| 1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556 |
- # -*- coding: UTF-8 -*-
- import numpy as np
- import os
- import sys
- import time
- from figure import Figure
- from dataset import DataSet
- from logger import load_logger
- from config import myargparse
- frame = "keras"
- if frame == "keras":
- from model.model_keras import train, predict
- os.environ["TF_CPP_MIN_LOG_LEVEL"] = '3'
- else:
- raise Exception("Wrong frame seletion")
- def main():
- parse = myargparse(discription="training config", add_help=False)
- opt = parse.parse_args_and_yaml()
- logger = load_logger(opt)
- try:
- np.random.seed(opt.Model["random_seed"])
- # 在这里获取数据集
- ds = DataSet(opt=opt)
- if opt.do_train:
- train_X, valid_X, train_Y, valid_Y = ds.get_train_and_valid_data(case=2)
- train(opt, [train_X, train_Y, valid_X, valid_Y])
- if opt.do_predict:
- test_X, test_Y, dq_Y = ds.get_test_data(return_label_data=True)
- result = predict(opt, test_X) # 这里输出的是未还原的归一化预测数据
- fig = Figure(opt, logger, ds)
- fig.draw(test_Y, dq_Y, result)
- except Exception:
- logger.error("Run Error", exc_info=True)
- if __name__=="__main__":
- import argparse
- # argparse方便于命令行下输入参数，可以根据需要增加更多
- # parser = argparse.ArgumentParser()
- # parser.add_argument("-t", "--do_train", default=False, type=bool, help="whether to train")
- # parser.add_argument("-p", "--do_predict", default=True, type=bool, help="whether to train")
- # parser.add_argument("-b", "--batch_size", default=64, type=int, help="batch size")
- # parser.add_argument("-e", "--epoch", default=20, type=int, help="epochs num")
- # args = parser.parse_args()
- # con = Config()
- # for key in dir(args): # dir(args) 函数获得args所有的属性
- # if not key.startswith("_"): # 去掉 args 自带属性，比如__name__等
- # setattr(con, key, getattr(args, key)) # 将属性值赋给Config
- main()
|
