Binary cross entropy python. If p and q are the same distributions (e.

Binary cross entropy python Pela fórmula If you are using keras, just put sigmoids on your output layer and binary_crossentropy on your cost function. When you say that this is a multi-label problem, are you in fact trying to predict a label? Mar 16, 2018 · A naive implementation of Binary Cross Entropy will suffer numerical problem on 0 output or larger than one output, eg log(0) -> NaN. Sigmoid cross entropy is typically used for binary classification. classes: ["normal", "bullish"] class indices: {'bullish': 0, 'normal': 1} Nov 14, 2019 · I think you're confusing the nn api with the functional F api. torch. The height along the vertical axis H represents the magnitude of the Cross Entropy for the particular input parameter values. To implement binary cross-entropy in Python, we can use the binary_crossentropy() function from the Keras library. Figure 1: Cross Entropy as a function of p_c and q_c, for the specific case where there are only 2 classes (see equation (2)). Cross-entropy is the default loss function to use for binary classification problems. Mathematically speaking, if your label is 1 and your predicted probability is low (like 0. I have used the binary-crossentropy as the loss function and sigmoid as the activation function. binary_cross_entropy. A related quantity, the cross entropy CE(pk, qk), satisfies the equation CE(pk, qk) = H(pk) + D(pk|qk) and can also be calculated with the formula CE =-sum(pk * log(qk)). Here is a simple implementation of the Binary Cross Entropy loss function in Python: def binary_cross_entropy(y, y_hat): Oct 24, 2018 · One of the difference between BCE and CE losses (in case there are 2 classes) is mathematically CE loss only penalizes the neuron that is responsible for predicting the ground truth class (y_i log(y_hat_i)) and let the softmax function make the logits mutually exclusive whereas in the single neuron BCE loss, penalizing the prediction for making it converge to ground truth distribution Sep 1, 2024 · Implementing Binary Cross Entropy in Python. Dec 11, 2024 · In this tutorial, we delve into the intricacies of Binary Cross Entropy loss function and its pivotal role in optimizing machine learning models, particularly within the realms of Python-based regression and neural networks. keras. (as explained in this answer) Also, I understood that tf. Feb 6, 2022 · The binary loss value is calculated for each sample which is then summated to get the total binary log loss/binary cross entropy. 1 - sigmoid(x)) is the negative class. It measures the performance of a model by comparing the predicted probability distribution with the actual distribution. See BCELoss for details. Jun 30, 2023 · Therefore, when t =1, the binary cross-entropy loss is equal to the negative logarithm of the predicted probability p. predicting whether an image has 0 dogs, 0 cats or 1 dog, 0 cats or 0 dogs, 1 cat or 1 dog, 1 cat. log(1 - y_hat) return jnp. Slide 1: Introduction to Cross-Entropy. binary_cross_entropy are two PyTorch interfaces to the same operations. It is intended for use with binary classification where the target values are in the set {0, 1}. preprocessing import LabelBinarizer from sklearn. Args: y_true: True labels (0 or 1 for binary classification). I hope you will enjoy. The other points, such as the benefits and challenges of cross entropy, are also covered in this article. Alpha could be the inverse class frequency or a hyper-parameter that is determined by cross-validation. Aug 10, 2024 · In this instance, we must use binary cross-entropy, which is the average cross-entropy across all data samples: Binary cross entropy formula [Source: Cross-Entropy Loss Function] If we were to calculate the loss of a single data point where the correct value is y=1, here’s how our equation would look: Calculating the binary cross-entropy for Sep 27, 2023 · The formula for cross-entropy loss in binary classification (two classes) is:. Explore Teams Cross Entropy is a loss function commonly used in machine learning, particularly in classification tasks. the model's parameters. sigmoid_cross_entropy_with_logits, which actually is intended for classification tasks with multiple, independent classes that are not mutually exclusive. Jul 27, 2021 · I have a binary cross entropy model two classes: [normal, bullish]. But the gap between the two is great. compile(optimizer=opt, loss='binary_crossentropy', metrics=['acc']) In my data I can say that the probabilty of 1 values occur in output is %5-10. This question is specifically asking about the "Fastest" way but I only see times on one answer so I'll post a comparison of using scipy and numpy to the original poster's entropy2 answer with slight alterations. Binary Cross-Entropy Loss is a widely used loss function in binary classification problems. Jan 16, 2019 · How can I find the binary cross entropy between these 2 lists in terms of python code? I tried using the log_loss function from sklearn: log_loss(test_list,prediction_list) but the output of the loss function was like 10. And that’s all with regards to the math! Let’s go over the binary cross entropy loss function next. Balanced Cross-Entropy loss adds a weighting factor to each class, which is represented by the Greek letter alpha, [0, 1]. py. Nov 4, 2017 · $\begingroup$ dJ/dw is derivative of sigmoid binary cross entropy with logits, binary cross entropy is dJ/dz where z can be something else rather than sigmoid $\endgroup$ – Charles Chow Commented May 28, 2020 at 20:20 For instance, when you call tensorflow binary cross entropy loss function, it will compute this sum and divide by the number of items (check here for a detailed example): The term n in this formula will be the number of items that were summed along the specified axis, and not the number of the batch size. Apr 9, 2020 · Then I use cross-entropy loss to measure: BCEloss and CrossEntropyLoss. The quantity \(−[ylny+(1−y)ln(1−y)]\) is sometimes known as the binary entropy. It measures the dissimilarity between two probability distributions. If you are using tensorflow, then can use sigmoid_cross_entropy_with_logits. In order to apply gradient descent we must calculate the derivative (gradient) of the loss function w. def weighted_bce_dice_loss(y_true, y_pred): Oct 4, 2024 · Losses 1. onde y é o rótulo (1 para pontos verdes e 0 para pontos vermelhos) e p(y) é a previsão da probabilidade do ponto ser verde para todos N pontos. binary_crossentropy(tf. input – Tensor of arbitrary shape as probabilities. BCEloss and F. log(predY), Y) + np. Essentially the idea is to add a lambda layer that computes the pixel-wise weighted cross-entropy within the model itself and then use an "identity loss" that just copies the output of the network. Personally, when I try to implement a new concept, I often opt for naive implementations before optimizing things, for example, using linear algebra concepts. Jan 21, 2024 · バイナリクロスエントロピーロス（BCE Loss）とは2値分類に用いられるロス関数です．例えば，メールの文章からスパムかどうかを判定するタスクなどで使用できます．このロスを使用することで，スパ… Aug 19, 2020 · Also from the documentation: "Use this cross-entropy loss when there are only two label classes (assumed to be 0 and 1). Since each bit is independent, you should use a sigmoid activation at the output, not a softmax one, in order to use the binary_crossentropy loss: Feb 22, 2021 · Of course, you probably don’t need to implement binary cross entropy yourself. Binary cross-entropy is a mathematical measure used to evaluate how well a model is performing in binary classification tasks. Deriving the gradient is usually the most tedious part of training a Dec 19, 2017 · I thought binary_crossentropy should not be a multi-class loss function and would most likely use binary labels, but in fact Keras (TF Python backend) calls tf. However, your loss should be the sum Jun 19, 2018 · I'm new to Keras (and ML in general) and I'm trying to train a binary classifier. But the performance is still not good. Aug 25, 2020 · Although an MLP is used in these examples, the same loss functions can be used when training CNN and RNN models for binary classification. 2656. compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['accuracy'], from_logits=True) From what I understand what you have here is a regression task, even if you're trying to output integers. Returns: The average cross-entropy loss across all data points. def sigmoid(x): return 1/(1+np. Jan 8, 2020 · The solution suggested in this answer may actually not be what you (reader) are looking for. Note that binary cross-entropy cost functions, categorical cross-entropy, and sparse categorical cross-entropy are provided with the Keras API. y_pred: Predicted probabilities for the correct class. That being said the formula for the binary cross-entropy is: bce = -[y*log(sigmoid(x)) + (1-y)*log(1- sigmoid(x))] Where y (respectively sigmoid(x) is for the positive class associated with that logit, and 1 - y (resp. While both binary and categorical cross-entropy are used to calculate loss in classification problems, they differ in use cases and how they handle multiple classes: Binary Cross-Entropy is used for binary classification problems where there are only two possible outcomes (e. ; To perform this particular task we are going to use the tf. Import the Numpy Library; Define the Cross-Entropy Loss function. For a binary bit ground truth of shape (None, 10), the model output should be of the same shape. multiply(np. Binary Cross-entropy. For a text classification problem -> MSE loss is calculated. In neural networks that are multilabel classifiers, sigmoid is used as the last layer (Kaggle kernel you linked uses sigmoid as activation in the last layer). If A and B are NxM, where M > 1, then binary_crossentropy(A, B) will not compute the binary cross-entropy element-wise, but binary_crossentropy(A, B) returns an array of shape Nx1, where binary_crossentropy(A, B)[i] correspond to the average binary cross-entropy between A[i] and B[i] (i. After I realize the sign of labels, I tried binary cross-entropy as well. In functional api, loss function F. Dec 15, 2019 · I am using binary cross entropy as an loss function like: model. A lower cross-entropy value indicates a better model performance. It gives the average number of units of information needed per symbol if an encoding is optimized for the probability distribution qk when the true distribution is pk . Before showing you the code, let me refresh your memory on the math: and as for the binary-cross entropy: Note that: The base of the logarithm is e. Apr 12, 2022 · Binary Cross entropy TensorFlow. g. When i call model. A Neural Network library coded from scratch. You can see the Python code below with manual implementation and Feb 28, 2024 · import numpy as np def cross_entropy_loss(y_true, y_pred): """ Calculates cross-entropy loss for a batch of data points. BinaryCrossentropy() function and this method is used to generate the cross-entropy loss between predicted values and actual values. model_selection Aug 28, 2018 · We can also go of the definition of the cross entropy which is generally entropy(p) + kullback-leibler divergence(p,q). Fig 5. Introduction to Binary Cross Entropy Loss. Where: H(y,p) is the cross-entropy loss. Nov 21, 2018 · Binary Cross-Entropy / Log Loss. Keras. Implementation of Binary Cross Entropy in Python. with reduction set to 'none' ) loss can be described as: Apr 12, 2022 · Binary Cross entropy TensorFlow. For each example, there should be a single floating-point value per prediction. Binary cross entropy contrasts each of the predicted probability to actual output which can be 0 or 1. In this Understanding and implementing Neural Network with Softmax in Python from scratch we will go through the mathematical derivation of the Jul 11, 2020 · For a binary classification problem -> binary_crossentropy. Yes, it can handle multiple labels, but sigmoid cross entropy basically makes a (binary) decision on each of them -- for example, for a face recognition net, those (not Jan 3, 2024 · Binary Cross-Entropy Loss and Multiclass Cross-Entropy Loss are two variants of cross-entropy loss, each tailored to different types of classification tasks. Mar 23, 2021 · Single Label可以使用標準Cross Entropy則是因為Activation Function為Softmax，只考慮正樣本的同時會降低負樣本的機率(對所有output歸一化)，因此可以使用Cross Entropy。 總結. py This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. py --dataset dataset # import the necessary packages from sklearn. May 20, 2021 · I am implementing the Binary Cross-Entropy loss function with Raw python but it gives me a very different answer than Tensorflow. The training process will then start and eventually finish, while you'll see a visualization of the data you generated first. ; p is the predicted probability that the input belongs to class 1. Here what I did: Sep 17, 2024 · Differences Between Categorical and Binary Cross-Entropy. r. In nn api, you need to create an object of the loss class such as criterion = nn. Since this library doesn't come with an implementation of binary cross entropy, I wrote my own: def binary_cross_entropy(y_hat, y): bce = y * jnp. Generally, relu activation function is used, but for a binary classification problem sometimes tanh . Cross-entropy is a widely used loss function in machine learning, particularly in classification problems. Manual Calculation with NumPy:The function binary_cross_entropy manually calculates BCE loss using the formula, averaging individual losses for true labels (y_true) and predicted probabilities (y_pred). binary_cross_entropy_with_logits:. Is this an accurate implementation of weighted binary cross entropy? How could I test if it is? Oct 2, 2021 · Cute Dogs & Cats [1] Cross-Entropy loss is a popular choice if the problem at hand is a classification problem, and in and of itself it can be classified into either categorical cross-entropy or multi-class cross-entropy (with binary cross-entropy being a special case of the former. Jan 17, 2024 · Binary Cross-Entropy, also known as log loss, is a loss function used in machine learning for binary classification problems. It's used for multi-class problems. Binary Cross-Entropy Loss. Creates a criterion that measures the Binary Cross Entropy between the target and the input probabilities: The unreduced (i. As I explained above, it seems we can utilize two loss functions and sum them up. Feb 27, 2023 · Implementing Binary Cross Entropy Loss in Python. log(p) vanishes, and the expression for binary cross-entropy loss reduces to: Now, let’s plot the binary cross-entropy loss for different values of the predicted probability p. Multi-Label時，不能使用標準Cross Entropy，Single Label則是標準Cross Entropy或Binary Cross Entropy都可以使用。 binary_cross_entropy. Am I using the function the wrong way or should I use another implementation ? I am sure that as a Neural Network enthusiasts, you are familiar with the idea of the sigmoid() function and the binary-cross entropy function. ; y is the true label (0 or 1). You must change this: model. t. In defining this function: We pass the true and predicted values for a data point. Apr 25, 2018 · loss = np. predict() the output returns a single probability, how do i know to which class the probability belongs to? I use a keras Functional model. ]), tf. constant([0. 17603033705165633 Accuracy: 1. The gradient descent is not converging, may be I'm doing it wrong. model = Sequential() Oct 5, 2020 · I'm creating a fully convolutional neural network, which given an image in input is capable to identify zones in it (black, 0), and also identify background (white, 255). ) Feb 27, 2022 · With 1 output neuron and binary cross-entropy, python; lstm; cross-entropy; or ask your own question. an Anaconda prompt or your regular terminal, cd to the folder and execute python binary-cross-entropy. losses. May 5, 2023 · In sparse categorical cross-entropy, truth labels are labeled with integral values. Binary cross-entropy loss is often used for binary (0 or 1) classification tasks. Binary Cross Entropy Loss: 0. The code is available in this colab notebook. binary_cross_entropy¶ torch. binary_cross_entropy_with_logitsを本来の用途で用いる場合には問題ありませんでした。しかし，binary_cross_entropyと同様のはたらきをさせるために，pos_weightにtorch. Apr 30, 2020 · I am running this model for 1080 training images and 270 validation set. log(y_hat) + (1 - y) * jnp. Parameters. Let's open up a Python terminal, e. Aug 1, 2021 · Looking into F. Module which makes it handy to be used in a two-step fashion, as you would always do in OOP (Object Oriented Programming): initialize then use. Nov 24, 2018 · Binary Cross-Entropy / Log Loss. sigmoid_cross_entropy_with_logits. Code: In the following code, we will import May 1, 2024 · Cross-entropy Loss Explained with Python Example. Below we discuss the Implementation of Cross-Entropy Loss using Python and the Numpy Library. References Jan 18, 2021 · But while binary cross-entropy is certainly a valid choice of loss function, it’s not the only choice (or even the best choice). Problems Many-layer multi-neuron networks In the notation introduced in the last chapter, show that for the quadratic cost the partial derivative with respect to weights in the output layer is Sep 11, 2019 · I'm implementing a Single Layer Perceptron for binary classification in python. Jul 24, 2024 · Implementing Binary Cross Entropy in Python. p is uniform when we have the same number of examples for each class, and q is around uniform for random networks) then the KL divergence becomes 0 and we are left with entropy(p). But the alpha(i) does not belong to the sample, it is an aggregate property. Module): def __init__(self, Mar 20, 2020 · 原因. . I'm trying to develop a binary classifier with Huggingface's BertModel and Pytorch. It’s commonly referred to as log loss, so keep in mind these are synonyms. Oct 4, 2021 · Binary Cross Entropy Loss Normalmente es usada en problemas binarios de clasificación, aunque también puede ser en problemas donde las variables a predecir toman valores entre cero y uno. This means the Apr 29, 2019 · However often most lectures or books goes through Binary classification using Binary Cross Entropy Loss in detail and skips the derivation of the backpropagation using the Softmax Activation. constant([1. sigmoid_cross_entropy_with_logits to calculate cross entropy and return to you the mean of that. In PyTorch, it's implemented as a built-in function. 0. e. binary_cross_entropy (input, target, weight = None, size_average = None, reduce = None, reduction = 'mean') [source] ¶ Measure Binary Cross Entropy between the target and input probabilities. binary_cross_entropy can be used as a function directly. Jun 15, 2017 · Note that weighted_cross_entropy_with_logits is the weighted variant of sigmoid_cross_entropy_with_logits. Besides, if you have any other suggestion for this specific dataset, please let me know. Computes focal cross-entropy loss between true labels and predictions. log(1 - predY)) #cross entropy cost = -np. Binary Cross Entropy Loss. The following deduction is from tf. 5 which seemed off to me. Next, we compute the softmax of the predicted Mar 31, 2022 · In this section, we will learn about the PyTorch Binary cross entropy with logits in python. In this section, you will learn about cross-entropy loss using Python code examples. For a multi-class problem -> categoricol_crossentropy. Overview; LogicalDevice; LogicalDeviceConfiguration; PhysicalDevice; experimental_connect_to_cluster; experimental_connect_to_host; experimental_functions_run_eagerly Jan 6, 2022 · # USAGE # python lego_trainer. While most deep learning frameworks like PyTorch and TensorFlow have built-in BCE loss functions, it‘s instructive to implement it from scratch to understand what‘s really going on under the hood. It also computes the score that deals with the probability based on the distance from the expected value. final layer: 1 unit, sigmoid activation. In the case of the MNIST dataset, you actually have a multiclass classification problem (you're trying to predict the correct digit out of 10 possible digits), so the binary cross-entropy loss isn't suitable, and you should the general cross-entropy loss instead. In this section, we will discuss how to calculate a Binary Cross-Entropy loss in Python TensorFlow. We need to use them during the forward-pass. The Tanh method transforms the input to values in the range -1 to 1 which cross entropy can't handle. The loss function comes out of the box in PyTorch and TensorFlow. Similarly, when the true label t=0, the term t. The performance of the former is normal, but the latter has a very low precision rate and a high recall rate. I'm using weighted binary cross entropy as a loss function but I am unsure how I can test if my implementation is correct. Jun 1, 2020 · The binary cross-entropy being a convex function in the present case, any technique from convex optimization is nonetheless guaranteed to find the global minimum. Oct 7, 2021 · Ask questions, find answers and collaborate at work with Stack Overflow for Teams. May 27, 2024 · Therefore, the Binary Cross-Entropy loss for these observations is approximately 0. (as explained in this question) Feb 2, 2024 · Let’s build a bare-bones Python implementation of cross-entropy loss. ones([(クラス数)])を指定したところ，binary_cross_entropyとは 異なる挙動を示しました。 May 1, 2019 · To use the from_logits in your loss function, you must pass it into the BinaryCrossentropy object initialization, not in the model compile. This model is for skin lesion segmentation and data is from ISIC 2016. If p and q are the same distributions (e. The activation function is also depending on the problem type. with reduction set to 'none' ) loss can be described as: Apr 24, 2023 · Implementing Cross Entropy Loss using Python and Numpy. I should use a binary cross-entropy function. Aug 30, 2019 · When considering the problem of classifying an input to one of 2 classes, 99% of the examples I saw used a NN with a single output and sigmoid as their activation followed by a binary cross-entropy loss. When you use the loss function in these deep learning frameworks, you get automatic differentiation so you can easily learn weights that minimize the loss. The loss function requires the following inputs: y_true (true label): This is either 0 or 1. But I don't know how to write the code. Keras is a popular deep learning library that provides a high-level interface for building neural networks. exp( Apr 26, 2022 · Balanced Cross-Entropy Loss. The former, torch. Let us see them in detail. Minimization In training, the goal is to minimize the cross entropy loss. The classifier module is something like this: class SSTClassifierModel(nn. I don't understand Why the Binary_Cross-entropy Loss Jul 22, 2019 · Binary cross-entropy is a confusing name. This is the answer I got from Tensorflow:- import numpy as np from Computes the cross-entropy loss between true labels and predicted labels. Mar 5, 2021 · I was trying to build a gradient descent function in python. 1), the cross entropy can be greater than 1, like losses. Binary cross entropy is a common cost (or loss) function for evaluating binary classification models. Cross-Entropy Loss Function Dec 23, 2021 · The binary cross entropy loss function is the preferred loss function in binary classification tasks, and is utilized to estimate the value of the model's parameters through gradient descent. where y is the label (1 for green points and 0 for red points) and p(y) is the predicted probability of the point being green for all N points. I'm using binary Cross-Entropy loss function and gradient descent. Some possible fixes would be to rescale the input in the final layer in the input is tanh and the cost cross-entropy. This is the function we will need to represent in form of a Python function. sum(loss)/m #num of examples in batch is m Probability of Y predY is computed using sigmoid and logits can be thought as the outcome of from a neural network before reaching the classification step Apr 17, 2018 · Then it uses tf. May 8, 2022 · 多クラス分類と同じ結果です。 回帰(MeanSquaredError) 回帰は分類ではなく数値を予測するタスクです。 今回はタスク自体は重要ではないので確率をそのまま数値として予測します。 Jul 5, 2016 · Cross entropy expects it's inputs to be logits, which are in the range 0 to 1. multiply((1 - Y), np. E. Nov 20, 2018 · I am using weighted Binary cross entropy Dice loss for a segmentation problem with class imbalance (80 times more black pixels than white pixels) . It does NOT mean binary in the sense of each datapoint getting either a 0 or a 1. BCELoss() The same behavior would occur with any objective function; modify the example above to use 'binary_crossentropy' instead, and that works as well. Su fórmula es la siguiente: Jun 28, 2021 · I'm trying to implement and train a neural network using the JAX library and its little neural network submodule, "Stax". In binary classification, the goal is to predict whether a certain event will occur (e. 1])). To review, open the file in an editor that reveals hidden Unicode characters. , an email is spam or not, an image contains a cat or not). , "yes" or Dec 19, 2017 · And just for the completeness of the discussion, if, for whatever reason, you insist in using binary cross entropy as your loss function (as I said, nothing wrong with this, at least in principle) while still getting the categorical accuracy required by the problem at hand, you should ask explicitly for categorical_accuracy in the model Jul 25, 2020 · I have seen many questions of this problem online, but there are no definitive solutions and my case might be different, as it is with time series data and a LSTM architecture. it computes Mar 16, 2013 · @Sanjeet Gupta answer is good but could be condensed. Each class has its own separate prediction of whether or not it's present. BCELoss, is a class and inherits from nn. Featured on Meta The December 2024 Community Asks Sprint has Easy to use class balanced cross entropy and focal loss implementation for Pytorch python machine-learning computer-vision deep-learning pypi pytorch pip image-classification cvpr loss-functions cross-entropy focal-loss binary-crossentropy class-balanced-loss balanced-loss Mar 18, 2019 · to be able to do a pixel-wise weighted binary cross-entropy, as they do in the original U-Net paper , to force my U-Net to learn border pixels. Jun 30, 2023 · Here, all topics like what is cross-entropy, the formula to calculate cross-entropy, SoftMax function, cross-entropy across-entropy using numpy, cross-entropy using PyTorch, and their differences are covered. The formula you posted is reformulated to ensure stability and avoid underflow. functional. nn. Contribute to omaraflak/python-neural-networks development by creating an account on GitHub. For example, if a 3-class problem is taken into consideration, the labels would be encoded as [1], [2], [3]. May 24, 2018 · It makes sense to use binary cross-entropy for that, since the way most neural network framework work makes it behave like you calculate average binary cross-entropy over these binary tasks. We’ll illustrate this point below using two such techniques, namely gradient descent with optimal learning rate and Newton-Raphson’s method . Reading this formula, it tells you that, for each green point (y=1), it adds log(p(y)) to the loss, that is, the log probability of it being green. Code: Apr 4, 2022 · This will allow us to implement the logistic loss (which we will call binary cross-entropy from now on) from scratch by using a Python for-loop (for the sum) and if-else statements. This can be used either with from_logits True or False. You could simply try to reconstruct the input x on the output layer with a matrix of real numbers, and eventually round them for computing your loss, which could be a simple RMSE (binary cross entropy is for classification tasks). May 9, 2022 · The difference is that nn. Understanding Cross Entropy. mean(-bce) Computes focal cross-entropy loss between true labels and predictions. State-of-the-art siamese networks tend to use some form of either contrastive loss or triplet loss when training — these loss functions are better suited for siamese networks and tend to improve accuracy. In order words only 5-10 values are 1, all of the other values are 0 in output. The alpha parameter replaces the actual label term in the Cross-Entropy equation. BinaryCrossentropy() is a wrapper around tensorflow's sigmoid_cross_entropy_with_logits. egbp ukny cubd vqetn mmuxe lreksx rmjxzsl kqf nvzxnpg szv