Torch和Numpy之间的简单对比

忘是亡心i 2021-08-26 23:20 767阅读 0赞

*  numpy和torch两者用法十分相似，torch可以说是神经网络中的numpy。

### numpy和torch之间的互相转换： ###

import torch
    import numpy as np
    
    np_data = np.arange(6).reshape(2, 3)
    print("Numpy 数据：", np_data)
    torch_data = torch.tensor([[0, 1, 2], [3, 4, 5]])
    print("Torch 数据：", torch_data)
    
    np2torch = torch.from_numpy(np_data)
    print("numpy数据转torch数据:", np2torch)
    torch2np = torch_data.numpy()
    print("torch数据转numpy数据：", torch2np)

*  result：
    
    ![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDkxMjE1OQ_size_16_color_FFFFFF_t_70]

### numpy和torch的数学运算： ###

*  详细的API用法请参考官方地址：[https://pytorch.org/docs/stable/torch.html][https_pytorch.org_docs_stable_torch.html]
 *  中文文档地址：[https://pytorch-cn.readthedocs.io/zh/latest/package\_references/Tensor/][https_pytorch-cn.readthedocs.io_zh_latest_package_references_Tensor]

1.  **计算绝对值abs**
    
        import torch
        import numpy as np
        
        data = [-1,2,-3,4]
        np_abs = np.abs(np.array(data))
        print("numpy的abs：", np_abs)
        
        torch_abs = torch.abs(torch.tensor(data))
        print("torch的abs：", torch_abs)
    
    result:
    
     ![在这里插入图片描述][20200310202341357.png]
2.  **计算sin** [torch.tensor和torch.Torch的区别][torch.tensor_torch.Torch]
    
        import torch
        import numpy as np
        
        data = [-1,2,-3,4]
        np_sin = np.sin(np.array(data))
        print("numpy的sin：", np_sin)
        # 这里改了大写的Tensor
        torch_sin = torch.sin(torch.Tensor(data))
        print("torch的sin：", torch_sin)
    
    result:
    
     ![在这里插入图片描述][20200310202350814.png]
3.  **计算均值mean**
    
        import torch
        import numpy as np
        
        data = [-1,2,-3,4]
        np_mean = np.mean(np.array(data))
        print("numpy的均值：", np_mean)
        # 这里改了大写的Tensor
        torch_mean = torch.mean(torch.Tensor(data))
        print("torch的均值：", torch_mean)
    
    result:
    
     ![在这里插入图片描述][20200310202359177.png]
4.  矩阵相乘
    
        import torch
        import numpy as np
        
        matrix_2x3 = [[1,2,3], [4,5,6]]
        matrix_3x2 = [[1,2],[3,4],[5,6]]
        
        np_matmul = np.matmul(matrix_2x3, matrix_3x2)
        print("numpy的矩阵相乘：", np_matmul)
        torch_matmul = torch.mm(torch.tensor(matrix_2x3), torch.tensor(matrix_3x2))
        print("torch的矩阵相乘：", torch_matmul)
    
    result:
    
     ![在这里插入图片描述][20200310202406157.png]

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDkxMjE1OQ_size_16_color_FFFFFF_t_70]: /images/20210511/37f1b7471c724dafb959d9cf12f4874d.png
[https_pytorch.org_docs_stable_torch.html]: https://pytorch.org/docs/stable/torch.html
[https_pytorch-cn.readthedocs.io_zh_latest_package_references_Tensor]: https://pytorch-cn.readthedocs.io/zh/latest/package_references/Tensor/
[20200310202341357.png]: /images/20210511/60bf8ae1672840dd80f2ecca9e1ba527.png
[torch.tensor_torch.Torch]: https://blog.csdn.net/weixin_44912159/article/details/104776922
[20200310202350814.png]: /images/20210511/59f38064536844bea1121ecb42effa62.png
[20200310202359177.png]: /images/20210511/426d85930fef4f5793bf7f45f6d28394.png
[20200310202406157.png]: /images/20210511/40264f2f600b4304a539ba64b8a0496d.png