曲线中的损失表明可以通过调整超参数来改进训练，尤其是学习率和/或批量大小。因此，最佳决策是继续完善模型，而不是使用已经训练好的模型。

如果调整超参数不是一种选择，至少您应该重新拆分训练和验证数据，或者至少使用不同的随机种子重复。

如果这些都不是一个选项，你应该采取最好的验证损失。原因是我们假设验证数据没有泄漏到训练数据中，并且验证数据代表了将要测试模型的数据。在没有任何其他证据的情况下，我们应该假设您找到的“最佳位置”也会在模型以前从未见过的测试数据中产生更好的结果。

You should always look at validation loss, you don't care about training when evaluating your model's performance.

But your idea is quite in line with the principle of early stopping is: keep training the model, checking its performance at each epoch; once you find a best loss value, save the model; stop training once you don't find any loss improvement for a number of epochs defined by your patience hyperparameter.

However, about your specific problem I agree with @ncasas, i.e. that looking at your image it seems your model can be improved.