第一个pytorch程序（Pycharm）

不念不忘少年蓝@ 2023-06-24 12:29 39阅读 0赞

pytorch开发环境搭建(win10)

上一篇（[pytorch开发环境搭建(win10) ][pytorch_win10]  ）完成了pytorch环境搭建

本篇就来介绍第一个pytorch程序及

我的是windows环境，开发工具Pycharm

一、新建工程

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3liNTQ2ODIyNjEy_size_16_color_FFFFFF_t_70][]

工程名：pytorch-test

画红圈的选中

点击creat 创建

二、创建pytorch测试文件

创建test-one.py文件

详细 代码如下

#!～/anaconda3/bin/python
    # _*_ coding:UTF-8 _*_
    from __future__ import print_function
    from itertools import count
    
    import numpy as np
    import torch
    import torch.autograd
    
    import torch.nn.functional as F
    from torch.autograd import Variable
    import matplotlib.pyplot as plt
    
    random_state = 5000
    torch.manual_seed(random_state)
    poly_degree = 4
    W_target = torch.randn(poly_degree, 1) * 5
    b_target = torch.randn(1) * 5
    
    
    def make_features(x):
        """
        创建一个特征矩阵结构为[x, x^2, x^3, x^4].
        """
        x = x.unsqueeze(1)
        return torch.cat([x ** i for i in range(1, poly_degree + 1)], 1)
    
    
    def f(x):
        """近似函数"""
        return x.mm(W_target) + b_target[0]
    
    
    def poly_desc(W, b):
        """生成多项式描述内容"""
        result = 'y = '
        for i, w in enumerate(W):
            result += '{:+.2f} x^{} '.format(w, len(W) - i)
        result += '{:+.2f}'.format(b[0])
        return result
    
    
    def get_batch(batch_size=32):
        """创建类似(x, f(x))批数据"""
        random = torch.from_numpy(np.sort(torch.randn(batch_size)))
        x = make_features(random)
        y = f(x)
        return Variable(x), Variable(y)
    
    
    # 声明模型
    fc = torch.nn.Linear(W_target.size(0), 1)
    
    for batch_idx in count(1):
        # 获取数据
        batch_x, batch_y = get_batch()
        # 重制求导
        fc.zero_grad()
    
        # 前向传播
        output = F.smooth_l1_loss(fc(batch_x), batch_y)
        loss = output.item()
    
        # 后向传播
        output.backward()
    
        # 应用导数
        for param in fc.parameters():
            param.data.add_(-0.1 * param.grad.data)
    
        # 停止条件
        if loss < 1e-3:
            plt.cla()
            plt.scatter(batch_x.data.numpy()[:, 0], batch_y.data.numpy()[:, 0], label='real curve', color='b')
            plt.plot(batch_x.data.numpy()[:, 0], fc(batch_x).data.numpy()[:, 0], label='fitting curve', color='r')
            plt.title('$Y=W^T*X+b$')
            plt.legend()
            plt.savefig('1.png')
            plt.show()
            break
    
    print('Loss: {:.6f} after {} batches'.format(loss, batch_idx))
    print('==> Learned function:\t' + poly_desc(fc.weight.data.view(-1), fc.bias.data))
    print('==> Actual function:\t' + poly_desc(W_target.view(-1), b_target))

运行文件

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3liNTQ2ODIyNjEy_size_16_color_FFFFFF_t_70 1][]

结果如下

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3liNTQ2ODIyNjEy_size_16_color_FFFFFF_t_70 2][]

这是个经典线性函数  
此处创建了一些随机训练样本，使其符合经典函数Y=MTX+b Y=M^TX+bY=M   
T  
 X+b分布，再增加一点噪声处理使得样本出现一定的偏差。然后创建一个线性回归的模型，在训练过程中对训练样本进行反向传播，求导后根据指定的损失边界结束训练。

上图白色就是线性回归的结果

[pytorch_win10]: https://blog.csdn.net/yb546822612/article/details/103664182
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