1.3 张量的操作及线性回归（笔记）
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2022-03-23 09:11:17
任务简介：学习张量的基本操作与线性回归模型的实现；学习计算图概念，理解动态图和静态图的差异详细说明：本节介绍张量的基本操作，如张量拼接切分、索引和变换，同时学习张量的数学运算，并基于所学习的知识，实现线性回归模型的训练，以加深知识点的认识。本节第二部分介绍pytorch最大的特性——动态图机制，动态图机制是pytorch与tensorflow最大的区别，该部分首先介绍计算图的概念，并通过演示动态图与静态图的搭建过程来理解动态图与静态图的差异。1. 张量的操作:拼接、切分、索引和变换量的量的操...
目录
3.2 torch.transpose()和torch.t()
3.3 torch.squeeze()和torch.unsqueeze()
任务简介：
学习张量的基本操作与线性回归模型的实现。
详细说明：
本节介绍张量的基本操作，如张量拼接切分、索引和变换，同时学习张量的数学运算，并基于所学习的知识，实现线性回归模型的训练，以加深知识点的认识。
一、张量的操作
1. 拼接、切分
1.1 torch.cat()
测试代码
# ======================================= example 1 =======================================
# torch.cat
flag = True
#flag = False
if flag:
t = torch.ones((2, 3))
t_0 = torch.cat([t, t], dim=0)
t_1 = torch.cat([t, t, t], dim=1)
print("t_0:{} shape:{}\nt_1:{} shape:{}".format(t_0, t_0.shape, t_1, t_1.shape))
输出
t_0:tensor([[1., 1., 1.],
[1., 1., 1.],
[1., 1., 1.],
[1., 1., 1.]]) shape:torch.Size([4, 3])
t_1:tensor([[1., 1., 1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1., 1., 1.]]) shape:torch.Size([2, 9])
1.2 torch.stack()
测试代码
# ======================================= example 2 =======================================
# torch.stack
flag = True
#flag = False
if flag:
t = torch.ones((2, 3))
t_stack = torch.stack([t, t, t], dim=0) # 如果第0维有数据则原来第0、1...维的向后顺延一位
print("\nt_stack:{} shape:{}".format(t_stack, t_stack.shape))
输出
t_stack:tensor([[[1., 1., 1.],
[1., 1., 1.]],
[[1., 1., 1.],
[1., 1., 1.]],
[[1., 1., 1.],
[1., 1., 1.]]]) shape:torch.Size([3, 2, 3])
1.3 torch.chunk()
测试代码
# ======================================= example 3 =======================================
# torch.chunk
flag = True
#flag = False
if flag:
a = torch.ones((2, 7)) # 7
list_of_tensors = torch.chunk(a, dim=1, chunks=3) # 3
for idx, t in enumerate(list_of_tensors):
print("第{}个张量：{}, shape is {}".format(idx+1, t, t.shape))
输出
第1个张量：tensor([[1., 1., 1.],
[1., 1., 1.]]), shape is torch.Size([2, 3])
第2个张量：tensor([[1., 1., 1.],
[1., 1., 1.]]), shape is torch.Size([2, 3])
第3个张量：tensor([[1.],
[1.]]), shape is torch.Size([2, 1])
1.4 torch.split()
list元素的和必须等于指定维度上张量的长度。
测试代码
# ======================================= example 4 =======================================
# torch.split
# flag = True
flag = True
if flag:
t = torch.ones((2, 5))
t1 = torch.ones((2, 5))
list_of_tensors = torch.split(t, 2, dim=1) # [2 , 1, 2]
for idx, t in enumerate(list_of_tensors):
print("第{}个张量：{}, shape is {}".format(idx+1, t, t.shape))
list_of_tensors = torch.split(t1, [2, 1, 2], dim=1)
for idx, t in enumerate(list_of_tensors):
print("第{}个张量：{}, shape is {}".format(idx+1, t, t.shape))
输出
第1个张量：tensor([[1., 1.],
[1., 1.]]), shape is torch.Size([2, 2])
第2个张量：tensor([[1., 1.],
[1., 1.]]), shape is torch.Size([2, 2])
第3个张量：tensor([[1.],
[1.]]), shape is torch.Size([2, 1])
第1个张量：tensor([[1., 1.],
[1., 1.]]), shape is torch.Size([2, 2])
第2个张量：tensor([[1.],
[1.]]), shape is torch.Size([2, 1])
第3个张量：tensor([[1., 1.],
[1., 1.]]), shape is torch.Size([2, 2])
2. 索引
2.1 torch.index_select()
测试代码
# ======================================= example 5 =======================================
# torch.index_select
flag = True
#flag = False
if flag:
t = torch.randint(0, 9, size=(3, 3))
idx = torch.tensor([0, 2], dtype=torch.long) # float
t_select = torch.index_select(t, dim=0, index=idx)
print("t:\n{}\nt_select:\n{}".format(t, t_select))
输出
t:
tensor([[4, 5, 0],
[5, 7, 1],
[2, 5, 8]])
t_select:
tensor([[4, 5, 0],
[2, 5, 8]])
2.2 torch.make_select()
测试代码
# ======================================= example 6 =======================================
# torch.masked_select
flag = True
#flag = False
if flag:
t = torch.randint(0, 9, size=(3, 3))
mask = t.le(5) # ge is mean greater than or equal/ gt: greater than le lt
t_select = torch.masked_select(t, mask)
print("t:\n{}\nmask:\n{}\nt_select:\n{} ".format(t, mask, t_select))
输出
t:
tensor([[4, 5, 0],
[5, 7, 1],
[2, 5, 8]])
mask:
tensor([[ True, True, True],
[ True, False, True],
[ True, True, False]])
t_select:
tensor([4, 5, 0, 5, 1, 2, 5])
3. 变换
3.1 torch.reshape()
测试代码
# ======================================= example 7 =======================================
# torch.reshape
flag = True
#flag = False
if flag:
t = torch.randperm(8)
t_reshape = torch.reshape(t, (-1, 2, 2)) # -1代表第0维度数值为8/2/2=2
print("t:{}\nt_reshape:\n{}".format(t, t_reshape))
t[0] = 1024
print("t:{}\nt_reshape:\n{}".format(t, t_reshape))
print("t.data 内存地址:{}".format(id(t.data)))
print("t_reshape.data 内存地址:{}".format(id(t_reshape.data)))
输出
t:tensor([5, 4, 2, 6, 7, 3, 1, 0])
t_reshape:
tensor([[[5, 4],
[2, 6]],
[[7, 3],
[1, 0]]])
t:tensor([1024, 4, 2, 6, 7, 3, 1, 0])
t_reshape:
tensor([[[1024, 4],
[ 2, 6]],
[[ 7, 3],
[ 1, 0]]])
t.data 内存地址:1169043563128
t_reshape.data 内存地址:1169043563128
3.2 torch.transpose()和torch.t()
测试代码
# ======================================= example 8 =======================================
# torch.transpose
flag = True
#flag = False
if flag:
# torch.transpose
t = torch.rand((2, 3, 4))
t_transpose = torch.transpose(t, dim0=1, dim1=2) # c*h*w h*w*c
print("t shape:{}\nt_transpose shape: {}".format(t.shape, t_transpose.shape))
输出
t shape:torch.Size([2, 3, 4])
t_transpose shape: torch.Size([2, 4, 3])
3.3 torch.squeeze()和torch.unsqueeze()
测试代码
# ======================================= example 9 =======================================
# torch.squeeze
flag = True
#flag = False
if flag:
t = torch.rand((1, 2, 3, 1))
t_sq = torch.squeeze(t)
t_0 = torch.squeeze(t, dim=0)
t_1 = torch.squeeze(t, dim=1)
print(t.shape)
print(t_sq.shape)
print(t_0.shape)
print(t_1.shape)
输出
torch.Size([1, 2, 3, 1])
torch.Size([2, 3])
torch.Size([2, 3, 1])
torch.Size([1, 2, 3, 1])
二、张量数学运算
1. 加法运算torch.add()
测试代码
# ======================================= example 8 =======================================
# torch.add
flag = True
#flag = False
if flag:
t_0 = torch.randn((3, 3))
t_1 = torch.ones_like(t_0)
t_add = torch.add(t_0, 10, t_1)
print("t_0:\n{}\nt_1:\n{}\nt_add_10:\n{}".format(t_0, t_1, t_add))
输出
t_0:
tensor([[ 0.6614, 0.2669, 0.0617],
[ 0.6213, -0.4519, -0.1661],
[-1.5228, 0.3817, -1.0276]])
t_1:
tensor([[1., 1., 1.],
[1., 1., 1.],
[1., 1., 1.]])
t_add_10:
tensor([[10.6614, 10.2669, 10.0617],
[10.6213, 9.5481, 9.8339],
[ 8.4772, 10.3817, 8.9724]])
三、线性回归
测试代码
import torch
import matplotlib.pyplot as plt
torch.manual_seed(10)
lr = 0.05 # 学习率 20191015修改
# 创建训练数据
x = torch.rand(20, 1) * 10 # x data (tensor), shape=(20, 1)
y = 2*x + (5 + torch.randn(20, 1)) # y data (tensor), shape=(20, 1)
# 构建线性回归参数
w = torch.randn((1), requires_grad=True)
b = torch.zeros((1), requires_grad=True)
for iteration in range(1000):
# 前向传播
wx = torch.mul(w, x)
y_pred = torch.add(wx, b)
# 计算 MSE loss
loss = (0.5 * (y - y_pred) ** 2).mean()
# 反向传播
loss.backward()
# 更新参数
b.data.sub_(lr * b.grad)
w.data.sub_(lr * w.grad)
# 清零张量的梯度
w.grad.zero_()
b.grad.zero_()
# 绘图
if iteration % 20 == 0:
plt.scatter(x.data.numpy(), y.data.numpy())
plt.plot(x.data.numpy(), y_pred.data.numpy(), 'r-', lw=5)
plt.text(2, 20, 'Loss=%.4f' % loss.data.numpy(), fontdict={'size': 20, 'color': 'red'})
plt.xlim(1.5, 10)
plt.ylim(8, 28)
plt.title("Iteration: {}\nw: {} b: {}".format(iteration, w.data.numpy(), b.data.numpy()))
plt.pause(0.5)
if loss.data.numpy() < 1:
break
输出
当迭代100次时，损失值小于1
本文地址：https://blog.csdn.net/weixin_40633696/article/details/107887186