Questions tagged [multilayer-perceptron]
For question about Multi Layer Perceptron model/architecture, its training and other related details and parameters associated with the model.
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What are the math theorems regarding the Multilayer Perceptron?
I've come across a theorem "Convergence theorem
Simple Perceptron" for the first time, here-> https://zaguan.unizar.es/record/69205/files/TAZ-TFG-2018-148.pdf, page 27, (is in Spanish)
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Why is the Backpropagation algorithm used to train the Multilayer Perceptron?
I've read on the book NND by Martin Hagan et al (chapter 11 IIRC), that to train the feed-forward neural network: Multilayer Perceptron one uses the Backpropagation algorithm.
Why?
Why not another?
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What is the difference between the forward pass of the Multi-Layer Perceptron, Deep AutoEncoder and Deep Belief Network?
Multi-Layer Perceptron (MLP), Deep AutoEncoder (DAE), and Deep Belief Network (DBN) are trained differently.
However, do they follow the same process during the inference phase, i.e., do they ...
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40 views
Can RNNs get inputs and produce outputs similar to the inputs and outputs of FFNNs?
RNN and LSTM models have many interesting architectures that can be modified in various ways. We can also compose their input and output data in quite interesting ways. However, in the examples that I ...
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55 views
What are examples of good free books that cover the back-propagation algorithm?
What are examples of good free books that cover the back-propagation used to train multilayer perceptrons? I've just started to learn about artificial neural networks, so I'm looking for books that ...
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201 views
Why do feedforward neural networks require the inputs to be of a fixed size, while RNNs can process variable-size inputs?
Why does a vanilla feedforward neural network only accept a fixed input size, while RNNs are capable of taking a series of inputs with no predetermined limit on the size? Can anyone elaborate on this ...
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What is the number of neurons required to approximate a polynomial of degree n?
I learned about the universal approximation theorem from this guide. It states that a network even with a single hidden layer can approximate any function within some bound, given a sufficient number ...
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How to draw a 3-dimensonal shape's neural network
I am reading an exam question about NN (that I cannot publish, for copyright reasons). The question says: 'Construct a rectangle in 2D space. Define the lines, and then define the weights and ...
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NN for multivariate function interpolation
I have a multivariate function. It takes 4, real valued inputs: a, b, c, d and returns 1 complex number, z.
I wanted to use Neural Networks to predict the value z' for a generic input a', b', c', d'.
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How to perform data augmentation on multiple input classification task?
I would like to add some more samples to my dataset which consists of two parts: 1. image and 2. numerical data. For each image in the dataset there is its corresponding numerical data as well.
If it ...
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Why does 0.8:0.2 divided dataset have a much greater AUROC than its 5-fold cross validated counterpart's mean AUROC?
I trained a dataset with a 5-fold cross validation to search for hyper-parameters by an AUROC metric. For splitting I used
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Why don't neural networks project the data into higher dimensions first, then reduce the size of each layer thereafter?
Background
From my understanding (and following along with this blog post), (deep) neural networks apply transformations to the data such that the data's representation to the next layer (or ...
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140 views
Keras DQN Model with Multiple Inputs and Multiple Outputs [closed]
I am trying to create a DQN agent where I have 2 inputs: the agent's position and a matrix of 0s and 1s.
The output is composed of the agent's new chosen position, a matrix of 0s and 1s (different ...
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59 views
Is there a common way to build a neural network that seeks to extract spatial and temporal information simultaneously?
Is there a common way to build a neural network that seeks to extract spatial and temporal information simultaneously? Is there an agreed up protocol on how to extract this information?
What ...
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How is the error calculated with multiple output neurons in the neural network?
Machine Learning books generally explains that the error calculated for a given sample $i$ is:
$e_i = y_i - \hat{y_i}$
Where $\hat{y}$ is the target output and $y$ is the actual output given by the ...
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Random Initializations with ReLU gives puzzling results
this may sound naive, but I’m getting a really puzzling result. I was experimenting with MNIST on vanilla MLP (784, 256, 128, 10) with ...
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What is the purpose to have fully connected layers?
What is the purpose of a fully connected multi layer perceptron in which every input is connected to every output by a weight?
After all, the information is only distributed over several channels, but ...
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Backpropagation implementation with Java
I've been trying to implement a Multilayer Perceptron Network using java language with the ultimate goal of creating and teaching a neural network to recognize handwritten digits. Pretty simple and ...
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Why does every neuron in hidden layers of a multi-layer perceptron typically have the same activation function? [duplicate]
Why does every neuron in a hidden layer of a multi-layer perceptron (MLP) typically have the same activation function as every other neuron in the same or other hidden layers (so I exclude the output ...
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Problem fitting data using mlpregressor
I'm training a sklearn.neural_network.mlpregressor by a large data of students performance (an excel file with 740 students and 27 columns that are their qualities) and I want to predict their grades. ...
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369 views
Why can't MLPs perform non-linear regression and classification?
In this page it's told:
In Single Perceptron / Multi-layer Perceptron(MLP), we only have linear separability because they are composed of input and output layers(some hidden layers in MLP)
What ...
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What are standard datasets for fully connected neural networks?
I am looking for datasets that are used as a testing standard in the fully connected neural networks (FCNN). For example, in the image recognition and CNN, CIFAR datasets are used in most of the ...
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How can I adapt this script (to test the robustness of a perceptron) to test the robustness of a multi-layer perceptron?
The following script is from Trappenberg's Fundamentals of Computational Neuroscience and is used to test a perceptron's robustness against noise.
However, how would one alter it to test the output ...
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Why is it called back-propagation?
While looking at the mathematics of the back-propagation algorithm for a multi-layer perceptron, I noticed that in order to find the partial derivative of the cost function with respect to a weight (...
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Can neurons in MLP and filters in CNN be compared?
I know they are not the same in working, but an input layer sends the input to $n$ neurons with a set of weights, based on these weights and the activation layer, it produces an output that can be fed ...
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Why MLP cannot approximate a closed shape function?
[TL;DR]
I generated two classes Red and Blue on a 2D space. Red are points on Unit Circle and Blue are points on a Circle Ring with radius limits (3,4). I tried to train a Multi Layer Perceptron ...
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Recent algorithms for correcting mislabeled data using multilayer perceptrons
I am doing literature research on algorithms for correcting mislabeled data using multilayer perceptrons. Found an "old" paper An algorithm for correcting mislabeled data (2001) by Xinchuan Zeng et al....
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Are the labels updated during training in the algorithm presented in “An algorithm for correcting mislabeled data”?
I am trying to understand an algorithm for correcting mislabeled data in the paper An algorithm for correcting mislabeled data (2001) by Xinchuan Zeng et al. The authors are suggesting to update the ...
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Model unfit for some part of spiral data despite low error
I'm current testing a model for spiral data. After 500 epoches, loss is 0.04 but the result is still unmatch with some part of the training data. (bottom left)
The model has 2 hidden tanh x 16 units ...
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1answer
124 views
Is there a mathematical theory behind why MLP can classify handwritten digits?
I'm trying to really understand how multi-layer perceptrons work. I want to prove mathematically that MLP's can classify handwritten digits. The only thing I really have is that each perceptron can ...
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Keras MLP returns always loss 0.0 [closed]
I'm implementing a multilayer perceptron with Keras to predict the correct words order in a sentence.
I'm using train_on_batch()because I convert each sentence in a ...
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How I can predict the next number in a sequence with a neural network?
I've been dabbling with machine learning and neural networks (namely, resnet50) for a few months now, mostly doing image recognition. I am currently trying to make a program that, given a string of ...
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Why does my model overfit on pseudo-random numbers training data?
I am trying to predict pseudo-random numbers using the past numbers with a multiplayer perceptron. The error while training is very low. However, as soon as I test it with a test set, the model ...
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Is it expected that adding an additional hidden layer to my 3-layer ANN reduces accuracy significantly?
I've been using several resources to implement my own artificial neural network package in C++.
Among some of the resources I've been using are
https://www.anotsorandomwalk.com/backpropagation-...
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Does a varying ANN model accuracy mean underfitting or overfitting?
Background:
This is for a simulated robot with four legs, walking on a flat terrain. The ANN (an MLP) is given inputs as the robot's body angle, positions and angle of each leg with respect to the ...
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Applying Machine Learning to 2D Laser Scanner Data
We are using 2D Laser Scanner to scan various objects of different geometric shapes for e.g. cylinder, spiked, cylinder with notch, cylinder with curved edges e.t.c. The dataset contains points in the ...
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Why use a recurrent neural network over a feedforward neural network for sequence prediction?
If recurrent neural networks (RNNs) are used to capture prior information, couldn't the same thing be achieved by a feedforward neural network (FFNN) or multi-layer perceptron (MLP) where the inputs ...
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How can I access the weights at each training iteration of an MLP with scikit-learn?
I'm building an MLP with scikit-learn. Is there a way I can access the weights and biases of the output layer per iteration? There is an option mlp.coefs_, but it ...
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How can we print weights per iteration in a simple feed forward MLP for an specific class?
im working on a project in which I have to make a multi-layer perceptron with two hidden layers with 3 nodes in each. The target value in my data contains 8 unique values/classes. One of the tasks ...
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Is there data available about successful neural network architectures?
I am curious to if there is data available for MLP architectures in use today, their initial architecture, the steps that were taken to improve the architecture to an acceptable state and what the ...
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One vs multiple output neurons
Consider an MLP that outputs an integer 'rating' of 0 to 4. Would it be correct to say this could be modeled in either of the following ways:
map each rating in the dataset to a 'normalized set' ...
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Backpropagation equation for a variant on the usual Linear Neuron architecture
Recently I encountered a variant on the normal linear neural layer architecture: Instead of $Z = XW + B$, we now have $Z = (X-A)W + B$. So we have a 'pre-bias' $A$ that affects the activation of the ...
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How can an ANN efficiently predict multiple numbers with fixed sum (in other words, proportions)?
I need a neural network (or any other solution) to predict 3 values which sum equals a fixed number (100). This will help me calculate proportions. Which is the most efficient way to do this?
The ...
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1answer
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Why are activation functions independent layers in CNNs rather than part of convolutional layers?
I have been reading up on CNNs. One of the different confusing things has been that people always talk of normalization layers. A common normalization layer is a ReLU layer. But I never encountered an ...
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Which online machine learning technique to use for multi-class classification problem with multiple inputs?
I have the following problem. We have $4$ separate discrete inputs, which can take any integer value between $-63$ and $63$. The output is also supposed to be a discrete value between $-63$ and $63$. ...
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How does a single hidden layer affect output? [duplicate]
I'm learning about multilayer perceptrons, and I have a quick theory question in regards to hidden layer neurons.
I know we can use two hidden layers to solve a non-linearly separable problem by ...
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Is a multilayer perceptron a recursive function?
I read somewhere that a multilayer perceptron is a recursive function in its forward propagation phase. I am not sure, what is the recursive part? For me, I would see an MLP as a chained function. So, ...
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How can a neural network learn to play sudoku?
I'm just beginning to understand neural networks and I've performed a couple of successful tests with numerical series where the NN was trained to find the odd one or a missing value. It all works ...
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Why MLP momentum term must be in the range 0-1?
Why momentum factor greater than 1 is a bad idea?
What are the mathematical conclusions?
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Unable to overfit using MLP
I'm building a 5-class classifier with a private dataset. Each data sample has 67 features and there are about 40000 samples. Samples of a particular class were duplicated to overcome class imbalance ...
