Comments (6)
txie-93
commented on August 23, 2024
Thanks for reaching out. Can you provide more details? What is your train/test data? What are other hyperparameters?
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Rich2333
commented on August 23, 2024
My train/test data is crystal structures downloaded from Materials Project database (using MPRester API).
In the case of using my own trained model, I use same data to train and predict. And I'm quite confused that the prediction MAE(e.g. 5.977) is 100 times more than the training MAE(e.g. 0.0586).
As for parameters, I think I didn't change any default ones other than epochs:
python main.py --epochs 500 --train-ratio 0.6 --val-ratio 0.2 --test-ratio 0.2 mp/
python predict.py E:/cgcnn-master/trained_files/from_cmd/mp-2/mp_model_best.pth.tar mp/
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Rich2333
commented on August 23, 2024
And for the size mismatch problem, I'm wondering if my environment is different from the environment of pre-trained models.
The packages in my vritual environment are listed as follows:
#Name Version Build Channel
ase 3.22.1 pyhd8ed1ab_1 conda-forge
blas 2.114 mkl conda-forge
blas-devel 3.9.0 14_win64_mkl conda-forge
brotli 1.0.9 h8ffe710_7 conda-forge
brotli-bin 1.0.9 h8ffe710_7 conda-forge
brotlipy 0.7.0 py310he2412df_1004 conda-forge
bzip2 1.0.8 h8ffe710_4 conda-forge
ca-certificates 2021.10.8 h5b45459_0 conda-forge
certifi 2021.10.8 py310h5588dad_2 conda-forge
cffi 1.15.0 py310hcbf9ad4_0 conda-forge
cftime 1.6.0 py310h2873277_1 conda-forge
charset-normalizer 2.0.12 pyhd8ed1ab_0 conda-forge
click 8.1.3 py310h5588dad_0 conda-forge
colorama 0.4.4 pyh9f0ad1d_0 conda-forge
cryptography 36.0.2 py310ha857299_1 conda-forge
cudatoolkit 11.3.1 h59b6b97_2
curl 7.83.0 h789b8ee_0 conda-forge
cycler 0.11.0 pyhd8ed1ab_0 conda-forge
cython 0.29.28 py310h8a704f9_2 conda-forge
double-conversion 3.2.0 h0e60522_0 conda-forge
eigen 3.4.0 h2d74725_0 conda-forge
expat 2.4.8 h39d44d4_0 conda-forge
ffmpeg 4.3.1 ha925a31_0 conda-forge
flask 2.1.2 pyhd8ed1ab_1 conda-forge
fonttools 4.33.3 py310he2412df_0 conda-forge
freetype 2.10.4 h546665d_1 conda-forge
future 0.18.2 py310h5588dad_5 conda-forge
gl2ps 1.4.2 h0597ee9_0 conda-forge
glew 2.1.0 h39d44d4_2 conda-forge
hdf4 4.2.15 h0e5069d_3 conda-forge
hdf5 1.12.1 nompi_h2a0e4a3_104 conda-forge
icu 69.1 h0e60522_0 conda-forge
idna 3.3 pyhd8ed1ab_0 conda-forge
importlib-metadata 4.11.3 py310h5588dad_1 conda-forge
intel-openmp 2022.0.0 h57928b3_3663 conda-forge
itsdangerous 2.1.2 pyhd8ed1ab_0 conda-forge
jbig 2.1 h8d14728_2003 conda-forge
jinja2 3.1.2 pyhd8ed1ab_0 conda-forge
joblib 1.1.0 pyhd8ed1ab_0 conda-forge
jpeg 9e h8ffe710_1 conda-forge
jsoncpp 1.9.5 h2d74725_1 conda-forge
kiwisolver 1.4.2 py310h476a331_1 conda-forge
krb5 1.19.3 h1176d77_0 conda-forge
latexcodec 2.0.1 pyh9f0ad1d_0 conda-forge
lcms2 2.12 h2a16943_0 conda-forge
lerc 3.0 h0e60522_0 conda-forge
libblas 3.9.0 14_win64_mkl conda-forge
libbrotlicommon 1.0.9 h8ffe710_7 conda-forge
libbrotlidec 1.0.9 h8ffe710_7 conda-forge
libbrotlienc 1.0.9 h8ffe710_7 conda-forge
libcblas 3.9.0 14_win64_mkl conda-forge
libclang 13.0.1 default_h81446c8_0 conda-forge
libcurl 7.83.0 h789b8ee_0 conda-forge
libdeflate 1.10 h8ffe710_0 conda-forge
libffi 3.4.2 h8ffe710_5 conda-forge
libiconv 1.16 he774522_0 conda-forge
liblapack 3.9.0 14_win64_mkl conda-forge
liblapacke 3.9.0 14_win64_mkl conda-forge
libnetcdf 4.8.1 nompi_h1cc8e9d_102 conda-forge
libogg 1.3.4 h8ffe710_1 conda-forge
libpng 1.6.37 h1d00b33_2 conda-forge
libssh2 1.10.0 h680486a_2 conda-forge
libtheora 1.1.1 h8d14728_1005 conda-forge
libtiff 4.3.0 hc4061b1_3 conda-forge
libuv 1.43.0 h8ffe710_0 conda-forge
libwebp 1.2.2 h57928b3_0 conda-forge
libwebp-base 1.2.2 h8ffe710_1 conda-forge
libxcb 1.13 hcd874cb_1004 conda-forge
libxml2 2.9.14 hf5bbc77_0 conda-forge
libzip 1.8.0 hfed4ece_1 conda-forge
libzlib 1.2.11 h8ffe710_1014 conda-forge
loguru 0.6.0 py310h5588dad_1 conda-forge
lz4-c 1.9.3 h8ffe710_1 conda-forge
m2w64-gcc-libgfortran 5.3.0 6 conda-forge
m2w64-gcc-libs 5.3.0 7 conda-forge
m2w64-gcc-libs-core 5.3.0 7 conda-forge
m2w64-gmp 6.1.0 2 conda-forge
m2w64-libwinpthread-git 5.0.0.4634.697f757 2 conda-forge
markupsafe 2.1.1 py310he2412df_1 conda-forge
matplotlib-base 3.5.2 py310h79a7439_0 conda-forge
mkl 2022.0.0 h0e2418a_796 conda-forge
mkl-devel 2022.0.0 h57928b3_797 conda-forge
mkl-include 2022.0.0 h0e2418a_796 conda-forge
monty 2022.4.26 pyhd8ed1ab_0 conda-forge
mpmath 1.2.1 pyhd8ed1ab_0 conda-forge
msys2-conda-epoch 20160418 1 conda-forge
munkres 1.1.4 pyh9f0ad1d_0 conda-forge
netcdf4 1.5.8 nompi_py310h5489b47_101 conda-forge
networkx 2.8 pyhd8ed1ab_0 conda-forge
numpy 1.22.3 py310hed7ac4c_2 conda-forge
openjpeg 2.4.0 hb211442_1 conda-forge
openssl 1.1.1o h8ffe710_0 conda-forge
packaging 21.3 pyhd8ed1ab_0 conda-forge
palettable 3.3.0 py_0 conda-forge
pandas 1.4.2 py310hf5e1058_1 conda-forge
pillow 9.1.0 py310h767b3fd_2 conda-forge
pip 22.0.4 pyhd8ed1ab_0 conda-forge
plotly 5.7.0 pyhd8ed1ab_0 conda-forge
proj 9.0.0 h1cfcee9_1 conda-forge
pthread-stubs 0.4 hcd874cb_1001 conda-forge
pugixml 1.11.4 h0e60522_0 conda-forge
pybtex 0.24.0 pyhd8ed1ab_2 conda-forge
pycparser 2.21 pyhd8ed1ab_0 conda-forge
pymatgen 2022.4.26 py310h476a331_0 conda-forge
pyopenssl 22.0.0 pyhd8ed1ab_0 conda-forge
pyparsing 3.0.8 pyhd8ed1ab_0 conda-forge
pysocks 1.7.1 py310h5588dad_5 conda-forge
python 3.10.4 h9a09f29_0_cpython conda-forge
python-dateutil 2.8.2 pyhd8ed1ab_0 conda-forge
python_abi 3.10 2_cp310 conda-forge
pytorch 1.11.0 py3.10_cuda11.3_cudnn8_0 pytorch
pytorch-mutex 1.0 cuda pytorch
pytz 2022.1 pyhd8ed1ab_0 conda-forge
pyyaml 6.0 py310he2412df_4 conda-forge
qt 5.12.9 h556501e_6 conda-forge
requests 2.27.1 pyhd8ed1ab_0 conda-forge
ruamel.yaml 0.17.21 py310he2412df_1 conda-forge
ruamel.yaml.clib 0.2.6 py310he2412df_1 conda-forge
scikit-learn 1.0.2 py310h4dafddf_0 conda-forge
scipy 1.8.0 py310h33db832_1 conda-forge
setuptools 62.1.0 py310h5588dad_0 conda-forge
six 1.16.0 pyh6c4a22f_0 conda-forge
spglib 1.16.4 py310h2873277_0 conda-forge
sqlite 3.38.4 h8ffe710_0 conda-forge
sympy 1.10.1 py310h5588dad_0 conda-forge
tabulate 0.8.9 pyhd8ed1ab_0 conda-forge
tbb 2021.5.0 h2d74725_1 conda-forge
tbb-devel 2021.5.0 h2d74725_1 conda-forge
tenacity 8.0.1 pyhd8ed1ab_0 conda-forge
threadpoolctl 3.1.0 pyh8a188c0_0 conda-forge
tk 8.6.12 h8ffe710_0 conda-forge
torchvision 0.12.0 py310_cu113 pytorch
tqdm 4.64.0 pyhd8ed1ab_0 conda-forge
typing_extensions 4.2.0 pyha770c72_1 conda-forge
tzdata 2022a h191b570_0 conda-forge
ucrt 10.0.20348.0 h57928b3_0 conda-forge
uncertainties 3.1.6 pyhd8ed1ab_0 conda-forge
unicodedata2 14.0.0 py310he2412df_1 conda-forge
urllib3 1.26.9 pyhd8ed1ab_0 conda-forge
utfcpp 3.2.1 h57928b3_0 conda-forge
vc 14.2 hb210afc_6 conda-forge
vs2015_runtime 14.29.30037 h902a5da_6 conda-forge
vtk 9.1.0 qt_py310h99a8838_207 conda-forge
werkzeug 2.1.2 pyhd8ed1ab_1 conda-forge
wheel 0.37.1 pyhd8ed1ab_0 conda-forge
win32_setctime 1.1.0 pyhd8ed1ab_0 conda-forge
win_inet_pton 1.1.0 py310h5588dad_4 conda-forge
xorg-libxau 1.0.9 hcd874cb_0 conda-forge
xorg-libxdmcp 1.1.3 hcd874cb_0 conda-forge
xz 5.2.5 h62dcd97_1 conda-forge
yaml 0.2.5 h8ffe710_2 conda-forge
zipp 3.8.0 pyhd8ed1ab_0 conda-forge
zlib 1.2.11 h8ffe710_1014 conda-forge
zstd 1.5.2 h6255e5f_0 conda-forge
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SANTKJD
commented on August 23, 2024
Dear author,
I have trained 40000+ cifs and I have set “tarin size 0.8”，“epoch 1000”，I think I will get the same result of yours ,but I just get the MAE (0.049).Only 300~400 cifs of all cifs are different from yours.Is my result correct within the margin of error?
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liaokkkkk
commented on August 23, 2024
yeah，i also found this problem
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liaokkkkk
commented on August 23, 2024
I know, it’s because there are differences in prediction codes between models trained using GPU and models trained using CPU. You can go to some tutorials online and it’s easier to solve.
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Related Issues (20)
- Description for Materials Project .csv files HOT 4
- Predict at atom level HOT 3
- why the cgcnn model cannot predict graphene and diamond? HOT 3
- The setting of hyperparameters to get the best performance HOT 2
- Data construction specifics HOT 2
- On understanding the AtomInitializer class
- The problem of using the Materials Project database and the Perovskite database HOT 3
- Add CIF module to be installed in the environment in README file HOT 2
- How to understand the shape of "nbr_fea_idx" in the code HOT 2
- Could you please tell me why do you use one hot encode?
- Question about fetching Materials Project Data HOT 2
- Problem with importing MPDataRetrieval HOT 1
- Atom features specifics
- Loading data issue with certain CIF files
- Multiclass classification HOT 1
- Bulid the element vector HOT 1
- Questions about recreating a data set
- MAE for predicting and training shear modulus and bulk modulus is very large HOT 3
- struct = Structure.from_dict(entry["structure"]) # Pymatgen structure KeyError: 'structure'
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