Questions tagged [mlp]

MLP stands for multi-layer perceptron, the most basic kind of neural network. Also called DNN (deep neural network), as opposed to CNN or RNN (convolutional and recurrent neural networks).

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What what will happen if all the layers of a MLP or any DL architecture are set as same in the beginning?

Setting the initial weights as all zeros will have the output dependent on the bias and setting the weights of all the neurons of a layer as same, will update the gradients in same way thus removing ...
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Null Inputs/Inhibiting Inputs & Outputs with Scikit-Learn MLPRegressor

I'm trying to build a general predictive model of a model of a machine. I've got a variable number of sensor inputs, and I'd like to create a MLPRegressor that can estimate outputs from the input ...
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What is the difference between keras tuned hyperparameters and manually defined Sequential model with same hyperparameters?

I have a dataset that I divided into 10 splits of training, validation and test sets for a regression problem. I used the first split and RandomSearch in ...
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Can a multilayer perceptron classify binary values?

I have a dataset in which the response variable is Sick(1) or not sick (2). As for the variables, there are a few numeric ones (2/14), all the others are variables by levels (example: 1-Abdominal pain,...
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Inbetween CNN and MLP: neural network architecture for "close to convolutional" problem?

I am looking to approximate an (expensive to calculate precisely) forward problem using a NN. Input and output are vectors of identical length. Although not linear, the output somewhat resembles a ...
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Should I normalise or detrend time series data before creating MLP models

Am building MLP models on forecasting timeseries data. Am new in the field of machine learning and I have read about Detrending and normalisation. So which method (normalisation or detrending) will be ...
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How to improve the learning rate of an MLP for regression when tanh is used with the Adam solver as an activation function?

I'm trying to use an MLP to approximate a smooth function f : R^3 -> R, that takes a point in space as an argument, and returns a scalar value. The MLP architecture has a 3-dimensional (for 3 point ...
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Linear regression with Pytorch not converging

I am trying to perform a simple linear regression using Pytorch lightning (a network with only one neuron). The network is supposed to learn a simple function: y=-4x...
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What does "expansion layer" mean?

Recently, I found "expansion layer" term in the next paper: Liu, Ze, et al. "Swin transformer: Hierarchical vision transformer using shifted windows." arXiv preprint arXiv:2103....
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Is it possible to see the output for each layer in a mlp? using SKLearn

I have a simple NN which I have made with SKLearn. I have extracted: The weights sent to each node The bias assigned to each activation function But I can't see a way to get the output of the ...
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Why there is a marked difference in metric scores using linear regression or MLP as readout for echo state network?

I am using a reservoir computing architecture comprising of an echo state network as per the paper Reservoir Computing Approaches for Representation and Classification of Multivariate Time Series ...
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Improve model accuracy in multi-classification problem

I use a MLP to classify three different classes A, B, C. The loss function I use is categorical cross entropy and the optimiser ...
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Testing a Binary Classifier

I have been training a binary multilayer perceptron on a database made out of roughly 3600 0 values, and 4 1 values. Afterwards, I'm testing the MLP on a test set made out of 7 0 values and 7 1 ...
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Is it possible that MLP has better accuracy than CNN?

I am working on the epilepsy classification system which consumes EEG signals and in the result says if withing the certain period is a seizure or not. I take an advantage of Keras API for the sake of ...
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Predicting Y Values Properly in a Regression Task using Scaled Values (Random Forest & MLP)

I have a supervised learning regression task: I am trying to forecast demand for a product based on sales in past years. Data description: Samples (rows) - Demand for a certain product (at a certain ...
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Multiple solutions with same minima in MLP with same weights

I came across an excercise on deep learning from here. It goes as follows: Consider a simple MLP with a single hidden layer of $d$ dimensions in the hidden layer and a single output. Show that for any ...
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Developing Custom MLP for Mnist

I am trying to develop a custom MLP for MNIST Dataset with 2 hidden fully connected layers Mnist: 28281 FC1: input 28*28, output 512 FC2: input 512, output 128 Classifier: FC: input 128, output 10 I ...
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SHAP for Deep Neural Network taking long time

I have 60,000 samples with each having 1,800 features. I have made a multilayer perceptron in Keras and I want to use SHAP values to arrive at global feature importance. Is the matrix too big for SHAP?...
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avoiding premature convergence with neural networks (EA's)

I am currently writing a program that would be able to play snake on an 25*25 grid. It works by optimizing a set of weights of 300 different solutions (each solution would be a different neural ...
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Why does the MAE still remain, at all?

This may seem to be a silly question. But I just wonder why the MAE doesn't reduce to values close to 0. It's the result of an MLP with 2 hidden layers and 6 neurons per hidden layer, trying to ...
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Why we use an activation function for introducing nonlinearity instead of a polynomial Perceptron implementation?

I perceive a single perceptron as a single linear function $y = a_1x_1 + a_2x_2 + ... + a_nx_n + b_0$ with a goal to calculate the best weights combination $ w_1, w_2, ..., w_n $ that minimizes the ...
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Is a multi-layer perceptron exactly the same as a simple fully connected neural network?

I've been learning a little about StyleGans lately and somebody told me that a Multi-Layer Perceptron, MLP, is used in parts of the architecture for transforming noise. When I saw this person's code, ...
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Input changing row count matrices in an MLP

I want to input a numpy 2d array into MLP but I have an array of 50395 rows that contains many 2d array of shape (x, 129). x ...
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1 answer
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What are the differences between MLP and DNN?

So I have been reading about the topic for a while, but i did not find a clear answer why MLP and DNN are being used interchangeably even though there are some differences between them. So far I have ...
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Lower training accuracy than testing accuracy (MLP/Dropout)

I am working on a problem of multi-class classification by MLP. I have set dropout to each middle layer. Now I observe the training accuracy is around 10% less than ...
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model tuning by using loss curves

I have been practicing with the following dataset: http://archive.ics.uci.edu/ml/datasets/Concrete+Compressive+Strength for building a prediction model based on a MLP, but I have some doubts if the ...
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Neural Network regression negative performance

I have a problem with the performance of a multi layer perceptron regressor (neural network) and I cannot figure out why. Task: I am trying to improve a time series prediction. I have predictions of a ...
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How to structure the output layer of an MLP that finds the quadrant of an arbitrary point in a rectangle?

I'm trying to write a neural network that outputs the quadrant of a rectangle that an arbitrary point lies in. This rectangle has its upper left at {0, 0} and its lower right at {1, 1} (e.g. point {0....
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implementing forward and backward of a Linear model

I'm implementing the code of this abstraction. The forward is easy and looks like that: I don't understand the backward path and how it fit's the abstraction in the first image: Why is db defined as ...
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MLP classifier Gridsearch CV parameters to tune?

I'm looking to tune the parameters for sklearn's MLP classifier but don't know which to tune/how many options to give them? Example is learning rate. should i give it[.0001,.001,.01,.1,.2,.3]? or is ...
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Feature scaling for MLP neural network sklearn

I am working with a dataset where the features have multiple scales. Before running scikit-learns's MLP neural network I was reading around and found a variety of different opinions for feature ...
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Training loss stuck in the starting epochs but then starts decreasing. What could be the reason for it?

I am training a model where I found a unique problem that for starting 4 epochs, my loss did not change with the epochs but after that, it started changing. Could it be because of the high learning ...
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Are weights of a neural network reset between epochs?

If an epoch is defined as the neural network training process after seeing the whole training data once. How is it that when starting the next epoch, the loss is almost always smaller than the first ...
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(De-)Scaling/normalizing input and output data inside Keras model as layer

I am building a 2-hidden layer MLP using Keras. I'm using a SciKit learn wrapper to be able to use the GridSearchCV functionality. My sample-size is limited, ...
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2 votes
1 answer
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SciKit Learn: Multilayer perceptron early stopping, restore best weights

In the SciKit documentation of the MLP classifier, there is the early_stopping flag which allows to stop the learning if there is not any improvement in several ...
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PyTorch MultiLayer Perceptron Classification Size of Features vs Labels Wrong

I am getting the following error: ...
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Which MSE (total or individual) back-propagate for multi out regression neural network

When we have multi output regression neural network, we can calculate total MSE and individual MSE per output. How this MSE should back-propagate ? Shouldn't we back-propagate individual MSE through ...
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Structuring experiment/training data with months in mind

We're using a whole year's data to predict a certain target variable.The model works like data - OneHot encoding the categorical variables - MinMaxScaler - PCA (to choose a subset of 2000 components ...
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LabelEncoder with a Multi-Layer Perceptron?

So we're working on a machine learning project at work and it's the first time I'm working with an actual team on this. I got pretty good results with a model that uses the following SKLearn pipeline: ...
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Logistic Regression outperforms MLPClassifier

I am new in ML and I am trying to train classifier. I have a tiny dataset, just 90 examples, I divided it 70/30 train/test set and started to train. As I know MLP must outperform Logistic Regression, ...
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3 votes
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Understanding computations of Perceptron and Multi-Layer Perceptrons on Geometric level

I am currently watching amazing Deep Learning lecture series from Carnegie Melllon University, but I am having little bit of trouble understanding how Perceptrons and MLP are making their decisions on ...
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Is it possible to decompose a scalar value to a inter-dependent vector neural network?

My data contains a scalar feature $r$, I found this feature is important for training my deep model. My idea is supposing there is a 3-layer MLP $f(x), x \in \mathbb{R}^{n}$, where $n=1$. It outputs a ...
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1 vote
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Different hidden layer architectures deliver the same classification results, is that normal?

I have a data set with 600 data points with about 10 attributes (binary). The dataset has been normalized: Xnormalized = StandardScaler().fit_transform(X) The ...
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can i get weights per iteration of MLP?

im building an mlp with scikit learn. Is there a way I can access weights and biases of the output layer per iteration? There is an option mlp.coefs_ But it ...
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2 votes
2 answers
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how to access weights of individual Neurons in the output layers in MLPs?

im working on a neural network using Keras. Its an mlp(multi-layer perceptron). With 8 Neurons in the output layer. Is there a way I can access weights and biases of individual neurons of the output ...
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2 votes
1 answer
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Scikit model is not able to predict sequence correctly

I am trying to create a regression model using scikit-learn for predicting car price. The input data are, car model(trim), kilometers used, past resale price of similar car and age of used car. I am ...
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2 votes
3 answers
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About different structures of neural network

https://www.mathworks.com/help/deeplearning/ref/fitnet.html is the tutorial that I am following to understand fitting data to a function. I have few doubts regarding structure and terminologies which ...
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Conceptual questions on MLP and Perceptrons

I am facing some confusion regarding the terminologies assocaiated to classification and regression problems esp. using the MLP and Perceptron models. These are the following: 1) When the data is ...
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Help with MLP convergence

I posted this question on AI SE and got advised to ask here for guidance. I've been stuck for a couple of days trying to figure it out how the standard MLP works and why my code doesn't converge at ...
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How to improve the accuracy of my MLP (Current benchmark 77%)

My MLP code: ...
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