基于Pytorch版本的深度学习相关代码.zip

import torch u = torch.tensor([3.0,-4.0]) print(torch.abs(u).sum()) # print(torch.norm(u)) #平方和，再开方 # #矩阵乘法!!!! # x=torch.arange(12).reshape((4,3)) # y=torch.arange(3) # print(x) # print(y) # print(x.shape,y.shape,torch.mv(x,y)) #torch.mv()只适用于二维乘一维 # # b=torch.arange(15).reshape((5,3)) # c=torch.arange(12).reshape((3,4)) # print(b.shape,c.shape,torch.mm(b,c)) # a = torch.arange(20).reshape((4,5)) # a_sum_axis0 = a.sum(axis=0,keepdims=True) # axis=0,按照第0维度进行求和操作 # keepdims：保存现有维度 # a_sum_axis1 = a.sum(axis=1,keepdims=True) # print(a,a_sum_axis0,a_sum_axis0.shape,a_sum_axis1,a_sum_axis1.shape) # a = torch.arange(12).reshape((3,4)) # print(a) # print(a.T) # #矩阵的转置操纵 # b=a.clone() # clone可以理解为深拷贝 # print(a*b) # # X = torch.arange(12) # a=X.numpy() # b=torch.tensor(a) # print(type(a),type(b)) #c=torch.tensor([3.99]) #print(c,c.item(),float(c),int(c)) #torch数据类型的item方法是得到只有一个元素张量里面的元素值 # x=torch.arange(3).reshape((3,1)) # print(x) # y=torch.arange(2).reshape((1,2)) # print(y) # print(x+y) # 此情况下会触发广播机制 # x = torch.arange(12, dtype=torch.float32).reshape((3,4)) # #print(x) # y= torch.tensor([[2.0,1,3,4],[1,2,3,4],[4,3,2,1]]) # print(y.sum())#对张量y中的元素进行求和操作 # print(x==y)#判断x与y中的每个元素是否相等 # print(torch.cat((x,y),dim=0))#dim=0，原样式合并 # print(torch.cat((x,y),dim=1))#dim=1，按一维合并，即按照行进行拼接，把每一行的元素合起来 # x=torch.tensor([1.2,3,5,7]) # # y=torch.tensor([2,4.3,5,9]) # # print(x+y) # print(x-y) # print(x/y) # print(x*y) # print(x**y) x的y次方 #print(torch.exp(x)) #e的x次方