LZ之前一直使用的都是tensorflow，对pytorch这个深度框架一直有所耳闻，只是看过部分代码，自己却没怎么写过，感觉还是要学习下，不然跟不上年轻人的时代呢，主要都是pytorch的tutorial的example，自己运行下，然后在稍微注释下，内容比较简单，Pytorch高阶玩家可忽略。
#载入对应包 from __future__ import print_function import torch #看下版本呗 print(torch.__version__) 1.1.0 #当使用的matrix为空时，初始值相当随意。。。 x = torch.empty(5, 3) print(x) tensor([[ 0.0000e+00, 0.0000e+00, 0.0000e+00], [ 0.0000e+00, 0.0000e+00, 0.0000e+00], [ 0.0000e+00, 0.0000e+00, 0.0000e+00], [ 0.0000e+00, 9.1084e-44, 0.0000e+00], [-1.5327e+33, 3.0915e-41, 2.5906e-15]]) #随机初始化个矩阵，这个和tf差不多 x = torch.rand(5, 3) print(x) tensor([[0.9989, 0.1704, 0.8226], [0.0776, 0.9894, 0.5031], [0.3808, 0.3658, 0.4930], [0.0226, 0.0428, 0.9701], [0.2032, 0.0144, 0.8480]]) #初始化为0，type=long x = torch.zeros(5, 3, dtype=torch.long) print(x) tensor([[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]]) #创建个tensor x = torch.tensor([5.5, 3]) print(x) tensor([5.5000, 3.0000]) #对于已有tensor重新赋值使用 x = x.new_ones(5, 3, dtype=torch.double) print(x) x = torch.randn_like(x, dtype=torch.float) print(x) tensor([[1., 1., 1.], [1., 1., 1.], [1., 1., 1.], [1., 1., 1.], [1., 1., 1.]], dtype=torch.float64) tensor([[-1.2955, -0.5346, -0.3851], [ 0.0423, 0.7751, -0.8816], [-1.0434, -0.6452, 1.6098], [-0.7102, -1.1221, -0.5180], [-0.0641, 0.0103, 0.2643]]) #输出尺寸 print(x.size()) torch.Size([5, 3]) #开始两个变量了，加减乘除。。。 y = torch.rand(5, 3) print(x, y) tensor([[-1.2955, -0.5346, -0.3851], [ 0.0423, 0.7751, -0.8816], [-1.0434, -0.6452, 1.6098], [-0.7102, -1.1221, -0.5180], [-0.0641, 0.0103, 0.2643]]) tensor([[0.0126, 0.4379, 0.6608], [0.6111, 0.3980, 0.6987], [0.5446, 0.8241, 0.3343], [0.0104, 0.6765, 0.3375], [0.5786, 0.2635, 0.2601]]) y.add_(x) print(y) tensor([[-1.2829, -0.0967, 0.2757], [ 0.6534, 1.1731, -0.1829], [-0.4988, 0.1789, 1.9440], [-0.6999, -0.4456, -0.1805], [ 0.5145, 0.2738, 0.5244]]) print(x[:,1]) tensor([-0.5346, 0.7751, -0.6452, -1.1221, 0.0103]) #resize的操作啦 x = torch.randn(4, 4) y = x.view(16) z = x.view(-1, 8) print(x.size(), y.size(), z.size()) torch.Size([4, 4]) torch.Size([16]) torch.Size([2, 8]) #输出对应tensor值，tf要session run才行 x = torch.randn(1) print(x) print(x.item()) tensor([-1.0967]) -1.0967001914978027 a = torch.ones(5) print(a) tensor([1., 1., 1., 1., 1.]) b = a.numpy() print(b) [1. 1. 1. 1. 1.] a.add_(1) print(a) print(b) tensor([2., 2., 2., 2., 2.]) [2. 2. 2. 2. 2.] import numpy as np a = np.ones(5) b = torch.from_numpy(a) np.add(a, 1, out=a) print(a) print(b) [2. 2. 2. 2. 2.] tensor([2., 2., 2., 2., 2.], dtype=torch.float64) #移动数据GPU或者CPU if torch.cuda.is_available(): device = torch.device("cuda") y = torch.ones_like(x, device=device) x = x.to(device) z = x + y print(z) print(z.to("cpu", torch.double)) tensor([-0.0967], device='cuda:0') tensor([-0.0967], dtype=torch.float64)好像基本操作还不是很难，后续继续学习中。。。
参考地址： https://pytorch.org/tutorials/beginner/blitz/tensor_tutorial.html#sphx-glr-beginner-blitz-tensor-tutorial-py
