Neural network hyperparameters. html>um

Thus, the objective of this work is to propose a rigorous methodology for hyperparameter tuning of Convolutional Neural Network for building construction image classification, especially in roofs structure analysis. Multi-layer Perceptron (MLP) is a supervised learning algorithm that learns a function f: R m → R o by training on a dataset, where m is the number of dimensions for input and o is the number of dimensions for output. 1 — Learning rate Aug 15, 2019 · To optimize hyperparameters with the proposed method, the cost functions were selected as the average of the difference between the decoded value and the original image for the autoencoder, and the inverse of the evaluation accuracy for the convolution neural network. It uses information from the rest of the population to refine the hyperparameters and determine the value of hyperparameter to try. You must specify values for these parameters when configuring your network. is Oct 25, 2017 · Hyperparameters for a deep neural network: - Number of iterations - Number of layers LL in the neural network - Number of hidden units in each layer - Learning rate α - Step size - Choice of the activation function - Losss function - Mini-batch Size - Momentum - Regularization - Drop out rate - Weight Decay. Feb 14, 2022 · The correct hyperparameter settings are critical to the success of a Feedforward Neural Network. As before, hyper-parameter tuning is enabled by specifying the tuner constructor argument of the model. Number of hidden layers. This module is fairly comprehensive, and is thus further divided into three parts: Part I: Setting up your Machine Learning Application. On the other hand, neural networks have two hyperparameters that are very easy to see and understand: the number of hidden layers, and the Aug 30, 2023 · PBT starts by training many neural networks in parallel with random hyperparameters. Nov 22, 2022 · In general, you will get better results by increasing the number of layers instead of the number of neurons per layer. Apr 28, 2023 · Well-tuned hyperparameters are crucial for obtaining good generalization behavior in neural networks. First, we define a model-building function. 3. The hyperparameters of a convolutional neural network always have been important, because the performance of the convolutional neural network depends largely on them. The Keras Tuner is a library that helps you pick the optimal set of hyperparameters for your TensorFlow program. Unlike these parameters, hyperparameters must be set before the training process starts. Hyperparameters can be divided into 2 categories: 1) Optimizer hyperparameters. Gaussian process based Bayesian optimization (GPEI) has proven to be an effective algorithm to optimize several hyperparameters. Given a set of features X = x 1, x 2,, x m and a target y, it can learn a non-linear Feb 15, 2024 · The major problem facing users of Hopfield neural networks is the automatic choice of hyperparameters depending on the optimisation problem. • Examine the hyperparameter optimization algorithms by highlighting their strengths and weaknesses. Random search is appropriate for discovering new hyperparameter values or new combinations of hyperparameters, often resulting in better performance, although it may take more time to complete. We give details of the hyperparameters to be tuned in deep neural networks and provide a general guide for doing this task with more probability of success. The ith element represents the number of neurons in the ith hidden layer. If our hypothesis is correct, the Jan 22, 2021 · The hyperparameters for model design are more related to the structure of neural networks a trivial example being the number of hidden layers and the width of these layers. Setting the hyper-parameters remains a black art that requires years of experience to acquire. This report proposes several efficient ways to set the hyper Aug 28, 2020 · Machine learning algorithms have hyperparameters that allow you to tailor the behavior of the algorithm to your specific dataset. The term “deep” in deep learning refers to the number of hidden layers (depth) of a neural network. ‘logistic’, the logistic sigmoid function, returns f (x) = 1 / (1 + exp (-x)). Mar 18, 2023 · In the context of Convolutional Neural Networks (CNNs), hyperparameters are used to control various aspects of the model architecture, such as the number and size of filters, the size of the Added in version 0. By contrast, the values of other parameters are derived via training the data. For example, Neural Networks has many hyperparameters, including: number of hidden layers; number of neurons; learning rate Examples. keras, there are 2 ways to use Keras, either directly import Keras or from tf import Keras. Part II: Regularizing your Neural Network. Finding a set of hyperparameters that gives an accurate model in a reasonable time is also part of the hyperparameter optimization problem [2]. The selected hyperparameters for training convolutional neural network (CNN) models have a significant effect on the performance. Model Structure. Smaller values yield slow learning speed, while Jul 16, 2021 · 3. Then we explain the most popular deep learning frameworks that can be used to implement these models as well as the most popular Hyperparameters Tuning for Neural Networks. Hyperparameters determine the network section depth, initial learning rate, stochastic gradient descent momentum, and L2 regularization strength. Part III: Setting up your Optimization Problem. Feb 12, 2024 · The best hyperparameters for neural network training are usually very near the edge of stability. Caviar • 6 minutes; Normalizing Activations in a Network • 8 minutes; Fitting Batch Norm into a Neural Network • 12 minutes; Why does Batch Norm work? • 11 minutes; Batch Norm at Test Time • 5 minutes; Softmax Regression Jul 25, 2017 · Model hyperparameters are often referred to as model parameters which can make things confusing. We investigate the effect of hyperparameters on the NN model’s performance and Hyperparameters determine how well your neural network learns and processes information. Keras is an excellent platform for constructing neural networks. Currently, the optimization methods are widely used in the training of neural networks in order to minimize the cost function. Hyperparameters are different from parameters, which are the internal coefficients or weights for a model found by the learning algorithm. Selecting and tuning these hyperparameters can be PaperArtificial Neural Network Hyperparameters Optimization: A Survey While depending on the value regarding another hyperparameter, conditional hyperpa rameters might need to be employed or Sep 18, 2020 · Grid search is appropriate for small and quick searches of hyperparameter values that are known to perform well generally. Mar 15, 2023 · Number of layers: A neural network is made up of vertically arranged components, which are called layers. e. This methods ensures the feasibility of optimal solution obtained by decomposing the set of the feasible solutions Sep 16, 2022 · Hyperparameters that define neural network structure. % Train final model on full training set using the best hyperparameters. ) and, voilà , we obtain our output. When building machine learning models, you need to choose various hyperparameters, such as the dropout rate in a layer or the learning rate Jul 10, 2017 · Tuning hyperparameters for deep neural network is difficult as it is slow to train a deep neural network and there are numerours parameters to configure. In this article, we will describe the techniques for optimizing the hyperparameters in the models. A hyperparameter is a parameter whose value is used to control the learning process. Running the example shows the same general trend in performance as a batch size of 4, perhaps with a higher RMSE on the final epoch. Most methods that use SI algorithms for the CNN’s hyperparameters tuning propose the PSO or its modifications. […] Apr 3, 2024 · Neural networks are powerful and flexible models that can learn complex patterns from data. NNs can take different shapes and structures, nevertheless, the core skeleton is the following: So we have our inputs (x), we take the weighted sum of them (with weights equal to w), pass it through an activation function f(. The process of selecting the right set of hyperparameters for your machine learning (ML) application is called hyperparameter tuning or hypertuning. Hyperparameters are the parameters that are explicitly defined to control the learning process before applying a machine-learning algorithm to a dataset. Since it makes a huge impact on the learned model. Batch size can refer to the full data sample where mini-batch size would be a smaller sample set. Choosing min_resources and the number of candidates#. Beside factor, the two main parameters that influence the behaviour of a successive halving search are the min_resources parameter, and the number of candidates (or parameter combinations) that are evaluated. 2 Hypertparameter optimization for deep neural networks. However, due to its complex network structure, the intertwining of hyperparameters, and the time-consuming procedure for network training, finding an efficient network configuration for CNN Jul 5, 2019 · Neural Networks (NNs) are the typical algorithms used in Deep Learning analysis. Typically, it is challenging […] ” Some examples of model hyperparameters include: The learning rate for training a neural network. In machine learning, hyperparameter optimization [1] or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. The network was built using the PyTorch framework without the use of specialized PINN-oriented libraries. network which w e have designed and built to act on the Quick Draw data. This idea isn't new, it was also discussed in Return of the Devil in the Details: Delving Deep into Convolutional Networks by the Oxford VGG team. On top of that, individual models can be very slow to train. Change network complexity by changing the network parameters (values of weights). 0. This work introduces an automatic method to overcome this problem based on an original mathematical model minimizing the energy function. I find it more difficult to find the latter tutorials than the former. Hyperparameter tuning is the process of selecting the optimal values for a machine learning model’s hyperparameters. They allow for efficient model training and tuning, utilizing powerful libraries that can handle the vast possible combinations of hyperparameters without excessive consumption of Nov 6, 2018 · Learn more about hyperparameter tuning, neural network, bayesopt MATLAB. The required computation time is approximately 37 hours 57 minutes 55 seconds, with generation 3 taking the longest time at around 16 hours 45 minutes 4 seconds. Jan 16, 2023 · Tackling new machine learning problems with neural networks always means optimizing numerous hyperparameters that define their structure and strongly impact their performances. The meta-loss landscapes associated with neural network hyperparameters are often pathological and chaotic, and descending this badly behaved landscape is a central challenge in meta-learning [10]. Model parameters are learned during training. Hyperparameters are the variables that govern the training process and the Aug 6, 2019 · Artificial neural networks have two main hyperparameters that control the architecture or topology of the network: the number of layers and the number of nodes in each hidden layer. The C and sigma hyperparameters for support vector machines. 17. Hyperparameter optimization finds a tuple of hyperparameters that yields an optimal Aug 11, 2017 · It is hence a good method for meta-optimizing a neural network which is itself an optimisation problem: tuning a neural network uses gradient descent methods, and tuning the hyperparameters needs Jun 28, 2017 · Background In recent years, research in artificial neural networks has resurged, now under the deep-learning umbrella, and grown extremely popular. Mar 5, 2024 · Our neural network has 2 hidden layers and the hyperparameters that Hyperopt will help us to optimize are: activation function, optimizer, learning_rate, number of epochs, number of neurons on The variable of the network structure and the network trained of CNN are known as hyperparameters [1]. The aim of this work was dual, first large number of Dec 28, 2018 · Hyperparameters for Neural Networks. But these networks aren’t fully independent of each other. Model parameters (like weights in neural networks) are the outcome of the training process, determining the model’s predictions. The use of convolutional neural networks involves hyperparameters optimization. Multi-layer Perceptron #. A genetic algorithm (GA) approach as Jan 14, 2022 · This chapter provides elements for implementing deep neural networks (deep learning) for continuous outcomes. 18. Model parameters are the properties of the training data that are learnt during training by the classifier or other ml model. These studies show that well tuning the hyperparameters of each algorithm increases the performance of An example of a model hyperparameter is the topology and size of a neural network. In this paper, we use Q-learning algorithm to search for good hyperparameter configurations for neural networks, where the learning agent searches for the optimal hyperparameter configuration by continuously Dec 7, 2023 · Hyperparameter Tuning. The correct hyperparameter settings are critical to the success of a Feedforward Neural Network. Examples are the number of hidden layers and the choice of activation functions. Choosing good hyperparameters gives two benefits: Mar 23, 2023 · For a deep learning network, AutoML not only performs Hyperparameter Optimization (HPO) to automatically set the optimal hyperparameters but also selects the right neural architecture for each layer. The learning rate is considered to be one of the most important hyperparameters you can optimize in a neural network model. Recently reported success of DL techniques in crowd-sourced QSAR and predictive toxicology competitions has showcased these methods as powerful tools in drug-discovery and toxicology research. Oct 7, 2023 · Keras Tuner is an open-source Python library exclusively designed to tune the hyperparameters of the deep neural network (DNN)-based application domains. In this article, I will demonstrate the process to tune 2 things of Neural Network: (1) the hyperparameters and (2) the layers. • Categorize ten hyperparameter optimization algorithms into four classes. Seems crazy, right? Typically, network trains much longer and we need to tune more hyperparameters, which means that it can take forever to run grid search for typical neural network. Hyperparameters live in spaces that are Oct 8, 2020 · Convolution neural network (CNN) is a kind of powerful and efficient deep learning approach that has obtained great success in many real-world applications. T his aim of this chapter. In this work, we propose a simple and efficient way for optimizing hyperparameters inspired by the marginal likelihood, an optimization objective that Jul 19, 2020 · Using gradient checking to verify the correctness of our backpropagation implementation. However, one of the challenges in this field is the definition of hyperparameters. May 25, 2020 · Bayesian optimization is most useful while optimizing the hyperparameters of a deep neural network, where evaluating the accuracy of the model can take few days for training. Keras Tuner comes with some built-in HPO algorithms like Random search, Hyperband, and Bayesian optimization. Apr 17, 2017 · In addition to the answer above. The hyperparameters classified under this criterion directly affect the structure of the neural network. We define the following hyperparameters for training: Number of Epochs - the number times to iterate over the dataset. The most reliable way to configure these hyperparameters for your specific predictive modeling problem is […] Apr 1, 2022 · Several studies propose methods to tune hyperparameters for Decision Tree (Alawad et al. The Adam optimization algorithm is an extension to stochastic gradient descent that has recently seen broader adoption for deep learning applications in computer vision and natural language processing. You can check this article for more information on PBT. A 3-layered neural network gives a better performance than a 2-layered network. 1. More hidden layers allow the model to learn more complex representations, useful for tasks requiring high-level Jan 12, 2022 · Deep Learning models have important applications in image processing. Examples of algorithm hyperparameters are learning rate and batch size as well as mini-batch size. To train a deep neural network, you must specify the neural network architecture, as well as options of the training algorithm. Jan 29, 2024 · Hyperparameters control aspects like learning rate and model complexity, influencing how the model learns. Hyperparameters are settings that control the learning process of the model, such as the learning rate, the number of neurons in a neural network, or the kernel size in a support vector machine. ‘tanh’, the hyperbolic tan function, returns f (x) = tanh (x). In this video we take a high-level look on all main hyperpar Feb 9, 2019 · Hyperparameters are varaibles that we need to set before applying a learning algorithm to a dataset. The origin of hyperparameter optimization is dated back to the 1990s. Jan 7, 2024 · Creating a complete Python code example for experimenting with hyperparameters in deep learning involves several steps: generating a synthetic dataset, designing a neural network model, training Jan 29, 2020 · Here’s a simple end-to-end example. Activation function for the hidden layer. Jul 13, 2024 · Overview. Consequently, we can simply apply the optimal hyperparameters of a small model directly onto a scaled-up version. Nov 9, 2023 · Convolutional neural networks (CNNs) are widely used deep learning (DL) models for image classification. The aim of optimizing the hyperparameters is to find an algorithm that returns best and accurate performance obtained on a validation set. Learning rate controls how much to update the weight in the optimization algorithm. For example, if the learning rate is too low, the model will miss the important patterns in the data. The hyperparameters were optimized using the proposed method with fast May 17, 2021 · 2. Learning Rate - how much to update models parameters at each batch/epoch. In this study, Adolescent Identity Search Algorithm (AISA) and . Jan 21, 2021 · If you look at my series on emulating PID controllers with an LSTM neural network, you’ll see that LSTMs worked really well with this type of problem. Some examples of model hyperparameters include: The learning rate for training a neural network. 1. Oct 8, 2022 · Machine learning algorithms are sensitive to hyperparameters, and hyperparameter optimization techniques are often computationally expensive, especially for complex deep neural networks. , 2019), etc. Dropout prevents the weight from Mar 18, 2024 · More than 35 years later, the concepts of backpropagation and hyperparameters continue to be integral to training neural networks. Set use_predefined_hps=True to automatically configure the search space for the hyper-parameters. But those are a little abstract and hard to see. The optimization of CNN hyperparameters is a complex problem involving multiple local optima that poses a challenge for Jun 1, 2024 · Present a comprehensive review of hyperparameters of Convolution Neural Networks. Ironically, it is as relevant as Top Gun or Legend of Zelda is today. One way to find a good learning rate is to train the model for a few hundred Aug 4, 2022 · The reason is that neural networks are notoriously difficult to configure, and a lot of parameters need to be set. They can enforce appropriate inductive biases, regularize the model and improve performance -- especially in the presence of limited data. We call this practical procedure µTransfer. Hyperparameter optimization is a problem that identifies a good model of hyperparameter [3] or a Mar 26, 2024 · Some common examples of hyperparameters are the depth of trees (decision trees), the number of trees (random forest), the number of neighbors (KNN), batch size (neural networks), and alpha (lasso Feb 9, 2024 · Many types of meta-learning optimize hyperparameters associated with neural network training (e. The k in k-nearest neighbors. ; however, overfitting may occur when the model is over optimized for the training data [67]. Jul 1, 2019 · Abstract. A good rule of thumb to overcome this confusion is as follows: If you have to specify a model parameter manually then it is probably a model hyperparameter. this is done in learned optimizers [11]). Feb 14, 2022. Apr 25, 2015 · Use stacks of smaller receptive field convolutional layers instead of using a single large receptive field convolutional layers, i. The goal is to get a more practical understanding of decisions one has to make building a neural network like this, especially on how to chose some of the hyperparameters. Share. In the next section, you will discover the importance of the right set of hyperparameter values in a machine learning model. Mar 8, 2022 · If, as discussed above, µP networks of different widths share similar training dynamics, they likely also share similar optimal hyperparameters. There are mainly input layers, hidden layers, and output layers. For instance, as suggested above, the best learning rate in a grid search is typically the largest learning rate for which training converges rather than diverges. 1 Convolutional Neural Networks Tuned by SI Algorithms. Therefore, hyperparameter optimization (HPO) is an important process to design optimal CNN models. , 2019), Random Forest (Probst et al. The full article with code and outputs can be found on Github as a Notebook. On the contrary, hyperparameters are the parameters of a neural network that is fixed by design and not tuned by training. Convolutional neural networks (CNNs) have been used over the past years to solve many different artificial intelligence (AI) problems, providing significant advances in some domains and leading to Dec 6, 2022 · Neural Networks (NNs) are one of the most es-sential ways to ML; the most challenging element of designing a NN is de-termining which hyperparameters to employ to generate the optimal model, in Dec 17, 2016 · Assuming that network trains 10 minutes on average we will have finished hyperparameter tuning in almost 2 years. Batch Size - the number of data samples propagated through the network before the parameters are updated. For a Convolutional Neural network, a greater number of layers make a better model. In the case of neural networks, the complexity can be varied by changing the Jul 16, 2021 · Hyperparameters determine layer architecture in the feature extraction step of a convolutional neural network (CNN), and this affects classification accuracy and learning time. Learning rate. Frameworks such as PyTorch and TensorFlow provide robust, flexible environments that simplify the implementation of complex models like SVMs or neural networks. Unlike parameters, hyperparameters are specified by the practitioner when configuring the model. Comparison between grid search and successive halving. The model training hyperparameters in most cases could well serve as a way to measure a model’s learning capacity🧠. Aug 31, 2019 · Neural Networks Hyperparameter tuning in tensorflow 2. For this, the HyperTuningSK Nov 24, 2023 · This is a process whereby the best hyperparameters such as learning rate and number of hidden layers are “tuned” to find the most optimal ones for our network to boost its performance. Jul 3, 2024 · A. Apr 11, 2017 · In this section, we look at halving the batch size from 4 to 2. Since Tensorflow 2 comes up with a tight integration of Keras and an intuitive high-level API tf. Conclusion. Note: The automatic hyper-parameter configuration explores some powerful but slow to train hyper-parameters. Jun 13, 2024 · Hyperparameter-tuning is important to find the possible best sets of hyperparameters to build the model from a specific dataset. 2 stacks of 3x3 conv layers vs a single 7x7 conv layer. g. The challenge with hyperparameters is that there are no magic number that works everywhere. Nov 29, 2018 · Instead, we will only focus on the high-level implementation using Keras. Hyperparameter optimization. In this part, we briefly survey the hyperparameters for convnet. • Assess the performance of hyperparameter optimization algorithms on benchmark Jul 14, 2021 · Abstract and Figures. Unfortunately, this tuning process for large deep neural networks (deep learning) is painstakingly slow. May 13, 2018 · Finally, chapter 3 will show a case study with numerical results of a convolutional neural. Sep 23, 2020 · Keras is an open-source neural network library written in Python that is an API designed for human beings, not machines. Mar 15, 2020 · What are the hyperparameters anyway? A hyperparameter is a parameter whose value is set before the learning process begins. In this post, you will […] Sep 8, 2023 · Neural Network (NN) Learning rate : Increasing : A higher learning rate can make the training process converge faster, but it may also lead to overshooting the optimal weights and result in Jul 3, 2018 · Choosing appropriate hyperparameters plays a crucial role in the success of our neural network architecture. This change is made to the n_batch parameter in the run () function; for example: n_batch = 2. However, they also require careful tuning of various hyperparameters, such as the number of layers, the Jan 1, 2024 · This research proposes the use of a genetic algorithm (GA) as the main method to optimize hyperparameters in the convolutional neural network (CNN) model for FER. Apr 8, 2023 · The “weights” of a neural network is referred as “parameters” in PyTorch code and it is fine-tuned by optimizer during training. To find The choice of optimization algorithm for your deep learning model can mean the difference between good results in minutes, hours, and days. In this work, we study the use of goal-oriented sensitivity analysis, based on the Hilbert–Schmidt independence criterion (HSIC), for hyperparameter analysis and optimization. Hyperparameters are parameters whose values control the learning process and determine the values of model parameters that a learning algorithm ends up learning. Dec 30, 2020 · Hyperparameters. , 2018), Support Vector Machine (SVM) (Duarte and Wainer, 2017), Deep Neural Network (DNN) (Zhou et al. It takes an hp argument from which you can sample hyperparameters, such as hp. 2. Learning Rate. The better solution is random search. Hyperparameters are variables that determine the structure of the deep neural network. This example shows how to apply Bayesian optimization to deep learning and find optimal network hyperparameters and training options for convolutional neural networks. With the revolution of artificial intelligence and deep learning, many built-in libraries such as Pytorch and tensorflow can be used to train a model to Mar 26, 2018 · Although deep learning has produced dazzling successes for applications of image, speech, and video processing in the past few years, most trainings are with suboptimal hyper-parameters, requiring unnecessarily long training times. However, traditional methods for hyperparameter configuration, such as grid searches and random searches, are time consuming and labor intensive. For example in case of some NLP task: word frequency, sentence length, noun or verb distribution per sentence, the number of specific character n-grams per word, lexical diversity, etc. Then deep networks for global optimization algorithm (DNGO) that used neural network as an alternative to Gaussian process was Aug 6, 2019 · Therefore, we can reduce the complexity of a neural network to reduce overfitting in one of two ways: Change network complexity by changing the network structure (number of weights). Mar 27, 2023 · The configuration of the hyperparameters in convolutional neural networks (CNN) is crucial for determining their performance. The best numbers depend on each task and each dataset. If it is high, it may have collisions. Successive Halving Iterations. 3. The prefix ‘hyper_’ suggests that they are ‘top-level’ parameters that control the learning process and the model parameters that result from it. The particle swarm optimization (PSO) algorithm is one of the earliest and most studied SI algorithms. Examples of hyperparameters that need to be tuned in neural networks include: Number of hidden layers: Each hidden layer can capture different levels of abstraction from the input data. What we want to do is train an LSTM model that would follow this same type of FOPDT model behavior. Int('units', min_value=32, max_value=512, step=32) (an integer from a certain range). The solver for weight optimization. Notice how the hyperparameters can be defined inline with the model-building code. Jan 31, 2024 · A typical neural network has many different hyperparameters that you can tune including things like batch size, learning rate, and the type of optimizer. Sep 14, 2020 · Unlike the internal model parameters, such as the neural network’s weights, which can be learned from the data during the model training phase, hyperparameters are set prior to the learning process. Find optimal hyperparameters and training options for convolutional neural network. Wikipedia. Using an Appropriate Scale to pick Hyperparameters • 8 minutes; Hyperparameters Tuning in Practice: Pandas vs. For a Convolutional Neural network, a greater number of layers, ideally 5-7, make a better model. This is also called the depth of the network. In this post, you will discover how to use the grid search capability from the scikit-learn Python machine learning library to tune the hyperparameters of Keras’s deep Jan 17, 2024 · Abstract In this work, we study the effectiveness of common hyperparameter optimization (HPO) methods for physics-informed neural networks (PINNs) with an application to the multidimensional Helmholtz problem. In complex DNN models, neurons can become overly dependent on a particular feature, which can lead to problems with learning. n_batch=2. Examples highlighting these differences include: Hyperparameter: Learning rate in a neural network. Feb 14, 2022 · Developer Educator. We see where in the lifecycle of the NNs they belong, what they mean, and also how to set them using Python and Keras. In this video, we take a high-level look on all main hyperparameters of Neural Networks. Sep 15, 2023 · When a neural network model learns, the neuron weights are adjusted to fit specific features. Model parameters differ for each experiment and Sep 21, 2022 · This paper evaluates a comparison between three machine learning algorithms (MLAs), namely support vector machine (SVM), multilayer perceptron artificial neural network (MLP-ANN) and random forest (RF), in landslide susceptibility mapping and addresses a optimization algorithm to optimize the performance of a MLA to yield more accurate and reliable results. ua nx st jn wk nh dn um ua cg