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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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18 views

Probability distribution for lstm and mlp

I have total of 6300 samples, 5800 of which are training data, and 500 of which are testing data. We compare the performance of LSTM and multilayer perceptron (MLP) with one hidden layer in terms of ...
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5 views

MLPclassifier on text and categorical features don't learn, is there a normalisation step missing?

I trained MLPclassifier with 'lbfgs' solver model for document multilabel classification. The data are separated on train and test with 'iterative_train_test_split' from 'skmultilearn.model_selection'....
0 votes
1 answer
32 views

Difference between LSTM and MLP

My teacher said me that: We compare the performance of LSTM and multilayer perceptron (MLP) with one hidden layer in terms of training process, prediction accuracy and learning ability. Anybody ...
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1 answer
1k views

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 ...
1 vote
1 answer
237 views

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 ...
2 votes
1 answer
3k views

Multilayer perceptron does not converge

I have been coding my own multi layer perceptron in MATLAB and it compiles without error. My training data features, x, has values from 1 to 360, and the training data output, y, has the value of $\...
3 votes
1 answer
252 views

What are the key differences between a MLP with lagged features and a RNN

I've been working with multilayer perceptron (MLP) for a while. Whenever I assumed that the past values of a feature might be useful for predicting the future values of y, I would just create a new ...
3 votes
1 answer
539 views

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 ...
2 votes
2 answers
5k views

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 ...
1 vote
1 answer
94 views

How to Improve MLP ANN accuracy

I am trying to improve the accuracy of my model over the UCI Breast Cancer Dataset. There's 426 records, and it is a binary classification model. ...
0 votes
1 answer
37 views

My custom neural network is converging but keras model not

in most cases it is probably the other way round but... I have implemented a basic MLP neural network structure with backpropagation. My data is just a shifted quadratic function with 100 samples. I ...
0 votes
1 answer
188 views

Scikit-learn and TensorFlow with very different MLP models

I'm using Multilayer Perceptron ANNs at the very beginning of my project (it's a binary classification problem). Because it's simpler, I started with Scikit-learn. I got a magic result, with my model ...
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21 views

Mean Square Error not decreasing during gradient descent

I am currently writing a program which is supposed to implement gradient descent to train a prediction model. I am encountering an error whereby my MSE continuously increases, and the network never ...
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0 answers
16 views

XAI model to evaluate MLP

I need to build a XAI model and I don't know where to begin. I have seen different algorithms but can't think of how to create a model with them.Could anyone help me?
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16 views

eXplainable Artificial Intelligence (XAI). Need help building a XAI model to explain the results of an IDS classifier

I need some help building a XAI model with Keras to explain the results of an MLP working as an IDS. I have resarched about XAI but the only thing I find is small portions of code that just use ...
2 votes
3 answers
2k views

Why might a neural network consistently underestimate its target?

I have a neural network (MLP) that is consistently underestimating the target variable on the validation set, test set, and on the training set (by about the same amount as on the validation set and ...
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23 views

Is it possible okay to use regression MLP for ordinal classification problem when target variable is numerical?

I have a target variable of 1-10 that represent difficulty level. These are individual classes represented by integers with 1 being the easiest and 10 most difficult. I have decided to use regression ...
1 vote
1 answer
44 views

Finding parameter combinations for zero gradients in an artificial neural network

Consider the following network: There are two weights, say $w_1$ and $w_2$, and two biases, $b_1$ and $b_2$. The hidden layer has a ReLU activation function $g^{(1)}$ and the output layer has a ...
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1 answer
39 views

What is the highest possible prediction accuracy when I flip some labels at random?

I want to predict MNIST labels in a binary setting using a simple MLP model (0 for digits 0-4 and 1 for 5-9). For the train and test data, I randomly flip 25% of the labels. Is the maximum achievable ...
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65 views

Need insights in how to reduce overfitting with MLPClassifier

I am new to data science. Please bear with me as I ask this long question. I am trying to do Speech Emotion Recognition with MLPCLassifier on RAVDESS and Crema datasets. I am predicting only three ...
1 vote
2 answers
1k views

How to obtain with a RRN a version of a temporal XOR function using keras/tensorflow? [closed]

I'm trying to implement a model of a recurrent neural network to solve a temporal version of the XOR problem, but I am not still able to do that. Any hints?
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0 answers
21 views

classification with rejection and how to interpret a 2D ambiguous data

Let's say we're going to train a classifier with the full data set. There's also a reject logic for ambiguous regions in the data. So, at the end, the final system outputs reject or 0 or 1. That is, ...
1 vote
2 answers
193 views

Neural network not learning at all

I am training a MLP on a tabular dataset, the pendigits dataset. Problem is that training loss and accuracy are more or less stable, while validation and test loss and accuracy are completely constant....
1 vote
1 answer
440 views

How to determine which combinations of parameters to include in GridSearchCV

I am using MLPClassifier from sklearn and I would like to tune it with GridSearchCV. But I don't know which set of values to include for hidden_layer_sizes, max_iter, activation, solver, etc. How can ...
3 votes
1 answer
4k views

How can I Implement Dropout in SciKit-Learn?

I am working on an air-gapped PC and can only access the SciKit-Learn machine learning library. Unfortunately, its MLPClassifier doesn't include a Dropout ...
1 vote
1 answer
3k views

Failed to convert a NumPy array to a Tensor (Unsupported object type float) in Python

I am trying to build a MLP with Keras and an error appears. I do not have experience with neural networks so it is difficult for me. When I run the code for the NN after some time it says: ...
4 votes
4 answers
3k views

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 ...
1 vote
0 answers
32 views

Loss is very erratic in the 100s and val_loss is at 0, something - what is the reason for that?

I have a problem. I would like to solve a NLP classification problem. For this I have trained a CNN and since I have other features, I wanted to include them in the model training. Thus I have ...
22 votes
2 answers
99k views

How to adjust the hyperparameters of MLP classifier to get more perfect performance

I am just getting touch with Multi-layer Perceptron. And, I got this accuracy when classifying the DEAP data with MLP. However, I have no idea how to adjust the hyperparameters for improving the ...
0 votes
1 answer
342 views

Can MLP model sequential data?

When modeling sequential data, RNNs are introduced as an improvement of MLP as they can model the time dependency between the inputs. It is said that feeding the last N data points in the sequence to ...
0 votes
1 answer
56 views

Will summing features improve the Machine Learning models?

Assuming that I have two features, x and y for an MLP model. I know that depending on the model, the multiplication of features ...
0 votes
1 answer
116 views

Spot Logistic Regression Training Error

My friend gave me this puzzle awhile ago and I've never figured it out. ...
1 vote
1 answer
1k views

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....
0 votes
1 answer
36 views

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 ...
1 vote
1 answer
93 views

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 ...
0 votes
1 answer
468 views

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,...
2 votes
0 answers
591 views

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...
0 votes
0 answers
26 views

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 ...
0 votes
1 answer
159 views

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 ...
1 vote
0 answers
28 views

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 ...
0 votes
0 answers
515 views

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 ...
2 votes
1 answer
37 views

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 ...
1 vote
0 answers
31 views

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 ...
0 votes
1 answer
505 views

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 ...
0 votes
0 answers
51 views

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 ...
0 votes
1 answer
326 views

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 ...
1 vote
0 answers
26 views

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 ...
1 vote
0 answers
310 views

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?...
2 votes
1 answer
164 views

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 ...
3 votes
1 answer
405 views

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 ...