4.8
今天学习LSTM
创建模型
# 二、创建LSTM模型 hidden_size = 10 output_size = 2 input_size = miData.shape[1] class RNN(nn.Module): def __init__(self): super(RNN,self).__init__() #面向对象中的继承 # TODO: 调整参数 self.lstm = nn.LSTM(input_size = input_size, hidden_size = hidden_size, num_layers = 2) #输入数据2个特征维度，6个隐藏层维度，2个LSTM串联，第二个LSTM接收第一个的计算结果 self.out = nn.Linear(hidden_size, hidden_size) #线性拟合，接收数据的维度为6，输出数据的维度为1 self.out2 = nn.Linear(hidden_size, output_size) self.s = nn.ReLU() def forward(self,x): x1,_ = self.lstm(x) # output (seq_len, batch, hidden_size * num_directions) a,b,c = x1.shape out = self.out(x1.view(-1, hidden_size)) #因为线性层输入的是个二维数据，所以此处应该将lstm输出的三维数据x1调整成二维数据，最后的特征维度不能变 out = self.s(out) out2 = self.out2(out) out2 = self.s(out2) # out1 = out.view(a, b, -1) #因为是循环神经网络，最后的时候要把二维的out调整成三维数据，下一次循环使用 output = out2.view(a, b, -1) return output rnn = RNN() print(rnn) #参数寻优，计算损失函数 optimizer = torch.optim.Adam(rnn.parameters(),lr = 0.001) # TODO：损失函数 loss_func = nn.CrossEntropyLoss() scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.1, patience=20, verbose=True) # SR : Segmentation Result # GT : Ground Truth def get_accuracy(SR,GT,threshold=0.5): SR = SR > threshold GT = GT == torch.max(GT) corr = torch.sum(SR==GT) # tensor_size = SR.size(0)*SR.size(1)*SR.size(2)*SR.size(3) tensor_size = SR.size(0)*SR.size(1) acc = float(corr)/float(tensor_size) return acc def get_recall(SR,GT,threshold=0.5): # Sensitivity == Recall SR = SR > threshold GT = GT == torch.max(GT) # TP : True Positive # FN : False Negative TP = ((SR==1)&(GT==1)) FN = ((SR==0)&(GT==1)) SE = float(torch.sum(TP))/(float(torch.sum(TP+FN)) + 1e-6) return SE def get_specificity(SR,GT,threshold=0.5): SR = SR > threshold GT = GT == torch.max(GT) # TN : True Negative # FP : False Positive TN = ((SR==0)&(GT==0)) FP = ((SR==1)&(GT==0)) SP = float(torch.sum(TN))/(float(torch.sum(TN+FP)) + 1e-6) return SP def get_precision(SR,GT,threshold=0.5): SR = SR > threshold GT = GT == torch.max(GT) # TP : True Positive # FP : False Positive TP = ((SR==1)&(GT==1)) FP = ((SR==1)&(GT==0)) PC = float(torch.sum(TP))/(float(torch.sum(TP+FP)) + 1e-6) return PC
模型训练
loss_history = [] accuracy_his = [] precision_his = [] recall_his = [] specificity_his = [] all_y = torch.tensor([]) all_p = torch.tensor([]) #三、训练模型 for k in range(len(end_ptr)): if end_ptr[k] >= len(miData): break trainX, trainY = create_dataset(miData[train_ptr[k]:test_ptr[k],:], yData[train_ptr[k]:test_ptr[k]], 10) trainLoaderX, trainLoaderY, validateLoaderX, validateLoaderY = trainSet_split(trainX, trainY) testLoaderX, testLoaderY = create_Test_dataset(miData[test_ptr[k]-10:end_ptr[k],:], yData[test_ptr[k]-10:end_ptr[k]], 10) print('\nDataSet No.{} data row {}-{}-{}'.format(k, train_ptr[k], test_ptr[k], end_ptr[k])) # 训练集和验证集 loss_sum_flag = 10 # 用来判断loss是否下降 fall_cnt = 0 train_len = len(trainLoaderX) train_loss_his = [] for epoch in range(0, 1000): loss_sum_item = 0 for i, var_x in enumerate(trainLoaderX, 0): # var_x = Variable(x_train).type(torch.FloatTensor) # var_y = Variable(y_train).type(torch.FloatTensor) var_y = trainLoaderY[i] out = rnn(var_x) # loss = loss_func(out[-1], var_y[-1].view(-1)) loss = loss_func(out.view(-1, 2), var_y.view(-1)) optimizer.zero_grad() loss.backward() optimizer.step() loss_sum_item += loss.item() # if (epoch+1) % 50 == 0: # print('Train Epoch:{}, step:{}, Loss:{:.5f}'.format(epoch, i, loss.item())) #loss_history.append(loss.item()) if loss_sum_item < loss_sum_flag: loss_sum_flag = loss_sum_item fall_cnt += 1 if fall_cnt % 100 == 0: print('\nDataSet No.{}, Train Epoch:{}, Avg Loss:{:.10f}'.format(k, epoch, loss_sum_item/train_len)) else: print('>',end='') else: # fall_cnt += 1 print('-',end='') train_loss_his.append(loss_sum_item) loss_sum_validate = 0 for i, var in enumerate(validateLoaderX, 0): var_y = trainLoaderY[i] out = rnn(var_x) # loss = loss_func(out[-1], var_y[-1].view(-1)) loss = loss_func(out.view(-1, 2), var_y.view(-1)) loss_sum_validate += loss.item() # optimizer.zero_grad() # loss.backward() # optimizer.step() # if (epoch+1) % 50==0: # print('Validate Epoch:{}, step:{}, Loss:{:.5f}'.format(epoch, i, loss.item())) # print('Validate Epoch:{}, Loss:{:.5f}'.format(epoch, i, loss.item())) # scheduler.step(loss, epoch=epoch) # TODO: 不知道这种用法对不对 # scheduler.step(loss_sum_validate) if (epoch+1) % 200==0: print('\nValidate Epoch:{}, Loss Avg:{:.5f}'.format(epoch, loss_sum_validate/len(validateLoaderX))) plt.plot(train_loss_his) plt.show() torch.save(obj=rnn.state_dict(), f="main_models/LSTM_k"+str(k)+".pth") # 测试 print('Test') test_y = torch.tensor([]) test_p = torch.tensor([]) softm_p = torch.tensor([]) for i, var_x in enumerate(testLoaderX, 0): var_y = testLoaderY[i] out = rnn(var_x) # loss = loss_func(out[-1], var_y[-1].view(-1)) loss = loss_func(out.view(-1, 2), var_y.view(-1)) # 取最后一个数，由于batch_size不为1 test_y = torch.cat((test_y, var_y[-1]), 0) # test_p = torch.cat((test_p, out[-1]), 1) # if (i+1) % 20==0: # print('DataSet No.{}, Test step:{}, Loss:{:.5f}'.format(k, i, loss.item())) loss_history.append(loss.item()) # ind_y = torch.max(var_y[-1], dim = 1) ind_p = torch.max(out[-1], dim = 1) # print(out[-1],end=' ') # 用于计算ROC softMax_func = nn.Softmax(dim=1) out_p = softMax_func(out[-1]) softm_p = torch.cat((softm_p, out_p), 0) # test_y = torch.cat((test_y, ind_y.indices.view(-1, 1)), 1) test_p = torch.cat((test_p, ind_p.indices.view(-1, 1)), 0) all_y = torch.cat((all_y, test_y), 0) all_p = torch.cat((all_p, softm_p), 0) print((test_p + test_y*10).view(-1)) Acc = get_accuracy(test_p, test_y) accuracy_his.append(Acc) print('------------- DataSet:{}, Accuracy:{:.5f} -------------'.format(k, Acc)) Pc = get_precision(test_p, test_y) precision_his.append(Pc) print('------------- DataSet:{}, Precision:{:.5f} -------------'.format(k, Pc)) Recall = get_recall(test_p, test_y) recall_his.append(Recall) print('------------- DataSet:{}, Recall:{:.5f} -------------'.format(k, Recall)) Sp = get_specificity(test_p, test_y) specificity_his.append(Sp) print('------------- DataSet:{}, Specificity:{:.5f} -------------'.format(k, Sp))
