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- """
- @Author : Lee, Qin
- @StartTime : 2018/08/13
- @Filename : train.py
- @Software : Pycharm
- @Framework : Pytorch
- @LastModify : 2019/05/07
- """
-
- from utils.module import ModelManager
- from utils.loader import DatasetManager
- from utils.process import Processor
-
- import torch
-
- import os
- import json
- import random
- import argparse
- import numpy as np
-
- parser = argparse.ArgumentParser()
-
- # Training parameters.
- parser.add_argument('--data_dir', '-dd', type=str, default='data/atis')
- parser.add_argument('--save_dir', '-sd', type=str, default='save')
- parser.add_argument("--random_state", '-rs', type=int, default=0)
- parser.add_argument('--num_epoch', '-ne', type=int, default=300)
- parser.add_argument('--batch_size', '-bs', type=int, default=16)
- parser.add_argument('--l2_penalty', '-lp', type=float, default=1e-6)
- parser.add_argument("--learning_rate", '-lr', type=float, default=0.001)
- parser.add_argument('--dropout_rate', '-dr', type=float, default=0.4)
- parser.add_argument('--intent_forcing_rate', '-ifr', type=float, default=0.9)
- parser.add_argument("--differentiable", "-d", action="store_true", default=False)
- parser.add_argument('--slot_forcing_rate', '-sfr', type=float, default=0.9)
-
- # model parameters.
- parser.add_argument('--word_embedding_dim', '-wed', type=int, default=64)
- parser.add_argument('--encoder_hidden_dim', '-ehd', type=int, default=256)
- parser.add_argument('--intent_embedding_dim', '-ied', type=int, default=8)
- parser.add_argument('--slot_embedding_dim', '-sed', type=int, default=32)
- parser.add_argument('--slot_decoder_hidden_dim', '-sdhd', type=int, default=64)
- parser.add_argument('--intent_decoder_hidden_dim', '-idhd', type=int, default=64)
- parser.add_argument('--attention_hidden_dim', '-ahd', type=int, default=1024)
- parser.add_argument('--attention_output_dim', '-aod', type=int, default=128)
-
- if __name__ == "__main__":
- args = parser.parse_args()
-
- # Save training and model parameters.
- if not os.path.exists(args.save_dir):
- os.system("mkdir -p " + args.save_dir)
-
- log_path = os.path.join(args.save_dir, "param.json")
- with open(log_path, "w") as fw:
- fw.write(json.dumps(args.__dict__, indent=True))
-
- # Fix the random seed of package random.
- random.seed(args.random_state)
- np.random.seed(args.random_state)
-
- # Fix the random seed of Pytorch when using GPU.
- if torch.cuda.is_available():
- torch.cuda.manual_seed_all(args.random_state)
- torch.cuda.manual_seed(args.random_state)
-
- # Fix the random seed of Pytorch when using CPU.
- torch.manual_seed(args.random_state)
- torch.random.manual_seed(args.random_state)
-
- # Instantiate a dataset object.
- dataset = DatasetManager(args)
- dataset.quick_build()
- dataset.show_summary()
-
- # Instantiate a network model object.
- model = ModelManager(
- args, len(dataset.word_alphabet),
- len(dataset.slot_alphabet),
- len(dataset.intent_alphabet))
- model.show_summary()
-
- # To train and evaluate the models.
- process = Processor(dataset, model, args.batch_size)
- process.train()
-
- print('\nAccepted performance: ' + str(Processor.validate(
- os.path.join(args.save_dir, "model/model.pkl"),
- os.path.join(args.save_dir, "model/dataset.pkl"),
- args.batch_size)) + " at test dataset;\n")
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