def where(condition, name=''):
'''
Given a symbolic sequence `condition` of boolean-like values, it will return
a new sequence containing the indices for which the values were true.
Example:
>>> import cntk.ops as C
>>> import numpy as np
>>> x = C.input_variable(shape=(3,2))
>>> z = C.greater(C.reduce_sum(x),60)
>>> y = C.sequence.where(z)
>>> x0 = np.reshape(np.arange(24.0,dtype=np.float32),(4,3,2))
>>> z.eval({x:x0})
array([[ 0., 0., 1., 1.]], dtype=float32)
>>> y.eval({x:x0})
array([[ 2., 3.]], dtype=float32)
Args:
condition: the symbolic sequence of booleans
name (str): the name of the node in the network
Returns:
:class:`cntk.Function`
'''
from cntk.cntk_py import where
condition = sanitize_input(condition, get_data_type(condition))
return where(condition, name)
def where(condition, name=''):
'''
Given a symbolic sequence ``condition`` of boolean-like values, it will return
a new sequence containing the indices for which the values were true.
Example:
>>> x = C.input_variable(shape=(3,2))
>>> z = C.greater(C.reduce_sum(x), 60)
>>> # create one sequence of 4 tensors each with shape (3,2)
>>> x0 = np.reshape(np.arange(24.0, dtype=np.float32), (1,4,3,2))
>>> z.eval({x:x0})
array([[[ 0.],
[ 0.],
[ 1.],
[ 1.]]], dtype=float32)
>>> y = C.sequence.where(z)
>>> y.eval({x:x0})
array([[[ 2.],
[ 3.]]], dtype=float32)
Args:
condition: the symbolic sequence of booleans
name (str): the name of the node in the network
Returns:
:class:`~cntk.ops.functions.Function`
'''
from cntk.cntk_py import where
condition = sanitize_input(condition, get_data_type(condition))
return where(condition, name)
def where(condition, name=''):
'''
TBA
Example:
TBA
Args:
condition: the symbolic tensor operand denoting a boolean condition flag for each step of a sequence
name (str): the name of the node in the network
Returns:
:class:`cntk.Function`
'''
from cntk.cntk_py import where
condition = sanitize_input(condition, get_data_type(condition))
return where(condition, name)
def where(condition, name=''):
'''
TBA
Example:
TBA
Args:
condition: the symbolic tensor operand denoting a boolean condition flag for each step of a sequence
name (str): the name of the node in the network
Returns:
:class:`cntk.Function`
'''
from cntk.cntk_py import where
condition = sanitize_input(condition, get_data_type(condition))
return where(condition, name)
def where(condition, name=''):
'''
Given a symbolic sequence ``condition`` of boolean-like (1/0) values, it will return
a new sequence containing the indices for which the values were true.
If ``condition`` has a value other than 0 or 1, it will denote a repeat factor.
If a repeat factor is fractional, it will round up but deduct the overshoot from the
next repeat factor.
Example:
>>> x = C.sequence.input(shape=(3,2))
>>> z = C.greater(C.reduce_sum(x), 60)
>>> # create one sequence of 4 tensors each with shape (3,2)
>>> x0 = np.reshape(np.arange(24.0, dtype=np.float32), (1,4,3,2))
>>> z.eval({x:x0})
[array([[ 0.],
[ 0.],
[ 1.],
[ 1.]], dtype=float32)]
>>> y = C.sequence.where(z)
>>> y.eval({x:x0})
[array([ 2., 3.], dtype=float32)]
>>> # repeat frame[1] twice, frame[3] three times, and frame[4] twice
>>> C.sequence.where(C.sequence.input(1)).eval([[[1], [2], [1], [3], [2]]])
[array([ 0., 1., 1., 2., 3., 3., 3., 4., 4.], dtype=float32)]
>>> # note that the above are the indices that are passed to
>>> # repeat frames with a fractional factor
>>> C.sequence.where(C.sequence.input(1)).eval([[[1.2]]*10])
[array([ 0., 0., 1., 2., 3., 4., 5., 5., 6., 7., 8., 9.],
dtype=float32)]
>>> # as a result, a 1.2 times stretch is realized by duplicating frame[0] and frame[5]
Args:
condition: sequence of 0 or 1 values for filtering, or other positive values for repetition (also fractional)
name (str): the name of the node in the network
Returns:
:class:`~cntk.ops.functions.Function`
'''
from cntk.cntk_py import where
condition = sanitize_input(condition, get_data_type(condition))
return where(condition, name)
def where(condition, name=''):
'''
Given a symbolic sequence ``condition`` of boolean-like (1/0) values, it will return
a new sequence containing the indices for which the values were true.
If ``condition`` has a value other than 0 or 1, it will denote a repeat factor.
If a repeat factor is fractional, it will round up but deduct the overshoot from the
next repeat factor.
Example:
>>> x = C.sequence.input_variable(shape=(3,2))
>>> z = C.greater(C.reduce_sum(x), 60)
>>> # create one sequence of 4 tensors each with shape (3,2)
>>> x0 = np.reshape(np.arange(24.0, dtype=np.float32), (1,4,3,2))
>>> z.eval({x:x0})
[array([ 0., 0., 1., 1.], dtype=float32)]
>>> y = C.sequence.where(z)
>>> y.eval({x:x0})
[array([ 2., 3.], dtype=float32)]
>>> # repeat frame[1] twice, frame[3] three times, and frame[4] twice
>>> C.sequence.where(C.sequence.input_variable(1)).eval([[[1], [2], [1], [3], [2]]])
[array([ 0., 1., 1., 2., 3., 3., 3., 4., 4.], dtype=float32)]
>>> # note that the above are the indices that are passed to
>>> # repeat frames with a fractional factor
>>> C.sequence.where(C.sequence.input_variable(1)).eval([[[1.2]]*10])
[array([ 0., 0., 1., 2., 3., 4., 5., 5., 6., 7., 8., 9.],
dtype=float32)]
>>> # as a result, a 1.2 times stretch is realized by duplicating frame[0] and frame[5]
Args:
condition: sequence of 0 or 1 values for filtering, or other positive values for repetition (also fractional)
name (str): the name of the node in the network
Returns:
:class:`~cntk.ops.functions.Function`
'''
from cntk.cntk_py import where
condition = sanitize_input(condition, get_data_type(condition))
return where(condition, name)