MATLAB: Is the “patternnet” a fully connected neural network

MATLABnnstart patternnet neural networks

I am using thi "nnstart" Neural network tool box which is descriped here :

https://uk.mathworks.com/help/deeplearning/gs/classify-patterns-with-a-neural-network.html

I am using "patternnet" network. My question is: is the "patternnet" network a fully connected neural network?

Which means every neuron in the input layer is connected to every neuron in the hidden layers, also every neuron in the hidden layer is connected to every neuron in the output layer.

Best Answer

Yes.

The only difference between my classifiers and regressors is the sigmoid output layer instead of linear.

Hope this helps.

Thank you for formally accepting my answer

Greg

Related Solutions

MATLAB: How to train a feedfordward neural network with error weights

 Use the command window
 help mse
 doc mse

Hope this helps.

Thank you for formally accepting my answer

Greg

MATLAB: Can “feedforwardnet” and “patternnet” accept images (2-D or 3-D) as inputs

The input data for training classical neural networks is expected to be an R x Q dimensional matrix (or a cell array of R x Q matrices), where Q is the batch size or number of observations and R is the number of variables. So "feedforwardnet" and "patternnet" can only accept inputs that are each 1D.

I recommend you try our deep learning functionalities. Deep learning networks are now the state of the art for image classification/regression (as opposed to classical/shallow neural nets such as patternnet and feedforwardnet) and have also been developed with good GPU performance in mind. Note that are our deep learning networks currently only support 2D data (or 3D data reshaped into 2D). Here are some links with information on our deep learning:

https://www.mathworks.com/solutions/deep-learning.html

https://www.mathworks.com/help/nnet/deep-learning-image-classification.html

https://www.mathworks.com/help/nnet/ug/deep-learning-in-matlab.html

https://www.mathworks.com/help/nnet/ref/regressionlayer.html

Depending on the data, the best option might be to use convolutional neural networks (CNNs). These networks are designed to model spatial dependencies between variables/features and are the state-of-the-art for tasks such as image classification.

Please see here how to create a simple classification deep learning network for 2-D image data:

https://www.mathworks.com/help/nnet/examples/create-simple-deep-learning-network-for-classification.html

Best Answer

Related Solutions

MATLAB: How to train a feedfordward neural network with error weights

MATLAB: Can “feedforwardnet” and “patternnet” accept images (2-D or 3-D) as inputs

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