From 8aa6a27c24679b2ae68c26a57cb0fb5d2bbdebcf Mon Sep 17 00:00:00 2001 From: littletomatodonkey Date: Mon, 28 Sep 2020 02:25:50 +0000 Subject: [PATCH] fix code example and doc --- python/paddle/regularizer.py | 14 ++++++-------- 1 file changed, 6 insertions(+), 8 deletions(-) diff --git a/python/paddle/regularizer.py b/python/paddle/regularizer.py index 26fcb944944..bcde4f3dcb2 100644 --- a/python/paddle/regularizer.py +++ b/python/paddle/regularizer.py @@ -28,11 +28,11 @@ class L1Decay(fluid.regularizer.L1Decay): in its ParamAttr, then the regularizer in Optimizer will be ignored. Otherwise the regularizer in Optimizer will be used. - In the implementation, the formula of L1 Weight Decay Regularization is as follows: + In the implementation, the penalty of L1 Weight Decay Regularization is as follows: .. math:: - L1WeightDecay = reg\_coeff * sign(parameter) + loss = coeff * reduce\_sum(abs(x)) Args: coeff(float, optional): regularization coeff. Default:0.0. @@ -45,9 +45,8 @@ class L1Decay(fluid.regularizer.L1Decay): from paddle.regularizer import L1Decay import numpy as np paddle.disable_static() - inp = np.random.uniform(-0.1, 0.1, [10, 10]).astype("float32") linear = paddle.nn.Linear(10, 10) - inp = paddle.to_tensor(inp) + inp = paddle.rand(shape=[10, 10], dtype="float32") out = linear(inp) loss = paddle.mean(out) beta1 = paddle.to_tensor([0.9], dtype="float32") @@ -92,11 +91,11 @@ class L2Decay(fluid.regularizer.L2Decay): in its ParamAttr, then the regularizer in Optimizer will be ignored. Otherwise the regularizer in Optimizer will be used. - In the implementation, the formula of L2 Weight Decay Regularization is as follows: + In the implementation, the penalty of L2 Weight Decay Regularization is as follows: .. math:: - L2WeightDecay = reg\_coeff * parameter + loss = coeff * reduce\_sum(square(x)) Args: regularization_coeff(float, optional): regularization coeff. Default:0.0 @@ -109,9 +108,8 @@ class L2Decay(fluid.regularizer.L2Decay): from paddle.regularizer import L2Decay import numpy as np paddle.disable_static() - inp = np.random.uniform(-0.1, 0.1, [10, 10]).astype("float32") linear = paddle.nn.Linear(10, 10) - inp = paddle.to_tensor(inp) + inp = paddle.rand(shape=[10, 10], dtype="float32") out = linear(inp) loss = paddle.mean(out) beta1 = paddle.to_tensor([0.9], dtype="float32") -- GitLab