激活函数之tanh介绍及C++/PyTorch实现

「爱情、让人受尽委屈。」 2022-09-02 00:42 277阅读 0赞

深度神经网络中使用的激活函数有很多种，这里介绍下tanh。它的公式如下，截图来自于维基百科([https://en.wikipedia.org/wiki/Activation\_function][https_en.wikipedia.org_wiki_Activation_function])：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2ZlbmdiaW5nY2h1bg_size_16_color_FFFFFF_t_70][]

tanh又称双曲正切，它解决了sigmoid非零中心问题。tanh取值范围在(-1, 1)内，它也是非线性的。它也不能完全解决梯度消失问题。

C++实现如下：

template<typename _Tp>
    int activation_function_tanh(const _Tp* src, _Tp* dst, int length)
    {
    	for (int i = 0; i < length; ++i) {
    		_Tp ep = std::exp(src[i]);
    		_Tp em = std::exp(-src[i]);
    
    		dst[i] = (ep - em) / (ep + em);
    	}
    
    	return 0;
    }
    
    template<typename _Tp>
    int activation_function_tanh_derivative(const _Tp* src, _Tp* dst, int length)
    {
    	for (int i = 0; i < length; ++i) {
    		dst[i] = (_Tp)1. - src[i] * src[i];
    	}
    
    	return 0;
    }
    
    int test_activation_function()
    {
    	std::vector<float> src{ 1.1f, -2.2f, 3.3f, 0.4f, -0.5f, -1.6f };
    	int length = src.size();
    	std::vector<float> dst(length);
    
    	fprintf(stderr, "source vector: \n");
    	fbc::print_matrix(src);
    	fprintf(stderr, "calculate activation function:\n");
    
    	fprintf(stderr, "type: tanh result: \n");
    	fbc::activation_function_tanh(src.data(), dst.data(), length);
    	fbc::print_matrix(dst);
    	fprintf(stderr, "type: tanh derivative result: \n");
    	fbc::activation_function_tanh_derivative(dst.data(), dst.data(), length);
    	fbc::print_matrix(dst);
    }

执行结果如下：

![20210729084702978.png][]

Python和PyTorch实现如下：

import numpy as np
    import torch
    
    data = [1.1, -2.2, 3.3, 0.4, -0.5, -1.6]
    
    # numpy impl
    def tanh(x):
    	lists = list()
    	for i in range(len(x)):
    		lists.append((np.exp(x[i]) - np.exp(-x[i])) / (np.exp(x[i]) + np.exp(-x[i])))
    	return lists
    
    def tanh_derivative(x):
    	return 1 - np.power(tanh(x), 2)
    
    output = [round(value, 4) for value in tanh(data)] # 通过round保留小数点后4位
    print("numpy tanh:", output)
    print("numpt tanh derivative:", [round(value, 4) for value in tanh_derivative(data)])
    print("numpt tanh derivative2:", [round(1. - value*value, 4) for value in tanh(data)])
    
    # call pytorch interface
    input = torch.FloatTensor(data)
    m = torch.nn.Tanh()
    output2 = m(input)
    print("pytorch tanh:", output2)
    print("pytorch tanh derivative:", 1. - output2*output2)

执行结果如下：

![20210729084927260.png][]

由以上执行结果可知：C++、Python、PyTorch三种实现方式结果完全一致。

GitHub：

[https://github.com/fengbingchun/NN\_Test][https_github.com_fengbingchun_NN_Test]

[https://github.com/fengbingchun/PyTorch\_Test][https_github.com_fengbingchun_PyTorch_Test]

[https_en.wikipedia.org_wiki_Activation_function]: https://en.wikipedia.org/wiki/Activation_function
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2ZlbmdiaW5nY2h1bg_size_16_color_FFFFFF_t_70]: /images/20220829/fbf626cc6dae4023af9304e6a1629676.png
[20210729084702978.png]: /images/20220829/b488d7ac550b4ba0aab8278c47fa3072.png
[20210729084927260.png]: /images/20220829/69771ca6422e49af9dd673f76164689f.png
[https_github.com_fengbingchun_NN_Test]: https://github.com/fengbingchun/NN_Test
[https_github.com_fengbingchun_PyTorch_Test]: https://github.com/fengbingchun/PyTorch_Test