def train(): # 训练 print('Start training ===========================================>') best_epo = -1 max_pck = -1 cur_lr = learning_rate print('Learning Rate: {}'.format(learning_rate)) for epoch in range(1, epochs + 1): print('Epoch[{}/{}] ==============>'.format(epoch, epochs)) model.train() train_loss = [] for step, (img, label, img_name, w, h) in enumerate(train_loader): label = torch.stack([label] * 6, dim=1) # bz * 6 * 21 * 46 * 46 if cuda: img = img.cuda() label = label.cuda() optimizer.zero_grad() pred_maps = model(img) # (FloatTensor.cuda) size:(bz,6,21,46,46) loss = sum_mse_loss(pred_maps, label) # total loss loss.backward() optimizer.step() if step % 100 == 0: print('STEP: {} LOSS {}'.format(step, loss.item())) loss_final = sum_mse_loss(pred_maps[:, -1, ...].cpu(), label[:, -1, ...].cpu()) train_loss.append(loss_final) # save sample image **** save_images(label[:, -1, ...].cpu(), pred_maps[:, -1, ...].cpu(), epoch, img_name, save_dir) # eval model after one epoch eval_loss, cur_pck = eval(epoch, mode='valid') print('EPOCH {} Valid PCK {}'.format(epoch, cur_pck)) print('EPOCH {} TRAIN_LOSS {}'.format(epoch, sum(train_loss)/len(train_loss))) print('EPOCH {} VALID_LOSS {}'.format(epoch, eval_loss)) if cur_pck > max_pck: torch.save(model.state_dict(), os.path.join(save_dir, 'best_model.pth')) max_pck = cur_pck best_epo = epoch print('Current Best EPOCH is : {}\n**************\n'.format(best_epo)) torch.save(model.state_dict(), os.path.join(save_dir, 'final_epoch.pth')) if epoch % lr_decay_epoch == 0: cur_lr /= 2 update_lr(optimizer, cur_lr) print('Train Done!') print('Best epoch is {}'.format(best_epo))
时间: 2023-05-29 20:06:50 浏览: 95
train()是一个函数,具体实现的内容需要根据上下文和代码细节来确定。一般来说,train()函数用于训练机器学习模型,包括读取数据、构建模型、设置优化器和损失函数、进行训练迭代等步骤。在训练过程中,train()函数通常需要不断调用其他函数来执行具体的操作,如计算梯度、更新参数等。
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train_l=int(0.75*l) X_train=r[0:train_l,0:b-1] Y_train=r[0:train_l,b-1] X_test=r[train_l:l,0:b-1] Y_test=r[train_l:l,b-1] X_train = normalize(X_train, axis=0, norm='max') X_test = normalize(X_test, axis=0, norm='max') scaler = StandardScaler() scaler.fit(X_train) X_train = scaler.transform(X_train) X_test = scaler.transform(X_test) # In[58]: def mySvm(training, training_labels, testing, testing_labels): #Support Vector Machine start = datetime.datetime.now() clf = svm.SVC() clf.fit(training, training_labels) print ("+++++++++ Finishing training the SVM classifier ++++++++++++") result = clf.predict(testing) print ("SVM accuracy:", accuracy_score(testing_labels, result)) #keep the time finish = datetime.datetime.now() print ((finish-start).seconds)帮我解释一下这段代码
这段代码实现了一个支持向量机分类器的训练和测试过程,并且输出了分类器的准确率和训练时间。
首先,代码把数据集分成了训练集和测试集。其中,前75%的数据作为训练集,后25%的数据作为测试集。接着,代码对训练集和测试集进行了归一化处理和标准化处理,以便更好地训练和测试分类器。
接下来,代码定义了一个函数 `mySvm()`,用于训练和测试支持向量机分类器。该函数接受四个参数,分别是训练集数据、训练集标签、测试集数据和测试集标签。
在函数内部,首先记录了开始训练的时间。然后,使用 sklearn 库中的 svm.SVC() 函数创建了一个支持向量机分类器,并使用训练集数据和标签进行训练。训练完成后,使用分类器对测试集数据进行预测,并计算出分类器的准确率。最后,输出准确率和训练时间。
总体来说,这段代码实现了一个简单的支持向量机分类器的训练和测试过程,并且给出了分类器的准确率和训练时间。
#@save def train_ch6(net, train_iter, test_iter, num_epochs, lr, device): """用GPU训练模型(在第六章定义)""" def init_weights(m): if type(m) == nn.Linear or type(m) == nn.Conv2d: nn.init.xavier_uniform_(m.weight) net.apply(init_weights) print('training on', device) net.to(device) optimizer = torch.optim.SGD(net.parameters(), lr=lr) loss = nn.CrossEntropyLoss() animator = d2l.Animator(xlabel='epoch', xlim=[1, num_epochs], legend=['train loss', 'train acc', 'test acc']) timer, num_batches = d2l.Timer(), len(train_iter) for epoch in range(num_epochs): # 训练损失之和,训练准确率之和,样本数 metric = d2l.Accumulator(3) net.train() for i, (X, y) in enumerate(train_iter): timer.start() optimizer.zero_grad() X, y = X.to(device), y.to(device) y_hat = net(X) l = loss(y_hat, y) l.backward() optimizer.step() with torch.no_grad(): metric.add(l * X.shape[0], d2l.accuracy(y_hat, y), X.shape[0]) timer.stop() train_l = metric[0] / metric[2] train_acc = metric[1] / metric[2] if (i + 1) % (num_batches // 5) == 0 or i == num_batches - 1: animator.add(epoch + (i + 1) / num_batches, (train_l, train_acc, None)) test_acc = evaluate_accuracy_gpu(net, test_iter) animator.add(epoch + 1, (None, None, test_acc)) print(f'loss {train_l:.3f}, train acc {train_acc:.3f}, ' f'test acc {test_acc:.3f}') print(f'{metric[2] * num_epochs / timer.sum():.1f} examples/sec ' f'on {str(device)}')
这段代码是用GPU训练模型的代码,可以使用SGD优化器和交叉熵损失函数对模型进行训练,并且使用Accumulator类来计算训练损失之和、训练准确率之和和样本数,以及使用d2l.Animator类绘制训练和测试的准确率和损失的动态图。你可以通过修改代码中的参数来进行训练,比如设置训练的epoch数、学习率等。