mindspore.train.CosineSimilarity
- class mindspore.train.CosineSimilarity(similarity='cosine', reduction='none', zero_diagonal=True)[source]
Computes representation similarity.
- Parameters
similarity (str) – the computation logit.
'cosine'
means computing similarity.'dot'
means computing dots of arrays, Default:'cosine'
.reduction (str) – Specifies the reduction to be applied to the output. Support
'none'
,'sum'
,'mean'
(all along dim -1). Default:'none'
.zero_diagonal (bool) – If
True
, diagonals of results will be set to zero. Default:True
.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> import numpy as np >>> from mindspore.train import CosineSimilarity >>> >>> 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.]]
- eval()[source]
Computes the similarity matrix.
- Returns
numpy.ndarray. The similarity matrix.
- Raises
RuntimeError – If the update method is not called first, an error will be reported.
- update(inputs)[source]
Updates the internal evaluation result with 'inputs'.
- Parameters
inputs (Union[Tensor, list, numpy.ndarray]) – The input matrix including y and y_pred.