Hello,
I am trying to train a new PLC model with train_plc.py and and several things have caused me difficulties:
- Can I follow the same data preprocessing set up with
./dump_data -train input.s16 features.f32 data.s16 to get input features.f32 for PLC model training?
- How should lost_file for training look like? Is it a single
.txt file - a concatenation of smaller .txt files with one entry per 20ms packet, where 1 means "packet lost" and 0 means "packet not lost"? How to create a single file if original data was augmented after running ./dump_data? Is there any script for it?
- To close the above questions with lost_file preprocessing, can I just uncomment the line and train the model with random packets marked as lost? Have you noticed any significant degradation in how this works?
- Following
test_plc.py, the output is: features + (1-lost)*out, but the shapes:
- features: [bs, seq_len, nb_used_features+nb_burg_features]
- lost: [bs, seq_len, 1]
- out: [bs, seq_len, nb_used_features]
Did I think of the wrong shapes? What should be the shape of a correct output for writing to output.f32?
Thank you for sharing your code and supporting this repository!
Hello,
I am trying to train a new PLC model with train_plc.py and and several things have caused me difficulties:
./dump_data -train input.s16 features.f32 data.s16to get input features.f32 for PLC model training?.txtfile - a concatenation of smaller .txt files with one entry per 20ms packet, where 1 means "packet lost" and 0 means "packet not lost"? How to create a single file if original data was augmented after running./dump_data?Is there any script for it?test_plc.py, the output is: features + (1-lost)*out, but the shapes:Did I think of the wrong shapes? What should be the shape of a correct output for writing to
output.f32?Thank you for sharing your code and supporting this repository!