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未验证 提交 fa79f486 编写于 作者: X xsrobin 提交者： GitHub
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Cherrypick1.5 (#1028) 

* add 1.5.1 whl and fix some bugs (#1010)

* add windows install whl

* whl and bug

* fix some bugs

* update 1.5.1 cn API

* add url
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Showing with 503 addition and 212 deletion
doc/fluid/api_cn/average_cn.rst
浏览文件 @ fa79f486
...@@ -19,6 +19,7 @@ WeightedAverage ...@@ -19,6 +19,7 @@ WeightedAverage
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
avg = fluid.average.WeightedAverage() avg = fluid.average.WeightedAverage()
avg.add(value=2.0, weight=1) avg.add(value=2.0, weight=1)
avg.add(value=4.0, weight=2) avg.add(value=4.0, weight=2)
......
doc/fluid/api_cn/backward_cn.rst
浏览文件 @ fa79f486
...@@ -36,6 +36,7 @@ append_backward ...@@ -36,6 +36,7 @@ append_backward
# 网络配置 # 网络配置
# 损失计算 # 损失计算
import paddle.fluid as fluid
x = fluid.layers.data(name='x', shape=[13], dtype='float32') x = fluid.layers.data(name='x', shape=[13], dtype='float32')
y = fluid.layers.data(name='y', shape=[1], dtype='float32') y = fluid.layers.data(name='y', shape=[1], dtype='float32')
......
doc/fluid/api_cn/data/data_reader_cn.rst
浏览文件 @ fa79f486
...@@ -292,7 +292,7 @@ Data Reader Interface ...@@ -292,7 +292,7 @@ Data Reader Interface
返回：新的数据读取器 返回：新的数据读取器
抛出异常： ``ComposeNotAligned`` – reader的输出不一致。 当check_alignment设置为False，不会升高。 抛出异常： ``ComposeNotAligned`` – reader的输出不一致。 当check_alignment设置为False，不会抛出异常。
...@@ -378,22 +378,15 @@ PipeReader通过流从一个命令中读取数据，将它的stdout放到管道 ...@@ -378,22 +378,15 @@ PipeReader通过流从一个命令中读取数据，将它的stdout放到管道
.. py:method:: get_line(cut_lines=True, line_break='\n') .. py:method:: get_line(cut_lines=True, line_break='\n')
param cut_lines:
cut buffer to lines
type cut_lines: bool 参数：
- **cut_lines** （bool） - 将缓冲区分行。
param line_break: - **line_break** （string） - 文件中的行分割符，比如 ‘\\n’ 或者 ‘\\r’。
line break of the file, like
or
type line_break:
string
return: one line or a buffer of bytes 返回：一行或者一段缓冲区。
rtype: string 返回类型： string
......
doc/fluid/api_cn/executor_cn.rst
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...@@ -267,6 +267,7 @@ scope_guard ...@@ -267,6 +267,7 @@ scope_guard
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
import numpy import numpy
new_scope = fluid.Scope() new_scope = fluid.Scope()
......
doc/fluid/api_cn/fluid_cn.rst
浏览文件 @ fa79f486
...@@ -249,7 +249,7 @@ cpu_places ...@@ -249,7 +249,7 @@ cpu_places
创建 ``fluid.CPUPlace`` 对象列表。 创建 ``fluid.CPUPlace`` 对象列表。
如果 ``device_count`` 为None，则设备数目将由环境变量 ``CPU_NUM`` 确定。如果未设置 ``CPU_NUM`` ，则设备数目将由 ``multiprocessing.cpu_count()`` 确定。 如果 ``device_count`` 为None，则设备数目将由环境变量 ``CPU_NUM`` 确定。如果未设置 ``CPU_NUM`` ，则设备数目默认为1，也就是说， ``CPU_NUM`` =1。
参数： 参数：
- **device_count** (None|int) - 设备数目 - **device_count** (None|int) - 设备数目
...@@ -262,6 +262,7 @@ cpu_places ...@@ -262,6 +262,7 @@ cpu_places
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
cpu_places = fluid.cpu_places() cpu_places = fluid.cpu_places()
...@@ -279,6 +280,7 @@ CPUPlace是设备的描述符。它代表一个CPU，可以访问CPUPlace对应 ...@@ -279,6 +280,7 @@ CPUPlace是设备的描述符。它代表一个CPU，可以访问CPUPlace对应
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
cpu_place = fluid.CPUPlace() cpu_place = fluid.CPUPlace()
...@@ -397,6 +399,7 @@ cuda_pinned_places ...@@ -397,6 +399,7 @@ cuda_pinned_places
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
cuda_pinned_places_cpu_num = fluid.cuda_pinned_places() cuda_pinned_places_cpu_num = fluid.cuda_pinned_places()
# 或者 # 或者
cuda_pinned_places = fluid.cuda_pinned_places(1) cuda_pinned_places = fluid.cuda_pinned_places(1)
...@@ -428,6 +431,7 @@ cuda_places ...@@ -428,6 +431,7 @@ cuda_places
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
cuda_places = fluid.cuda_places() cuda_places = fluid.cuda_places()
.. _cn_api_fluid_CUDAPinnedPlace: .. _cn_api_fluid_CUDAPinnedPlace:
...@@ -443,6 +447,7 @@ CUDAPinnedPlace是一个设备描述符，它所指代的存储空间可以被GP ...@@ -443,6 +447,7 @@ CUDAPinnedPlace是一个设备描述符，它所指代的存储空间可以被GP
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
place = fluid.CUDAPinnedPlace() place = fluid.CUDAPinnedPlace()
.. _cn_api_fluid_CUDAPlace: .. _cn_api_fluid_CUDAPlace:
...@@ -458,6 +463,7 @@ CUDAPlace是一个设备描述符，它代表一个GPU，并且每个CUDAPlace ...@@ -458,6 +463,7 @@ CUDAPlace是一个设备描述符，它代表一个GPU，并且每个CUDAPlace
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
gpu_place = fluid.CUDAPlace(0) gpu_place = fluid.CUDAPlace(0)
...@@ -482,6 +488,7 @@ DataFeedDesc应由来自磁盘的有效protobuf消息初始化。 ...@@ -482,6 +488,7 @@ DataFeedDesc应由来自磁盘的有效protobuf消息初始化。
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
f = open("data.proto", "w") f = open("data.proto", "w")
print >> f, 'name: "MultiSlotDataFeed"' print >> f, 'name: "MultiSlotDataFeed"'
print >> f, 'batch_size: 2' print >> f, 'batch_size: 2'
...@@ -508,6 +515,7 @@ DataFeedDesc也可以在运行时更改。一旦你熟悉了每个字段的含 ...@@ -508,6 +515,7 @@ DataFeedDesc也可以在运行时更改。一旦你熟悉了每个字段的含
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
data_feed = fluid.DataFeedDesc('data.proto') data_feed = fluid.DataFeedDesc('data.proto')
data_feed.set_batch_size(128) data_feed.set_batch_size(128)
data_feed.set_dense_slots('wd') # 名为'wd'的slot将被设置为密集的 data_feed.set_dense_slots('wd') # 名为'wd'的slot将被设置为密集的
...@@ -534,6 +542,7 @@ DataFeedDesc也可以在运行时更改。一旦你熟悉了每个字段的含 ...@@ -534,6 +542,7 @@ DataFeedDesc也可以在运行时更改。一旦你熟悉了每个字段的含
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
f = open("data.proto", "w") f = open("data.proto", "w")
print >> f, 'name: "MultiSlotDataFeed"' print >> f, 'name: "MultiSlotDataFeed"'
print >> f, 'batch_size: 2' print >> f, 'batch_size: 2'
...@@ -569,6 +578,7 @@ DataFeedDesc也可以在运行时更改。一旦你熟悉了每个字段的含 ...@@ -569,6 +578,7 @@ DataFeedDesc也可以在运行时更改。一旦你熟悉了每个字段的含
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
f = open("data.proto", "w") f = open("data.proto", "w")
print >> f, 'name: "MultiSlotDataFeed"' print >> f, 'name: "MultiSlotDataFeed"'
print >> f, 'batch_size: 2' print >> f, 'batch_size: 2'
...@@ -606,6 +616,7 @@ DataFeedDesc也可以在运行时更改。一旦你熟悉了每个字段的含 ...@@ -606,6 +616,7 @@ DataFeedDesc也可以在运行时更改。一旦你熟悉了每个字段的含
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
f = open("data.proto", "w") f = open("data.proto", "w")
print >> f, 'name: "MultiSlotDataFeed"' print >> f, 'name: "MultiSlotDataFeed"'
print >> f, 'batch_size: 2' print >> f, 'batch_size: 2'
...@@ -642,6 +653,7 @@ DataFeedDesc也可以在运行时更改。一旦你熟悉了每个字段的含 ...@@ -642,6 +653,7 @@ DataFeedDesc也可以在运行时更改。一旦你熟悉了每个字段的含
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
f = open("data.proto", "w") f = open("data.proto", "w")
print >> f, 'name: "MultiSlotDataFeed"' print >> f, 'name: "MultiSlotDataFeed"'
print >> f, 'batch_size: 2' print >> f, 'batch_size: 2'
...@@ -993,6 +1005,7 @@ DistributeTranspiler ...@@ -993,6 +1005,7 @@ DistributeTranspiler
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
x = fluid.layers.data(name='x', shape=[13], dtype='float32') x = fluid.layers.data(name='x', shape=[13], dtype='float32')
y = fluid.layers.data(name='y', shape=[1], dtype='float32') y = fluid.layers.data(name='y', shape=[1], dtype='float32')
y_predict = fluid.layers.fc(input=x, size=1, act=None) y_predict = fluid.layers.fc(input=x, size=1, act=None)
...@@ -1053,6 +1066,7 @@ DistributeTranspiler ...@@ -1053,6 +1066,7 @@ DistributeTranspiler
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
transpiler = fluid.DistributeTranspiler() transpiler = fluid.DistributeTranspiler()
t.transpile( t.transpile(
trainer_id=0, trainer_id=0,
...@@ -1162,6 +1176,7 @@ DistributeTranspiler ...@@ -1162,6 +1176,7 @@ DistributeTranspiler
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
pserver_endpoints = "192.168.0.1:6174,192.168.0.2:6174" pserver_endpoints = "192.168.0.1:6174,192.168.0.2:6174"
trainer_endpoints = "192.168.0.1:6174,192.168.0.2:6174" trainer_endpoints = "192.168.0.1:6174,192.168.0.2:6174"
current_endpoint = "192.168.0.1:6174" current_endpoint = "192.168.0.1:6174"
...@@ -1207,6 +1222,7 @@ block中分割(split)出的元素个数的最小值。 ...@@ -1207,6 +1222,7 @@ block中分割(split)出的元素个数的最小值。
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
config = fluid.DistributeTranspilerConfig() config = fluid.DistributeTranspilerConfig()
config.slice_var_up = True config.slice_var_up = True
...@@ -1226,6 +1242,7 @@ ExecutionStrategy ...@@ -1226,6 +1242,7 @@ ExecutionStrategy
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
x = fluid.layers.data(name='x', shape=[13], dtype='float32') x = fluid.layers.data(name='x', shape=[13], dtype='float32')
y = fluid.layers.data(name='y', shape=[1], dtype='float32') y = fluid.layers.data(name='y', shape=[1], dtype='float32')
y_predict = fluid.layers.fc(input=x, size=1, act=None) y_predict = fluid.layers.fc(input=x, size=1, act=None)
...@@ -1578,6 +1595,7 @@ in_dygraph_mode ...@@ -1578,6 +1595,7 @@ in_dygraph_mode
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
if fluid.in_dygraph_mode(): if fluid.in_dygraph_mode():
pass pass
...@@ -1875,6 +1893,7 @@ name_scope ...@@ -1875,6 +1893,7 @@ name_scope
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
with fluid.name_scope("s1"): with fluid.name_scope("s1"):
a = fluid.layers.data(name='data', shape=[1], dtype='int32') a = fluid.layers.data(name='data', shape=[1], dtype='int32')
b = a + 1 b = a + 1
...@@ -2043,6 +2062,7 @@ ParallelExecutor ...@@ -2043,6 +2062,7 @@ ParallelExecutor
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
pe = fluid.ParallelExecutor(use_cuda=use_cuda, pe = fluid.ParallelExecutor(use_cuda=use_cuda,
loss_name=avg_cost.name, loss_name=avg_cost.name,
main_program=fluid.default_main_program()) main_program=fluid.default_main_program())
...@@ -2211,6 +2231,7 @@ Program ...@@ -2211,6 +2231,7 @@ Program
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
test_program = fluid.default_main_program().clone(for_test=True) test_program = fluid.default_main_program().clone(for_test=True)
optimizer = fluid.optimizer.Momentum(learning_rate=0.01, momentum=0.9) optimizer = fluid.optimizer.Momentum(learning_rate=0.01, momentum=0.9)
optimizer.minimize() optimizer.minimize()
...@@ -2538,6 +2559,7 @@ scope_guard ...@@ -2538,6 +2559,7 @@ scope_guard
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
import numpy import numpy
new_scope = fluid.Scope() new_scope = fluid.Scope()
......
doc/fluid/api_cn/initializer_cn.rst
浏览文件 @ fa79f486
...@@ -26,6 +26,7 @@ BilinearInitializer ...@@ -26,6 +26,7 @@ BilinearInitializer
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
factor = 2 factor = 2
C = 2 C = 2
w_attr = fluid.initializer.ParamAttr( w_attr = fluid.initializer.ParamAttr(
...@@ -77,6 +78,7 @@ ConstantInitializer ...@@ -77,6 +78,7 @@ ConstantInitializer
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
x = fluid.layers.data(name="data", shape=[32, 32], dtype="float32") x = fluid.layers.data(name="data", shape=[32, 32], dtype="float32")
fc = fluid.layers.fc(input=x, size=10, fc = fluid.layers.fc(input=x, size=10,
param_attr=fluid.initializer.Constant(value=2.0)) param_attr=fluid.initializer.Constant(value=2.0))
...@@ -104,6 +106,7 @@ force_init_on_cpu ...@@ -104,6 +106,7 @@ force_init_on_cpu
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
if fluid.initializer.force_init_on_cpu(): if fluid.initializer.force_init_on_cpu():
step = fluid.layers.create_global_var(shape=[2,3], value=1.0, dtype='float32') step = fluid.layers.create_global_var(shape=[2,3], value=1.0, dtype='float32')
...@@ -130,6 +133,7 @@ init_on_cpu ...@@ -130,6 +133,7 @@ init_on_cpu
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
with fluid.initializer.init_on_cpu(): with fluid.initializer.init_on_cpu():
step = fluid.layers.create_global_var(shape=[2,3], value=1.0, dtype='float32') step = fluid.layers.create_global_var(shape=[2,3], value=1.0, dtype='float32')
...@@ -183,6 +187,7 @@ MSRAInitializer ...@@ -183,6 +187,7 @@ MSRAInitializer
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
x = fluid.layers.data(name="data", shape=[32, 32], dtype="float32") x = fluid.layers.data(name="data", shape=[32, 32], dtype="float32")
fc = fluid.layers.fc(input=x, size=10, param_attr=fluid.initializer.MSRA(uniform=False)) fc = fluid.layers.fc(input=x, size=10, param_attr=fluid.initializer.MSRA(uniform=False))
...@@ -219,6 +224,7 @@ NormalInitializer ...@@ -219,6 +224,7 @@ NormalInitializer
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
x = fluid.layers.data(name="data", shape=[32, 32], dtype="float32") x = fluid.layers.data(name="data", shape=[32, 32], dtype="float32")
fc = fluid.layers.fc(input=x, size=10, fc = fluid.layers.fc(input=x, size=10,
param_attr=fluid.initializer.Normal(loc=0.0, scale=2.0) param_attr=fluid.initializer.Normal(loc=0.0, scale=2.0)
...@@ -240,6 +246,7 @@ NumpyArrayInitializer ...@@ -240,6 +246,7 @@ NumpyArrayInitializer
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
x = fluid.layers.data(name="x", shape=[5], dtype='float32') x = fluid.layers.data(name="x", shape=[5], dtype='float32')
fc = fluid.layers.fc(input=x, size=10, fc = fluid.layers.fc(input=x, size=10,
param_attr=fluid.initializer.NumpyArrayInitializer(numpy.array([1,2]))) param_attr=fluid.initializer.NumpyArrayInitializer(numpy.array([1,2])))
......
doc/fluid/api_cn/io_cn.rst
浏览文件 @ fa79f486
...@@ -96,6 +96,7 @@ load_params ...@@ -96,6 +96,7 @@ load_params
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
exe = fluid.Executor(fluid.CPUPlace()) exe = fluid.Executor(fluid.CPUPlace())
param_path = "./my_paddle_model" param_path = "./my_paddle_model"
prog = fluid.default_main_program() prog = fluid.default_main_program()
...@@ -131,6 +132,7 @@ load_persistables ...@@ -131,6 +132,7 @@ load_persistables
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
exe = fluid.Executor(fluid.CPUPlace()) exe = fluid.Executor(fluid.CPUPlace())
param_path = "./my_paddle_model" param_path = "./my_paddle_model"
prog = fluid.default_main_program() prog = fluid.default_main_program()
...@@ -237,6 +239,7 @@ PyReader ...@@ -237,6 +239,7 @@ PyReader
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
EPOCH_NUM = 3 EPOCH_NUM = 3
ITER_NUM = 5 ITER_NUM = 5
BATCH_SIZE = 3 BATCH_SIZE = 3
...@@ -278,6 +281,7 @@ PyReader ...@@ -278,6 +281,7 @@ PyReader
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
EPOCH_NUM = 3 EPOCH_NUM = 3
ITER_NUM = 5 ITER_NUM = 5
BATCH_SIZE = 10 BATCH_SIZE = 10
...@@ -346,6 +350,7 @@ PyReader ...@@ -346,6 +350,7 @@ PyReader
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
BATCH_SIZE = 10 BATCH_SIZE = 10
def generator(): def generator():
...@@ -376,6 +381,7 @@ PyReader ...@@ -376,6 +381,7 @@ PyReader
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
BATCH_SIZE = 10 BATCH_SIZE = 10
def generator(): def generator():
...@@ -418,6 +424,7 @@ PyReader ...@@ -418,6 +424,7 @@ PyReader
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
EPOCH_NUM = 3 EPOCH_NUM = 3
ITER_NUM = 15 ITER_NUM = 15
BATCH_SIZE = 3 BATCH_SIZE = 3
...@@ -464,6 +471,7 @@ PyReader ...@@ -464,6 +471,7 @@ PyReader
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
EPOCH_NUM = 3 EPOCH_NUM = 3
ITER_NUM = 15 ITER_NUM = 15
BATCH_SIZE = 3 BATCH_SIZE = 3
...@@ -510,6 +518,7 @@ PyReader ...@@ -510,6 +518,7 @@ PyReader
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
EPOCH_NUM = 3 EPOCH_NUM = 3
ITER_NUM = 15 ITER_NUM = 15
BATCH_SIZE = 3 BATCH_SIZE = 3
...@@ -636,6 +645,7 @@ save_params ...@@ -636,6 +645,7 @@ save_params
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
exe = fluid.Executor(fluid.CPUPlace()) exe = fluid.Executor(fluid.CPUPlace())
param_path = "./my_paddle_model" param_path = "./my_paddle_model"
prog = fluid.default_main_program() prog = fluid.default_main_program()
......
doc/fluid/api_cn/layers_cn/control_flow_cn.rst
浏览文件 @ fa79f486
...@@ -149,6 +149,7 @@ create_array ...@@ -149,6 +149,7 @@ create_array
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.create_array(dtype='float32') data = fluid.layers.create_array(dtype='float32')
...@@ -391,6 +392,7 @@ greater_equal ...@@ -391,6 +392,7 @@ greater_equal
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
out = fluid.layers.greater_equal(x=label, y=limit) out = fluid.layers.greater_equal(x=label, y=limit)
...@@ -417,6 +419,7 @@ greater_than ...@@ -417,6 +419,7 @@ greater_than
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
out = fluid.layers.greater_than(x=label, y=limit) out = fluid.layers.greater_than(x=label, y=limit)
...@@ -564,6 +567,7 @@ less_equal ...@@ -564,6 +567,7 @@ less_equal
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
out = fluid.layers.less_equal(x=label, y=limit) out = fluid.layers.less_equal(x=label, y=limit)
...@@ -594,6 +598,7 @@ less_than ...@@ -594,6 +598,7 @@ less_than
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
label = fluid.layers.data(name='y', shape=[1], dtype='int64') label = fluid.layers.data(name='y', shape=[1], dtype='int64')
limit = fluid.layers.fill_constant(shape=[1], dtype='int64', value=5) limit = fluid.layers.fill_constant(shape=[1], dtype='int64', value=5)
cond = fluid.layers.less_than(x=label, y=limit) cond = fluid.layers.less_than(x=label, y=limit)
...@@ -621,6 +626,7 @@ not_equal ...@@ -621,6 +626,7 @@ not_equal
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
out = fluid.layers.not_equal(x=label, y=limit) out = fluid.layers.not_equal(x=label, y=limit)
......
doc/fluid/api_cn/layers_cn/detection_cn.rst
浏览文件 @ fa79f486
...@@ -34,6 +34,7 @@ anchor_generator ...@@ -34,6 +34,7 @@ anchor_generator
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
conv1 = fluid.layers.data(name='conv1', shape=[48, 16, 16], dtype='float32') conv1 = fluid.layers.data(name='conv1', shape=[48, 16, 16], dtype='float32')
anchor, var = fluid.layers.anchor_generator( anchor, var = fluid.layers.anchor_generator(
input=conv1, input=conv1,
...@@ -86,6 +87,7 @@ bipartite_match ...@@ -86,6 +87,7 @@ bipartite_match
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
x = fluid.layers.data(name='x', shape=[4], dtype='float32') x = fluid.layers.data(name='x', shape=[4], dtype='float32')
y = fluid.layers.data(name='y', shape=[4], dtype='float32') y = fluid.layers.data(name='y', shape=[4], dtype='float32')
iou = fluid.layers.iou_similarity(x=x, y=y) iou = fluid.layers.iou_similarity(x=x, y=y)
...@@ -131,6 +133,7 @@ box_clip ...@@ -131,6 +133,7 @@ box_clip
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
boxes = fluid.layers.data( boxes = fluid.layers.data(
name='boxes', shape=[8, 4], dtype='float32', lod_level=1) name='boxes', shape=[8, 4], dtype='float32', lod_level=1)
im_info = fluid.layers.data(name='im_info', shape=[3]) im_info = fluid.layers.data(name='im_info', shape=[3])
...@@ -210,6 +213,7 @@ Bounding Box Coder ...@@ -210,6 +213,7 @@ Bounding Box Coder
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
prior_box = fluid.layers.data(name='prior_box', prior_box = fluid.layers.data(name='prior_box',
shape=[512, 4], shape=[512, 4],
dtype='float32', dtype='float32',
...@@ -278,6 +282,7 @@ box decode过程得出decode_box，然后分配方案如下所述： ...@@ -278,6 +282,7 @@ box decode过程得出decode_box，然后分配方案如下所述：
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
pb = fluid.layers.data( pb = fluid.layers.data(
name='prior_box', shape=[4], dtype='float32') name='prior_box', shape=[4], dtype='float32')
pbv = fluid.layers.data( pbv = fluid.layers.data(
...@@ -322,6 +327,7 @@ collect_fpn_proposals ...@@ -322,6 +327,7 @@ collect_fpn_proposals
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
multi_rois = [] multi_rois = []
multi_scores = [] multi_scores = []
for i in range(4): for i in range(4):
...@@ -402,6 +408,7 @@ density prior box的量由fixed_sizes and fixed_ratios决定。显然地，fixed ...@@ -402,6 +408,7 @@ density prior box的量由fixed_sizes and fixed_ratios决定。显然地，fixed
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
input = fluid.layers.data(name="input", shape=[3,6,9]) input = fluid.layers.data(name="input", shape=[3,6,9])
images = fluid.layers.data(name="images", shape=[3,9,12]) images = fluid.layers.data(name="images", shape=[3,9,12])
box, var = fluid.layers.density_prior_box( box, var = fluid.layers.density_prior_box(
...@@ -468,6 +475,8 @@ detection_map ...@@ -468,6 +475,8 @@ detection_map
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
from fluid.layers import detection
detect_res = fluid.layers.data( detect_res = fluid.layers.data(
name='detect_res', name='detect_res',
shape=[10, 6], shape=[10, 6],
...@@ -581,6 +590,7 @@ distribute_fpn_proposals ...@@ -581,6 +590,7 @@ distribute_fpn_proposals
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
fpn_rois = fluid.layers.data( fpn_rois = fluid.layers.data(
name='data', shape=[4], dtype='float32', lod_level=1) name='data', shape=[4], dtype='float32', lod_level=1)
multi_rois, restore_ind = fluid.layers.distribute_fpn_proposals( multi_rois, restore_ind = fluid.layers.distribute_fpn_proposals(
...@@ -962,6 +972,7 @@ multiclass_nms ...@@ -962,6 +972,7 @@ multiclass_nms
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
boxes = fluid.layers.data(name='bboxes', shape=[81, 4], boxes = fluid.layers.data(name='bboxes', shape=[81, 4],
dtype='float32', lod_level=1) dtype='float32', lod_level=1)
scores = fluid.layers.data(name='scores', shape=[81], scores = fluid.layers.data(name='scores', shape=[81],
...@@ -1047,6 +1058,7 @@ prior_box ...@@ -1047,6 +1058,7 @@ prior_box
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
input = fluid.layers.data(name="input", shape=[3,6,9]) input = fluid.layers.data(name="input", shape=[3,6,9])
images = fluid.layers.data(name="images", shape=[3,9,12]) images = fluid.layers.data(name="images", shape=[3,9,12])
box, var = fluid.layers.prior_box( box, var = fluid.layers.prior_box(
...@@ -1422,6 +1434,7 @@ ssd_loss ...@@ -1422,6 +1434,7 @@ ssd_loss
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
pb = fluid.layers.data( pb = fluid.layers.data(
name='prior_box', name='prior_box',
shape=[10, 4], shape=[10, 4],
...@@ -1669,6 +1682,7 @@ yolov3_loss ...@@ -1669,6 +1682,7 @@ yolov3_loss
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
x = fluid.layers.data(name='x', shape=[255, 13, 13], dtype='float32') x = fluid.layers.data(name='x', shape=[255, 13, 13], dtype='float32')
gt_box = fluid.layers.data(name='gtbox', shape=[6, 4], dtype='float32') gt_box = fluid.layers.data(name='gtbox', shape=[6, 4], dtype='float32')
gt_label = fluid.layers.data(name='gtlabel', shape=[6], dtype='int32') gt_label = fluid.layers.data(name='gtlabel', shape=[6], dtype='int32')
......
doc/fluid/api_cn/layers_cn/io_cn.rst
浏览文件 @ fa79f486
...@@ -24,6 +24,7 @@ batch ...@@ -24,6 +24,7 @@ batch
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
raw_reader = fluid.layers.io.open_files(filenames=['./data1.recordio', raw_reader = fluid.layers.io.open_files(filenames=['./data1.recordio',
'./data2.recordio'], './data2.recordio'],
shapes=[(3,224,224), (1,)], shapes=[(3,224,224), (1,)],
...@@ -153,6 +154,7 @@ data ...@@ -153,6 +154,7 @@ data
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.data(name='x', shape=[784], dtype='float32') data = fluid.layers.data(name='x', shape=[784], dtype='float32')
...@@ -299,6 +301,7 @@ reader变量中数据预处理块。 ...@@ -299,6 +301,7 @@ reader变量中数据预处理块。
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
reader = fluid.layers.io.open_files( reader = fluid.layers.io.open_files(
filenames=['./data1.recordio', './data2.recordio'], filenames=['./data1.recordio', './data2.recordio'],
shapes=[(3, 224, 224), (1, )], shapes=[(3, 224, 224), (1, )],
...@@ -331,7 +334,7 @@ py_reader ...@@ -331,7 +334,7 @@ py_reader
创建一个由在Python端提供数据的reader 创建一个由在Python端提供数据的reader
该layer返回一个Reader Variable。reader提供了 ``decorate_paddle_reader()`` 和 ``decorate_tensor_provider()`` 来设置Python generator作为数据源。更多细节请参考异步数据读取:ref:`user_guide_use_py_reader`，在c++端调用 ``Executor::Run()`` 时，来自generator的数据将被自动读取。与 ``DataFeeder.feed()`` 不同，数据读取进程和 ``Executor::Run()`` 进程可以使用 ``py_reader`` 并行运行。reader的 ``start()`` 方法应该在每次数据传递开始时调用，在传递结束和抛出 ``fluid.core.EOFException`` 后执行 ``reset()`` 方法。注意， ``Program.clone()`` 方法不能克隆 ``py_reader`` 。 该layer返回一个Reader Variable。reader提供了 ``decorate_paddle_reader()`` 和 ``decorate_tensor_provider()`` 来设置Python generator作为数据源。更多细节请参考 :ref:`user_guides_use_py_reader`，在c++端调用 ``Executor::Run()`` 时，来自generator的数据将被自动读取。与 ``DataFeeder.feed()`` 不同，数据读取进程和 ``Executor::Run()`` 进程可以使用 ``py_reader`` 并行运行。reader的 ``start()`` 方法应该在每次数据传递开始时调用，在传递结束和抛出 ``fluid.core.EOFException`` 后执行 ``reset()`` 方法。注意， ``Program.clone()`` 方法不能克隆 ``py_reader`` 。
参数: 参数:
- **capacity** (int) – ``py_reader`` 维护的缓冲区容量 - **capacity** (int) – ``py_reader`` 维护的缓冲区容量
...@@ -485,6 +488,7 @@ random_data_generator ...@@ -485,6 +488,7 @@ random_data_generator
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
reader = fluid.layers.random_data_generator( reader = fluid.layers.random_data_generator(
low=0.0, low=0.0,
high=1.0, high=1.0,
...@@ -563,6 +567,7 @@ shuffle ...@@ -563,6 +567,7 @@ shuffle
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
raw_reader = fluid.layers.io.open_files(filenames=['./data1.recordio', raw_reader = fluid.layers.io.open_files(filenames=['./data1.recordio',
'./data2.recordio'], './data2.recordio'],
shapes=[(3,224,224), (1,)], shapes=[(3,224,224), (1,)],
......
doc/fluid/api_cn/layers_cn/learning_rate_scheduler_cn.rst
浏览文件 @ fa79f486
...@@ -29,6 +29,7 @@ cosine_decay ...@@ -29,6 +29,7 @@ cosine_decay
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
base_lr = 0.1 base_lr = 0.1
lr = fluid.layers.cosine_decay( learning_rate = base_lr, step_each_epoch=10000, epochs=120) lr = fluid.layers.cosine_decay( learning_rate = base_lr, step_each_epoch=10000, epochs=120)
...@@ -156,6 +157,7 @@ linear_lr_warmup ...@@ -156,6 +157,7 @@ linear_lr_warmup
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
boundaries = [100, 200] boundaries = [100, 200]
lr_steps = [0.1, 0.01, 0.001] lr_steps = [0.1, 0.01, 0.001]
warmup_steps = 50 warmup_steps = 50
...@@ -225,6 +227,7 @@ Noam衰减方法。noam衰减的numpy实现如下。 ...@@ -225,6 +227,7 @@ Noam衰减方法。noam衰减的numpy实现如下。
.. code-block:: python .. code-block:: python
import padde.fluid as fluid
import numpy as np import numpy as np
# 设置超参数 # 设置超参数
d_model = 2 d_model = 2
......
doc/fluid/api_cn/layers_cn/nn_cn.rst
浏览文件 @ fa79f486
...@@ -60,6 +60,7 @@ pooling2d操作根据输入 ``input`` ， ``pool_size`` ， ``pool_type`` 参数 ...@@ -60,6 +60,7 @@ pooling2d操作根据输入 ``input`` ， ``pool_size`` ， ``pool_type`` 参数
# wend = ceil((i + 1) * W / n) # wend = ceil((i + 1) * W / n)
# output[:, :, i, j] = avg(input[:, :, hstart: hend, wstart: wend]) # output[:, :, i, j] = avg(input[:, :, hstart: hend, wstart: wend])
# #
import paddle.fluid as fluid
data = fluid.layers.data( data = fluid.layers.data(
name='data', shape=[3, 32, 32], dtype='float32') name='data', shape=[3, 32, 32], dtype='float32')
pool_out = fluid.layers.adaptive_pool2d( pool_out = fluid.layers.adaptive_pool2d(
...@@ -377,6 +378,7 @@ autoincreased_step_counter ...@@ -377,6 +378,7 @@ autoincreased_step_counter
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
global_step = fluid.layers.autoincreased_step_counter( global_step = fluid.layers.autoincreased_step_counter(
counter_name='@LR_DECAY_COUNTER@', begin=0, step=1) counter_name='@LR_DECAY_COUNTER@', begin=0, step=1)
...@@ -447,6 +449,7 @@ batch_norm ...@@ -447,6 +449,7 @@ batch_norm
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
x = fluid.layers.data(name='x', shape=[3, 7, 3, 7], dtype='float32', append_batch_size=False) x = fluid.layers.data(name='x', shape=[3, 7, 3, 7], dtype='float32', append_batch_size=False)
hidden1 = fluid.layers.fc(input=x, size=200, param_attr='fc1.w') hidden1 = fluid.layers.fc(input=x, size=200, param_attr='fc1.w')
hidden2 = fluid.layers.batch_norm(input=hidden1) hidden2 = fluid.layers.batch_norm(input=hidden1)
...@@ -621,6 +624,7 @@ bilinear_tensor_product ...@@ -621,6 +624,7 @@ bilinear_tensor_product
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
layer1 = fluid.layers.data("t1", shape=[-1, 5], dtype="float32") layer1 = fluid.layers.data("t1", shape=[-1, 5], dtype="float32")
layer2 = fluid.layers.data("t2", shape=[-1, 4], dtype="float32") layer2 = fluid.layers.data("t2", shape=[-1, 4], dtype="float32")
tensor = fluid.layers.bilinear_tensor_product(x=layer1, y=layer2, size=1000) tensor = fluid.layers.bilinear_tensor_product(x=layer1, y=layer2, size=1000)
...@@ -693,6 +697,7 @@ BRelu 激活函数 ...@@ -693,6 +697,7 @@ BRelu 激活函数
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
x = fluid.layers.data(name="x", shape=[2,3,16,16], dtype=”float32”) x = fluid.layers.data(name="x", shape=[2,3,16,16], dtype=”float32”)
y = fluid.layers.brelu(x, t_min=1.0, t_max=20.0) y = fluid.layers.brelu(x, t_min=1.0, t_max=20.0)
...@@ -876,6 +881,7 @@ ClipByNorm算子 ...@@ -876,6 +881,7 @@ ClipByNorm算子
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
input = fluid.layers.data( input = fluid.layers.data(
name='data', shape=[1], dtype='float32') name='data', shape=[1], dtype='float32')
reward = fluid.layers.clip_by_norm(x=input, max_norm=1.0) reward = fluid.layers.clip_by_norm(x=input, max_norm=1.0)
...@@ -912,6 +918,7 @@ continuous_value_model ...@@ -912,6 +918,7 @@ continuous_value_model
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
input = fluid.layers.data(name="input", shape=[-1, 1], lod_level=1, append_batch_size=False, dtype="int64")#, stop_gradient=False) input = fluid.layers.data(name="input", shape=[-1, 1], lod_level=1, append_batch_size=False, dtype="int64")#, stop_gradient=False)
label = fluid.layers.data(name="label", shape=[-1, 1], append_batch_size=False, dtype="int64") label = fluid.layers.data(name="label", shape=[-1, 1], append_batch_size=False, dtype="int64")
embed = fluid.layers.embedding( embed = fluid.layers.embedding(
...@@ -997,6 +1004,7 @@ conv2d ...@@ -997,6 +1004,7 @@ conv2d
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.data(name='data', shape=[3, 32, 32], dtype='float32') data = fluid.layers.data(name='data', shape=[3, 32, 32], dtype='float32')
conv2d = fluid.layers.conv2d(input=data, num_filters=2, filter_size=3, act="relu") conv2d = fluid.layers.conv2d(input=data, num_filters=2, filter_size=3, act="relu")
...@@ -1094,6 +1102,7 @@ conv2d_transpose ...@@ -1094,6 +1102,7 @@ conv2d_transpose
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.data(name='data', shape=[3, 32, 32], dtype='float32') data = fluid.layers.data(name='data', shape=[3, 32, 32], dtype='float32')
conv2d_transpose = fluid.layers.conv2d_transpose(input=data, num_filters=2, filter_size=3) conv2d_transpose = fluid.layers.conv2d_transpose(input=data, num_filters=2, filter_size=3)
...@@ -1174,6 +1183,7 @@ conv3d ...@@ -1174,6 +1183,7 @@ conv3d
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.data(name='data', shape=[3, 12, 32, 32], dtype='float32') data = fluid.layers.data(name='data', shape=[3, 12, 32, 32], dtype='float32')
conv3d = fluid.layers.conv3d(input=data, num_filters=2, filter_size=3, act="relu") conv3d = fluid.layers.conv3d(input=data, num_filters=2, filter_size=3, act="relu")
...@@ -1278,6 +1288,7 @@ conv3d_transpose ...@@ -1278,6 +1288,7 @@ conv3d_transpose
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.data(name='data', shape=[3, 12, 32, 32], dtype='float32') data = fluid.layers.data(name='data', shape=[3, 12, 32, 32], dtype='float32')
conv3d_transpose = fluid.layers.conv3d_transpose(input=data, num_filters=2, filter_size=3) conv3d_transpose = fluid.layers.conv3d_transpose(input=data, num_filters=2, filter_size=3)
...@@ -1319,6 +1330,7 @@ cos_sim ...@@ -1319,6 +1330,7 @@ cos_sim
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
x = fluid.layers.data(name='x', shape=[3, 7], dtype='float32', append_batch_size=False) x = fluid.layers.data(name='x', shape=[3, 7], dtype='float32', append_batch_size=False)
y = fluid.layers.data(name='y', shape=[1, 7], dtype='float32', append_batch_size=False) y = fluid.layers.data(name='y', shape=[1, 7], dtype='float32', append_batch_size=False)
out = fluid.layers.cos_sim(x, y) out = fluid.layers.cos_sim(x, y)
...@@ -1359,6 +1371,7 @@ crf_decoding ...@@ -1359,6 +1371,7 @@ crf_decoding
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
images = fluid.layers.data(name='pixel', shape=[784], dtype='float32') images = fluid.layers.data(name='pixel', shape=[784], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int32') label = fluid.layers.data(name='label', shape=[1], dtype='int32')
hidden = fluid.layers.fc(input=images, size=2) hidden = fluid.layers.fc(input=images, size=2)
...@@ -1506,6 +1519,7 @@ cross_entropy ...@@ -1506,6 +1519,7 @@ cross_entropy
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
classdim = 7 classdim = 7
x = fluid.layers.data(name='x', shape=[3, 7], dtype='float32', append_batch_size=False) x = fluid.layers.data(name='x', shape=[3, 7], dtype='float32', append_batch_size=False)
label = fluid.layers.data(name='label', shape=[3, 1], dtype='float32', append_batch_size=False) label = fluid.layers.data(name='label', shape=[3, 1], dtype='float32', append_batch_size=False)
...@@ -1705,6 +1719,7 @@ deformable_conv ...@@ -1705,6 +1719,7 @@ deformable_conv
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.data(name='data', shape=[3, 32, 32], dtype='float32') data = fluid.layers.data(name='data', shape=[3, 32, 32], dtype='float32')
offset = fluid.layers.data(name='offset', shape=[18, 32, 32], dtype='float32') offset = fluid.layers.data(name='offset', shape=[18, 32, 32], dtype='float32')
mask = fluid.layers.data(name='mask', shape=[9, 32, 32], dtype='float32') mask = fluid.layers.data(name='mask', shape=[9, 32, 32], dtype='float32')
...@@ -1747,6 +1762,7 @@ deformable_roi_pooling ...@@ -1747,6 +1762,7 @@ deformable_roi_pooling
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
input = fluid.layers.data(name="input", input = fluid.layers.data(name="input",
shape=[2, 192, 64, 64], shape=[2, 192, 64, 64],
dtype='float32', dtype='float32',
...@@ -1866,6 +1882,7 @@ dropout操作符可以从程序中移除，程序变得高效。 ...@@ -1866,6 +1882,7 @@ dropout操作符可以从程序中移除，程序变得高效。
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
x = fluid.layers.data(name="data", shape=[32, 32], dtype="float32") x = fluid.layers.data(name="data", shape=[32, 32], dtype="float32")
droped = fluid.layers.dropout(x, dropout_prob=0.5) droped = fluid.layers.dropout(x, dropout_prob=0.5)
...@@ -2051,6 +2068,7 @@ W 代表了权重矩阵(weight matrix)，例如 :math:`W_{xi}` 是从输入门 ...@@ -2051,6 +2068,7 @@ W 代表了权重矩阵(weight matrix)，例如 :math:`W_{xi}` 是从输入门
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
emb_dim = 256 emb_dim = 256
vocab_size = 10000 vocab_size = 10000
hidden_dim = 512 hidden_dim = 512
...@@ -2162,6 +2180,7 @@ LSTMP层(具有循环映射的LSTM)在LSTM层后有一个分离的映射层， ...@@ -2162,6 +2180,7 @@ LSTMP层(具有循环映射的LSTM)在LSTM层后有一个分离的映射层，
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
dict_dim, emb_dim = 128, 64 dict_dim, emb_dim = 128, 64
data = fluid.layers.data(name='sequence', shape=[1], data = fluid.layers.data(name='sequence', shape=[1],
dtype='int32', lod_level=1) dtype='int32', lod_level=1)
...@@ -2929,31 +2948,37 @@ elementwise_pow ...@@ -2929,31 +2948,37 @@ elementwise_pow
.. code-block:: python .. code-block:: python
# 例1: shape(x) = (2, 3, 4, 5), shape(y) = (2, 3, 4, 5) # 例1: shape(x) = (2, 3, 4, 5), shape(y) = (2, 3, 4, 5)
import paddle.fluid as fluid
x0 = fluid.layers.data(name="x0", shape=[2, 3, 4, 5], dtype='float32') x0 = fluid.layers.data(name="x0", shape=[2, 3, 4, 5], dtype='float32')
y0 = fluid.layers.data(name="y0", shape=[2, 3, 4, 5], dtype='float32') y0 = fluid.layers.data(name="y0", shape=[2, 3, 4, 5], dtype='float32')
z0 = fluid.layers.elementwise_pow(x0, y0) z0 = fluid.layers.elementwise_pow(x0, y0)
# 例2: shape(X) = (2, 3, 4, 5), shape(Y) = (5) # 例2: shape(X) = (2, 3, 4, 5), shape(Y) = (5)
import paddle.fluid as fluid
x1 = fluid.layers.data(name="x1", shape=[2, 3, 4, 5], dtype='float32') x1 = fluid.layers.data(name="x1", shape=[2, 3, 4, 5], dtype='float32')
y1 = fluid.layers.data(name="y1", shape=[5], dtype='float32') y1 = fluid.layers.data(name="y1", shape=[5], dtype='float32')
z1 = fluid.layers.elementwise_pow(x1, y1) z1 = fluid.layers.elementwise_pow(x1, y1)
# 例3: shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5), with axis=-1(default) or axis=2 # 例3: shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5), with axis=-1(default) or axis=2
import paddle.fluid as fluid
x2 = fluid.layers.data(name="x2", shape=[2, 3, 4, 5], dtype='float32') x2 = fluid.layers.data(name="x2", shape=[2, 3, 4, 5], dtype='float32')
y2 = fluid.layers.data(name="y2", shape=[4, 5], dtype='float32') y2 = fluid.layers.data(name="y2", shape=[4, 5], dtype='float32')
z2 = fluid.layers.elementwise_pow(x2, y2, axis=2) z2 = fluid.layers.elementwise_pow(x2, y2, axis=2)
# 例4: shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1 # 例4: shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
import paddle.fluid as fluid
x3 = fluid.layers.data(name="x3", shape=[2, 3, 4, 5], dtype='float32') x3 = fluid.layers.data(name="x3", shape=[2, 3, 4, 5], dtype='float32')
y3 = fluid.layers.data(name="y3", shape=[3, 4], dtype='float32') y3 = fluid.layers.data(name="y3", shape=[3, 4], dtype='float32')
z3 = fluid.layers.elementwise_pow(x3, y3, axis=1) z3 = fluid.layers.elementwise_pow(x3, y3, axis=1)
# 例5: shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0 # 例5: shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
import paddle.fluid as fluid
x4 = fluid.layers.data(name="x4", shape=[2, 3, 4, 5], dtype='float32') x4 = fluid.layers.data(name="x4", shape=[2, 3, 4, 5], dtype='float32')
y4 = fluid.layers.data(name="y4", shape=[2], dtype='float32') y4 = fluid.layers.data(name="y4", shape=[2], dtype='float32')
z4 = fluid.layers.elementwise_pow(x4, y4, axis=0) z4 = fluid.layers.elementwise_pow(x4, y4, axis=0)
# 例6: shape(X) = (2, 3, 4, 5), shape(Y) = (2, 1), with axis=0 # 例6: shape(X) = (2, 3, 4, 5), shape(Y) = (2, 1), with axis=0
import paddle.fluid as fluid
x5 = fluid.layers.data(name="x5", shape=[2, 3, 4, 5], dtype='float32') x5 = fluid.layers.data(name="x5", shape=[2, 3, 4, 5], dtype='float32')
y5 = fluid.layers.data(name="y5", shape=[2], dtype='float32') y5 = fluid.layers.data(name="y5", shape=[2], dtype='float32')
z5 = fluid.layers.elementwise_pow(x5, y5, axis=0) z5 = fluid.layers.elementwise_pow(x5, y5, axis=0)
...@@ -3079,6 +3104,7 @@ ELU激活层（ELU Activation Operator） ...@@ -3079,6 +3104,7 @@ ELU激活层（ELU Activation Operator）
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
x = fluid.layers.data(name="x", shape=[3,10,32,32], dtype="float32") x = fluid.layers.data(name="x", shape=[3,10,32,32], dtype="float32")
y = fluid.layers.elu(x, alpha=0.2) y = fluid.layers.elu(x, alpha=0.2)
...@@ -3168,6 +3194,7 @@ expand运算会按给定的次数对输入各维度进行复制（tile）运算 ...@@ -3168,6 +3194,7 @@ expand运算会按给定的次数对输入各维度进行复制（tile）运算
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
x = fluid.layers.data(name='x', shape=[10], dtype='float32') x = fluid.layers.data(name='x', shape=[10], dtype='float32')
out = fluid.layers.expand(x=x, expand_times=[1, 2, 2]) out = fluid.layers.expand(x=x, expand_times=[1, 2, 2])
...@@ -3253,6 +3280,7 @@ fc ...@@ -3253,6 +3280,7 @@ fc
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
# 当输入为单个张量时 # 当输入为单个张量时
data = fluid.layers.data(name="data", shape=[32, 32], dtype="float32") data = fluid.layers.data(name="data", shape=[32, 32], dtype="float32")
...@@ -3323,6 +3351,7 @@ flatten ...@@ -3323,6 +3351,7 @@ flatten
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
x = fluid.layers.data(name="x", shape=[4, 4, 3], dtype="float32") x = fluid.layers.data(name="x", shape=[4, 4, 3], dtype="float32")
out = fluid.layers.flatten(x=x, axis=2) out = fluid.layers.flatten(x=x, axis=2)
...@@ -3411,6 +3440,7 @@ gather ...@@ -3411,6 +3440,7 @@ gather
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
x = fluid.layers.data(name='x', shape=[-1, 5], dtype='float32') x = fluid.layers.data(name='x', shape=[-1, 5], dtype='float32')
index = fluid.layers.data(name='index', shape=[-1, 1], dtype='int32') index = fluid.layers.data(name='index', shape=[-1, 1], dtype='int32')
output = fluid.layers.gather(x, index) output = fluid.layers.gather(x, index)
...@@ -3449,6 +3479,7 @@ gaussian_random算子。 ...@@ -3449,6 +3479,7 @@ gaussian_random算子。
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
import paddle.fluid.layers as layers import paddle.fluid.layers as layers
out = fluid.layers.gaussian_random(shape=[20, 30]) out = fluid.layers.gaussian_random(shape=[20, 30])
...@@ -3488,6 +3519,7 @@ gaussian_random_batch_size_like ...@@ -3488,6 +3519,7 @@ gaussian_random_batch_size_like
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
input = fluid.layers.data(name="input", shape=[13, 11], dtype='float32') input = fluid.layers.data(name="input", shape=[13, 11], dtype='float32')
out = fluid.layers.gaussian_random_batch_size_like( out = fluid.layers.gaussian_random_batch_size_like(
...@@ -3636,6 +3668,7 @@ group_norm ...@@ -3636,6 +3668,7 @@ group_norm
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.data(name='data', shape=[8, 32, 32], data = fluid.layers.data(name='data', shape=[8, 32, 32],
dtype='float32') dtype='float32')
x = fluid.layers.group_norm(input=data, groups=4) x = fluid.layers.group_norm(input=data, groups=4)
...@@ -4175,6 +4208,7 @@ https://en.wikipedia.org/wiki/Bilinear_interpolation。 ...@@ -4175,6 +4208,7 @@ https://en.wikipedia.org/wiki/Bilinear_interpolation。
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
input = fluid.layers.data(name="input", shape=[3,6,9], dtype="float32") input = fluid.layers.data(name="input", shape=[3,6,9], dtype="float32")
out = fluid.layers.image_resize(input, out_shape=[12, 12], resample="NEAREST") out = fluid.layers.image_resize(input, out_shape=[12, 12], resample="NEAREST")
...@@ -4211,6 +4245,7 @@ image_resize_short ...@@ -4211,6 +4245,7 @@ image_resize_short
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
input = fluid.layers.data(name="input", shape=[3,6,9], dtype="float32") input = fluid.layers.data(name="input", shape=[3,6,9], dtype="float32")
out = fluid.layers.image_resize_short(input, out_short_len=3) out = fluid.layers.image_resize_short(input, out_short_len=3)
...@@ -4253,6 +4288,7 @@ kL发散损失计算如下： ...@@ -4253,6 +4288,7 @@ kL发散损失计算如下：
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
x = fluid.layers.data(name='x', shape=[4,2,2], dtype='float32') x = fluid.layers.data(name='x', shape=[4,2,2], dtype='float32')
target = fluid.layers.data(name='target', shape=[4,2,2], dtype='float32') target = fluid.layers.data(name='target', shape=[4,2,2], dtype='float32')
loss = fluid.layers.kldiv_loss(x=x, target=target, reduction='batchmean') loss = fluid.layers.kldiv_loss(x=x, target=target, reduction='batchmean')
...@@ -4294,6 +4330,7 @@ L2正则（L2 normalize Layer） ...@@ -4294,6 +4330,7 @@ L2正则（L2 normalize Layer）
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.data(name="data", data = fluid.layers.data(name="data",
shape=(3, 17, 13), shape=(3, 17, 13),
dtype="float32") dtype="float32")
...@@ -4343,6 +4380,7 @@ label_smooth ...@@ -4343,6 +4380,7 @@ label_smooth
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
import paddle.fluid.layers as layers import paddle.fluid.layers as layers
label = fluid.layers.data(name="label", shape=[1], dtype="float32") label = fluid.layers.data(name="label", shape=[1], dtype="float32")
...@@ -4401,6 +4439,7 @@ layer_norm ...@@ -4401,6 +4439,7 @@ layer_norm
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.data(name='data', shape=[3, 32, 32], data = fluid.layers.data(name='data', shape=[3, 32, 32],
dtype='float32') dtype='float32')
x = fluid.layers.layer_norm(input=data, begin_norm_axis=1) x = fluid.layers.layer_norm(input=data, begin_norm_axis=1)
...@@ -4433,6 +4472,7 @@ LeakyRelu 激活函数 ...@@ -4433,6 +4472,7 @@ LeakyRelu 激活函数
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
x = fluid.layers.data(name="x", shape=[2,3,16,16], dtype="float32") x = fluid.layers.data(name="x", shape=[2,3,16,16], dtype="float32")
y = fluid.layers.leaky_relu(x, alpha=0.01) y = fluid.layers.leaky_relu(x, alpha=0.01)
...@@ -4605,6 +4645,7 @@ lod_reset ...@@ -4605,6 +4645,7 @@ lod_reset
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
x = fluid.layers.data(name='x', shape=[10]) x = fluid.layers.data(name='x', shape=[10])
y = fluid.layers.data(name='y', shape=[10, 20], lod_level=2) y = fluid.layers.data(name='y', shape=[10, 20], lod_level=2)
out = fluid.layers.lod_reset(x=x, y=y) out = fluid.layers.lod_reset(x=x, y=y)
...@@ -4644,6 +4685,7 @@ log ...@@ -4644,6 +4685,7 @@ log
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
x = fluid.layers.data(name="x", shape=[3, 4], dtype="float32") x = fluid.layers.data(name="x", shape=[3, 4], dtype="float32")
output = fluid.layers.log(x) output = fluid.layers.log(x)
...@@ -4688,6 +4730,7 @@ log_loss ...@@ -4688,6 +4730,7 @@ log_loss
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
label = fluid.layers.data(name='label', shape=[1], dtype='int64') label = fluid.layers.data(name='label', shape=[1], dtype='int64')
prob = fluid.layers.data(name='prob', shape=[10], dtype='float32') prob = fluid.layers.data(name='prob', shape=[10], dtype='float32')
cost = fluid.layers.log_loss(input=prob, label=label) cost = fluid.layers.log_loss(input=prob, label=label)
...@@ -4731,6 +4774,7 @@ logical_and算子 ...@@ -4731,6 +4774,7 @@ logical_and算子
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
left = fluid.layers.data( left = fluid.layers.data(
name='left', shape=[1], dtype='int32') name='left', shape=[1], dtype='int32')
right = fluid.layers.data( right = fluid.layers.data(
...@@ -4773,6 +4817,7 @@ logical_not算子 ...@@ -4773,6 +4817,7 @@ logical_not算子
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
left = fluid.layers.data( left = fluid.layers.data(
name='left', shape=[1], dtype='int32') name='left', shape=[1], dtype='int32')
result = fluid.layers.logical_not(x=left) result = fluid.layers.logical_not(x=left)
...@@ -4814,6 +4859,7 @@ logical_or算子 ...@@ -4814,6 +4859,7 @@ logical_or算子
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
left = fluid.layers.data( left = fluid.layers.data(
name='left', shape=[1], dtype='int32') name='left', shape=[1], dtype='int32')
right = fluid.layers.data( right = fluid.layers.data(
...@@ -4855,6 +4901,7 @@ logical_xor算子 ...@@ -4855,6 +4901,7 @@ logical_xor算子
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
left = fluid.layers.data( left = fluid.layers.data(
name='left', shape=[1], dtype='int32') name='left', shape=[1], dtype='int32')
right = fluid.layers.data( right = fluid.layers.data(
...@@ -4908,6 +4955,7 @@ lrn ...@@ -4908,6 +4955,7 @@ lrn
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.data( data = fluid.layers.data(
name="data", shape=[3, 112, 112], dtype="float32") name="data", shape=[3, 112, 112], dtype="float32")
lrn = fluid.layers.lrn(input=data) lrn = fluid.layers.lrn(input=data)
...@@ -4980,6 +5028,7 @@ sigmoid的计算公式为： :math:`sigmoid(x) = 1 / (1 + e^{-x})` 。 ...@@ -4980,6 +5028,7 @@ sigmoid的计算公式为： :math:`sigmoid(x) = 1 / (1 + e^{-x})` 。
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
emb_dim = 256 emb_dim = 256
vocab_size = 10000 vocab_size = 10000
data = fluid.layers.data(name='x', shape=[-1, 100, 1], data = fluid.layers.data(name='x', shape=[-1, 100, 1],
...@@ -5115,6 +5164,7 @@ margin rank loss（差距排序损失）层。在排序问题中，它可以比 ...@@ -5115,6 +5164,7 @@ margin rank loss（差距排序损失）层。在排序问题中，它可以比
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
label = fluid.layers.data(name="label", shape=[-1, 1], dtype="float32") label = fluid.layers.data(name="label", shape=[-1, 1], dtype="float32")
left = fluid.layers.data(name="left", shape=[-1, 1], dtype="float32") left = fluid.layers.data(name="left", shape=[-1, 1], dtype="float32")
right = fluid.layers.data(name="right", shape=[-1, 1], dtype="float32") right = fluid.layers.data(name="right", shape=[-1, 1], dtype="float32")
...@@ -5191,6 +5241,7 @@ matmul ...@@ -5191,6 +5241,7 @@ matmul
# x: [M], y: [N] # x: [M], y: [N]
# fluid.layers.matmul(x, y, True, True) # out: [M, N] # fluid.layers.matmul(x, y, True, True) # out: [M, N]
import paddle.fluid as fluid
x = fluid.layers.data(name='x', shape=[2, 3], dtype='float32') x = fluid.layers.data(name='x', shape=[2, 3], dtype='float32')
y = fluid.layers.data(name='y', shape=[3, 2], dtype='float32') y = fluid.layers.data(name='y', shape=[3, 2], dtype='float32')
out = fluid.layers.matmul(x, y, True, True) out = fluid.layers.matmul(x, y, True, True)
...@@ -5237,6 +5288,7 @@ maxout ...@@ -5237,6 +5288,7 @@ maxout
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
input = fluid.layers.data( input = fluid.layers.data(
name='data', name='data',
shape=[256, 32, 32], shape=[256, 32, 32],
...@@ -5273,6 +5325,7 @@ mean算子计算X中所有元素的平均值 ...@@ -5273,6 +5325,7 @@ mean算子计算X中所有元素的平均值
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
input = fluid.layers.data( input = fluid.layers.data(
name='data', shape=[2, 3], dtype='float32') name='data', shape=[2, 3], dtype='float32')
mean = fluid.layers.mean(input) mean = fluid.layers.mean(input)
...@@ -5370,6 +5423,7 @@ merge_selected_rows ...@@ -5370,6 +5423,7 @@ merge_selected_rows
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
b = fluid.default_main_program().global_block() b = fluid.default_main_program().global_block()
var = b.create_var( var = b.create_var(
name="X", dtype="float32", persistable=True, name="X", dtype="float32", persistable=True,
...@@ -5555,6 +5609,7 @@ nce ...@@ -5555,6 +5609,7 @@ nce
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
import numpy as np import numpy as np
window_size = 5 window_size = 5
...@@ -5619,6 +5674,7 @@ NPair损失需要成对的数据。NPair损失分为两部分：第一部分是 ...@@ -5619,6 +5674,7 @@ NPair损失需要成对的数据。NPair损失分为两部分：第一部分是
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
anchor = fluid.layers.data( anchor = fluid.layers.data(
name = 'anchor', shape = [18, 6], dtype = 'float32', append_batch_size=False) name = 'anchor', shape = [18, 6], dtype = 'float32', append_batch_size=False)
positive = fluid.layers.data( positive = fluid.layers.data(
...@@ -5654,6 +5710,7 @@ one_hot ...@@ -5654,6 +5710,7 @@ one_hot
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
label = fluid.layers.data(name="label", shape=[1], dtype="int64") label = fluid.layers.data(name="label", shape=[1], dtype="int64")
one_hot_label = fluid.layers.one_hot(input=label, depth=10) one_hot_label = fluid.layers.one_hot(input=label, depth=10)
...@@ -5886,6 +5943,7 @@ pixel shuffle 层（像素重组层） ...@@ -5886,6 +5943,7 @@ pixel shuffle 层（像素重组层）
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
input = fluid.layers.data(name="input", shape=[9,4,4]) input = fluid.layers.data(name="input", shape=[9,4,4])
output = fluid.layers.pixel_shuffle(x=input, upscale_factor=3) output = fluid.layers.pixel_shuffle(x=input, upscale_factor=3)
...@@ -5975,6 +6033,7 @@ pooling2d操作符根据 ``input`` ， 池化类型 ``pool_type`` ， 池化核 ...@@ -5975,6 +6033,7 @@ pooling2d操作符根据 ``input`` ， 池化类型 ``pool_type`` ， 池化核
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.data( data = fluid.layers.data(
name='data', shape=[3, 32, 32], dtype='float32') name='data', shape=[3, 32, 32], dtype='float32')
pool2d = fluid.layers.pool2d( pool2d = fluid.layers.pool2d(
...@@ -6071,6 +6130,7 @@ pooling3d操作根据input，pool_type，pool_size，strides和paddings参数计 ...@@ -6071,6 +6130,7 @@ pooling3d操作根据input，pool_type，pool_size，strides和paddings参数计
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.data( data = fluid.layers.data(
name='data', shape=[3, 32, 32, 32], dtype='float32') name='data', shape=[3, 32, 32, 32], dtype='float32')
pool3d = fluid.layers.pool3d( pool3d = fluid.layers.pool3d(
...@@ -6306,6 +6366,7 @@ random_crop ...@@ -6306,6 +6366,7 @@ random_crop
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
img = fluid.layers.data("img", [3, 256, 256]) img = fluid.layers.data("img", [3, 256, 256])
cropped_img = fluid.layers.random_crop(img, shape=[3, 224, 224]) cropped_img = fluid.layers.random_crop(img, shape=[3, 224, 224])
...@@ -6335,6 +6396,7 @@ rank ...@@ -6335,6 +6396,7 @@ rank
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
input = layers.data( input = layers.data(
name="input", shape=[3, 100, 100], dtype="float32") name="input", shape=[3, 100, 100], dtype="float32")
rank = layers.rank(input) # 4 rank = layers.rank(input) # 4
...@@ -6379,6 +6441,7 @@ P 的取值可为： {0, 1} 或 {0, 0.5, 1}, 其中，0.5表示输入的两文 ...@@ -6379,6 +6441,7 @@ P 的取值可为： {0, 1} 或 {0, 0.5, 1}, 其中，0.5表示输入的两文
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
label = fluid.layers.data(name="label", shape=[-1, 1], dtype="float32") label = fluid.layers.data(name="label", shape=[-1, 1], dtype="float32")
left = fluid.layers.data(name="left", shape=[-1, 1], dtype="float32") left = fluid.layers.data(name="left", shape=[-1, 1], dtype="float32")
right = fluid.layers.data(name="right", shape=[-1, 1], dtype="float32") right = fluid.layers.data(name="right", shape=[-1, 1], dtype="float32")
...@@ -6414,6 +6477,9 @@ reduce_all ...@@ -6414,6 +6477,9 @@ reduce_all
# [[True, False] # [[True, False]
# [True, True]] # [True, True]]
# 接下来的示例中，我们在每处函数调用后面都标注出了它的结果张量。 # 接下来的示例中，我们在每处函数调用后面都标注出了它的结果张量。
import paddle.fluid as fluid
import paddle.fluid.layers as layers
import numpy as np
fluid.layers.reduce_all(x) # False fluid.layers.reduce_all(x) # False
fluid.layers.reduce_all(x, dim=0) # [True, False] fluid.layers.reduce_all(x, dim=0) # [True, False]
fluid.layers.reduce_all(x, dim=-1) # [False, True] fluid.layers.reduce_all(x, dim=-1) # [False, True]
...@@ -6448,6 +6514,9 @@ reduce_any ...@@ -6448,6 +6514,9 @@ reduce_any
# [[True, False] # [[True, False]
# [False, False]] # [False, False]]
# 接下来的示例中，我们在每处函数调用后面都标注出了它的结果张量。 # 接下来的示例中，我们在每处函数调用后面都标注出了它的结果张量。
import paddle.fluid as fluid
import paddle.fluid.layers as layers
import numpy as np
fluid.layers.reduce_any(x) # True fluid.layers.reduce_any(x) # True
fluid.layers.reduce_any(x, dim=0) # [True, False] fluid.layers.reduce_any(x, dim=0) # [True, False]
fluid.layers.reduce_any(x, dim=-1) # [True, False] fluid.layers.reduce_any(x, dim=-1) # [True, False]
...@@ -6740,6 +6809,7 @@ Relu接受一个输入数据(张量)，输出一个张量。将线性函数y = m ...@@ -6740,6 +6809,7 @@ Relu接受一个输入数据(张量)，输出一个张量。将线性函数y = m
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
x = fluid.layers.data(name="x", shape=[3, 4], dtype="float32") x = fluid.layers.data(name="x", shape=[3, 4], dtype="float32")
output = fluid.layers.relu(x) output = fluid.layers.relu(x)
...@@ -6780,6 +6850,7 @@ relu6激活算子（Relu6 Activation Operator） ...@@ -6780,6 +6850,7 @@ relu6激活算子（Relu6 Activation Operator）
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
x = fluid.layers.data(name="x", shape=[3,10,32,32], dtype="float32") x = fluid.layers.data(name="x", shape=[3,10,32,32], dtype="float32")
y = fluid.layers.relu6(x, threshold=6.0) y = fluid.layers.relu6(x, threshold=6.0)
...@@ -6833,6 +6904,7 @@ reshape ...@@ -6833,6 +6904,7 @@ reshape
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.data( data = fluid.layers.data(
name='data', shape=[2, 4, 6], dtype='float32') name='data', shape=[2, 4, 6], dtype='float32')
reshaped = fluid.layers.reshape( reshaped = fluid.layers.reshape(
...@@ -6916,6 +6988,7 @@ align_corners和align_mode是可选参数，插值的计算方法可以由它们 ...@@ -6916,6 +6988,7 @@ align_corners和align_mode是可选参数，插值的计算方法可以由它们
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
input = fluid.layers.data(name="input", shape=[3,6,9], dtype="float32") input = fluid.layers.data(name="input", shape=[3,6,9], dtype="float32")
out = fluid.layers.resize_bilinear(input, out_shape=[12, 12]) out = fluid.layers.resize_bilinear(input, out_shape=[12, 12])
...@@ -6988,6 +7061,7 @@ resize_nearest ...@@ -6988,6 +7061,7 @@ resize_nearest
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
input = fluid.layers.data(name="input", shape=[3,6,9], dtype="float32") input = fluid.layers.data(name="input", shape=[3,6,9], dtype="float32")
out = fluid.layers.resize_nearest(input, out_shape=[12, 12]) out = fluid.layers.resize_nearest(input, out_shape=[12, 12])
...@@ -7032,6 +7106,7 @@ Region of Interests align(直译：有意义、有价值选区对齐) 用于实 ...@@ -7032,6 +7106,7 @@ Region of Interests align(直译：有意义、有价值选区对齐) 用于实
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
x = fluid.layers.data( x = fluid.layers.data(
name='data', shape=[256, 32, 32], dtype='float32') name='data', shape=[256, 32, 32], dtype='float32')
rois = fluid.layers.data( rois = fluid.layers.data(
...@@ -7229,6 +7304,7 @@ sampling_id算子。用于从输入的多项分布中对id进行采样的图层 ...@@ -7229,6 +7304,7 @@ sampling_id算子。用于从输入的多项分布中对id进行采样的图层
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
x = fluid.layers.data( x = fluid.layers.data(
name="X", name="X",
shape=[13, 11], shape=[13, 11],
...@@ -7500,6 +7576,7 @@ sequence_enumerate ...@@ -7500,6 +7576,7 @@ sequence_enumerate
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
x = fluid.layers.data(shape[-1, 1], dtype='int32', lod_level=1) x = fluid.layers.data(shape[-1, 1], dtype='int32', lod_level=1)
out = fluid.layers.sequence_enumerate(input=x, win_size=3, pad_value=0) out = fluid.layers.sequence_enumerate(input=x, win_size=3, pad_value=0)
...@@ -7570,6 +7647,7 @@ sequence_expand ...@@ -7570,6 +7647,7 @@ sequence_expand
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
import paddle.fluid.layers as layers import paddle.fluid.layers as layers
x = fluid.layers.data(name='x', shape=[10], dtype='float32') x = fluid.layers.data(name='x', shape=[10], dtype='float32')
y = fluid.layers.data(name='y', shape=[10, 20], y = fluid.layers.data(name='y', shape=[10, 20],
...@@ -7642,9 +7720,14 @@ Sequence Expand As Layer ...@@ -7642,9 +7720,14 @@ Sequence Expand As Layer
.. code-block:: python .. code-block:: python
x = fluid.layers.data(name='x', shape=[7, 1], import paddle.fluid as fluid
import paddle.fluid.layers as layers
x = fluid.layers.data(name='x', shape=[10], dtype='float32')
y = fluid.layers.data(name='y', shape=[10, 20],
dtype='float32', lod_level=1) dtype='float32', lod_level=1)
x_first_step = fluid.layers.sequence_first_step(input=x) out = layers.sequence_expand_as(x=x, y=y)
...@@ -7684,6 +7767,7 @@ sequence_first_step ...@@ -7684,6 +7767,7 @@ sequence_first_step
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
x = fluid.layers.data(name='x', shape=[7, 1], x = fluid.layers.data(name='x', shape=[7, 1],
dtype='float32', lod_level=1) dtype='float32', lod_level=1)
x_first_step = fluid.layers.sequence_first_step(input=x) x_first_step = fluid.layers.sequence_first_step(input=x)
...@@ -7731,6 +7815,7 @@ sequence_last_step ...@@ -7731,6 +7815,7 @@ sequence_last_step
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
x = fluid.layers.data(name='x', shape=[7, 1], x = fluid.layers.data(name='x', shape=[7, 1],
dtype='float32', lod_level=1) dtype='float32', lod_level=1)
x_last_step = fluid.layers.sequence_last_step(input=x) x_last_step = fluid.layers.sequence_last_step(input=x)
...@@ -7772,6 +7857,7 @@ sequence_mask ...@@ -7772,6 +7857,7 @@ sequence_mask
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
import paddle.fluid.layers as layers import paddle.fluid.layers as layers
x = fluid.layers.data(name='x', shape=[10], dtype='float32', lod_level=1) x = fluid.layers.data(name='x', shape=[10], dtype='float32', lod_level=1)
...@@ -7869,6 +7955,7 @@ sequence_pad ...@@ -7869,6 +7955,7 @@ sequence_pad
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
import numpy import numpy
x = fluid.layers.data(name='y', shape=[10, 5], x = fluid.layers.data(name='y', shape=[10, 5],
...@@ -8110,6 +8197,7 @@ sequence_scatter ...@@ -8110,6 +8197,7 @@ sequence_scatter
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
import paddle.fluid.layers as layers import paddle.fluid.layers as layers
input = layers.data( name="x", shape=[3, 6], append_batch_size=False, dtype='float32' ) input = layers.data( name="x", shape=[3, 6], append_batch_size=False, dtype='float32' )
...@@ -8171,6 +8259,7 @@ sequence_slice ...@@ -8171,6 +8259,7 @@ sequence_slice
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
import numpy as np import numpy as np
seqs = fluid.layers.data(name='x', shape=[10, 5], seqs = fluid.layers.data(name='x', shape=[10, 5],
dtype='float32', lod_level=1) dtype='float32', lod_level=1)
...@@ -8220,6 +8309,7 @@ sequence_softmax ...@@ -8220,6 +8309,7 @@ sequence_softmax
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
x = fluid.layers.data(name='x', shape=[7, 1], x = fluid.layers.data(name='x', shape=[7, 1],
dtype='float32', lod_level=1) dtype='float32', lod_level=1)
x_sequence_softmax = fluid.layers.sequence_softmax(input=x) x_sequence_softmax = fluid.layers.sequence_softmax(input=x)
...@@ -8277,6 +8367,7 @@ sequence_unpad ...@@ -8277,6 +8367,7 @@ sequence_unpad
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
x = fluid.layers.data(name='x', shape=[10, 5], dtype='float32') x = fluid.layers.data(name='x', shape=[10, 5], dtype='float32')
len = fluid.layers.data(name='length', shape=[1], dtype='int64') len = fluid.layers.data(name='length', shape=[1], dtype='int64')
out = fluid.layers.sequence_unpad(x=x, length=len) out = fluid.layers.sequence_unpad(x=x, length=len)
...@@ -8382,6 +8473,7 @@ shuffle_channel ...@@ -8382,6 +8473,7 @@ shuffle_channel
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
input = fluid.layers.data(name='input', shape=[4,2,2], dtype='float32') input = fluid.layers.data(name='input', shape=[4,2,2], dtype='float32')
out = fluid.layers.shuffle_channel(x=input, group=2) out = fluid.layers.shuffle_channel(x=input, group=2)
...@@ -8443,6 +8535,7 @@ sigmoid_cross_entropy_with_logits ...@@ -8443,6 +8535,7 @@ sigmoid_cross_entropy_with_logits
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
input = fluid.layers.data( input = fluid.layers.data(
name='data', shape=[10], dtype='float32') name='data', shape=[10], dtype='float32')
label = fluid.layers.data( label = fluid.layers.data(
...@@ -8481,6 +8574,7 @@ sign ...@@ -8481,6 +8574,7 @@ sign
.. code-block:: python .. code-block:: python
# [1, 0, -1] # [1, 0, -1]
import paddle.fluid as fluid
data = fluid.layers.sign(np.array([3, 0, -2])) data = fluid.layers.sign(np.array([3, 0, -2]))
...@@ -8571,6 +8665,7 @@ similarity_focus ...@@ -8571,6 +8665,7 @@ similarity_focus
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.data( data = fluid.layers.data(
name='data', shape=[-1, 3, 2, 2], dtype='float32') name='data', shape=[-1, 3, 2, 2], dtype='float32')
fluid.layers.similarity_focus(input=data, axis=1, indexes=[0]) fluid.layers.similarity_focus(input=data, axis=1, indexes=[0])
...@@ -8673,6 +8768,7 @@ smooth_l1 ...@@ -8673,6 +8768,7 @@ smooth_l1
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.data(name='data', shape=[128], dtype='float32') data = fluid.layers.data(name='data', shape=[128], dtype='float32')
label = fluid.layers.data( label = fluid.layers.data(
name='label', shape=[100], dtype='float32') name='label', shape=[100], dtype='float32')
...@@ -8824,6 +8920,7 @@ softmax_with_cross_entropy ...@@ -8824,6 +8920,7 @@ softmax_with_cross_entropy
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.data(name='data', shape=[128], dtype='float32') data = fluid.layers.data(name='data', shape=[128], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64') label = fluid.layers.data(name='label', shape=[1], dtype='int64')
fc = fluid.layers.fc(input=data, size=100) fc = fluid.layers.fc(input=data, size=100)
...@@ -9028,6 +9125,7 @@ square_error_cost ...@@ -9028,6 +9125,7 @@ square_error_cost
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
y = fluid.layers.data(name='y', shape=[1], dtype='float32') y = fluid.layers.data(name='y', shape=[1], dtype='float32')
y_predict = fluid.layers.data(name='y_predict', shape=[1], dtype='float32') y_predict = fluid.layers.data(name='y_predict', shape=[1], dtype='float32')
cost = fluid.layers.square_error_cost(input=y_predict, label=y) cost = fluid.layers.square_error_cost(input=y_predict, label=y)
...@@ -9080,6 +9178,7 @@ squeeze ...@@ -9080,6 +9178,7 @@ squeeze
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
import paddle.fluid.layers as layers import paddle.fluid.layers as layers
x = fluid.layers.data(name='x', shape=[5, 1, 10]) x = fluid.layers.data(name='x', shape=[5, 1, 10])
y = fluid.layers.sequeeze(input=x, axes=[1]) y = fluid.layers.sequeeze(input=x, axes=[1])
...@@ -9159,6 +9258,7 @@ stack ...@@ -9159,6 +9258,7 @@ stack
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
import paddle.fluid.layers as layers import paddle.fluid.layers as layers
x1 = layers.data(name='x1', shape=[1, 2], dtype='int32') x1 = layers.data(name='x1', shape=[1, 2], dtype='int32')
x2 = layers.data(name='x2', shape=[1, 2], dtype='int32') x2 = layers.data(name='x2', shape=[1, 2], dtype='int32')
...@@ -9196,6 +9296,7 @@ STanh 激活算子（STanh Activation Operator.） ...@@ -9196,6 +9296,7 @@ STanh 激活算子（STanh Activation Operator.）
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
x = fluid.layers.data(name="x", shape=[3,10,32,32], dtype="float32") x = fluid.layers.data(name="x", shape=[3,10,32,32], dtype="float32")
y = fluid.layers.stanh(x, scale_a=0.67, scale_b=1.72) y = fluid.layers.stanh(x, scale_a=0.67, scale_b=1.72)
...@@ -9228,6 +9329,7 @@ sum算子。 ...@@ -9228,6 +9329,7 @@ sum算子。
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
import paddle.fluid.layers as layers import paddle.fluid.layers as layers
input0 = fluid.layers.data(name="input0", shape=[13, 11], dtype='float32') input0 = fluid.layers.data(name="input0", shape=[13, 11], dtype='float32')
input1 = layers.data(name="input1", shape=[13, 11], dtype='float32') input1 = layers.data(name="input1", shape=[13, 11], dtype='float32')
...@@ -9265,6 +9367,7 @@ Swish 激活函数 ...@@ -9265,6 +9367,7 @@ Swish 激活函数
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
x = fluid.layers.data(name="x", shape=[3,10,32,32], dtype="float32") x = fluid.layers.data(name="x", shape=[3,10,32,32], dtype="float32")
y = fluid.layers.swish(x, beta=2.0) y = fluid.layers.swish(x, beta=2.0)
...@@ -9358,6 +9461,7 @@ temporal_shift ...@@ -9358,6 +9461,7 @@ temporal_shift
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
input = fluid.layers.data(name='input', shape=[4,2,2], dtype='float32') input = fluid.layers.data(name='input', shape=[4,2,2], dtype='float32')
out = fluid.layers.temporal_shift(x=input, seg_num=2, shift_ratio=0.2) out = fluid.layers.temporal_shift(x=input, seg_num=2, shift_ratio=0.2)
...@@ -9411,6 +9515,7 @@ topk ...@@ -9411,6 +9515,7 @@ topk
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
import paddle.fluid.layers as layers import paddle.fluid.layers as layers
input = layers.data(name="input", shape=[13, 11], dtype='float32') input = layers.data(name="input", shape=[13, 11], dtype='float32')
top5_values, top5_indices = fluid.layers.topk(input, k=5) top5_values, top5_indices = fluid.layers.topk(input, k=5)
...@@ -9488,6 +9593,7 @@ tree_conv ...@@ -9488,6 +9593,7 @@ tree_conv
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
# 10 代表数据集的最大节点大小max_node_size，5 代表向量宽度 # 10 代表数据集的最大节点大小max_node_size，5 代表向量宽度
nodes_vector = fluid.layers.data(name='vectors', shape=[10, 5], dtype='float32') nodes_vector = fluid.layers.data(name='vectors', shape=[10, 5], dtype='float32')
# 10 代表数据集的最大节点大小max_node_size, 2 代表每条边连接两个节点 # 10 代表数据集的最大节点大小max_node_size, 2 代表每条边连接两个节点
...@@ -9542,6 +9648,7 @@ uniform_random_batch_size_like算子。 ...@@ -9542,6 +9648,7 @@ uniform_random_batch_size_like算子。
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
import paddle.fluid.layers as layers import paddle.fluid.layers as layers
input = fluid.layers.data(name="input", shape=[13, 11], dtype='float32') input = fluid.layers.data(name="input", shape=[13, 11], dtype='float32')
...@@ -9685,6 +9792,9 @@ where ...@@ -9685,6 +9792,9 @@ where
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
import paddle.fluid.layers as layers
import numpy as np
# condition为张量[True, False, True] # condition为张量[True, False, True]
out = fluid.layers.where(condition) # [[0], [2]] out = fluid.layers.where(condition) # [[0], [2]]
......
doc/fluid/api_cn/layers_cn/ops_cn.rst
浏览文件 @ fa79f486
...@@ -26,6 +26,7 @@ abs ...@@ -26,6 +26,7 @@ abs
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.data(name="input", shape=[32, 784]) data = fluid.layers.data(name="input", shape=[32, 784])
result = fluid.layers.abs(data) result = fluid.layers.abs(data)
...@@ -51,6 +52,7 @@ arccosine激活函数。 ...@@ -51,6 +52,7 @@ arccosine激活函数。
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.data(name="input", shape=[32, 784]) data = fluid.layers.data(name="input", shape=[32, 784])
result = fluid.layers.acos(data) result = fluid.layers.acos(data)
...@@ -76,6 +78,7 @@ arcsine激活函数。 ...@@ -76,6 +78,7 @@ arcsine激活函数。
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.data(name="input", shape=[32, 784]) data = fluid.layers.data(name="input", shape=[32, 784])
result = fluid.layers.asin(data) result = fluid.layers.asin(data)
...@@ -102,6 +105,7 @@ arctanh激活函数。 ...@@ -102,6 +105,7 @@ arctanh激活函数。
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.data(name="input", shape=[32, 784]) data = fluid.layers.data(name="input", shape=[32, 784])
result = fluid.layers.atan(data) result = fluid.layers.atan(data)
...@@ -134,6 +138,7 @@ ceil ...@@ -134,6 +138,7 @@ ceil
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.data(name="input", shape=[32, 784]) data = fluid.layers.data(name="input", shape=[32, 784])
result = fluid.layers.ceil(data) result = fluid.layers.ceil(data)
...@@ -172,6 +177,7 @@ Cosine余弦激活函数。 ...@@ -172,6 +177,7 @@ Cosine余弦激活函数。
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.data(name="input", shape=[32, 784]) data = fluid.layers.data(name="input", shape=[32, 784])
result = fluid.layers.cos(data) result = fluid.layers.cos(data)
...@@ -203,6 +209,7 @@ cumsum ...@@ -203,6 +209,7 @@ cumsum
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.data(name="input", shape=[32, 784]) data = fluid.layers.data(name="input", shape=[32, 784])
result = fluid.layers.cumsum(data, axis=0) result = fluid.layers.cumsum(data, axis=0)
...@@ -238,6 +245,7 @@ Exp激活函数(Exp指以自然常数e为底的指数运算)。 ...@@ -238,6 +245,7 @@ Exp激活函数(Exp指以自然常数e为底的指数运算)。
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.data(name="input", shape=[32, 784]) data = fluid.layers.data(name="input", shape=[32, 784])
result = fluid.layers.exp(data) result = fluid.layers.exp(data)
...@@ -275,6 +283,7 @@ floor ...@@ -275,6 +283,7 @@ floor
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.data(name="input", shape=[32, 784]) data = fluid.layers.data(name="input", shape=[32, 784])
result = fluid.layers.floor(data) result = fluid.layers.floor(data)
...@@ -315,6 +324,7 @@ HardShrink激活函数(HardShrink activation operator) ...@@ -315,6 +324,7 @@ HardShrink激活函数(HardShrink activation operator)
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.data(name="input", shape=[784]) data = fluid.layers.data(name="input", shape=[784])
result = fluid.layers.hard_shrink(x=data, threshold=0.3) result = fluid.layers.hard_shrink(x=data, threshold=0.3)
...@@ -351,6 +361,7 @@ Logsigmoid激活函数。 ...@@ -351,6 +361,7 @@ Logsigmoid激活函数。
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.data(name="input", shape=[32, 784]) data = fluid.layers.data(name="input", shape=[32, 784])
result = fluid.layers.logsigmoid(data) result = fluid.layers.logsigmoid(data)
...@@ -386,6 +397,7 @@ Reciprocal（取倒数）激活函数 ...@@ -386,6 +397,7 @@ Reciprocal（取倒数）激活函数
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.data(name="input", shape=[32, 784]) data = fluid.layers.data(name="input", shape=[32, 784])
result = fluid.layers.reciprocal(data) result = fluid.layers.reciprocal(data)
...@@ -425,6 +437,7 @@ Round取整激活函数。 ...@@ -425,6 +437,7 @@ Round取整激活函数。
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.data(name="input", shape=[32, 784]) data = fluid.layers.data(name="input", shape=[32, 784])
result = fluid.layers.round(data) result = fluid.layers.round(data)
...@@ -456,6 +469,7 @@ rsqrt激活函数 ...@@ -456,6 +469,7 @@ rsqrt激活函数
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.data(name="input", shape=[32, 784]) data = fluid.layers.data(name="input", shape=[32, 784])
result = fluid.layers.rsqrt(data) result = fluid.layers.rsqrt(data)
...@@ -485,6 +499,7 @@ sigmoid激活函数 ...@@ -485,6 +499,7 @@ sigmoid激活函数
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.data(name="input", shape=[32, 784]) data = fluid.layers.data(name="input", shape=[32, 784])
result = fluid.layers.sigmoid(data) result = fluid.layers.sigmoid(data)
...@@ -524,6 +539,7 @@ sin ...@@ -524,6 +539,7 @@ sin
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.data(name="input", shape=[32, 784]) data = fluid.layers.data(name="input", shape=[32, 784])
result = fluid.layers.sin(data) result = fluid.layers.sin(data)
...@@ -561,6 +577,7 @@ softplus激活函数。 ...@@ -561,6 +577,7 @@ softplus激活函数。
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.data(name="input", shape=[32, 784]) data = fluid.layers.data(name="input", shape=[32, 784])
result = fluid.layers.softplus(data) result = fluid.layers.softplus(data)
...@@ -600,6 +617,7 @@ Softshrink激活算子 ...@@ -600,6 +617,7 @@ Softshrink激活算子
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.data(name="input", shape=[32, 784]) data = fluid.layers.data(name="input", shape=[32, 784])
result = fluid.layers.softshrink(data) result = fluid.layers.softshrink(data)
...@@ -638,6 +656,7 @@ softsign激活函数。 ...@@ -638,6 +656,7 @@ softsign激活函数。
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.data(name="input", shape=[32, 784]) data = fluid.layers.data(name="input", shape=[32, 784])
result = fluid.layers.softsign(data) result = fluid.layers.softsign(data)
...@@ -677,6 +696,7 @@ sqrt ...@@ -677,6 +696,7 @@ sqrt
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.data(name="input", shape=[32, 784]) data = fluid.layers.data(name="input", shape=[32, 784])
result = fluid.layers.sqrt(data) result = fluid.layers.sqrt(data)
...@@ -714,6 +734,7 @@ square ...@@ -714,6 +734,7 @@ square
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.data(name="input", shape=[32, 784]) data = fluid.layers.data(name="input", shape=[32, 784])
result = fluid.layers.square(data) result = fluid.layers.square(data)
...@@ -754,6 +775,7 @@ tanh 激活函数。 ...@@ -754,6 +775,7 @@ tanh 激活函数。
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.data(name="input", shape=[32, 784]) data = fluid.layers.data(name="input", shape=[32, 784])
result = fluid.layers.tanh(data) result = fluid.layers.tanh(data)
...@@ -792,6 +814,7 @@ tanh_shrink激活函数。 ...@@ -792,6 +814,7 @@ tanh_shrink激活函数。
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.data(name="input", shape=[32, 784]) data = fluid.layers.data(name="input", shape=[32, 784])
result = fluid.layers.tanh_shrink(data) result = fluid.layers.tanh_shrink(data)
...@@ -829,6 +852,7 @@ ThresholdedRelu激活函数 ...@@ -829,6 +852,7 @@ ThresholdedRelu激活函数
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.data(name="input", shape=[1]) data = fluid.layers.data(name="input", shape=[1])
result = fluid.layers.thresholded_relu(data, threshold=0.4) result = fluid.layers.thresholded_relu(data, threshold=0.4)
......
doc/fluid/api_cn/layers_cn/tensor_cn.rst
浏览文件 @ fa79f486
...@@ -26,6 +26,7 @@ argmax ...@@ -26,6 +26,7 @@ argmax
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
x = fluid.layers.data(name="x", shape=[3, 4], dtype="float32") x = fluid.layers.data(name="x", shape=[3, 4], dtype="float32")
out = fluid.layers.argmax(x=in, axis=0) out = fluid.layers.argmax(x=in, axis=0)
out = fluid.layers.argmax(x=in, axis=-1) out = fluid.layers.argmax(x=in, axis=-1)
...@@ -61,6 +62,7 @@ argmin ...@@ -61,6 +62,7 @@ argmin
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
x = fluid.layers.data(name="x", shape=[3, 4], dtype="float32") x = fluid.layers.data(name="x", shape=[3, 4], dtype="float32")
out = fluid.layers.argmin(x=in, axis=0) out = fluid.layers.argmin(x=in, axis=0)
out = fluid.layers.argmin(x=in, axis=-1) out = fluid.layers.argmin(x=in, axis=-1)
...@@ -113,6 +115,7 @@ argsort ...@@ -113,6 +115,7 @@ argsort
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
x = fluid.layers.data(name="x", shape=[3, 4], dtype="float32") x = fluid.layers.data(name="x", shape=[3, 4], dtype="float32")
out, indices = fluid.layers.argsort(input=x, axis=0) out, indices = fluid.layers.argsort(input=x, axis=0)
...@@ -180,6 +183,7 @@ cast ...@@ -180,6 +183,7 @@ cast
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.data(name='x', shape=[13], dtype='float32') data = fluid.layers.data(name='x', shape=[13], dtype='float32')
result = fluid.layers.cast(x=data, dtype='float64') result = fluid.layers.cast(x=data, dtype='float64')
...@@ -215,6 +219,7 @@ concat ...@@ -215,6 +219,7 @@ concat
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
a = fluid.layers.data(name='a', shape=[2, 13], dtype='float32') a = fluid.layers.data(name='a', shape=[2, 13], dtype='float32')
b = fluid.layers.data(name='b', shape=[2, 3], dtype='float32') b = fluid.layers.data(name='b', shape=[2, 3], dtype='float32')
c = fluid.layers.data(name='c', shape=[2, 2], dtype='float32') c = fluid.layers.data(name='c', shape=[2, 2], dtype='float32')
...@@ -254,6 +259,7 @@ create_global_var ...@@ -254,6 +259,7 @@ create_global_var
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
import paddle.fluid.layers as layers import paddle.fluid.layers as layers
var = layers.create_global_var(shape=[2,3], value=1.0, dtype='float32', var = layers.create_global_var(shape=[2,3], value=1.0, dtype='float32',
persistable=True, force_cpu=True, name='new_var') persistable=True, force_cpu=True, name='new_var')
...@@ -290,6 +296,7 @@ create_parameter ...@@ -290,6 +296,7 @@ create_parameter
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
import paddle.fluid.layers as layers import paddle.fluid.layers as layers
W = fluid.layers.create_parameter(shape=[784, 200], dtype='float32') W = fluid.layers.create_parameter(shape=[784, 200], dtype='float32')
...@@ -323,6 +330,7 @@ create_tensor ...@@ -323,6 +330,7 @@ create_tensor
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
tensor = fluid.layers.create_tensor(dtype='float32') tensor = fluid.layers.create_tensor(dtype='float32')
...@@ -350,6 +358,7 @@ diag ...@@ -350,6 +358,7 @@ diag
# [3, 0, 0] # [3, 0, 0]
# [0, 4, 0] # [0, 4, 0]
# [0, 0, 5] # [0, 0, 5]
import paddle.fluid as fluid
data = fluid.layers.diag(np.arange(3, 6)) data = fluid.layers.diag(np.arange(3, 6))
...@@ -508,6 +517,7 @@ isfinite ...@@ -508,6 +517,7 @@ isfinite
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
var = fluid.layers.data(name="data", var = fluid.layers.data(name="data",
shape=(4, 6), shape=(4, 6),
dtype="float32") dtype="float32")
...@@ -540,6 +550,7 @@ linspace ...@@ -540,6 +550,7 @@ linspace
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.linspace(0, 10, 5, 'float32') # [0.0, 2.5, 5.0, 7.5, 10.0] data = fluid.layers.linspace(0, 10, 5, 'float32') # [0.0, 2.5, 5.0, 7.5, 10.0]
data = fluid.layers.linspace(0, 10, 1, 'float32') # [0.0] data = fluid.layers.linspace(0, 10, 1, 'float32') # [0.0]
...@@ -602,6 +613,7 @@ range ...@@ -602,6 +613,7 @@ range
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.range(0, 10, 2, 'int32') data = fluid.layers.range(0, 10, 2, 'int32')
...@@ -808,6 +820,7 @@ zeros_like ...@@ -808,6 +820,7 @@ zeros_like
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
x = fluid.layers.data(name='x', dtype='float32', shape=[3], append_batch_size=False) x = fluid.layers.data(name='x', dtype='float32', shape=[3], append_batch_size=False)
data = fluid.layers.zeros_like(x) # [0.0, 0.0, 0.0] data = fluid.layers.zeros_like(x) # [0.0, 0.0, 0.0]
......
doc/fluid/api_cn/metrics_cn.rst
浏览文件 @ fa79f486
...@@ -21,6 +21,7 @@ https://en.wikipedia.org/wiki/Accuracy_and_precision ...@@ -21,6 +21,7 @@ https://en.wikipedia.org/wiki/Accuracy_and_precision
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
# 假设有batch_size = 128 # 假设有batch_size = 128
batch_size=128 batch_size=128
accuracy_manager = fluid.metrics.Accuracy() accuracy_manager = fluid.metrics.Accuracy()
...@@ -77,6 +78,7 @@ auc函数创建四个局部变量true_positives, true_negatives, false_positives ...@@ -77,6 +78,7 @@ auc函数创建四个局部变量true_positives, true_negatives, false_positives
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
import numpy as np import numpy as np
# 初始化auc度量 # 初始化auc度量
auc_metric = fluid.metrics.Auc("ROC") auc_metric = fluid.metrics.Auc("ROC")
...@@ -134,6 +136,7 @@ ChunkEvaluator ...@@ -134,6 +136,7 @@ ChunkEvaluator
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
# 初始化chunck-level的评价管理。 # 初始化chunck-level的评价管理。
metric = fluid.metrics.ChunkEvaluator() metric = fluid.metrics.ChunkEvaluator()
...@@ -185,6 +188,7 @@ CompositeMetric ...@@ -185,6 +188,7 @@ CompositeMetric
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
import numpy as np import numpy as np
preds = [[0.1], [0.7], [0.8], [0.9], [0.2], preds = [[0.1], [0.7], [0.8], [0.9], [0.2],
[0.2], [0.3], [0.5], [0.8], [0.6]] [0.2], [0.3], [0.5], [0.8], [0.6]]
...@@ -271,6 +275,7 @@ DetectionMAP ...@@ -271,6 +275,7 @@ DetectionMAP
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
import paddle.fluid.layers as layers import paddle.fluid.layers as layers
batch_size = -1 # 可以为任意大小 batch_size = -1 # 可以为任意大小
...@@ -339,6 +344,7 @@ EditDistance ...@@ -339,6 +344,7 @@ EditDistance
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
import numpy as np import numpy as np
# 假设batch_size为128 # 假设batch_size为128
...@@ -366,6 +372,7 @@ EditDistance ...@@ -366,6 +372,7 @@ EditDistance
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
edit_distances_batch2 = np.random.randint(low = 0, high = 10, size = (batch_size, 1)) edit_distances_batch2 = np.random.randint(low = 0, high = 10, size = (batch_size, 1))
seq_num_batch2 = batch_size seq_num_batch2 = batch_size
distance_evaluator.update(edit_distances_batch2, seq_num_batch2) distance_evaluator.update(edit_distances_batch2, seq_num_batch2)
...@@ -456,6 +463,7 @@ https://en.wikipedia.org/wiki/Evaluation_of_binary_classifiers ...@@ -456,6 +463,7 @@ https://en.wikipedia.org/wiki/Evaluation_of_binary_classifiers
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
import numpy as np import numpy as np
metric = fluid.metrics.Precision() metric = fluid.metrics.Precision()
...@@ -499,6 +507,7 @@ https://en.wikipedia.org/wiki/Precision_and_recall ...@@ -499,6 +507,7 @@ https://en.wikipedia.org/wiki/Precision_and_recall
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
import numpy as np import numpy as np
metric = fluid.metrics.Recall() metric = fluid.metrics.Recall()
......
doc/fluid/api_cn/nets_cn.rst
浏览文件 @ fa79f486
...@@ -30,6 +30,7 @@ he Gated Linear Units(GLU)由切分（split），sigmoid激活函数和按元素 ...@@ -30,6 +30,7 @@ he Gated Linear Units(GLU)由切分（split），sigmoid激活函数和按元素
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.data( data = fluid.layers.data(
name="words", shape=[-1, 6, 3, 9], dtype="float32") name="words", shape=[-1, 6, 3, 9], dtype="float32")
# 输出的形状为[-1, 3, 3, 9] # 输出的形状为[-1, 3, 3, 9]
...@@ -74,6 +75,7 @@ Image Convolution Group由Convolution2d，BatchNorm，DropOut和Pool2d组成。 ...@@ -74,6 +75,7 @@ Image Convolution Group由Convolution2d，BatchNorm，DropOut和Pool2d组成。
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32') img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
conv_pool = fluid.nets.img_conv_group(input=img, conv_pool = fluid.nets.img_conv_group(input=img,
conv_padding=1, conv_padding=1,
......
doc/fluid/api_cn/optimizer_cn.rst
浏览文件 @ fa79f486
...@@ -340,6 +340,7 @@ DGC还使用动量因子掩藏(momentum factor masking)和预训练(warm-up)来 ...@@ -340,6 +340,7 @@ DGC还使用动量因子掩藏(momentum factor masking)和预训练(warm-up)来
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
optimizer = fluid.optimizer.DGCMomentumOptimizer( optimizer = fluid.optimizer.DGCMomentumOptimizer(
learning_rate=0.0001, learning_rate=0.0001,
momentum=0.9, momentum=0.9,
...@@ -349,84 +350,6 @@ DGC还使用动量因子掩藏(momentum factor masking)和预训练(warm-up)来 ...@@ -349,84 +350,6 @@ DGC还使用动量因子掩藏(momentum factor masking)和预训练(warm-up)来
.. _cn_api_fluid_optimizer_PipelineOptimizer:
PipelineOptimizer
-------------------------------
.. py:class:: paddle.fluid.optimizer.PipelineOptimizer(optimizer, cut_list=None, place_list=None, concurrency_list=None, queue_size=30, sync_steps=1, start_cpu_core_id=0)
Pipeline 优化器训练。该程序将由cut_list分割。如果cut_list的长度是k，则整个程序（包括向后部分）将被分割为2 * k-1个部分。 所以place_list和concurrency_list的长度也必须是2 * k-1。
.. note::
虽然异步模式应用于管道训练中以加速，但最终的性能取决于每个管道的训练进度。 我们将在未来尝试同步模式。
参数:
- **optimizer** (Optimizer) - 基础优化器，如SGD
- **cut_list** (list of Variable list) - main_program的cut变量
- **place_lis** (list of Place) - 某部分运行的位置
- **concurrency_lis** (list of int) - 并发度
- **queue_size** (int) - 每个部分都将使用其范围内队列(in-scope queue)中的范围并将范围生成到范围外队列(out-scope queue)。 而这个参数定范围队列大小。 这一参数可选，默认值：30。
- **sync_steps** (int) - 不同显卡之间的同步步数
- **start_cpu_core_id** (int) - 设置第一个cpu核的id。这一参数可选，默认值：0。
**代码示例**
.. code-block:: python
x = fluid.layers.data(name='x', shape=[1], dtype='int64', lod_level=0)
y = fluid.layers.data(name='y', shape=[1], dtype='int64', lod_level=0)
emb_x = layers.embedding(input=x, param_attr=fluid.ParamAttr(name="embx"), size=[10,2], is_sparse=False)
emb_y = layers.embedding(input=y, param_attr=fluid.ParamAttr(name="emby",learning_rate=0.9), size=[10,2], is_sparse=False)
concat = layers.concat([emb_x, emb_y], axis=1)
fc = layers.fc(input=concat, name="fc", size=1, num_flatten_dims=1, bias_attr=False)
loss = layers.reduce_mean(fc)
optimizer = fluid.optimizer.SGD(learning_rate=0.5)
optimizer = fluid.optimizer.PipelineOptimizer(optimizer,
cut_list=[[emb_x, emb_y], [loss]],
place_list=[fluid.CPUPlace(), fluid.CUDAPlace(0), fluid.CPUPlace()],
concurrency_list=[1, 1, 4],
queue_size=2,
sync_steps=1,
)
optimizer.minimize(loss)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
filelist = [] # 您应该根据需求自行设置文件列表, 如: filelist = ["dataA.txt"]
dataset = fluid.DatasetFactory().create_dataset("FileInstantDataset")
dataset.set_use_var([x,y])
dataset.set_batch_size(batch_size)
dataset.set_filelist(filelist)
exe.train_from_dataset(
fluid.default_main_program(),
dataset,
thread=2,
debug=False,
fetch_list=[],
fetch_info=[],
print_period=1)
.. py:method:: extract_section_opt_ops(ops, cut_point_name)
获取指定section的优化算子(opt ops)
.. py:method:: extract_section_opt_ops(ops, cut_point_name)
获取指定section的输入和输出
.. py:method:: find_persistable_vars(ops, whole_parameters)
获取指定section的持久性输入变量
.. py:method:: extract_section_ops(ops, cut_point_name)
获取指定的section的算子(ops)
.. _cn_api_fluid_optimizer_ExponentialMovingAverage: .. _cn_api_fluid_optimizer_ExponentialMovingAverage:
ExponentialMovingAverage ExponentialMovingAverage
...@@ -624,15 +547,16 @@ FTRL 原始论文: ( `https://www.eecs.tufts.edu/~dsculley/papers/ad-click-predi ...@@ -624,15 +547,16 @@ FTRL 原始论文: ( `https://www.eecs.tufts.edu/~dsculley/papers/ad-click-predi
LambOptimizer LambOptimizer
------------------------------- -------------------------------
.. py:class:: paddle.fluid.optimizer.LambOptimizer(learning_rate=0.001, lamb_weight_decay=0.01, beta1=0.9, beta2=0.999, epsilon=1e-06, regularization=None, name=None) .. py:class:: paddle.fluid.optimizer.LambOptimizer(learning_rate=0.001, lamb_weight_decay=0.01, beta1=0.9, beta2=0.999, epsilon=1e-06, regularization=None, exclude_from_weight_decay_fn=None, name=None)
LAMB（Layer-wise Adaptive Moments optimizer for Batching training）优化器 LAMB（Layer-wise Adaptive Moments optimizer for Batching training）优化器
LAMB优化器旨在不降低准确性的条件下扩大训练的批量大小，支持自适应元素更新和精确的分层校正。 更多信息请参考Reducing BERT Pre-Training Time from 3 Days to 76 Minutes。 LAMB优化器旨在不降低准确性的条件下扩大训练的批量大小，支持自适应元素更新和精确的分层校正。 更多信息请参考 `Large Batch Optimization for
Deep Learning: Training BERT in 76 minutes <https://arxiv.org/pdf/1904.00962.pdf>`_ 。
参数更新如下： 参数更新如下：
.. math:: .. math::
\begin{align}\begin{aligned}m_t^l & = \beta_1 m_{t - 1}^l + (1 - \beta_1)g_t^l\\v_t^l & = \beta_2 v_{t - 1}^l + (1 - \beta_2)g_t^l \odot g_t^l\\\widehat{m}_t^l & = m_t^l/(1 - \beta_1^t)\\\widehat{v}_t^l & = v_t^l/(1 - \beta_2^t)\\r_1 & = \left \| w_{t-1}^l \right \|_2\\r_2 & = \left \| \frac{\widehat{m}_t^l}{\sqrt{\widehat{v}_t^l+\epsilon}} + \lambda w_{t-1}^l \right \|_2\\r & = r_1 / r_2\\\eta^l & = r \times \eta\\w_t^l & = w_{t-1}^l -\eta ^l \times (\frac{\widehat{m}_t^l}{\sqrt{\widehat{v}_t^l+\epsilon}} + \lambda w_{t-1}^l)\end{aligned}\end{align} \begin{align}\begin{aligned}m_t &= \beta_1 m_{t - 1}+ (1 - \beta_1)g_t \\\v_t &= \beta_2 v_{t - 1} + (1 - \beta_2)g_t^2 \\\r_t &= \frac{m_t}{\sqrt{v_t}+\epsilon} \\\w_t &= w_{t-1} -\eta_t \frac{\left \| w_{t-1}\right \|}{\left \| r_t + \lambda w_{t-1}\right \|} (r_t + \lambda w_{t-1})\end{aligned}\end{align}
其中 :math:`m` 为第一个时刻，:math:`v` 为第二个时刻，:math:`\eta` 为学习率，:math:`\lambda` 为LAMB权重衰减率。 其中 :math:`m` 为第一个时刻，:math:`v` 为第二个时刻，:math:`\eta` 为学习率，:math:`\lambda` 为LAMB权重衰减率。
...@@ -642,7 +566,8 @@ LAMB优化器旨在不降低准确性的条件下扩大训练的批量大小， ...@@ -642,7 +566,8 @@ LAMB优化器旨在不降低准确性的条件下扩大训练的批量大小，
- **beta1** (float) – 第一个时刻估计的指数衰减率。 - **beta1** (float) – 第一个时刻估计的指数衰减率。
- **beta2** (float) – 第二个时刻估计的指数衰减率。 - **beta2** (float) – 第二个时刻估计的指数衰减率。
- **epsilon** (float) – 一个小的浮点值，目的是维持数值稳定性。 - **epsilon** (float) – 一个小的浮点值，目的是维持数值稳定性。
- **regularization** – 一个正则化器，如fluid.regularizer.L1DecayRegularizer。 - **regularization** (Regularizer) – 一个正则化器，如fluid.regularizer.L1DecayRegularizer。
- **exclude_from_weight_decay_fn** (function) – 当返回值为True时从权重衰减中去除某个参数。
- **name** (str|None) – 名字前缀（可选项）。 - **name** (str|None) – 名字前缀（可选项）。
**代码示例** **代码示例**
...@@ -655,7 +580,11 @@ LAMB优化器旨在不降低准确性的条件下扩大训练的批量大小， ...@@ -655,7 +580,11 @@ LAMB优化器旨在不降低准确性的条件下扩大训练的批量大小，
hidden = fluid.layers.fc(input=data, size=10) hidden = fluid.layers.fc(input=data, size=10)
cost = fluid.layers.mean(hidden) cost = fluid.layers.mean(hidden)
optimizer = fluid.optimizer.Lamb(learning_rate=0.002) def exclude_fn(param):
return param.name.endswith('.b_0')
optimizer = fluid.optimizer.Lamb(learning_rate=0.002,
exclude_from_weight_decay_fn=exclude_fn)
optimizer.minimize(cost) optimizer.minimize(cost)
...@@ -703,6 +632,7 @@ LARS支持的Momentum优化器 ...@@ -703,6 +632,7 @@ LARS支持的Momentum优化器
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
optimizer = fluid.optimizer.LarsMomentum(learning_rate=0.2, momentum=0.1, lars_weight_decay=0.001) optimizer = fluid.optimizer.LarsMomentum(learning_rate=0.2, momentum=0.1, lars_weight_decay=0.001)
optimizer.minimize(cost) optimizer.minimize(cost)
...@@ -859,6 +789,70 @@ MomentumOptimizer ...@@ -859,6 +789,70 @@ MomentumOptimizer
.. _cn_api_fluid_optimizer_PipelineOptimizer:
PipelineOptimizer
-------------------------------
.. py:class:: paddle.fluid.optimizer.PipelineOptimizer(optimizer, cut_list=None, place_list=None, concurrency_list=None, queue_size=30, sync_steps=1, start_cpu_core_id=0)
使用流水线模式进行训练。
Program会根据切分列表cut_list进行分割。如果cut_list的长度是k，则整个program（包括反向部分）将被分割为2*k-1个section。 所以place_list和concurrency_list的长度也必须是2*k-1。
.. note::
虽然我们在流水线训练模式中采用异步更新的方式来加速，但最终的效果会依赖于每条流水线的训练进程。我们将在未来尝试同步模式。
参数:
- **optimizer** (Optimizer) - 基础优化器，如SGD
- **cut_list** (list of Variable list) - main_program的cut变量列表
- **place_list** (list of Place) - 对应section运行所在的place
- **concurrency_list** (list of int) - 指定每个section的并发度列表
- **queue_size** (int) - 每个section都会消费其输入队列(in-scope queue)中的scope，并向输出队列(out-scope queue)产出scope。 此参数的作用就是指定队列的大小。 可选，默认值：30
- **sync_steps** (int) - 不同显卡之间的同步周期数。可选，默认值：1
- **start_cpu_core_id** (int) - 指定所使用的第一个CPU核的id。可选，默认值：0
**代码示例**
.. code-block:: python
import paddle.fluid as fluid
import paddle.fluid.layers as layers
x = fluid.layers.data(name='x', shape=[1], dtype='int64', lod_level=0)
y = fluid.layers.data(name='y', shape=[1], dtype='int64', lod_level=0)
emb_x = layers.embedding(input=x, param_attr=fluid.ParamAttr(name="embx"), size=[10,2], is_sparse=False)
emb_y = layers.embedding(input=y, param_attr=fluid.ParamAttr(name="emby",learning_rate=0.9), size=[10,2], is_sparse=False)
concat = layers.concat([emb_x, emb_y], axis=1)
fc = layers.fc(input=concat, name="fc", size=1, num_flatten_dims=1, bias_attr=False)
loss = layers.reduce_mean(fc)
optimizer = fluid.optimizer.SGD(learning_rate=0.5)
optimizer = fluid.optimizer.PipelineOptimizer(optimizer,
cut_list=[[emb_x, emb_y], [loss]],
place_list=[fluid.CPUPlace(), fluid.CUDAPlace(0), fluid.CPUPlace()],
concurrency_list=[1, 1, 4],
queue_size=2,
sync_steps=1,
)
optimizer.minimize(loss)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
filelist = [] # you should set your own filelist, e.g. filelist = ["dataA.txt"]
dataset = fluid.DatasetFactory().create_dataset("FileInstantDataset")
dataset.set_use_var([x,y])
dataset.set_batch_size(batch_size)
dataset.set_filelist(filelist)
exe.train_from_dataset(
fluid.default_main_program(),
dataset,
thread=2,
debug=False,
fetch_list=[],
fetch_info=[],
print_period=1)
.. _cn_api_fluid_optimizer_RMSPropOptimizer: .. _cn_api_fluid_optimizer_RMSPropOptimizer:
......
doc/fluid/api_cn/profiler_cn.rst
浏览文件 @ fa79f486
...@@ -82,6 +82,7 @@ profile interface 。与cuda_profiler不同，此profiler可用于分析CPU和GP ...@@ -82,6 +82,7 @@ profile interface 。与cuda_profiler不同，此profiler可用于分析CPU和GP
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
import paddle.fluid.profiler as profiler import paddle.fluid.profiler as profiler
import numpy as np import numpy as np
...@@ -118,6 +119,7 @@ reset_profiler ...@@ -118,6 +119,7 @@ reset_profiler
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
import paddle.fluid.profiler as profiler import paddle.fluid.profiler as profiler
with profiler.profiler('CPU', 'total', '/tmp/profile'): with profiler.profiler('CPU', 'total', '/tmp/profile'):
for iter in range(10): for iter in range(10):
...@@ -155,6 +157,7 @@ start_profiler ...@@ -155,6 +157,7 @@ start_profiler
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
import paddle.fluid.profiler as profiler import paddle.fluid.profiler as profiler
profiler.start_profiler('GPU') profiler.start_profiler('GPU')
...@@ -195,6 +198,7 @@ stop_profiler ...@@ -195,6 +198,7 @@ stop_profiler
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
import paddle.fluid.profiler as profiler import paddle.fluid.profiler as profiler
profiler.start_profiler('GPU') profiler.start_profiler('GPU')
......
doc/fluid/beginners_guide/install/Tables.md
浏览文件 @ fa79f486
...@@ -218,16 +218,16 @@ PaddePaddle通过编译时指定路径来实现引用各种BLAS/CUDA/cuDNN库。 ...@@ -218,16 +218,16 @@ PaddePaddle通过编译时指定路径来实现引用各种BLAS/CUDA/cuDNN库。
</thead> </thead>
<tbody> <tbody>
<tr> <tr>
<td> paddlepaddle==[版本号] 例如 paddlepaddle==1.5.0 </td> <td> paddlepaddle==[版本号] 例如 paddlepaddle==1.5.1 </td>
<td> 只支持CPU对应版本的PaddlePaddle，具体版本请参见<a href=https://pypi.org/project/paddlepaddle/#history>Pypi</a> </td> <td> 只支持CPU对应版本的PaddlePaddle，具体版本请参见<a href=https://pypi.org/project/paddlepaddle/#history>Pypi</a> </td>
</tr> </tr>
<tr> <tr>
<td> paddlepaddle-gpu==1.5.0 </td> <td> paddlepaddle-gpu==1.5.1 </td>
<td> 使用CUDA 9.0和cuDNN 7编译的1.5.0版本 </td> <td> 使用CUDA 9.0和cuDNN 7编译的1.5.1版本 </td>
</tr> </tr>
<tr> <tr>
<td> paddlepaddle-gpu==1.5.0.post87 </td> <td> paddlepaddle-gpu==1.5.1.post87 </td>
<td> 使用CUDA 8.0和cuDNN 7编译的1.5.0版本 </td> <td> 使用CUDA 8.0和cuDNN 7编译的1.5.1版本 </td>
</tr> </tr>
</tbody> </tbody>
</table> </table>
...@@ -259,74 +259,110 @@ PaddePaddle通过编译时指定路径来实现引用各种BLAS/CUDA/cuDNN库。 ...@@ -259,74 +259,110 @@ PaddePaddle通过编译时指定路径来实现引用各种BLAS/CUDA/cuDNN库。
<tbody> <tbody>
<tr> <tr>
<td> cpu-mkl </td> <td> cpu-mkl </td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-cpu-mkl/paddlepaddle-1.5.0-cp27-cp27mu-linux_x86_64.whl"> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-cpu-mkl/paddlepaddle-1.5.1-cp27-cp27mu-linux_x86_64.whl">
paddlepaddle-1.5.0-cp27-cp27mu-linux_x86_64.whl</a></td> paddlepaddle-1.5.1-cp27-cp27mu-linux_x86_64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-cpu-mkl/paddlepaddle-1.5.0-cp27-cp27m-linux_x86_64.whl"> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-cpu-mkl/paddlepaddle-1.5.1-cp27-cp27m-linux_x86_64.whl">
paddlepaddle-1.5.0-cp27-cp27m-linux_x86_64.whl</a></td> paddlepaddle-1.5.1-cp27-cp27m-linux_x86_64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-cpu-mkl/paddlepaddle-1.5.0-cp35-cp35m-linux_x86_64.whl"> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-cpu-mkl/paddlepaddle-1.5.1-cp35-cp35m-linux_x86_64.whl">
paddlepaddle-1.5.0-cp35-cp35m-linux_x86_64.whl</a></td> paddlepaddle-1.5.1-cp35-cp35m-linux_x86_64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-cpu-mkl/paddlepaddle-1.5.0-cp36-cp36m-linux_x86_64.whl"> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-cpu-mkl/paddlepaddle-1.5.1-cp36-cp36m-linux_x86_64.whl">
paddlepaddle-1.5.0-cp36-cp36m-linux_x86_64.whl</a></td> paddlepaddle-1.5.1-cp36-cp36m-linux_x86_64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-cpu-mkl/paddlepaddle-1.5.0-cp37-cp37m-linux_x86_64.whl"> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-cpu-mkl/paddlepaddle-1.5.1-cp37-cp37m-linux_x86_64.whl">
paddlepaddle-1.5.0-cp37-cp37m-linux_x86_64.whl</a></td> paddlepaddle-1.5.1-cp37-cp37m-linux_x86_64.whl</a></td>
</tr> </tr>
<tr> <tr>
<td> cpu-openblas </td> <td> cpu-openblas </td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-cpu-openblas/paddlepaddle-1.5.0-cp27-cp27mu-linux_x86_64.whl"> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-cpu-openblas/paddlepaddle-1.5.1-cp27-cp27mu-linux_x86_64.whl">
paddlepaddle-1.5.0-cp27-cp27mu-linux_x86_64.whl</a></td> paddlepaddle-1.5.1-cp27-cp27mu-linux_x86_64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-cpu-openblas/paddlepaddle-1.5.0-cp27-cp27m-linux_x86_64.whl"> paddlepaddle-1.5.0-cp27-cp27m-linux_x86_64.whl</a></td> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-cpu-openblas/paddlepaddle-1.5.1-cp27-cp27m-linux_x86_64.whl"> paddlepaddle-1.5.1-cp27-cp27m-linux_x86_64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-cpu-openblas/paddlepaddle-1.5.0-cp35-cp35m-linux_x86_64.whl"> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-cpu-openblas/paddlepaddle-1.5.1-cp35-cp35m-linux_x86_64.whl">
paddlepaddle-1.5.0-cp35-cp35m-linux_x86_64.whl</a></td> paddlepaddle-1.5.1-cp35-cp35m-linux_x86_64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-cpu-openblas/paddlepaddle-1.5.0-cp36-cp36m-linux_x86_64.whl"> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-cpu-openblas/paddlepaddle-1.5.1-cp36-cp36m-linux_x86_64.whl">
paddlepaddle-1.5.0-cp36-cp36m-linux_x86_64.whl</a></td> paddlepaddle-1.5.1-cp36-cp36m-linux_x86_64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-cpu-openblas/paddlepaddle-1.5.0-cp37-cp37m-linux_x86_64.whl"> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-cpu-openblas/paddlepaddle-1.5.1-cp37-cp37m-linux_x86_64.whl">
paddlepaddle-1.5.0-cp37-cp37m-linux_x86_64.whl</a></td> paddlepaddle-1.5.1-cp37-cp37m-linux_x86_64.whl</a></td>
</tr> </tr>
<tr> <tr>
<td> cuda8-cudnn7-openblas </td> <td> cuda8-cudnn7-openblas </td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-gpu-cuda8-cudnn7-openblas/paddlepaddle_gpu-1.5.0-cp27-cp27mu-linux_x86_64.whl"> paddlepaddle_gpu-1.5.0-cp27-cp27mu-linux_x86_64.whl</a></td> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-gpu-cuda8-cudnn7-openblas/paddlepaddle_gpu-1.5.1-cp27-cp27mu-linux_x86_64.whl"> paddlepaddle_gpu-1.5.1-cp27-cp27mu-linux_x86_64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-gpu-cuda8-cudnn7-openblas/paddlepaddle_gpu-1.5.0-cp27-cp27m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.0-cp27-cp27m-linux_x86_64.whl</a></td> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-gpu-cuda8-cudnn7-openblas/paddlepaddle_gpu-1.5.1-cp27-cp27m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.1-cp27-cp27m-linux_x86_64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-gpu-cuda8-cudnn7-openblas/paddlepaddle_gpu-1.5.0-cp35-cp35m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.0-cp35-cp35m-linux_x86_64.whl</a></td> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-gpu-cuda8-cudnn7-openblas/paddlepaddle_gpu-1.5.1-cp35-cp35m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.1-cp35-cp35m-linux_x86_64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-gpu-cuda8-cudnn7-openblas/paddlepaddle_gpu-1.5.0-cp36-cp36m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.0-cp36-cp36m-linux_x86_64.whl</a></td> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-gpu-cuda8-cudnn7-openblas/paddlepaddle_gpu-1.5.1-cp36-cp36m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.1-cp36-cp36m-linux_x86_64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-gpu-cuda8-cudnn7-openblas/paddlepaddle_gpu-1.5.0-cp37-cp37m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.0-cp37-cp37m-linux_x86_64.whl</a></td> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-gpu-cuda8-cudnn7-openblas/paddlepaddle_gpu-1.5.1-cp37-cp37m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.1-cp37-cp37m-linux_x86_64.whl</a></td>
</tr> </tr>
<tr> <tr>
<td> cuda8-cudnn7-mkl </td> <td> cuda8-cudnn7-mkl </td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-gpu-cuda8-cudnn7-mkl/paddlepaddle_gpu-1.5.0.post87-cp27-cp27mu-linux_x86_64.whl"> paddlepaddle_gpu-1.5.0-cp27-cp27mu-linux_x86_64.whl</a></td> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-gpu-cuda8-cudnn7-mkl/paddlepaddle_gpu-1.5.1.post87-cp27-cp27mu-linux_x86_64.whl"> paddlepaddle_gpu-1.5.1-cp27-cp27mu-linux_x86_64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-gpu-cuda8-cudnn7-mkl/paddlepaddle_gpu-1.5.0.post87-cp27-cp27m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.0-cp27-cp27m-linux_x86_64.whl</a></td> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-gpu-cuda8-cudnn7-mkl/paddlepaddle_gpu-1.5.1.post87-cp27-cp27m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.1-cp27-cp27m-linux_x86_64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-gpu-cuda8-cudnn7-mkl/paddlepaddle_gpu-1.5.0.post87-cp35-cp35m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.0-cp35-cp35m-linux_x86_64.whl</a></td> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-gpu-cuda8-cudnn7-mkl/paddlepaddle_gpu-1.5.1.post87-cp35-cp35m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.1-cp35-cp35m-linux_x86_64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-gpu-cuda8-cudnn7-mkl/paddlepaddle_gpu-1.5.0.post87-cp36-cp36m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.0-cp36-cp36m-linux_x86_64.whl</a></td> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-gpu-cuda8-cudnn7-mkl/paddlepaddle_gpu-1.5.1.post87-cp36-cp36m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.1-cp36-cp36m-linux_x86_64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-gpu-cuda8-cudnn7-mkl/paddlepaddle_gpu-1.5.0.post87-cp37-cp37m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.0-cp37-cp37m-linux_x86_64.whl</a></td> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-gpu-cuda8-cudnn7-mkl/paddlepaddle_gpu-1.5.1.post87-cp37-cp37m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.1-cp37-cp37m-linux_x86_64.whl</a></td>
</tr> </tr>
<tr> <tr>
<td> cuda9-cudnn7-mkl </td> <td> cuda9-cudnn7-mkl </td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-gpu-cuda9-cudnn7-mkl/paddlepaddle_gpu-1.5.0.post97-cp27-cp27mu-linux_x86_64.whl"> paddlepaddle_gpu-1.5.0-cp27-cp27mu-linux_x86_64.whl</a></td> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-gpu-cuda9-cudnn7-mkl/paddlepaddle_gpu-1.5.1.post97-cp27-cp27mu-linux_x86_64.whl"> paddlepaddle_gpu-1.5.1-cp27-cp27mu-linux_x86_64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-gpu-cuda9-cudnn7-mkl/paddlepaddle_gpu-1.5.0.post97-cp27-cp27m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.0-cp27-cp27m-linux_x86_64.whl</a></td> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-gpu-cuda9-cudnn7-mkl/paddlepaddle_gpu-1.5.1.post97-cp27-cp27m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.1-cp27-cp27m-linux_x86_64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-gpu-cuda9-cudnn7-mkl/paddlepaddle_gpu-1.5.0.post97-cp35-cp35m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.0-cp35-cp35m-linux_x86_64.whl</a></td> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-gpu-cuda9-cudnn7-mkl/paddlepaddle_gpu-1.5.1.post97-cp35-cp35m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.1-cp35-cp35m-linux_x86_64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-gpu-cuda9-cudnn7-mkl/paddlepaddle_gpu-1.5.0.post97-cp36-cp36m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.0-cp36-cp36m-linux_x86_64.whl</a></td> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-gpu-cuda9-cudnn7-mkl/paddlepaddle_gpu-1.5.1.post97-cp36-cp36m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.1-cp36-cp36m-linux_x86_64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-gpu-cuda9-cudnn7-mkl/paddlepaddle_gpu-1.5.0.post97-cp37-cp37m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.0-cp37-cp37m-linux_x86_64.whl</a></td> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-gpu-cuda9-cudnn7-mkl/paddlepaddle_gpu-1.5.1.post97-cp37-cp37m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.1-cp37-cp37m-linux_x86_64.whl</a></td>
</tr> </tr>
<tr> <tr>
<td> cuda10_cudnn7-mkl </td> <td> cuda10_cudnn7-mkl </td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-gpu-cuda10-cudnn7-mkl/paddlepaddle_gpu-1.5.0.post107-cp27-cp27mu-linux_x86_64.whl"> paddlepaddle_gpu-1.5.0-cp27-cp27mu-linux_x86_64.whl</a></td> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-gpu-cuda10-cudnn7-mkl/paddlepaddle_gpu-1.5.1.post107-cp27-cp27mu-linux_x86_64.whl"> paddlepaddle_gpu-1.5.1-cp27-cp27mu-linux_x86_64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-gpu-cuda10-cudnn7-mkl/paddlepaddle_gpu-1.5.0.post107-cp27-cp27m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.0-cp27-cp27m-linux_x86_64.whl</a></td> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-gpu-cuda10-cudnn7-mkl/paddlepaddle_gpu-1.5.1.post107-cp27-cp27m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.1-cp27-cp27m-linux_x86_64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-gpu-cuda10-cudnn7-mkl/paddlepaddle_gpu-1.5.0.post107-cp35-cp35m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.0-cp35-cp35m-linux_x86_64.whl</a></td> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-gpu-cuda10-cudnn7-mkl/paddlepaddle_gpu-1.5.1.post107-cp35-cp35m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.1-cp35-cp35m-linux_x86_64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-gpu-cuda10-cudnn7-mkl/paddlepaddle_gpu-1.5.0.post107-cp36-cp36m-linux_x86_64.whl"> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-gpu-cuda10-cudnn7-mkl/paddlepaddle_gpu-1.5.1.post107-cp36-cp36m-linux_x86_64.whl">
paddlepaddle_gpu-1.5.0-cp36-cp36m-linux_x86_64.whl</a></td> paddlepaddle_gpu-1.5.1-cp36-cp36m-linux_x86_64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-gpu-cuda10-cudnn7-mkl/paddlepaddle_gpu-1.5.0.post107-cp37-cp37m-linux_x86_64.whl"> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-gpu-cuda10-cudnn7-mkl/paddlepaddle_gpu-1.5.1.post107-cp37-cp37m-linux_x86_64.whl">
paddlepaddle_gpu-1.5.0-cp37-cp37m-linux_x86_64.whl</a></td> paddlepaddle_gpu-1.5.1-cp37-cp37m-linux_x86_64.whl</a></td>
</tr>
<tr>
<td> win_cpu_openblas </td>
<td> - </td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-win-open/paddlepaddle-1.5.1-cp27-cp27m-win_amd64.whl">
paddlepaddle-1.5.1-cp27-cp27m-win_amd64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-win-open/paddlepaddle-1.5.1-cp35-cp35m-win_amd64.whl">
paddlepaddle-1.5.1-cp35-cp35m-win_amd64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-win-open/paddlepaddle-1.5.1-cp36-cp36m-win_amd64.whl">
paddlepaddle-1.5.1-cp36-cp36m-win_amd64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-win-open/paddlepaddle-1.5.1-cp37-cp37m-win_amd64.whl">
paddlepaddle-1.5.1-cp37-cp37m-win_amd64.whl</a></td>
</tr>
<tr>
<td> win_cuda8_cudnn7_openblas </td>
<td> - </td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-win-open/paddlepaddle_gpu-1.5.1.post87-cp27-cp27m-win_amd64.whl">
paddlepaddle_gpu-1.5.1-cp27-cp27m-win_amd64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-win-open/paddlepaddle_gpu-1.5.1.post87-cp35-cp35m-win_amd64.whl">
paddlepaddle_gpu-1.5.1-cp35-cp35m-win_amd64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-win-open/paddlepaddle_gpu-1.5.1.post87-cp36-cp36m-win_amd64.whl">
paddlepaddle_gpu-1.5.1-cp36-cp36m-win_amd64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-win-open/paddlepaddle_gpu-1.5.1.post87-cp37-cp37m-win_amd64.whl">
paddlepaddle_gpu-1.5.1-cp37-cp37m-win_amd64.whl</a></td>
</tr>
<tr>
<td> win_cuda9_cudnn7_openblas </td>
<td> - </td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-win-open/paddlepaddle_gpu-1.5.1.post97-cp27-cp27m-win_amd64.whl">
paddlepaddle_gpu-1.5.1-cp27-cp27m-win_amd64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-win-open/paddlepaddle_gpu-1.5.1.post97-cp35-cp35m-win_amd64.whl">
paddlepaddle_gpu-1.5.1-cp35-cp35m-win_amd64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-win-open/paddlepaddle_gpu-1.5.1.post97-cp36-cp36m-win_amd64.whl">
paddlepaddle_gpu-1.5.1-cp36-cp36m-win_amd64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-win-open/paddlepaddle_gpu-1.5.1.post97-cp37-cp37m-win_amd64.whl">
paddlepaddle_gpu-1.5.1-cp37-cp37m-win_amd64.whl</a></td>
</tr> </tr>
<tr> <tr>
<td> mac_cpu </td> <td> mac_cpu </td>
<td> - </td> <td> - </td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-cpu-mac/paddlepaddle-1.5.0-cp27-cp27m-macosx_10_6_intel.whl"> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-cpu-mac/paddlepaddle-1.5.1-cp27-cp27m-macosx_10_6_intel.whl">
paddlepaddle-1.5.0-cp27-cp27m-macosx_10_6_intel.whl</a></td> paddlepaddle-1.5.1-cp27-cp27m-macosx_10_6_intel.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-cpu-mac/paddlepaddle-1.5.0-cp35-cp35m-macosx_10_6_intel.whl"> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-cpu-mac/paddlepaddle-1.5.1-cp35-cp35m-macosx_10_6_intel.whl">
paddlepaddle-1.5.0-cp35-cp35m-macosx_10_6_intel.whl</a></td> paddlepaddle-1.5.1-cp35-cp35m-macosx_10_6_intel.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-cpu-mac/paddlepaddle-1.5.0-cp36-cp36m-macosx_10_6_intel.whl"> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-cpu-mac/paddlepaddle-1.5.1-cp36-cp36m-macosx_10_6_intel.whl">
paddlepaddle-1.5.0-cp36-cp36m-macosx_10_6_intel.whl</a></td> paddlepaddle-1.5.1-cp36-cp36m-macosx_10_6_intel.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-cpu-mac/paddlepaddle-1.5.0-cp37-cp37m-macosx_10_6_intel.whl"> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-cpu-mac/paddlepaddle-1.5.1-cp37-cp37m-macosx_10_6_intel.whl">
paddlepaddle-1.5.0-cp37-cp37m-macosx_10_6_intel.whl</a></td> paddlepaddle-1.5.1-cp37-cp37m-macosx_10_6_intel.whl</a></td>
</tr> </tr>
</tbody> </tbody>
</table> </table>
......
doc/fluid/beginners_guide/install/Tables_en.md
浏览文件 @ fa79f486
...@@ -239,16 +239,16 @@ PaddePaddle implements references to various BLAS/CUDA/cuDNN libraries by specif ...@@ -239,16 +239,16 @@ PaddePaddle implements references to various BLAS/CUDA/cuDNN libraries by specif
</thead> </thead>
<tbody> <tbody>
<tr> <tr>
<td> paddlepaddle==[version code] such as paddlepaddle==1.5.0 </td> <td> paddlepaddle==[version code] such as paddlepaddle==1.5.1 </td>
<td> Only support the corresponding version of the CPU PaddlePaddle, please refer to <a href=https://pypi.org/project/paddlepaddle/#history>Pypi</a> for the specific version. </td> <td> Only support the corresponding version of the CPU PaddlePaddle, please refer to <a href=https://pypi.org/project/paddlepaddle/#history>Pypi</a> for the specific version. </td>
</tr> </tr>
<tr> <tr>
<td> paddlepaddle-gpu==1.5.0 </td> <td> paddlepaddle-gpu==1.5.1 </td>
<td> Using version 1.5.0 compiled with CUDA 9.0 and cuDNN 7 </td> <td> Using version 1.5.1 compiled with CUDA 9.0 and cuDNN 7 </td>
</tr> </tr>
<tr> <tr>
<td> paddlepaddle-gpu==1.5.0.post87 </td> <td> paddlepaddle-gpu==1.5.1.post87 </td>
<td> Using version 1.5.0 compiled with CUDA 8.0 and cuDNN 7 </td> <td> Using version 1.5.1 compiled with CUDA 8.0 and cuDNN 7 </td>
</tr> </tr>
</tbody> </tbody>
</table> </table>
...@@ -319,74 +319,110 @@ You can find the docker image for each release of PaddlePaddle in the [DockerHub ...@@ -319,74 +319,110 @@ You can find the docker image for each release of PaddlePaddle in the [DockerHub
<tbody> <tbody>
<tr> <tr>
<td> cpu-mkl </td> <td> cpu-mkl </td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-cpu-mkl/paddlepaddle-1.5.0-cp27-cp27mu-linux_x86_64.whl"> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-cpu-mkl/paddlepaddle-1.5.1-cp27-cp27mu-linux_x86_64.whl">
paddlepaddle-1.5.0-cp27-cp27mu-linux_x86_64.whl</a></td> paddlepaddle-1.5.1-cp27-cp27mu-linux_x86_64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-cpu-mkl/paddlepaddle-1.5.0-cp27-cp27m-linux_x86_64.whl"> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-cpu-mkl/paddlepaddle-1.5.1-cp27-cp27m-linux_x86_64.whl">
paddlepaddle-1.5.0-cp27-cp27m-linux_x86_64.whl</a></td> paddlepaddle-1.5.1-cp27-cp27m-linux_x86_64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-cpu-mkl/paddlepaddle-1.5.0-cp35-cp35m-linux_x86_64.whl"> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-cpu-mkl/paddlepaddle-1.5.1-cp35-cp35m-linux_x86_64.whl">
paddlepaddle-1.5.0-cp35-cp35m-linux_x86_64.whl</a></td> paddlepaddle-1.5.1-cp35-cp35m-linux_x86_64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-cpu-mkl/paddlepaddle-1.5.0-cp36-cp36m-linux_x86_64.whl"> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-cpu-mkl/paddlepaddle-1.5.1-cp36-cp36m-linux_x86_64.whl">
paddlepaddle-1.5.0-cp36-cp36m-linux_x86_64.whl</a></td> paddlepaddle-1.5.1-cp36-cp36m-linux_x86_64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-cpu-mkl/paddlepaddle-1.5.0-cp37-cp37m-linux_x86_64.whl"> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-cpu-mkl/paddlepaddle-1.5.1-cp37-cp37m-linux_x86_64.whl">
paddlepaddle-1.5.0-cp37-cp37m-linux_x86_64.whl</a></td> paddlepaddle-1.5.1-cp37-cp37m-linux_x86_64.whl</a></td>
</tr> </tr>
<tr> <tr>
<td> cpu-openblas </td> <td> cpu-openblas </td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-cpu-openblas/paddlepaddle-1.5.0-cp27-cp27mu-linux_x86_64.whl"> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-cpu-openblas/paddlepaddle-1.5.1-cp27-cp27mu-linux_x86_64.whl">
paddlepaddle-1.5.0-cp27-cp27mu-linux_x86_64.whl</a></td> paddlepaddle-1.5.1-cp27-cp27mu-linux_x86_64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-cpu-openblas/paddlepaddle-1.5.0-cp27-cp27m-linux_x86_64.whl"> paddlepaddle-1.5.0-cp27-cp27m-linux_x86_64.whl</a></td> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-cpu-openblas/paddlepaddle-1.5.1-cp27-cp27m-linux_x86_64.whl"> paddlepaddle-1.5.1-cp27-cp27m-linux_x86_64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-cpu-openblas/paddlepaddle-1.5.0-cp35-cp35m-linux_x86_64.whl"> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-cpu-openblas/paddlepaddle-1.5.1-cp35-cp35m-linux_x86_64.whl">
paddlepaddle-1.5.0-cp35-cp35m-linux_x86_64.whl</a></td> paddlepaddle-1.5.1-cp35-cp35m-linux_x86_64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-cpu-openblas/paddlepaddle-1.5.0-cp36-cp36m-linux_x86_64.whl"> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-cpu-openblas/paddlepaddle-1.5.1-cp36-cp36m-linux_x86_64.whl">
paddlepaddle-1.5.0-cp36-cp36m-linux_x86_64.whl</a></td> paddlepaddle-1.5.1-cp36-cp36m-linux_x86_64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-cpu-openblas/paddlepaddle-1.5.0-cp37-cp37m-linux_x86_64.whl"> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-cpu-openblas/paddlepaddle-1.5.1-cp37-cp37m-linux_x86_64.whl">
paddlepaddle-1.5.0-cp37-cp37m-linux_x86_64.whl</a></td> paddlepaddle-1.5.1-cp37-cp37m-linux_x86_64.whl</a></td>
</tr> </tr>
<tr> <tr>
<td> cuda8-cudnn7-openblas </td> <td> cuda8-cudnn7-openblas </td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-gpu-cuda8-cudnn7-openblas/paddlepaddle_gpu-1.5.0-cp27-cp27mu-linux_x86_64.whl"> paddlepaddle_gpu-1.5.0-cp27-cp27mu-linux_x86_64.whl</a></td> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-gpu-cuda8-cudnn7-openblas/paddlepaddle_gpu-1.5.1-cp27-cp27mu-linux_x86_64.whl"> paddlepaddle_gpu-1.5.1-cp27-cp27mu-linux_x86_64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-gpu-cuda8-cudnn7-openblas/paddlepaddle_gpu-1.5.0-cp27-cp27m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.0-cp27-cp27m-linux_x86_64.whl</a></td> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-gpu-cuda8-cudnn7-openblas/paddlepaddle_gpu-1.5.1-cp27-cp27m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.1-cp27-cp27m-linux_x86_64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-gpu-cuda8-cudnn7-openblas/paddlepaddle_gpu-1.5.0-cp35-cp35m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.0-cp35-cp35m-linux_x86_64.whl</a></td> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-gpu-cuda8-cudnn7-openblas/paddlepaddle_gpu-1.5.1-cp35-cp35m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.1-cp35-cp35m-linux_x86_64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-gpu-cuda8-cudnn7-openblas/paddlepaddle_gpu-1.5.0-cp36-cp36m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.0-cp36-cp36m-linux_x86_64.whl</a></td> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-gpu-cuda8-cudnn7-openblas/paddlepaddle_gpu-1.5.1-cp36-cp36m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.1-cp36-cp36m-linux_x86_64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-gpu-cuda8-cudnn7-openblas/paddlepaddle_gpu-1.5.0-cp37-cp37m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.0-cp37-cp37m-linux_x86_64.whl</a></td> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-gpu-cuda8-cudnn7-openblas/paddlepaddle_gpu-1.5.1-cp37-cp37m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.1-cp37-cp37m-linux_x86_64.whl</a></td>
</tr> </tr>
<tr> <tr>
<td> cuda8-cudnn7-mkl </td> <td> cuda8-cudnn7-mkl </td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-gpu-cuda8-cudnn7-mkl/paddlepaddle_gpu-1.5.0.post87-cp27-cp27mu-linux_x86_64.whl"> paddlepaddle_gpu-1.5.0-cp27-cp27mu-linux_x86_64.whl</a></td> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-gpu-cuda8-cudnn7-mkl/paddlepaddle_gpu-1.5.1.post87-cp27-cp27mu-linux_x86_64.whl"> paddlepaddle_gpu-1.5.1-cp27-cp27mu-linux_x86_64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-gpu-cuda8-cudnn7-mkl/paddlepaddle_gpu-1.5.0.post87-cp27-cp27m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.0-cp27-cp27m-linux_x86_64.whl</a></td> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-gpu-cuda8-cudnn7-mkl/paddlepaddle_gpu-1.5.1.post87-cp27-cp27m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.1-cp27-cp27m-linux_x86_64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-gpu-cuda8-cudnn7-mkl/paddlepaddle_gpu-1.5.0.post87-cp35-cp35m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.0-cp35-cp35m-linux_x86_64.whl</a></td> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-gpu-cuda8-cudnn7-mkl/paddlepaddle_gpu-1.5.1.post87-cp35-cp35m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.1-cp35-cp35m-linux_x86_64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-gpu-cuda8-cudnn7-mkl/paddlepaddle_gpu-1.5.0.post87-cp36-cp36m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.0-cp36-cp36m-linux_x86_64.whl</a></td> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-gpu-cuda8-cudnn7-mkl/paddlepaddle_gpu-1.5.1.post87-cp36-cp36m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.1-cp36-cp36m-linux_x86_64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-gpu-cuda8-cudnn7-mkl/paddlepaddle_gpu-1.5.0.post87-cp37-cp37m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.0-cp37-cp37m-linux_x86_64.whl</a></td> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-gpu-cuda8-cudnn7-mkl/paddlepaddle_gpu-1.5.1.post87-cp37-cp37m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.1-cp37-cp37m-linux_x86_64.whl</a></td>
</tr> </tr>
<tr> <tr>
<td> cuda9-cudnn7-mkl </td> <td> cuda9-cudnn7-mkl </td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-gpu-cuda9-cudnn7-mkl/paddlepaddle_gpu-1.5.0.post97-cp27-cp27mu-linux_x86_64.whl"> paddlepaddle_gpu-1.5.0-cp27-cp27mu-linux_x86_64.whl</a></td> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-gpu-cuda9-cudnn7-mkl/paddlepaddle_gpu-1.5.1.post97-cp27-cp27mu-linux_x86_64.whl"> paddlepaddle_gpu-1.5.1-cp27-cp27mu-linux_x86_64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-gpu-cuda9-cudnn7-mkl/paddlepaddle_gpu-1.5.0.post97-cp27-cp27m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.0-cp27-cp27m-linux_x86_64.whl</a></td> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-gpu-cuda9-cudnn7-mkl/paddlepaddle_gpu-1.5.1.post97-cp27-cp27m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.1-cp27-cp27m-linux_x86_64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-gpu-cuda9-cudnn7-mkl/paddlepaddle_gpu-1.5.0.post97-cp35-cp35m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.0-cp35-cp35m-linux_x86_64.whl</a></td> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-gpu-cuda9-cudnn7-mkl/paddlepaddle_gpu-1.5.1.post97-cp35-cp35m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.1-cp35-cp35m-linux_x86_64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-gpu-cuda9-cudnn7-mkl/paddlepaddle_gpu-1.5.0.post97-cp36-cp36m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.0-cp36-cp36m-linux_x86_64.whl</a></td> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-gpu-cuda9-cudnn7-mkl/paddlepaddle_gpu-1.5.1.post97-cp36-cp36m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.1-cp36-cp36m-linux_x86_64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-gpu-cuda9-cudnn7-mkl/paddlepaddle_gpu-1.5.0.post97-cp37-cp37m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.0-cp37-cp37m-linux_x86_64.whl</a></td> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-gpu-cuda9-cudnn7-mkl/paddlepaddle_gpu-1.5.1.post97-cp37-cp37m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.1-cp37-cp37m-linux_x86_64.whl</a></td>
</tr> </tr>
<tr> <tr>
<td> cuda10_cudnn7-mkl </td> <td> cuda10_cudnn7-mkl </td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-gpu-cuda10-cudnn7-mkl/paddlepaddle_gpu-1.5.0.post107-cp27-cp27mu-linux_x86_64.whl"> paddlepaddle_gpu-1.5.0-cp27-cp27mu-linux_x86_64.whl</a></td> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-gpu-cuda10-cudnn7-mkl/paddlepaddle_gpu-1.5.1.post107-cp27-cp27mu-linux_x86_64.whl"> paddlepaddle_gpu-1.5.1-cp27-cp27mu-linux_x86_64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-gpu-cuda10-cudnn7-mkl/paddlepaddle_gpu-1.5.0.post107-cp27-cp27m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.0-cp27-cp27m-linux_x86_64.whl</a></td> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-gpu-cuda10-cudnn7-mkl/paddlepaddle_gpu-1.5.1.post107-cp27-cp27m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.1-cp27-cp27m-linux_x86_64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-gpu-cuda10-cudnn7-mkl/paddlepaddle_gpu-1.5.0.post107-cp35-cp35m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.0-cp35-cp35m-linux_x86_64.whl</a></td> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-gpu-cuda10-cudnn7-mkl/paddlepaddle_gpu-1.5.1.post107-cp35-cp35m-linux_x86_64.whl"> paddlepaddle_gpu-1.5.1-cp35-cp35m-linux_x86_64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-gpu-cuda10-cudnn7-mkl/paddlepaddle_gpu-1.5.0.post107-cp36-cp36m-linux_x86_64.whl"> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-gpu-cuda10-cudnn7-mkl/paddlepaddle_gpu-1.5.1.post107-cp36-cp36m-linux_x86_64.whl">
paddlepaddle_gpu-1.5.0-cp36-cp36m-linux_x86_64.whl</a></td> paddlepaddle_gpu-1.5.1-cp36-cp36m-linux_x86_64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-gpu-cuda10-cudnn7-mkl/paddlepaddle_gpu-1.5.0.post107-cp37-cp37m-linux_x86_64.whl"> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-gpu-cuda10-cudnn7-mkl/paddlepaddle_gpu-1.5.1.post107-cp37-cp37m-linux_x86_64.whl">
paddlepaddle_gpu-1.5.0-cp37-cp37m-linux_x86_64.whl</a></td> paddlepaddle_gpu-1.5.1-cp37-cp37m-linux_x86_64.whl</a></td>
</tr>
<tr>
<td> win_cpu_openblas </td>
<td> - </td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-win-open/paddlepaddle-1.5.1-cp27-cp27m-win_amd64.whl">
paddlepaddle-1.5.1-cp27-cp27m-win_amd64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-win-open/paddlepaddle-1.5.1-cp35-cp35m-win_amd64.whl">
paddlepaddle-1.5.1-cp35-cp35m-win_amd64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-win-open/paddlepaddle-1.5.1-cp36-cp36m-win_amd64.whl">
paddlepaddle-1.5.1-cp36-cp36m-win_amd64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-win-open/paddlepaddle-1.5.1-cp37-cp37m-win_amd64.whl">
paddlepaddle-1.5.1-cp37-cp37m-win_amd64.whl</a></td>
</tr>
<tr>
<td> win_cuda8_cudnn7_openblas </td>
<td> - </td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-win-open/paddlepaddle_gpu-1.5.1.post87-cp27-cp27m-win_amd64.whl">
paddlepaddle_gpu-1.5.1-cp27-cp27m-win_amd64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-win-open/paddlepaddle_gpu-1.5.1.post87-cp35-cp35m-win_amd64.whl">
paddlepaddle_gpu-1.5.1-cp35-cp35m-win_amd64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-win-open/paddlepaddle_gpu-1.5.1.post87-cp36-cp36m-win_amd64.whl">
paddlepaddle_gpu-1.5.1-cp36-cp36m-win_amd64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-win-open/paddlepaddle_gpu-1.5.1.post87-cp37-cp37m-win_amd64.whl">
paddlepaddle_gpu-1.5.1-cp37-cp37m-win_amd64.whl</a></td>
</tr>
<tr>
<td> win_cuda9_cudnn7_openblas </td>
<td> - </td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-win-open/paddlepaddle_gpu-1.5.1.post97-cp27-cp27m-win_amd64.whl">
paddlepaddle_gpu-1.5.1-cp27-cp27m-win_amd64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-win-open/paddlepaddle_gpu-1.5.1.post97-cp35-cp35m-win_amd64.whl">
paddlepaddle_gpu-1.5.1-cp35-cp35m-win_amd64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-win-open/paddlepaddle_gpu-1.5.1.post97-cp36-cp36m-win_amd64.whl">
paddlepaddle_gpu-1.5.1-cp36-cp36m-win_amd64.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-win-open/paddlepaddle_gpu-1.5.1.post97-cp37-cp37m-win_amd64.whl">
paddlepaddle_gpu-1.5.1-cp37-cp37m-win_amd64.whl</a></td>
</tr> </tr>
<tr> <tr>
<td> mac_cpu </td> <td> mac_cpu </td>
<td> - </td> <td> - </td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-cpu-mac/paddlepaddle-1.5.0-cp27-cp27m-macosx_10_6_intel.whl"> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-cpu-mac/paddlepaddle-1.5.1-cp27-cp27m-macosx_10_6_intel.whl">
paddlepaddle-1.5.0-cp27-cp27m-macosx_10_6_intel.whl</a></td> paddlepaddle-1.5.1-cp27-cp27m-macosx_10_6_intel.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-cpu-mac/paddlepaddle-1.5.0-cp35-cp35m-macosx_10_6_intel.whl"> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-cpu-mac/paddlepaddle-1.5.1-cp35-cp35m-macosx_10_6_intel.whl">
paddlepaddle-1.5.0-cp35-cp35m-macosx_10_6_intel.whl</a></td> paddlepaddle-1.5.1-cp35-cp35m-macosx_10_6_intel.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-cpu-mac/paddlepaddle-1.5.0-cp36-cp36m-macosx_10_6_intel.whl"> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-cpu-mac/paddlepaddle-1.5.1-cp36-cp36m-macosx_10_6_intel.whl">
paddlepaddle-1.5.0-cp36-cp36m-macosx_10_6_intel.whl</a></td> paddlepaddle-1.5.1-cp36-cp36m-macosx_10_6_intel.whl</a></td>
<td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.0-cpu-mac/paddlepaddle-1.5.0-cp37-cp37m-macosx_10_6_intel.whl"> <td> <a href="https://paddle-wheel.bj.bcebos.com/1.5.1-cpu-mac/paddlepaddle-1.5.1-cp37-cp37m-macosx_10_6_intel.whl">
paddlepaddle-1.5.0-cp37-cp37m-macosx_10_6_intel.whl</a></td> paddlepaddle-1.5.1-cp37-cp37m-macosx_10_6_intel.whl</a></td>
</tr> </tr>
</tbody> </tbody>
</table> </table>
......
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