importnumpyasnp a=np.array([[1,2],[3,5]]) x=np.expand_dims(a,axis=0) y=np.expand_dims(a,axis=1) z=np.expand_dims(a,axis=2) print(a.shape) print(x.shape) print(y.shape) print(z.shape) 输出结果: x: y: z: 参考知乎https://www.zhihu.com/question/265545749...
>>> x = np.array([1,2])>>>x.shape (2,)>>> y = np.expand_dims(x, axis=0)>>>y array([[1, 2]])>>>y.shape (1, 2) 1. 2. 3. 4. 5. 6. 7. 8. >>> x = np.array([1,2])>>>x.shape (2,)>>> y = np.expand_dims(x, axis=1)>>>y array([[1], [2]]...
把numpy翻译为一个中文库. Contribute to pebble329/-numpy- development by creating an account on GitHub.
2是负例,这样计算出的loss应该是16-8 = 8 pairwise_distances = test_pairwise_distances() anchor_positive = np.expand_dims(pairwise_distances, axis=2) anchor_negative = np.expand_dims(pairwise_distances, axis=1) triplet_loss = anchor_positive - anchor_negative + marg...
即扩展维度,np.expand_dims(a,axis=)即在 a的相应的axis轴上扩展维度a =np.array([[1,2],[3,5]])y=np.expand_dims(a,axis=2)z=np.expand_dims(a,axis=1) print(a.shape) print(y.shape) print(z.shape) 输出 (2,2) (2,2 图像分割将label和得到的mask画入原图 ...
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2是负例,这样计算出的loss应该是16-8 = 8 pairwise_distances = test_pairwise_distances() anchor_positive = np.expand_dims(pairwise_distances, axis=2) anchor_negative = np.expand_dims(pairwise_distances, axis=1) triplet_loss = anchor_positive - anchor_negative + margin ...
2是负例,这样计算出的loss应该是16-8 = 8 pairwise_distances = test_pairwise_distances() anchor_positive = np.expand_dims(pairwise_distances, axis=2) anchor_negative = np.expand_dims(pairwise_distances, axis=1) triplet_loss = anchor_positive - anchor_negative + mar...
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2是负例,这样计算出的loss应该是16-8 = 8 pairwise_distances = test_pairwise_distances() anchor_positive = np.expand_dims(pairwise_distances, axis=2) anchor_negative = np.expand_dims(pairwise_distances, axis=1) triplet_loss = anchor_positive - anchor_negative + ma...