PyTorch回归训练

傷城~ 2022-01-23 23:07 370阅读 0赞

### 1. 创建用于回归的虚拟数据集 ###

### 2. 划分训练集和测试集 ###

### 3. 参数初始化比较 ###

### 4 批训练方法 ###

#!/usr/bin/env python
    # -*- coding: utf-8 -*-
    """
    __title__ = ''
    
    """
    import torch
    from torch import nn
    import torch.nn.functional as F
    import torch.utils.data as Data
    import matplotlib.pyplot as plt
    from sklearn.model_selection import train_test_split
    import numpy as np
    from torchvision import datasets, transforms
    from torch.nn import init
    
    
    
    #创建fake data
    # torch.manual_seed(99)
    # x = torch.unsqueeze(torch.linspace(-1, 1, 1000), dim=1)
    # y = x.pow(2) + 0.1 * torch.normal(torch.zeros(x.size()))
    # plt.scatter(x.numpy(), y.numpy())
    # plt.show()
    
    np.random.seed(666)
    X = np.linspace(-1, 1, 1000)
    y = np.power(X, 2) + 0.1 * np.random.normal(0, 1, X.size)
    print(X.shape)
    print(y.shape)
    # plt.scatter(X, y)
    # plt.show()
    
    # 创建训练集和测试集
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1024)
    X_train = torch.from_numpy(X_train).type(torch.FloatTensor)
    X_train = torch.unsqueeze(X_train, dim=1)  #转换成二维
    y_train = torch.from_numpy(y_train).type(torch.FloatTensor)
    y_train = torch.unsqueeze(y_train, dim=1)
    print(X_train.type)
    X_test = torch.from_numpy(X_test).type(torch.FloatTensor)
    X_test = torch.unsqueeze(X_test, dim=1)  #转换成二维
    
    # train_size = int(0.7 * len(X))
    # test_size = len(X) - train_size
    # X_train, X_test = Data.random_split(X, [train_size, test_size])
    # print(len(X_train), len(X_test))
    
    
    
    BATCH_SIZE = 50
    LR = 0.02
    EPOCH = 5
    
    #将数据装载镜data中, 对数据进行分批训练
    torch_data  = Data.TensorDataset(X_train, y_train)
    loader = Data.DataLoader(dataset=torch_data, batch_size=BATCH_SIZE, shuffle=True)
    #创建自己的nn
    class Net(nn.Module):
        def __init__(self):
            super(Net, self).__init__()
            self.hidden = nn.Linear(1, 20)
            self.predict = nn.Linear(20, 1)
    
        def forward(self, x):
            x = F.relu(self.hidden(x))
            x = self.predict(x)
            return x
    def weights_init(m):
        if isinstance(m, nn.Linear):
            init.kaiming_normal(m.weight.data)
            # init.xavier_normal(m.bias.data)
    
    adam_net = Net()
    # adam_net.apply(weights_init) # 对整个网络层进行参数初始化
    # 有初始化损失 xavier_normal  ：
    #{1: tensor(0.0972, grad_fn=<MseLossBackward>), 2: tensor(0.0731, grad_fn=<MseLossBackward>),
    # 3: tensor(0.0881, grad_fn=<MseLossBackward>), 4: tensor(0.1120, grad_fn=<MseLossBackward>),
    # 5: tensor(0.1012, grad_fn=<MseLossBackward>)}
    # 有初始化损失 kaiming_normal  ： 表现相对较好---
    #{1: tensor(0.1476, grad_fn=<MseLossBackward>), 2: tensor(0.0234, grad_fn=<MseLossBackward>),
    # 3: tensor(0.0162, grad_fn=<MseLossBackward>), 4: tensor(0.0170, grad_fn=<MseLossBackward>),
    # 5: tensor(0.0218, grad_fn=<MseLossBackward>)}
    
    # 没有初始化损失
    # {1: tensor(0.0265, grad_fn=<MseLossBackward>), 2: tensor(0.0121, grad_fn=<MseLossBackward>),
    # 3: tensor(0.0096, grad_fn=<MseLossBackward>), 4: tensor(0.0109, grad_fn=<MseLossBackward>),
    # 5: tensor(0.0104, grad_fn=<MseLossBackward>)}
    #设置优化器和损失函数
    opt_adam = torch.optim.Adam(adam_net.parameters(), lr=LR)
    loss_func = nn.MSELoss()
    #对数据进行分批训练
    # 在神经网络中传递完整的数据集一次是不够的，
    # 而且我们需要将完整的数据集在同样的神经网络中传递多次。
    # 但是请记住，我们使用的是有限的数据集，
    # 并且我们使用一个迭代过程即梯度下降。因此仅仅更新权重一次或者说使用一个 epoch 是不够的。
    # 比如对于一个有 2000 个训练样本的数据集。将 2000 个样本分成大小为 500 的 batch，那么完成一个 epoch 需要 4 个 iteration。
    all_loss = {}
    for epoch in range(EPOCH):
        print('epoch', epoch)
        for step, (b_x, b_y) in enumerate(loader):
            print('step', step)
            pre = adam_net(b_x)
            loss = loss_func(pre, b_y)
            opt_adam.zero_grad()
            loss.backward()
            opt_adam.step()
            # print(loss)
            all_loss[epoch+1] = loss
    print(all_loss)
    
    #对测试集进行预测
    adam_net.eval()
    predict = adam_net(X_test)
    predict = predict.data.numpy()
    plt.scatter(X_test.numpy(), y_test, label='origin')
    plt.scatter(X_test.numpy(), predict, color='red', label='predict')
    plt.legend()
    plt.show()

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQwNTg3NTc1_size_16_color_FFFFFF_t_70][]

**没有使用参数初始化的结果**

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQwNTg3NTc1_size_16_color_FFFFFF_t_70 1][]

**使用参数初始化的结果**

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