Matrix size of layer weights in neural network(Er​ror:net.LW​{2,1} must be a 0-by-3 matrix.)

Hello, I had a Elman network with 3 hidden layers, 3 input layers and 1 output layer. I try to initial the hidden layer weights with a decent trained result which is saved before. But when I re-input the hidden layer weights into the network. Errors always bumped out. Error:net.LW{2,1} must be a 0-by-3 matrix. The error also happened when I valued the bias and input layer weights. I have two questions: (1) What does "0-by-3 matrix" mean? Does it mean the matrix should be a empty matrix? (2) I got the hidden layer weights, input layer weights and bias from the trainning result. Why it doesn't work when I re-input them into the network? They were saved in ".mat" format. They were all "cell" structure.

    *%Code for saving the trainning result
    iw = net.iw;  % "iw" is variable of input layer weight
    lw = net.lw;  % "lw" is variable of hidden layer weight
    theta = net.b;  % "theta" is variable of bias

    *%%Code for re-input the result
    net.b{1} = theta{1,1}; 
    net.lw{2,1} = lw{2,1};
    net.iw{1,1} = iw{1,1};
    net.b{2} = theta{2,1};

>> net.lw{2,1}

ans =

   Empty matrix: 0-by-3

>> lw{2,1} % saved trainning result of hidden layer weight (variable "lw")

ans =

   -1.3208   -1.4288   -0.7487

>> net.iw{1,1}

ans =

   Empty matrix: 3-by-0

>> iw{1,1} % saved trainning result of input layer weight (variable "iw")

ans =

    0.7936    0.7638    0.6892
   -0.0306   -0.3250   -0.0438
    0.8408   -1.0601   -0.9286

*
    Error: network/subsasgn>network_subsasgn (line 553)
    net.LW{2,1} must be a 0-by-3 matrix.
    Error: network/subsasgn (line 13)
    net = network_subsasgn(net,subscripts,v,netname);
    Error: elman_PepperHTHS_v2 (line 65)
    net.lw{2,1} = lw{2,1};
*

I can value net.b{1} successfuly but not the other three. That confused me a lot. If it could be answered, that would be appreciated so much.

Answer 1

[Greg Heath]

Hello, I had a Elman network with 3 hidden layers, 3 input layers and 1 output layer.

This makes no sense. Do you mean

Hello, I had a Elman network with 3 hidden nodes, 3 input nodes and 1 output node?

In addition, you never defined "net"

You'd be better off posting your code rather than trying to explain it.

Hope this helps.

Greg