Pytorch(一)

目录

一、基本操作

二、自动求导机制

 三、线性回归DEMO

3.1模型的读取与保存

3.2利用GPU训练时

四、常见的Tensor形式

五、Hub模块

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一、基本操作

操作代码如下:

import torch
import numpy as np

#创建一个矩阵
x1 = torch.empty(5,3)

# 随机值
x2 = torch.rand(5,3)

# 初始化一个全零的矩阵
x3 = torch.zeros(5,3,dtype = torch.long)

# view操作改变矩阵维度
x4 = torch.randn(4,4) #4*4矩阵
y = x4.view(16) #变成一行的矩阵
z = x4.view(-1,8) #变为2*8的矩阵
print(y.size()) #矩阵的尺寸

#与numpy的协同操作
# tensor转array
a = torch.ones(5)
b = a.numpy()

# array转tensor
a1 = np.ones(5)
b1 = torch.from_numpy(a)

二、自动求导机制

案例代码如下:

 

import torch

#计算流程
x = torch.rand(1)
b = torch.rand(1,requires_grad=True)
w = torch.rand(1,requires_grad=True)
y = w * x
z = y + b

# 反向传播计算
z.backward(retain_graph = True)
print(w.grad)
print(b.grad)

 三、线性回归DEMO

 

import numpy as np
import torch
import torch.nn as nn

# 构建线性回归模型
class LinearRegressionModel(nn.Module):
    def __init__(self,input_dim,output_dim):
        super(LinearRegressionModel,self).__init__()
        self.linear = nn.Linear(input_dim,output_dim)

    def forward(self,x):
        out = self.linear(x)
        return out

x_values = [i for i in range(11)]
x_train = np.array(x_values,dtype=np.float32)
x_train = x_train.reshape(-1,1)
print(x_train.shape)

#y = 2x + 1
y_values = [2*i + 1 for i in range(11)]
y_train = np.array(x_values,dtype=np.float32)
y_train = x_train.reshape(-1,1)

# 构建model
input_dim = 1
output_dim = 1

model = LinearRegressionModel(input_dim,output_dim)

# 指定好参数和损失函数
epochs = 1000 #训练次数
learning_rate = 0.01 #学习率
optimizer = torch.optim.SGD(model.parameters(),lr = learning_rate) #优化器
criterion = nn.MSELoss() #损失函数

# 训练模型
for epoch in range(epochs):
    epoch += 1
    #注意转行为tensor
    inputs = torch.from_numpy(x_train)
    labels = torch.from_numpy(y_train)


    #梯度要清零每一次迭代
    optimizer.zero_grad()

    #前向传播
    outputs = model(inputs)

    #计算损失
    loss = criterion(outputs,labels)

    #反向传播
    loss.backward()

    #更新权重参数
    optimizer.step()
    if epoch % 50 ==0:
        print('epoch {},loss {}'.format(epoch,loss.item()))

3.1模型的读取与保存

# 模型的保存与读取
torch.save(model.state.dict(),'model.pkl') #保存
model.load_state_dict(torch.load('model.pkl')) #读取

3.2利用GPU训练时

利用GPU训练时要将数据与模型导入cuda

#注意转行为tensor
inputs = torch.from_numpy(x_train)
labels = torch.from_numpy(y_train)
#利用GPU训练数据时的数据
inputs = torch.from_numpy(x_train).to(device)
labels = torch.from_numpy(y_train).to(device)


model = LinearRegressionModel(input_dim,output_dim)

#使用GPU进行训练
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model.to(device)

四、常见的Tensor形式

• 1.scalar:通常是指一个数值
• 2.vector:通常是指一个向量
• 3.matrix:通常是指一个数据矩阵
• 4.n-dimensional tensor:高维数据

五、Hub模块

Github地址:https://github.com/pytorch/hub

Hub已有模型:https://pytorch.org/hub/research-models