int a[2][3] = {{1,2,3},{4,5,6}}; 我们就写成 a = Array((3,2),[1,2,3,4,5,6]) 先看单目运算。sum的输入是一个rank-n的数组,输出是rank-(n-1)的数组 sum of Array((3,), [1,2,3]) 1 + 2 + 3 = 6 sum of Array((3,2), [1,2,3,4,5,6]) 1 2 3 + + + ...
rank=1: [0,1,2] rank=2: [[0,1],[2,3]] rank=3: [[[0,1],[2,3]],[[4,5],[6,7]]] 什么是Shape shape是形状,他是指明每一层有多少个元素。比如[2,3,4]是指第一层2个元素,第二层3个元素,第三层4个元素,通过这个我们就可以知道这个张量一共有2 × 3 × 4=24 个元素。上面的r...
rank=1: [0,1,2] rank=2: [[0,1],[2,3]] rank=3: [[[0,1],[2,3]],[[4,5],[6,7]]] 什么是Shape shape是形状,他是指明每一层有多少个元素。比如[2,3,4]是指第一层2个元素,第二层3个元素,第三层4个元素,通过这个我们就可以知道这个张量一共有2 × 3 × 4=24 个元素。上面的r...
2-form gauge theoryBRST transformationfield/antifield formulationWe generalize the BRST transformations in Abelian rank-2 tensor field theory by allowing the parameter to be finite and field-dependent and show that such transformations play crucial role in studying the Abelian 2-form gauge theory in ...
2R:表示该内存有 2 个 Rank *8:表示每个内存颗粒的位宽是 8 bit, 接下来我们分两个小节,深入地看看 Rank、位宽与内存颗粒的内部结构。 内存的 Rank 与位宽 在内存中,其中每一个黑色的内存颗粒叫一个 Chip。所谓 Rank 指的是属于同一个组的 Chip 的总和。这些 Chip 并行工作,共同组成组成一个 64 bit 的...
We construct a Harder-Narasimhan filtration for rank $2$ tensors, where there does not exist any such notion a priori, as coming from a GIT notion of maximal unstability. The filtration associated to the 1-parameter subgroup of Kempf giving the maximal way to destabilize, in the GIT sense,...
tensor([[1.4142, 1.4142], [1.4142, 1.4142]]) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 只有一个参数时,表示对整个张量求范数,参数表示范数的幂指数值。
inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512).to(device) print(f"inputs_time is {time.time()}") scores = model(**inputs, return_dict=True).logits.view(-1, ).float() print(scores) end_time = time.time() print(end_time ...
pairs = [['what is panda?','hi'], ['what is panda?','The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]with torch.no_grad():inputs = tokenizer(pai...
形状可以通过Python中的整数列表或元祖(int list或tuples)来表示,也或者用TensorShape. 例子: AI检测代码解析 >>> matrix=tf.constant([[1,2,3],[4,5,6]]) >>> matrix <tf.Tensor 'Const_3:0' shape=(2, 3) dtype=int32> 1. 2.