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提交 48a3cc39 authored 作者: Gijs van Tulder's avatar Gijs van Tulder
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Add functions to create tensor5, dtensor5 etc.

上级 c5d3942b
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正在显示 包含 116 行增加 和 75 行删除
doc/generate_dtype_tensor_table.py
浏览文件 @ 48a3cc39
......@@ -17,17 +17,18 @@ shapes = [
('col', (False, True)),
('matrix', (False,False)),
('tensor3', (False,False,False)),
('tensor4', (False,False,False,False)),]
('tensor4', (False,False,False,False)),
('tensor5', (False,False,False,False,False)),]
hdr = '============ =========== ==== =========== ================================='
hdr = '============ =========== ==== ============ ==================================='
print(hdr)
print('Constructor dtype ndim shape broadcastable')
print('Constructor dtype ndim shape broadcastable')
print(hdr)
for letter in letters:
for shape in shapes:
suff = ',)' if len(shape[1])==1 else ')'
s = '(' + ','.join('1' if b else '?' for b in shape[1]) + suff
print('%s%-10s %-10s %-4s %-10s %-20s' %(
print('%s%-10s %-10s %-4s %-11s %-20s' %(
letter[0], shape[0], letter[1], len(shape[1]), s, shape[1]
))
print(hdr)
doc/library/tensor/basic.txt
浏览文件 @ 48a3cc39
......@@ -85,6 +85,10 @@ floating-point precision.
Return a Variable for a 4-dimensional ndarray
.. function:: tensor5(name=None, dtype=config.floatX)
Return a Variable for a 5-dimensional ndarray
.. #COMMENT
Each of the types described above can be constructed by two methods:
a singular version (e.g., :ref:`dmatrix <libdoc_tensor_creation>`)
......@@ -112,66 +116,74 @@ They are all callable, and accept an optional ``name`` argument. So for example
table generated by
$ python Theano/doc/generate_dtype_tensor_table.py
============ =========== ==== =========== =================================
Constructor dtype ndim shape broadcastable
============ =========== ==== =========== =================================
bscalar int8 0 () ()
bvector int8 1 (?,) (False,)
brow int8 2 (1,?) (True, False)
bcol int8 2 (?,1) (False, True)
bmatrix int8 2 (?,?) (False, False)
btensor3 int8 3 (?,?,?) (False, False, False)
btensor4 int8 4 (?,?,?,?) (False, False, False, False)
wscalar int16 0 () ()
wvector int16 1 (?,) (False,)
wrow int16 2 (1,?) (True, False)
wcol int16 2 (?,1) (False, True)
wmatrix int16 2 (?,?) (False, False)
wtensor3 int16 3 (?,?,?) (False, False, False)
wtensor4 int16 4 (?,?,?,?) (False, False, False, False)
iscalar int32 0 () ()
ivector int32 1 (?,) (False,)
irow int32 2 (1,?) (True, False)
icol int32 2 (?,1) (False, True)
imatrix int32 2 (?,?) (False, False)
itensor3 int32 3 (?,?,?) (False, False, False)
itensor4 int32 4 (?,?,?,?) (False, False, False, False)
lscalar int64 0 () ()
lvector int64 1 (?,) (False,)
lrow int64 2 (1,?) (True, False)
lcol int64 2 (?,1) (False, True)
lmatrix int64 2 (?,?) (False, False)
ltensor3 int64 3 (?,?,?) (False, False, False)
ltensor4 int64 4 (?,?,?,?) (False, False, False, False)
dscalar float64 0 () ()
dvector float64 1 (?,) (False,)
drow float64 2 (1,?) (True, False)
dcol float64 2 (?,1) (False, True)
dmatrix float64 2 (?,?) (False, False)
dtensor3 float64 3 (?,?,?) (False, False, False)
dtensor4 float64 4 (?,?,?,?) (False, False, False, False)
fscalar float32 0 () ()
fvector float32 1 (?,) (False,)
frow float32 2 (1,?) (True, False)
fcol float32 2 (?,1) (False, True)
fmatrix float32 2 (?,?) (False, False)
ftensor3 float32 3 (?,?,?) (False, False, False)
ftensor4 float32 4 (?,?,?,?) (False, False, False, False)
cscalar complex64 0 () ()
cvector complex64 1 (?,) (False,)
crow complex64 2 (1,?) (True, False)
ccol complex64 2 (?,1) (False, True)
cmatrix complex64 2 (?,?) (False, False)
ctensor3 complex64 3 (?,?,?) (False, False, False)
ctensor4 complex64 4 (?,?,?,?) (False, False, False, False)
zscalar complex128 0 () ()
zvector complex128 1 (?,) (False,)
zrow complex128 2 (1,?) (True, False)
zcol complex128 2 (?,1) (False, True)
zmatrix complex128 2 (?,?) (False, False)
ztensor3 complex128 3 (?,?,?) (False, False, False)
ztensor4 complex128 4 (?,?,?,?) (False, False, False, False)
============ =========== ==== =========== =================================
============ =========== ==== ============ ===================================
Constructor dtype ndim shape broadcastable
============ =========== ==== ============ ===================================
bscalar int8 0 () ()
bvector int8 1 (?,) (False,)
brow int8 2 (1,?) (True, False)
bcol int8 2 (?,1) (False, True)
bmatrix int8 2 (?,?) (False, False)
btensor3 int8 3 (?,?,?) (False, False, False)
btensor4 int8 4 (?,?,?,?) (False, False, False, False)
btensor5 int8 5 (?,?,?,?,?) (False, False, False, False, False)
wscalar int16 0 () ()
wvector int16 1 (?,) (False,)
wrow int16 2 (1,?) (True, False)
wcol int16 2 (?,1) (False, True)
wmatrix int16 2 (?,?) (False, False)
wtensor3 int16 3 (?,?,?) (False, False, False)
wtensor4 int16 4 (?,?,?,?) (False, False, False, False)
wtensor5 int16 5 (?,?,?,?,?) (False, False, False, False, False)
iscalar int32 0 () ()
ivector int32 1 (?,) (False,)
irow int32 2 (1,?) (True, False)
icol int32 2 (?,1) (False, True)
imatrix int32 2 (?,?) (False, False)
itensor3 int32 3 (?,?,?) (False, False, False)
itensor4 int32 4 (?,?,?,?) (False, False, False, False)
itensor5 int32 5 (?,?,?,?,?) (False, False, False, False, False)
lscalar int64 0 () ()
lvector int64 1 (?,) (False,)
lrow int64 2 (1,?) (True, False)
lcol int64 2 (?,1) (False, True)
lmatrix int64 2 (?,?) (False, False)
ltensor3 int64 3 (?,?,?) (False, False, False)
ltensor4 int64 4 (?,?,?,?) (False, False, False, False)
ltensor5 int64 5 (?,?,?,?,?) (False, False, False, False, False)
dscalar float64 0 () ()
dvector float64 1 (?,) (False,)
drow float64 2 (1,?) (True, False)
dcol float64 2 (?,1) (False, True)
dmatrix float64 2 (?,?) (False, False)
dtensor3 float64 3 (?,?,?) (False, False, False)
dtensor4 float64 4 (?,?,?,?) (False, False, False, False)
dtensor5 float64 5 (?,?,?,?,?) (False, False, False, False, False)
fscalar float32 0 () ()
fvector float32 1 (?,) (False,)
frow float32 2 (1,?) (True, False)
fcol float32 2 (?,1) (False, True)
fmatrix float32 2 (?,?) (False, False)
ftensor3 float32 3 (?,?,?) (False, False, False)
ftensor4 float32 4 (?,?,?,?) (False, False, False, False)
ftensor5 float32 5 (?,?,?,?,?) (False, False, False, False, False)
cscalar complex64 0 () ()
cvector complex64 1 (?,) (False,)
crow complex64 2 (1,?) (True, False)
ccol complex64 2 (?,1) (False, True)
cmatrix complex64 2 (?,?) (False, False)
ctensor3 complex64 3 (?,?,?) (False, False, False)
ctensor4 complex64 4 (?,?,?,?) (False, False, False, False)
ctensor5 complex64 5 (?,?,?,?,?) (False, False, False, False, False)
zscalar complex128 0 () ()
zvector complex128 1 (?,) (False,)
zrow complex128 2 (1,?) (True, False)
zcol complex128 2 (?,1) (False, True)
zmatrix complex128 2 (?,?) (False, False)
ztensor3 complex128 3 (?,?,?) (False, False, False)
ztensor4 complex128 4 (?,?,?,?) (False, False, False, False)
ztensor5 complex128 5 (?,?,?,?,?) (False, False, False, False, False)
============ =========== ==== ============ ===================================
Plural Constructors
--------------------------
......@@ -220,11 +232,11 @@ If you would like to construct a tensor variable with a non-standard
broadcasting pattern, or a larger number of dimensions you'll need to create
your own :class:`TensorType` instance. You create such an instance by passing
the dtype and broadcasting pattern to the constructor. For example, you
can create your own 5-dimensional tensor type
can create your own 6-dimensional tensor type
>>> dtensor5 = TensorType('float64', (False,)*5)
>>> x = dtensor5()
>>> z = dtensor5('z')
>>> dtensor6 = TensorType('float64', (False,)*6)
>>> x = dtensor6()
>>> z = dtensor6('z')
You can also redefine some of the provided types and they will interact
correctly:
......
doc/tutorial/adding.txt
浏览文件 @ 48a3cc39
......@@ -175,13 +175,13 @@ by :ref:`broadcasting <libdoc_tensor_broadcastable>`.
The following types are available:
* **byte**: ``bscalar, bvector, bmatrix, brow, bcol, btensor3, btensor4``
* **16-bit integers**: ``wscalar, wvector, wmatrix, wrow, wcol, wtensor3, wtensor4``
* **32-bit integers**: ``iscalar, ivector, imatrix, irow, icol, itensor3, itensor4``
* **64-bit integers**: ``lscalar, lvector, lmatrix, lrow, lcol, ltensor3, ltensor4``
* **float**: ``fscalar, fvector, fmatrix, frow, fcol, ftensor3, ftensor4``
* **double**: ``dscalar, dvector, dmatrix, drow, dcol, dtensor3, dtensor4``
* **complex**: ``cscalar, cvector, cmatrix, crow, ccol, ctensor3, ctensor4``
* **byte**: ``bscalar, bvector, bmatrix, brow, bcol, btensor3, btensor4, btensor5``
* **16-bit integers**: ``wscalar, wvector, wmatrix, wrow, wcol, wtensor3, wtensor4, wtensor5``
* **32-bit integers**: ``iscalar, ivector, imatrix, irow, icol, itensor3, itensor4, itensor5``
* **64-bit integers**: ``lscalar, lvector, lmatrix, lrow, lcol, ltensor3, ltensor4, ltensor5``
* **float**: ``fscalar, fvector, fmatrix, frow, fcol, ftensor3, ftensor4, ftensor5``
* **double**: ``dscalar, dvector, dmatrix, drow, dcol, dtensor3, dtensor4, dtensor5``
* **complex**: ``cscalar, cvector, cmatrix, crow, ccol, ctensor3, ctensor4, ctensor5``
The previous list is not exhaustive and a guide to all types compatible
with NumPy arrays may be found here: :ref:`tensor creation<libdoc_tensor_creation>`.
......
theano/tensor/basic.py
浏览文件 @ 48a3cc39
......@@ -1030,6 +1030,34 @@ def tensor4(name=None, dtype=None):
tensor4s, ftensor4s, dtensor4s, itensor4s, ltensor4s = _multi(
tensor4, ftensor4, dtensor4, itensor4, ltensor4)
ctensor5 = TensorType('complex64', ((False,) * 5))
ztensor5 = TensorType('complex128', ((False,) * 5))
ftensor5 = TensorType('float32', ((False,) * 5))
dtensor5 = TensorType('float64', ((False,) * 5))
btensor5 = TensorType('int8', ((False,) * 5))
wtensor5 = TensorType('int16', ((False,) * 5))
itensor5 = TensorType('int32', ((False,) * 5))
ltensor5 = TensorType('int64', ((False,) * 5))
def tensor5(name=None, dtype=None):
"""Return a symbolic 5-D variable.
Parameters
----------
dtype: numeric type
None means to use theano.config.floatX.
name
A name to attach to this variable.
"""
if dtype is None:
dtype = config.floatX
type = TensorType(dtype, (False, False, False, False, False))
return type(name)
tensor5s, ftensor5s, dtensor5s, itensor5s, ltensor5s = _multi(
tensor5, ftensor5, dtensor5, itensor5, ltensor5)
Tensor = TensorType
......
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