基于深度迁移学习的水质预测研究.zip

from data_provider.data_factory import data_provider from exp.exp_basic import Exp_Basic from models import Informer, Autoformer, Transformer, LSTM, CNN, MLP, BiLSTM, CLA from utils.tools import EarlyStopping, adjust_learning_rate, visual from utils.metrics import metric import numpy as np import torch import torch.nn as nn from torch import optim import os import time import warnings import matplotlib.pyplot as plt import numpy as np warnings.filterwarnings('ignore') # 传统迁移学习 # class Exp_Main_Transfer(Exp_Basic): # def __init__(self, args): # super(Exp_Main_Transfer, self).__init__(args) # # def _build_model(self): # model_dict = { # 'Autoformer': Autoformer, # 'Transformer': Transformer, # 'Informer': Informer, # 'LSTM': LSTM, # 'CNN': CNN, # 'MLP': MLP, # 'BiLSTM': BiLSTM, # 'CLA': CLA # # } # model = model_dict[self.args.model].Model(self.args).float() # # if self.args.use_multi_gpu and self.args.use_gpu: # model = nn.DataParallel(model, device_ids=self.args.device_ids) # return model # # def _get_data(self, flag): # data_set, data_loader = data_provider(self.args, flag) # return data_set, data_loader # # def _select_optimizer(self): # model_optim = optim.Adam(self.model.parameters(), lr=self.args.learning_rate) # return model_optim # # def _select_criterion(self): # criterion = nn.MSELoss() # return criterion # # def vali(self, vali_data, vali_loader, criterion): # total_loss = [] # self.model.eval() # with torch.no_grad(): # for i, (batch_x, batch_y, batch_x_mark, batch_y_mark) in enumerate(vali_loader): # batch_x = batch_x.float().to(self.device) # batch_y = batch_y.float() # # batch_x_mark = batch_x_mark.float().to(self.device) # batch_y_mark = batch_y_mark.float().to(self.device) # # # decoder input # dec_inp = torch.zeros_like(batch_y[:, -self.args.pred_len:, :]).float() # dec_inp = torch.cat([batch_y[:, :self.args.label_len, :], dec_inp], dim=1).float().to(self.device) # # encoder - decoder # if self.args.use_amp: # with torch.cuda.amp.autocast(): # if self.args.output_attention: # outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)[0] # else: # outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark) # else: # if self.args.model == 'LSTM' or self.args.model == 'CNN' or self.args.model == 'MLP' or self.args.model == 'BiLSTM' or self.args.model == 'CLA': # outputs = self.model(batch_x) # outputs = outputs[:, -self.args.pred_len:, :] # else: # if self.args.output_attention: # outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)[0] # else: # outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark, batch_y) # # if self.args.features == 'MS': # target_map = {'PH': 0, 'DO': 1, 'COD': 2, 'NH3-N': 3} # set_target = target_map[self.args.target] # f_dim = set_target # batch_y = batch_y[:, -self.args.pred_len:, f_dim].unsqueeze(2).to(self.device) # else: # f_dim = 0 # batch_y = batch_y[:, -self.args.pred_len:, f_dim:].to(self.device) # # pred = outputs.detach().cpu() # true = batch_y.detach().cpu() # # loss = criterion(pred, true) # # total_loss.append(loss) # total_loss = np.average(total_loss) # self.model.train() # return total_loss # # def tunning_train(self, setting): # vali_data, vali_loader = self._get_data(flag='val') # test_data, test_loader = self._get_data(flag='test') # path = os.path.join(self.args.checkpoints, setting) # print('loading model') # self.model.load_state_dict(torch.load(os.path.join('./checkpoints/' + setting, 'checkpoint.pth'))) # # tunning_steps = len(vali_loader) # print('tunning_steps:{} '.format(tunning_steps)) # # model_optim = self._select_optimizer() # 优化器 # criterion = self._select_criterion() # 损失函数 # # for epoch in range(self.args.train_epochs): # iter_count = 0 # tunning_loss = [] # self.model.train() # # for i, (batch_x, batch_y, batch_x_mark, batch_y_mark) in enumerate(vali_loader): # iter_count += 1 # model_optim.zero_grad() # batch_x = batch_x.float().to(self.device) # batch_y = batch_y.float() # # batch_x_mark = batch_x_mark.float().to(self.device) # batch_y_mark = batch_y_mark.float().to(self.device) # # # decoder input # dec_inp = torch.zeros_like(batch_y[:, -self.args.pred_len:, :]).float() # dec_inp = torch.cat([batch_y[:, :self.args.label_len, :], dec_inp], dim=1).float().to(self.device) # # encoder - decoder # # if self.args.model == 'LSTM' or self.args.model == 'CNN' or self.args.model == 'MLP' or self.args.model == 'BiLSTM' or self.args.model == 'CLA': # outputs = self.model(batch_x) # outputs = outputs[:, -self.args.pred_len:, :] # else: # if self.args.output_attention: # outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)[0] # else: # outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark, batch_y) # # if self.args.features == 'MS': # target_map = {'PH': 0, 'DO': 1, 'COD': 2, 'NH3-N': 3} # set_target = target_map[self.args.target] # f_dim = set_target # batch_y = batch_y[:, -self.args.pred_len:, f_dim].unsqueeze(2).to(self.device) # else: # f_dim = 0 # batch_y = batch_y[:, -self.args.pred_len:, f_dim:].to(self.device) # loss = criterion(outputs, batch_y) # tunning_loss.append(loss.item()) # loss.backward() # model_optim.step() # # tunning_loss = np.average(tunning_loss) # test_loss = self.vali(test_data, test_loader, criterion) # # print("Epoch: {0}, Steps: {1} | Tunning Loss: {2:.7f} Test Loss: {3:.7f}".format( # epoch + 1, tunning_steps, tunning_loss, test_loss)) # # torch.save(self.model.state_dict(), path + '/' + 'checkpoint.pth') # # return self.model # # def train(self, setting): # train_data, train_loader = self._get_data(flag='train') # vali_data, vali_loader = self._get_data(flag='val') # test_data, test_loader = self._get_data(flag='test') # # 水质三维时空数据调整为二维数据 # # path = os.path.join(self.args.checkpoints, setting) # if not os.path.exists(path): # os.makedirs(path) # # time_now = time.time() # # train_steps = len(train_loader) # print('train_steps:{} '.format(train_steps)) # early_stopping = EarlyStopping(patience=self.args.patience, verbose=True) # # model_optim = self._select_optimizer() # 优化器 # criterion = self._select_criterion() # 损失函数 #