# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import print_function import logging from . import framework from .framework import _non_static_mode, _varbase_creator from . import core from paddle import _C_ops __all__ = ['L1Decay', 'L2Decay', 'L1DecayRegularizer', 'L2DecayRegularizer'] class WeightDecayRegularizer(object): """Base class for weight decay regularizers Defines the common interface of weight-decay regularizers. Weight-decay regularizers are added only during the backward pass for faster regularization. They add operations to the network that correspond to gradient of the regularization function. Users should not use this class directly, but need to use one of its implementations """ def __init__(self): pass def __call__(self, param, grad, block): """Add corresponding weight decay operations to the network """ raise NotImplementedError() def __str__(self): """Debug string """ raise NotImplementedError() class L2DecayRegularizer(WeightDecayRegularizer): r""" Implement the L2 Weight Decay Regularization, which helps to prevent the model over-fitting. It can be set in :ref:`api_fluid_ParamAttr` or ``optimizer`` (such as :ref:`api_fluid_optimizer_SGDOptimizer` ). When set in ``ParamAttr`` , it only takes effect for trainable parameters in this layer. When set in ``optimizer`` , it takes effect for all trainable parameters. When set together, ``ParamAttr`` has higher priority than ``optimizer`` . In the implementation, the formula of L2 Weight Decay Regularization is as follows: .. math:: L2WeightDecay = reg\_coeff * parameter Args: regularization_coeff(float, optional): regularization coeff. Default:0.0 Examples: .. code-block:: python # Example1: set Regularizer in optimizer 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.L2Decay( regularization_coeff=0.1)) optimizer.minimize(avg_loss) # Example2: set Regularizer both in ParamAttr and optimizer import paddle.fluid as fluid l1 = fluid.regularizer.L1Decay(regularization_coeff=0.1) l2 = fluid.regularizer.L2Decay(regularization_coeff=0.1) x = fluid.layers.uniform_random([3,4]) # set L1 regularization in fluid.ParamAttr w_param = fluid.ParamAttr(regularizer=l1) hidden1 = fluid.layers.fc(x, 8, param_attr=w_param) # fc_0.w_0(L1), fc_0.b_0 hidden2 = fluid.layers.fc(hidden1, 16, param_attr=w_param) # fc_1.w_0(L1), fc_1.b_0 predict = fluid.layers.fc(hidden2, 32) # fc_3.w_0, fc_3.b_0 avg_loss = fluid.layers.mean(predict) # set L2 regularization in optimizer optimizer = fluid.optimizer.SGD(learning_rate=1e-4, regularization=l2) optimizer.minimize(avg_loss) # it will Print Message: # Regularization of [fc_0.w_0, fc_1.w_0] have been set by ParamAttr or WeightNormParamAttr already. # So, the Regularization of Optimizer will not take effect for these parameters! """ def __init__(self, regularization_coeff=0.0): assert regularization_coeff is not None super(L2DecayRegularizer, self).__init__() self._regularization_coeff = regularization_coeff def __call__(self, param, grad, block): """Add L2 weight decay ops to network Adds L2 weight decay ops. L2WeightDecay = reg_coeff * parameter Args: param: parameter variable for which regularization is applied block: block in which variable is to be created Returns: new variable for weight decay """ assert isinstance(param, framework.Variable) assert isinstance(block, framework.Block) if framework._non_static_mode(): if framework.in_dygraph_mode(): return _C_ops.final_state_scale(param, self._regularization_coeff, 0.0, True) else: return _C_ops.scale(param, "scale", self._regularization_coeff) else: decay = block.create_var(dtype=param.dtype, shape=param.shape, lod_level=param.lod_level) # Append Op to calculate decay block.append_op(type='scale', inputs={"X": param}, outputs={"Out": decay}, attrs={"scale": self._regularization_coeff}) return decay def __str__(self): return "L2Decay, regularization_coeff=%f" % self._regularization_coeff class L1DecayRegularizer(WeightDecayRegularizer): r""" Implement the L1 Weight Decay Regularization, which encourages the weights to be sparse. It can be set in :ref:`api_fluid_ParamAttr` or ``optimizer`` (such as :ref:`api_fluid_optimizer_SGDOptimizer` ). When set in ``ParamAttr`` , it only takes effect for trainable parameters in this layer. When set in ``optimizer`` , it takes effect for all trainable parameters. When set together, ``ParamAttr`` has higher priority than ``optimizer`` . In the implementation, the formula of L1 Weight Decay Regularization is as follows: .. math:: L1WeightDecay = reg\_coeff * sign(parameter) Args: regularization_coeff(float, optional): regularization coeff. Default:0.0. Examples: .. code-block:: python # Example1: set Regularizer in optimizer 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) # Example2: set Regularizer both in ParamAttr and optimizer import paddle.fluid as fluid l1 = fluid.regularizer.L1Decay(regularization_coeff=0.1) l2 = fluid.regularizer.L2Decay(regularization_coeff=0.1) x = fluid.layers.uniform_random([3,4]) # set L1 regularization in fluid.ParamAttr w_param = fluid.ParamAttr(regularizer=l1) hidden1 = fluid.layers.fc(x, 8, param_attr=w_param) # fc_0.w_0(L1), fc_0.b_0 hidden2 = fluid.layers.fc(hidden1, 16, param_attr=w_param) # fc_1.w_0(L1), fc_1.b_0 predict = fluid.layers.fc(hidden2, 32) # fc_3.w_0, fc_3.b_0 avg_loss = fluid.layers.mean(predict) # set L2 regularization in optimizer optimizer = fluid.optimizer.SGD(learning_rate=1e-4, regularization=l2) optimizer.minimize(avg_loss) # it will Print Message: # Regularization of [fc_0.w_0, fc_1.w_0] have been set by ParamAttr or WeightNormParamAttr already. # So, the Regularization of Optimizer will not take effect for these parameters! """ def __init__(self, regularization_coeff=0.0): assert regularization_coeff is not None super(L1DecayRegularizer, self).__init__() self._regularization_coeff = regularization_coeff def __call__(self, param, grad, block): """Add L1 weight decay ops to network Adds L1 weight decay ops. L1WeightDecay = reg_coeff * sign(parameter) Args: param: parameter variable for which regularization is applied block: block in which variable is to be created Returns: new variable for weight decay """ assert isinstance(param, framework.Variable) assert isinstance(block, framework.Block) if framework._non_static_mode(): sign = block.create_var(dtype=param.dtype, shape=param.shape) decay = block.create_var(dtype=param.dtype, shape=param.shape) else: sign = block.create_var(dtype=param.dtype, shape=param.shape, lod_level=param.lod_level) decay = block.create_var(dtype=param.dtype, shape=param.shape, lod_level=param.lod_level) # Append sign op block.append_op(type='sign', inputs={"X": param}, outputs={"Out": sign}) # Append scale op to the output of sign op block.append_op(type='scale', inputs={"X": sign}, outputs={"Out": decay}, attrs={"scale": self._regularization_coeff}) return decay def __str__(self): return "L1Decay, regularization_coeff=%f" % self._regularization_coeff # We short the class name, since users will use the regulaizer with the package # name. The sample code: # # import paddle.fluid as fluid # # hidden = fluid.layers.fc(..., # param_attr=fluid.regularizer.Xavier()) # # It is no need to add a `Regularizer` as the class suffix L1Decay = L1DecayRegularizer L2Decay = L2DecayRegularizer