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开源软件名称(OpenSource Name):davidstutz/matlab-mnist-two-layer-perceptron开源软件地址(OpenSource Url):https://github.com/davidstutz/matlab-mnist-two-layer-perceptron开源编程语言(OpenSource Language):MATLAB 100.0%开源软件介绍(OpenSource Introduction):Recognizing Handwritten Digits using a Two-layer PerceptronThis repository contains code corresponding to the seminar paper: D. Stutz. Introduction to Neural Networks. Seminar Report, Human Language Technology and Pattern Recognition Group, RWTH Aachen University, 2014. Advisor: Pavel Golik Update: The code can be adapted to allow mini-batch training as done in this fork. MNIST DatasetThe MNIST dataset provides a training set of 60,000 handwritten digits and a validation set of 10,000 handwritten digits. The images have size 28 x 28 pixels. Therefore, when using a two-layer perceptron, we need 28 x 28 = 784 input units and 10 output units (representing the 10 different digits). The methods Methods and UsageThe main method to train the two-layer perceptron is
The above method requires the activation function used for both the hidden and the output layer to be given as parameter. I used the logistic sigmoid activation function:
In addition, the error backpropagation algorithm needs the derivative of the used activation function:
The method
LicenseLicense for source code corresponding to: D. Stutz. Introduction to Neural Networks. Seminar Report, Human Language Technology and Pattern Recognition Group, RWTH Aachen University, 2014. Copyright (c) 2014-2018 David Stutz Please read carefully the following terms and conditions and any accompanying documentation before you download and/or use this software and associated documentation files (the "Software"). The authors hereby grant you a non-exclusive, non-transferable, free of charge right to copy, modify, merge, publish, distribute, and sublicense the Software for the sole purpose of performing non-commercial scientific research, non-commercial education, or non-commercial artistic projects. Any other use, in particular any use for commercial purposes, is prohibited. This includes, without limitation, incorporation in a commercial product, use in a commercial service, or production of other artefacts for commercial purposes. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. You understand and agree that the authors are under no obligation to provide either maintenance services, update services, notices of latent defects, or corrections of defects with regard to the Software. The authors nevertheless reserve the right to update, modify, or discontinue the Software at any time. The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. You agree to cite the corresponding papers (see above) in documents and papers that report on research using the Software. |
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