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提交 76a58197 编写于 作者: D DuYao 提交者： hong
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update English document (#20330) 

* update English document, test=document_fix

* update api.spec, test=document_fix

* update api.spec, test=document_fix

* update, test=document_fix
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paddle/fluid/API.spec
浏览文件 @ 76a58197
......@@ -199,7 +199,7 @@ paddle.fluid.layers.lod_append (ArgSpec(args=['x', 'level'], varargs=None, keywo
paddle.fluid.layers.lrn (ArgSpec(args=['input', 'n', 'k', 'alpha', 'beta', 'name'], varargs=None, keywords=None, defaults=(5, 1.0, 0.0001, 0.75, None)), ('document', 'fa565b65fb98d3ca82361c79f41b06b2'))
paddle.fluid.layers.pad (ArgSpec(args=['x', 'paddings', 'pad_value', 'name'], varargs=None, keywords=None, defaults=(0.0, None)), ('document', '46b3ada86dd2c79042dca90a55e08f66'))
paddle.fluid.layers.pad_constant_like (ArgSpec(args=['x', 'y', 'pad_value', 'name'], varargs=None, keywords=None, defaults=(0.0, None)), ('document', '89aa122a50dc20ee116ae49d66854d20'))
paddle.fluid.layers.label_smooth (ArgSpec(args=['label', 'prior_dist', 'epsilon', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, 0.1, 'float32', None)), ('document', '214f1dfbe95a628600bbe99e836319cf'))
paddle.fluid.layers.label_smooth (ArgSpec(args=['label', 'prior_dist', 'epsilon', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, 0.1, 'float32', None)), ('document', '70b6f4ab59e60650231b1ead4ad46222'))
paddle.fluid.layers.roi_pool (ArgSpec(args=['input', 'rois', 'pooled_height', 'pooled_width', 'spatial_scale'], varargs=None, keywords=None, defaults=(1, 1, 1.0)), ('document', '6fc9bae94518bbf3e1a9e479f38f6537'))
paddle.fluid.layers.roi_align (ArgSpec(args=['input', 'rois', 'pooled_height', 'pooled_width', 'spatial_scale', 'sampling_ratio', 'name'], varargs=None, keywords=None, defaults=(1, 1, 1.0, -1, None)), ('document', '3885fd76e122ac0563fa8369bcab7363'))
paddle.fluid.layers.dice_loss (ArgSpec(args=['input', 'label', 'epsilon', 'name'], varargs=None, keywords=None, defaults=(1e-05, None)), ('document', '08d94daffbea3935178810bdc1633f07'))
......@@ -604,7 +604,7 @@ paddle.fluid.dygraph.Conv2D.set_dict (ArgSpec(args=['self', 'stat_dict', 'includ
paddle.fluid.dygraph.Conv2D.state_dict (ArgSpec(args=['self', 'destination', 'include_sublayers'], varargs=None, keywords=None, defaults=(None, True)), ('document', '9d689f44592cd22812c7ec06a9654eac'))
paddle.fluid.dygraph.Conv2D.sublayers (ArgSpec(args=['self', 'include_sublayers'], varargs=None, keywords=None, defaults=(True,)), ('document', '00a881005ecbc96578faf94513bf0d62'))
paddle.fluid.dygraph.Conv2D.train (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.dygraph.Conv3D ('paddle.fluid.dygraph.nn.Conv3D', ('document', '50412bd3fbf3557a8ef48e25c6517025'))
paddle.fluid.dygraph.Conv3D ('paddle.fluid.dygraph.nn.Conv3D', ('document', 'f81dee6781d6c18d0e7f5ca66b2fb010'))
paddle.fluid.dygraph.Conv3D.__init__ (ArgSpec(args=['self', 'name_scope', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.dygraph.Conv3D.add_parameter (ArgSpec(args=['self', 'name', 'parameter'], varargs=None, keywords=None, defaults=None), ('document', 'f35ab374c7d5165c3daf3bd64a5a2ec1'))
paddle.fluid.dygraph.Conv3D.add_sublayer (ArgSpec(args=['self', 'name', 'sublayer'], varargs=None, keywords=None, defaults=None), ('document', '839ff3c0534677ba6ad8735c3fd4e995'))
......@@ -689,7 +689,7 @@ paddle.fluid.dygraph.Embedding.set_dict (ArgSpec(args=['self', 'stat_dict', 'inc
paddle.fluid.dygraph.Embedding.state_dict (ArgSpec(args=['self', 'destination', 'include_sublayers'], varargs=None, keywords=None, defaults=(None, True)), ('document', '9d689f44592cd22812c7ec06a9654eac'))
paddle.fluid.dygraph.Embedding.sublayers (ArgSpec(args=['self', 'include_sublayers'], varargs=None, keywords=None, defaults=(True,)), ('document', '00a881005ecbc96578faf94513bf0d62'))
paddle.fluid.dygraph.Embedding.train (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.dygraph.GRUUnit ('paddle.fluid.dygraph.nn.GRUUnit', ('document', '389e860e455b67aab1f4d472ac9d7e49'))
paddle.fluid.dygraph.GRUUnit ('paddle.fluid.dygraph.nn.GRUUnit', ('document', 'f0e648f0a8d3389f755698dde488dc93'))
paddle.fluid.dygraph.GRUUnit.__init__ (ArgSpec(args=['self', 'name_scope', 'size', 'param_attr', 'bias_attr', 'activation', 'gate_activation', 'origin_mode', 'dtype'], varargs=None, keywords=None, defaults=(None, None, 'tanh', 'sigmoid', False, 'float32')), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.dygraph.GRUUnit.add_parameter (ArgSpec(args=['self', 'name', 'parameter'], varargs=None, keywords=None, defaults=None), ('document', 'f35ab374c7d5165c3daf3bd64a5a2ec1'))
paddle.fluid.dygraph.GRUUnit.add_sublayer (ArgSpec(args=['self', 'name', 'sublayer'], varargs=None, keywords=None, defaults=None), ('document', '839ff3c0534677ba6ad8735c3fd4e995'))
......@@ -757,7 +757,7 @@ paddle.fluid.dygraph.PRelu.set_dict (ArgSpec(args=['self', 'stat_dict', 'include
paddle.fluid.dygraph.PRelu.state_dict (ArgSpec(args=['self', 'destination', 'include_sublayers'], varargs=None, keywords=None, defaults=(None, True)), ('document', '9d689f44592cd22812c7ec06a9654eac'))
paddle.fluid.dygraph.PRelu.sublayers (ArgSpec(args=['self', 'include_sublayers'], varargs=None, keywords=None, defaults=(True,)), ('document', '00a881005ecbc96578faf94513bf0d62'))
paddle.fluid.dygraph.PRelu.train (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.dygraph.BilinearTensorProduct ('paddle.fluid.dygraph.nn.BilinearTensorProduct', ('document', 'be70d0f6d43729d9cb80c9a34ed5f26b'))
paddle.fluid.dygraph.BilinearTensorProduct ('paddle.fluid.dygraph.nn.BilinearTensorProduct', ('document', 'ddea5bc0668a636ded7db09538511c20'))
paddle.fluid.dygraph.BilinearTensorProduct.__init__ (ArgSpec(args=['self', 'name_scope', 'size', 'name', 'act', 'param_attr', 'bias_attr'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.dygraph.BilinearTensorProduct.add_parameter (ArgSpec(args=['self', 'name', 'parameter'], varargs=None, keywords=None, defaults=None), ('document', 'f35ab374c7d5165c3daf3bd64a5a2ec1'))
paddle.fluid.dygraph.BilinearTensorProduct.add_sublayer (ArgSpec(args=['self', 'name', 'sublayer'], varargs=None, keywords=None, defaults=None), ('document', '839ff3c0534677ba6ad8735c3fd4e995'))
......@@ -791,7 +791,7 @@ paddle.fluid.dygraph.Conv2DTranspose.set_dict (ArgSpec(args=['self', 'stat_dict'
paddle.fluid.dygraph.Conv2DTranspose.state_dict (ArgSpec(args=['self', 'destination', 'include_sublayers'], varargs=None, keywords=None, defaults=(None, True)), ('document', '9d689f44592cd22812c7ec06a9654eac'))
paddle.fluid.dygraph.Conv2DTranspose.sublayers (ArgSpec(args=['self', 'include_sublayers'], varargs=None, keywords=None, defaults=(True,)), ('document', '00a881005ecbc96578faf94513bf0d62'))
paddle.fluid.dygraph.Conv2DTranspose.train (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.dygraph.Conv3DTranspose ('paddle.fluid.dygraph.nn.Conv3DTranspose', ('document', '91ba132bc690eaf76eabdbde8f87e4a0'))
paddle.fluid.dygraph.Conv3DTranspose ('paddle.fluid.dygraph.nn.Conv3DTranspose', ('document', '0ef981fd6a74aaff21673f9925736ac7'))
paddle.fluid.dygraph.Conv3DTranspose.__init__ (ArgSpec(args=['self', 'name_scope', 'num_filters', 'output_size', 'filter_size', 'padding', 'stride', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, None, 0, 1, 1, None, None, None, True, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.dygraph.Conv3DTranspose.add_parameter (ArgSpec(args=['self', 'name', 'parameter'], varargs=None, keywords=None, defaults=None), ('document', 'f35ab374c7d5165c3daf3bd64a5a2ec1'))
paddle.fluid.dygraph.Conv3DTranspose.add_sublayer (ArgSpec(args=['self', 'name', 'sublayer'], varargs=None, keywords=None, defaults=None), ('document', '839ff3c0534677ba6ad8735c3fd4e995'))
......@@ -870,31 +870,31 @@ paddle.fluid.dygraph.Tracer.train_mode (ArgSpec(args=['self'], varargs=None, key
paddle.fluid.dygraph.prepare_context (ArgSpec(args=['strategy'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.dygraph.save_dygraph (ArgSpec(args=['state_dict', 'model_path'], varargs=None, keywords=None, defaults=None), ('document', '7c2bd58a69f9bca3b884f44154c84569'))
paddle.fluid.dygraph.load_dygraph (ArgSpec(args=['model_path'], varargs=None, keywords=None, defaults=None), ('document', 'd6d98002c39d2484835f4748e35b761c'))
paddle.fluid.dygraph.NoamDecay ('paddle.fluid.dygraph.learning_rate_scheduler.NoamDecay', ('document', '9ccfea97dbf15134d406a23aae1e1fa2'))
paddle.fluid.dygraph.NoamDecay ('paddle.fluid.dygraph.learning_rate_scheduler.NoamDecay', ('document', '3441619381487db8d1929a205f3c6d41'))
paddle.fluid.dygraph.NoamDecay.__init__ (ArgSpec(args=['self', 'd_model', 'warmup_steps', 'begin', 'step', 'dtype'], varargs=None, keywords=None, defaults=(1, 1, 'float32')), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.dygraph.NoamDecay.create_lr_var (ArgSpec(args=['self', 'lr'], varargs=None, keywords=None, defaults=None), ('document', '013bc233558149d0757b3df57845b866'))
paddle.fluid.dygraph.NoamDecay.step (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.dygraph.PiecewiseDecay ('paddle.fluid.dygraph.learning_rate_scheduler.PiecewiseDecay', ('document', '8f4d37eaad4e2f5b12850f3663856758'))
paddle.fluid.dygraph.PiecewiseDecay ('paddle.fluid.dygraph.learning_rate_scheduler.PiecewiseDecay', ('document', '0fccf303b94a13ae670fb3dd51931f73'))
paddle.fluid.dygraph.PiecewiseDecay.__init__ (ArgSpec(args=['self', 'boundaries', 'values', 'begin', 'step', 'dtype'], varargs=None, keywords=None, defaults=(1, 'float32')), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.dygraph.PiecewiseDecay.create_lr_var (ArgSpec(args=['self', 'lr'], varargs=None, keywords=None, defaults=None), ('document', '013bc233558149d0757b3df57845b866'))
paddle.fluid.dygraph.PiecewiseDecay.step (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.dygraph.NaturalExpDecay ('paddle.fluid.dygraph.learning_rate_scheduler.NaturalExpDecay', ('document', '94bed58b392a5a71b6d1abd39eed7111'))
paddle.fluid.dygraph.NaturalExpDecay ('paddle.fluid.dygraph.learning_rate_scheduler.NaturalExpDecay', ('document', '5fef27468d49ca8ca6c6a9635ad0f5c1'))
paddle.fluid.dygraph.NaturalExpDecay.__init__ (ArgSpec(args=['self', 'learning_rate', 'decay_steps', 'decay_rate', 'staircase', 'begin', 'step', 'dtype'], varargs=None, keywords=None, defaults=(False, 0, 1, 'float32')), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.dygraph.NaturalExpDecay.create_lr_var (ArgSpec(args=['self', 'lr'], varargs=None, keywords=None, defaults=None), ('document', '013bc233558149d0757b3df57845b866'))
paddle.fluid.dygraph.NaturalExpDecay.step (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.dygraph.ExponentialDecay ('paddle.fluid.dygraph.learning_rate_scheduler.ExponentialDecay', ('document', 'a259689c649c5f82636536386ce2ef19'))
paddle.fluid.dygraph.ExponentialDecay ('paddle.fluid.dygraph.learning_rate_scheduler.ExponentialDecay', ('document', '846eb564df136d8a8917bf16b5b8ac9b'))
paddle.fluid.dygraph.ExponentialDecay.__init__ (ArgSpec(args=['self', 'learning_rate', 'decay_steps', 'decay_rate', 'staircase', 'begin', 'step', 'dtype'], varargs=None, keywords=None, defaults=(False, 0, 1, 'float32')), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.dygraph.ExponentialDecay.create_lr_var (ArgSpec(args=['self', 'lr'], varargs=None, keywords=None, defaults=None), ('document', '013bc233558149d0757b3df57845b866'))
paddle.fluid.dygraph.ExponentialDecay.step (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.dygraph.InverseTimeDecay ('paddle.fluid.dygraph.learning_rate_scheduler.InverseTimeDecay', ('document', '6a868b2c7cc0f09f57ef71902bbc93ca'))
paddle.fluid.dygraph.InverseTimeDecay ('paddle.fluid.dygraph.learning_rate_scheduler.InverseTimeDecay', ('document', '1a74f0370e2e64f9e786d3c336526e6d'))
paddle.fluid.dygraph.InverseTimeDecay.__init__ (ArgSpec(args=['self', 'learning_rate', 'decay_steps', 'decay_rate', 'staircase', 'begin', 'step', 'dtype'], varargs=None, keywords=None, defaults=(False, 0, 1, 'float32')), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.dygraph.InverseTimeDecay.create_lr_var (ArgSpec(args=['self', 'lr'], varargs=None, keywords=None, defaults=None), ('document', '013bc233558149d0757b3df57845b866'))
paddle.fluid.dygraph.InverseTimeDecay.step (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.dygraph.PolynomialDecay ('paddle.fluid.dygraph.learning_rate_scheduler.PolynomialDecay', ('document', 'bb90314cee58952f13522dcd571ca832'))
paddle.fluid.dygraph.PolynomialDecay ('paddle.fluid.dygraph.learning_rate_scheduler.PolynomialDecay', ('document', 'e222a066a2bcf31bc52a14271048e034'))
paddle.fluid.dygraph.PolynomialDecay.__init__ (ArgSpec(args=['self', 'learning_rate', 'decay_steps', 'end_learning_rate', 'power', 'cycle', 'begin', 'step', 'dtype'], varargs=None, keywords=None, defaults=(0.0001, 1.0, False, 0, 1, 'float32')), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.dygraph.PolynomialDecay.create_lr_var (ArgSpec(args=['self', 'lr'], varargs=None, keywords=None, defaults=None), ('document', '013bc233558149d0757b3df57845b866'))
paddle.fluid.dygraph.PolynomialDecay.step (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.dygraph.CosineDecay ('paddle.fluid.dygraph.learning_rate_scheduler.CosineDecay', ('document', '46dadadee1a8a92d70bd277d9345bfb0'))
paddle.fluid.dygraph.CosineDecay ('paddle.fluid.dygraph.learning_rate_scheduler.CosineDecay', ('document', '0d7fe2b87492a0eb5cde60dbe268ea17'))
paddle.fluid.dygraph.CosineDecay.__init__ (ArgSpec(args=['self', 'learning_rate', 'step_each_epoch', 'epochs', 'begin', 'step', 'dtype'], varargs=None, keywords=None, defaults=(0, 1, 'float32')), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.dygraph.CosineDecay.create_lr_var (ArgSpec(args=['self', 'lr'], varargs=None, keywords=None, defaults=None), ('document', '013bc233558149d0757b3df57845b866'))
paddle.fluid.dygraph.CosineDecay.step (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
......
python/paddle/fluid/dygraph/learning_rate_scheduler.py
浏览文件 @ 76a58197
......@@ -69,30 +69,33 @@ class LearningRateDecay(object):
class PiecewiseDecay(LearningRateDecay):
"""
piecewise decay scheduler
Piecewise decay scheduler.
The algorithm can be described as the code below.
.. code-block:: text
boundaries = [10000, 20000]
values = [1.0, 0.5, 0.1]
if step < 10000:
learning_rate = 1.0
elif 10000 <= step < 20000:
learning_rate = 0.5
else:
learning_rate = 0.1
Args:
boundaries: A list of steps numbers.
values: A list of learning rate values that will be picked during
different step boundaries.
begin: The begin step to initilize the self.step_num
step: The step_size using when calculate the new step_num (Defalult is 1)
dtype: The dtype used to create the learning rate variable
boundaries = [10000, 20000]
values = [1.0, 0.5, 0.1]
if global_step < 10000:
learning_rate = 1.0
elif 10000 <= global_step < 20000:
learning_rate = 0.5
else:
learning_rate = 0.1
Parameters:
boundaries(list): A list of steps numbers. The type of element in the list is python int.
values(list): A list of learning rate values that will be picked during
different step boundaries. The type of element in the list is python float.
begin(int): The begin step to initilize the global_step in the description above.
step(int, optional): The step size used to calculate the new global_step in the description above.
The defalult value is 1.
dtype(str, optional): The data type used to create the learning rate variable. The data type can be set as
'float32', 'float64'. The default value is 'float32'.
Returns:
The decayed learning rate.
None.
Examples:
.. code-block:: python
......@@ -125,25 +128,40 @@ class NaturalExpDecay(LearningRateDecay):
"""
Applies natural exponential decay to the initial learning rate.
.. code-block:: python
The algorithm can be described as following.
if not staircase:
decayed_learning_rate = learning_rate * exp(- decay_rate * (global_step / decay_steps))
else:
decayed_learning_rate = learning_rate * exp(- decay_rate * (global_step / decay_steps))
Args:
learning_rate: A scalar float32 value or a Variable. This
will be the initial learning rate during training
decay_steps: A Python `int32` number.
decay_rate: A Python `float` number.
staircase: Boolean. If set true, decay the learning rate every decay_steps.
begin: A Python 'int32' number, the begin step (Default is 0)
step: A Python 'int32' number, the step size (Default is 1)
dtype: A Python 'str', the dtype used to create learning rate variable (Default is 'float32')
.. math::
decayed\_learning\_rate = learning\_rate * e^{y}
If staircase is set to False, then:
.. math::
y = - decay\_rate * \\frac{global\_step}{decay\_steps}
If staircase is set to True, then:
.. math::
y = - decay\_rate * math.floor(\\frac{global\_step}{decay\_steps})
Parameters:
learning_rate(Variable|float): The initial learning rate. If the type
is Variable, it's a tensor with shape [1], the data type can be
float32 or float64. It also can be set to python int number.
decay_steps(int): The decay step size. It determines the decay cycle.
decay_rate(int): The decay rate.
staircase(bool, optional): If set to True, decay the learning rate at discrete intervals. The
default value is False.
begin(int, optional): The begin step. The initial value of global_step described above. The default value is 0.
step(int, optional): The step size used to calculate the new global_step in the description above.
The defalult value is 1.
dtype(str, optional): The data type used to create the learning rate variable. The data type can be set as
'float32', 'float64'. The default value is 'float32'.
Returns:
The decayed learning rate.
None.
Examples:
.. code-block:: python
......@@ -189,29 +207,41 @@ class ExponentialDecay(LearningRateDecay):
"""
Applies exponential decay to the learning rate.
When training a model, it is often recommended to lower the learning rate as the
training progresses. By using this function, the learning rate will be decayed by
'decay_rate' every 'decay_steps' steps.
The algorithm can be described as following.
.. code-block:: python
.. math::
if staircase == True:
decayed_learning_rate = learning_rate * decay_rate ^ floor(global_step / decay_steps)
else:
decayed_learning_rate = learning_rate * decay_rate ^ (global_step / decay_steps)
Args:
learning_rate(Variable|float): The initial learning rate.
decay_steps(int): See the decay computation above.
decay_rate(float): The decay rate. See the decay computation above.
staircase(Boolean): If True, decay the learning rate at discrete intervals.
Default: False
begin(int): The begin step (default is 0)
step(int): The step size (default is 1)
dtype(str): The dtype used to create learning rate (default is 'float32')
decayed\_learning\_rate = learning\_rate * decay\_rate ^ y
If staircase is set to False, then:
.. math::
y = \\frac{global\_step}{decay\_steps}
If staircase is set to True, then:
.. math::
y = math.floor(\\frac{global\_step}{decay\_steps})
Parameters:
learning_rate(Variable|float): The initial learning rate. If the type
is Variable, it's a tensor with shape [1], the data type can be
float32 or float64. It also can be set to python int number.
decay_steps(int): The decay step size. It determines the decay cycle.
decay_rate(float): The decay rate.
staircase(bool, optional): If set to True, decay the learning rate at discrete intervals. The
default value is False.
begin(int, optional): The begin step. The initial value of global_step described above. The default value is 0.
step(int, optional): The step size used to calculate the new global_step in the description above.
The defalult value is 1.
dtype(str, optional): The data type used to create the learning rate variable. The data type can be set as
'float32', 'float64'. The default value is 'float32'.
Returns:
The decayed learning rate.
None.
Examples:
.. code-block:: python
......@@ -257,27 +287,35 @@ class InverseTimeDecay(LearningRateDecay):
"""
Applies inverse time decay to the initial learning rate.
When training a model, it is often recommended to lower the learning rate as the
training progresses. By using this function, an inverse decay function will be
applied to the initial learning rate.
>>> if staircase == True:
>>> decayed_learning_rate = learning_rate / (1 + decay_rate * floor(global_step / decay_step))
>>> else:
>>> decayed_learning_rate = learning_rate / (1 + decay_rate * global_step / decay_step)
Args:
learning_rate(Variable|float): The initial learning rate.
decay_steps(int): See the decay computation above.
decay_rate(float): The decay rate. See the decay computation above.
staircase(Boolean): If True, decay the learning rate at discrete intervals.
Default: False
begin(int): The begin step (default is 0)
step(int): The step size (default is 1)
dtype(str): The dtype used to create learning rate (default is 'float32')
The algorithm can be described as following.
If staircase is set to False, then:
.. math::
decayed\_learning\_rate = \\frac{learning\_rate}{1 + decay\_rate * \\frac{global\_step}{decay\_step}}
If staircase is set to True, then:
.. math::
decayed\_learning\_rate = \\frac{learning\_rate}{1 + decay\_rate * math.floor(\\frac{global\_step}{decay\_step})}
Parameters:
learning_rate(Variable|float): The initial learning rate. If the type
is Variable, it's a tensor with shape [1], the data type can be
float32 or float64. It also can be set to python int number.
decay_steps(int): The decay step size. It determines the decay cycle.
decay_rate(float): The decay rate.
staircase(bool, optional): If set to True, decay the learning rate at discrete intervals. The
default value is False.
begin(int, optional): The begin step. The initial value of global_step described above. The default value is 0.
step(int, optional): The step size used to calculate the new global_step in the description above.
The defalult value is 1.
dtype(str, optional): The data type used to create the learning rate variable. The data type can be
'float32', 'float64'. The default value is 'float32'.
Returns:
The decayed learning rate.
None.
Examples:
.. code-block:: python
......@@ -323,28 +361,40 @@ class PolynomialDecay(LearningRateDecay):
"""
Applies polynomial decay to the initial learning rate.
.. code-block:: text
The algorithm can be described as following.
If cycle is set to True, then:
.. math::
decay\_steps & = decay\_steps * math.ceil(\\frac{global\_step}{decay\_steps})
if cycle:
decay_steps = decay_steps * ceil(global_step / decay_steps)
else:
global_step = min(global_step, decay_steps)
decayed_learning_rate = (learning_rate - end_learning_rate) *
(1 - global_step / decay_steps) ^ power + end_learning_rate
Args:
learning_rate(Variable|float32): A scalar float32 value or a Variable. This
will be the initial learning rate during training.
decay_steps(int32): A Python `int32` number.
end_learning_rate(float): A Python `float` number.
power(float): A Python `float` number.
cycle(bool): If set true, decay the learning rate every decay_steps.
begin(int): The begin step (default is 0)
step(int): The step size (default is 1)
dtype(str): The dtype used to create learning rate (default is 'float32')
decayed\_learning\_rate & = (learning\_rate-end\_learning\_rate)*(1-\\frac{global\_step}{decay\_steps})^{power}+end\_learning\_rate
If cycle is set to False, then:
.. math::
global\_step & = min(global\_step, decay\_steps)
decayed\_learning\_rate & = (learning\_rate-end\_learning\_rate)*(1-\\frac{global\_step}{decay\_steps})^{power}+end\_learning\_rate
Parameters:
learning_rate(Variable|float): The initial learning rate. If the type
is Variable, it's a tensor with shape [1], the data type can be
float32 or float64. It also can be set to python int number.
decay_steps(int32): The decay step size. It determines the decay cycle.
end_learning_rate(float, optional): The minimum final learning rate. The default value is 0.0001.
power(float, optional): Power of polynomial. The default value is 1.0.
cycle(bool, optional): If set true, decay the learning rate every decay_steps. The default value is False.
begin(int, optional): The begin step. The initial value of global_step described above. The default value is 0.
step(int, optional): The step size used to calculate the new global_step in the description above.
The defalult value is 1.
dtype(str, optional): The data type used to create the learning rate variable. The data type can be set as
'float32', 'float64'. The default value is 'float32'.
Returns:
The decayed learning rate.
None.
Examples:
.. code-block:: python
......@@ -401,24 +451,26 @@ class CosineDecay(LearningRateDecay):
"""
Applies cosine decay to the learning rate.
when training a model, it is often recommended to lower the learning rate as the
training progresses. By using this function, the learning rate will be decayed by
following cosine decay strategy.
The algorithm can be described as following.
.. math::
decayed\_lr = learning\_rate * 0.5 * (math.cos * (epoch * \\frac{math.pi}{epochs} ) + 1)
decayed\_learning\_rate = learning\_rate * 0.5 * (math.cos(global\_step * \\frac{math.pi}{step\_each\_epoch} ) + 1)
Args:
learning_rate(Variable|float): The initial learning rate.
step_each_epoch(int): the number of steps in an epoch.
epochs(int): the number of epochs.
begin(int): The begin step (default is 0).
step(int): The step size (default is 1).
dtype(str): The dtype used to create learning rate (default is 'float32').
Parameters:
learning_rate(Variable|float): The initial learning rate. If the type
is Variable, it's a tensor with shape [1], the data type can be
float32 or float64. It also can be set to python int number.
step_each_epoch(int): The number of steps in an epoch.
epochs(int): The number of epochs.
begin(int, optional): The begin step. The initial value of global_step described above. The default value is 0.
step(int, optional): The step size used to calculate the new global_step in the description above.
The defalult value is 1.
dtype(str, optional): The data type used to create the learning rate variable. The data type can be set as
'float32', 'float64'. The default value is 'float32'.
Returns:
The decayed learning rate.
None.
Examples:
.. code-block:: python
......@@ -453,33 +505,29 @@ class CosineDecay(LearningRateDecay):
class NoamDecay(LearningRateDecay):
"""
Noam decay method. The numpy implementation of noam decay as follows.
.. code-block:: python
import numpy as np
# set hyper parameters
d_model = 2
current_steps = 20
warmup_steps = 200
# compute
lr_value = np.power(d_model, -0.5) * np.min([
np.power(current_steps, -0.5),
np.power(warmup_steps, -1.5) * current_steps])
Please reference `attention is all you need
<https://arxiv.org/pdf/1706.03762.pdf>`_.
Args:
d_model(Variable): The dimensionality of input and output of model.
warmup_steps(Variable): A super parameter.
begin(int): The begin step (default is 0)
step(int): The step size (default is 1)
dtype(str): The dtype used to create learning rate (default is 'float32')
Applies Noam decay to the initial learning rate.
The algorithm can be described as following.
.. math::
decayed\_learning\_rate = d_{model}^{-0.5} * min(global\_step^{-0.5}, global\_step * warmup\_steps^{-1.5})
Please reference `attention is all you need <https://arxiv.org/pdf/1706.03762.pdf>`_
Parameters:
d$_{model}$(Variable|int): The dimensionality of input and output feature vector of model. If type is Variable,
it's a tensor with shape [1] and the data type can be int32 or int64. The type can also be python int.
warmup_steps(Variable|int): The number of warmup steps. A super parameter. If type is Variable,
it's a tensor with shape [1] and the data type can be int32 or int64. The type can also be python int.
begin(int, optional): The begin step. The initial value of global_step described above. The default value is 0.
step(int, optional): The step size used to calculate the new global_step in the description above.
The defalult value is 1.
dtype(str, optional): The data type used to create the learning rate variable. The data type can be set as
'float32', 'float64'. The default value is 'float32'.
Returns:
The decayed learning rate.
None.
Examples:
.. code-block:: python
......
python/paddle/fluid/dygraph/nn.py
浏览文件 @ 76a58197
此差异已折叠。
python/paddle/fluid/layers/nn.py
浏览文件 @ 76a58197
......@@ -8937,8 +8937,8 @@ def label_smooth(label,
dtype="float32",
name=None):
"""
Label smoothing is a mechanism to regularize the classifier layer and is
called label-smoothing regularization (LSR).
Label smoothing is a mechanism to regularize the classifier layer and is called
label-smoothing regularization (LSR).
Label smoothing is proposed to encourage the model to be less confident,
since optimizing the log-likelihood of the correct label directly may
......@@ -8957,19 +8957,23 @@ def label_smooth(label,
See more details about label smoothing in https://arxiv.org/abs/1512.00567.
Args:
Parameters:
label(Variable): The input variable containing the label data. The
label data should use one-hot representation.
prior_dist(Variable): The prior distribution to be used to smooth
labels. If not provided, an uniform distribution
is used. The shape of :attr:`prior_dist` should
be :math:`(1, class\_num)`.
epsilon(float): The weight used to mix up the original ground-truth
distribution and the fixed distribution.
dtype(np.dtype|core.VarDesc.VarType|str): The type of data : float32,
float_64, int etc.
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
label data should use one-hot representation. It's
a multidimensional tensor with a shape of
:math:`[N_1, ..., Depth]`, where Depth is class number.
prior_dist(Variable, optional): The prior distribution to be used to smooth
labels. If not provided, an uniform distribution
is used. It's a multidimensional tensor with a shape of
:math:`[1, class\_num]` . The default value is None.
epsilon(float, optional): The weight used to mix up the original ground-truth
distribution and the fixed distribution. The default value is
0.1.
dtype(np.dtype|core.VarDesc.VarType|str, optional): The data type can be set
as 'float32', 'float64'. The default value is 'float32'.
name(str, optional): The default value is None. Normally there is no need for user
to set this property. For more information, please refer to
:ref:`api_guide_Name`.
Returns:
Variable: The tensor variable containing the smoothed labels.
......
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