Python theano.foldr() Examples
The following are 11
code examples of theano.foldr().
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Example #1
| Source File: theano_backend.py From Att-ChemdNER with Apache License 2.0 | 6 votes |
|
def foldr(fn, elems, initializer=None, name=None):
'''Reduce elems using fn to combine them from right to left.
# Arguments
fn: Callable that will be called upon each element in elems and an
accumulator, for instance lambda acc, x: acc + x
elems: tensor
initializer: The first value used (elems[-1] in case of None)
name: A string name for the foldr node in the graph
# Returns
Same type and shape as initializer
'''
if initializer is None:
initializer = elems[-1]
elems = elems[:-1]
# We need to change the order of the arguments because theano accepts x as
# first parameter and accumulator as second
fn2 = lambda x, acc: fn(acc, x)
return theano.foldr(fn2, elems, initializer, name=name)[0]
Example #2
| Source File: theano_backend.py From GraphicDesignPatternByPython with MIT License | 6 votes |
|
def foldr(fn, elems, initializer=None, name=None):
"""Reduce elems using fn to combine them from right to left.
# Arguments
fn: Callable that will be called upon each element in elems and an
accumulator, for instance lambda acc, x: acc + x
elems: tensor
initializer: The first value used (elems[-1] in case of None)
name: A string name for the foldr node in the graph
# Returns
Same type and shape as initializer
"""
if initializer is None:
initializer = elems[-1]
elems = elems[:-1]
# We need to change the order of the arguments because theano accepts x as
# first parameter and accumulator as second
return theano.foldr(lambda x, acc: fn(acc, x),
elems, initializer, name=name)[0]
Example #3
| Source File: theano_backend.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
|
def foldr(fn, elems, initializer=None, name=None):
"""Reduce elems using fn to combine them from right to left.
# Arguments
fn: Callable that will be called upon each element in elems and an
accumulator, for instance lambda acc, x: acc + x
elems: tensor
initializer: The first value used (elems[-1] in case of None)
name: A string name for the foldr node in the graph
# Returns
Same type and shape as initializer
"""
if initializer is None:
initializer = elems[-1]
elems = elems[:-1]
# We need to change the order of the arguments because theano accepts x as
# first parameter and accumulator as second
return theano.foldr(lambda x, acc: fn(acc, x),
elems, initializer, name=name)[0]
Example #4
| Source File: theano_backend.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
|
def foldr(fn, elems, initializer=None, name=None):
"""Reduce elems using fn to combine them from right to left.
# Arguments
fn: Callable that will be called upon each element in elems and an
accumulator, for instance lambda acc, x: acc + x
elems: tensor
initializer: The first value used (elems[-1] in case of None)
name: A string name for the foldr node in the graph
# Returns
Same type and shape as initializer
"""
if initializer is None:
initializer = elems[-1]
elems = elems[:-1]
# We need to change the order of the arguments because theano accepts x as
# first parameter and accumulator as second
return theano.foldr(lambda x, acc: fn(acc, x),
elems, initializer, name=name)[0]
Example #5
| Source File: theano_backend.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
|
def foldr(fn, elems, initializer=None, name=None):
"""Reduce elems using fn to combine them from right to left.
# Arguments
fn: Callable that will be called upon each element in elems and an
accumulator, for instance lambda acc, x: acc + x
elems: tensor
initializer: The first value used (elems[-1] in case of None)
name: A string name for the foldr node in the graph
# Returns
Same type and shape as initializer
"""
if initializer is None:
initializer = elems[-1]
elems = elems[:-1]
# We need to change the order of the arguments because theano accepts x as
# first parameter and accumulator as second
return theano.foldr(lambda x, acc: fn(acc, x),
elems, initializer, name=name)[0]
Example #6
| Source File: theano_backend.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
|
def foldr(fn, elems, initializer=None, name=None):
"""Reduce elems using fn to combine them from right to left.
# Arguments
fn: Callable that will be called upon each element in elems and an
accumulator, for instance lambda acc, x: acc + x
elems: tensor
initializer: The first value used (elems[-1] in case of None)
name: A string name for the foldr node in the graph
# Returns
Same type and shape as initializer
"""
if initializer is None:
initializer = elems[-1]
elems = elems[:-1]
# We need to change the order of the arguments because theano accepts x as
# first parameter and accumulator as second
return theano.foldr(lambda x, acc: fn(acc, x),
elems, initializer, name=name)[0]
Example #7
| Source File: theano_backend.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
|
def foldr(fn, elems, initializer=None, name=None):
"""Reduce elems using fn to combine them from right to left.
# Arguments
fn: Callable that will be called upon each element in elems and an
accumulator, for instance lambda acc, x: acc + x
elems: tensor
initializer: The first value used (elems[-1] in case of None)
name: A string name for the foldr node in the graph
# Returns
Same type and shape as initializer
"""
if initializer is None:
initializer = elems[-1]
elems = elems[:-1]
# We need to change the order of the arguments because theano accepts x as
# first parameter and accumulator as second
return theano.foldr(lambda x, acc: fn(acc, x),
elems, initializer, name=name)[0]
Example #8
| Source File: theano_backend.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
|
def foldr(fn, elems, initializer=None, name=None):
"""Reduce elems using fn to combine them from right to left.
# Arguments
fn: Callable that will be called upon each element in elems and an
accumulator, for instance lambda acc, x: acc + x
elems: tensor
initializer: The first value used (elems[-1] in case of None)
name: A string name for the foldr node in the graph
# Returns
Same type and shape as initializer
"""
if initializer is None:
initializer = elems[-1]
elems = elems[:-1]
# We need to change the order of the arguments because theano accepts x as
# first parameter and accumulator as second
return theano.foldr(lambda x, acc: fn(acc, x),
elems, initializer, name=name)[0]
Example #9
| Source File: theano_backend.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
|
def foldr(fn, elems, initializer=None, name=None):
"""Reduce elems using fn to combine them from right to left.
# Arguments
fn: Callable that will be called upon each element in elems and an
accumulator, for instance lambda acc, x: acc + x
elems: tensor
initializer: The first value used (elems[-1] in case of None)
name: A string name for the foldr node in the graph
# Returns
Same type and shape as initializer
"""
if initializer is None:
initializer = elems[-1]
elems = elems[:-1]
# We need to change the order of the arguments because theano accepts x as
# first parameter and accumulator as second
return theano.foldr(lambda x, acc: fn(acc, x),
elems, initializer, name=name)[0]
Example #10
| Source File: theano_backend.py From deepQuest with BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def foldr(fn, elems, initializer=None, name=None):
"""Reduce elems using fn to combine them from right to left.
# Arguments
fn: Callable that will be called upon each element in elems and an
accumulator, for instance lambda acc, x: acc + x
elems: tensor
initializer: The first value used (elems[-1] in case of None)
name: A string name for the foldr node in the graph
# Returns
Same type and shape as initializer
"""
if initializer is None:
initializer = elems[-1]
elems = elems[:-1]
# We need to change the order of the arguments because theano accepts x as
# first parameter and accumulator as second
return theano.foldr(lambda x, acc: fn(acc, x),
elems, initializer, name=name)[0]
Example #11
| Source File: theano_backend.py From keras-lambda with MIT License | 6 votes |
|
def foldr(fn, elems, initializer=None, name=None):
"""Reduce elems using fn to combine them from right to left.
# Arguments
fn: Callable that will be called upon each element in elems and an
accumulator, for instance lambda acc, x: acc + x
elems: tensor
initializer: The first value used (elems[-1] in case of None)
name: A string name for the foldr node in the graph
# Returns
Same type and shape as initializer
"""
if initializer is None:
initializer = elems[-1]
elems = elems[:-1]
# We need to change the order of the arguments because theano accepts x as
# first parameter and accumulator as second
fn2 = lambda x, acc: fn(acc, x)
return theano.foldr(fn2, elems, initializer, name=name)[0]
