# -*- coding:utf-8 -*- """ @file name : lesson-02.py @author : TingsongYu https://github.com/TingsongYu @date : 2018-08-26 @brief : 張量的創(chuàng)建 """ import torch import numpy as np torch.manual_seed(1)# =============================== example 1 =============================== # 通過torch.tensor創(chuàng)建張量 # # flag = True flag = False if flag:arr = np.ones((3, 3))print("ndarray的數(shù)據(jù)類型：", arr.dtype)#t = torch.tensor(arr, device='cuda')t = torch.tensor(arr)print(t) # =============================== example 2 =============================== # 通過torch.from_numpy創(chuàng)建張量 # flag = True flag = False if flag:arr = np.array([[1, 2, 3], [4, 5, 6]])t = torch.from_numpy(arr)# print("numpy array: ", arr)# print("tensor : ", t)# print("\n修改arr")# arr[0, 0] = 0# print("numpy array: ", arr)# print("tensor : ", t)print("\n修改tensor")t[0, 0] = -1print("numpy array: ", arr)print("tensor : ", t)# =============================== example 3 =============================== # 通過torch.zeros創(chuàng)建張量 # flag = True flag = False if flag:out_t = torch.tensor([1])t = torch.zeros((3, 3), out=out_t)print(t, '\n', out_t)print(id(t), id(out_t), id(t) == id(out_t))# =============================== example 4 =============================== # 通過torch.full創(chuàng)建全1張量 # flag = True flag = False if flag:t = torch.full((3, 3), 10.0)print(t)# =============================== example 5 =============================== # 通過torch.arange創(chuàng)建等差數(shù)列張量 # flag = True flag = False if flag:t = torch.arange(2, 10, 2)print(t)# =============================== example 6 =============================== # 通過torch.linspace創(chuàng)建均分?jǐn)?shù)列張量 # flag = True flag = False if flag:# t = torch.linspace(2, 10, 5)t = torch.linspace(2, 10, 6)print(t)# =============================== example 7 =============================== # 通過torch.normal創(chuàng)建正態(tài)分布張量 flag = True # flag = False if flag:# # mean：張量 std: 張量# mean = torch.arange(1, 5, dtype=torch.float)# std = torch.arange(1, 5, dtype=torch.float)# t_normal = torch.normal(mean, std)# print("mean:{}\nstd:{}".format(mean, std))# print(t_normal)# mean：標(biāo)量 std: 標(biāo)量# t_normal = torch.normal(0., 1., size=(4,))# print(t_normal)# mean：張量 std: 標(biāo)量mean = torch.arange(1, 5, dtype=torch.float)std = 1t_normal = torch.normal(mean, std)print("mean:{}\nstd:{}".format(mean, std))print(t_normal)

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