莫烦pytorch学习01：pytorch与numpy之间的转换

import torch
import numpy as np

#abs 绝对值转换
data = [-1,-2,-3,4]
tensor = torch.FloatTensor(data) #32bit浮点数

print(
'\nabs',
'\nnumpy',np.abs(data),
'\ntorch',torch.abs(tensor),
)
print(
'\nsin',
'\nnumpy',np.sin(data),
'\ntorch',torch.sin(tensor),
)
print(
'\nmean',
'\nnumpy',np.mean(data),
'\ntorch',torch.mean(tensor),
)

data = [[1,2], [3,4]]
tensor = torch.FloatTensor(data)  # 转换成32位浮点 tensor
print(
'\nnumpy',np.matmul(data,data),
'\ntorch',torch.mm(tensor,tensor),
)

data = np.array([1,2,3,4])
tensor = torch.FloatTensor(data)
print(
'\nnumpy',data.dot(data),
'\ntorch',tensor.dot(tensor), # torch 会转换成 [1,2,3,4].dot([1,2,3,4) = 30.0
)

#dot()的使用 https://docs.scipy.org/doc/numpy/reference/generated/numpy.dot.html
#numpy   a.dot(b) = np.dot(a,b) np.dot(a,b) != np.dot(b,a)
#一维数组返回内积
data = [1,2,3,4]
data = np.array(data)
print(
'\nnumpy',data.dot(data),
)
#二维数组（矩阵）返回矩阵积
data = [[1,2], [3,4]]
data = np.array(data)
print(
'\nnumpy',data.dot(data),
)

#pytorch
#仅支持一维数据
data = [1,2,3,4]
data = np.array(data)
tensor = torch.FloatTensor(data)
print(
'\ntorch',tensor.dot(tensor),
)
#二维会报错
# data = [[1,2], [3,4]]
# data = np.array(data)
# tensor = torch.FloatTensor(data)
# print(
#  '\ntorch',tensor.dot(tensor),

4000
# )