1026-pytorch学习笔记
作者:互联网
Pytorch学习笔记
张量Tensor
张量是一个统称,其中包含很多类型:
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0阶张量:标量、常数,0-D Tensor
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1阶张量:向量,1-D Tensor
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2阶张量:矩阵,2-D Tensor
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3阶张量
Pytorch中创建张量
使用python中的列表或者序列创建tensor
torch.tensor([[1., -1.], [1., -1.]]) tensor([[ 1.0000, -1.0000], [ 1.0000, -1.0000]])
使用numpy中的数组创建tensor
torch.tensor(np.array([[1, 2, 3], [4, 5, 6]])) tensor([[ 1, 2, 3], [ 4, 5, 6]])
使用torch的api创建tensor
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torch.empty(3,4)
创建3行4列的空的tensor,会用无用数据进行填充 -
torch.ones([3,4])
创建3行4列的全为1的tensor -
torch.zeros([3,4])
创建3行4列的全为0的tensor -
torch.rand([3,4])
创建3行4列的随机值的tensor,随机值的区间是[0, 1)
torch.rand(2, 3) tensor([[ 0.8237, 0.5781, 0.6879], [ 0.3816, 0.7249, 0.0998]])
torch.randint(low=0,high=10,size=[3,4])
创建3行4列的随机整数的tensor,随机值的区间是[low, high)
torch.randint(3, 10, (2, 2)) tensor([[4, 5], [6, 7]])
torch.randn([3,4])
创建3行4列的随机数的tensor,随机值的分布式均值为0,方差为1
Pytorch中tensor的常用方法
获取tensor中的数据(当tensor中只有一个元素可用):tensor.item()
In [10]: a = torch.tensor(np.arange(1)) In [11]: a Out[11]: tensor([0]) In [12]: a.item() Out[12]: 0
转化为numpy数组
In [55]: z.numpy() Out[55]: array([[-2.5871205], [ 7.3690367], [-2.4918075]], dtype=float32)
获取形状:tensor.size()
x Out[72]: tensor([[ 1, 2], [ 3, 4], [ 5, 10]], dtype=torch.int32) In [73]: x.size() Out[73]: torch.Size([3, 2])
形状改变:tensor.view((3,4))
。类似numpy中的reshape,是一种浅拷贝,仅仅是形状发生改变
In [76]: x.view(2,3) Out[76]: tensor([[ 1, 2, 3], [ 4, 5, 10]], dtype=torch.int32)
获取阶数:tensor.dim()
获取最大值:tensor.max()
转置:tensor.t()
tensor的数据类型
类型的修改
In [17]: a Out[17]: tensor([1, 2], dtype=torch.int32) In [18]: a.type(torch.float) Out[18]: tensor([1., 2.]) In [19]: a.double() Out[19]: tensor([1., 2.], dtype=torch.float64)
tensor的其他操作
tensor和tensor相加
In [94]: x = x.new_ones(5, 3, dtype=torch.float) In [95]: y = torch.rand(5, 3) In [96]: x+y Out[96]: tensor([[1.6437, 1.9439, 1.5393], [1.3491, 1.9575, 1.0552], [1.5106, 1.0123, 1.0961], [1.4382, 1.5939, 1.5012], [1.5267, 1.4858, 1.4007]]) In [98]: torch.add(x,y) Out[98]: tensor([[1.6437, 1.9439, 1.5393], [1.3491, 1.9575, 1.0552], [1.5106, 1.0123, 1.0961], [1.4382, 1.5939, 1.5012], [1.5267, 1.4858, 1.4007]]) In [99]: x.add(y) Out[99]: tensor([[1.6437, 1.9439, 1.5393], [1.3491, 1.9575, 1.0552], [1.5106, 1.0123, 1.0961], [1.4382, 1.5939, 1.5012], [1.5267, 1.4858, 1.4007]]) In [100]: x.add_(y) #带下划线的方法会对x进行就地修改 Out[100]: tensor([[1.6437, 1.9439, 1.5393], [1.3491, 1.9575, 1.0552], [1.5106, 1.0123, 1.0961], [1.4382, 1.5939, 1.5012], [1.5267, 1.4858, 1.4007]]) In [101]: x #x发生改变 Out[101]: tensor([[1.6437, 1.9439, 1.5393], [1.3491, 1.9575, 1.0552], [1.5106, 1.0123, 1.0961], [1.4382, 1.5939, 1.5012], [1.5267, 1.4858, 1.4007]])
带下划线的方法(比如:add_
)会对tensor进行就地修改
手写线性回归
import torch import matplotlib.pyplot as plt learning_rate=0.01 #1.准备数据 #y=3x+0.8 x=torch.rand([500,1]) y_true=3*x+0.8 #2.通过模型计算y_predict w = torch.rand([1,1],requires_grad=True) b = torch.tensor(0,requires_grad=True,dtype=torch.float32) #4.通过循环,反向传播,更新参数 for i in range(2000): y_predict = torch.matmul(x, w) + b # 3.计算loss loss = (y_true - y_predict).pow(2).mean() if w.grad is not None: w.grad.data.zero_() if b.grad is not None: b.grad.data.zero_() loss.backward() #反向传播 w.data = w.data - learning_rate * w.grad b.data = b.data - learning_rate * b.grad if i % 50==0: print("w ,b ,loss",w.item(),b.item(),loss.item()) plt.figure(figsize=(20,8)) plt.scatter(x.numpy().reshape(-1),y_true.numpy().reshape(-1)) y_predict = torch.matmul(x, w) + b plt.plot(x.numpy().reshape(-1),y_predict.detach().numpy().reshape(-1),c='r') plt.show()
结果:
标签:1026,tensor,numpy,torch,笔记,pytorch,dtype,grad,Out 来源: https://www.cnblogs.com/xiaofengzai/p/15468367.html