【666】语义分割减少过拟合

Keras的Adam优化器decay理解及自适应学习率

CNN学习笔记：正则化缓解过拟合

Deep Learning #3: More on CNNs & Handling Overfitting

吴恩达机器学习笔记（三）——正则化（Regularization）

卷积神经网络(CNN)防止过拟合的方法

• 设置 decay 值，可以调整学习率，使学习率逐渐减小

  • keras.optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)

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• 通过添加正则化来实现

  • model.add(Dense(units=200,input_dim=784,activation='tanh',kernel_regularizer=l2(0.01)))

 

Steps for reducing overfitting:

1. Add more data
2. Use data augmentation
3. Use architectures that generalize well
4. Add regularization (mostly dropout, L1/L2 regularization are also possible)
5. Reduce architecture complexity.