All Questions
Tagged with perceptron weights
10 questions
4
votes
1
answer
172
views
How do I check if the weights of my perceptron/step activation function are correct
I am new to stack overflow and deep learning so I hope I am doing this the right way. I tried to find the solution myself but it has not been successful so I am seeking some help. This is the ...
- 43
1
vote
0
answers
76
views
Differentiating output of layer with respect to its input [duplicate]
Say we have a relationship $ z = Wx$ for a multi layer perceptron where $z$ and $x$ are $n$ dimensional vectors. When we find $\frac{dz}{dx}$ , I would assume this would just be $W$, not $W^T$.
I was ...
- 11
1
vote
1
answer
708
views
Rank of gradient-of-loss with respect to layer weights in an MLP
The paper: https://arxiv.org/abs/2110.11309, makes the following claim at the end of page 3:
The gradient of loss $L$ with respect to weights $W_l$ of an MLP is a rank-1 matrix for each of B batch ...
- 13
1
vote
1
answer
944
views
Kernelized perceptron algorithm weights update
I'm asked to find the maximum margin decision surface separating positive from negative samples by inspection. The positive examples are (1,1) and (-1,-1), the negative ones are (1,-1) and (-1,1). The ...
- 45
2
votes
1
answer
885
views
Why the weight vector is a linear combination of the inputs and the outputs in the Perceptron
I was studying Support Vector Machines and I've got stuck with this relation regarding the weight vector of the hyperplane.
$w=\sum\limits_{i\in I}^{} y_i x_i$
For reference, I'm studying from the ...
- 45
0
votes
1
answer
244
views
Weight of MLP is larger than 1
I noticed when training MLP that weights of neurons can be larger than 1. Would this have negative effects on the outcome of the network? If yes, how to mitigate this problem?
- 61
5
votes
1
answer
794
views
Why do nodes in hidden layer produce different results?
Assuming a simple, fully connected Multilayer Perceptron network with one input layer, one hidden layer with multiple nodes and one output layer. In this case the nodes in hidden layer are ...
- 450
2
votes
1
answer
3k
views
Question regarding weight update rule in Perceptron
In the single-layer perceptron, the weight update rule is given by:-
$w_j := w_j + (y^{(i)} - \hat{y^{(i)}}) \times x_j^{(i)}$,
where $w_j$ is the weight of the $j$th feature, $y^{(i)}$ is the ...
- 133
0
votes
1
answer
2k
views
Initialization of perceptron weights
In this deck about the perceptron, there's a pseudo-code for the batch version on page 12. During each iteration of the inner loop, the dot product $y_ix_i\theta$ is compared to 0, however since the ...
- 219
2
votes
1
answer
229
views
Combine two single-level perceptrons
Suppose we have a training dataset with $s$ instances, and we split it to 2 sets, of lengths $n, m$ such that $s = n + m$ . We train two perceptron models $p$ and $q$, both are defined by a k-...
- 219