 0:00:00 Step loss: 144.39176453339 Overall loss: 132.97095160544072 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: 67%|███████████████████████████▍ | ETA: 0:00:00 Step loss: 143.89213924099897 Overall loss: 132.59214508105526 Overall accuracy: 0.6667       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... 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: 56%|██████████████████████▊ | ETA: 0:00:00 Step loss: 129.39207882397304 Overall loss: 134.21831789958514 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... Progress: 67%|███████████████████████████▍ | ETA: 0:00:00 Step loss: 136.2665052695647 Overall loss: 135.36709303725198     Progress: 100%|█████████████████████████████████████████| Time: 0:00:00 Step loss: 135.36709303725198 Overall loss: 134.44507860114422 Done! Test Summary: | Pass Total Time deep learning tests | 96 96 3m07.4s Testing JudiLing tests passed Testing completed after 430.56s PkgEval succeeded after 1163.94s