PyTorch中的数据类型
分为浮点型和整数型两种：
import torch # 浮点型 float_a = torch.FloatTensor(4, 5) # size为2*3的矩阵 float_b = torch.FloatTensor([1, 2, 3, 4]) # [1,2,3,4]矩阵 # 整形 int_a = torch.IntTensor(4, 5) int_b = torch.IntTensor([1, 2, 3, 4])
初始化数组有以下四种方式
import torch # 随机生成浮点型Tensor rand_a = torch.rand(2, 3) # 随机生成E=0，D=1 正态分布的Tensor randn_a = torch.randn(2, 3) # 设定起始值、结束值、步长 的Tensor 此方法在0.5版本将被更改 range_a = torch.range(1, 20, 1) # 设定数据为0的Tensor zeros_a=torch.zeros(2, 3)
运算
torch.abs取绝对值；torch.add相加；torch.clamp裁剪;torch.div求商；torch.mul求积；torch.pow求幂；torch.mm矩阵相乘；torch.mv矩阵和向量相乘（前后位置不能换）
import torch # 取绝对值 abs_b = torch.abs(torch.randn(2, 3)) print(abs_b) # 相加 add_b = torch.add(torch.randn(2, 3), torch.randn(2, 3)) # 矩阵相加 add_c = torch.add(torch.randn(2, 3), 10) # 矩阵和数字相加 print(add_b) print(add_c) # 裁剪 clamp_b = torch.clamp(torch.randn(2, 3), -0.1, 0.1) # 输入被裁剪矩阵，裁剪边界 print(clamp_b) # 相除 div_b = torch.div(torch.randn(2, 3), torch.randn(2, 3)) # 矩阵相除 div_c = torch.div(torch.randn(2, 3), 10) # 矩阵和数相除 # 求积 mul_b = torch.mul(torch.randn(2, 3), torch.randn(2, 3)) # 矩阵对应位置相乘 mul_c = torch.mul(torch.randn(2, 3), 10) # 矩阵和数字相乘 # 求幂 pow_b = torch.pow(torch.randn(2, 3), 2) # 矩阵乘法 mm_b = torch.mm(torch.randn(2, 3), torch.randn(3, 2)) # t()为矩阵转置 # 矩阵向量乘法 mv_b = torch.mv(torch.randn(2, 3), torch.randn(3))