YAO-optimizer/model/model_pytorch.py

import torch
from torch.nn import Module, LSTM, Linear
from torch.utils.data import DataLoader, TensorDataset
import numpy as np


class Net(Module):
	def __init__(self, config):
		super(Net, self).__init__()
		self.lstm = LSTM(input_size=config.input_size, hidden_size=config.hidden_size,
		                 num_layers=config.lstm_layers, batch_first=True, dropout=config.dropout_rate)
		self.linear = Linear(in_features=config.hidden_size, out_features=config.output_size)

	def forward(self, x, hidden=None):
		lstm_out, hidden = self.lstm(x, hidden)
		linear_out = self.linear(lstm_out)
		return linear_out, hidden


def train(config, train_X, train_Y, valid_X, valid_Y):
	train_X, train_Y = torch.from_numpy(train_X).float(), torch.from_numpy(train_Y).float()
	train_loader = DataLoader(TensorDataset(train_X, train_Y), batch_size=config.batch_size)

	valid_X, valid_Y = torch.from_numpy(valid_X).float(), torch.from_numpy(valid_Y).float()
	valid_loader = DataLoader(TensorDataset(valid_X, valid_Y), batch_size=config.batch_size)

	model = Net(config)
	if config.add_train:
		model.load_state_dict(torch.load(config.model_save_path + config.model_name))
	optimizer = torch.optim.Adam(model.parameters(), lr=config.learning_rate)
	criterion = torch.nn.MSELoss()

	valid_loss_min = float("inf")
	bad_epoch = 0
	for epoch in range(config.epoch):
		print("Epoch {}/{}".format(epoch, config.epoch))
		model.train()
		train_loss_array = []
		hidden_train = None
		for i, _data in enumerate(train_loader):
			_train_X, _train_Y = _data
			optimizer.zero_grad()
			pred_Y, hidden_train = model(_train_X, hidden_train)

			if not config.do_continue_train:
				hidden_train = None
			else:
				h_0, c_0 = hidden_train
				h_0.detach_(), c_0.detach_()
				hidden_train = (h_0, c_0)
			loss = criterion(pred_Y, _train_Y)
			loss.backward()
			optimizer.step()
			train_loss_array.append(loss.item())

		model.eval()
		valid_loss_array = []
		hidden_valid = None
		for _valid_X, _valid_Y in valid_loader:
			pred_Y, hidden_valid = model(_valid_X, hidden_valid)
			if not config.do_continue_train: hidden_valid = None
			loss = criterion(pred_Y, _valid_Y)
			valid_loss_array.append(loss.item())

		valid_loss_cur = np.mean(valid_loss_array)
		print("The train loss is {:.4f}. ".format(np.mean(train_loss_array)),
		      "The valid loss is {:.4f}.".format(valid_loss_cur))

		if valid_loss_cur < valid_loss_min:
			valid_loss_min = valid_loss_cur
			bad_epoch = 0
			torch.save(model.state_dict(), config.model_save_path + config.model_name)
		else:
			bad_epoch += 1
			if bad_epoch >= config.patience:
				print(" The training stops early in epoch {}".format(epoch))
				break


def predict(config, test_X):
	test_X = torch.from_numpy(test_X).float()
	test_set = TensorDataset(test_X)
	test_loader = DataLoader(test_set, batch_size=1)

	model = Net(config)
	model.load_state_dict(torch.load(config.model_save_path + config.model_name))

	result = torch.Tensor()

	model.eval()
	hidden_predict = None
	for _data in test_loader:
		data_X = _data[0]
		pred_X, hidden_predict = model(data_X, hidden_predict)
		cur_pred = torch.squeeze(pred_X, dim=0)
		result = torch.cat((result, cur_pred), dim=0)

	return result.detach().numpy()