softmax

How to create a layer to invert a softmax (TensforFlow,python)?

由 笑着哭i 提交于 2021-02-05 12:09:37
问题 I am building a deconvolution network. I would like to add a layer to it which is the reverse of a softmax. I tried to write a basic python function that returns the inverse of a softmax for a given matrix and put that in a tensorflow Lambda and add it to my model. I have no error but when I doing a predict I only have 0 at the exit. When I don't add this layer to my network I have output something other than zeros. This therefore justifies that they are due to my inv_softmax function which

How to create a layer to invert a softmax (TensforFlow,python)?

由 ￡可爱￡侵袭症＋ 提交于 2021-02-05 12:08:30
问题 I am building a deconvolution network. I would like to add a layer to it which is the reverse of a softmax. I tried to write a basic python function that returns the inverse of a softmax for a given matrix and put that in a tensorflow Lambda and add it to my model. I have no error but when I doing a predict I only have 0 at the exit. When I don't add this layer to my network I have output something other than zeros. This therefore justifies that they are due to my inv_softmax function which

How to create a layer to invert a softmax (TensforFlow,python)?

由 谁都会走 提交于 2021-02-05 12:08:26
问题 I am building a deconvolution network. I would like to add a layer to it which is the reverse of a softmax. I tried to write a basic python function that returns the inverse of a softmax for a given matrix and put that in a tensorflow Lambda and add it to my model. I have no error but when I doing a predict I only have 0 at the exit. When I don't add this layer to my network I have output something other than zeros. This therefore justifies that they are due to my inv_softmax function which

Softmax Cross Entropy implementation in Tensorflow Github Source Code

由 我的梦境 提交于 2021-01-29 22:23:59
问题 I am trying to implement a Softmax Cross-Entropy loss in python. So, I was looking at the implementation of Softmax Cross-Entropy loss in the GitHub Tensorflow repository. I am trying to understand it but I run into a loop of three functions and I don't understand which line of code in the function is computing the Loss? The function softmax_cross_entropy_with_logits_v2(labels, logits, axis=-1, name=None) returns the function softmax_cross_entropy_with_logits_v2_helper(labels=labels, logits

Should I use softmax as output when using cross entropy loss in pytorch?

由 ≯℡__Kan透↙ 提交于 2020-07-18 04:24:48
问题 I have a problem with classifying fully connected deep neural net with 2 hidden layers for MNIST dataset in pytorch . I want to use tanh as activations in both hidden layers, but in the end, I should use softmax . For the loss, I am choosing nn.CrossEntropyLoss() in pytorch, which (as I have found out) does not want to take one-hot encoded labels as true labels, but takes LongTensor of classes instead. My model is nn.Sequential() and when I am using softmax in the end, it gives me worse

Should I use softmax as output when using cross entropy loss in pytorch?

由 ℡╲_俬逩灬. 提交于 2020-07-18 04:23:59
问题 I have a problem with classifying fully connected deep neural net with 2 hidden layers for MNIST dataset in pytorch . I want to use tanh as activations in both hidden layers, but in the end, I should use softmax . For the loss, I am choosing nn.CrossEntropyLoss() in pytorch, which (as I have found out) does not want to take one-hot encoded labels as true labels, but takes LongTensor of classes instead. My model is nn.Sequential() and when I am using softmax in the end, it gives me worse

Imbalanced classes in multi-class classification problem

由 北战南征 提交于 2020-06-25 09:18:28
问题 I'm trying to use TensorFlow's DNNClassifier for my multi-class (softmax) classification problem with 4 different classes. I have an imbalanced dataset with the following distribution: Class 0: 14.8% Class 1: 35.2% Class 2: 27.8% Class 3: 22.2% How do I assign the weights for the DNNClassifier's weight_column for each class? I know how to code this, but I am wondering what values should I give for each class. 回答1: there are various options to build weights for un unbalance classification

RuntimeWarning: invalid value encountered in greater

由 大城市里の小女人 提交于 2020-04-08 18:53:27
问题 I tried to implement soft-max with the following code ( out_vec is a numpy vector of floats): numerator = np.exp(out_vec) denominator = np.sum(np.exp(out_vec)) out_vec = numerator/denominator However, I got an overflow error because of np.exp(out_vec) . Therefore, I checked (manually) what the upper limit of np.exp() is, and found that np.exp(709) is a number, but np.exp(710) is considered to be np.inf . Thus, to try to avoid the overflow error, I modified my code as follows: out_vec[out_vec

logistic函数和softmax函数

由 早过忘川 提交于 2020-04-04 03:22:13
　　 简单总结一下机器学习最常见的两个函数，一个是logistic函数，另一个是softmax函数，若有不足之处，希望大家可以帮忙指正。本文首先分别介绍logistic函数和softmax函数的定义和应用，然后针对两者的联系和区别进行了总结。 1. logistic函数 1.1 logistic函数定义 　　引用wiki百科的定义： 　　A logistic function or logistic curve is a common "S" shape (sigmoid curve). 　　其实逻辑斯谛函数也就是经常说的sigmoid函数，它的几何形状也就是一条sigmoid曲线。 　　logistic函数的公式形式如下： f ( x ) = L 1 + e − k ( x − x 0 ) f(x)=L1+e−k(x−x0) 　　其中， x 0 x0表示了函数曲线的中心（sigmoid midpoint）， k k是曲线的坡度。 　　logistic的几何形状如下所示： 1.2 logistic函数的应用 　　logistic函数本身在众多领域中都有很多应用，我们只谈统计学和机器学习领域。 　　logistic函数在统计学和机器学习领域应用最为广泛或者最为人熟知的肯定是逻辑斯谛回归模型了。逻辑斯谛回归（Logistic Regression，简称LR）作为一种对数线性模型（log

Logistic 与 softmax

由 拥有回忆 提交于 2020-03-31 03:47:19
之前写的一篇感觉太 Naive ，这里重新写一篇作为总结。Logistic 与 Softmax 都是一种概率判别模型（PRML p203），Softmax 通常用在 Neural Network 里最后全连接层 ，Logistic 在业界更是普及，因为简单有效、便于并行、计算量小快，适合大规模数据等优点，而且采用 SGD 的 Logistic 相当于直接 Online Learning ，非常方便。本文将对两个模型展开详细介绍，从 exponential family 到 parallel 等都会涉及 。 Sigmod Function Logistic Regression 是用来处理二分类问题的，它与一个函数密切相关，即 sigmod 函数： \[h(z) = \frac{ e^z}{1+ e^z}= \frac{ 1}{1+ e^{-z}} \] 通过 sigmod 函数可以将函数值 映射到 $(0,1)$ 区间， 这里 $h$ 即代表了 sigmod 函数，其图形如下： Logistic Regression 正是在线性回归的基础上加了 sigmod 而得到的 对数几率比解释 线性回归中，值域落在 $(-\infty,+\infty)$，然而对于二分类来说只需两种取值 0、1 即可，线性回归中事件发生的几率用 $\frac{p}{1-p}$ 表示的话，其值域为 $[0,
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