本篇内容主要讲解“PyTorch create_tensor怎么使用”,感兴趣的朋友不妨来看看。本文介绍的方法操作简单快捷,实用性强。下面就让小编来带大家学习“PyTorch create_tensor怎么使用”吧!
课程代码
1. create_tensor
import torch import numpy as np a = np.ones((3, 3)) print(a, id(a)) b = torch.tensor(a) print(b, id(b), b.device) # b_gpu = torch.tensor(a, device = 'cuda') b_gpu = torch.tensor(a, device = 'cpu') print(b_gpu, id(b_gpu), b_gpu.device) c = torch.from_numpy(a) print(c, id(c)) a[0, 0] = 2 print(a, c) c[0, 1] = 3 print(a, c) d = torch.zeros((3, 3, 3)) print(d, d.dtype, d.shape) dd = torch.zeros_like(d) print(d, d.type, d.shape) e = torch.full((2, 2), 233) print(e, e.dtype) ee = torch.full((2, 2), 233.) print(ee, ee.dtype) f = torch.arange(1, 5) print(f, f.dtype) ff = torch.arange(1., 5.1) print(ff, ff.dtype) g = torch.linspace(1, 6, 6) print(g, g.dtype) h = torch.normal(0, 1, (3, 3)) print(h, h.dtype) hh = torch.randn((3, 3)) print(hh, hh.dtype) i = torch.rand((2, 2)) print(i) ii = torch.randint(1, 5, (2, 2)) print(ii) j = torch.randperm(20) print(j, j.dtype)
2. reshape_tensor
import torch import numpy as np a = torch.arange(0, 10, dtype = torch.int64) b = torch.reshape(a, (2, 5)) print(b) b_T = torch.t(b) print(b_T, b_T.shape) c = torch.reshape(torch.arange(0, 24, dtype = torch.int64), (2, 3, 4)) print(c) d = torch.transpose(c, 0, 1) print(d) e = torch.tensor([1]) print(e, e.shape) f = torch.squeeze(e) print(f, f.shape) f = f * 2 print(f, e) ee = torch.unsqueeze(f, dim = 0) print(ee)
3. concat_split_tensor
import torch import numpy as np t1 = torch.ones((2, 2)) t2 = torch.zeros((2, 2)) a = torch.cat([t1, t2], dim = 0) print(a, a.shape) b = torch.stack([t1, t2], dim = 0) print(b, b.shape) print(b[0], b[1]) x = torch.split(b, [1, 1], dim = 0) print(type(x)) c, d = x print(c, d) e = torch.index_select(a, dim = 0, index = torch.tensor([0, 2])) print(e) mask = a.ge(1) f = torch.masked_select(a, mask) print(mask, f)
4. tensor_operator
# 通过一元线性回归, 来熟悉和展示常用的tensor的运算操作 import torch import numpy as np torch.manual_seed(10) # data x = torch.rand((20, 1)) * 10 y = 2 * x + 5 + torch.randn(20, 1) # model w = torch.tensor(np.asarray([0.3]), requires_grad=True) b = torch.tensor(np.asarray([0.]), requires_grad=True) print(w, b) # iteration for _ in range(1000): # flow y_pre = w * x + b loss = ( 0.5 * (y_pre - y) ** 2 ).mean() # backwords loss.backward() w.data.sub_(0.05 * w.grad) b.data.sub_(0.05 * b.grad) w.grad.zero_() b.grad.zero_() # show if _ % 100 == 0: print(str(_) + ' loss is', loss.data.numpy()) if loss.data.numpy() < 0.47: break print('finish...')
作业
1. 安装anaconda,pycharm, CUDA+CuDNN(可选),虚拟环境,pytorch,并实现hello pytorch查看pytorch的版本
2. 张量与矩阵、向量、标量的关系是怎么样的?
3. Variable“赋予”张量什么功能?
4. 采用torch.from_numpy创建张量,并打印查看ndarray和张量数据的地址;
5. 实现torch.normal()创建张量的四种模式。
1. 装环境
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conda create -n torch_p36 python=3.6.5
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conda activate torch_p36
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pip install torch==1.7.1+cu110 torchvision==0.8.2+cu110 torchaudio===0.7.2 -f https://download.pytorch.org/whl/torch_stable.html
2. 概念解释
标量(scalar)
一个标量表示一个单独的数,它不同于线性代数中研究的其他大部分对象
向量(vector)
一个向量表示一组有序排列的数。通过次序中的索引,我们可以确定每个单独的数
矩阵(matrix)
矩阵是具有相同特征和纬度的对象的集合,表现为一张二维数据表。其意义是一个对象表示为矩阵中的一行,一个特征表示为矩阵中的一列,每个特征都有数值型的取值
张量(tensor)
在某些情况下,我们会讨论坐标超过两维的数组。一般地,一个数组中的元素分布在若干维坐标的规则网格中,我们将其称之为张量
3. Variable“赋予”张量功能
Variable是torch.autograd中的数据类型,主要用于封装Tensor,使得tensor可以进行自动求导
主要有五个属性:
1.data:被包装的Tensor
2.grad:data的梯度
3.grad_fn:创建Tensor的Function(创建张量所用到的方法,如加法或乘法),是自动求导的关键
4.requires.grad:指示张量是否需要梯度,不需要梯度的张量可以设置为false
5.is_leaf:指示张量在计算图中是否是叶子结点。
现在variable不需要出现在代码中了, 并入到了tensor
Tensor
dtype
shape
device
4. 创建张量
import torch import numpy as np a = np.ones((3, 3)) print(a, id(a)) b = torch.tensor(a) print(b, id(b), b.device) # b_gpu = torch.tensor(a, device = 'cuda') b_gpu = torch.tensor(a, device = 'cpu') print(b_gpu, id(b_gpu), b_gpu.device) c = torch.from_numpy(a) print(c, id(c)) a[0, 0] = 2 print(a, c) c[0, 1] = 3 print(a, c) d = torch.zeros((3, 3, 3)) print(d, d.dtype, d.shape) dd = torch.zeros_like(d) print(d, d.type, d.shape) e = torch.full((2, 2), 233) print(e, e.dtype) ee = torch.full((2, 2), 233.) print(ee, ee.dtype) f = torch.arange(1, 5) print(f, f.dtype) ff = torch.arange(1., 5.1) print(ff, ff.dtype) g = torch.linspace(1, 6, 6) print(g, g.dtype) h = torch.normal(0, 1, (3, 3)) print(h, h.dtype) hh = torch.randn((3, 3)) print(hh, hh.dtype) i = torch.rand((2, 2)) print(i) ii = torch.randint(1, 5, (2, 2)) print(ii) j = torch.randperm(20) print(j, j.dtype)
到此,相信大家对“PyTorch create_tensor怎么使用”有了更深的了解,不妨来实际操作一番吧!这里是亿速云网站,更多相关内容可以进入相关频道进行查询,关注我们,继续学习!
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