train.py

"""
@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")