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b77c886e
编写于
6月 17, 2018
作者:
Q
qiaolongfei
浏览文件
操作
浏览文件
下载
差异文件
Merge branch 'develop' of
https://github.com/PaddlePaddle/Paddle
into update-api-reference-1
上级
82a4cf19
e6654c1c
变更
12
展开全部
隐藏空白更改
内联
并排
Showing
12 changed file
with
517 addition
and
175 deletion
+517
-175
paddle/fluid/operators/activation_op.cc
paddle/fluid/operators/activation_op.cc
+1
-1
paddle/fluid/operators/clip_by_norm_op.cc
paddle/fluid/operators/clip_by_norm_op.cc
+10
-1
paddle/fluid/operators/pool_op.cc
paddle/fluid/operators/pool_op.cc
+13
-6
paddle/fluid/operators/uniform_random_batch_size_like_op.cc
paddle/fluid/operators/uniform_random_batch_size_like_op.cc
+2
-2
python/paddle/fluid/framework.py
python/paddle/fluid/framework.py
+39
-2
python/paddle/fluid/layers/control_flow.py
python/paddle/fluid/layers/control_flow.py
+52
-21
python/paddle/fluid/layers/io.py
python/paddle/fluid/layers/io.py
+64
-3
python/paddle/fluid/layers/learning_rate_scheduler.py
python/paddle/fluid/layers/learning_rate_scheduler.py
+55
-19
python/paddle/fluid/layers/metric.py
python/paddle/fluid/layers/metric.py
+25
-1
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+220
-98
python/paddle/fluid/layers/ops.py
python/paddle/fluid/layers/ops.py
+0
-2
python/paddle/fluid/layers/tensor.py
python/paddle/fluid/layers/tensor.py
+36
-19
未找到文件。
paddle/fluid/operators/activation_op.cc
浏览文件 @
b77c886e
...
...
@@ -443,7 +443,7 @@ class SwishOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment
(
R"DOC(
Swish Activation Operator.
$$out = \frac{x}{1 + e^{- \beta x}}$$
$$out = \
\
frac{x}{1 + e^{- \beta x}}$$
)DOC"
);
}
...
...
paddle/fluid/operators/clip_by_norm_op.cc
浏览文件 @
b77c886e
...
...
@@ -54,10 +54,19 @@ be linearly scaled to make the L2 norm of $Out$ equal to $max\_norm$, as
shown in the following formula:
$$
Out = \
frac{max
\_norm * X}{norm(X)},
Out = \
\frac{max\
\_norm * X}{norm(X)},
$$
where $norm(X)$ represents the L2 norm of $X$.
Examples:
.. code-block:: python
data = fluid.layer.data(
name='data', shape=[2, 4, 6], dtype='float32')
reshaped = fluid.layers.clip_by_norm(
x=data, max_norm=0.5)
)DOC"
);
}
};
...
...
paddle/fluid/operators/pool_op.cc
浏览文件 @
b77c886e
...
...
@@ -204,8 +204,6 @@ void Pool2dOpMaker::Make() {
// TODO(dzhwinter): need to registered layout transform function
AddComment
(
R"DOC(
Pool2d Operator.
The pooling2d operation calculates the output based on
the input, pooling_type and ksize, strides, paddings parameters.
Input(X) and output(Out) are in NCHW format, where N is batch size, C is the
...
...
@@ -215,19 +213,28 @@ These two elements represent height and width, respectively.
The input(X) size and output(Out) size may be different.
Example:
Input:
X shape: $(N, C, H_{in}, W_{in})$
Output:
Out shape: $(N, C, H_{out}, W_{out})$
For ceil_mode = false:
$$
H_{out} = \frac{(H_{in} - ksize[0] + 2 * paddings[0])}{strides[0]} + 1 \\
W_{out} = \frac{(W_{in} - ksize[1] + 2 * paddings[1])}{strides[1]} + 1
H_{out} = \\frac{(H_{in} - ksize[0] + 2 * paddings[0])}{strides[0]} + 1
$$
$$
W_{out} = \\frac{(W_{in} - ksize[1] + 2 * paddings[1])}{strides[1]} + 1
$$
For ceil_mode = true:
$$
H_{out} = \frac{(H_{in} - ksize[0] + 2 * paddings[0] + strides[0] - 1)}{strides[0]} + 1 \\
W_{out} = \frac{(W_{in} - ksize[1] + 2 * paddings[1] + strides[1] - 1)}{strides[1]} + 1
H_{out} = \\frac{(H_{in} - ksize[0] + 2 * paddings[0] + strides[0] - 1)}{strides[0]} + 1
$$
$$
W_{out} = \\frac{(W_{in} - ksize[1] + 2 * paddings[1] + strides[1] - 1)}{strides[1]} + 1
$$
)DOC"
);
...
...
paddle/fluid/operators/uniform_random_batch_size_like_op.cc
浏览文件 @
b77c886e
...
...
@@ -35,10 +35,10 @@ class UniformRandomBatchSizeLikeOpMaker : public BatchSizeLikeOpMaker {
protected:
void
Apply
()
override
{
AddComment
(
R"DOC(
Uniform
random operator
Uniform
RandomBatchSizeLike operator.
This operator initializes a tensor with the same batch_size as the Input tensor
with random values sampled from a uniform distribution.
with random values sampled from a uniform distribution.
)DOC"
);
AddAttr
<
float
>
(
"min"
,
...
...
python/paddle/fluid/framework.py
浏览文件 @
b77c886e
...
...
@@ -1034,6 +1034,37 @@ class Block(object):
class
Program
(
object
):
"""
Python Program. Beneath it is a ProgramDesc, which is used for
create c++ Program. A program is a self-contained programing
language like container. It has at least one Block, when the
control flow op like conditional_block, while_op is included,
it will contains nested block.
Please reference the framework.proto for details.
Notes: we have default_startup_program and default_main_program
by default, a pair of them will shared the parameters.
The default_startup_program only run once to initialize parameters,
default_main_program run in every minibatch and adjust the weights.
Args:
None
Returns:
Python Program
Examples:
.. code-block:: python
main_program = Program()
startup_program = Program()
with fluid.program_guard(main_program=main_program, startup_program=startup_program):
fluid.layers.data(name="x", shape=[-1, 784], dtype='float32')
fluid.layers.data(name="y", shape=[-1, 1], dtype='int32')
fluid.layers.fc(name="fc", shape=[10], dtype='float32', act="relu")
"""
def
__init__
(
self
):
self
.
desc
=
core
.
ProgramDesc
()
self
.
blocks
=
[
Block
(
self
,
0
)]
...
...
@@ -1099,6 +1130,8 @@ class Program(object):
def
clone
(
self
,
for_test
=
False
):
"""Clone the Program object
Args:
for_test(bool): indicate whether clone for test.
Set for_test to False when we want to clone the program for training.
Set for_test to True when we want to clone the program for testing.
...
...
@@ -1109,8 +1142,9 @@ class Program(object):
the is_test attributes in these operators will be set to True for
testing purposes, otherwise, they remain unchanged.
Returns(Program):
The cloned Program object.
Returns:
Program: The cloned Program object.
"""
if
for_test
:
p
=
self
.
inference_optimize
()
...
...
@@ -1228,6 +1262,7 @@ class Program(object):
def
copy_param_info_from
(
self
,
other
):
"""
Copy the information of parameters from other program.
Args:
other(Program): Other program
...
...
@@ -1246,6 +1281,7 @@ class Program(object):
def
copy_data_info_from
(
self
,
other
):
"""
Copy the information of data variables from other program.
Args:
other(Program): Other program
...
...
@@ -1299,6 +1335,7 @@ class Parameter(Variable):
def
to_string
(
self
,
throw_on_error
,
with_details
=
False
):
"""
To debug string.
Args:
throw_on_error(bool): raise exception when self is not initialized
when throw_on_error is True
...
...
python/paddle/fluid/layers/control_flow.py
浏览文件 @
b77c886e
...
...
@@ -822,17 +822,25 @@ def max_sequence_len(rank_table):
def
lod_tensor_to_array
(
x
,
table
):
""" Convert a LOD_TENSOR to an LOD_TENSOR_ARRAY.
"""
Convert a LoDTensor to a LoDTensorArray.
This function split a LoDTesnor to a LoDTensorArray according to its LoD
information. LoDTensorArray is an alias of C++ std::vector<LoDTensor> in
PaddlePaddle. The generated LoDTensorArray of this function can be further read
or written by `read_from_array()` and `write_to_array()` operators. However,
this function is generally an internal component of PaddlePaddle `DynamicRNN`.
Users should not use it directly.
Args:
x (Variable|list): The L
OD tensor to be converted to a LOD tensor a
rray.
x (Variable|list): The L
oDTensor to be converted to a LoDTensorA
rray.
table (ParamAttr|list): The variable that stores the level of lod
which is ordered by sequence length in
descending order.
descending order. It is generally generated
by `layers.lod_rank_table()` API.
Returns:
Variable: The variable of type array that has been converted from a
tensor.
Variable: The LoDTensorArray that has been converted from the input tensor.
Examples:
.. code-block:: python
...
...
@@ -897,8 +905,7 @@ def increment(x, value=1.0, in_place=True):
in_place (bool): If the increment should be performed in-place.
Returns:
Variable: The tensor variable storing the transformation of
element-wise increment of each value in the input.
Variable: The elementwise-incremented object.
Examples:
.. code-block:: python
...
...
@@ -940,7 +947,7 @@ def array_write(x, i, array=None):
Variable: The output LOD_TENSOR_ARRAY where the input tensor is written.
Examples:
.. code-block::python
.. code-block::
python
tmp = fluid.layers.zeros(shape=[10], dtype='int32')
i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=10)
...
...
@@ -1054,14 +1061,31 @@ def equal(x, y, cond=None, **ignored):
def
array_read
(
array
,
i
):
"""This function performs the operation to read the data in as an
"""
This function performs the operation to read the data in as an
LOD_TENSOR_ARRAY.
.. code-block:: text
Given:
array = [0.6, 0.1, 0.3, 0.1]
And:
i = 2
Then:
output = 0.3
Args:
array (Variable|list): The input tensor that
will be written to an array
.
i (Variable|list): The
subscript index in tensor array, that points the
place where data will be written to.
array (Variable|list): The input tensor that
store data to be read
.
i (Variable|list): The
index of the data to be read from input array.
Returns:
Variable: The tensor type variable that has the data written to it.
Examples:
.. code-block:: python
...
...
@@ -1154,6 +1178,13 @@ def array_length(array):
class
ConditionalBlockGuard
(
BlockGuard
):
"""
ConditionalBlockGuard is derived from BlockGuard. It is dedicated for
holding a ConditionalBlock, and helping users entering and exiting the
ConditionalBlock via Python's 'with' keyword. However, ConditionalBlockGuard
is generally an internal component of IfElse, users should not use it directly.
"""
def
__init__
(
self
,
block
):
if
not
isinstance
(
block
,
ConditionalBlock
):
raise
TypeError
(
"block should be conditional block"
)
...
...
@@ -1875,26 +1906,26 @@ def reorder_lod_tensor_by_rank(x, rank_table):
def
is_empty
(
x
,
cond
=
None
,
**
ignored
):
"""
**Is Empty**
This layer returns the truth value of whether the variable is empty.
Test whether a Variable is empty.
Args:
x
(Variable): Operand of *is_empty*
cond
(Variable|None): Optional output variable to store the result
of *is_empty*
x
(Variable): The Variable to be tested.
cond
(Variable|None): Output parameter. Returns the test result
of given 'x'. Default: None
Returns:
Variable:
The tensor variable storing the output of *is_empty*
.
Variable:
A bool scalar. True if 'x' is an empty Variable
.
Raises:
TypeError: If input cond is not a variable, or cond's dtype is
not bool
not bool
.
Examples:
.. code-block:: python
less = fluid.layers.is_empty(x=input)
res = fluid.layers.is_empty(x=input)
# or:
fluid.layers.is_empty(x=input, cond=res)
"""
helper
=
LayerHelper
(
"is_empty"
,
**
locals
())
if
cond
is
None
:
...
...
python/paddle/fluid/layers/io.py
浏览文件 @
b77c886e
...
...
@@ -544,6 +544,41 @@ def shuffle(reader, buffer_size):
def
batch
(
reader
,
batch_size
):
"""
This layer is a reader decorator. It takes a reader and adds
'batching' decoration on it. When reading with the result
decorated reader, output data will be automatically organized
to the form of batches.
Args:
reader(Variable): The reader to be decorated with 'batching'.
batch_size(int): The batch size.
Returns:
Variable: The reader which has been decorated with 'batching'.
Examples:
.. code-block:: python
raw_reader = fluid.layers.io.open_files(filenames=['./data1.recordio',
'./data2.recordio'],
shapes=[(3,224,224), (1)],
lod_levels=[0, 0],
dtypes=['float32', 'int64'],
thread_num=2,
buffer_size=2)
batch_reader = fluid.layers.batch(reader=raw_reader, batch_size=5)
# If we read data with the raw_reader:
# data = fluid.layers.read_file(raw_reader)
# We can only get data instance by instance.
#
# However, if we read data with the batch_reader:
# data = fluid.layers.read_file(batch_reader)
# Each 5 adjacent instances will be automatically combined together
# to become a batch. So what we get('data') is a batch data instead
# of an instance.
"""
return
__create_unshared_decorated_reader__
(
'create_batch_reader'
,
reader
,
{
'batch_size'
:
int
(
batch_size
)})
...
...
@@ -589,15 +624,41 @@ def parallel(reader):
{})
def
read_file
(
file_obj
):
def
read_file
(
reader
):
"""
Execute the given reader and get data via it.
A reader is also a Variable. It can be a raw reader generated by
`fluid.layers.open_files()` or a decorated one generated by
`fluid.layers.double_buffer()` and so on.
Args:
reader(Variable): The reader to execute.
Returns:
Tuple[Variable]: Data read via the given reader.
Examples:
.. code-block:: python
data_file = fluid.layers.open_files(
filenames=['mnist.recordio'],
shapes=[(-1, 748), (-1, 1)],
lod_levels=[0, 0],
dtypes=["float32", "int64"])
data_file = fluid.layers.double_buffer(
fluid.layers.batch(data_file, batch_size=64))
input, label = fluid.layers.read_file(data_file)
"""
helper
=
LayerHelper
(
'read_file'
)
out
=
[
helper
.
create_tmp_variable
(
stop_gradient
=
True
,
dtype
=
'float32'
)
for
_
in
range
(
len
(
file_obj
.
desc
.
shapes
()))
for
_
in
range
(
len
(
reader
.
desc
.
shapes
()))
]
helper
.
append_op
(
type
=
'read'
,
inputs
=
{
'Reader'
:
[
file_obj
]},
outputs
=
{
'Out'
:
out
})
type
=
'read'
,
inputs
=
{
'Reader'
:
[
reader
]},
outputs
=
{
'Out'
:
out
})
if
len
(
out
)
==
1
:
return
out
[
0
]
else
:
...
...
python/paddle/fluid/layers/learning_rate_scheduler.py
浏览文件 @
b77c886e
...
...
@@ -71,21 +71,40 @@ def noam_decay(d_model, warmup_steps):
def
exponential_decay
(
learning_rate
,
decay_steps
,
decay_rate
,
staircase
=
False
):
"""Applies exponential decay to the learning rate.
"""
Applies exponential decay to the learning rate.
When training a model, it is often recommended to lower the learning rate as the
training progresses. By using this function, the learning rate will be decayed by
'decay_rate' every 'decay_steps' steps.
>>> if staircase == True:
>>> decayed_learning_rate = learning_rate * decay_rate ^ floor(global_step / decay_steps)
>>> else:
>>> decayed_learning_rate = learning_rate * decay_rate ^ (global_step / decay_steps)
```python
decayed_learning_rate = learning_rate *
decay_rate ^ (global_step / decay_steps)
```
Args:
learning_rate
: A scalar float32 value or a Variable. This
will be the initial learning rate during training
decay_
steps: A Python `int32` number
.
decay_rate: A Python `float` number
.
staircase: Boolean. If set true, decay the learning rate every decay_steps.
learning_rate
(Variable|float): The initial learning rate.
decay_steps(int): See the decay computation above.
decay_
rate(float): The decay rate. See the decay computation above
.
staircase(Boolean): If True, decay the learning rate at discrete intervals
.
Default: False
Returns:
The decayed learning rate
Variable: The decayed learning rate
Examples:
.. code-block:: python
base_lr = 0.1
sgd_optimizer = fluid.optimizer.SGD(
learning_rate=fluid.layers.exponential_decay(
learning_rate=base_lr,
decay_steps=10000,
decay_rate=0.5,
staircase=True))
sgd_optimizer.minimize(avg_cost)
"""
global_step
=
_decay_step_counter
()
...
...
@@ -129,22 +148,39 @@ def natural_exp_decay(learning_rate, decay_steps, decay_rate, staircase=False):
def
inverse_time_decay
(
learning_rate
,
decay_steps
,
decay_rate
,
staircase
=
False
):
"""Applies inverse time decay to the initial learning rate.
"""
Applies inverse time decay to the initial learning rate.
>>> if staircase:
When training a model, it is often recommended to lower the learning rate as the
training progresses. By using this function, an inverse decay function will be
applied to the initial learning rate.
>>> if staircase == True:
>>> decayed_learning_rate = learning_rate / (1 + decay_rate * floor(global_step / decay_step))
>>> else:
>>> decayed_learning_rate = learning_rate / (1 + decay_rate * global_step / decay_step)
Args:
learning_rate
: A scalar float32 value or a Variable. This
will be the initial learning rate during training
.
decay_
steps: A Python `int32` number
.
decay_rate: A Python `float` number
.
staircase: Boolean. If set true, decay the learning rate every decay_steps.
learning_rate
(Variable|float): The initial learning rate.
decay_steps(int): See the decay computation above
.
decay_
rate(float): The decay rate. See the decay computation above
.
staircase(Boolean): If True, decay the learning rate at discrete intervals
.
Default: False
Returns:
The decayed learning rate
Variable: The decayed learning rate
Examples:
.. code-block:: python
base_lr = 0.1
sgd_optimizer = fluid.optimizer.SGD(
learning_rate=fluid.layers.inverse_time_decay(
learning_rate=base_lr,
decay_steps=10000,
decay_rate=0.5,
staircase=True))
sgd_optimizer.minimize(avg_cost)
"""
global_step
=
_decay_step_counter
()
...
...
python/paddle/fluid/layers/metric.py
浏览文件 @
b77c886e
...
...
@@ -27,8 +27,32 @@ __all__ = ['accuracy', 'auc']
def
accuracy
(
input
,
label
,
k
=
1
,
correct
=
None
,
total
=
None
):
"""
accuracy layer.
Refer to the https://en.wikipedia.org/wiki/Precision_and_recall
This function computes the accuracy using the input and label.
The output is the top k inputs and their indices.
If the correct label occurs in top k predictions, then correct will increment by one.
Note: the dtype of accuracy is determined by input. the input and label dtype can be different.
Args:
input(Variable): The input of accuracy layer, which is the predictions of network.
Carry LoD information is supported.
label(Variable): The label of dataset.
k(int): The top k predictions for each class will be checked.
correct(Variable): The correct predictions count.
total(Variable): The total entries count.
Returns:
Variable: The correct rate.
Examples:
.. code-block:: python
data = fluid.layers.data(name="data", shape=[-1, 32, 32], dtype="float32")
label = fluid.layers.data(name="data", shape=[-1,1], dtype="int32")
predict = fluid.layers.fc(input=data, size=10)
acc = fluid.layers.accuracy(input=predict, label=label, k=5)
"""
helper
=
LayerHelper
(
"accuracy"
,
**
locals
())
topk_out
,
topk_indices
=
nn
.
topk
(
input
,
k
=
k
)
...
...
python/paddle/fluid/layers/nn.py
浏览文件 @
b77c886e
此差异已折叠。
点击以展开。
python/paddle/fluid/layers/ops.py
浏览文件 @
b77c886e
...
...
@@ -17,7 +17,6 @@ __activations__ = [
'sigmoid'
,
'logsigmoid'
,
'exp'
,
'relu'
,
'tanh'
,
'tanh_shrink'
,
'softshrink'
,
...
...
@@ -29,7 +28,6 @@ __activations__ = [
'sin'
,
'round'
,
'reciprocal'
,
'log'
,
'square'
,
'softplus'
,
'softsign'
,
...
...
python/paddle/fluid/layers/tensor.py
浏览文件 @
b77c886e
...
...
@@ -108,16 +108,29 @@ def create_global_var(shape,
force_cpu
=
False
,
name
=
None
):
"""
Create a global variable. such as global_step
Create a new variable in the global block(block 0).
Args:
shape(list[int]): shape of the variable
value(float): the value of the variable
dtype(string): element type of the parameter
persistable(bool): if this variable is persistable
force_cpu(bool): force this variable to be on CPU
value(float): the value of the variable. The new created
variable will be filled with it.
dtype(string): data type of the variable
persistable(bool): if this variable is persistable.
Default: False
force_cpu(bool): force this variable to be on CPU.
Default: False
name(str|None): The name of the variable. If set to None the variable
name will be generated automatically.
Default: None
Returns:
Variable: the created Variable
Examples:
.. code-block:: python
var = fluid.create_global_var(shape=[2,3], value=1.0, dtype='float32',
persistable=True, force_cpu=True, name='new_var')
"""
helper
=
LayerHelper
(
"global_var"
,
**
locals
())
var
=
helper
.
create_global_variable
(
...
...
@@ -175,7 +188,8 @@ def concat(input, axis=0, name=None):
Examples:
.. code-block:: python
out = fluid.layers.concat(input=[Efirst, Esecond, Ethird, Efourth])
out = fluid.layers.concat(input=[Efirst, Esecond, Ethird, Efourth])
"""
helper
=
LayerHelper
(
'concat'
,
**
locals
())
out
=
helper
.
create_tmp_variable
(
dtype
=
helper
.
input_dtype
())
...
...
@@ -188,19 +202,21 @@ def concat(input, axis=0, name=None):
def
sums
(
input
,
out
=
None
):
"""This function performs the sum operation on the input and returns the
"""
This function performs the sum operation on the input and returns the
result as the output.
Args:
input (Variable|list): The input tensor that has the elements
that need to be summed up.
out (Variable|None): Output parameter. The sum result.
Default: None
Returns:
Variable: The tensor type variable that has the sum of input
written to it.
Variable: the sum of input. The same as the argument 'out'
Examples:
.. code-block::python
.. code-block::
python
tmp = fluid.layers.zeros(shape=[10], dtype='int32')
i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=10)
...
...
@@ -371,13 +387,13 @@ def argmin(x, axis=0):
x(Variable): The input to compute the indices of
the min elements.
axis(int): Axis to compute indices along.
Returns:
Variable: The tensor variable storing the output
Examples:
.. code-block:: python
out = fluid.layers.argmin(x=in, axis=0)
out = fluid.layers.argmin(x=in, axis=-1)
"""
...
...
@@ -402,13 +418,13 @@ def argmax(x, axis=0):
x(Variable): The input to compute the indices of
the max elements.
axis(int): Axis to compute indices along.
Returns:
Variable: The tensor variable storing the output
Examples:
.. code-block:: python
out = fluid.layers.argmax(x=in, axis=0)
out = fluid.layers.argmax(x=in, axis=-1)
"""
...
...
@@ -456,11 +472,12 @@ def zeros(shape, dtype, force_cpu=False):
It also sets *stop_gradient* to True.
Args:
shape(tuple|list|None): Shape of output tensor
dtype(np.dtype|core.VarDesc.VarType|str): Data type of output tensor
shape(tuple|list|None): Shape of output tensor.
dtype(np.dtype|core.VarDesc.VarType|str): Data type of output tensor.
force_cpu(bool, default False): Whether to make output stay on CPU.
Returns:
Variable: The tensor variable storing the output
Variable: The tensor variable storing the output
.
Examples:
.. code-block:: python
...
...
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