Comments (4)
feiranl
commented on August 26, 2024
1
I created a new environment python==3.8.13 with pytorch=1.11.0 today, I tested on my computer(cpu), it works fine. I am also testing that on gpu now.
pip install torch==1.11.0
pip install scikit-learn==0.23.2
pip install Biopython==1.78
pip install rdkit-pypi
pip install seaborn==0.11.0
pip install Matplotlib==3.3.2
pip install pandas==1.1.3
pip install SciPy==1.5.2
pip install NumPy==1.20.2
Training...
Epoch Time(sec) RMSE_train R2_train MAE_dev MAE_test RMSE_dev RMSE_test R2_dev R2_test
1 1850.890769584 1.3438182532736704 0.1998537546862883 0.9035335875736547 0.9133727147194339 1.1992452517734593 1.2359207287270122 0.3549627178018241 0.32299921362051043
2 3714.328295292 1.084357741076502 0.4790051045211742 0.8155829147555056 0.827786833740544 1.0718060717840199 1.1102980169965346 0.4847698989405582 0.4536297183959668
3 5565.796247834 1.0039601096588002 0.5533974334491122 0.7873367654527741 0.8069042074566374 1.046123391943784 1.0935254266866503 0.509166011683243 0.4700123957534842
4 7421.475990792 0.9586988949066316 0.5927578201651297 0.7794618119884426 0.7914288478966054 1.0353950991924916 1.084159005412381 0.5191816743983877 0.4790525652715526
5 9266.206740167 0.9310297750368882 0.6159255295480108 0.7454487619816593 0.7643532149817572 1.011139254568574 1.0703361718545579 0.5414457282064841 0.4922518545061749
6 11125.5231325 0.9118198494526261 0.631611228691773 0.790947889391016 0.8055422126099699 1.0413691513693535 1.0933207478520355 0.5136171889336283 0.470210776313323
7 12984.743249333 0.8906566336945769 0.6485132858747704 0.7597130137872721 0.7625249781298686 1.0223852634093529 1.0676801557246463 0.5311888160688671 0.4947686600756299
8 14841.661516167 0.8750188583259594 0.6607474459657958 0.7765459976505641 0.7862476204838971 1.0301065805234337 1.0810044343642546 0.5240809108014289 0.48207975010564863
9 16692.103153875 0.8614303673156085 0.6712023877816913 0.7394888517778917 0.7643921505036454 1.0224698312506153 1.0881460149657793 0.5311112562860693 0.4752139373068053
10 18538.492715834 0.790652954533467 0.7230125473773831 0.7279709099352978 0.7501985592397513 1.0082084445331096 1.0707727209979077 0.5441001355827655 0.4918375879860142from dlkcat.
feiranl
commented on August 26, 2024
We have tested it in pytorch = 1.4.0 and 1.8.0. Could this be the reason? I will test again today in pytorch 1.11.0, will let you know.
from dlkcat.
hmunozb
commented on August 26, 2024
I tested a CPU run on pytorch=1.4.0, but I got a similar training history with the command I wrote. Here are the first 10 epochs
Epoch Time(sec) RMSE_train R2_train MAE_dev MAE_test RMSE_dev RMSE_test R2_dev R2_test
1 1799.3738492260454 1.4300474419985105 0.093872750637233 1.1220249858832454 1.120269724750849 1.4452466784056024 1.4633177762541145 0.06318731754995843 0.050958426455659955
2 3592.004232672043 1.361057561028385 0.17919255889212427 1.0730845817573005 1.0592566097688458 1.3718942067704682 1.3679814609925314 0.15586861831560928 0.17059172438793757
3 5395.483067929046 1.3306088168449604 0.21550696240926992 1.0587110773535187 1.056803738489438 1.355633245599969 1.3546850722451287 0.17576087913482286 0.1866365905160432
4 7205.411594508099 1.3139003745364772 0.23508500839068713 1.0498388265642649 1.0392077809579552 1.3514482404223975 1.3500219145964145 0.18084207726986423 0.19222654487030677
5 9061.130795708043 1.2988018518113706 0.25256385102031076 1.0517304778519334 1.0403759229673326 1.3645734090219308 1.3635168211241457 0.16485360677059235 0.17599671949939144
6 10892.622826825012 1.2909488882966267 0.2615749932689544 1.045592042422458 1.0386182284694494 1.3451212587044103 1.3451953064872948 0.18849411317486797 0.19799213481702715
7 12664.473319050041 1.281445919877912 0.2724064086264315 1.057109985958508 1.047079589826518 1.3602581657653914 1.3598782660418802 0.17012728644975328 0.1803885699595642
8 14405.505319613032 1.2747839340068512 0.27995197614892 1.0336260921603988 1.0305034704046387 1.340438009349556 1.3482086459813252 0.1941350446624892 0.19439499351976908
9 16294.816087790066 1.271886118601683 0.28322185526858723 1.0325258553756487 1.023831375006762 1.3334349881072143 1.3375600889186805 0.20253341498729838 0.20707055823955345
...
25 43949.70711359603 1.2212125746116513 0.33919878185838725 1.0221514104633171 1.0242531846619454 1.3326459844589709 1.3445351463183686 0.20347686989604497 0.19877911902582002
26 45594.636059042066 1.2194461549093851 0.3411090276759614 1.0218019473397688 1.0243150300529025 1.3316524246429056 1.3446511777670838 0.2046641294841648 0.1986408246401863and the exported conda environment
Conda export
channels:
- pytorch
- conda-forge
- defaults
dependencies:
- _libgcc_mutex=0.1=main
- _openmp_mutex=5.1=1_gnu
- blas=1.0=mkl
- boost=1.70.0=py37h9de70de_1
- boost-cpp=1.70.0=ha2d47e9_1
- bottleneck=1.3.4=py37hce1f21e_0
- brotli=1.0.9=he6710b0_2
- brotlipy=0.7.0=py37h27cfd23_1003
- bzip2=1.0.8=h7f98852_4
- ca-certificates=2022.4.26=h06a4308_0
- cairo=1.16.0=h18b612c_1001
- certifi=2022.5.18.1=py37h06a4308_0
- cffi=1.15.0=py37hd667e15_1
- charset-normalizer=2.0.4=pyhd3eb1b0_0
- cpuonly=2.0=0
- cryptography=37.0.1=py37h9ce1e76_0
- cycler=0.11.0=pyhd3eb1b0_0
- dbus=1.13.18=hb2f20db_0
- expat=2.4.4=h295c915_0
- fontconfig=2.13.1=h6c09931_0
- fonttools=4.25.0=pyhd3eb1b0_0
- freetype=2.11.0=h70c0345_0
- giflib=5.2.1=h7b6447c_0
- glib=2.69.1=h4ff587b_1
- gst-plugins-base=1.14.0=h8213a91_2
- gstreamer=1.14.0=h28cd5cc_2
- icu=58.2=he6710b0_3
- idna=3.3=pyhd3eb1b0_0
- intel-openmp=2021.4.0=h06a4308_3561
- joblib=1.1.0=pyhd8ed1ab_0
- jpeg=9e=h7f8727e_0
- kiwisolver=1.4.2=py37h295c915_0
- lcms2=2.12=h3be6417_0
- ld_impl_linux-64=2.38=h1181459_1
- libblas=3.9.0=12_linux64_mkl
- libcblas=3.9.0=12_linux64_mkl
- libffi=3.3=he6710b0_2
- libgcc-ng=11.2.0=h1234567_1
- libgfortran-ng=7.5.0=ha8ba4b0_17
- libgfortran4=7.5.0=ha8ba4b0_17
- libgomp=11.2.0=h1234567_1
- libpng=1.6.37=hbc83047_0
- libstdcxx-ng=11.2.0=h1234567_1
- libtiff=4.2.0=h2818925_1
- libuuid=1.0.3=h7f8727e_2
- libwebp=1.2.2=h55f646e_0
- libwebp-base=1.2.2=h7f8727e_0
- libxcb=1.15=h7f8727e_0
- libxml2=2.9.14=h74e7548_0
- lz4-c=1.9.3=h295c915_1
- matplotlib=3.5.1=py37h06a4308_1
- matplotlib-base=3.5.1=py37ha18d171_1
- mkl=2021.4.0=h06a4308_640
- mkl-service=2.4.0=py37h7f8727e_0
- mkl_fft=1.3.1=py37hd3c417c_0
- mkl_random=1.2.2=py37h51133e4_0
- munkres=1.1.4=py_0
- ncurses=6.3=h7f8727e_2
- ninja=1.10.2=h06a4308_5
- ninja-base=1.10.2=hd09550d_5
- numexpr=2.8.1=py37h6abb31d_0
- numpy=1.21.5=py37h6c91a56_3
- numpy-base=1.21.5=py37ha15fc14_3
- openssl=1.1.1o=h7f8727e_0
- packaging=21.3=pyhd3eb1b0_0
- pandas=1.3.5=py37h8c16a72_0
- patsy=0.5.2=pyhd8ed1ab_0
- pcre=8.45=h295c915_0
- pillow=9.0.1=py37h22f2fdc_0
- pip=21.2.2=py37h06a4308_0
- pixman=0.38.0=h516909a_1003
- pycairo=1.21.0=py37h0afab05_1
- pycparser=2.21=pyhd3eb1b0_0
- pyopenssl=22.0.0=pyhd3eb1b0_0
- pyparsing=3.0.4=pyhd3eb1b0_0
- pyqt=5.9.2=py37h05f1152_2
- pysocks=1.7.1=py37_1
- python=3.7.13=h12debd9_0
- python-dateutil=2.8.2=pyhd3eb1b0_0
- python_abi=3.7=2_cp37m
- pytorch=1.4.0=py3.7_cpu_0
- pytorch-mutex=1.0=cpu
- pytz=2022.1=py37h06a4308_0
- qt=5.9.7=h5867ecd_1
- rdkit=2019.09.3=py37hb31dc5d_0
- readline=8.1.2=h7f8727e_1
- requests=2.27.1=pyhd3eb1b0_0
- scikit-learn=1.0.2=py37hf9e9bfc_0
- scipy=1.7.3=py37hc147768_0
- seaborn=0.11.2=hd8ed1ab_0
- seaborn-base=0.11.2=pyhd8ed1ab_0
- setuptools=61.2.0=py37h06a4308_0
- sip=4.19.8=py37hf484d3e_0
- six=1.16.0=pyhd3eb1b0_1
- sqlite=3.38.3=hc218d9a_0
- statsmodels=0.13.2=py37hb1e94ed_0
- threadpoolctl=3.1.0=pyh8a188c0_0
- tk=8.6.12=h1ccaba5_0
- torchvision=0.5.0=py37_cpu
- tornado=6.1=py37h27cfd23_0
- typing_extensions=4.1.1=pyh06a4308_0
- urllib3=1.26.9=py37h06a4308_0
- wheel=0.37.1=pyhd3eb1b0_0
- xorg-kbproto=1.0.7=h7f98852_1002
- xorg-libice=1.0.10=h7f98852_0
- xorg-libsm=1.2.2=h470a237_5
- xorg-libx11=1.7.2=h7f98852_0
- xorg-libxext=1.3.4=h7f98852_1
- xorg-libxrender=0.9.10=h7f98852_1003
- xorg-renderproto=0.11.1=h7f98852_1002
- xorg-xextproto=7.3.0=h7f98852_1002
- xorg-xproto=7.0.31=h7f98852_1007
- xz=5.2.5=h7f8727e_1
- zlib=1.2.12=h7f8727e_2
- zstd=1.5.2=ha4553b6_0
- pip:
- biopython==1.79
from dlkcat.
Mojgan-Asadi
commented on August 26, 2024
I was also facing the same problem training the model with both Pytorch 1.4 and 1.11. After debugging further, I found that the ReLU activations of the CNN can die in the first epoch when running run_model.py on the data. After changing the activations to leaky ReLUs, the training output is now more similar to @feiranl's log.
def attention_cnn(self, x, xs, layer):
"""The attention mechanism is applied to the last layer of CNN."""
xs = torch.unsqueeze(torch.unsqueeze(xs, 0), 0)
for i in range(layer):
xs = F.leaky_relu(self.W_cnn[i](xs))
xs = torch.squeeze(torch.squeeze(xs, 0), 0)Epoch Time(sec) RMSE_train R2_train MAE_dev MAE_test RMSE_dev RMSE_test R2_dev R2_test
1 108.62964485096745 1.3361766198360103 0.2033351364647331 0.9049852923208807 0.9035405645037045 1.1739503139604477 1.1962411549966967 0.38448252864269594 0.36331743904275005
2 215.53552481881343 1.1098560563182645 0.45035631927606645 0.8470982153573635 0.8373281794411068 1.118362950754359 1.1249759460757636 0.4413928441627386 0.43691760597692275
3 321.9014725498855 1.0284413477237342 0.5280380527224801 0.8129164308897753 0.8113835559586549 1.1062356064091905 1.1162905596245978 0.4534420469215621 0.4455786076611852
4 428.2206061058678 0.9848536956129555 0.5671959016923331 0.8319972615900284 0.8101702824537884 1.0914312427031414 1.0855119277410503 0.46797294830478653 0.4757304007854727
5 530.6794445267878 0.95100420025565 0.5964356479384888 0.8040076379878801 0.803888922863107 1.0908744412588334 1.0882729923015786 0.46851564464039996 0.4730599867096059
6 627.7551401329692 0.9307128999705022 0.6134733938115456 0.7895671279593413 0.7741680549853144 1.071451121014253 1.0666324692731286 0.48727360031773326 0.4938082356513165
7 724.7671971449163 0.9097414091836883 0.6306961348218802 0.8244057570181237 0.8146809501164135 1.092587815943168 1.090002043599753 0.46684478877643476 0.4713842489036103
8 821.8540824640077 0.8983011608152153 0.6399259293182231 0.7782960763231104 0.7795859852150637 1.0553856260032444 1.0727548623233278 0.502534116338722 0.4879805502048369
9 918.9646761238109 0.88158551056637 0.653201816500695 0.7618141485525484 0.7723324211238441 1.0382033122967171 1.0696761191005835 0.5186003474926817 0.4909152642062631from dlkcat.
Related Issues (20)
- Dear author, you know that you have done a famous and complex work. Then, I meet a question that proteome data NOT 404 HOT 1
- two substrates HOT 1
- error running example HOT 3
- Failed parsing SMILES HOT 3
- Dataset problem HOT 3
- Example input.tsv file in DeeplearningApproach yields no kcats when run HOT 4
- Input a new sequence for measure Kcat using DLKcat HOT 1
- Reason for two kcat values in each PredcitedKcat343species file HOT 1
- Hyperparameter file for DLKcat HOT 3
- The dimension of the adjacency matrix is not consistent
- isssue about runing prediction_for_input.py HOT 4
- RMSE HOT 2
- result inconsistency HOT 1
- I can't find the file I'm looking for HOT 1
- RMSE and data HOT 7
- Unable to reproduce the results in Fig. 2a. HOT 2
- find the sequence HOT 3
- Can DLKcat parse the smiles including '.' ? HOT 2
- Loss calculation confusion HOT 2
- nomoudule name SOAPpy HOT 1
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