A single hidden layered NN but the weight matrix is a diagonal matrix (with a bias) -- how to design that?

3 次查看（过去 30 天）
Shashi Kant
Shashi Kant 2020-4-30
评论： Shashi Kant 2020-5-29
I am wondering if I can create a single hidden layered NN but the weight matrix is a diagonal matrix (with a bias). How to design that?
---------------------------------------------------------
Background
Let's say that a single layered hidden model (with m neurons) is given as
, where
  • is input vector,
  • is a first weight matrix connecting input with the hidden layer with m nodes,
  • is a bias
  • is an activation function which operates component wise, say $\tanh$
  • is second weight matrix connecting the hidden layer with the output,
  • is a bias, and
  • is an output vector.
My model wants the weight matrices and as only diagonal matrices. How to do that?

请先登录，再回答此问题。

采纳的回答

Srivardhan Gadila
Refer to Weight and Bias Values, Input Weights, Layer Weights & net.layerWeights{i,j}.learn.
You can access the layer weights as follows:
net.LW{i,j}
You can set any values to the above weights and set the net.layerWeights{i,j}.learn to 0 so that the weights won't be altered during the training & adaption. In this case setting a specific weight for a connection is not possible since the property net.layerWeights{i,j}.learn is defined for the entire connections between layers i and j.
net.layerWeights{i,j}.learn = 0
net.LW{i,j} = ones(size(net.LW{i,j})) % any weights of size(net.LW{i,j})
If your network architecture is defined and trained already:
Then you can set weight of a connection between nodes k & l of layers i & j as follows:
net.LW{i,j}(k,l) = 1
and then use the network.
The above things can be done to Input wieghts too.
  4 个评论
显示 2更早的评论
Srivardhan Gadila
Srivardhan Gadila 2020-5-29
编辑：Srivardhan Gadila 2020-5-29
@Shashi Kant, as per my knowledge on shallow neural networks:
  1. I listed the possible things you can do w.r.t the weights of layers of shallow nerual networks in the Answer.
  2. The property net.layerWeights{i,j}.learn is defined for the entire connections between layers i and j hence you cannot set the diagonal weights to learn only & non-diagonal weights to not learn.
  3. You can instead define custom Deep Learning layer to achieve your functionality. Refer to Define Custom Deep Learning Layers & Define Custom Deep Learning Layer with Learnable Parameters
Shashi Kant
Shashi Kant 2020-5-29
@Srivardhan Gadila Thank you for your suggestion. I will look into that. I am not very familar with the deep learning toolbox. But I will dig into it.

请先登录，再进行评论。

更多回答（0 个）

类别

在 Help Center 和 File Exchange 中查找有关 Define Shallow Neural Network Architectures 的更多信息

标签

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by