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提交 f6beb0fa 编写于 作者: Z zhouwei25 提交者： liuwei1031
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fix English Doc of API:layers.array_read, array_write, array_length, py_func and sum(#20498)

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Showing with 269 addition and 145 deletion
paddle/fluid/API.spec 100644 → 100755
浏览文件 @ f6beb0fa
......@@ -257,7 +257,7 @@ paddle.fluid.layers.uniform_random_batch_size_like (ArgSpec(args=['input', 'shap
paddle.fluid.layers.gaussian_random (ArgSpec(args=['shape', 'mean', 'std', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32')), ('document', 'dd4ddb66c78a2564e5d1e0e345d8286f'))
paddle.fluid.layers.sampling_id (ArgSpec(args=['x', 'min', 'max', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32')), ('document', '2490492db3b41af9144bb1539e4e9116'))
paddle.fluid.layers.gaussian_random_batch_size_like (ArgSpec(args=['input', 'shape', 'input_dim_idx', 'output_dim_idx', 'mean', 'std', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0, 0, 0.0, 1.0, 0, 'float32')), ('document', '2aed0f546f220364fb1da724a3176f74'))
paddle.fluid.layers.sum (ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None), ('document', 'f4b60847cb0f1ae00823ba6fb1b11310'))
paddle.fluid.layers.sum (ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None), ('document', '42c43fc74347bfe9528850aa7f59b2b2'))
paddle.fluid.layers.slice (ArgSpec(args=['input', 'axes', 'starts', 'ends'], varargs=None, keywords=None, defaults=None), ('document', '8c622791994a0d657d8c6c9cefa5bf34'))
paddle.fluid.layers.strided_slice (ArgSpec(args=['input', 'axes', 'starts', 'ends', 'strides'], varargs=None, keywords=None, defaults=None), ('document', '33b8dfd6708443ae93f1a0016ff6a5ef'))
paddle.fluid.layers.shape (ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None), ('document', '39534cccdb8e727e287316c7c42e6663'))
......@@ -288,7 +288,7 @@ paddle.fluid.layers.get_tensor_from_selected_rows (ArgSpec(args=['x', 'name'], v
paddle.fluid.layers.lstm (ArgSpec(args=['input', 'init_h', 'init_c', 'max_len', 'hidden_size', 'num_layers', 'dropout_prob', 'is_bidirec', 'is_test', 'name', 'default_initializer', 'seed'], varargs=None, keywords=None, defaults=(0.0, False, False, None, None, -1)), ('document', '5193cf1113f9d8d8f682ee5a5fc8b391'))
paddle.fluid.layers.shuffle_channel (ArgSpec(args=['x', 'group', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '276a1213dd431228cefa33c3146df34a'))
paddle.fluid.layers.temporal_shift (ArgSpec(args=['x', 'seg_num', 'shift_ratio', 'name'], varargs=None, keywords=None, defaults=(0.25, None)), ('document', 'd5945431cdcae3cda21914db5bbf383e'))
paddle.fluid.layers.py_func (ArgSpec(args=['func', 'x', 'out', 'backward_func', 'skip_vars_in_backward_input'], varargs=None, keywords=None, defaults=(None, None)), ('document', '8404e472ac12b4a30a505d3d3a3e5fdb'))
paddle.fluid.layers.py_func (ArgSpec(args=['func', 'x', 'out', 'backward_func', 'skip_vars_in_backward_input'], varargs=None, keywords=None, defaults=(None, None)), ('document', '231f91231430f5dae2b757df22317c67'))
paddle.fluid.layers.psroi_pool (ArgSpec(args=['input', 'rois', 'output_channels', 'spatial_scale', 'pooled_height', 'pooled_width', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '9bf0cc6b0717010b8ceec5dc2541d566'))
paddle.fluid.layers.prroi_pool (ArgSpec(args=['input', 'rois', 'output_channels', 'spatial_scale', 'pooled_height', 'pooled_width', 'name'], varargs=None, keywords=None, defaults=(1.0, 1, 1, None)), ('document', '454c7ea8c73313dd41513929d7526303'))
paddle.fluid.layers.teacher_student_sigmoid_loss (ArgSpec(args=['input', 'label', 'soft_max_up_bound', 'soft_max_lower_bound'], varargs=None, keywords=None, defaults=(15.0, -15.0)), ('document', 'b0e07aa41caae04b07a8e8217cc96020'))
......@@ -347,7 +347,7 @@ paddle.fluid.layers.Switch.__init__ (ArgSpec(args=['self', 'name'], varargs=None
paddle.fluid.layers.Switch.case (ArgSpec(args=['self', 'condition'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.layers.Switch.default (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.layers.increment (ArgSpec(args=['x', 'value', 'in_place'], varargs=None, keywords=None, defaults=(1.0, True)), ('document', 'f88b5787bb80ae6b8bf513a70dabbdc1'))
paddle.fluid.layers.array_write (ArgSpec(args=['x', 'i', 'array'], varargs=None, keywords=None, defaults=(None,)), ('document', '3f913b5069ad40bd85d89b33e4aa5939'))
paddle.fluid.layers.array_write (ArgSpec(args=['x', 'i', 'array'], varargs=None, keywords=None, defaults=(None,)), ('document', 'd357f71a280bf06aab4c79de9bd4facf'))
paddle.fluid.layers.create_array (ArgSpec(args=['dtype'], varargs=None, keywords=None, defaults=None), ('document', '556de793fdf24d515f3fc91260e2c048'))
paddle.fluid.layers.less_than (ArgSpec(args=['x', 'y', 'force_cpu', 'cond'], varargs=None, keywords=None, defaults=(None, None)), ('document', '329bdde01cba69463b08b8c13015560a'))
paddle.fluid.layers.less_equal (ArgSpec(args=['x', 'y', 'cond'], varargs=None, keywords=None, defaults=(None,)), ('document', '04e5623dd39b4437b9b08e0ce11071ca'))
......@@ -355,8 +355,8 @@ paddle.fluid.layers.greater_than (ArgSpec(args=['x', 'y', 'cond'], varargs=None,
paddle.fluid.layers.greater_equal (ArgSpec(args=['x', 'y', 'cond'], varargs=None, keywords=None, defaults=(None,)), ('document', '44bdacd11299d72c0a52d2181e7ae6ca'))
paddle.fluid.layers.equal (ArgSpec(args=['x', 'y', 'cond'], varargs=None, keywords=None, defaults=(None,)), ('document', '781eac1f980916c68623659f639e2b8c'))
paddle.fluid.layers.not_equal (ArgSpec(args=['x', 'y', 'cond'], varargs=None, keywords=None, defaults=(None,)), ('document', '8b76aaac4ba7cf9111750b9c2c9418cb'))
paddle.fluid.layers.array_read (ArgSpec(args=['array', 'i'], varargs=None, keywords=None, defaults=None), ('document', 'caf0d94349cdc28e1bda3b8a19411ac0'))
paddle.fluid.layers.array_length (ArgSpec(args=['array'], varargs=None, keywords=None, defaults=None), ('document', '6f24a9b872027634ad758ea2826c9727'))
paddle.fluid.layers.array_read (ArgSpec(args=['array', 'i'], varargs=None, keywords=None, defaults=None), ('document', 'b75c821cc1d22355c3c17e7bdf509510'))
paddle.fluid.layers.array_length (ArgSpec(args=['array'], varargs=None, keywords=None, defaults=None), ('document', 'c90d305395eb44e6dc772fab24ff2ef5'))
paddle.fluid.layers.IfElse ('paddle.fluid.layers.control_flow.IfElse', ('document', '720054043e55273224682fdb6b9ad13b'))
paddle.fluid.layers.IfElse.__init__ (ArgSpec(args=['self', 'cond', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.layers.IfElse.false_block (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
......
paddle/fluid/operators/sum_op.cc 100644 → 100755
浏览文件 @ f6beb0fa
......@@ -175,19 +175,21 @@ class SumOp : public framework::OperatorWithKernel {
class SumOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "(vector<Tensor>) The input tensors of sum operator.")
AddInput("X",
"A Varaible list. The shape and data type of the list elements"
"should be consistent. Variable can be multi-dimensional Tensor"
"or LoDTensor, and data types can be: float32, float64, int32, "
"int64.")
.AsDuplicable();
AddOutput("Out", "(Tensor) The output tensor of sum operator.");
AddOutput("Out",
"the sum of input :code:`x`. its shape and data types are "
"consistent with :code:`x`.");
AddAttr<bool>("use_mkldnn",
"(bool, default false) Only used in mkldnn kernel")
.SetDefault(false);
AddComment(R"DOC(
Sum operator.
This operators sums the input tensors. All the inputs can carry the
LoD (Level of Details) information. However, the output only shares
the LoD information with the first input.
)DOC");
AddComment(R"DOC(This OP is used to sum one or more Tensor or LoDTensor
of the input. If the input is LoDTensor, the output only
shares LoD information with the first input.)DOC");
}
};
......
python/paddle/fluid/layers/control_flow.py 100644 → 100755
浏览文件 @ f6beb0fa
......@@ -935,31 +935,53 @@ def increment(x, value=1.0, in_place=True):
def array_write(x, i, array=None):
"""
This function writes the given input variable to the specified position
indicating by the arrary index to an output LOD_TENSOR_ARRAY. If the
output LOD_TENSOR_ARRAY is not given(None), a new one will be created and
returned.
This OP writes the input ``x`` into the i-th position of the ``array``
:ref:`api_fluid_LoDTensorArray` and returns the modified array.
If ``array`` is none, a new LoDTensorArray will be created and returned.
This OP is often used together with :ref:`api_fluid_layers_array_read` OP.
Args:
x (Variable|list): The input tensor from which the data will be read.
i (Variable|list): The index of the output LOD_TENSOR_ARRAY, pointing to
the position to which the input tensor will be
written.
array (Variable|list): The output LOD_TENSOR_ARRAY to which the input
tensor will be written. If this parameter is
NONE, a new LOD_TENSOR_ARRAY will be created and
returned.
x (Variable): The input data to be written into array. It's multi-dimensional
Tensor or LoDTensor. Data type: float32, float64, int32, int64.
i (Variable): 1-D Tensor with shape [1], which represents the position into which
``x`` is written. Data type: int64.
array (LoDTensorArray, optional): The LoDTensorArray into which ``x`` is written.
The default value is None, when a new LoDTensorArray will be created and returned
as a result.
Returns:
Variable: The output LOD_TENSOR_ARRAY where the input tensor is written.
Variable: The input ``array`` after ``x`` is written into.
Examples:
.. code-block:: python
import paddle.fluid as fluid
tmp = fluid.layers.zeros(shape=[10], dtype='int32')
tmp = fluid.layers.fill_constant(shape=[3, 2], dtype='int64', value=5)
i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=10)
# Write tmp into the position of arr with subscript 10 and return arr.
arr = fluid.layers.array_write(tmp, i=i)
# Now, arr is a LoDTensorArray with length 11. We can use array_read OP to read
# the data at subscript 10 and print it out.
item = fluid.layers.array_read(arr, i=i)
input = fluid.layers.Print(item, message="The content of i-th LoDTensor:")
main_program = fluid.default_main_program()
exe = fluid.Executor(fluid.CPUPlace())
exe.run(main_program)
# The printed result is:
# 1570533133 The content of i-th LoDTensor: The place is:CPUPlace
# Tensor[array_read_0.tmp_0]
# shape: [3,2,]
# dtype: l
# data: 5,5,5,5,5,5,
# the output is 2-D Tensor with shape [3,2], which is tmp above.
# dtype is the corresponding C++ data type, which may vary in different environments.
# Eg: if the data type of tensor is int64, then the corresponding C++ data type is int64_t,
# so the dtype value is typeid(int64_t).Name(), which is 'x' on MacOS, 'l' on Linux,
# and '__int64' on Windows. They both represent 64-bit integer variables.
"""
helper = LayerHelper('array_write', **locals())
if array is None:
......@@ -1265,38 +1287,66 @@ def not_equal(x, y, cond=None):
def array_read(array, i):
"""
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]
This OP is used to read data at the specified position from the input array
:ref:`api_fluid_LoDTensorArray` . ``array`` is the input array and ``i``
is the specified read position. This OP is often used together with
:ref:`api_fluid_layers_array_write` OP.
Case 1:
::
Input:
The shape of first three tensors are [1], and that of the last one is [1,2]:
array = ([0.6], [0.1], [0.3], [0.4, 0.2])
And:
i = [3]
i = 2
Then:
output = 0.3
Output:
output = [0.4, 0.2]
Args:
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.
array (LoDTensorArray): The input LoDTensorArray.
i (Variable): 1-D Tensor, whose shape is [1] and dtype is int64. It represents the
specified read position of ``array``.
Returns:
Variable: The tensor type variable that has the data written to it.
Variable: The LoDTensor or Tensor that is read at the specified position of ``array``.
Examples:
.. code-block:: python
# First we're going to create a LoDTensorArray, then we're going to write the Tensor into
# the specified position, and finally we're going to read the Tensor at that position.
import paddle.fluid as fluid
array = fluid.layers.create_array(dtype='float32')
arr = fluid.layers.create_array(dtype='float32')
tmp = fluid.layers.fill_constant(shape=[3, 2], dtype='int64', value=5)
i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=10)
item = fluid.layers.array_read(array, i)
# tmp is the Tensor with shape [3,2], and if we write it into the position with subscript 10
# of the empty-array: arr, then the length of arr becomes 11.
arr = fluid.layers.array_write(tmp, i, array=arr)
# Read the data of the position with subscript 10.
item = fluid.layers.array_read(arr, i)
# You can print out the data via executor.
input = fluid.layers.Print(item, message="The LoDTensor of the i-th position:")
main_program = fluid.default_main_program()
exe = fluid.Executor(fluid.CPUPlace())
exe.run(main_program)
# The printed result is:
# 1569588169 The LoDTensor of the i-th position: The place is:CPUPlace
# Tensor[array_read_0.tmp_0]
# shape: [3,2,]
# dtype: l
# data: 5,5,5,5,5,5,
# the output is 2-D Tensor with shape [3,2].
# dtype is the corresponding C++ data type, which may vary in different environments.
# Eg: if the data type of tensor is int64, then the corresponding C++ data type is int64_t,
# so the dtype value is typeid(int64_t).Name(), which is 'x' on MacOS, 'l' on Linux,
# and '__int64' on Windows. They both represent 64-bit integer variables.
"""
helper = LayerHelper('array_read', **locals())
if not isinstance(
array,
......@@ -1350,19 +1400,15 @@ def shrink_memory(x, i, table):
def array_length(array):
"""
**Get the Length of Input LoDTensorArray**
This function performs the operation to find the length of the input
LOD_TENSOR_ARRAY.
Related API: array_read, array_write, While.
This OP is used to get the length of the input array :ref:`api_fluid_LoDTensorArray` .
It can be used together with :ref:`api_fluid_layers_array_read` , :ref:`api_fluid_layers_array_write` ,
:ref:`api_fluid_layers_While` OP to traverse, read and wirte LoDTensorArray.
Args:
array (LOD_TENSOR_ARRAY): The input array that will be used
to compute the length.
array (LoDTensorArray): The input array that will be used to compute the length.
Returns:
Variable: The length of the input LoDTensorArray.
Variable: 1-D Tensor with shape [1], which is the length of array. Datatype: int64.
Examples:
.. code-block:: python
......@@ -1370,9 +1416,32 @@ def array_length(array):
import paddle.fluid as fluid
tmp = fluid.layers.zeros(shape=[10], dtype='int32')
i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=10)
# tmp is 1-D Tensor with shape [10]. We write tmp into arr on subscript 10,
# then the length of arr becomes 11.
arr = fluid.layers.array_write(tmp, i=i)
# return the length of arr
arr_len = fluid.layers.array_length(arr)
# You can use executor to print out the length of LoDTensorArray.
input = fluid.layers.Print(arr_len, message="The length of LoDTensorArray:")
main_program = fluid.default_main_program()
exe = fluid.Executor(fluid.CPUPlace())
exe.run(main_program)
# The printed result is:
# 1569576542 The length of LoDTensorArray: The place is:CPUPlace
# Tensor[array_length_0.tmp_0]
# shape: [1,]
# dtype: l
# data: 11,
# 1-D Tensor with shape [1], whose value is 11. It means that the length of LoDTensorArray
# is 11.
# dtype is the corresponding C++ data type, which may vary in different environments.
# Eg: if the data type of tensor is int64, then the corresponding C++ data type is int64_t,
# so the dtype value is typeid(int64_t).Name(), which is 'x' on MacOS, 'l' on Linux,
# and '__int64' on Windows. They both represent 64-bit integer variables.
"""
helper = LayerHelper('array_length', **locals())
tmp = helper.create_variable_for_type_inference(dtype='int64')
......
python/paddle/fluid/layers/nn.py
浏览文件 @ f6beb0fa
......@@ -12489,20 +12489,68 @@ def sum(x):
"""
${comment}
Case 1:
::
Input:
Input. Shape = [2, 3]
Input = [[1, 2, 3],
[4, 5, 6]]
Output:
The output. Shape = [2, 3]
Output = [[1, 2, 3],
[4, 5, 6]]
Case 2:
::
Input:
First input:
Input1. Shape = [2, 3]
Input1 = [[1, 2, 3],
[4, 5, 6]]
The second input:
Input2. Shape = [2, 3]
Input2 = [[7, 8, 9],
[10, 11, 12]]
Output:
The output. Shape = [2, 3]
Output = [[8, 10, 12],
[14, 16, 18]]
Args:
x (Variable): ${x_comment}
x (Variable|list(Variable)): ${x_comment}
Returns:
out (Variable): ${out_comment}
Variable: ${out_comment}
Examples:
.. code-block:: python
import paddle.fluid as fluid
import paddle.fluid.layers as layers
input0 = layers.data(name="input0", shape=[13, 11], dtype='float32')
input1 = layers.data(name="input1", shape=[13, 11], dtype='float32')
out = layers.sum([input0,input1])
input0 = fluid.layers.fill_constant(shape=[2, 3], dtype='int64', value=5)
input1 = fluid.layers.fill_constant(shape=[2, 3], dtype='int64', value=3)
sum = fluid.layers.sum([input0, input1])
# You can print out 'sum' via executor.
out = fluid.layers.Print(sum, message="the sum of input0 and input1: ")
exe = fluid.Executor(fluid.CPUPlace())
exe.run(fluid.default_main_program())
# The printed result is:
# 1570701754 the sum of input0 and input1: The place is:CPUPlace
# Tensor[sum_0.tmp_0]
# shape: [2,3,]
# dtype: l
# data: 8,8,8,8,8,8,
# the sum of input0 and input1 is 2-D Tensor with shape [2,3].
# dtype is the corresponding C++ data type, which may vary in different environments.
# Eg: if the data type of tensor is int64, then the corresponding C++ data type is int64_t,
# so the dtype value is typeid(int64_t).Name(), which is 'x' on MacOS, 'l' on Linux,
# and '__int64' on Windows. They both represent 64-bit integer variables.
"""
helper = LayerHelper('sum', **locals())
......@@ -15095,85 +15143,90 @@ class PyFuncRegistry(object):
@templatedoc()
def py_func(func, x, out, backward_func=None, skip_vars_in_backward_input=None):
"""
PyFunc Operator.
User can use :code:`py_func` to register operators in Python side.
The inputs of :code:`func` is :code:`LoDTensor` and outputs can be
numpy array or :code:`LoDTensor`. Paddle would call the registered
:code:`func` in forward part, and call :code:`backward_func` in
backward part (if :code:`backward_func` is not None).
This API is used to register customized OP to Fluid. The forward function
of the registered OP is ``func`` and the backward function of that is
``backward_func``. Paddle will call ``func`` at forward runtime and call
``backward_func`` at backward runtime(if ``backward_func`` is not None).
``x`` is the input of ``func``, whose type must be LoDTensor; ``out`` is
the output of ``func``, whose type can be either LoDTensor or NumPy array.
User should set the right data type and shape of :code:`out` before
calling this function. However, data types and shapes of gradients of
:code:`out` and :code:`x` would be inferred automatically.
The input of the backward function ``backward_func`` is ``x``, ``out`` and
the gradient of ``out``. If some variables of ``out`` have no gradient, the
relevant input variable of ``backward_func`` is None. If some variables of
``x`` do not have a gradient, the user should return None in ``backward_func``.
Input orders of :code:`backward_func` would be: forward inputs
:code:`x`, forward outputs :code:`out` and backward input gradients of
:code:`out`. If some variables of :code:`out` have no gradient, the input
tensor would be None in Python side. If some variables of :code:`in` have
no gradient, users should return None.
The data type and shape of ``out`` should also be set correctly before this
API is called, and the data type and shape of the gradient of ``out`` and
``x`` will be inferred automatically.
This function can also be used to debug the running network. User can
add a :code:`py_func` operator without output, and print input
:code:`x` inside :code:`func`.
This API can also be used to debug the neural network by setting the ``func``
as a function that only print variables.
Args:
func (callable): forward Python function.
x (Variable|list(Variable)|tuple(Variable)): inputs of :code:`func`.
out (Variable|list(Variable)|tuple(Variable)): outputs of :code:`func`.
Paddle cannot infer shapes and data types of :code:`out`. Users
should create :code:`out` beforehand.
backward_func (callable|None): backward Python function.
None means no backward. Default None.
skip_vars_in_backward_input (Variable|list(Variable)|tuple(Variable)):
Variables that are not needed in :code:`backward_func` inputs.
These variables must be any of :code:`x` and :code:`out`.
If set, these vars would not be inputs of :code:`backward_func`,
Only useful when :code:`backward_func` is not None. Default None.
func (callable): The forward function of the registered OP. When the network
is running, the forward output ``out`` will be calculated according to this
function and the forward input ``x``.
x (Variable): The input of the forward function ``func``, its type can be
Variable | tuple[Variable] | list[Variale], in which Variable is LoDTensor.
out (Variable): The output of the forward function ``func``, its type can be
Variable | tuple[Variable] | list[Variale], in which Variable can be either
LoDTensor or NumPy array. Since Paddle cannot automatically infer the shape
and data type of ``out``, ``out`` must be created in advance.
backward_func (callable, optional): The backward function of the registered OP.
Its default value is None, which means there is no reverse calculation. If
it is not None, ``backward_func`` is called to calculate the gradient of
``x`` when the network is at backward runtime.
skip_vars_in_backward_input (Variable, optional): It's used to limit the input
variable list of ``backward_func``, and it can be single Variable, tuple[Variable]
or list[Variable]. It must belong to either ``x`` or ``out``. The default
value is None, which means that no variables need to be removed from ``x``
and ``out``. If it is not None, these variables will not be the input of
``backward_func``. This parameter is only useful when ``backward_func`` is
not None.
Returns:
out (Variable|list(Variable)|tuple(Variable)): input :code:`out`
Variable: The output ``out`` of the forward function ``func``.
Examples:
.. code-block:: python
>>> import paddle.fluid as fluid
>>> import six
>>>
>>> def create_tmp_var(name, dtype, shape):
>>> return fluid.default_main_program().current_block().create_var(
>>> name=name, dtype=dtype, shape=shape)
>>>
>>> # tanh activation has been provided by Paddle C++ op
>>> # Here, we only use tanh to be an example to show the usage
>>> # of py_func
>>> def tanh(x):
>>> return np.tanh(x)
>>>
>>> # forward input x is skipped
>>> def tanh_grad(y, dy):
>>> return np.array(dy) * (1 - np.square(np.array(y)))
>>>
>>> def debug_func(x):
>>> print(x)
>>>
>>> def simple_net(img, label):
>>> hidden = img
>>> for idx in six.moves.range(4):
>>> hidden = fluid.layers.fc(hidden, size=200)
>>> new_hidden = create_tmp_var(name='hidden_{}'.format(idx),
>>> dtype=hidden.dtype, shape=hidden.shape)
>>>
>>> # user-defined layers with forward and backward
>>> hidden = fluid.layers.py_func(func=tanh, x=hidden,
>>> out=new_hidden, backward_func=tanh_grad,
>>> skip_vars_in_backward_input=hidden)
>>>
>>> # user-defined debug layers to print variables
>>> fluid.layers.py_func(func=debug_func, x=hidden, out=None)
>>>
>>> prediction = fluid.layers.fc(hidden, size=10, act='softmax')
>>> loss = fluid.layers.cross_entropy(input=prediction, label=label)
>>> return fluid.layers.mean(loss)
import paddle.fluid as fluid
import six
def create_tmp_var(name, dtype, shape):
return fluid.default_main_program().current_block().create_var(
name=name, dtype=dtype, shape=shape)
# Tanh activation function provided by Paddle C++ op
# Here, tanh is used as an example to show how to use py_func
def tanh(x):
return np.tanh(x)
# Skip forward input x
def tanh_grad(y, dy):
return np.array(dy) * (1 - np.square(np.array(y)))
def debug_func(x):
print(x)
def simple_net(img, label):
hidden = img
for idx in six.moves.range(4):
hidden = fluid.layers.fc(hidden, size=200)
new_hidden = create_tmp_var(name='hidden_{}'.format(idx),
dtype=hidden.dtype, shape=hidden.shape)
# User-defined forward and backward
hidden = fluid.layers.py_func(func=tanh, x=hidden,
out=new_hidden, backward_func=tanh_grad,
skip_vars_in_backward_input=hidden)
# User-defined debugging layer, which can print out variable details
fluid.layers.py_func(func=debug_func, x=hidden, out=None)
prediction = fluid.layers.fc(hidden, size=10, act='softmax')
loss = fluid.layers.cross_entropy(input=prediction, label=label)
return fluid.layers.mean(loss)
"""
helper = LayerHelper('py_func', **locals())
if x is None:
......
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