Row 5460

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{
  "text": "The following describes the task at hand, I feel I'm stuck at this stage of the neural network implementation, so please help or any tips on how to push this further. My project on github:  \n[https://github.com/janrzepkowski/MultilayerPerceptron/tree/main/MLP](https://github.com/janrzepkowski/MultilayerPerceptron/tree/main/MLP)\n\nThe task is to implement and test a Multi-layer Perceptron (MLP) neural network.\n\nThe implementation of the perceptron is to be universal in nature, and therefore allow easy scalability of its architecture. This means that the code should guarantee the correct operation and learning of the perceptron regardless of the number of layers and neurons in each layer, which parameters should be determined at the time of network development. Processing neurons, i.e. hidden and output neurons (input neurons, if any, are not counted as processing neurons) are to be nonlinear and use a sigmoidal activation function, the slope coefficient of which is to be equal to 1. The program is to make it possible to specify whether or not to take into account the value of the load input (bias) when calculating the excitation of a neuron (the weighted sum of its inputs). The weights of the network, unless loaded from a file, are to be initialized pseudo-randomly with values from a small interval surrounding 0 (e.g., from the interval \\[-0.5; 0.5\\] or \\[-1; 1\\]). The program is supposed to allow the network to be saved to a file and to load the saved network from the file. It is also to allow loading a set of patterns from a file.\n\nThe program is to provide two modes of network operation: learning mode and testing mode.\n\nIn the learning mode, the perceptron is taught using the method with a teacher. At each epoch, all the training patterns are presented to the network, and the program should provide a choice between an invariant and a random order of their presentation. The sequence of actions that is performed for a single pattern here is as follows: the training pattern is fed to the inputs of the network, then its forward propagation takes place, further on, based on the value of the response generated by the network and the value of the desired response pattern, the errors are determined, after which they are propagated backward, and finally the correction of the weights takes place. The update of the scales is therefore online (it takes place after each presentation of the pattern). The program is also supposed to make it possible to determine whether or not a momentum member is to be included during learning.\n\n\n\nThe duration of learning should be determined either by the realization of the number of epochs entered by the user, or by the network reaching the error level specified by the user (however, it should be possible here to stop learning at a certain point, should the assumed error level prove unattainable), or by the fulfillment of any of the two conditions.\n\nDuring learning, there is to be a logging to a file of the value of the global error made by the network in a given epoch, but every certain number of epochs with a certain assumed jump (e.g.: in each epoch, every 10, 20 or 50 epochs). The jump can be predetermined by the programmer or entered by the user.\n\n\n\nIn testing mode, the network's responses for individual patterns are determined, while learning, and therefore modification of the weights, does not occur. The sequence of operations in this case is limited to three: the training pattern is fed to the network inputs, then forward propagation takes place, and finally, based on the value of the response generated by the network and the value of the desired response pattern, the errors are determined.\n\n\n\nDuring testing, certain quantities are also to be recorded into a file, namely: the input pattern, the error committed by the network for the whole pattern, the desired response pattern, the errors committed at the individual outputs of the network, the output values of the output neurons, the weights of the output neurons, the output values of the hidden neurons, the weights of the hidden neurons (in the order of layers from farther relative to the inputs of the network to closer). Alternatively, the program may allow the user to select only some of them for registration in a given run.\n\n\n\nThe research part involves using the implemented network to perform two tasks:\n\n\n\n1. classification of a set of Irises (https://archive.ics.uci.edu/ml/datasets/iris);",
  "label": "r/neuralnetworks",
  "dataType": "post",
  "communityName": "r/neuralnetworks",
  "datetime": "2024-04-30",
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  "url_encoded": "Z0FBQUFBQm5Lak9GTjRjOHMwdEtuXy1zcUFUdFp5N0dPOEk5RlNtaWpXSUV5NUI3UU05Z2Zvd0l4UUUzc09FR1V3ZTRybld1RmI4QktPcmlnSXJVUmgzSkQtNzN6OGNKVmdXQnA2Ull5bkI3c3QwQ3RzMTltUXEyTzhZeG1tcDAwdUlvREdfZk1iTEtQMXBuZlNlOEc2TldZVU9NelExdF9QQ3lyZkZWWUZQa2RmWl8wSmc0eEhsNUlSRnI1aGZ5c21VTnR4Vlp3aWZ0SWk3OHY5OTVxdXdUUmlmR0c5UHRFZz09"
}

Entry Information

• Entry ID: 5460
• Repository: Axioma AXP
• Dataset: arrmlet/reddit_dataset_36
• Total Entries: 100,000