Tf nn softmax nan. Softmax is defined as: Softmax (x i) = exp ⁡ (x i) ∑ j exp ⁡ (x j) \text {Softmax} (x_ {i}) = \frac {\exp (x_i)} {\sum_j \exp (x_j)} Softmax(xi )= ∑j exp(xj )exp(xi ) It is applied to all slices along dim, and will re-scale them so that the elements Computes softmax activations. But I've read that tf. When I just split up the training set and run it on the training set I get a ~93% 文章浏览阅读1. If you get NaN values this is probably caused at an earlier stage in your network, using a debugger in an IDE might help in that case. When loss become nan loading of saved weights doesn't help to continue training (weights become corrupted on first training iteration). For soft softmax classification with a probability distribution for each entry, see softmax_cross_entropy_with_logits_v2. Softmax is defined as: Softmax (x i) = exp ⁡ (x i) ∑ j exp ⁡ (x j) \text {Softmax} (x_ {i}) = \frac {\exp (x_i)} {\sum_j \exp (x_j)} Softmax(xi )= ∑j exp(xj )exp(xi ) It is applied to all slices along dim, and will re-scale them so that the elements Softmax is often used as the activation for the last layer of a classification network because the result could be interpreted as a probability distribution. The softmax of each vector x is computed as exp(x) / sum(exp(x)). softmax(sorted_logits, dim=-1), dim=-1) + # Remove tokens with cumulative probability above the threshold Softmax # class torch. exp和tf. The axis argument sets which axis of the input the function is applied along. optimizer 模型的评估： tf. Instead, they return nan for all entries. softmax_cross_entropy_with_logits(logits=out_final_system, labels=y_system_)) optimizer = tf. Could you have the same problem with multiple input heads to your network, if it's always at the top of the graph? torch. Defaults to None. Do not call this op with the output of softmax, as it will produce incorrect results. A name for the operation (optional). exp, keeping everything else like it was, causes not only the gradients to contain NaN but also the intermediate variable s. However, why trainng this I am getting NAN as my predictions even before completeing the first batch of training (batch …. keras. Softmax(dim=None) [source] # Applies the Softmax function to an n-dimensional input Tensor. metrics softmax_cross_entropy_with_logits (logits, labels) softmax_cross_entropy_with_logits_v2 (logits, labels) sparse_softmax_cross_entropy_with_logits (logits, labels) 其中logits都是 未 经激活函数（sigmoid、tanh、relu）和softmax 放缩 后的神经网络输出值，labels为样本标签（真实值）； tf. py. nn. 文章浏览阅读9. 6w次，点赞189次，收藏418次。本文深入解析PyTorch中softmax函数的dim参数作用，通过实例演示不同维度下softmax运算的具体效果，帮助读者掌握softmax在神经网络中的应用。 tf. Softmax(a) should produce near zero output. Try to lower it until NaN errors disappear and the loss starts to decrease. activations. SUM_BY_NONZERO_WEIGHTS ) weights acts as a coefficient for the loss. 1 softmax_cross_entropy_with_logits 该函数定义如下 For this reason, I ended up replacing any None gradients with tf. The input values in are the log-odds of the resulting probability. Code example >>> import torch >>> import torch. autograd. I'm trying to get a 1, 0, or hopefully a probability in result to a real test set. 0, scope=None, loss_collection=ops. Is the only difference that training vectors y have to be one-hot encoded when using sparse_softmax_cross_entropy_with_logits? # to define the softmax classifier and cross entropy cost # we can do the following # matrix multiplication using the . softmax_cross_entropy_with_logits() to compute the loss, I get nan. softmax_cross_entropy_with_logits. losses 模型的优化器： tf. Tensorflow 中交叉熵损失函数实现 在Tensorflow中给出了两种交叉熵损失函数的实现，分别是： tf. mask: A boolean mask of the same shape as inputs. You should use tf. nn. softmax() and compute the cross entropy manually, I get all 0 and 1 in the predictions, if I use tf. 2k次，点赞5次，收藏25次。这篇博客详细解释了TensorFlow中tf. softmax with -inf input? It looks like torch does not output NaN anymore in this case. log (output / (1 - output)) 而在categorical_crossentropy中，由于难以对softmax的输出进行还原，故重使用了自定义代码计算CE，而没有调用TF接口，其计算方法与上文定义的公式（1）一致。 二、应用场景分析 But I've read that tf. train. gumbel_softmax # torch. reduce_mean(tf. Call arguments inputs: The inputs (logits) to the softmax layer. softmax( x, axis=-1 ) The elements of the output vector are in range [0, 1] and sum to 1. Defined in tensorflow/python/ops/nn_ops. I know how to make softmax stable by adding to element -max _i x_i. Was this helpful? Simply exchanging the nn. loss_system = tf. 7k次，点赞4次，收藏17次。本文详细分析了在Tensorflow中训练神经网络时遇到的NAN问题，包括梯度爆炸和梯度消失，以及数据越界等。提出了解决方案，如减小学习率、合理初始化权重、预测结果裁剪、梯度修剪和使用BatchNormalization等，并给出了相应的代码示例。建议在实际应用中结合 之前在TensorFlow中实现不同的神经网络，作为新手，发现经常会出现计算的loss中，出现Nan值的情况，总的来说，TensorFlow中出现Nan值的情况有两种，一种是在loss中计算后得到了Nan值，另一种是在更新网络权重等等数据的时候出现了Nan值，本文接下来，首先解决计算loss中得到N Simply exchanging the nn. Dec 22, 2025 · The softmax activation function is a cornerstone of modern neural networks, particularly in multi-class classification tasks. sparse_softmax_cross_entropy_with_logits handles the case of log(0) for you, you don't have to worry about it. WARNING: This op expects unscaled logits, since it performs a softmax on logits internally for efficiency. cumsum(F. Hi, I am trying to train an existing neural network from a published paper, using custom dataset. Has the same type and shape as logits. gumbel_softmax(logits, tau=1, hard=False, eps=1e-10, dim=-1) [source] # Sample from the Gumbel-Softmax distribution (Link 1 Link 2) and optionally discretize. It converts raw output scores (logits) from a model into probabilities that sum to 1, making it easy to interpret and compare class likelihoods. 9w次，点赞8次，收藏47次。本文详细探讨了神经网络训练中出现nan和inf的原因及解决方案，包括调整学习率、使用受限激活函数、梯度裁剪等方法，并提供了TensorFlow实现示例。 Safe Softmax 是一种改进的Softmax计算方法，主要用于解决传统Softmax在数值计算中可能出现的**数值溢出（overflow）或下溢（underflow）**问题。 其核心思想是通过数学优化，确保在指数计算和概率归一化过程中保持数值稳定性。 1. 训练网络loss出现Nan解决办法 一. softmax function for a combination which uses tf. Still use softmax_cross_entropy method because it is more stable for training # prediction = tf. A Tensor. AdamOptimizer(learning_rate=1e-10) LogSoftmax (x i) = log ⁡ (exp ⁡ (x i) ∑ j exp ⁡ (x j)) \text {LogSoftmax} (x_ {i}) = \log\left (\frac {\exp (x_i) } { \sum_j \exp (x_j)} \right) LogSoftmax(xi The NaN softmax issue occurs whether I run with my custom activation function implemented as a nn. Theretically, every element of a is a super small negative value, and nn. log softmax(x) can evaluate to zero, leading to - 4、所以到头来，Gumbel-Softmax采样出来的仅仅是“像离散值”的连续值？ 对，数学本质而言，Gumbel-Softmax采样出来的不是离散值，是连续值。 但是在代码层面，可以用Hard Gumbel-Softmax。 但是务必牢记，Hard Gumbel-Softmax是代码层级的trick，而不是数学层级的trick！ 实例 For soft softmax classification with a probability distribution for each entry, see softmax_cross_entropy_with_logits. The dimension softmax would be performed on. matmul(X, W) + b) # cost function: cross entropy, the reduce mean is simply the average of the # cost function across all observations cross_entropy = tf Arguments axis: Integer, or list of Integers, axis along which the softmax normalization is applied. if logits is empty or axis is beyond the last dimension of logits. softmax # torch. functional as F I'm using TensorFlow and I modified the tutorial example to take my RGB images. v1. See the guides: Layers (contrib) > Higher level ops for building neural network layers, Neural Network > Classification May 23, 2017 · From reading the doc and code of tf. softmax_cross_entropy for that, using the outputs of the last layer before the softmax activation (the "logits"). softmax_cross_entropy_with_logits_v2是不一样的，因为tf. softmax_cross_entropy_with_logits_v2的label是one-hot表示的，所以可以有多个类别，是哪一个就在哪个位置写1就可以了。 比如 Are you by any chance using the log_softmax? "Normalized softmax" doesn't make much sense, as SoftMax itself already provides a form of normalization. **kwargs: Base layer keyword arguments, such as name and dtype. 简介 TensorFlow Probability 是 TensorFlow 中用于概率推理和统计分析的库。 安装 安装最新版本的 TensorFlow Probability： pip install --upgrade tensorflow-probability 安装指定版本的 TensorF As I've mentioned, if I output tf. Aug 22, 2019 · tf. tf. layers. is _ nan On this page Used in the notebooks Args Returns numpy compatibility 2. Issue description F. Each input vector is handled independently. sparse_softmax_cross_entropy_with_logits 我们分别进行介绍 2. softmax(tf. reduce_sum #問題と実現したいこと Tensorflowでlossがnanとなったため、誤差関数を用いることで解決しようと考えました。 しかし、自作の誤差関数で学習を行った結果、まったく学習が進みません。 In my case problem occurred randomly, the probability of getting nan is increasing with model's complexity (and memory usage). The default is -1 which indicates the last dimension. But instead I have huge numbers, so I (naturally) thought that to avoid log (0) a small constant must have been added to the problematic numbers. This avoids overflow and underflow. math. compat. softmax(input, dim=None, _stacklevel=3, dtype=None) [source] # Apply a softmax function. Returns Softmaxed output with the same shape as inputs @SandPhoenix, Can u check for each value in tensor x any values nan using isNAN() funciton. Model 和 tf. softmax should return one-hot representation when only 1 value is Inf and the others are all finite or -Inf. As I've mentioned, if I output tf. exp, keeping everything else like it was, causes not only the gradients to contain NaN but also the intermediate variable s CSDN问答为您找到softmax分类器，分类概率都是nan是怎么回事？相关问题答案，如果想了解更多关于softmax分类器，分类概率都是nan是怎么回事？ python 技术问题等相关问答，请访问CSDN问答。 TensorFlowの学習周りのコマンドについて確認 CNNなどのconvolution層やpooling層は詳細な説明がたくさんあります。モデル記述後、損失関数や学習あたりのfunctionをよく把握していないので、その辺りを確認したいと思います。 特に、 tf. Function with my own implementation of backward (). Must be one of the following types: half, float32, float64. softmax_cross_entropy_with_logits_v2 or tf. sparse_softmax_cross_entropy ( labels, logits, weights= 1. Because softmax is unstable when the logits are too large you could instead try using log_softmax which will have the same relative ordering as softmax but doesn't have the same numeric instabilities. 写在前面 在文章 [TensorFlow] argmax, softmax_cross_entropy_with_logits, sparse_softmax_cross_entropy_with_logits函数详解 中，提到了交叉熵损失函数的计算方式以及 tensorflow 中的输入和输出等。本篇文章会更细地讲一下tensorflow中交叉熵损失函数的应用，以及在优化过程中可能用到加权交叉熵损失函数的使用方式。 一 Hi, I am trying to train an existing neural network from a published paper, using custom dataset. 文章浏览阅读10w+次，点赞72次，收藏242次。本文详细介绍了Softplus函数，它是ReLU的一个平滑版本，与神经元激活相似，推动了神经网络的发展。Softplus函数表达式为Softplus (x)=log (1+e^x)，其图像平滑且无导数为零的问题，常用于深度学习模型中。 Here is the code that I am using. 传统Softmax的数值问题 传统Softmax公式 这就对应了函数中的代码： output = tf. Rescales them so that the elements of the n-dimensional output Tensor lie in the range [0,1] and sum to 1. (underflow can happen, too) The dimension softmax would be performed on. Only recompilation or creating new model allow to continue training. sparse_softmax_cross_entropy_with_logits and I can not figure out what the difference is compared to tf. losses. softmax, it looks like this function naively call a _softmax function which may have the overflow problem if the x in exp (x) is too big. TensorFlow 模型建立与训练 ¶ 本章介绍如何使用 TensorFlow 快速搭建动态模型。 模型的构建： tf. Usually a NaN is due to a high learning rate of your optimization algorithm. Now, taking log of this can cause underflow. zeros_like and it was possible to proceed with training. 原因 一般来说，出现NaN有以下几种情况： 1. Module or as a torch. sparse_softmax_cross_entropy_with_logits() handle that case, and that's the case, cause I would have some Nan output. softmax_cross_entropy_with_logits tf. layers 模型的损失函数： tf. 如果在迭代的100轮以内，出现NaN，一般情况下的原因是因为你的 学习率过高，需要降低学习率。 可以不断降低学习率直至不出现NaN为止，一般来说低于现有学习率1-10倍即可。 tf. Those functions are designed to handle extreme cases correctly. Softmax( axis=-1, **kwargs ) Used in the notebooks Used in the tutorials Basic classification: Classify images of clothing TensorFlow 2 quickstart for beginners Building Your Own Federated Learning Algorithm Composing Learning Algorithms Federated Learning for Image Classification torch. The algorithm works flawlessly out of the box on the new image set, until suddenly (still converging, it's around 92% You should use tf. + cumulative_probs = torch. Softmax is defined as: torch. Parameters: logits (Tensor) – […, num_features] unnormalized log probabilities tau (float) – non-negative scalar temperature hard (bool) – if True, the returned samples 128 I recently came across tf. Warning: This op expects unscaled logits, since it performs a softmax on logits internally for efficiency. reduce_sum How does torch handle nn. functional. labels的条件是互斥的，也就是说，一个样本只能被分为一类，不能有其他的类别 这跟tf. If weights is a tensor of shape [batch_size], then the loss weights apply to each corresponding sample. Sometimes with Keras the combination of Relu and Softmax causes numerical troubles as Relu can produce large positive values corresponding to very small probabilities. softmax(neural_net_layer) # use logits (and prediction only for c++) G検定の頻出コア（決定境界・活性化・交差エントロピー・逆伝播・最適化）を、実務の定石であるlogits前提の損失（BCEWithLogitsとsoftmax交差エントロピーは同型の安定化）へ最短でつなぎます。数式とPythonは補足なので読み飛ばしOKです。 文章浏览阅读3. LOSSES, reduction=Reduction. If a scalar is provided, then the loss is simply scaled by the given value. matmul command # and add the softmax output output = tf. GraphKeys. softmax函数的作用和参数，它用于将全连接层的输出转换为概率分布。softmax将每个类别的得分转换为0到1之间的概率值，使得所有类别概率之和为1。文章还介绍了softmax的计算公式，并通过示例展示了如何使用tf. Arguments x: Input tensor. However, why trainng this I am getting NAN as my predictions even before completeing the first batch of training (batch … 文章浏览阅读3. It is true that the probability value in the output of a softmax for a logit that tend to -inf should tend to 0. The mask specifies 1 to keep and 0 to mask. grsg8, hphvs, 5usav, aeimyl, k6ymz4, ejqm, 6akh8, eijq, byvtx, g4oh,