pytorch基础入门（一）
pytorch基础入门（一）
一、tensor属性
import torch
a = torch.tensor([1,2,3], dtype = int)
print(a)
tensor([1, 2, 3])
a.type
b = torch.tensor([4,5,6], dtype = float)
print(b)
tensor([4., 5., 6.], dtype=torch.float64)
tensor = torch.tensor([[1,2,3],
[4,5,6]])
# 查看数据的维度
tensor.ndim
2
# 查看数据的形状
tensor.shape # 属性
torch.Size([2, 3])
tensor.size() # 方法
torch.Size([2, 3])
二、数据生成
import torch
# 生成权重为1的两行三列的矩阵
torch.ones(2,3)
tensor([[1., 1., 1.],
[1., 1., 1.]])
# 生成权重为0的三行三列的矩阵
torch.zeros(3,3)
tensor([[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.]])
# 生成三行四列的随机数矩阵
torch.rand(3,4)
tensor([[0.2095, 0.2981, 0.9930, 0.2970],
[0.5243, 0.6877, 0.2672, 0.1739],
[0.3329, 0.4882, 0.5752, 0.0886]])
# 生成0-10之间两行三列的随机整数矩阵
torch.randint(0,10,(2,3))
tensor([[1, 6, 6],
[9, 4, 7]])
# 生成三行四列的符合正态分布矩阵
torch.randn(3,4)
tensor([[ 1.6634, 0.8821, 0.3271, 0.7346],
[-0.7800, -0.8522, 0.3166, -1.0605],
[-0.2146, -0.4478, 1.0225, 0.7094]])
a = torch.tensor([[1,2],
[3,4],
[5,6]])
a
tensor([[1, 2],
[3, 4],
[5, 6]])
# 根据a的形状生成b
b = torch.rand_like(a, dtype = float)
b
tensor([[0.0610, 0.8480],
[0.9012, 0.8032],
[0.8593, 0.0249]], dtype=torch.float64)
# 改变数据形状
d = b.view(2,3)
d
tensor([[0.0610, 0.8480, 0.9012],
[0.8032, 0.8593, 0.0249]], dtype=torch.float64)
# 改变数据形状
d = d.reshape(6)
d
tensor([0.0610, 0.8480, 0.9012, 0.8032, 0.8593, 0.0249], dtype=torch.float64)
d[1]
tensor(0.8480, dtype=torch.float64)
# 转化为正常数据，非tensor,只能改变一个
d[1].item()
0.8479788102877875
import numpy as np
# 将tenser转numpy array
array = np.array(d)
array
array([0.06101681, 0.84797881, 0.90117927, 0.80323335, 0.85928866,
0.02492348])
tensor = torch.tensor(array)
tensor
tensor([0.0610, 0.8480, 0.9012, 0.8032, 0.8593, 0.0249], dtype=torch.float64)
三、数据索引
import torch
tensor = torch.arange(2,14)
tensor
tensor([ 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13])
print(tensor[2])
tensor(4)
tensor[1:4]
tensor([3, 4, 5])
tensor[2:-1]
tensor([ 4, 5, 6, 7, 8, 9, 10, 11, 12])
tensor[:5]
tensor([2, 3, 4, 5, 6])
tensor[::2]
tensor([ 2, 4, 6, 8, 10, 12])
tensor[-3:]
tensor([11, 12, 13])
index = [1,4,5,5,7]
tensor[index]
tensor([3, 6, 7, 7, 9])
四、torch的基本运算操作
import torch
a = torch.randint(1,5,(2,3))
b = torch.randint(1,5,(2,3))
print(a)
print(b)
tensor([[4, 2, 2],
[2, 2, 3]])
tensor([[4, 1, 3],
[3, 1, 2]])
a+b
tensor([[8, 3, 5],
[5, 3, 5]])
torch.add(a,b)
tensor([[8, 3, 5],
[5, 3, 5]])
result = torch.zeros(2,3)
torch.add(a,b,out= result)
tensor([[8., 3., 5.],
[5., 3., 5.]])
# a + b
# 注意 任何使张量tensor会发生变化的操作都有一个前缀'_'，例如：a.add_()
a.add_(b)
tensor([[8, 3, 5],
[5, 3, 5]])
tensor = torch.ones(3,5)
tensor
tensor([[1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.]])
a = a.float()
# 矩阵乘法
torch.matmul(a,tensor)
tensor([[16., 16., 16., 16., 16.],
[13., 13., 13., 13., 13.]])
a = torch.rand(3,2)
a
tensor([[0.3879, 0.3897],
[0.6362, 0.4619],
[0.9510, 0.0311]])
torch.sum(a)
tensor(2.8577)
torch.min(a)
tensor(0.0311)
# 求最小值所在位置（索引）
torch.argmin(a)
tensor(5)
# 平均值
torch.mean(a)
tensor(0.4763)
# 中位数
torch.median(a)
tensor(0.3897)
torch.sqrt(a)
tensor([[0.6228, 0.6243],
[0.7976, 0.6796],
[0.9752, 0.1763]])
五、自动求导
import torch
x = torch.ones(2,2,requires_grad=True)
x
tensor([[1., 1.],
[1., 1.]], requires_grad=True)
y = x + 2
y
tensor([[3., 3.],
[3., 3.]], grad_fn=)
z = y * y * 3
z
tensor([[27., 27.],
[27., 27.]], grad_fn=)
out = z.mean()
out
tensor(27., grad_fn=)
# 求导
out.backward()
print(x.grad)
tensor([[4.5000, 4.5000],
[4.5000, 4.5000]])
参考：31 项目实战 猫狗分类模型预测 - YouTube