dump_dataset.py -o <num_of_samples> -c <num_of_variables> -j <random_seed>
Run on TPU like this:
python neurosat_tpu.py \
--use_tpu=True \
--tpu=$TPU_NAME \
--train_file=$TRAINNIG_FILE \
--test_file=$TEST_FILE \
--train_steps=1200000 \
--test_steps=80 \
--model_dir=$MODEL_DIR \
--export_dir=$EXPORT_DIR \
--variable_number=30 \
--clause_number=300 \ # 10 * variable_number
--train_files_gzipped=False \
--batch_size=128 \
--export_model \
--attention=$ATTENTION \
--level_number=$LEVEL_NUMBER
Examples and hyperparameters can be read in notebooks/iclr2019/tpu_grid.sh
.
- For DPLL with 1000 step limit see
notebooks/iclr2019/dpll_1000_steps.ipynb
. - For DPLL without a step limit see
notebooks/iclr2019/hybrid_dpll.ipynb
. - For CDCL without a step limit see
notebooks/iclr2019/hybrid_cdcl.ipynb
.