Currently, I am implementing artificial neural networks by myself.
I have done to implement multi-layer perceptron and some experiments.
And I noticed that some weights exceed bounds between [-1, 1].
Should I limit this weight between [-1, 1]?
Thank you very much in advance.
:)
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I mean, should I adjust the bias the way weights are?
Yes, the bias is part of the trainable parameters and should change during training.
Consider this Neural Network:
My Neural Network
the numbers which are on connections in this Neural Network, show the connection weights and numbers on each node (neuron) are thresholds of hard-lim transfer function.
Now here are my two questions:
First: for implementing this neural network I used below code, but I couldn't specify connection weights. How can I specify them?
net=feedforwardnet([2,4]);
net.numInputs=2;
net.inputConnect=[1 1;0 0;0 0];
net.inputs{1}.size=4;
net.inputs{2}.size=4;
net.layers{3}.size=2
view(net)
Second: (x1,x2) is the coordinate of input dots and this Neural Network specify if this dot belong to s1 or s2. For example:
(3,3)∈s2
(0,1)∈s2
(1,3)∈s1
(4,1)∈s1
now how should I train this neural network for this kind of inputs and targets?
(I use MATLAB R2014a and am a little new in MATLAB Neural Network, so please be kind)
Any suggestion will help so much. Thanks in advance.
We have weights and optimizer in the neural network.
Why cant we just W * input then apply activation, estimate loss and minimize it?
Why do we need to do W * input + b?
Thanks for your answer!
There are two ways to think about why biases are useful in neural nets. The first is conceptual, and the second is mathematical.
Neural nets are loosely inspired by biological neurons. The basic idea is that human neurons take a bunch of inputs and "add" them together. If the sum of the inputs is greater than some threshold, then the neuron will "fire" (produce an output that goes to other neurons). This threshold is essentially the same thing as a bias. So, in this way, the bias in artificial neural nets helps to replicate the behavior of real, human neurons.
Another way to think about biases is simply by considering any linear function, y = mx + b. Let's say you are using y to approximate some linear function z. If z has a non-zero z-intercept, and you have no bias in the equation for y (i.e. y = mx), then y can never perfectly fit z. Similarly, if the neurons in your network have no bias terms, then it can be harder for your network to approximate some functions.
All that said, you don't "need" biases in neural nets--and, indeed, recent developments (like batch normalization) have made biases less frequent in convolutional neural nets.
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 training a neural network and it stopped training due to the gradient stopping condition. From what I can see the gradient 8.14e-0.6 is larger than minimum gradient 1e-0.5, so why did it stop? Is it because the gradient wasn't improving so there was little point continuing?
I am very new to neural networks (and using MATLAB's nntool) so any help/explanation would be much appreciated.
This is not a neural network problem, it is a problem of understanding floating point representations:
8.14e-06 = 8.14×10^−6 = 0.00000814 < 0.00001 = 1.0x10^-5 = 1e-05