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polish append_backward en doc (#20199)

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* polish append_backward, test=document_fix

* test=document_fix, test=develop

* test=document_fix, test=develop

* polish append_backward, test=document_fix, test=develop
revert-20712-fix_depthwise_conv
Youwei Song 6 years ago committed by Jiabin Yang
parent fb21a743ae
commit 478e4d6882
2 changed files with 27 additions and 22 deletions
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2
paddle/fluid/API.spec
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@ -1041,7 +1041,7 @@ paddle.fluid.optimizer.RecomputeOptimizer.backward (ArgSpec(args=['self', 'loss'
paddle.fluid.optimizer.RecomputeOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.RecomputeOptimizer.load (ArgSpec(args=['self', 'stat_dict'], varargs=None, keywords=None, defaults=None), ('document', '7b2b8ae72011bc4decb67e97623f2c56'))
paddle.fluid.optimizer.RecomputeOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.backward.append_backward (ArgSpec(args=['loss', 'parameter_list', 'no_grad_set', 'callbacks', 'checkpoints'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', '52488008103886c793843a3828bacd5e'))
paddle.fluid.backward.append_backward (ArgSpec(args=['loss', 'parameter_list', 'no_grad_set', 'callbacks', 'checkpoints'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'c68fe1cb95d90762b57c309cae9b99d9'))
paddle.fluid.backward.gradients (ArgSpec(args=['targets', 'inputs', 'target_gradients', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'e2097e1e0ed84ae44951437bfe269a1b'))
paddle.fluid.regularizer.L1DecayRegularizer ('paddle.fluid.regularizer.L1DecayRegularizer', ('document', '34603757e70974d2fcc730643b382925'))
paddle.fluid.regularizer.L1DecayRegularizer.__init__ (ArgSpec(args=['self', 'regularization_coeff'], varargs=None, keywords=None, defaults=(0.0,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))

47
python/paddle/fluid/backward.py
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@ -936,29 +936,31 @@ def append_backward(loss,
callbacks=None,
checkpoints=None):
"""
Append backward part to main_program.
This function appends backward part to main_program.
A complete neural network training is made up of forward and backward
propagation. However, when we configure a network, we only need to
specify its forwrd part. The backward part is generated automatically
according to the forward part by this function.
specify its forward part. This function uses the chain rule to automatically
generate the backward part according to the forward part.
In most cases, users do not need to invoke this function manually. It
will be automatically invoked by the optimizer's `minimize` function.
In most cases, users do not need to invoke this function manually.
It will be automatically invoked by the optimizer's `minimize` function.
Args:
loss(Variable): The loss variable of the network.
parameter_list(list[string]|None): Names of parameters that need
Parameters:
loss( :ref:`api_guide_Variable_en` ): The loss variable of the network.
parameter_list(list of str, optional): Names of parameters that need
to be updated by optimizers.
If it is None, all parameters
will be updated.
Default: None
no_grad_set(set|None): Variables in the Block 0 whose gradients
Default: None.
no_grad_set(set of str, optional): Variable names in the :ref:`api_guide_Block_en` 0 whose gradients
should be ignored. All variables with
`stop_gradient=True` from all blocks will
be automatically added into this set.
Default: None
callbacks(list[callable object]|None): The callbacks are used for
If this parameter is not None, the names in this set will be added to the default set.
Default: None.
callbacks(list of callable object, optional): List of callback functions.
The callbacks are used for
doing some custom jobs during
backward part building. All
callable objects in it will
@ -967,23 +969,23 @@ def append_backward(loss,
into the program. The callable
object must has two input
parameters: 'block' and 'context'.
The 'block' is the block which
The 'block' is the :ref:`api_guide_Block_en` which
the new gradient operator will
be added to. The 'context' is a
map, whose keys are gradient
variable names and values are
corresponding original variables.
corresponding original :ref:`api_guide_Variable_en` .
In addition to this, the 'context'
has another special key-value pair:
the key is string '__current_op_desc__'
and the value is the op_desc of the
gradient operator who has just
triggered the callable object.
Default: None.
Returns:
list[(Variable,Variable)]: Pairs of parameter and its
corresponding gradients. The key is the parameter and the
value is gradient variable.
list of tuple ( :ref:`api_guide_Variable_en` , :ref:`api_guide_Variable_en` ): Pairs of parameter and its corresponding gradients.
The key is the parameter and the value is gradient variable.
Raises:
AssertionError: If `loss` is not an instance of Variable.
@ -991,17 +993,20 @@ def append_backward(loss,
Examples:
.. code-block:: python
# network configuration code
# loss from ...
import paddle.fluid as fluid
x = fluid.layers.data(name='x', shape=[13], dtype='float32')
y = fluid.layers.data(name='y', shape=[1], dtype='float32')
x = fluid.data(name='x', shape=[None, 13], dtype='float32')
y = fluid.data(name='y', shape=[None, 1], dtype='float32')
y_predict = fluid.layers.fc(input=x, size=1, act=None)
loss = fluid.layers.square_error_cost(input=y_predict, label=y)
avg_loss = fluid.layers.mean(loss)
param_grad_list = fluid.backward.append_backward(loss=avg_loss)
p_g_list1 = fluid.backward.append_backward(loss=avg_loss) # len(p_g_list1) == 2
p_g_list2 = fluid.backward.append_backward(loss=avg_loss, parameter_list=[p_g_list1[0][0].name]) # len(p_g_list1) == 1
p_g_list3 = fluid.backward.append_backward(loss=avg_loss, no_grad_set=set([p_g_list1[0][0].name])) # len(p_g_list1) == 1
p_g_list4 = fluid.backward.append_backward(loss=avg_loss, parameter_list=[p_g_list1[0][0].name], no_grad_set=set([p_g_list1[0][0].name])) # len(p_g_list1) == 0
"""
assert isinstance(loss, framework.Variable)

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