\u200E
###################
fluid.regularizer
###################
.. _cn_api_fluid_regularizer_L1Decay:
L1Decay
-------------------------------
.. py:attribute:: paddle.fluid.regularizer.L1Decay
``L1DecayRegularizer`` 的别名
.. _cn_api_fluid_regularizer_L1DecayRegularizer:
L1DecayRegularizer
-------------------------------
.. py:class:: paddle.fluid.regularizer.L1DecayRegularizer(regularization_coeff=0.0)
实现 L1 权重衰减正则化。
L1正则将会稀疏化权重矩阵。
.. math::
\\L1WeightDecay=reg\_coeff∗sign(parameter)\\
参数:
- **regularization_coeff** (float) – 正则化系数
**代码示例**
.. code-block:: python
import paddle.fluid as fluid
main_prog = fluid.Program()
startup_prog = fluid.Program()
with fluid.program_guard(main_prog, startup_prog):
data = fluid.layers.data(name='image', shape=[3, 28, 28], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
hidden = fluid.layers.fc(input=data, size=128, act='relu')
prediction = fluid.layers.fc(input=hidden, size=10, act='softmax')
loss = fluid.layers.cross_entropy(input=prediction, label=label)
avg_loss = fluid.layers.mean(loss)
optimizer = fluid.optimizer.Adagrad(
learning_rate=1e-4,
regularization=fluid.regularizer.L1DecayRegularizer(
regularization_coeff=0.1))
optimizer.minimize(avg_loss)
.. _cn_api_fluid_regularizer_L2Decay:
L2Decay
-------------------------------
.. py:attribute:: paddle.fluid.regularizer.L2Decay
``L2DecayRegularizer`` 的别名
.. _cn_api_fluid_regularizer_L2DecayRegularizer:
L2DecayRegularizer
-------------------------------
.. py:class:: paddle.fluid.regularizer.L2DecayRegularizer(regularization_coeff=0.0)
实现L2 权重衰减正则化。
较小的 L2 的有助于防止对训练数据的过度拟合。
.. math::
\\L2WeightDecay=reg\_coeff*parameter\\
参数:
- **regularization_coeff** (float) – 正则化系数
**代码示例**
.. code-block:: python
import paddle.fluid as fluid
main_prog = fluid.Program()
startup_prog = fluid.Program()
with fluid.program_guard(main_prog, startup_prog):
data = fluid.layers.data(name='image', shape=[3, 28, 28], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
hidden = fluid.layers.fc(input=data, size=128, act='relu')
prediction = fluid.layers.fc(input=hidden, size=10, act='softmax')
loss = fluid.layers.cross_entropy(input=prediction, label=label)
avg_loss = fluid.layers.mean(loss)
optimizer = fluid.optimizer.Adagrad(
learning_rate=1e-4,
regularization=fluid.regularizer.L2DecayRegularizer(
regularization_coeff=0.1))
optimizer.minimize(avg_loss)
