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pytensor
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2ef495d9
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2ef495d9
authored
4月 30, 2012
作者:
Frederic
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1 个修改的文件
包含
57 行增加
和
31 行删除
+57
-31
randomstreams.py
theano/tensor/randomstreams.py
+57
-31
没有找到文件。
theano/tensor/randomstreams.py
浏览文件 @
2ef495d9
"""Define RandomStreams, providing random number variables for Theano graphs."""
"""Define RandomStreams, providing random number variables for Theano
graphs.
"""
__docformat__
=
"restructuredtext en"
import
sys
...
...
@@ -8,6 +11,7 @@ from theano.compile import module, In, Component
from
theano.gof
import
Container
from
theano.tensor
import
raw_random
class
RandomStreamsInstance
(
object
):
"""RandomStreamsInstance"""
def
__init__
(
self
,
random_streams
,
memo
,
default_seed
):
...
...
@@ -18,44 +22,51 @@ class RandomStreamsInstance(object):
def
initialize
(
self
,
seed
=
None
):
"""Initialize each random stream
:param seed: each random stream will be assigned a unique
state that depends
deterministically on this value.
:param seed: each random stream will be assigned a unique
state that depends
deterministically on this value.
:type seed: None or integer in range 0 to 2**30
:rtype: None
"""
self
.
seed
(
seed
)
def
seed
(
self
,
seed
=
None
):
"""Re-initialize each random stream
:param seed: each random stream will be assigned a unique
state that depends
deterministically on this value.
:param seed: each random stream will be assigned a unique
state that depends
deterministically on this value.
:type seed: None or integer in range 0 to 2**30
:rtype: None
"""
if
seed
is
None
:
seed
=
self
.
default_seed
seed
=
self
.
default_seed
#backport
#seed = self.default_seed if seed is None else seed
seedgen
=
numpy
.
random
.
RandomState
(
seed
)
for
old_r
,
new_r
in
self
.
random_streams
.
random_state_variables
:
old_r_seed
=
seedgen
.
randint
(
2
**
30
)
old_r_seed
=
seedgen
.
randint
(
2
**
30
)
old_r_container
=
self
.
memo
[
old_r
]
.
value
if
old_r_container
.
value
is
None
:
#the cast to int here makes it work on 32bit machines, not sure why
old_r_container
.
value
=
numpy
.
random
.
RandomState
(
int
(
old_r_seed
))
#the cast to int here makes it work on 32bit machines,
#not sure why
old_r_container
.
value
=
numpy
.
random
.
RandomState
(
int
(
old_r_seed
))
else
:
#the cast to int here makes it work on 32bit machines, not sure why
#the cast to int here makes it work on 32bit machines,
#not sure why
old_r_container
.
value
.
seed
(
int
(
old_r_seed
))
def
__getitem__
(
self
,
item
):
"""Retrieve the numpy RandomState instance associated with a particular stream
"""Retrieve the numpy RandomState instance associated with a
particular stream
:param item: a variable of type RandomStateType, associated with this RandomStream
:param item: a variable of type RandomStateType, associated
with this RandomStream
:rtype: numpy RandomState (or None, before initialize)
...
...
@@ -67,9 +78,11 @@ class RandomStreamsInstance(object):
raise
KeyError
(
item
)
def
__setitem__
(
self
,
item
,
val
):
"""Set the numpy RandomState instance associated with a particular stream
"""Set the numpy RandomState instance associated with a
particular stream
:param item: a variable of type RandomStateType, associated with this RandomStream
:param item: a variable of type RandomStateType, associated
with this RandomStream
:param val: the new value
:type val: numpy RandomState
...
...
@@ -78,7 +91,8 @@ class RandomStreamsInstance(object):
"""
if
type
(
val
)
is
not
numpy
.
random
.
RandomState
:
raise
TypeError
(
'only values of type RandomState are permitted'
,
val
)
raise
TypeError
(
'only values of type RandomState are permitted'
,
val
)
for
old_r
,
new_r
in
self
.
random_streams
.
random_state_variables
:
if
item
is
old_r
:
container
=
self
.
memo
[
item
]
.
value
...
...
@@ -86,24 +100,34 @@ class RandomStreamsInstance(object):
return
raise
KeyError
(
item
)
class
RandomStreams
(
Component
,
raw_random
.
RandomStreamsBase
):
"""Module component with similar interface to numpy.random (numpy.random.RandomState)"""
"""Module component with similar interface to numpy.random
(numpy.random.RandomState)
"""
random_state_variables
=
[]
"""A list of pairs of the form (input_r, output_r). This will be over-ridden by the module
instance to contain stream generators.
"""A list of pairs of the form (input_r, output_r). This will be
over-ridden by the module instance to contain stream
generators.
"""
default_instance_seed
=
None
"""Instance variable should take None or integer value. Used to seed the random number
generator that provides seeds for member streams"""
"""Instance variable should take None or integer value. Used to
seed the random number generator that provides seeds for member
streams
"""
def
__init__
(
self
,
seed
=
None
):
"""
:type seed: None or int
""":type seed: None or int
:param seed: a default seed to initialize the RandomState
instances after build. See `RandomStreamsInstance.__init__`
for more details.
:param seed: a default seed to initialize the RandomState instances after build. See
`RandomStreamsInstance.__init__` for more details.
"""
super
(
RandomStreams
,
self
)
.
__init__
()
self
.
random_state_variables
=
[]
...
...
@@ -115,7 +139,7 @@ class RandomStreams(Component, raw_random.RandomStreamsBase):
if
old_r
in
memo
:
assert
memo
[
old_r
]
.
update
is
new_r
else
:
memo
[
old_r
]
=
In
(
old_r
,
memo
[
old_r
]
=
In
(
old_r
,
value
=
Container
(
old_r
,
storage
=
[
None
]),
update
=
new_r
,
mutable
=
True
)
...
...
@@ -124,26 +148,28 @@ class RandomStreams(Component, raw_random.RandomStreamsBase):
"""override `Component.build` """
if
self
not
in
memo
:
print
'creating RandomStreamsInstance'
memo
[
self
]
=
RandomStreamsInstance
(
self
,
memo
,
self
.
default_instance_seed
)
memo
[
self
]
=
RandomStreamsInstance
(
self
,
memo
,
self
.
default_instance_seed
)
return
memo
[
self
]
def
gen
(
self
,
op
,
*
args
,
**
kwargs
):
"""Create a new random stream in this container.
:param op: a RandomFunction instance to
:param op: a RandomFunction instance to
:param args: interpreted by `op`
:param kwargs: interpreted by `op`
:returns: The symbolic random draw part of op()'s return value. This function stores
the updated RandomStateType Variable for use at `build` time.
:returns: The symbolic random draw part of op()'s return
value. This function stores the updated RandomStateType
Variable for use at `build` time.
:rtype: TensorVariable
"""
random_state_variable
=
raw_random
.
random_state_type
()
new_r
,
out
=
op
(
random_state_variable
,
*
args
,
**
kwargs
)
out
.
rng
=
random_state_variable
self
.
random_state_variables
.
append
((
random_state_variable
,
new_r
))
return
out
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