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Modify lod tensor doc based on new LoDTensor Python API (#11253)

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* Modify lod_tensor.md and nn.py

* Modify control_flow.py doc

* undo change in lod_tensor.md
wangkuiyi-patch-1
Kexin Zhao 7 years ago committed by GitHub
parent 609dccfb55
commit 3a25f21eae
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2 changed files with 28 additions and 28 deletions
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4
python/paddle/fluid/layers/control_flow.py
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@ -746,8 +746,8 @@ def lod_rank_table(x, level=0):
.. code-block:: text
x is a LoDTensor:
x.lod = [[0, 2, 3],
[0, 5, 6, 7]]
x.lod = [[2, 1],
[5, 1, 1]]
x.data = [a, b, c, d, e, f, g]
1. set level to 0:

52
python/paddle/fluid/layers/nn.py
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@ -1621,13 +1621,13 @@ def sequence_pool(input, pool_type):
.. code-block:: text
x is a 1-level LoDTensor:
x.lod = [[0, 2, 5, 7]]
x.lod = [[2, 3, 2]]
x.data = [1, 3, 2, 4, 6, 5, 1]
x.dims = [7, 1]
then output is a Tensor:
out.dim = [3, 1]
with condition len(x.lod[-1]) - 1 == out.dims[0]
with condition len(x.lod[-1]) == out.dims[0]
for different pool_type:
average: out.data = [2, 4, 3], where 2=(1+3)/2, 4=(2+4+6)/3, 3=(5+1)/2
@ -1686,13 +1686,13 @@ def sequence_first_step(input):
.. code-block:: text
x is a 1-level LoDTensor:
x.lod = [[0, 2, 5, 7]]
x.lod = [[2, 3, 2]]
x.data = [1, 3, 2, 4, 6, 5, 1]
x.dims = [7, 1]
then output is a Tensor:
out.dim = [3, 1]
with condition len(x.lod[-1]) - 1 == out.dims[0]
with condition len(x.lod[-1]) == out.dims[0]
out.data = [1, 2, 5], where 1=first(1,3), 2=first(2,4,6), 5=first(5,1)
Args:
@ -1719,13 +1719,13 @@ def sequence_last_step(input):
.. code-block:: text
x is a 1-level LoDTensor:
x.lod = [[0, 2, 5, 7]]
x.lod = [[2, 3, 2]]
x.data = [1, 3, 2, 4, 6, 5, 1]
x.dims = [7, 1]
then output is a Tensor:
out.dim = [3, 1]
with condition len(x.lod[-1]) - 1 == out.dims[0]
with condition len(x.lod[-1]) == out.dims[0]
out.data = [3, 6, 1], where 3=last(1,3), 6=last(2,4,6), 1=last(5,1)
Args:
@ -2468,18 +2468,18 @@ def sequence_expand(x, y, ref_level=-1, name=None):
* Case 1
x is a LoDTensor:
x.lod = [[0, 2, 4]]
x.lod = [[2, 2]]
x.data = [[a], [b], [c], [d]]
x.dims = [4, 1]
y is a LoDTensor:
y.lod = [[0, 2, 4],
[0, 3, 6, 7, 8]]
y.lod = [[2, 2],
[3, 3, 1, 1]]
ref_level: 0
then output is a 1-level LoDTensor:
out.lod = [[0, 2, 4, 6, 8]]
out.lod = [[2, 2, 2, 2]]
out.data = [[a], [b], [a], [b], [c], [d], [c], [d]]
out.dims = [8, 1]
@ -2489,7 +2489,7 @@ def sequence_expand(x, y, ref_level=-1, name=None):
x.dims = [3, 1]
y is a LoDTensor:
y.lod = [[0, 2, 2, 5]]
y.lod = [[2, 0, 3]]
ref_level: -1
@ -3343,7 +3343,7 @@ def ctc_greedy_decoder(input, blank, name=None):
[0.2, 0.2, 0.1, 0.5],
[0.5, 0.1, 0.3, 0.1]]
input.lod = [[0, 4, 8]]
input.lod = [[4, 4]]
Then:
@ -3351,7 +3351,7 @@ def ctc_greedy_decoder(input, blank, name=None):
[1],
[3]]
output.lod = [[0, 2, 3]]
output.lod = [[2, 1]]
Args:
@ -3368,7 +3368,7 @@ def ctc_greedy_decoder(input, blank, name=None):
Returns:
Variable: CTC greedy decode result. If all the sequences in result were
empty, the result LoDTensor will be [-1] with LoD [[0]] and dims [1, 1].
empty, the result LoDTensor will be [-1] with LoD [[]] and dims [1, 1].
Examples:
.. code-block:: python
@ -3458,7 +3458,7 @@ def sequence_reshape(input, new_dim):
.. code-block:: text
x is a LoDTensor:
x.lod = [[0, 2, 6]]
x.lod = [[2, 4]]
x.data = [[1, 2], [3, 4],
[5, 6], [7, 8], [9, 10], [11, 12]]
x.dims = [6, 2]
@ -3466,7 +3466,7 @@ def sequence_reshape(input, new_dim):
set new_dim = 4
then out is a LoDTensor:
out.lod = [[0, 1, 3]]
out.lod = [[1, 2]]
out.data = [[1, 2, 3, 4],
[5, 6, 7, 8], [9, 10, 11, 12]]
out.dims = [3, 4]
@ -3737,7 +3737,7 @@ def im2sequence(input, filter_size=1, stride=1, padding=0, name=None):
output.dims = {8, 9}
output.lod = [[0, 4, 8]]
output.lod = [[4, 4]]
The simple usage is:
@ -4133,47 +4133,47 @@ def lod_reset(x, y=None, target_lod=None):
* Example 1:
Given a 1-level LoDTensor x:
x.lod = [[ 0, 2, 5 6 ]]
x.lod = [[ 2, 3, 1 ]]
x.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
x.dims = [6, 1]
target_lod: [0, 4, 6]
target_lod: [4, 2]
then we get a 1-level LoDTensor:
out.lod = [[ 0, 4, 6 ]]
out.lod = [[4, 2]]
out.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
out.dims = [6, 1]
* Example 2:
Given a 1-level LoDTensor x:
x.lod = [[ 0, 2, 5 6 ]]
x.lod = [[2, 3, 1]]
x.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
x.dims = [6, 1]
y is a Tensor:
y.data = [[0, 2, 6]]
y.data = [[2, 4]]
y.dims = [1, 3]
then we get a 1-level LoDTensor:
out.lod = [[ 0, 2, 6 ]]
out.lod = [[2, 4]]
out.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
out.dims = [6, 1]
* Example 3:
Given a 1-level LoDTensor x:
x.lod = [[ 0, 2, 5 6 ]]
x.lod = [[2, 3, 1]]
x.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
x.dims = [6, 1]
y is a 2-level LoDTensor:
y.lod = [[0, 2, 4], [0, 2, 5, 6]]
y.lod = [[2, 2], [2, 2, 1, 1]]
y.data = [[1.1], [2.1], [3.1], [4.1], [5.1], [6.1]]
y.dims = [6, 1]
then we get a 2-level LoDTensor:
out.lod = [[0, 2, 4], [0, 2, 5, 6]]
out.lod = [[2, 2], [2, 2, 1, 1]]
out.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
out.dims = [6, 1]

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