mindspore.nn.CosineSimilarity

class mindspore.nn.CosineSimilarity(similarity='cosine', reduction='none', zero_diagonal=True)

Computes representation similarity

Parameters

• similarity (str) – ‘dot’ or ‘cosine’. Default: ‘cosine’

• reduction (str) – ‘none’, ‘sum’, ‘mean’ (all along dim -1). Default: ‘none’

• zero_diagonal (bool) – if True, the diagonals are set to zero. Default: True

Returns

A square matrix (input1, input1) with the similarity scores between all elements. If sum or mean are used, then returns (b, 1) with the reduced value for each row

Example

>>> test_data = np.array([[1, 3, 4, 7], [2, 4, 2, 5], [3, 1, 5, 8]])
>>> metric = CosineSimilarity()
>>> metric.clear()
>>> metric.update(test_data)
>>> square_matrix = metric.eval()
>>> print(square_matrix)
[[0.  0.94025615  0.95162452]
 [0.94025615  0.  0.86146098]
 [0.95162452  0.86146098  0.]]

clear()

Clears the internal evaluation result.

eval()

Computes the Cosine_Similarity square matrix.

Returns

A square matrix.

update(inputs)

Updates the internal evaluation result with ‘input1’.

Parameters

inputs – input_data input1. The input_data is a Tensor or an array.