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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Why does MLPClassifier only give linear decision boundaries?
I am trying to learn about the MLPClassifier and see how it fits when I use different activation functions. Unexpectedly, with any activation function other than ReLU, I can only get linear decision ...
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Probability distribution for lstm and mlp [closed]
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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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'....
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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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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 ...
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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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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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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 ...
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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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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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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, ...
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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....
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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 ...
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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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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 ...
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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:
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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 ...
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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.
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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 ...
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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 ...
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Spot Logistic Regression Training Error
My friend gave me this puzzle awhile ago and I've never figured it out.
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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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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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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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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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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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