Package evaluation of JudiLing on Julia 1.10.8 (92f03a4775*) started at 2025-02-25T12:57:30.032 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Set-up completed after 5.14s ################################################################################ # Installation # Installing JudiLing... Resolving package versions... Updating `~/.julia/environments/v1.10/Project.toml` [b43a184b] + JudiLing v0.12.0 Updating `~/.julia/environments/v1.10/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 v0.18.20 [e2d170a0] + DataValueInterfaces v1.0.0 [8bb1440f] + DelimitedFiles v1.9.1 [b4f34e82] + Distances v0.10.12 [31c24e10] + Distributions v0.25.117 ⌅ [ffbed154] + DocStringExtensions v0.8.6 [c5bfea45] + Embeddings v0.4.6 [460bff9d] + ExceptionUnwrapping v0.1.11 [48062228] + FilePathsBase v0.9.23 [1a297f60] + FillArrays v1.13.0 [92fee26a] + GZip v0.6.2 ⌅ [91feb7a0] + GoogleDrive v0.1.3 [cd3eb016] + HTTP v1.10.15 [34004b35] + HypergeometricFunctions v0.3.27 [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 v0.11.32 [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 v1.10.2 [43287f4e] + PtrArrays v1.3.0 [1fd47b50] + QuadGK v2.11.2 [3cdcf5f2] + RecipesBase v1.3.4 [189a3867] + Reexport v1.2.2 [ae029012] + Requires v1.3.0 [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 [82ae8749] + StatsAPI v1.7.0 ⌅ [2913bbd2] + StatsBase v0.33.21 [4c63d2b9] + StatsFuns v1.3.2 [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 v1.5.1 [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 v1.1.1 [56f22d72] + Artifacts [2a0f44e3] + Base64 [ade2ca70] + Dates [8ba89e20] + Distributed [f43a241f] + Downloads v1.6.0 [7b1f6079] + FileWatching [9fa8497b] + Future [b77e0a4c] + InteractiveUtils [b27032c2] + LibCURL v0.6.4 [76f85450] + LibGit2 [8f399da3] + Libdl [37e2e46d] + LinearAlgebra [56ddb016] + Logging [d6f4376e] + Markdown [a63ad114] + Mmap [ca575930] + NetworkOptions v1.2.0 [de0858da] + Printf [9abbd945] + Profile [9a3f8284] + Random [ea8e919c] + SHA v0.7.0 [9e88b42a] + Serialization [6462fe0b] + Sockets [2f01184e] + SparseArrays v1.10.0 [10745b16] + Statistics v1.10.0 [4607b0f0] + SuiteSparse [fa267f1f] + TOML v1.0.3 [8dfed614] + Test [cf7118a7] + UUIDs [4ec0a83e] + Unicode [e66e0078] + CompilerSupportLibraries_jll v1.1.1+0 [deac9b47] + LibCURL_jll v8.4.0+0 [e37daf67] + LibGit2_jll v1.6.4+0 [29816b5a] + LibSSH2_jll v1.11.0+1 [c8ffd9c3] + MbedTLS_jll v2.28.2+1 [14a3606d] + MozillaCACerts_jll v2023.1.10 [4536629a] + OpenBLAS_jll v0.3.23+4 [05823500] + OpenLibm_jll v0.8.1+4 [bea87d4a] + SuiteSparse_jll v7.2.1+1 [83775a58] + Zlib_jll v1.2.13+1 [8e850b90] + libblastrampoline_jll v5.11.0+0 [8e850ede] + nghttp2_jll v1.52.0+1 [3f19e933] + p7zip_jll v17.4.0+2 Info Packages marked with ⌃ 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 7.03s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... Precompiling package dependencies... Precompilation completed after 523.19s ################################################################################ # Testing # Testing JudiLing Status `/tmp/jl_Jd0pTi/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 [2f01184e] SparseArrays v1.10.0 [8dfed614] Test Status `/tmp/jl_Jd0pTi/Manifest.toml` [621f4979] AbstractFFTs v1.5.0 [7d9f7c33] Accessors v0.1.41 [79e6a3ab] Adapt v4.2.0 [66dad0bd] AliasTables v1.1.3 [dce04be8] ArgCheck v2.4.0 [a9b6321e] Atomix v1.1.0 ⌅ [15f4f7f2] AutoHashEquals v0.2.0 [fbb218c0] BSON v0.3.9 [198e06fe] BangBang v0.4.3 [9718e550] Baselet v0.1.1 ⌅ [6e4b80f9] BenchmarkTools v1.5.0 [d1d4a3ce] BitFlags v0.1.9 [fa961155] CEnum v0.5.0 [336ed68f] CSV v0.10.15 [082447d4] ChainRules v1.72.2 [d360d2e6] ChainRulesCore v1.25.1 [944b1d66] CodecZlib v0.7.8 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v11.2.2 [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 v0.3.27 [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 [929cbde3] LLVM v9.2.0 [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 v1.6.11 ⌃ [9920b226] MLDataPattern v0.5.4 [66a33bbf] MLLabelUtils v0.5.7 [d8e11817] MLStyle v0.4.17 [f1d291b0] MLUtils v0.4.7 [1914dd2f] MacroTools v0.5.15 [dbb5928d] MappedArrays v0.4.2 [739be429] 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[2a0f44e3] Base64 [ade2ca70] Dates [8ba89e20] Distributed [f43a241f] Downloads v1.6.0 [7b1f6079] FileWatching [9fa8497b] Future [b77e0a4c] InteractiveUtils [4af54fe1] LazyArtifacts [b27032c2] LibCURL v0.6.4 [76f85450] LibGit2 [8f399da3] Libdl [37e2e46d] LinearAlgebra [56ddb016] Logging [d6f4376e] Markdown [a63ad114] Mmap [ca575930] NetworkOptions v1.2.0 [44cfe95a] Pkg v1.10.0 [de0858da] Printf [9abbd945] Profile [3fa0cd96] REPL [9a3f8284] Random [ea8e919c] SHA v0.7.0 [9e88b42a] Serialization [6462fe0b] Sockets [2f01184e] SparseArrays v1.10.0 [10745b16] Statistics v1.10.0 [4607b0f0] SuiteSparse [fa267f1f] TOML v1.0.3 [a4e569a6] Tar v1.10.0 [8dfed614] Test [cf7118a7] UUIDs [4ec0a83e] Unicode [e66e0078] CompilerSupportLibraries_jll v1.1.1+0 [deac9b47] LibCURL_jll v8.4.0+0 [e37daf67] LibGit2_jll v1.6.4+0 [29816b5a] LibSSH2_jll v1.11.0+1 [c8ffd9c3] MbedTLS_jll v2.28.2+1 [14a3606d] MozillaCACerts_jll v2023.1.10 [4536629a] OpenBLAS_jll v0.3.23+4 [05823500] OpenLibm_jll v0.8.1+4 [bea87d4a] SuiteSparse_jll v7.2.1+1 [83775a58] Zlib_jll v1.2.13+1 [8e850b90] libblastrampoline_jll v5.11.0+0 [8e850ede] nghttp2_jll v1.52.0+1 [3f19e933] p7zip_jll v17.4.0+2 Info Packages marked with ⌃ 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-3.0.12-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) (2.2.3) 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python_dateutil-2.9.0.post0-py2.py3-none-any.whl.metadata (8.4 kB) Collecting pytz>=2020.1 (from pandas>=1.4.3->pyndl) Downloading pytz-2025.1-py2.py3-none-any.whl.metadata (22 kB) Collecting tzdata>=2022.7 (from pandas>=1.4.3->pyndl) Downloading tzdata-2025.1-py2.py3-none-any.whl.metadata (1.4 kB) Requirement already satisfied: packaging>=23.2 in /home/pkgeval/.julia/conda/3/x86_64/lib/python3.12/site-packages (from xarray>=2022.6.0->pyndl) (24.2) Collecting six>=1.5 (from python-dateutil>=2.8.2->pandas>=1.4.3->pyndl) Downloading six-1.17.0-py2.py3-none-any.whl.metadata (1.7 kB) Using cached Cython-3.0.12-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.5 MB) Downloading netCDF4-1.7.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (9.3 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 9.3/9.3 MB 109.3 MB/s eta 0:00:00 Downloading pandas-2.2.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.7 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 12.7/12.7 MB 139.0 MB/s eta 0:00:00 Downloading scipy-1.15.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (37.3 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 37.3/37.3 MB 123.0 MB/s eta 0:00:00 Downloading toml-0.10.2-py2.py3-none-any.whl (16 kB) Downloading xarray-2025.1.2-py3-none-any.whl (1.2 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.2/1.2 MB 84.8 MB/s eta 0:00:00 Downloading python_dateutil-2.9.0.post0-py2.py3-none-any.whl (229 kB) Downloading pytz-2025.1-py2.py3-none-any.whl (507 kB) Downloading tzdata-2025.1-py2.py3-none-any.whl (346 kB) Downloading cftime-1.6.4.post1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.4 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.4/1.4 MB 84.1 MB/s eta 0:00:00 Downloading six-1.17.0-py2.py3-none-any.whl (11 kB) Building wheels for collected packages: pyndl Building wheel for pyndl (pyproject.toml): started Building wheel for pyndl (pyproject.toml): finished with status 'done' Created wheel for pyndl: filename=pyndl-1.2.3-cp312-cp312-manylinux_2_36_x86_64.whl size=393264 sha256=eafc3e3314c2625a12642837a9fe00a25e8f9842c3a934d9144cb54d952d7162 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-2025.1 scipy-1.15.2 six-1.17.0 toml-0.10.2 tzdata-2025.1 xarray-2025.1.2 /home/pkgeval/.julia/conda/3/x86_64/lib/python3.12/multiprocessing/popen_fork.py:66: DeprecationWarning: This process (pid=509) 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=509) 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=509) 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=509) 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=509) 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=509) 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=509) 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=509) 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=509) 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=509) 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=509) 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=509) 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=509) 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=509) 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=509) 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=509) 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 4m02.7s Test Summary: | Pass Total Time input tests | 27 27 10.4s Test Summary: | Pass Total Time cholesky tests | 10 10 15.4s Test Summary: | Pass Total Time frequency tests | 3 3 10.6s 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 -0.0013789 -0.0013789 -0.0236124 -0.0132235 -0.0104839 0.0222335 2 │ S2 -0.0026455 -0.0026455 0.00430249 0.000297545 0.00463446 -0.00694799 3 │ S3 -0.00144289 -0.00144289 -0.0191179 -0.00532917 -0.00962582 0.017675 4 │ S4 -0.00136834 -0.00136834 0.00155156 0.00289332 0.00965301 -0.00291989 5 │ S5 -0.0029053 -0.0029053 -0.0173081 -0.00290699 0.0789402 0.0144028 6 │ S6 0.00240335 0.00240335 0.0128883 -0.00295148 0.0262302 -0.0104849 6×7 DataFrame Row │ Data #vo voc oco coo oo# oca │ String15 Float64 Float64 Float64 Float64 Float64 Float64 ─────┼────────────────────────────────────────────────────────────────────────────── 1 │ vocoo 0.988967 0.988967 0.863576 0.866626 0.870662 0.125391 2 │ vocaas 1.00248 1.00248 -0.0441335 -0.0335163 -0.0380084 1.04661 3 │ vocat 1.00099 1.00099 0.00415934 -0.0184501 0.00873488 0.996827 4 │ vocaamus 0.998959 0.998959 0.00745898 0.0163423 0.0557051 0.9915 5 │ vocaatis 1.01121 1.01121 0.0806596 0.0566645 -0.0173351 0.93055 6 │ vocant 0.995747 0.995747 0.0018249 -0.0189341 -0.0549372 0.993922 6×7 DataFrame Row │ Data S1 S2 S3 S4 S5 S6 │ String Float64 Float64 Float64 Float64 Float64 Float64 ─────┼───────────────────────────────────────────────────────────────────────── 1 │ #vo 5.72851 -7.76108 3.50812 -2.02855 -0.436928 1.589 2 │ voc 5.72851 -7.76108 3.50812 -2.02855 -0.436928 1.589 3 │ oco 1.42971 -3.03506 1.62505 -1.31801 -0.234167 0.88857 4 │ coo 0.820988 0.0194814 1.3853 -0.821628 -0.0331436 0.0733962 5 │ oo# 1.52235 -2.32403 5.54024 -3.70934 1.54308 0.754508 6 │ oca 4.29879 -4.72601 1.88307 -0.710533 -0.202761 0.70043 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 4.89447 2 │ vocaas 21.1856 -21.497 18.7376 -8.27066 -6.04744 10.5077 3 │ vocat 22.2289 -28.9307 9.79305 -5.54923 -1.80306 4.54154 4 │ vocaamus 16.8829 -18.2816 7.83294 -8.25267 8.51863 -1.95153 5 │ vocaatis 19.7887 -19.1196 10.4814 -6.73917 1.86 3.5266 6 │ vocant 20.6622 -26.5988 5.84833 -5.02826 4.97536 -3.94895 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 0.942801 0.383536 0.412454 0.369812 0.402581 0.36661 2 │ vocaas 0.322266 0.951594 0.523392 0.633262 0.574722 0.487659 3 │ vocat 0.380784 0.564788 0.964372 0.485519 0.462794 0.538526 4 │ vocaamus 0.292915 0.525256 0.449793 0.975419 0.493892 0.377126 5 │ vocaatis 0.325229 0.574494 0.47668 0.480103 0.948104 0.405212 6 │ vocant 0.330508 0.509389 0.522855 0.442543 0.400384 0.98746 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: | Time display tests | None 28.7s ┌ 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 33.0s Test Summary: |Time find_path tests | None 3.6s Test Summary: | Pass Total Time make_adjacency_matrix tests | 7 7 0.4s true Test Summary: | Pass Total Time make_cue_matrix tests | 21 21 5.0s true Test Summary: | Pass Total Time make_semantic_matrix tests | 72 72 4.9s Test Summary: |Time make_yt_matrix tests | None 0.1s Test Summary: | Pass Total Time output_matrix tests | 10 10 11.6s Test Summary: |Time preprocess tests | None 0.5s ┌ 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: | Time test_combo tests | None 15.1s Test Summary: | Pass Total Time wh tests | 5 5 4.7s Setting up model... ┌ Warning: No functional GPU backend found! Defaulting to CPU. │ │ 1. If no GPU is available, nothing needs to be done. │ 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/7YnMq/src/internal.jl:94 model = Chain(Dense(33 => 1000, relu), Dense(1000 => 200)) Setting up data structures... Setting up optimizer... Training... Progress: 2%|▉ | ETA: 1:02:07 Training loss: 136.2665052702724   Progress: 74%|██████████████████████████████▍ | ETA: 0:00:28 Training loss: 8.157322318122155   Progress: 91%|█████████████████████████████████████▎ | ETA: 0:00:08 Training loss: 5.446414653872179   Progress: 97%|███████████████████████████████████████▊ | ETA: 0:00:02 Training loss: 4.787791317238355   Progress: 100%|█████████████████████████████████████████| Time: 0:01:19 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: 0:17:37 Training loss: 7.5811825   Progress: 7%|██▉ | ETA: 0:04:51 Training loss: 2.308484   Progress: 9%|███▊ | ETA: 0:03:43 Training loss: 7.591371   Progress: 11%|████▌ | ETA: 0:03:00 Training loss: 5.2658377   Progress: 13%|█████▍ | ETA: 0:02:30 Training loss: 0.851448   Progress: 16%|██████▌ | ETA: 0:01:58 Training loss: 2.5420182   Progress: 19%|███████▊ | ETA: 0:01:37 Training loss: 1.79281   Progress: 21%|████████▋ | ETA: 0:01:26 Training loss: 0.32272664   Progress: 25%|██████████▎ | ETA: 0:01:09 Training loss: 1.1387806   Progress: 28%|███████████▌ | ETA: 0:00:59 Training loss: 0.5026373   Progress: 31%|████████████▊ | ETA: 0:00:52 Training loss: 0.12263489   Progress: 34%|██████████████ | ETA: 0:00:45 Training loss: 0.47882608   Progress: 38%|███████████████▋ | ETA: 0:00:38 Training loss: 0.048797842   Progress: 42%|█████████████████▎ | ETA: 0:00:32 Training loss: 0.1947911   Progress: 46%|██████████████████▉ | ETA: 0:00:28 Training loss: 0.029697938   Progress: 50%|████████████████████▌ | ETA: 0:00:24 Training loss: 0.083399534   Progress: 55%|██████████████████████▌ | ETA: 0:00:20 Training loss: 0.00787846   Progress: 57%|███████████████████████▍ | ETA: 0:00:18 Training loss: 0.027322868   Progress: 59%|████████████████████████▎ | ETA: 0:00:17 Training loss: 0.0360049   Progress: 60%|████████████████████████▋ | ETA: 0:00:17 Training loss: 0.026762815   Progress: 62%|█████████████████████████▍ | ETA: 0:00:15 Training loss: 0.0053788233   Progress: 64%|██████████████████████████▎ | ETA: 0:00:14 Training loss: 0.008427495   Progress: 66%|███████████████████████████ | ETA: 0:00:13 Training loss: 0.017089667   Progress: 67%|███████████████████████████▌ | ETA: 0:00:13 Training loss: 0.015207298   Progress: 68%|███████████████████████████▉ | ETA: 0:00:12 Training loss: 0.010124475   Progress: 70%|████████████████████████████▊ | ETA: 0:00:11 Training loss: 0.0019406169   Progress: 72%|█████████████████████████████▌ | ETA: 0:00:10 Training loss: 0.0049975896   Progress: 75%|██████████████████████████████▊ | ETA: 0:00:09 Training loss: 0.0060106283   Progress: 78%|████████████████████████████████ | ETA: 0:00:07 Training loss: 0.0009678599   Progress: 80%|████████████████████████████████▊ | ETA: 0:00:07 Training loss: 0.0031347119   Progress: 86%|███████████████████████████████████▎ | ETA: 0:00:04 Training loss: 0.0008392447   Progress: 89%|████████████████████████████████████▌ | ETA: 0:00:03 Training loss: 0.0015391329   Progress: 92%|█████████████████████████████████████▊ | ETA: 0:00:02 Training loss: 0.00025809451   Progress: 95%|███████████████████████████████████████ | ETA: 0:00:01 Training loss: 0.0008231473   Progress: 98%|████████████████████████████████████████▏| ETA: 0:00:01 Training loss: 0.00022815836   Progress: 100%|█████████████████████████████████████████| Time: 0:00:27 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: 0:01:37 Training loss: 136.2665052702724 Validation loss: 115.86517105004413 Validation accuracy: 0.0       Progress: 8%|███▎ | ETA: 0:00:25 Training loss: 130.2244481256631 Validation loss: 111.87290646038484 Validation accuracy: 0.0       Progress: 13%|█████▍ | ETA: 0:00:15 Training loss: 122.9682799017793 Validation loss: 107.71823659660603 Validation accuracy: 0.0       Progress: 16%|██████▌ | ETA: 0:00:13 Training loss: 117.27420305866508 Validation loss: 104.73628984640311 Validation accuracy: 0.0       Progress: 21%|████████▋ | ETA: 0:00:09 Training loss: 105.3576064632318 Validation loss: 98.92197601252431 Validation accuracy: 0.0       Progress: 26%|██████████▋ | ETA: 0:00:07 Training loss: 90.66557981385432 Validation loss: 92.29905506306721 Validation accuracy: 0.0       Progress: 32%|█████████████▏ | ETA: 0:00:06 Training loss: 70.73453400930393 Validation loss: 84.1826190090735 Validation accuracy: 0.0       Progress: 36%|██████████████▊ | ETA: 0:00:05 Training loss: 57.32190033082655 Validation loss: 79.39037711557003 Validation accuracy: 0.0       Progress: 40%|████████████████▍ | ETA: 0:00:05 Training loss: 44.92076266483825 Validation loss: 75.58061462687004 Validation accuracy: 0.0       Progress: 46%|██████████████████▉ | ETA: 0:00:04 Training loss: 29.870952580777 Validation loss: 72.14063469086707 Validation accuracy: 0.0       Progress: 52%|█████████████████████▍ | ETA: 0:00:03 Training loss: 20.003568121576137 Validation loss: 70.84511748632153 Validation accuracy: 0.0       Progress: 58%|███████████████████████▊ | ETA: 0:00:02 Training loss: 14.485045419158766 Validation loss: 70.17235343366026 Validation accuracy: 0.0       Progress: 64%|██████████████████████████▎ | ETA: 0:00:02 Training loss: 11.349522587968652 Validation loss: 69.24207982736236 Validation accuracy: 0.0       Progress: 70%|████████████████████████████▊ | ETA: 0:00:02 Training loss: 9.236278321559821 Validation loss: 68.17248166695094 Validation accuracy: 0.0       Progress: 75%|██████████████████████████████▊ | ETA: 0:00:01 Training loss: 7.922924271112943 Validation loss: 67.47676382485899 Validation accuracy: 0.0       Progress: 81%|█████████████████████████████████▎ | ETA: 0:00:01 Training loss: 6.774134752560341 Validation loss: 67.02764766380811 Validation accuracy: 0.0       Progress: 85%|██████████████████████████████████▉ | ETA: 0:00:01 Training loss: 6.1892119805382 Validation loss: 66.8830797519387 Validation accuracy: 0.0       Progress: 91%|█████████████████████████████████████▎ | ETA: 0:00:00 Training loss: 5.446414653872179 Validation loss: 66.81482212500998 Validation accuracy: 0.0       Progress: 100%|█████████████████████████████████████████| Time: 0:00:04 Training loss: 4.482754494799172 Validation loss: 66.82992221533729 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: 0:01:37 Training loss: 7.5811825 Validation loss: 23.51899 Validation accuracy: 0.0       Progress: 7%|██▉ | ETA: 0:00:28 Training loss: 2.308484 Validation loss: 14.486548 Validation accuracy: 0.1667       Progress: 28%|███████████▌ | ETA: 0:00:06 Training loss: 0.5026373 Validation loss: 10.694138 Validation accuracy: 0.0       Progress: 38%|███████████████▋ | ETA: 0:00:04 Training loss: 0.048797842 Validation loss: 10.102867 Validation accuracy: 0.0       Progress: 59%|████████████████████████▎ | ETA: 0:00:02 Training loss: 0.0360049 Validation loss: 9.891784 Validation accuracy: 0.0       Progress: 70%|████████████████████████████▊ | ETA: 0:00:01 Training loss: 0.0019406169 Validation loss: 9.828575 Validation accuracy: 0.1667       Progress: 86%|███████████████████████████████████▎ | ETA: 0:00:00 Training loss: 0.0008392447 Validation loss: 9.812101 Validation accuracy: 0.0       Progress: 100%|█████████████████████████████████████████| Time: 0:00:03 Training loss: 0.00021136894 Validation loss: 9.807112 Validation accuracy: 0.1667 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: 0:00:11 Training loss: 136.1213472638287 Training accuracy: 1.0     Progress: 6%|██▌ | ETA: 0:00:05 Training loss: 132.0665580086667 Training accuracy: 0.6667     Progress: 11%|████▌ | ETA: 0:00:04 Training loss: 125.32990365321314 Training accuracy: 0.6667     Progress: 15%|██████▏ | ETA: 0:00:03 Training loss: 117.97306201708268 Training accuracy: 0.6667     Progress: 18%|███████▍ | ETA: 0:00:05 Training loss: 111.14874618344862 Training accuracy: 0.6667     Progress: 19%|███████▊ | ETA: 0:00:07 Training loss: 108.62411371914884 Training accuracy: 0.6667     Progress: 25%|██████████▎ | ETA: 0:00:05 Training loss: 91.10556553891337 Training accuracy: 0.6667     Progress: 31%|████████████▊ | ETA: 0:00:04 Training loss: 70.85782996416283 Training accuracy: 0.6667     Progress: 36%|██████████████▊ | ETA: 0:00:03 Training loss: 53.874136198795554 Training accuracy: 0.6667     Progress: 42%|█████████████████▎ | ETA: 0:00:03 Training loss: 36.19274447673215 Training accuracy: 0.6667     Progress: 48%|███████████████████▋ | ETA: 0:00:02 Training loss: 23.60107991773754 Training accuracy: 0.6667     Progress: 53%|█████████████████████▊ | ETA: 0:00:02 Training loss: 17.227254838708728 Training accuracy: 0.6667     Progress: 59%|████████████████████████▎ | ETA: 0:00:02 Training loss: 12.9205195096771 Training accuracy: 1.0     Progress: 65%|██████████████████████████▋ | ETA: 0:00:01 Training loss: 10.278983228154583 Training accuracy: 1.0     Progress: 71%|█████████████████████████████▏ | ETA: 0:00:01 Training loss: 8.379592825529798 Training accuracy: 1.0     Progress: 77%|███████████████████████████████▋ | ETA: 0:00:01 Training loss: 7.028107372335075 Training accuracy: 1.0     Progress: 83%|██████████████████████████████████ | ETA: 0:00:01 Training loss: 6.072854642078747 Training accuracy: 1.0     Progress: 89%|████████████████████████████████████▌ | ETA: 0:00:00 Training loss: 5.305060046203967 Training accuracy: 1.0     Progress: 95%|███████████████████████████████████████ | ETA: 0:00:00 Training loss: 4.621973191073119 Training accuracy: 1.0     Progress: 100%|█████████████████████████████████████████| Time: 0:00:03 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: 5%|██ | ETA: 0:00:02 Training loss: 133.51544664341876 Validation loss: 113.95206746423744 Validation accuracy: 0.0 Training accuracy: 1.0         Progress: 11%|████▌ | ETA: 0:00:02 Training loss: 126.07318760485117 Validation loss: 109.68211963966911 Validation accuracy: 0.0 Training accuracy: 1.0         Progress: 17%|███████ | ETA: 0:00:02 Training loss: 114.85274142425517 Validation loss: 104.03326495876938 Validation accuracy: 0.0 Training accuracy: 0.6667         Progress: 23%|█████████▍ | ETA: 0:00:02 Training loss: 99.38955491372346 Validation loss: 96.86521731289909 Validation accuracy: 0.0 Training accuracy: 0.6667         Progress: 29%|███████████▉ | ETA: 0:00:01 Training loss: 80.40652186395339 Validation loss: 88.72638737760704 Validation accuracy: 0.0 Training accuracy: 0.3333         Progress: 35%|██████████████▍ | ETA: 0:00:01 Training loss: 60.06523875241784 Validation loss: 80.90910838931845 Validation accuracy: 0.0 Training accuracy: 0.3333         Progress: 41%|████████████████▊ | ETA: 0:00:01 Training loss: 41.41996016563803 Validation loss: 74.88616458414299 Validation accuracy: 0.0 Training accuracy: 0.3333         Progress: 47%|███████████████████▎ | ETA: 0:00:01 Training loss: 27.161621946841272 Validation loss: 71.52450046765706 Validation accuracy: 0.0 Training accuracy: 0.6667         Progress: 53%|█████████████████████▊ | ETA: 0:00:01 Training loss: 18.269171252618744 Validation loss: 70.20489158077123 Validation accuracy: 0.0 Training accuracy: 0.6667         Progress: 59%|████████████████████████▎ | ETA: 0:00:01 Training loss: 13.482625175371723 Validation loss: 69.40070369407573 Validation accuracy: 0.0 Training accuracy: 1.0         Progress: 65%|██████████████████████████▋ | ETA: 0:00:01 Training loss: 10.68575794389141 Validation loss: 68.45291443145857 Validation accuracy: 0.0 Training accuracy: 1.0         Progress: 71%|█████████████████████████████▏ | ETA: 0:00:01 Training loss: 8.717441486588518 Validation loss: 67.50840909862347 Validation accuracy: 0.0 Training accuracy: 1.0         Progress: 77%|███████████████████████████████▋ | ETA: 0:00:00 Training loss: 7.286645662727883 Validation loss: 66.84866593408341 Validation accuracy: 0.0 Training accuracy: 1.0         Progress: 83%|██████████████████████████████████ | ETA: 0:00:00 Training loss: 6.2848531488083905 Validation loss: 66.57880048707224 Validation accuracy: 0.0 Training accuracy: 1.0         Progress: 89%|████████████████████████████████████▌ | ETA: 0:00:00 Training loss: 5.511636071363488 Validation loss: 66.50833337755533 Validation accuracy: 0.0 Training accuracy: 1.0         Progress: 100%|█████████████████████████████████████████| Time: 0:00:01 Training loss: 4.313025460671513 Validation loss: 66.537312151915 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... Progress: 2%|▉ | ETA: 0:00:06 Training loss: 136.2665052702724 Training accuracy: 1.0     Progress: 8%|███▎ | ETA: 0:00:03 Training loss: 130.2244481256631 Training accuracy: 1.0     Progress: 13%|█████▍ | ETA: 0:00:02 Training loss: 122.9682799017793 Training accuracy: 1.0     Progress: 17%|███████ | ETA: 0:00:02 Training loss: 115.13426013633551 Training accuracy: 0.6667     Progress: 23%|█████████▍ | ETA: 0:00:02 Training loss: 99.77496584310455 Training accuracy: 0.6667     Progress: 29%|███████████▉ | ETA: 0:00:02 Training loss: 80.87493713900922 Training accuracy: 0.6667     Progress: 35%|██████████████▍ | ETA: 0:00:02 Training loss: 60.61754172096076 Training accuracy: 0.6667     Progress: 41%|████████████████▊ | ETA: 0:00:01 Training loss: 42.07916442778076 Training accuracy: 0.6667     Progress: 45%|██████████████████▌ | ETA: 0:00:01 Training loss: 32.02662253311591 Training accuracy: 0.6667     Progress: 50%|████████████████████▌ | ETA: 0:00:01 Training loss: 22.727475472414458 Training accuracy: 0.6667     Progress: 54%|██████████████████████▏ | ETA: 0:00:01 Training loss: 17.773431847257562 Training accuracy: 0.6667     Progress: 60%|████████████████████████▋ | ETA: 0:00:01 Training loss: 13.26558732911634 Training accuracy: 1.0     Progress: 65%|██████████████████████████▋ | ETA: 0:00:01 Training loss: 10.947032814573426 Training accuracy: 1.0     Progress: 71%|█████████████████████████████▏ | ETA: 0:00:01 Training loss: 8.944534006313967 Training accuracy: 1.0     Progress: 76%|███████████████████████████████▏ | ETA: 0:00:01 Training loss: 7.70186050904711 Training accuracy: 1.0     Progress: 82%|█████████████████████████████████▋ | ETA: 0:00:00 Training loss: 6.617918412801383 Training accuracy: 1.0     Progress: 88%|████████████████████████████████████▏ | ETA: 0:00:00 Training loss: 5.8038710635679776 Training accuracy: 1.0     Progress: 94%|██████████████████████████████████████▌ | ETA: 0:00:00 Training loss: 5.108680811532662 Training accuracy: 1.0     Progress: 100%|█████████████████████████████████████████| Time: 0:00:02 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: 2%|▉ | ETA: 0:00:12 Training loss: 136.2665052702724 Validation loss: 115.86517105004413 Validation accuracy: 0.0       Progress: 6%|██▌ | ETA: 0:00:06 Training loss: 132.4684527797079 Validation loss: 113.29363121553904 Validation accuracy: 0.0       Progress: 12%|████▉ | ETA: 0:00:04 Training loss: 124.63243619206405 Validation loss: 108.62710541167057 Validation accuracy: 0.0       Progress: 18%|███████▍ | ETA: 0:00:03 Training loss: 112.87174311991774 Validation loss: 102.53156873624178 Validation accuracy: 0.0       Progress: 23%|█████████▍ | ETA: 0:00:02 Training loss: 99.77496584310455 Validation loss: 96.34143709445692 Validation accuracy: 0.0       Progress: 29%|███████████▉ | ETA: 0:00:02 Training loss: 80.87493713900922 Validation loss: 88.17909633094159 Validation accuracy: 0.0       Progress: 35%|██████████████▍ | ETA: 0:00:02 Training loss: 60.61754172096076 Validation loss: 80.50946048119178 Validation accuracy: 0.0       Progress: 41%|████████████████▊ | ETA: 0:00:02 Training loss: 42.07916442778076 Validation loss: 74.81446732861167 Validation accuracy: 0.0       Progress: 46%|██████████████████▉ | ETA: 0:00:01 Training loss: 29.870952580777 Validation loss: 72.14063469086707 Validation accuracy: 0.0       Progress: 51%|████████████████████▉ | ETA: 0:00:01 Training loss: 21.299648690478033 Validation loss: 70.97828504700902 Validation accuracy: 0.0       Progress: 56%|███████████████████████ | ETA: 0:00:01 Training loss: 15.96152919257431 Validation loss: 70.40700529406608 Validation accuracy: 0.0       Progress: 62%|█████████████████████████▍ | ETA: 0:00:01 Training loss: 12.237004811681919 Validation loss: 69.5826736515576 Validation accuracy: 0.0       Progress: 67%|███████████████████████████▌ | ETA: 0:00:01 Training loss: 10.208132753016475 Validation loss: 68.70278597263899 Validation accuracy: 0.0       Progress: 73%|█████████████████████████████▉ | ETA: 0:00:01 Training loss: 8.405613313483148 Validation loss: 67.71885965654198 Validation accuracy: 0.0       Progress: 79%|████████████████████████████████▍ | ETA: 0:00:01 Training loss: 7.113125882242806 Validation loss: 67.13700387404101 Validation accuracy: 0.0       Progress: 84%|██████████████████████████████████▌ | ETA: 0:00:00 Training loss: 6.3262308384091765 Validation loss: 66.91131652368342 Validation accuracy: 0.0       Progress: 89%|████████████████████████████████████▌ | ETA: 0:00:00 Training loss: 5.682227675346057 Validation loss: 66.82122898001825 Validation accuracy: 0.0       Progress: 98%|████████████████████████████████████████▏| ETA: 0:00:00 Training loss: 4.68441067248936 Validation loss: 66.83195878820192 Validation accuracy: 0.0       Progress: 100%|█████████████████████████████████████████| Time: 0:00:02 Training loss: 4.482754494799172 Validation loss: 66.82992221533729 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: 14%|█████▊ | ETA: 0:00:01 Training loss: 114.60724243383426 Validation loss: 108.75940237758253 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: 0:00:20 Training loss: 127.94594780431525 Validation loss: 115.32043880165149 Validation accuracy: 0.0       Progress: 1%|▍ | ETA: 0:00:21 Training loss: 121.5978813063485 Validation loss: 111.9256141207007 Validation accuracy: 0.0       Progress: 2%|▋ | ETA: 0:00:22 Training loss: 112.74216516305972 Validation loss: 107.72027239366956 Validation accuracy: 0.0       Progress: 2%|▊ | ETA: 0:00:23 Training loss: 103.61031292214219 Validation loss: 103.67362574714458 Validation accuracy: 0.0       Progress: 2%|█ | ETA: 0:00:23 Training loss: 89.76521631110387 Validation loss: 97.8765044846233 Validation accuracy: 0.0       Progress: 3%|█▎ | ETA: 0:00:22 Training loss: 73.88451346223633 Validation loss: 91.65397680265123 Validation accuracy: 0.0       Progress: 3%|█▍ | ETA: 0:00:22 Training loss: 57.25337266137101 Validation loss: 85.6401632590779 Validation accuracy: 0.0       Progress: 4%|█▋ | ETA: 0:00:22 Training loss: 41.637233091420896 Validation loss: 80.55950256014388 Validation accuracy: 0.0       Progress: 4%|█▊ | ETA: 0:00:22 Training loss: 28.696647910677488 Validation loss: 76.85244887646509 Validation accuracy: 0.0       Progress: 5%|█▉ | ETA: 0:00:23 Training loss: 22.639063700413896 Validation loss: 75.25348211719039 Validation accuracy: 0.0       Progress: 5%|██ | ETA: 0:00:25 Training loss: 17.894388836764882 Validation loss: 74.0525672265879 Validation accuracy: 0.0       Progress: 5%|██▏ | ETA: 0:00:25 Training loss: 14.323269918574995 Validation loss: 73.17958709671329 Validation accuracy: 0.0       Progress: 6%|██▍ | ETA: 0:00:25 Training loss: 10.971926919612853 Validation loss: 72.34085615830374 Validation accuracy: 0.0       Progress: 6%|██▌ | ETA: 0:00:26 Training loss: 9.184753822803698 Validation loss: 71.82010271596528 Validation accuracy: 0.0       Progress: 6%|██▋ | ETA: 0:00:27 Training loss: 7.3860064424702525 Validation loss: 71.1613053033522 Validation accuracy: 0.0       Progress: 7%|██▉ | ETA: 0:00:26 Training loss: 5.7266905100210215 Validation loss: 70.4354654339043 Validation accuracy: 0.0       Progress: 7%|███ | ETA: 0:00:27 Training loss: 4.967146184739027 Validation loss: 70.09428390360408 Validation accuracy: 0.0       Progress: 8%|███▏ | ETA: 0:00:27 Training loss: 4.347784641599201 Validation loss: 69.85689449680375 Validation accuracy: 0.0       Progress: 8%|███▎ | ETA: 0:00:27 Training loss: 3.550158684045246 Validation loss: 69.68031861510373 Validation accuracy: 0.0       Progress: 9%|███▊ | ETA: 0:00:24 Training loss: 2.324837375193413 Validation loss: 69.81008353161359 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: 7%|██▉ | ETA: 0:00:01 Training loss: 3.7844775 Validation loss: 10.471688 Validation accuracy: 0.1667       Progress: 13%|█████▍ | ETA: 0:00:01 Training loss: 0.70521224 Validation loss: 9.481736 Validation accuracy: 0.1667       Progress: 20%|████████▎ | ETA: 0:00:01 Training loss: 0.33774388 Validation loss: 8.596704 Validation accuracy: 0.3333       Progress: 29%|███████████▉ | ETA: 0:00:01 Training loss: 0.06749439 Validation loss: 8.707428 Validation accuracy: 0.1667       Progress: 41%|████████████████▊ | ETA: 0:00:01 Training loss: 0.012075873 Validation loss: 8.690061 Validation accuracy: 0.1667       Progress: 54%|██████████████████████▏ | ETA: 0:00:01 Training loss: 0.0010970138 Validation loss: 8.674412 Validation accuracy: 0.1667       Progress: 66%|███████████████████████████ | ETA: 0:00:00 Training loss: 0.00022777046 Validation loss: 8.677857 Validation accuracy: 0.1667       Progress: 78%|████████████████████████████████ | ETA: 0:00:00 Training loss: 0.00011660496 Validation loss: 8.7095785 Validation accuracy: 0.1667       Progress: 90%|████████████████████████████████████▉ | ETA: 0:00:00 Training loss: 0.0001971823 Validation loss: 8.655141 Validation accuracy: 0.1667       Progress: 100%|█████████████████████████████████████████| Time: 0:00:01 Training loss: 0.0001907741 Validation loss: 8.669787 Validation accuracy: 0.1667 Setting up model... model = Chain(Dense(200 => 1000, relu), Dense(1000 => 33)) Setting up data structures... Setting up optimizer... Training... Progress: 2%|▉ | ETA: 0:00:05 Training loss: 7.827197 Validation loss: 13.5191 Validation accuracy: 0.0       Progress: 4%|█▌ | ETA: 0:00:06 Training loss: 2.7352757 Validation loss: 9.837805 Validation accuracy: 0.1667       Progress: 6%|██▍ | ETA: 0:00:05 Training loss: 0.22318552 Validation loss: 8.964647 Validation accuracy: 0.0       Progress: 8%|███▎ | ETA: 0:00:06 Training loss: 0.09691016 Validation loss: 8.750064 Validation accuracy: 0.0       Progress: 10%|████▏ | ETA: 0:00:06 Training loss: 0.08506254 Validation loss: 8.470981 Validation accuracy: 0.1667       Progress: 12%|█████▏ | ETA: 0:00:05 Training loss: 0.005457099 Validation loss: 8.485021 Validation accuracy: 0.1667       Progress: 15%|██████▍ | ETA: 0:00:05 Training loss: 0.001443777 Validation loss: 8.463659 Validation accuracy: 0.1667       Progress: 20%|████████▏ | ETA: 0:00:04 Training loss: 8.805651e-5 Validation loss: 8.4530115 Validation accuracy: 0.1667       Progress: 24%|█████████▉ | ETA: 0:00:03 Training loss: 4.7207228e-5 Validation loss: 8.450605 Validation accuracy: 0.1667       Progress: 29%|███████████▊ | ETA: 0:00:03 Training loss: 3.8623916e-6 Validation loss: 8.452602 Validation accuracy: 0.1667       Progress: 33%|█████████████▌ | ETA: 0:00:03 Training loss: 6.179989e-7 Validation loss: 8.452609 Validation accuracy: 0.1667       Progress: 37%|███████████████▏ | ETA: 0:00:02 Training loss: 4.3120178e-8 Validation loss: 8.452297 Validation accuracy: 0.1667       Progress: 41%|████████████████▉ | ETA: 0:00:02 Training loss: 9.822807e-9 Validation loss: 8.452287 Validation accuracy: 0.1667       Progress: 45%|██████████████████▋ | ETA: 0:00:02 Training loss: 7.867624e-10 Validation loss: 8.452292 Validation accuracy: 0.1667       Progress: 50%|████████████████████▍ | ETA: 0:00:02 Training loss: 6.377142e-11 Validation loss: 8.452295 Validation accuracy: 0.1667       Progress: 54%|██████████████████████▎ | ETA: 0:00:02 Training loss: 3.3691585e-12 Validation loss: 8.452297 Validation accuracy: 0.1667       Progress: 58%|████████████████████████ | ETA: 0:00:01 Training loss: 1.1734079e-12 Validation loss: 8.452295 Validation accuracy: 0.1667       Progress: 63%|█████████████████████████▋ | ETA: 0:00:01 Training loss: 4.0746277e-13 Validation loss: 8.452296 Validation accuracy: 0.1667       Progress: 67%|███████████████████████████▍ | ETA: 0:00:01 Training loss: 2.6386865e-13 Validation loss: 8.452295 Validation accuracy: 0.1667       Progress: 71%|█████████████████████████████▎ | ETA: 0:00:01 Training loss: 1.9273377e-13 Validation loss: 8.452296 Validation accuracy: 0.1667       Progress: 76%|███████████████████████████████ | ETA: 0:00:01 Training loss: 2.1353398e-13 Validation loss: 8.452295 Validation accuracy: 0.1667       Progress: 80%|████████████████████████████████▊ | ETA: 0:00:01 Training loss: 2.9223425e-13 Validation loss: 8.452296 Validation accuracy: 0.1667       Progress: 84%|██████████████████████████████████▌ | ETA: 0:00:00 Training loss: 2.777197e-13 Validation loss: 8.452296 Validation accuracy: 0.1667       Progress: 89%|████████████████████████████████████▍ | ETA: 0:00:00 Training loss: 3.075944e-13 Validation loss: 8.452295 Validation accuracy: 0.1667       Progress: 93%|██████████████████████████████████████ | ETA: 0:00:00 Training loss: 3.066613e-13 Validation loss: 8.452296 Validation accuracy: 0.1667       Progress: 97%|███████████████████████████████████████▉ | ETA: 0:00:00 Training loss: 2.530445e-13 Validation loss: 8.452295 Validation accuracy: 0.1667       Progress: 100%|█████████████████████████████████████████| Time: 0:00:02 Training loss: 2.7676104e-13 Validation loss: 8.452294 Validation accuracy: 0.1667 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: 54.025524 Validation loss: 45.068626 Validation accuracy: 0.0       Progress: 10%|████▏ | ETA: 0:00:02 Training loss: 47.286404 Validation loss: 41.713417 Validation accuracy: 0.0       Progress: 15%|██████▏ | ETA: 0:00:02 Training loss: 42.13684 Validation loss: 39.11458 Validation accuracy: 0.0       Progress: 20%|████████▎ | ETA: 0:00:02 Training loss: 37.41176 Validation loss: 36.696583 Validation accuracy: 0.0       Progress: 25%|██████████▎ | ETA: 0:00:02 Training loss: 33.093 Validation loss: 34.464916 Validation accuracy: 0.0       Progress: 30%|████████████▎ | ETA: 0:00:02 Training loss: 29.17408 Validation loss: 32.41435 Validation accuracy: 0.0       Progress: 36%|██████████████▊ | ETA: 0:00:01 Training loss: 24.979359 Validation loss: 30.188534 Validation accuracy: 0.0       Progress: 41%|████████████████▊ | ETA: 0:00:01 Training loss: 21.889658 Validation loss: 28.520725 Validation accuracy: 0.0       Progress: 47%|███████████████████▎ | ETA: 0:00:01 Training loss: 18.62558 Validation loss: 26.728035 Validation accuracy: 0.0       Progress: 53%|█████████████████████▊ | ETA: 0:00:01 Training loss: 15.799977 Validation loss: 25.145638 Validation accuracy: 0.0       Progress: 59%|████████████████████████▎ | ETA: 0:00:01 Training loss: 13.363939 Validation loss: 23.751104 Validation accuracy: 0.0       Progress: 65%|██████████████████████████▋ | ETA: 0:00:01 Training loss: 11.283667 Validation loss: 22.527836 Validation accuracy: 0.0       Progress: 70%|████████████████████████████▊ | ETA: 0:00:01 Training loss: 9.790781 Validation loss: 21.62666 Validation accuracy: 0.0       Progress: 76%|███████████████████████████████▏ | ETA: 0:00:01 Training loss: 8.249056 Validation loss: 20.671581 Validation accuracy: 0.0       Progress: 81%|█████████████████████████████████▎ | ETA: 0:00:00 Training loss: 7.1492248 Validation loss: 19.9706 Validation accuracy: 0.0       Progress: 87%|███████████████████████████████████▋ | ETA: 0:00:00 Training loss: 6.021545 Validation loss: 19.228958 Validation accuracy: 0.0       Progress: 92%|█████████████████████████████████████▊ | ETA: 0:00:00 Training loss: 5.2250338 Validation loss: 18.681095 Validation accuracy: 0.0       Progress: 97%|███████████████████████████████████████▊ | ETA: 0:00:00 Training loss: 4.5401435 Validation loss: 18.192432 Validation accuracy: 0.0       Progress: 100%|█████████████████████████████████████████| Time: 0:00:02 Training loss: 4.177827 Validation loss: 17.926912 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: 13%|█████▍ | ETA: 0:00:01 Training loss: 0.29510415 Validation loss: 952.82074 Validation accuracy: 0.0       Progress: 35%|██████████████▍ | ETA: 0:00:00 Training loss: 0.42944366 Validation loss: 2319.511 Validation accuracy: 0.0       Progress: 56%|███████████████████████ | ETA: 0:00:00 Training loss: 0.44717836 Validation loss: 2559.376 Validation accuracy: 0.0       Progress: 78%|████████████████████████████████ | ETA: 0:00:00 Training loss: 0.44679818 Validation loss: 2593.2063 Validation accuracy: 0.0       Progress: 100%|█████████████████████████████████████████| Time: 0:00:00 Training loss: 0.44346893 Validation loss: 2597.2239 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: 7%|██▉ | ETA: 0:00:01 Training loss: 13.737583 Validation loss: 34.08393 Validation accuracy: 0.1667       Progress: 12%|████▉ | ETA: 0:00:02 Training loss: 7.9006376 Validation loss: 29.722157 Validation accuracy: 0.1667       Progress: 20%|████████▎ | ETA: 0:00:02 Training loss: 2.0036345 Validation loss: 25.262585 Validation accuracy: 0.1667       Progress: 70%|████████████████████████████▊ | ETA: 0:00:00 Training loss: 0.012798193 Validation loss: 23.40764 Validation accuracy: 0.1667       Progress: 100%|█████████████████████████████████████████| Time: 0:00:00 Training loss: 0.0005761981 Validation loss: 23.385372 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: 0:03:10 Training loss: 299.4347 Validation loss: 91.25949 Validation accuracy: 0.1667       Progress: 4%|█▋ | ETA: 0:01:36 Training loss: 32.32986 Validation loss: 69.628555 Validation accuracy: 0.3333       Progress: 18%|███████▍ | ETA: 0:00:19 Training loss: 5.86343 Validation loss: 47.395058 Validation accuracy: 0.3333       Progress: 31%|████████████▊ | ETA: 0:00:10 Training loss: 2.887811 Validation loss: 41.66309 Validation accuracy: 0.3333       Progress: 42%|█████████████████▎ | ETA: 0:00:06 Training loss: 0.78921324 Validation loss: 41.045143 Validation accuracy: 0.3333       Progress: 53%|█████████████████████▊ | ETA: 0:00:04 Training loss: 0.22962157 Validation loss: 41.321064 Validation accuracy: 0.3333       Progress: 64%|██████████████████████████▎ | ETA: 0:00:03 Training loss: 0.051198743 Validation loss: 41.17063 Validation accuracy: 0.3333       Progress: 78%|████████████████████████████████ | ETA: 0:00:01 Training loss: 0.020110909 Validation loss: 41.22193 Validation accuracy: 0.3333       Progress: 93%|██████████████████████████████████████▏ | ETA: 0:00:00 Training loss: 0.0024178752 Validation loss: 41.10287 Validation accuracy: 0.3333       Progress: 100%|█████████████████████████████████████████| Time: 0:00:04 Training loss: 0.0018686217 Validation loss: 41.11145 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: 0:17:04 Training loss: 0.39982933 Validation loss: 1.9041343 Validation accuracy: 0.0       Progress: 22%|█████████ | ETA: 0:01:14 Training loss: 0.0003009053 Validation loss: 3.951775 Validation accuracy: 0.0       Progress: 41%|████████████████▊ | ETA: 0:00:30 Training loss: 2.1771819e-5 Validation loss: 3.997645 Validation accuracy: 0.0       Progress: 62%|█████████████████████████▍ | ETA: 0:00:13 Training loss: 1.0096172e-5 Validation loss: 4.0138235 Validation accuracy: 0.0       Progress: 83%|██████████████████████████████████ | ETA: 0:00:04 Training loss: 6.1685473e-6 Validation loss: 4.022877 Validation accuracy: 0.0       Progress: 100%|█████████████████████████████████████████| Time: 0:00:21 Training loss: 4.923299e-6 Validation loss: 4.02695 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: 4%|█▋ | ETA: 0:00:03 Training loss: 134.40879278400018   Progress: 8%|███▎ | ETA: 0:00:03 Training loss: 130.13941916840923   Progress: 14%|█████▊ | ETA: 0:00:02 Training loss: 120.98872611133547   Progress: 20%|████████▎ | ETA: 0:00:02 Training loss: 107.68661231693514   Progress: 26%|██████████▋ | ETA: 0:00:02 Training loss: 90.25856808924895   Progress: 32%|█████████████▏ | ETA: 0:00:01 Training loss: 70.13147124589717   Progress: 38%|███████████████▋ | ETA: 0:00:01 Training loss: 50.095712301721555   Progress: 43%|█████████████████▋ | ETA: 0:00:01 Training loss: 35.774255217729554   Progress: 49%|████████████████████▏ | ETA: 0:00:01 Training loss: 23.32140950015062   Progress: 55%|██████████████████████▌ | ETA: 0:00:01 Training loss: 16.09625129555276   Progress: 61%|█████████████████████████ | ETA: 0:00:01 Training loss: 12.246790718661403   Progress: 67%|███████████████████████████▌ | ETA: 0:00:01 Training loss: 9.821376379338346   Progress: 72%|█████████████████████████████▌ | ETA: 0:00:01 Training loss: 8.312280326570281   Progress: 78%|████████████████████████████████ | ETA: 0:00:00 Training loss: 6.977868440421696   Progress: 83%|██████████████████████████████████ | ETA: 0:00:00 Training loss: 6.173659580727615   Progress: 88%|████████████████████████████████████▏ | ETA: 0:00:00 Training loss: 5.522939059141304   Progress: 94%|██████████████████████████████████████▌ | ETA: 0:00:00 Training loss: 4.835026190141939   Progress: 99%|████████████████████████████████████████▋| ETA: 0:00:00 Training loss: 4.311281319755048   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: 6%|██▌ | ETA: 0:00:02 Training loss: 1.4823678   Progress: 12%|████▉ | ETA: 0:00:02 Training loss: 1.4483393   Progress: 18%|███████▍ | ETA: 0:00:02 Training loss: 2.2443912   Progress: 24%|█████████▉ | ETA: 0:00:01 Training loss: 1.3232522   Progress: 30%|████████████▎ | ETA: 0:00:01 Training loss: 0.15564735   Progress: 36%|██████████████▊ | ETA: 0:00:01 Training loss: 0.10888151   Progress: 41%|████████████████▊ | ETA: 0:00:01 Training loss: 0.23198485   Progress: 46%|██████████████████▉ | ETA: 0:00:01 Training loss: 0.023441594   Progress: 51%|████████████████████▉ | ETA: 0:00:01 Training loss: 0.053920075   Progress: 57%|███████████████████████▍ | ETA: 0:00:01 Training loss: 0.045457494   Progress: 63%|█████████████████████████▉ | ETA: 0:00:01 Training loss: 0.015671683   Progress: 69%|████████████████████████████▎ | ETA: 0:00:01 Training loss: 0.0024874364   Progress: 74%|██████████████████████████████▍ | ETA: 0:00:01 Training loss: 0.003814346   Progress: 80%|████████████████████████████████▊ | ETA: 0:00:00 Training loss: 0.0037680187   Progress: 86%|███████████████████████████████████▎ | ETA: 0:00:00 Training loss: 0.0022662706   Progress: 92%|█████████████████████████████████████▊ | ETA: 0:00:00 Training loss: 0.0009902355   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: 0:00:04 Step loss: 131.14099893438816 Overall loss: 135.85572493843063 Overall accuracy: 0.3333       Progress: 100%|█████████████████████████████████████████| Time: 0:00:07 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: 67%|███████████████████████████▍ | ETA: 0:00:00 Step loss: 130.16812457299167 Overall loss: 134.46400128629588 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: 42%|█████████████████▏ | ETA: 0:00:00 Step loss: 144.80990708280137 Overall loss: 133.3798196402762 Overall accuracy: 0.3333       Progress: 92%|█████████████████████████████████████▋ | ETA: 0:00:00 Step loss: 138.14421181184488 Overall loss: 128.05366535605572 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: 56%|██████████████████████▊ | ETA: 0:00:00 Step loss: 129.1634043054313 Overall loss: 133.49724543761405 Overall accuracy: 1.0       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: 44%|██████████████████▎ | ETA: 0:00:00 Step loss: 130.16812457299167 Overall loss: 134.46400128629588 Overall accuracy: 0.3333       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#253 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: 60%|████████████████████████▋ | ETA: 0:00:00 Step loss: 137.822301324285 Overall loss: 137.14627030622287 Overall accuracy: 1.0   Done! WARNING: Method definition compute_target_corr(Any, Any, Any, Any, Any, Any, Any) in module ##deep learning tests#253 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: 33%|█████████████▋ | ETA: 0:00:00 Step loss: 147.07400355839548 Overall loss: 135.57267106773455 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 6m28.6s Testing JudiLing tests passed Testing completed after 768.0s PkgEval succeeded after 1340.4s