A Comparison of Back Propagation Implementations
نویسندگان
چکیده
Back propagation training algorithms have been implemented by many researchers for their own purposes and provided publicly on the internet for others to use in veriication of published results and for reuse in unrelated research projects. Often, the source code of a package is used as the basis for a new package for demonstrating new algorithm variations, or some functionality is added speciically for analysis of results. However, there are rarely any guarantees that the original implementation is faithful to the algorithm it represents, or that its code is bug free or accurate. This report attempts to look at a few implementations and provide a test suite which shows deeciencies in some software available which the average researcher may not be aware of, and may not have the time to discover on their own. This test suite may then be used to test the correctness of new packages.
منابع مشابه
MPI- and CUDA- implementations of modal finite difference method for P-SV wave propagation modeling
Among different discretization approaches, Finite Difference Method (FDM) is widely used for acoustic and elastic full-wave form modeling. An inevitable deficit of the technique, however, is its sever requirement to computational resources. A promising solution is parallelization, where the problem is broken into several segments, and the calculations are distributed over different processors. ...
متن کاملOn the Performance of Parallel Back-propagation Neural Network Implementations Using CUDA
In this paper, we study the impact of the many core Graphics Processing Units (GPUs) system on the implementation of parallel algorithm for back-propagation neural network training. We provide a comparison between the running times taken on the GPU and on the conventional CPU to perform the training of a back-propagation neural network. We design and implement a back-propagation neural network ...
متن کاملComparison of artificial neural network and multivariate regression methods in prediction of soil cation exchange capacity (Case study: Ziaran region)
Investigation of soil properties like Cation Exchange Capacity (CEC) plays important roles in study of environmental reaserches as the spatial and temporal variability of this property have been led to development of indirect methods in estimation of this soil characteristic. Pedotransfer functions (PTFs) provide an alternative by estimating soil parameters from more readily available soil data...
متن کاملAn Optimal Utilization of Cloud Resources using Adaptive Back Propagation Neural Network and Multi-Level Priority Queue Scheduling
With the innovation of cloud computing industry lots of services were provided based on different deployment criteria. Nowadays everyone tries to remain connected and demand maximum utilization of resources with minimum timeand effort. Thus, making it an important challenge in cloud computing for optimum utilization of resources. To overcome this issue, many techniques have been proposed ...
متن کاملApplication of Linear Regression and Artificial NeuralNetwork for Broiler Chicken Growth Performance Prediction
This study was conducted to investigate the prediction of growth performance using linear regression and artificial neural network (ANN) in broiler chicken. Artificial neural networks (ANNs) are powerful tools for modeling systems in a wide range of applications. The ANN model with a back propagation algorithm successfully learned the relationship between the inputs of metabolizable energy (kca...
متن کاملANN Based Modeling for Prediction of Evaporation in Reservoirs (RESEARCH NOTE)
This paper is an attempt to assess the potential and usefulness of ANN based modeling for evaporation prediction from a reservoir, where in classical and empirical equations failed to predict the evaporation accurately. The meteorological data set of daily pan evaporation, temperature, solar radiation, relative humidity, wind speed is used in this study. The performance of feed forward back pro...
متن کامل