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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1answer
260 views

Why would one crossvalidate the random state number?

Still learning about machine learning, I've stumbled across a kaggle (link), which I cannot understand. Here are lines 72 and 73: ...
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113 views

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

I've been working with MLP's 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 column in my data frame ...
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1answer
62 views

Why is my MLP with 2 features is doing worse than MLP with 1 feature where the one feature is a combination of feature1*feature2?

I have programmed a MLP for a dataset (~500 rows) containing the length (L) and width (W) of an organism and the output of biomass (the organisms weight in pounds, B). ...
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104 views

Deep learning(MLP) on multiclass classification. Model learns only one class

I am new to deep learning. I have imbalanced class data. I used one hot encoding and scaling to preprocess my data. I have used adamoptimizer as optimizer function and sparse categorical crossentropy ...
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2answers
352 views

Why is the reported loss different from the mean squared error calculated on the train data?

Why the loss in this code is not equal to the mean squared error in the training data? It should be equal because I set alpha =0 , therefore there is no regularization. ...
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1answer
157 views

How Does Sci-Kit Learn Train Regression Neural Networks (MLPRegressor) So Fast?

Why does using the scikit-learn library's MLPRegressor result in such a boost in training time when compared to constructing the network from scratch? I tried both methods and I found that writing the ...
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1answer
701 views

IN CIFAR 10 DATASET

After building up the mlp using ...
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2answers
180 views

Input data of variable length - two scenarios

I'm trying to figure out how I could train a neural network with inputs that have variable length. This issue comes up in the following 2 scenarios I'm trying to solve. Scenario 1: I have a long list ...
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1answer
92 views

How to utilize user feedback due to miss-classification when correct class label is unknown?

Suppose we are developing an app which is supposed to predict a dog's breed by it's picture. We trained a classifier (in my case an MLP) using some dataset and shipped the app to users. Now suppose ...
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1answer
158 views

Interpreting MLP output

I just wrote an MLP in Python. After having trained it, I pass in some test data to see the result, and I get an array of decimal numbers at the output, rather than the desired binary output. For ...
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1answer
54 views

Coding MLP: good practices?

I recently finished coding my own MLP neural network in Python. To make my code easier to read, I separated the MLP, into classes; the network class, the layers class and the neuron class, where the ...
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4answers
144 views

How to handle features which are not always available?

I have a feature in my feature vector that is not always available respectively sometimes (for some samples) it makes no sense to use it. I feed a sklearn MLPClassifier with this feature vector. Does ...
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2answers
12k views

How do I get the feature importace for a MLPClassifier?

I use the MLPClassifier from scikit learn. I have about 20 features. Is there a scikit method to get the feature importance? I found clf.feature_importances_ but it seems that it only exists for ...
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1answer
198 views

Can a neural network recognize a letter B as an A if your trained it so?

You have a neural network. And you have, say, pictures of $100,000$ hand-written letters (A-Z). Now you make a typical Training and the neural network will recognize an A as an A, a B as a B, ... Now ...
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3answers
519 views

One Hot Encoding of Age

My task is to predict how many years a person has left to live using an MLP. There is one specific feature I'd like to discuss: current age. Statistically, it's a conditional probability. Example: ...
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2answers
3k views

Adding more layers decreases accuracy

I have my ANN trained on MNIST dataset. Hidden layer has 128 neurons and input layer has 784 neurons. This gave me an accuracy of 94%. However when I added one more layer with 64 neurons in each then ...
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1answer
243 views

Validation loss differs on GPU vs CPU

I am consistently seeing higher validation loss when I train & evaluate a model on AWS GPU vs local CPU. I am using the exact same train/eval datasets and the exact same Tensorflow code and ...
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2answers
947 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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1answer
1k views

multilayer perceptron do not converge

I have been coding my own multi layer perceptron in MATLAB and it can be compiled without error. My training data features,x, has values from 1 to 360, and training data output, y, has the value of ...
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2answers
1k views

What is the difference between multi-layer perceptron and generalized feed forward neural network?

I'm reading this paper:An artificial neural network model for rainfall forecasting in Bangkok, Thailand. The author created 6 models, 2 of which have the following architecture: model B: ...
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1answer
603 views

Is the multilayer perceptron only able to accept 1d vector of input data? If yes, why is this so?

I am going through the tutorial at the link below which uses MNIST handwritten digit database. https://machinelearningmastery.com/handwritten-digit-recognition-using-convolutional-neural-networks-...
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2answers
59k 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 ...
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2answers
675 views

MLP conv layers

When should MLP conv layers be used instead of normal conv layers? Is there a consensus? Or is it the norm to try both and see which one performs better? I would love to better understand the ...
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1answer
42 views

Alternatives to regression to decide weights in an expression

I have a use case in which I am required to predict variable $y$ which depends on five variables $x_i$. Consider something like $$ y=w_1 x_1+ w_2 x_2+ w_3 x_3+ w_4 x_4+ w_5 x_5.$$ This expression ...
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3answers
1k views

More layers in NN give worse result

So I was working on a classification task with the help of a NN. The data-set was normalised, weights random between 0-1, and all the activations were sigmoid ...
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3answers
321 views

How each layer of a neural net is responsible for one feature

Through my study of neural networks, I came across the idea that each layer of a neural network is responsible for recognizing one feature of the input data. For example, if we build a neural network ...
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2answers
6k views

MLPRegressor Output Range

I am using Scikit's MLPRegressor for a timeseries prediction task. My data is scaled between 0 and 1 using the MinMaxScaler ...
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2answers
115 views

Data pre-processing before feeding into a deep learning model

Generally speaking, when training a deep learning model, like MLP, what kind of data pre-processing operation has to be conducted when the input is a numerical sequence.
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2answers
160 views

Neural Network Hidden Layer Selection

I am trying to build an MLP classifier model on a dataset containing 30000 samples and 23 features. What are the standards I need to consider while selecting the ...
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1answer
4k views

number of neurons for mnist dataset using mlp?

I am trying to find out what is optimum number of neurons that can be used in MNIST dataset(60,000 training and 10,000 testing data). I build a single hidden layer model using keras,with relu ...
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1answer
653 views

Polynomial regression vs. multilayer perceptron [closed]

Polynomial regression and multilayer perceptrons have different structures and different learning procedures. What are these two algorithms pros and cons? Are there some situations where one should ...
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2answers
800 views

How to obtain with a recurrent neural network the Xor function using keras? [closed]

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

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