def gen_best_23_error():
data_loader = DataLoader()
Checkpoint.CHECKPOINT_DIR_NAME = args.checkpoint_dir_name
checkpoint_path = os.path.join("./experiment", Checkpoint.CHECKPOINT_DIR_NAME, 'best')
checkpoint = Checkpoint.load(checkpoint_path)
seq2seq = checkpoint.model
if args.cuda_use:
seq2seq = seq2seq.cuda()
seq2seq.eval()
emb_model = seq2seq.encoder.embedding
emb_np = emb_model.weight.cpu().data.numpy()
np.save("./data/rl_train_data/emb.npy", emb_np)
evaluator = Evaluator(vocab_dict = data_loader.vocab_dict,
vocab_list = data_loader.vocab_list,
decode_classes_dict = data_loader.decode_classes_dict,
decode_classes_list = data_loader.decode_classes_list,
loss = NLLLoss(),
cuda_use = args.cuda_use)
evaluator.gen_rl_data(model = seq2seq,
data_loader = data_loader,
data_list = data_loader.math23k_train_list,
template_flag = False,
batch_size = 16,
evaluate_type = 0,
use_rule = False,
mode = args.mode,
filename = args.load_name)
def gen_math57k_error():
data_loader = DataLoader()
Checkpoint.CHECKPOINT_DIR_NAME = args.checkpoint_dir_name
checkpoint_path = os.path.join("./experiment", Checkpoint.CHECKPOINT_DIR_NAME, args.load_name)
checkpoint = Checkpoint.load(checkpoint_path)
seq2seq = checkpoint.model
if args.cuda_use:
seq2seq = seq2seq.cuda()
seq2seq.eval()
evaluator = Evaluator(vocab_dict = data_loader.vocab_dict,
vocab_list = data_loader.vocab_list,
decode_classes_dict = data_loader.decode_classes_dict,
decode_classes_list = data_loader.decode_classes_list,
loss = NLLLoss(),
cuda_use = args.cuda_use)
evaluator.gen_rl_data(model = seq2seq,
data_loader = data_loader,
data_list = data_loader.math57k_data_list,
template_flag = False,
batch_size = 16,
evaluate_type = 0,
use_rule = True,
mode = args.mode,
filename = args.load_name)