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Questions - mlp

What is the code for Logistic Regression and MLP

I am trying to use AutoSklearn with a specific list of algorithms Logistic Regression Random Forest Gaussian NB SVC ADA MLP I know I can use these parameters. mdl = autosklearn.classification.AutoSkl...

asmgx

python

scikit-learn

logistic-regression

mlp

auto-sklearn

Votes: 0

Answers: 1

Latest Answer

The documentation states that the strings used to identify estimators and preprocessors are the filenames without .py. You can find here the model_id you are looking for here. From the documentation M...

Antoine Dubuis

How to add two new dimensions to the input feature before the last two layers in MLP

Initially we had an MLP of multiple layers. We had an input embedding of 200 dimension. We now want to add two more dimensions to the original embedding to encode two important features. But as the or...

Leonard

machine-learning

deep-learning

pytorch

mlp

Votes: 0

Answers: 1

Latest Answer

You could simply create two input heads. One for the embedding, which goes through its own neural network and then one for the two features. The output of both networks are then simply concatinated an...

Theodor Peifer

MLP Classifier in SKlearn to include "softmax" activation

I am trying to fit MLP model but I would like to use 'SOFTMAX' as an output layer. Could you please let me know how can I incorporate the same?

Add

python

scikit-learn

deep-learning

neural-network

mlp

Votes: 0

Answers: 1

Latest Answer

"softmax can be used as an output function within sklearn as follow. mlp.out_activation_='softmax'

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How do I use a single neuron layer in the multiple layers of a multilayer network?

Is it conceptually wrong to put a layer of multiple neurons after a single layer of neurons? if yes, How do I use this single neuron layer in the multiple layers of a multilayer network? model = S...

zahra moradi

machine-learning

keras

neural-network

mlp

Votes: 0

Answers: 1

Latest Answer

It is definitely non standard to have any point in a deep neural network, where representation is collapsed to a single number. This creates an extreme information bottleneck, and thus, while in theor...

lejlot

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