1.weight decay. Weight decay是在每次更新的梯度基础上减去一个梯度（为模型参数向量，为时刻loss函数的梯度，为学习率）: . 2.L2 regularization. L2 regularization是给参数加上一个L2惩罚( 为loss函数)： (当时，与weight decay等价，仅在使用标准SGD优化时成立) Adam+L2 regularization

extend_with_decoupled_weight_decay(tf.keras.optimizers.Adam, weight_decay=weight_decay) Note: when applying a decay to the learning rate, be sure to manually apply the decay to the weight_decay as well. For example:

We observe that the momentum GD does not converge sufficiently to the optimum. The slowdown is explained by the decreased effective step sizes. # See the License for the specific language governing permissions and # limitations under the License. # ===== from functools import partial import tensorflow as tf from tensorforce import util from tensorforce.core import parameter_modules from tensorforce.core.optimizers import Optimizer tensorflow_optimizers = dict (adadelta = tf.

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trainable_weights ) # Update the weights of the model.

关于adam优化器的具体实现过程可以参考这篇博客，或者更简洁一点的这篇博客，这里我只想简单介绍一下adam优化器里decay的原理。 Adam in Keras. 在Keras的Adam优化器中各参数如下： keras.optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) lr: 学习率

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【tf.keras】AdamW: Adam with Weight decay wuliytTaotao 2020-01-11 论文 Decoupled Weight Decay Regularization 中提到，Adam 在使用时，L2 与 weight decay 并不等价，并提出了 AdamW，在神经网络需要正则项时，用 AdamW 替换 Adam+L2 会得到更好的性能。

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Note: when applying a decay to the learning rate, be sure to manually apply the decay to the weight_decay as well. For example: schedule = tf.train.piecewise_constant(tf.train.get_global_step(), [10000, 15000], [1e-0, 1e-1, 1e-2]) lr = 1e-1 * schedule() wd = lambda: 1e-4 * schedule() #

Let the prediction loss of your net is L and the weight decay loss R. Weight decay (commonly called L2 regularization), might be the most widely-used technique for regularizing parametric machine learning models.

GradientDescentOptimizer If you need SGD with momentum, use tf.train. MomentumOptimizer Weight decay has nothing to do with an optimizer. optimizers (SGD, RMSProp, Adagrad, Adadelta, Adam, Adamax, Nadam, and all those)? When

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TensorFlow 2.x 在 tensorflow_addons 库里面实现了 AdamW，可以直接 pip install tensorflow_addons 进行安装（在 windows 上需要 TF 2.1），也可以直接把这个仓库下载下来使用。. 【tf.keras】AdamW: Adam with Weight decay 由戏子无情提交于 2020-01-11 01:21:20 论文 Decoupled Weight Decay Regularization 中提到，Adam 在使用时，L2 regularization 与 weight decay 并不等价，并提出了 AdamW，在神经网络需要正则项时，用 AdamW 替换 Adam+L2 会得到更好的性能。【tf.keras】AdamW: Adam with Weight decay wuliytTaotao 2020-01-11 论文 Decoupled Weight Decay Regularization 中提到，Adam 在使用时，L2 与 weight decay 并不等价，并提出了 AdamW，在神经网络需要正则项时，用 AdamW 替换 Adam+L2 会得到更好的性能。 4.5.4. Concise Implementation¶. Because weight decay is ubiquitous in neural network optimization, the deep learning framework makes it especially convenient, integrating weight decay into the optimization algorithm itself for easy use in combination with any loss function. 论文 Decoupled Weight Decay Regularization 中提到，Adam 在使用时，L2 regularization 与 weight decay 并不等价，并提出了 AdamW，在神经网络需要正则项时，用 AdamW 替换 Adam+L2 会得到更好的性能。. TensorFlow 2.x 在 tensorflow_addons 库里面实现了 AdamW，可以直接 pip install tensorflow_addons 进行安装（在 windows 上需要 TF 2.1），也可以直接把这个仓库下载下来使用。.