从 Skorch 拟合中获取验证/训练损失

Question

有没有办法从 Skorch 中获取训练/验证损失，例如列表（如果你想做一些绘图、统计）？

Answer 1

您可以使用历史记录（允许随着时间的推移进行切片）来获取此信息。例如：

train_loss = net.history[:, 'train_loss']

它为每个记录的纪元返回train_loss。

这是一个基于following的示例。

import numpy as np
import torch
import torch.nn.functional as F
from matplotlib import pyplot as plt
from sklearn.datasets import make_classification
from skorch import NeuralNetClassifier
from torch import nn

torch.manual_seed(0)

class ClassifierModule(nn.Module):
    def __init__(
            self,
            num_units=10,
            nonlin=F.relu,
            dropout=0.5,
    ):
        super(ClassifierModule, self).__init__()
        self.num_units = num_units
        self.nonlin = nonlin
        self.dropout = dropout

        self.dense0 = nn.Linear(20, num_units)
        self.nonlin = nonlin
        self.dropout = nn.Dropout(dropout)
        self.dense1 = nn.Linear(num_units, 10)
        self.output = nn.Linear(10, 2)

    def forward(self, X, **kwargs):
        X = self.nonlin(self.dense0(X))
        X = self.dropout(X)
        X = F.relu(self.dense1(X))
        X = F.softmax(self.output(X), dim=-1)
        return X

net = NeuralNetClassifier(
    ClassifierModule,
    max_epochs=20,
    lr=0.1,
    # device='cuda',  # uncomment this to train with CUDA
)

X, y = make_classification(1000, 20, n_informative=10, random_state=0)
X, y = X.astype(np.float32), y.astype(np.int64)

net.fit(X, y)

train_loss = net.history[:, 'train_loss']
valid_loss = net.history[:, 'valid_loss']

plt.plot(train_loss, 'o-', label='training')
plt.plot(valid_loss, 'o-', label='validation')
plt.legend()
plt.show()

结果：