def main(arg_list: List[str]) -> None:
args = take_std_args(
arg_list, "non-recurrent neural network "
"model for Proverbot9001")
raw_dataset = get_text_data(args)
dataset, tokenizer, embedding = encode_bag_classify_data(
raw_dataset, tokenizers[args.tokenizer], args.num_keywords, 2)
checkpoints = train(dataset, tokenizer.numTokens(), args.hidden_size,
embedding.num_tokens(), args.num_decoder_layers,
args.batch_size, args.learning_rate, args.gamma,
args.epoch_step, args.num_epochs, args.print_every,
optimizers[args.optimizer])
for epoch, (network_state, training_loss) in enumerate(checkpoints):
state = {
'epoch': epoch,
'training-loss': training_loss,
'tokenizer': tokenizer,
'embedding': embedding,
'network-state': network_state,
'training-args': args,
}
with open(args.save_file, 'wb') as f:
print("=> Saving checkpoint at epoch {}".format(epoch))
torch.save(state, f)
def main(args_list : List[str]) -> None:
parser = argparse.ArgumentParser(description=
"A second-tier predictor which predicts tactic "
"stems based on word frequency in the goal")
parser.add_argument("--context-filter", dest="context_filter",
type=str, default="default")
parser.add_argument("--num-keywords", dest="num_keywords",
type=int, default=100)
parser.add_argument("--max-tuples", dest="max_tuples",
type=int, default=None)
parser.add_argument("scrape_file")
parser.add_argument("save_file")
args = parser.parse_args(args_list)
dataset = get_text_data(args)
samples, tokenizer, embedding = encode_bag_classify_data(dataset,
tokenizers["no-fallback"],
args.num_keywords, 2)
classifier, loss = train(samples, embedding.num_tokens())
state = {'stem-embeddings': embedding,
'tokenizer':tokenizer,
'classifier': classifier,
'options': [
("dataset size", str(len(samples))),
("context filter", args.context_filter),
("training loss", loss),
("# stems", embedding.num_tokens()),
("# tokens", args.num_keywords),
]}
with open(args.save_file, 'wb') as f:
pickle.dump(state, f)
def _encode_data(self, data : RawDataset, arg_values : argparse.Namespace) \
-> Tuple[ClassifyBagDataset, KNNMetadata]:
samples, tokenizer, embedding = \
encode_bag_classify_data(RawDataset(list(self._preprocess_data(data, arg_values))),
tokenizers[arg_values.tokenizer],
arg_values.num_keywords, 2)
return samples, KNNMetadata(embedding, tokenizer, arg_values.tokenizer,
len(samples), arg_values.context_filter)
def main(args_list : List[str]) -> None:
parser = argparse.ArgumentParser(description=
"A second-tier predictor which predicts tactic "
"stems based on word frequency in the goal")
parser.add_argument("--learning-rate", dest="learning_rate", default=.5, type=float)
parser.add_argument("--num-epochs", dest="num_epochs", default=10, type=int)
parser.add_argument("--batch-size", dest="batch_size", default=256, type=int)
parser.add_argument("--print-every", dest="print_every", default=10, type=int)
parser.add_argument("--epoch-step", dest="epoch_step", default=5, type=int)
parser.add_argument("--gamma", dest="gamma", default=0.5, type=float)
parser.add_argument("--optimizer", default="SGD",
choices=list(optimizers.keys()), type=str)
parser.add_argument("--context-filter", dest="context_filter",
type=str, default="default")
parser.add_argument("scrape_file")
parser.add_argument("save_file")
args = parser.parse_args(args_list)
print("Loading dataset...")
text_dataset = get_text_data(args)
samples, tokenizer, embedding = encode_bag_classify_data(text_dataset,
tokenizers["char-fallback"],
100, 2)
checkpoints = train(samples, args.learning_rate,
args.num_epochs, args.batch_size,
embedding.num_tokens(), args.print_every,
args.gamma, args.epoch_step, args.optimizer)
for epoch, (linear_state, loss) in enumerate(checkpoints, start=1):
state = {'epoch':epoch,
'text-encoder':tokenizer,
'linear-state': linear_state,
'stem-embeddings': embedding,
'options': [
("# epochs", str(epoch)),
("learning rate", str(args.learning_rate)),
("batch size", str(args.batch_size)),
("epoch step", str(args.epoch_step)),
("gamma", str(args.gamma)),
("dataset size", str(len(samples))),
("optimizer", args.optimizer),
("training loss", "{:10.2f}".format(loss)),
("context filter", args.context_filter),
]}
with open(args.save_file, 'wb') as f:
print("=> Saving checkpoint at epoch {}".
format(epoch))
torch.save(state, f)
