def cosine_distance_with_negative_samples(x,
y,
shift,
num_negative_samples,
name=''):
'''
Given minibatches for ``x`` and ``y``, this function computes for each element in `x` the cosine distance between
it and the corresponding `y` and additionally the cosine distance between ``x`` and some other elements of ``y``
(referred to a negative samples). The ``x`` and ``y`` pairs are samples often derived
from embeddings of textual data, though the function can be used for any form of numeric encodings.
When using this function to compute textual similarity, ``x`` represents search query term embedding
and ``y`` represents a document embedding. The negative samples are formed on the fly by shifting
the right side (``y``). The ``shift`` indicates how many samples in ``y`` one should shift while
forming each negative sample pair. It is often chosen to be 1. As the name suggests
``num_negative_samples`` indicates how many negative samples one would want to generate.
Example:
>>> qry = np.asarray([1., 1., 0., 0., 0., 1., 1., 0., 0., 0., 1., 1.], dtype=np.float32).reshape(3, 1, 4)
>>> doc = np.asarray([1., 1., 0., 0., 0., 1., 1., 0., 0., 0., 1., 1.], dtype=np.float32).reshape(3, 1, 4)
>>> x = C.sequence.input(shape=(4,))
>>> y = C.sequence.input(shape=(4,))
>>> model = C.cosine_distance_with_negative_samples(x, y, shift=1, num_negative_samples=2)
>>> np.round(model.eval({x: qry, y: doc}), decimals=4)
array([[[ 1. , 0.5, 0. ]],
<BLANKLINE>
[[ 1. , 0.5, 0.5]],
<BLANKLINE>
[[ 1. , 0. , 0.5]]], dtype=float32)
Args:
x: numpy array or any :class:`~cntk.ops.functions.Function` that outputs a tensor
y: numpy array or any :class:`~cntk.ops.functions.Function` that outputs a tensor
shift: non-zero positive integer representing number of shift to generate a negative sample
num_negative_samples: number of negative samples to generate, a non-zero positive integer
name (str, optional): the name of the Function instance in the network
Returns:
:class:`~cntk.ops.functions.Function`
'''
from cntk.cntk_py import cosine_distance_with_negative_samples
dtype = get_data_type(x, y)
x = sanitize_input(x, dtype)
y = sanitize_input(y, dtype)
return cosine_distance_with_negative_samples(x, y, shift,
num_negative_samples, name)
def cosine_distance_with_negative_samples(x, y, shift, num_negative_samples, name=''):
'''
Given minibatches for ``x`` and ``y``, this function computes for each element in `x` the cosine distance between
it and the corresponding `y` and additionally the cosine distance between ``x`` and some other elements of ``y``
(referred to a negative samples). The ``x`` and ``y`` pairs are samples often derived
from embeddings of textual data, though the function can be used for any form of numeric encodings.
When using this function to compute textual similarity, ``x`` represents search query term embedding
and ``y`` represents a document embedding. The negative samples are formed on the fly by shifting
the right side (``y``). The ``shift`` indicates how many samples in ``y`` one should shift while
forming each negative sample pair. It is often chosen to be 1. As the name suggests
``num_negative_samples`` indicates how many negative samples one would want to generate.
Example:
>>> qry = np.asarray([1., 1., 0., 0., 0., 1., 1., 0., 0., 0., 1., 1.], dtype=np.float32).reshape(3, 1, 4)
>>> doc = np.asarray([1., 1., 0., 0., 0., 1., 1., 0., 0., 0., 1., 1.], dtype=np.float32).reshape(3, 1, 4)
>>> x = C.sequence.input_variable(shape=(4,))
>>> y = C.sequence.input_variable(shape=(4,))
>>> model = C.cosine_distance_with_negative_samples(x, y, shift=1, num_negative_samples=2)
>>> np.round(model.eval({x: qry, y: doc}), decimals=4)
array([[[ 1. , 0.5, 0. ]],
<BLANKLINE>
[[ 1. , 0.5, 0.5]],
<BLANKLINE>
[[ 1. , 0. , 0.5]]], dtype=float32)
Args:
x: numpy array or any :class:`~cntk.ops.functions.Function` that outputs a tensor
y: numpy array or any :class:`~cntk.ops.functions.Function` that outputs a tensor
shift: non-zero positive integer representing number of shift to generate a negative sample
num_negative_samples: number of negative samples to generate, a non-zero positive integer
name (str, optional): the name of the Function instance in the network
Returns:
:class:`~cntk.ops.functions.Function`
'''
from cntk.cntk_py import cosine_distance_with_negative_samples
dtype = get_data_type(x, y)
x = sanitize_input(x, dtype)
y = sanitize_input(y, dtype)
return cosine_distance_with_negative_samples(x, y, shift, num_negative_samples, name)
