def edit_distance_error(input_a,
input_b,
subPen=1,
delPen=1,
insPen=1,
squashInputs=False,
tokensToIgnore=[],
name=''):
'''
Edit distance error evaluation function with the option of specifying penalty of substitution, deletion and insertion, as well as squashing the input sequences and ignoring certain samples.
Using the classic DP algorithm as described in https://en.wikipedia.org/wiki/Edit_distance, adjusted to take into account the penalties.
Each sequence in the inputs is expected to be a matrix. Prior to computation of the edit distance, the operation extracts the indices of maximum element in each column.
For example, a sequence matrix
1 2 9 1
3 0 3 2
will be represented as the vector of labels (indices) as [1, 0, 0, 1], on which edit distance will be actually evaluated.
The function allows to squash sequences of repeating labels and ignore certain labels. For example, if squashInputs is true and tokensToIgnore contains index of label '-' then
given first input sequence as s1="1-12-" and second as s2="-11--122" the edit distance will be computed against s1' = "112" and s2' = "112".
When used as an evaluation criterion, the Trainer will aggregate all values over an epoch and report the average, i.e. the error rate.
Primary objective of this node is for error evaluation of CTC training, see formula (1) in "Connectionist Temporal Classification: Labelling Unsegmented
Sequence Data with Recurrent Neural Networks", ftp://ftp.idsia.ch/pub/juergen/icml2006.pdf
Example:
>>> i1 = C.input(shape=(2,))
>>> i2 = C.input(shape=(2,))
>>> arguments = {i1 : [[1, 3], [2, 0]], i2 : [[2, 0], [2, 0]]}
>>> a = C.edit_distance_error(i1, i2, 0, 1, 1, True, [1])
>>> a.eval(arguments)
array(1.0, dtype=float32)
Args:
input_a: first input sequence
input_b: second input sequence
subPen: substitution penalty
delPen: deletion penalty
insPen: insertion penalty
squashInputs: whether to merge sequences of identical samples (in both input sequences). If true and tokensToIgnore contains label '-' then
given first input sequence as s1="a-ab-" and second as s2="-aa--abb" the edit distance will be computed against s1' = "aab" and s2' = "aab".
tokensToIgnore: list of indices of samples to ignore during edit distance evaluation (in both sequences)
name (str, optional): the name of the Function instance in the network
Returns:
:class:`~cntk.ops.functions.Function`
'''
from cntk.cntk_py import edit_distance_error
dtype = get_data_type(input_a, input_b)
input_a = sanitize_input(input_a, dtype)
input_b = sanitize_input(input_b, dtype)
return edit_distance_error(input_a, input_b, subPen, delPen, insPen,
squashInputs, tokensToIgnore, name)
def edit_distance_error(input_a, input_b, subPen=1, delPen=1, insPen=1, squashInputs=False, tokensToIgnore=[], name=''):
'''
Edit distance error evaluation function with the option of specifying penalty of substitution, deletion and insertion, as well as squashing the input sequences and ignoring certain samples.
Using the classic DP algorithm as described in https://en.wikipedia.org/wiki/Edit_distance, adjusted to take into account the penalties.
Each sequence in the inputs is expected to be a matrix. Prior to computation of the edit distance, the operation extracts the indices of maximum element in each column.
For example, a sequence matrix
1 2 9 1
3 0 3 2
will be represented as the vector of labels (indices) as [1, 0, 0, 1], on which edit distance will be actually evaluated.
The function allows to squash sequences of repeating labels and ignore certain labels. For example, if squashInputs is true and tokensToIgnore contains index of label '-' then
given first input sequence as s1="1-12-" and second as s2="-11--122" the edit distance will be computed against s1' = "112" and s2' = "112".
When used as an evaluation criterion, the Trainer will aggregate all values over an epoch and report the average, i.e. the error rate.
Primary objective of this node is for error evaluation of CTC training, see formula (1) in "Connectionist Temporal Classification: Labelling Unsegmented
Sequence Data with Recurrent Neural Networks", ftp://ftp.idsia.ch/pub/juergen/icml2006.pdf
Example:
>>> i1 = C.input(shape=(2,))
>>> i2 = C.input(shape=(2,))
>>> arguments = {i1 : [[1, 3], [2, 0]], i2 : [[2, 0], [2, 0]]}
>>> a = C.edit_distance_error(i1, i2, 0, 1, 1, True, [1])
>>> a.eval(arguments)
array(1.0, dtype=float32)
Args:
input_a: first input sequence
input_b: second input sequence
subPen: substitution penalty
delPen: deletion penalty
insPen: insertion penalty
squashInputs: whether to merge sequences of identical samples (in both input sequences). If true and tokensToIgnore contains label '-' then
given first input sequence as s1="a-ab-" and second as s2="-aa--abb" the edit distance will be computed against s1' = "aab" and s2' = "aab".
tokensToIgnore: list of indices of samples to ignore during edit distance evaluation (in both sequences)
name (str, optional): the name of the Function instance in the network
Returns:
:class:`~cntk.ops.functions.Function`
'''
from cntk.cntk_py import edit_distance_error
dtype = get_data_type(input_a, input_b)
input_a = sanitize_input(input_a, dtype)
input_b = sanitize_input(input_b, dtype)
return edit_distance_error(input_a, input_b, subPen, delPen, insPen, squashInputs, tokensToIgnore, name)