218 lines
8.9 KiB
Python
218 lines
8.9 KiB
Python
import torch
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import math
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import os
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import time
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import copy
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import numpy as np
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# import pynvml
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from lib.logger import get_logger
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from lib.loss_function import all_metrics
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class Trainer(object):
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def __init__(self, model, loss, optimizer, train_loader, val_loader, test_loader,
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scaler, args, lr_scheduler=None):
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super(Trainer, self).__init__()
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self.model = model
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self.loss = loss
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self.optimizer = optimizer
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self.train_loader = train_loader
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self.val_loader = val_loader
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self.test_loader = test_loader
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self.scaler = scaler
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self.args = args
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self.lr_scheduler = lr_scheduler
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self.train_per_epoch = len(train_loader)
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if val_loader != None:
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self.val_per_epoch = len(val_loader)
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self.best_path = os.path.join(self.args['log_dir'], 'best_model.pth')
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self.best_test_path = os.path.join(self.args['log_dir'], 'best_test_model.pth')
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self.loss_figure_path = os.path.join(self.args['log_dir'], 'loss.png')
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#log
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if os.path.isdir(args['log_dir']) == False and not args['debug']:
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os.makedirs(args['log_dir'], exist_ok=True)
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self.logger = get_logger(args['log_dir'], name=self.model.__class__.__name__, debug=args['debug'])
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self.logger.info('Experiment log path in: {}'.format(args['log_dir']))
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def val_epoch(self, epoch, val_dataloader):
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self.model.eval()
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total_val_loss = 0
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epoch_time = time.time()
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with torch.no_grad():
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for batch_idx, (data, target) in enumerate(val_dataloader):
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data = data
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label = target[..., :self.args['output_dim']]
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output = self.model(data)
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if self.args['real_value']:
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output = self.scaler.inverse_transform(output)
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loss = self.loss(output.cuda(), label)
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if not torch.isnan(loss):
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total_val_loss += loss.item()
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val_loss = total_val_loss / len(val_dataloader)
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self.logger.info('Val Epoch {}: average Loss: {:.6f}, train time: {:.2f} s'.format(epoch, val_loss, time.time() - epoch_time))
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return val_loss
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def test_epoch(self, epoch, test_dataloader):
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self.model.eval()
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total_test_loss = 0
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epoch_time = time.time()
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with torch.no_grad():
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for batch_idx, (data, target) in enumerate(test_dataloader):
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data = data
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label = target[..., :self.args['output_dim']]
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output = self.model(data)
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if self.args['real_value']:
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output = self.scaler.inverse_transform(output)
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loss = self.loss(output.cuda(), label)
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if not torch.isnan(loss):
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total_test_loss += loss.item()
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test_loss = total_test_loss / len(test_dataloader)
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self.logger.info('test Epoch {}: average Loss: {:.6f}, train time: {:.2f} s'.format(epoch, test_loss, time.time() - epoch_time))
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return test_loss
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def train_epoch(self, epoch):
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self.model.train()
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total_loss = 0
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epoch_time = time.time()
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for batch_idx, (data, target) in enumerate(self.train_loader):
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data = data
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label = target[..., :self.args['output_dim']]
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self.optimizer.zero_grad()
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output = self.model(data)
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if self.args['real_value']:
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output = self.scaler.inverse_transform(output)
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loss = self.loss(output.cuda(), label)
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loss.backward()
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# add max grad clipping
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if self.args['grad_norm']:
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torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.args['max_grad_norm'])
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self.optimizer.step()
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total_loss += loss.item()
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#log information
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if (batch_idx+1) % self.args['log_step'] == 0:
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self.logger.info('Train Epoch {}: {}/{} Loss: {:.6f}'.format(
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epoch, batch_idx+1, self.train_per_epoch, loss.item()))
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train_epoch_loss = total_loss/self.train_per_epoch
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self.logger.info(
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'Train Epoch {}: averaged Loss: {:.6f}, train time: {:.2f} s'.format(epoch, train_epoch_loss,
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time.time() - epoch_time))
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#learning rate decay
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if self.args['lr_decay']:
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self.lr_scheduler.step()
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return train_epoch_loss
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def train(self):
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best_model = None
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best_test_model =None
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not_improved_count = 0
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best_loss = float('inf')
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best_test_loss = float('inf')
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vaild_loss = []
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for epoch in range(0, self.args['epochs']):
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train_epoch_loss = self.train_epoch(epoch)
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if self.val_loader == None:
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val_dataloader = self.test_loader
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else:
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val_dataloader = self.val_loader
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test_dataloader = self.test_loader
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val_epoch_loss = self.val_epoch(epoch, val_dataloader)
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vaild_loss.append(val_epoch_loss)
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test_epoch_loss = self.test_epoch(epoch, test_dataloader)
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if train_epoch_loss > 1e6:
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self.logger.warning('Gradient explosion detected. Ending...')
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break
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if val_epoch_loss < best_loss:
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best_loss = val_epoch_loss
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not_improved_count = 0
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best_state = True
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else:
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not_improved_count += 1
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best_state = False
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# early stop
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if self.args['early_stop']:
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if not_improved_count == self.args['early_stop_patience']:
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self.logger.info("Validation performance didn\'t improve for {} epochs. "
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"Training stops.".format(self.args['early_stop_patience']))
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break
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# save the best state
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if best_state == True:
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self.logger.info('Current best model saved!')
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best_model = copy.deepcopy(self.model.state_dict())
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if test_epoch_loss< best_test_loss:
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best_test_loss = test_epoch_loss
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best_test_model = copy.deepcopy(self.model.state_dict())
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#save the best model to file
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if not self.args['debug']:
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torch.save(best_model, self.best_path)
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self.logger.info("Saving current best model to " + self.best_path)
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torch.save(best_test_model, self.best_test_path)
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self.logger.info("Saving current best model to " + self.best_test_path)
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#test
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self.model.load_state_dict(best_model)
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self.test(self.model, self.args, self.test_loader, self.scaler, self.logger)
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self.logger.info("This is best_test_model")
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self.model.load_state_dict(best_test_model)
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self.test(self.model, self.args, self.test_loader, self.scaler, self.logger)
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def save_checkpoint(self):
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state = {
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'state_dict': self.model.state_dict(),
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'optimizer': self.optimizer.state_dict(),
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'config': self.args
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}
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torch.save(state, self.best_path)
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self.logger.info("Saving current best model to " + self.best_path)
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@staticmethod
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def test(model, args, data_loader, scaler, logger, path=None):
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if path != None:
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check_point = torch.load(path)
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state_dict = check_point['state_dict']
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args = check_point['config']
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model.load_state_dict(state_dict)
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model.to(args['device'])
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model.eval()
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y_pred = []
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y_true = []
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with torch.no_grad():
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for batch_idx, (data, target) in enumerate(data_loader):
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data = data
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label = target[..., :args['output_dim']]
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output = model(data)
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y_true.append(label)
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y_pred.append(output)
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if args['real_value']:
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y_pred = scaler.inverse_transform(torch.cat(y_pred, dim=0))
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y_true = torch.cat(y_true, dim=0)
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else:
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y_pred = torch.cat(y_pred, dim=0)
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y_true = torch.cat(y_true, dim=0)
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for t in range(y_true.shape[1]):
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mae, rmse, mape = all_metrics(y_pred[:, t, ...], y_true[:, t, ...],
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args['mae_thresh'], args['mape_thresh'])
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logger.info("Horizon {:02d}, MAE: {:.4f}, RMSE: {:.4f}, MAPE: {:.4f}".format(
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t + 1, mae, rmse, mape))
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mae, rmse, mape = all_metrics(y_pred, y_true, args['mae_thresh'], args['mape_thresh'])
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logger.info("Average Horizon, MAE: {:.4f}, RMSE: {:.4f}, MAPE: {:.4f}".format(
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mae, rmse, mape))
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@staticmethod
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def _compute_sampling_threshold(global_step, k):
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"""
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Computes the sampling probability for scheduled sampling using inverse sigmoid.
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:param global_step:
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:param k:
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:return:
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"""
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return k / (k + math.exp(global_step / k)) |