提交 25f77680 authored 作者: Iban Harlouchet's avatar Iban Harlouchet

numpydoc for theano/sandbox/cuda/blocksparse.py

上级 1a9da258
...@@ -30,7 +30,9 @@ class SparseBlockGemvSS(GpuOp): ...@@ -30,7 +30,9 @@ class SparseBlockGemvSS(GpuOp):
This should not be directly called since the interface is subject This should not be directly called since the interface is subject
to change without notice. Use the sparse_block_dot_SS() function to change without notice. Use the sparse_block_dot_SS() function
for a stable interface. for a stable interface.
""" """
def __init__(self, inplace=False): def __init__(self, inplace=False):
self.inplace = inplace self.inplace = inplace
if self.inplace: if self.inplace:
...@@ -369,7 +371,9 @@ class SparseBlockOuterSS(GpuOp): ...@@ -369,7 +371,9 @@ class SparseBlockOuterSS(GpuOp):
This op should not be called directly since its interface is This op should not be called directly since its interface is
subject to change without notice. It is involved in the gradient subject to change without notice. It is involved in the gradient
of SparseBlockGemvSS. of SparseBlockGemvSS.
""" """
def __init__(self, inplace=False): def __init__(self, inplace=False):
self.inplace = inplace self.inplace = inplace
if self.inplace: if self.inplace:
...@@ -680,18 +684,26 @@ def sparse_block_dot_SS(W, h, inputIdx, b, outputIdx): ...@@ -680,18 +684,26 @@ def sparse_block_dot_SS(W, h, inputIdx, b, outputIdx):
Parameters Parameters
---------- ----------
var: shape, comment var
W: (iBlocks, oBlocks, iSize, oSize), weight matrix Shape, comment.
h: (batch, iWin, iSize), input from lower layer (sparse) W
inputIdx: (batch, iWin), indexes of the input blocks (iBlocks, oBlocks, iSize, oSize), weight matrix.
b: (oBlocks, oSize), bias vector h
outputIdx: (batch, oWin), indexes of the output blocks (batch, iWin, iSize), input from lower layer (sparse).
inputIdx
returns (batch, oWin, oSize), dot(W[i, j], h[i]) + b[j] (batch, iWin), indexes of the input blocks.
b
(oBlocks, oSize), bias vector.
outputIdx
(batch, oWin), indexes of the output blocks.
Returns
-------
(batch, oWin, oSize), dot(W[i, j], h[i]) + b[j]
but b[j] is only added once but b[j] is only added once
Notation Notes
-------- -----
- `batch` is the number of examples in a minibatch (batch size). - `batch` is the number of examples in a minibatch (batch size).
- `iBlocks` is the total number of blocks in the input (from lower layer). - `iBlocks` is the total number of blocks in the input (from lower layer).
- `iSize` is the size of each of these input blocks. - `iSize` is the size of each of these input blocks.
...@@ -701,7 +713,9 @@ def sparse_block_dot_SS(W, h, inputIdx, b, outputIdx): ...@@ -701,7 +713,9 @@ def sparse_block_dot_SS(W, h, inputIdx, b, outputIdx):
- `oSize` is the size of each of these output blocks. - `oSize` is the size of each of these output blocks.
- `oWin` is the number of output blocks that will actually be computed. - `oWin` is the number of output blocks that will actually be computed.
Which blocks will be computed is specified in `outputIdx`. Which blocks will be computed is specified in `outputIdx`.
""" """
assert inputIdx.ndim == h.ndim - 1 assert inputIdx.ndim == h.ndim - 1
assert outputIdx.ndim == inputIdx.ndim assert outputIdx.ndim == inputIdx.ndim
if h.ndim == 2: if h.ndim == 2:
......
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