update README

update readme and environment
clean up repositry
2025-12-15 20:08:21 +01:00 · 2025-02-27 13:09:10 +01:00 · 2025-02-27 12:48:58 +01:00 · 2025-02-27 12:09:42 +01:00
8 changed files with 267 additions and 323 deletions
--- a/README.md
+++ b/README.md
@ -1,5 +1,22 @@
 # Training of AI Surrogate Models
- run `git lfs pull` to get the data from the large file storage
+This repository contains the current experiments for training AI models that attempt to predict the chemistry component of POET and is structued as follows:
- create conda environment with `conda env create -f environment.yml`
+
 ```
 └── dataset
    └── Barite_50_Data_training.h5
    └── barite_50_4_corner.h5
 └── doc
 └── results
 └── src
    └── POET_Training.ipynb
    └── convert_data.jl
    └── optuna_runs.py
    └── preprocessing.py
 ```
 The datasets in `datasets` must first be pulled via `git lfs pull` to get the data from the large file storage.
 A conda environment can then be set up with the packages contained in environment.yml with `conda env create -f environment.yml`
 The `preprocessing.py` file defines all the necessary steps for preprocessing as well as the keras models used. The actual training and additional explanations then take place in `POET_Training.ipynb`.
--- a/barite_50_4_corner.h5
+++ b/barite_50_4_corner.h5
--- a/convert_barite.jl
+++ b/convert_barite.jl
@ -1,88 +0,0 @@
 #!/usr/bin/env julia
 # qs_read = []
 # # find all the files in 'barite_out'
 # files = readdir("barite_out"; join=true)
 # # remove files which do not have the extension '.qs2' and contains 'iter'
 # files = filter(x -> occursin(r".*\.qs2", x) && occursin(r"iter", x), files)
 # # remove first entry as it is iteration 0
 # files = files[2:end]
 # test1 = qs_read(files[1])
 # @rput test1
 # R"test1 <- test1$C"
 # @rget test1
 # check if ARGS contains 2 elements
 if length(ARGS) != 2
    println("Usage: julia convert.jl <directory> <output_file_name>.h5")
    exit(1)
 end
 to_read_dir = ARGS[1]
 output_file_name = ARGS[2] * ".h5"
 # check if the directory exists
 if !isdir(to_read_dir)
    println("The directory \"$to_read_dir\" does not exist")
    exit(1)
 end
 using HDF5, RCall, DataFrames
@rlibrary qs2
 # List all .rds files starting with "iter" in a given directory
 qs_files = filter(x -> occursin(r".*\.qs2", x) && occursin(r"iter", x), readdir(to_read_dir; join=true))[2:end]
 df_design = DataFrame()
 df_result = DataFrame()
 for file in qs_files
    # Load the RDS file
    data = qs_read(file)
    # get basename of the file
    basename = split(file, "/")[end]
    # get the iteration number by splitting the basename and parse the second element
    iteration = parse(Int, split(split(basename, "_")[2], ".")[1])
    @rput data
    R"transport <- data$T"
    R"chemistry <- data$C"
    @rget transport
    @rget chemistry
    # Add iteration number to the DataFrame
    transport.iteration = fill(iteration, size(transport, 1))
    chemistry.iteration = fill(iteration, size(chemistry, 1))
    # Append the DataFrame to the big DataFrame
    append!(df_design, transport)
    append!(df_result, chemistry)
 end
 # remove ID, Barite_p1, Celestite_p1 columns
 df_design = df_design[:, Not([:ID, :Barite_p1, :Celestite_p1])]
 df_result = df_result[:, Not([:ID, :Barite_p1, :Celestite_p1])]
 h5open(output_file_name, "w") do fid
    group_in = create_group(fid, "design")
    group_out = create_group(fid, "result")
    group_in["names"] = names(df_design)
    group_in["data", compress=9] = Matrix(df_design)
    group_out["names"] = names(df_result)
    group_out["data", compress=9] = Matrix(df_result)
 end
--- a/convert_data.jl
+++ b/convert_data.jl
@ -1,60 +0,0 @@
 using HDF5
 using RData
 using DataFrames
 # Load Training Data 
 # train_data = load("Barite_50_Data.rds")
 # training_h5_name = "Barite_50_Data.h5"
 # h5open(training_h5_name, "w") do fid
 #     for key in keys(train_data)
 #         group = create_group(fid, key)
 #         group["names"] = names(train_data[key])
 #         group["data", compress=3] = Matrix(train_data[key])
 #         # group = create_group(fid, key)
 #         # grou["names"] = coln
 #     end
 # end
 # List all .rds files starting with "iter" in a given directory
 rds_files = filter(x -> startswith(x, "iter"), readdir("barite_out/"))
 # remove "iter_0.rds" from the list 
 rds_files = rds_files[2:end]
 big_df_in = DataFrame()
 big_df_out = DataFrame()
 for rds_file in rds_files
    # Load the RDS file
    data = load("barite_out/$rds_file")
    # Convert the data to a DataFrame
    df_T = DataFrame(data["T"])
    df_C = DataFrame(data["C"])
    # Append the DataFrame to the big DataFrame
    append!(big_df_in, df_T)
    append!(big_df_out, df_C)
 end
 # remove ID, Barite_p1, Celestite_p1 columns
 big_df_in = big_df_in[:, Not([:ID, :Barite_p1, :Celestite_p1])]
 big_df_out = big_df_out[:, Not([:ID, :Barite_p1, :Celestite_p1])]
 inference_h5_name = "Barite_50_Data_inference.h5"
 h5open(inference_h5_name, "w") do fid
    fid["names"] = names(big_df_in)
    fid["data", compress=9] = Matrix(big_df_in)
 end
 training_h5_name = "Barite_50_Data_training.h5"
 h5open(training_h5_name, "w") do fid
    group_in = create_group(fid, "design")
    group_out = create_group(fid, "result")
    group_in["names"] = names(big_df_in)
    group_in["data", compress=9] = Matrix(big_df_in)
    group_out["names"] = names(big_df_out)
    group_out["data", compress=9] = Matrix(big_df_out)
 end
--- a/doc/measurement_plan.md
+++ b/doc/measurement_plan.md
@ -3,19 +3,11 @@
 ### Parameters to optimize
 ### Saved models
 `./results/model_large_standardization.keras`: Trained on `barite_50_4_corner.h5` dataset with extended Loss function (Huber loss with mass balance) and **standardized data**
 ### Experiments
 | **Experiment** | **Dataset**           | **Model** | **Lossfunction** | **Activation** | **Preprocessing** |
--- a/environment.yaml
+++ b/environment.yaml
@ -0,0 +1,248 @@
 name: training
 channels:
  - conda-forge
  - defaults
 dependencies:
  - _libgcc_mutex=0.1=main
  - _openmp_mutex=5.1=1_gnu
  - absl-py=2.1.0=py311h06a4308_0
  - alembic=1.13.3=py311h06a4308_0
  - anyio=4.6.2=py311h06a4308_0
  - argon2-cffi=21.3.0=pyhd3eb1b0_0
  - argon2-cffi-bindings=21.2.0=py311h5eee18b_1
  - asttokens=2.0.5=pyhd3eb1b0_0
  - astunparse=1.6.3=py_0
  - async-lru=2.0.4=py311h06a4308_0
  - attrs=24.3.0=py311h06a4308_0
  - babel=2.11.0=py311h06a4308_0
  - beautifulsoup4=4.12.3=py311h06a4308_0
  - blas=1.0=mkl
  - bleach=6.2.0=py311h06a4308_0
  - bottleneck=1.4.2=py311hf4808d0_0
  - brotli=1.0.9=h5eee18b_9
  - brotli-bin=1.0.9=h5eee18b_9
  - brotli-python=1.0.9=py311h6a678d5_9
  - bzip2=1.0.8=h5eee18b_6
  - c-ares=1.19.1=h5eee18b_0
  - ca-certificates=2024.12.31=h06a4308_0
  - certifi=2025.1.31=py311h06a4308_0
  - cffi=1.17.1=py311h1fdaa30_1
  - charset-normalizer=3.3.2=pyhd3eb1b0_0
  - colorlog=5.0.1=py311h06a4308_1
  - comm=0.2.1=py311h06a4308_0
  - contourpy=1.3.1=py311hdb19cb5_0
  - cycler=0.11.0=pyhd3eb1b0_0
  - cyrus-sasl=2.1.28=h52b45da_1
  - dbus=1.13.18=hb2f20db_0
  - debugpy=1.8.11=py311h6a678d5_0
  - decorator=5.1.1=pyhd3eb1b0_0
  - defusedxml=0.7.1=pyhd3eb1b0_0
  - executing=0.8.3=pyhd3eb1b0_0
  - expat=2.6.4=h6a678d5_0
  - flatbuffers=24.3.25=h6a678d5_0
  - fontconfig=2.14.1=h55d465d_3
  - fonttools=4.51.0=py311h5eee18b_0
  - freetype=2.12.1=h4a9f257_0
  - gast=0.5.3=pyhd3eb1b0_0
  - giflib=5.2.2=h5eee18b_0
  - glib=2.78.4=h6a678d5_0
  - glib-tools=2.78.4=h6a678d5_0
  - google-pasta=0.2.0=pyhd3eb1b0_0
  - greenlet=3.1.1=py311h6a678d5_0
  - grpcio=1.62.2=py311h6a678d5_0
  - gst-plugins-base=1.14.1=h6a678d5_1
  - gstreamer=1.14.1=h5eee18b_1
  - h11=0.14.0=py311h06a4308_0
  - h5py=3.12.1=py311hc0802c4_0
  - hdf5=1.12.1=h2b7332f_3
  - httpcore=1.0.2=py311h06a4308_0
  - httpx=0.27.0=py311h06a4308_0
  - icu=73.1=h6a678d5_0
  - idna=3.7=py311h06a4308_0
  - imbalanced-learn=0.12.3=py311h06a4308_1
  - intel-openmp=2023.1.0=hdb19cb5_46306
  - ipykernel=6.29.5=py311h06a4308_0
  - ipython=8.30.0=py311h06a4308_0
  - ipywidgets=8.1.5=py311h06a4308_0
  - jedi=0.19.2=py311h06a4308_0
  - jinja2=3.1.4=py311h06a4308_1
  - joblib=1.4.2=py311h06a4308_0
  - jpeg=9e=h5eee18b_3
  - json5=0.9.25=py311h06a4308_0
  - jsonschema=4.23.0=py311h06a4308_0
  - jsonschema-specifications=2023.7.1=py311h06a4308_0
  - jupyter=1.0.0=py311h06a4308_9
  - jupyter-lsp=2.2.0=py311h06a4308_0
  - jupyter_client=8.6.0=py311h06a4308_0
  - jupyter_console=6.6.3=py311h06a4308_0
  - jupyter_core=5.7.2=py311h06a4308_0
  - jupyter_events=0.10.0=py311h06a4308_0
  - jupyter_server=2.14.1=py311h06a4308_0
  - jupyter_server_terminals=0.4.4=py311h06a4308_1
  - jupyterlab=4.2.5=py311h06a4308_0
  - jupyterlab_pygments=0.1.2=py_0
  - jupyterlab_server=2.27.3=py311h06a4308_0
  - jupyterlab_widgets=3.0.13=py311h06a4308_0
  - keras=3.6.0=py311h06a4308_0
  - kiwisolver=1.4.4=py311h6a678d5_0
  - krb5=1.20.1=h143b758_1
  - lcms2=2.16=hb9589c4_0
  - ld_impl_linux-64=2.40=h12ee557_0
  - lerc=4.0.0=h6a678d5_0
  - libabseil=20240116.2=cxx17_h6a678d5_0
  - libbrotlicommon=1.0.9=h5eee18b_9
  - libbrotlidec=1.0.9=h5eee18b_9
  - libbrotlienc=1.0.9=h5eee18b_9
  - libclang=14.0.6=default_hc6dbbc7_2
  - libclang13=14.0.6=default_he11475f_2
  - libcups=2.4.2=h2d74bed_1
  - libcurl=8.11.1=hc9e6f67_0
  - libdeflate=1.22=h5eee18b_0
  - libedit=3.1.20230828=h5eee18b_0
  - libev=4.33=h7f8727e_1
  - libffi=3.4.4=h6a678d5_1
  - libgcc-ng=11.2.0=h1234567_1
  - libgfortran-ng=11.2.0=h00389a5_1
  - libgfortran5=11.2.0=h1234567_1
  - libglib=2.78.4=hdc74915_0
  - libgomp=11.2.0=h1234567_1
  - libgrpc=1.62.2=h2d74bed_0
  - libiconv=1.16=h5eee18b_3
  - libllvm14=14.0.6=hecde1de_4
  - libnghttp2=1.57.0=h2d74bed_0
  - libpng=1.6.39=h5eee18b_0
  - libpq=17.2=hdbd6064_0
  - libprotobuf=4.25.3=he621ea3_0
  - libsodium=1.0.18=h7b6447c_0
  - libssh2=1.11.1=h251f7ec_0
  - libstdcxx-ng=11.2.0=h1234567_1
  - libtiff=4.5.1=hffd6297_1
  - libuuid=1.41.5=h5eee18b_0
  - libwebp-base=1.3.2=h5eee18b_1
  - libxcb=1.15=h7f8727e_0
  - libxkbcommon=1.0.1=h097e994_2
  - libxml2=2.13.5=hfdd30dd_0
  - lz4-c=1.9.4=h6a678d5_1
  - mako=1.2.3=py311h06a4308_0
  - markdown=3.4.1=py311h06a4308_0
  - markdown-it-py=2.2.0=py311h06a4308_1
  - markupsafe=2.1.3=py311h5eee18b_1
  - matplotlib=3.10.0=py311h06a4308_0
  - matplotlib-base=3.10.0=py311hbfdbfaf_0
  - matplotlib-inline=0.1.6=py311h06a4308_0
  - mdurl=0.1.0=py311h06a4308_0
  - mistune=2.0.4=py311h06a4308_0
  - mkl=2023.1.0=h213fc3f_46344
  - mkl-service=2.4.0=py311h5eee18b_2
  - mkl_fft=1.3.11=py311h5eee18b_0
  - mkl_random=1.2.8=py311ha02d727_0
  - ml_dtypes=0.4.0=py311ha02d727_0
  - mysql=8.4.0=h29a9f33_1
  - namex=0.0.7=py311h06a4308_0
  - nbclient=0.8.0=py311h06a4308_0
  - nbconvert=7.16.4=py311h06a4308_0
  - nbformat=5.10.4=py311h06a4308_0
  - ncurses=6.4=h6a678d5_0
  - nest-asyncio=1.6.0=py311h06a4308_0
  - notebook=7.2.2=py311h06a4308_1
  - notebook-shim=0.2.3=py311h06a4308_0
  - numexpr=2.10.1=py311h3c60e43_0
  - numpy=1.26.4=py311h08b1b3b_0
  - numpy-base=1.26.4=py311hf175353_0
  - openjpeg=2.5.2=he7f1fd0_0
  - openldap=2.6.4=h42fbc30_0
  - openssl=3.0.15=h5eee18b_0
  - opt_einsum=3.3.0=pyhd3eb1b0_1
  - optree=0.12.1=py311hdb19cb5_0
  - optuna=4.2.1=pyhd8ed1ab_0
  - overrides=7.4.0=py311h06a4308_0
  - packaging=24.2=py311h06a4308_0
  - pandas=2.2.3=py311h6a678d5_0
  - pandocfilters=1.5.0=pyhd3eb1b0_0
  - parso=0.8.4=py311h06a4308_0
  - pcre2=10.42=hebb0a14_1
  - pexpect=4.8.0=pyhd3eb1b0_3
  - pillow=11.0.0=py311hcea889d_1
  - pip=24.2=py311h06a4308_0
  - platformdirs=3.10.0=py311h06a4308_0
  - ply=3.11=py311h06a4308_0
  - prometheus_client=0.21.0=py311h06a4308_0
  - prompt-toolkit=3.0.43=py311h06a4308_0
  - prompt_toolkit=3.0.43=hd3eb1b0_0
  - protobuf=4.25.3=py311he36ed58_1
  - psutil=5.9.0=py311h5eee18b_1
  - ptyprocess=0.7.0=pyhd3eb1b0_2
  - pure_eval=0.2.2=pyhd3eb1b0_0
  - pycparser=2.21=pyhd3eb1b0_0
  - pygments=2.15.1=py311h06a4308_1
  - pyparsing=3.2.0=py311h06a4308_0
  - pyqt=5.15.10=py311h6a678d5_0
  - pyqt5-sip=12.13.0=py311h5eee18b_0
  - pysocks=1.7.1=py311h06a4308_0
  - python=3.11.11=he870216_0
  - python-dateutil=2.9.0post0=py311h06a4308_2
  - python-fastjsonschema=2.20.0=py311h06a4308_0
  - python-flatbuffers=24.3.25=py311h06a4308_0
  - python-json-logger=3.2.1=py311h06a4308_0
  - python-tzdata=2023.3=pyhd3eb1b0_0
  - pytz=2024.1=py311h06a4308_0
  - pyyaml=6.0.2=py311h5eee18b_0
  - pyzmq=26.2.0=py311h6a678d5_0
  - qt-main=5.15.2=hb6262e9_11
  - qtconsole=5.6.0=py311h06a4308_0
  - qtpy=2.4.1=py311h06a4308_0
  - re2=2022.04.01=h295c915_0
  - readline=8.2=h5eee18b_0
  - referencing=0.30.2=py311h06a4308_0
  - requests=2.32.3=py311h06a4308_1
  - rfc3339-validator=0.1.4=py311h06a4308_0
  - rfc3986-validator=0.1.1=py311h06a4308_0
  - rich=13.9.4=py311h06a4308_0
  - rpds-py=0.22.3=py311h4aa5aa6_0
  - scikit-learn=1.5.2=py311h6a678d5_0
  - scipy=1.14.1=py311h08b1b3b_0
  - seaborn=0.13.2=py311h06a4308_1
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  - setuptools=75.1.0=py311h06a4308_0
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  - tbb=2021.8.0=hdb19cb5_0
  - tensorboard=2.17.0=py311h06a4308_0
  - tensorboard-data-server=0.7.0=py311h52d8a92_1
  - tensorflow=2.17.0=cpu_py311hbca4264_0
  - tensorflow-base=2.17.0=cpu_py311hb07566e_0
  - tensorflow-estimator=2.17.0=cpu_py311hfedf350_0
  - termcolor=2.1.0=py311h06a4308_0
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  - zeromq=4.3.5=h6a678d5_0
  - zlib=1.2.13=h5eee18b_1
  - zstd=1.5.6=hc292b87_0
  - pip:
      - smogn==0.1.2
 prefix: /home/signer/bin/miniconda3/envs/training
--- a/environment.yml
+++ b/environment.yml
@ -1,162 +0,0 @@
 name: ai
 channels:
  - conda-forge
  - defaults
  - https://repo.anaconda.com/pkgs/main
  - https://repo.anaconda.com/pkgs/r
 dependencies:
  - absl-py=2.1.0=py311hca03da5_0
  - appnope=0.1.4=pyhd8ed1ab_1
  - asttokens=3.0.0=pyhd8ed1ab_1
  - astunparse=1.6.3=py_0
  - blas=1.0=openblas
  - bottleneck=1.4.2=py311hb9f6ed7_0
  - brotli=1.0.9=h80987f9_9
  - brotli-bin=1.0.9=h80987f9_9
  - brotli-python=1.0.9=py311h313beb8_9
  - bzip2=1.0.8=h80987f9_6
  - c-ares=1.34.4=h5505292_0
  - ca-certificates=2024.12.31=hca03da5_0
  - cached-property=1.5.2=py_0
  - certifi=2024.12.14=py311hca03da5_0
  - charset-normalizer=3.3.2=pyhd3eb1b0_0
  - comm=0.2.2=pyhd8ed1ab_1
  - contourpy=1.3.1=py311h48ca7d4_0
  - cycler=0.11.0=pyhd3eb1b0_0
  - debugpy=1.8.11=py311h155a34a_0
  - decorator=5.1.1=pyhd8ed1ab_1
  - exceptiongroup=1.2.2=pyhd8ed1ab_1
  - executing=2.1.0=pyhd8ed1ab_1
  - flatbuffers=24.3.25=h313beb8_0
  - fonttools=4.51.0=py311h80987f9_0
  - freetype=2.12.1=hadb7bae_2
  - gast=0.5.3=pyhd3eb1b0_0
  - giflib=5.2.2=h80987f9_0
  - google-pasta=0.2.0=pyhd3eb1b0_0
  - grpcio=1.65.5=py311hc367efa_0
  - h5py=3.12.1=nompi_py311h5dd25b7_103
  - hdf5=1.14.4=nompi_ha698983_105
  - icu=75.1=hfee45f7_0
  - idna=3.7=py311hca03da5_0
  - importlib-metadata=8.5.0=pyha770c72_1
  - ipykernel=6.29.5=pyh57ce528_0
  - ipython=8.31.0=pyh707e725_0
  - jedi=0.19.2=pyhd8ed1ab_1
  - joblib=1.4.2=py311hca03da5_0
  - jupyter_client=8.6.3=pyhd8ed1ab_1
  - jupyter_core=5.7.2=pyh31011fe_1
  - keras=3.8.0=pyh753f3f9_0
  - kiwisolver=1.4.4=py311h313beb8_0
  - krb5=1.21.3=hf3e1bf2_0
  - lcms2=2.16=ha0e7c42_0
  - lerc=4.0.0=h313beb8_0
  - libabseil=20240722.0=cxx17_h07bc746_4
  - libaec=1.1.3=h313beb8_0
  - libbrotlicommon=1.0.9=h80987f9_9
  - libbrotlidec=1.0.9=h80987f9_9
  - libbrotlienc=1.0.9=h80987f9_9
  - libcurl=8.11.1=h73640d1_0
  - libcxx=19.1.6=ha82da77_1
  - libdeflate=1.23=hec38601_0
  - libedit=3.1.20230828=h80987f9_0
  - libev=4.33=h1a28f6b_1
  - libexpat=2.6.4=h286801f_0
  - libffi=3.4.4=hca03da5_1
  - libgfortran=5.0.0=13_2_0_hd922786_3
  - libgfortran5=13.2.0=hf226fd6_3
  - libgrpc=1.65.5=h3d9cf25_0
  - libjpeg-turbo=3.0.3=h80987f9_0
  - liblzma=5.6.3=h39f12f2_1
  - libnghttp2=1.64.0=h6d7220d_0
  - libopenblas=0.3.21=h269037a_0
  - libpng=1.6.45=h3783ad8_0
  - libprotobuf=5.27.5=h53f8970_2
  - libre2-11=2024.07.02=h07bc746_2
  - libsodium=1.0.20=h99b78c6_0
  - libsqlite=3.47.2=h3f77e49_0
  - libssh2=1.11.1=h9cc3647_0
  - libtiff=4.7.0=h551f018_3
  - libwebp-base=1.5.0=h2471fea_0
  - libxcb=1.17.0=hdb1d25a_0
  - libzlib=1.3.1=h8359307_2
  - llvm-openmp=14.0.6=hc6e5704_0
  - lz4-c=1.9.4=h313beb8_1
  - markdown=3.4.1=py311hca03da5_0
  - markdown-it-py=2.2.0=py311hca03da5_1
  - markupsafe=2.1.3=py311h80987f9_1
  - matplotlib=3.10.0=py311hca03da5_0
  - matplotlib-base=3.10.0=py311h7ef442a_0
  - matplotlib-inline=0.1.7=pyhd8ed1ab_1
  - mdurl=0.1.0=py311hca03da5_0
  - ml_dtypes=0.4.0=py311h7aedaa7_0
  - namex=0.0.7=py311hca03da5_0
  - ncurses=6.5=h5e97a16_2
  - nest-asyncio=1.6.0=pyhd8ed1ab_1
  - numexpr=2.10.1=py311h5d9532f_0
  - numpy=1.26.4=py311he598dae_0
  - numpy-base=1.26.4=py311hfbfe69c_0
  - openjpeg=2.5.3=h8a3d83b_0
  - openssl=3.4.0=h81ee809_1
  - opt_einsum=3.3.0=pyhd3eb1b0_1
  - optree=0.12.1=py311h48ca7d4_0
  - packaging=24.2=py311hca03da5_0
  - pandas=2.2.3=py311hcf29cfe_0
  - parso=0.8.4=pyhd8ed1ab_1
  - pexpect=4.9.0=pyhd8ed1ab_1
  - pickleshare=0.7.5=pyhd8ed1ab_1004
  - pillow=11.1.0=py311hb9ba9e9_0
  - pip=24.2=py311hca03da5_0
  - platformdirs=4.3.6=pyhd8ed1ab_1
  - prompt-toolkit=3.0.48=pyha770c72_1
  - protobuf=5.27.5=py311h3f08180_0
  - psutil=6.1.1=py311h917b07b_0
  - pthread-stubs=0.3=h1a28f6b_1
  - ptyprocess=0.7.0=pyhd8ed1ab_1
  - pure_eval=0.2.3=pyhd8ed1ab_1
  - pygments=2.15.1=py311hca03da5_1
  - pyparsing=3.2.0=py311hca03da5_0
  - pysocks=1.7.1=py311hca03da5_0
  - python=3.11.11=hc22306f_1_cpython
  - python-dateutil=2.9.0.post0=pyhff2d567_1
  - python-flatbuffers=24.3.25=py311hca03da5_0
  - python-tzdata=2023.3=pyhd3eb1b0_0
  - python_abi=3.11=5_cp311
  - pytz=2024.1=py311hca03da5_0
  - pyzmq=26.2.0=py311h730b646_3
  - re2=2024.07.02=h6589ca4_2
  - readline=8.2=h1a28f6b_0
  - requests=2.32.3=py311hca03da5_1
  - rich=13.9.4=py311hca03da5_0
  - scikit-learn=1.5.2=py311h313beb8_0
  - scipy=1.14.1=py311hac8794a_0
  - setuptools=75.1.0=py311hca03da5_0
  - six=1.16.0=pyhd3eb1b0_1
  - snappy=1.2.1=h313beb8_0
  - sqlite=3.47.2=hd7222ec_0
  - stack_data=0.6.3=pyhd8ed1ab_1
  - tensorboard=2.17.1=pyhd8ed1ab_0
  - tensorboard-data-server=0.7.0=py311ha6e5c4f_1
  - tensorflow=2.17.0=cpu_py311h9d3d1e9_3
  - tensorflow-base=2.17.0=cpu_py311ha270cad_3
  - tensorflow-estimator=2.17.0=cpu_py311h935fadc_3
  - termcolor=2.1.0=py311hca03da5_0
  - threadpoolctl=3.5.0=py311hb6e6a13_0
  - tk=8.6.13=h5083fa2_1
  - tornado=6.4.2=py311h917b07b_0
  - traitlets=5.14.3=pyhd8ed1ab_1
  - typing-extensions=4.12.2=py311hca03da5_0
  - typing_extensions=4.12.2=py311hca03da5_0
  - tzdata=2024b=h04d1e81_0
  - unicodedata2=15.1.0=py311h80987f9_1
  - urllib3=2.2.3=py311hca03da5_0
  - wcwidth=0.2.13=pyhd8ed1ab_1
  - werkzeug=3.0.6=py311hca03da5_0
  - wheel=0.44.0=py311hca03da5_0
  - wrapt=1.17.0=py311h80987f9_0
  - xorg-libxau=1.0.12=h5505292_0
  - xorg-libxdmcp=1.1.5=hd74edd7_0
  - xz=5.4.6=h80987f9_1
  - zeromq=4.3.5=hc1bb282_7
  - zipp=3.21.0=pyhd8ed1ab_1
  - zlib=1.3.1=h8359307_2
  - zstd=1.5.6=hb46c0d2_0
--- a/model_large.keras
+++ b/model_large.keras
Author	SHA1	Message	Date
Hannes Martin Signer	d62115b111	update README	2025-02-27 13:09:10 +01:00
Hannes Signer	05bbb87562	update readme and environment	2025-02-27 12:48:58 +01:00
Hannes Signer	d5d836bd98	clean up repositry	2025-02-27 12:09:42 +01:00