Python theano.foldl() Examples
The following are 12
code examples of theano.foldl().
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Example #1
| Source File: theano_backend.py From Att-ChemdNER with Apache License 2.0 | 6 votes |
|
def foldl(fn, elems, initializer=None, name=None):
'''Reduce elems using fn to combine them from left to right.
# 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[0] in case of None)
name: A string name for the foldl node in the graph
# Returns
Same type and shape as initializer
'''
if initializer is None:
initializer = elems[0]
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.foldl(fn2, elems, initializer, name=name)[0]
Example #2
| Source File: metrics.py From ntm-one-shot with MIT License | 6 votes |
|
def accuracy_instance(predictions, targets, n=[1, 2, 3, 4, 5, 10], \
nb_classes=5, nb_samples_per_class=10, batch_size=1):
accuracy_0 = theano.shared(np.zeros((batch_size, nb_samples_per_class), \
dtype=theano.config.floatX))
indices_0 = theano.shared(np.zeros((batch_size, nb_classes), \
dtype=np.int32))
batch_range = T.arange(batch_size)
def step_(p, t, acc, idx):
acc = T.inc_subtensor(acc[batch_range, idx[batch_range, t]], T.eq(p, t))
idx = T.inc_subtensor(idx[batch_range, t], 1)
return (acc, idx)
(raw_accuracy, _), _ = theano.foldl(step_, sequences=[predictions.dimshuffle(1, 0), \
targets.dimshuffle(1, 0)], outputs_info=[accuracy_0, indices_0])
accuracy = T.mean(raw_accuracy / nb_classes, axis=0)
return accuracy
Example #3
| Source File: theano_backend.py From GraphicDesignPatternByPython with MIT License | 6 votes |
|
def foldl(fn, elems, initializer=None, name=None):
"""Reduce elems using fn to combine them from left to right.
# 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[0] in case of None)
name: A string name for the foldl node in the graph
# Returns
Same type and shape as initializer
"""
if initializer is None:
initializer = elems[0]
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.foldl(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 foldl(fn, elems, initializer=None, name=None):
"""Reduce elems using fn to combine them from left to right.
# 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[0] in case of None)
name: A string name for the foldl node in the graph
# Returns
Same type and shape as initializer
"""
if initializer is None:
initializer = elems[0]
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.foldl(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 foldl(fn, elems, initializer=None, name=None):
"""Reduce elems using fn to combine them from left to right.
# 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[0] in case of None)
name: A string name for the foldl node in the graph
# Returns
Same type and shape as initializer
"""
if initializer is None:
initializer = elems[0]
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.foldl(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 foldl(fn, elems, initializer=None, name=None):
"""Reduce elems using fn to combine them from left to right.
# 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[0] in case of None)
name: A string name for the foldl node in the graph
# Returns
Same type and shape as initializer
"""
if initializer is None:
initializer = elems[0]
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.foldl(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 foldl(fn, elems, initializer=None, name=None):
"""Reduce elems using fn to combine them from left to right.
# 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[0] in case of None)
name: A string name for the foldl node in the graph
# Returns
Same type and shape as initializer
"""
if initializer is None:
initializer = elems[0]
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.foldl(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 foldl(fn, elems, initializer=None, name=None):
"""Reduce elems using fn to combine them from left to right.
# 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[0] in case of None)
name: A string name for the foldl node in the graph
# Returns
Same type and shape as initializer
"""
if initializer is None:
initializer = elems[0]
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.foldl(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 foldl(fn, elems, initializer=None, name=None):
"""Reduce elems using fn to combine them from left to right.
# 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[0] in case of None)
name: A string name for the foldl node in the graph
# Returns
Same type and shape as initializer
"""
if initializer is None:
initializer = elems[0]
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.foldl(lambda x, acc: fn(acc, x),
elems, initializer, name=name)[0]
Example #10
| Source File: theano_backend.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
|
def foldl(fn, elems, initializer=None, name=None):
"""Reduce elems using fn to combine them from left to right.
# 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[0] in case of None)
name: A string name for the foldl node in the graph
# Returns
Same type and shape as initializer
"""
if initializer is None:
initializer = elems[0]
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.foldl(lambda x, acc: fn(acc, x),
elems, initializer, name=name)[0]
Example #11
| Source File: theano_backend.py From deepQuest with BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def foldl(fn, elems, initializer=None, name=None):
"""Reduce elems using fn to combine them from left to right.
# 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[0] in case of None)
name: A string name for the foldl node in the graph
# Returns
Same type and shape as initializer
"""
if initializer is None:
initializer = elems[0]
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.foldl(lambda x, acc: fn(acc, x),
elems, initializer, name=name)[0]
Example #12
| Source File: theano_backend.py From keras-lambda with MIT License | 6 votes |
|
def foldl(fn, elems, initializer=None, name=None):
"""Reduce elems using fn to combine them from left to right.
# 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[0] in case of None)
name: A string name for the foldl node in the graph
# Returns
Same type and shape as initializer
"""
if initializer is None:
initializer = elems[0]
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.foldl(fn2, elems, initializer, name=name)[0]
