咱们在训练和验证模型时都会将训练指标保留成起来制作成图表，这样能够在完结后进行查看和剖析，然而你真的理解这些指标的图表的含意吗？
在本文中将对训练和验证可能产生的状况进行总结并介绍这些图表到底能为咱们提供什么样的信息。
让咱们从一些简略的代码开始，以下代码建设了一个根本的训练流程框架。
from sklearn.model_selection import train_test_splitfrom sklearn.datasets import make_classificationimport torchfrom torch.utils.data import Dataset, DataLoaderimport torch.optim as torch_optimimport torch.nn as nnimport torch.nn.functional as Fimport numpy as npimport matplotlib.pyplot as pltclass MyCustomDataset(Dataset): def __init__(self, X, Y, scale=False): self.X = torch.from_numpy(X.astype(np.float32)) self.y = torch.from_numpy(Y.astype(np.int64)) def __len__(self): return len(self.y) def __getitem__(self, idx): return self.X[idx], self.y[idx]def get_optimizer(model, lr=0.001, wd=0.0): parameters = filter(lambda p: p.requires_grad, model.parameters()) optim = torch_optim.Adam(parameters, lr=lr, weight_decay=wd) return optimdef train_model(model, optim, train_dl, loss_func): # Ensure the model is in Training mode model.train() total = 0 sum_loss = 0 for x, y in train_dl: batch = y.shape[0] # Train the model for this batch worth of data logits = model(x) # Run the loss function. We will decide what this will be when we call our Training Loop loss = loss_func(logits, y) # The next 3 lines do all the PyTorch back propagation goodness optim.zero_grad() loss.backward() optim.step() # Keep a running check of our total number of samples in this epoch total += batch # And keep a running total of our loss sum_loss += batch*(loss.item()) return sum_loss/totaldef train_loop(model, train_dl, valid_dl, epochs, loss_func, lr=0.1, wd=0): optim = get_optimizer(model, lr=lr, wd=wd) train_loss_list = [] val_loss_list = [] acc_list = [] for i in range(epochs): loss = train_model(model, optim, train_dl, loss_func) # After training this epoch, keep a list of progress of # the loss of each epoch train_loss_list.append(loss) val, acc = val_loss(model, valid_dl, loss_func) # Likewise for the validation loss and accuracy val_loss_list.append(val) acc_list.append(acc) print("training loss: %.5f valid loss: %.5f accuracy: %.5f" % (loss, val, acc)) return train_loss_list, val_loss_list, acc_listdef val_loss(model, valid_dl, loss_func): # Put the model into evaluation mode, not training mode model.eval() total = 0 sum_loss = 0 correct = 0 batch_count = 0 for x, y in valid_dl: batch_count += 1 current_batch_size = y.shape[0] logits = model(x) loss = loss_func(logits, y) sum_loss += current_batch_size*(loss.item()) total += current_batch_size # All of the code above is the same, in essence, to # Training, so see the comments there # Find out which of the returned predictions is the loudest # of them all, and that's our prediction(s) preds = logits.sigmoid().argmax(1) # See if our predictions are right correct += (preds == y).float().mean().item() return sum_loss/total, correct/batch_countdef view_results(train_loss_list, val_loss_list, acc_list): plt.rcParams["figure.figsize"] = (15, 5) plt.figure() epochs = np.arange(0, len(train_loss_list)) plt.subplot(1, 2, 1) plt.plot(epochs-0.5, train_loss_list) plt.plot(epochs, val_loss_list) plt.title('model loss') plt.ylabel('loss') plt.xlabel('epoch') plt.legend(['train', 'val', 'acc'], loc = 'upper left') plt.subplot(1, 2, 2) plt.plot(acc_list) plt.title('accuracy') plt.ylabel('accuracy') plt.xlabel('epoch') plt.legend(['train', 'val', 'acc'], loc = 'upper left') plt.show() def get_data_train_and_show(model, batch_size=128, n_samples=10000, n_classes=2, n_features=30, val_size=0.2, epochs=20, lr=0.1, wd=0, break_it=False): # We'll make a fictitious dataset, assuming all relevant # EDA / Feature Engineering has been done and this is our # resultant data X, y = make_classification(n_samples=n_samples, n_classes=n_classes, n_features=n_features, n_informative=n_features, n_redundant=0, random_state=1972) if break_it: # Specifically mess up the data X = np.random.rand(n_samples,n_features) X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=val_size, random_state=1972) train_ds = MyCustomDataset(X_train, y_train) valid_ds = MyCustomDataset(X_val, y_val) train_dl = DataLoader(train_ds, batch_size=batch_size, shuffle=True) valid_dl = DataLoader(valid_ds, batch_size=batch_size, shuffle=True) train_loss_list, val_loss_list, acc_list = train_loop(model, train_dl, valid_dl, epochs=epochs, loss_func=F.cross_entropy, lr=lr, wd=wd) view_results(train_loss_list, val_loss_list, acc_list)以上的代码很简略，就是获取数据，训练，验证这样一个根本的流程，上面咱们开始进入正题。
场景 1 - 模型仿佛能够学习，但在验证或准确性方面体现不佳
无论超参数如何，模型 Train loss 都会迟缓降落，但 Val loss 不会降落，并且其 Accuracy 并不表明它正在学习任何货色。
比方在这种状况下，二进制分类的准确率彷徨在 50% 左右。
class Scenario_1_Model_1(nn.Module): def __init__(self, in_features=30, out_features=2): super().__init__() self.lin1 = nn.Linear(in_features, out_features) def forward(self, x): x = self.lin1(x) return xget_data_train_and_show(Scenario_1_Model_1(), lr=0.001, break_it=True)数据中没有足够的信息来容许‘学习’，训练数据可能没有蕴含足够的信息来让模型“学习”。
在这种状况下（代码中训练数据是随机数据），这意味着它无奈学习任何本质内容。
数据必须有足够的信息能够从中学习。EDA 和特色工程是要害！模型学习能够学到的货色，而不是不是假造不存在的货色。
场景 2 — 训练、验证和准确度曲线都十分不稳
例如上面代码：lr=0.1，bs=128
class Scenario_2_Model_1(nn.Module): def __init__(self, in_features=30, out_features=2): super().__init__() self.lin1 = nn.Linear(in_features, out_features) def forward(self, x): x = self.lin1(x) return xget_data_train_and_show(Scenario_2_Model_1(), lr=0.1)“学习率太高”或“批量太小”能够尝试将学习率从 0.1 升高到 0.001，这意味着它不会“反弹”，而是会安稳地升高。
get_data_train_and_show(Scenario_1_Model_1(), lr=0.001)除了升高学习率外，减少批量大小也会使其更平滑。
get_data_train_and_show(Scenario_1_Model_1(), lr=0.001, batch_size=256)场景 3——训练损失接近于零，准确率看起来还不错，但验证 并没有降落，并且还回升了
class Scenario_3_Model_1(nn.Module): def __init__(self, in_features=30, out_features=2): super().__init__() self.lin1 = nn.Linear(in_features, 50) self.lin2 = nn.Linear(50, 150) self.lin3 = nn.Linear(150, 50) self.lin4 = nn.Linear(50, out_features) def forward(self, x): x = F.relu(self.lin1(x)) x = F.relu(self.lin2(x)) x = F.relu(self.lin3(x)) x = self.lin4(x) return xget_data_train_and_show(Scenario_3_Model_1(), lr=0.001)这必定是过拟合了：训练损失低和准确率高，而验证损失和训练损失越来越大，都是经典的过拟合指标。
从根本上说，你的模型学习能力太强了。它对训练数据的记忆太好，这意味着它也不能泛化到新数据。
咱们能够尝试的第一件事是升高模型的复杂性。
class Scenario_3_Model_2(nn.Module): def __init__(self, in_features=30, out_features=2): super().__init__() self.lin1 = nn.Linear(in_features, 50) self.lin2 = nn.Linear(50, out_features) def forward(self, x): x = F.relu(self.lin1(x)) x = self.lin2(x) return xget_data_train_and_show(Scenario_3_Model_2(), lr=0.001)这让它变得更好了，还能够引入 L2 权重衰减正则化，让它再次变得更好（实用于较浅的模型）。
get_data_train_and_show(Scenario_3_Model_2(), lr=0.001, wd=0.02)如果咱们想放弃模型的深度和大小，能够尝试应用 dropout（实用于更深的模型）。
class Scenario_3_Model_3(nn.Module): def __init__(self, in_features=30, out_features=2): super().__init__() self.lin1 = nn.Linear(in_features, 50) self.lin2 = nn.Linear(50, 150) self.lin3 = nn.Linear(150, 50) self.lin4 = nn.Linear(50, out_features) self.drops = nn.Dropout(0.4) def forward(self, x): x = F.relu(self.lin1(x)) x = self.drops(x) x = F.relu(self.lin2(x)) x = self.drops(x) x = F.relu(self.lin3(x)) x = self.drops(x) x = self.lin4(x) return xget_data_train_and_show(Scenario_3_Model_3(), lr=0.001)场景 4 - 训练和验证体现良好，但准确度没有进步
lr = 0.001，bs = 128（默认，分类类别= 5
class Scenario_4_Model_1(nn.Module): def __init__(self, in_features=30, out_features=2): super().__init__() self.lin1 = nn.Linear(in_features, 2) self.lin2 = nn.Linear(2, out_features) def forward(self, x): x = F.relu(self.lin1(x)) x = self.lin2(x) return xget_data_train_and_show(Scenario_4_Model_1(out_features=5), lr=0.001, n_classes=5)没有足够的学习能力：模型中的其中一层的参数少于模型可能输入中的类。在这种状况下，当有 5 个可能的输入类时，两头的参数只有 2 个。
这意味着模型会失落信息，因为它不得不通过一个较小的层来填充它，因而一旦层的参数再次扩充，就很难复原这些信息。
所以须要记录层的参数永远不要小于模型的输入大小。
class Scenario_4_Model_2(nn.Module): def __init__(self, in_features=30, out_features=2): super().__init__() self.lin1 = nn.Linear(in_features, 50) self.lin2 = nn.Linear(50, out_features) def forward(self, x): x = F.relu(self.lin1(x)) x = self.lin2(x) return xget_data_train_and_show(Scenario_4_Model_2(out_features=5), lr=0.001, n_classes=5)总结
以上就是一些常见的训练、验证时的曲线的示例，心愿你在遇到雷同状况时能够疾速定位并且改良。
https://avoid.overfit.cn/post/5f52eb0868ce41a3a847783d5e87a04f
作者：Martin Keywood