pytorch 学习（1）

pytorch

已经成为最炙手可热的深度学习框架之一，非常有必要学习

入门

常见形式
scalar： 常量

from torch import tensor

x = tensor(32,)
print tensor(32,)

vector: 向量（特征）

x = tensor([32,32,1,2,3]) 类似一个特征向量

matrix： 表示矩阵，通常是多维的

tensor([[1, 2, 3, 4], [2 ,3, 34,4]])

高维的
与numpy 的高维一样

线性回归案例

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

y_values = [2 * i + 1 for i in x_values]
y_train = np.array(y_values, dtype=np.float32)
y_train = y_train.reshape(-1, 1)

import torch.nn as nn
class LinearRegressionModel(nn.Module):
    def __init__(self, input_dim, out_dim):
        super(LinearRegressionModel, self).__init__()
        self.linear = nn.Linear(input_dim, out_dim)
        
    def forward(self, x):
        out = self.linear(x)
        return out

input_dim = 1; out_dim = 1
model = LinearRegressionModel(input_dim, out_dim)
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
model.to(device)

epochs = 1000
rate = 0.01
optimizer = torch.optim.SGD(model.parameters(), lr=rate)
criterion = nn.MSELoss()

for epoch in range(epochs):
    epoch += 1
    inputs = torch.from_numpy(x_train).to(device)
    labels = torch.from_numpy(y_train).to(device)
    
    # 清零梯度
    optimizer.zero_grad()
    
    # 前传播
    ouput = model(inputs)
    
    # 损失
    loss = criterion(ouput, labels)
    
    # 反向
    loss.backward()
    
    # 权重参数
    optimizer.step()
    
    if epoch % 50 == 0:
        print('epoch {}, loss {}'.format(epoch, loss.item()))

模型的保存

torch.save(model.state_dict(), "model.pkl")

读取

torch.load('model.pkl')

觉得有收获的可以加个微信交流技术

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