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GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. This repo is tested on Python 2. First you need to install one of, or both, TensorFlow 2. When TensorFlow 2. Here also, you first need to install one of, or both, TensorFlow 2.

According to the best-performing networks in TAIEX results Table 23 levels of decomposition, db9 wavelet, penalized high thresholding strategy, and a delay of one week with application of WPCA-NN, gain considerably high performance Nn model list index thumbnail return. However, Hsieh et al. Generally, inconsistency of a single technique could be resolved by combining two or more techniques to overcome the deficiencies of single models and yield more accurate results [ 34 — 36 ]. To train them, don't forget to set them back in training mode model. Fig

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This paper presents an integrated hybrid optimization algorithm for training the radial basis function neural network RBF NN. Training of neural networks is still a challenging exercise in machine learning domain. Traditional training algorithms in general suffer and trap in local optima and lead to premature convergence, which makes them ineffective when applied for datasets with diverse features.

Training algorithms based on evolutionary computations are becoming popular due to their robust nature in overcoming the drawbacks of the traditional algorithms. Accordingly, this paper proposes a hybrid training procedure with differential search DS algorithm functionally integrated with the particle swarm optimization PSO. To surmount the local trapping of the search procedure, a new population initialization scheme is proposed using Logistic chaotic sequence, which enhances the population diversity and aid the search capability.

To demonstrate the effectiveness of the proposed RBF hybrid training algorithm, experimental analysis on publicly available 7 benchmark datasets are performed. Subsequently, experiments were conducted on a practical application case for wind speed prediction to expound the superiority of the proposed RBF training algorithm in terms of prediction accuracy. Citation: Rani R. HJ, Victoire T. AA Training radial basis function networks for wind speed prediction using PSO enhanced differential search optimizer.

This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: The data underlying this study are third party.

The authors did not have special access privileges. Competing interests: The authors have declared that no competing interests exist. Artificial neural networks ANN are a section of artificial intelligence systems fundamentally designed to overcome some of the challenges the mathematical models fail with complex and ill-defined problems.

They are fault tolerant and solve the problem by learning from similar examples. ANN are capable of handling noisy and ambiguous data, with the ability to predict and generalize once efficiently trained [ 1 ].

Radial basis function RBF networks are another class of ANN simulating the locally tuned response observed in biologic neurons [ 2 ]. The structure of RBF consists of three layers, namely the input, hidden and output layers. The RBF training involves two stages, with centres of the hidden layer are determined first in a self-organising manner [ 3 ] and secondly, the weights connecting the hidden layer to the output layer are computed.

Generally, RBF training is accomplished by computing the weights and biases to obtain the target output by minimizing the error function. To accomplish this, the following methods are widely used in the literature, matrix inversion techniques, gradient-based training approaches and evolutionary computation methods [ 4 ].

Thus in this section, a review of literature in the topic of various RBF training methods will be discussed. As the training phase determines the success of any network, the training of radial basis network RBF involves three step learning [ 5 ] which is the fastest as the centres are determined by unsupervised method and output weights are also determined by less complex algorithms. Though gradient descent method offers precise results as it involves derivatives which affects the computation time it is not preferred to use alone.

Training the neural networks by heuristic search algorithms like differential evolution DE [ 6 ] was previously utilized and the results are compared with gradient descent methods, however it is observed that no significant improvement in the performance because of DE. Similarly, in [ 7 ] the authors suggests solutions for stagnation of Differential evolution DE when used with neural networks, as the individual does not improves even under favourable conditions. Taking care of the initialization, merging DE with specified mutation operators, size of population DE are some of the key areas have been discussed.

In [ 8 ], as the training of both MLP and RBFN is difficult, evolutionary algorithms like Genetic Algorithm optimizes the subset of input data for determining the number of centres which helps us in elevating the over-fitting problems. In another work [ 9 ], the authors carried out short term wind speed prediction with inputs from five different meteorological stations and tested with ANN trained by PSO.

Similar works which involves neural network and PSO are experimented in [ 10 ] in order to improve the reliability of electric power generation, wind power is predicted with enhanced particle swarm optimization EPSO in combination with standard neural networks and the weights of the networks are optimized. Similar time series data in [ 12 ], nonlinear time varying evolution PSO was proposed for training RBFN and tuning the acceleration coefficients for short term electric power prediction in Taiwan.

Similarly, in [ 14 ] both the global search and local search capabilities of Adaptive PSO and BP is been efficiently exploited for finding the global optimum in the given search space. In another work [ 15 ] the authors proposed an improved dynamic PSO, together with Ada Boost algorithm, authors adjust the parameters centers, widths, shape parameters and connection weights to train the RBF NN. Similar recently developed hybrid models[ 16 ]such as biogeography based optimization BBO algorithm is used for training Multi- layer Perceptron MLP networks and tested with several classification, approximation datasets.

In another work [ 17 ] modified bat algorithm is employed to optimize the weights, biases and the structure of neural network and tested on classification, benchmark time series and real time series e.

Again in [ 18 ], to improve the diversity of population, two strategies are proposed in modified bat inspired algorithm, proposing Ring and Master slave methods, the weights and the structure of ANN are simultaneously optimized. PSO replaces K-means clustering for finding centres, as in K-means random selection of centres was deficient. These two swarms exchanged the information of optimized parameters among themselves. Again in [ 21 ], the variant of PSO i.

Since GA is binary coded, and PSO is real value coded, this algorithm is a mixed coded one where the network structure is been optimized by GA and the weights and basis are optimized by PSO. In [ 24 ], the authors tried RBFN for solar power prediction with wind speed and two dimensional representation of solar irradiation as its inputs.

Again [ 25 ] uses a hybrid PSO-GA for finding the parameters of radial basis network in rainfall prediction. K-means clustering which is adapted for finding centres of RBF, weights linking the output and hidden layer are being optimized by AFSA. Meanwhile in [ 27 ], a hybrid perturbation artificial bee colony trainer for a local linear RBF NN is presented.

Another hybrid method integrating empirical mode decomposition with adaptive neural network based fuzzy inference system ANFIS for short-term wind speed forecasting is presented in [ 28 ]. To improve the diversity of individuals results in higher chance to search in the direction of global optimal [ 29 ], proposes an integrated hybrid method with PSO and GA for RBFNN training.

Generally, inconsistency of a single technique could be resolved by combining two or more techniques to overcome the deficiencies of single models and yield more accurate results [ 34 — 36 ]. Accordingly this paper proposes a hybrid model combining the salient features of PSO with the differential search algorithm.

Before establishing the applicability of the proposed technique to train RBF NN for wind speed prediction, seven publicly available test datasets are experimented to demonstrate the results produced by the new scheme is evidently superior in many aspects compared to other reported methods for training RBF NN.

Despite the fact that any developed technique can be experimented and proved to be effective for standard test problems, it is more insistence to justify its performance on a real time system. Accordingly this research after establishing the performance of the proposed trainer for RBF NN, will be experimented on a practical wind prediction problem. Wind is one of the green renewable energy, widely available for electric power generation. In spite of its chaotic nature the wind is effectively utilized for power generation with suitable planning.

Several factors influence the speed of the wind and hence prediction of wind speed will help electric power companies to well utilize the energy tapped from wind and minimize the expenses for fossil power generation. The literature for application of NN for wind speed prediction is comprehensive. Here selective articles are reviewed pertaining to the content of this research.

Wind speed prediction is done using three NN, namely adaptive linear element, back propagation and radial basis function and demonstrated that no particular NN outperforms the other in terms of all evaluation metrics [ 37 ]. In [ 38 ], a self-organising map is used to process the uncertainties of wind nature and then processed using RBF NN. Similarly an adaptive neuro fuzzy system is proposed along with similar day method and proved to be effective [ 39 ].

In [ 40 ], a NN model for predicting real time information obtained from various locations in the mountainous regions of Himalaya is presented. Similarly, a recurrent NN model is developed for predicting the wind power generated from wind turbines installed across the coastal region [ 41 ]. In another work [ 42 ], a Least square support vector machine LSSVM , with empirical wavelet transform as a pre-processor is presented.

Similarly, two different statistical models with same datasets of inputs ranges from atmospheric variables is presented [ 43 ]. Complexity is one of the key factors which trigger the advent of new solution techniques for finding possible solutions, where existing mathematical programming techniques fail. Evolutionary computation algorithms are promising alternatives, when attempting complex search space and further expounded to overcome several drawbacks the mathematical programming techniques face when applied.

The search range of a neural network, where weight determination is the key problem is also complex and cumbersome in nature. This solution space is not only a challenge for any method to produce quality solutions, there are other issues like local trapping and premature convergence. An inherent feature of most of the population based algorithm is their capability of balancing between exploration and exploitation when searching the complex solution space.

Similar, the key concern and shortcoming of any evolutionary computation algorithms is to overcome the trapping into local optima and to avoid poor convergence. The rest of this paper is organized as follows. Section 3, presents a brief introduction to the RBF NN followed by the Logistic chaotic sequence based Initial population generation algorithm. Section 5, summarizes the simulation results of the seven publicly available regression test datasets and finally for wind speed prediction problem.

Finally the paper concludes by summarizing the merits of the proposed approach. Radial basis function neural network RBF NN [ 2 , 3 ] is the general class of non-linear and three-layer feed forward neural networks: i an input layer with n nodes, ii a hidden layer with m neurons or RBFs, and iii an output layer with one or several nodes Fig 1.

The unsupervised layer is defined between input nodes and the hidden neurons in the RBF network, while the supervised layer exists between hidden neurons and the output nodes. According to [ 30 ], the basis function can be defined in several ways, while some of the most commonly used basis functions are as follows: Gaussian, multi-quadric, inverse multi-quadric, generalised inverse multi-quadric, thin plate spline, cubic and linear function.

The output layer applies a linear combination of this function and is represented as 2. The Gaussian form is defined as 3. Now, the j th output becomes: 4. To obtain the above parameters to design the RBF neural network used for prediction problems, the root mean square error RMSE is formulated as an optimization problem.

Accordingly the fitness function for the optimization procedure is given below. The fitness function is the key element in determining the suitable parameters for the better performance of the RBF NN. Thus the error function also considered as fitness function is given by Eq 5.

In any evolutionary computation procedure, the convergence speed and final optimum solution obtained are greatly influenced by the initialization of the candidate solution or population. Mostly, initial candidate solutions are randomly generated within the range of the variables limits as no information of the solution space is available [ 44 , 45 ].

Recently, several evolutionary computation procedures due to the randomness and sensitivity dependence on the initial conditions, adopts chaotic maps for initialization of the candidate solutions as chaotic maps are capable of extracting diversity within the solution space, thereby generate initial population that are much diversified throughout the search space than the regular randomly initialized population.

In this work the chaotic map adopted is the one proved to be most successful in various applications. Subsequently once the population initialization is done with chaotic maps, another improvisation is carried out by applying the opposition based population diversification [ 47 ]. This diversification is done for the entire size of the population and their place in the search will be decided based on their fitness.

So that out of twice the size of the candidates only the first half candidates with highest fitness will enter the PSODS routine. The combined algorithm for population initialization using chaotic maps and opposition based method is presented in Algorithm 1.

The Differential search DS is one of the recently developed evolutionary computation procedure to solve constrained global optimization problems. It is getting attention in recent times in wide range of applications which requires rigorous search of solution space [ 48 , 49 ].

The DS algorithm shall be briefed in three stages as follows:. The above procedure will be continued until all the artificial-super-organism examines and settle at an acceptable position as per the problem requirement.

The DS procedure is inspired by the movement of a super-organism well similar to the Brownian-like random-walk model. The flow chart of the Differential search algorithm is shown in Fig 2.

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How to Load and Manipulate Images for Deep Learning in Python With PIL/Pillow

GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

This repo is tested on Python 2. First you need to install one of, or both, TensorFlow 2. When TensorFlow 2. Here also, you first need to install one of, or both, TensorFlow 2. A series of tests are included for the library and the example scripts. Library tests can be found in the tests folder and examples tests in the examples folder.

These tests can be run using pytest install pytest if needed with pip install pytest. Depending on which framework is installed TensorFlow 2. Ensure that both frameworks are installed if you want to execute all tests. You should check out our swift-coreml-transformers repo.

It contains a set of tools to convert PyTorch or TensorFlow 2. At some point in the future, you'll be able to seamlessly move from pre-training or fine-tuning models to productizing them in CoreML, or prototype a model or an app in CoreML then research its hyperparameters or architecture from TensorFlow 2. Super exciting! These implementations have been tested on several datasets see the example scripts and should match the performances of the original implementations e.

You can find more details on the performances in the Examples section of the documentation. Write With Transformer , built by the Hugging Face team at transformer. Let's do a quick example of how a TensorFlow 2.

The General Language Understanding Evaluation GLUE benchmark is a collection of nine sentence- or sentence-pair language understanding tasks for evaluating and analyzing natural language understanding systems. Parallel training is a simple way to use several GPUs but is slower and less flexible than distributed training, see below.

This is the model provided as bert-large-uncased-whole-word-masking-finetuned-squad. A conditional generation script is also included to generate text from a prompt. The generation script includes the tricks proposed by Aman Rusia to get high-quality generation with memory models like Transformer-XL and XLNet include a predefined text to make short inputs longer. Here is a quick summary of what you should take care of when migrating from pytorch-transformers to transformers. If you used to call the models with keyword names for keyword arguments, e.

If you used to call the models with positional inputs for keyword arguments, e. Here is a quick summary of what you should take care of when migrating from pytorch-pretrained-bert to transformers. The main breaking change when migrating from pytorch-pretrained-bert to transformers is that every model's forward method always outputs a tuple with various elements depending on the model and the configuration parameters.

The exact content of the tuples for each model is detailed in the models' docstrings and the documentation. In pretty much every case, you will be fine by taking the first element of the output as the output you previously used in pytorch-pretrained-bert. Here is a pytorch-pretrained-bert to transformers conversion example for a BertForSequenceClassification classification model:. This allows users to easily access the embeddings final state. To train them, don't forget to set them back in training mode model.

They are now used to update the model configuration attribute instead, which can break derived model classes built based on the previous BertForSequenceClassification examples. The schedules are now standard PyTorch learning rate schedulers and not part of the optimizer anymore. Here is a conversion examples from BertAdam with a linear warmup and decay schedule to AdamW and the same schedule:. Skip to content. Permalink Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

Sign up. Branch: master Find file Copy path. Find file Copy path. Raw Blame History. SparseCategoricalAccuracy ' accuracy ' model. You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Quick tour: TF 2. Migrating from pytorch-transformers to transformers. Migrating from pytorch-pretrained-bert to pytorch-transformers.