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modify docs of program API, test=document_fix (#27600)

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my_2.0rc
Zhang Ting 4 years ago committed by GitHub
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python/paddle/fluid/framework.py
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@ -5278,32 +5278,32 @@ def default_startup_program():
"""
Get default/global startup program.
The layer function in :ref:`api_fluid_layers` will create parameters, :ref:`api_paddle_data_reader_reader` ,
`NCCL <https://developer.nvidia.com/nccl>`_ handles as global variables. The :code:`startup_program` will
initialize them by the OPs in startup :ref:`api_fluid_Program` . The :ref:`api_fluid_layers` function will
append these initialization operators into startup program.
The :code:`paddle.nn` function will append the initialization operators into startup program.
The :code:`startup_program` will initialize the parameters by the OPs.
This method will return the default or the current startup program. Users can use
:ref:`api_paddle_fluid_framework_program_guard` to switch :ref:`api_paddle_fluid_framework_Program` .
This method will return the :code:`default` or the :code:`current` startup
program. Users can use :ref:`api_fluid_program_guard` to switch :ref:`api_fluid_Program` .
Returns: current default startup :ref:`api_fluid_Program`
Returns:
Program: current default startup program.
Returns type: :ref:`api_fluid_Program`
Returns type:
Examples:
.. code-block:: python
import paddle.fluid as fluid
import paddle
main_program = fluid.Program()
startup_program = fluid.Program()
with fluid.program_guard(main_program=main_program, startup_program=startup_program):
x = fluid.layers.data(name="x", shape=[-1, 784], dtype='float32')
y = fluid.layers.data(name="y", shape=[-1, 1], dtype='int32')
z = fluid.layers.fc(name="fc", input=x, size=10, act="relu")
paddle.enable_static()
main_program = paddle.static.Program()
startup_program = paddle.static.Program()
with paddle.static.program_guard(main_program=main_program, startup_program=startup_program):
x = paddle.data(name="x", shape=[-1, 784], dtype='float32')
y = paddle.data(name="y", shape=[-1, 1], dtype='int32')
z = paddle.static.nn.fc(name="fc", input=x, size=10, act="relu")
print("main program is: {}".format(fluid.default_main_program()))
print("start up program is: {}".format(fluid.default_startup_program()))
print("main program is: {}".format(paddle.static.default_main_program()))
print("start up program is: {}".format(paddle.static.default_startup_program()))
"""
return _startup_program_
@ -5311,52 +5311,53 @@ def default_startup_program():
def default_main_program():
"""
This API can be used to get ``default main program`` which store the
descriptions of ``op`` and ``variable``.
descriptions of Ops and tensors.
For example ``z = fluid.layers.elementwise_add(x, y)`` will create a new ``elementwise_add``
``op`` and a new ``z`` ``variable``, and they will be recorded in ``default main program``
For example ``z = paddle.elementwise_add(x, y)`` will create a new ``elementwise_add``
Op and a new ``z`` tensor, and they will be recorded in ``default main program`` .
The ``default_main_program`` is the default value for ``Program`` parameter in
a lot of ``fluid`` APIs. For example, the :code:`Executor.run()` will execute the
The ``default main program`` is the default value for ``Program`` parameter in
a lot of APIs. For example, the :code:`Executor.run()` will execute the
:code:`default_main_program` when the program is not specified.
If you want to replace the ``default main program``, you can use :ref:`api_fluid_program_guard`
If you want to switch the ``default main program``, you can use :ref:`api_paddle_fluid_framework_program_guard` .
Returns:
:ref:`api_fluid_Program`: a ``Program`` which holding the descriptions of ops and variables in the network.
Program: A ``Program`` which holding the descriptions of OPs and tensors in the network.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import paddle
paddle.enable_static()
# Sample Network:
data = fluid.data(name='image', shape=[None, 3, 224, 224], dtype='float32')
label = fluid.data(name='label', shape=[None, 1], dtype='int64')
data = paddle.data(name='image', shape=[None, 3, 224, 224], dtype='float32')
label = paddle.data(name='label', shape=[None, 1], dtype='int64')
conv1 = fluid.layers.conv2d(data, 4, 5, 1, act=None)
bn1 = fluid.layers.batch_norm(conv1, act='relu')
pool1 = fluid.layers.pool2d(bn1, 2, 'max', 2)
conv2 = fluid.layers.conv2d(pool1, 16, 5, 1, act=None)
bn2 = fluid.layers.batch_norm(conv2, act='relu')
pool2 = fluid.layers.pool2d(bn2, 2, 'max', 2)
conv1 = paddle.static.nn.conv2d(data, 4, 5, 1, act=None)
bn1 = paddle.static.nn.batch_norm(conv1, act='relu')
pool1 = paddle.nn.functional.pool2d(bn1, 2, 'max', 2)
conv2 = paddle.static.nn.conv2d(pool1, 16, 5, 1, act=None)
bn2 = paddle.static.nn.batch_norm(conv2, act='relu')
pool2 = paddle.nn.functional.pool2d(bn2, 2, 'max', 2)
fc1 = fluid.layers.fc(pool2, size=50, act='relu')
fc2 = fluid.layers.fc(fc1, size=102, act='softmax')
fc1 = paddle.static.nn.fc(pool2, size=50, act='relu')
fc2 = paddle.static.nn.fc(fc1, size=102, act='softmax')
loss = fluid.layers.cross_entropy(input=fc2, label=label)
loss = fluid.layers.mean(loss)
opt = fluid.optimizer.Momentum(
loss = paddle.nn.functional.loss.cross_entropy(input=fc2, label=label)
loss = paddle.mean(loss)
opt = paddle.optimizer.Momentum(
learning_rate=0.1,
momentum=0.9,
regularization=fluid.regularizer.L2Decay(1e-4))
weight_decay=paddle.regularizer.L2Decay(1e-4))
opt.minimize(loss)
#print the number of blocks in the program, 1 in this case
print(fluid.default_main_program().num_blocks)
print(paddle.static.default_main_program().num_blocks) #[1]
#print the description of variable 'image'
print(fluid.default_main_program().blocks[0].var('image'))
print(paddle.static.default_main_program())
"""
return _main_program_

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