Try our neural network based nonogram solver at nonogram-solver.web.app
Usage of the scripts in src/
datagen.py
-s, --samples Number of generated samples
-r, --rows Number of rows in a nonogram
-c, --columns Number of columns in a nonogram
-t, --train Train split [0.0, 1.0]
-v, --valid Valid split [0.0, 1.0]
-o, --output Output folder
models.py
-i, --input Input folder, consisting of training and validation data, default value: ./
-o, --output Compiled model is saved to here, default value: compiled/
train.py
-i, --input Input folder, consisting training and validation data , default value: ./
-l, --logdir TensorBoard logging directory, default value: logs/scalars
-e, --earlystopping Early stopping patience, default value: 10
-m, --model Model path, default value: models/
-c, --checkpoints Use checkpoints (save best model during training), default value: False
-o, --output Trained model is saved to here, default value: trained
evaluate.py
-i, --input Input folder, consisting test data, default value: ./
-m, --model Model path, default value: trained/