If there is a forth channel of some constant values (ones, for instance) in an RGB image, will a neural network learn to nullify the weights related to this channel? Or will it leave the weights as is and adjust a bias? What about a column of constant values in some tabular data? Will it affect a neural network in some way?
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In a neural network, for an intermediate layer, I need to threshold the output. The output of each neuron in the layer is a real value, but I need to binarize it (to 0 or 1). But with hard thresholding, backpropagation won't work. Is there a way to achieve this?
Details:
I have a GAN kind of network i.e. there are 2 neural networks trained end-to-end. The output of first neural network is real values. I need them to be binary values. I read that Gumbel Softmax (Categorical Reparameterization) is used to handle discrete variables in a neural network. Is there a way to use that for my use-case? If yes, how? If not, is there any other way?
From what I could gather in internet is that Gumbel is a probability distribution. Using that we can generate a discrete distribution. But for use-case, I need a function that can take a real input and output a binary value. So, I need an activation function of that form. How can I achieve that?
Thanks!
I would like to know what max pooling and mean pooling are for recurrent neural networks like LSTM while using them for sentiment analysis.
I think as far as I know we pooling is mostly used in convolution neural networks.
and it is a method of concentration of higher order matrix to lower order matrix which contains properties of inherent matrix...in pooling a matrix smaller size and is moved over the original matrix and max value or average value in smaller matrix is selected to form a new resultant matrix of further computation. link-https://machinelearningmastery.com/pooling-layers-for-convolutional-neural-networks/
I am making 8 x 8 tiles of Images and I want to train a RBF Neural Network in Matlab using those tiles as inputs. I understand that I can convert the matrix into a vector and use it. But is there a way to train them as matrices? (to preserve the locality) Or is there any other technique to solve this problem?
There is no way to use a matrix as an input to such a neural network, but anyway this won't change anything:
Assume you have any neural network with an image as input, one hidden layer, and the output layer. There will be one weight from every input pixel to every hidden unit. All weights are initialized randomly and then trained using backpropagation. The development of these weights does not depend on any local information - it only depends on the gradient of the output error with respect to the weight. Having a matrix input will therefore make no difference to having a vector input.
For example, you could make a vector out of the image, shuffle that vector in any way (as long as you do it the same way for all images) and the result would be (more or less, due to the random initialization) the same.
The way to handle local structures in the input data is using convolutional neural networks (CNN).
After training and testing a neural net on Matlab, I got a satisfactory Net-output.
The problem I am facing now is how to get the weights/bias distributed by the network, as well as the threshold, as I intend to use them on a different program.
I just need a guide on how to retrieve these values from the network
Thanks for your suggestions.
The weights are saved in the network class. The values are contained in
net.IW
net.LW
net.b
where net.IW contains the input weight values, net.LW contains the layer weight values and net.b contains the bias values.
To help you with the implementation of the neural network, you could use genFunction to create a MATLAB function for your neural network.
I have trained a 3-layer (input, hidden and output) feedforward neural network in Matlab. After training, I would like to simulate the trained network with an input test vector and obtain the response of the neurons of the hidden layer (not the final output layer). How can I go about doing this?
Additionally, after training a neural network, is it possible to "cut away" the final output layer and make the current hidden layer as the new output layer (for any future use)?
Extra-info: I'm building an autoencoder network.
The trained weights for a trained network are available in the net.LW property. You can use these weights to get the hidden layer outputs
From Matlab Documentation
nnproperty.net_LW
Neural network LW property.
NET.LW
This property defines the weight matrices of weights going to layers
from other layers. It is always an Nl x Nl cell array, where Nl is the
number of network layers (net.numLayers).
The weight matrix for the weight going to the ith layer from the jth
layer (or a null matrix []) is located at net.LW{i,j} if
net.layerConnect(i,j) is 1 (or 0).
The weight matrix has as many rows as the size of the layer it goes to
(net.layers{i}.size). It has as many columns as the product of the size
of the layer it comes from with the number of delays associated with the
weight:
net.layers{j}.size * length(net.layerWeights{i,j}.delays)
Addition to using input and layer weights and biases, you may add a output connect from desired layer (after training the network). I found it possible and easy but I didn't exam the correctness of it.