% Since the initial weights and random data divisions depend on the RNG state, many more nets have to be designed before a definitive conclusion can be made about the ability of adapt to be used for this problem.
Question
I am trying to do adaptive training of a neural network using patternnet using all default settings, so the following is my complete code (except for array initialisation). The train code works, and is verified to produce accurate output : X is [101,12507] of training data, T is [133,12507] of classification data net = patternnet(101); net = train(net,X,T); If I try adaptive training with one sample : X is [101,1] of training data, T is [133,1] of classification data net = patternnet(101); net = adapt(net,X,T); I get the following error: Error using * Inner matrix dimensions must agree. Error in nn7.grad2 (line 108) gNi(:,qq) = Fdot{qq}' * gA{i}(:,qq); Error in adaptwb>adapt_network (line 100) [gB,gIW,gLW] = nn7.grad2(net,[],PD(:,:,ts),BZ,IWZ,LWZ,N,Ac(:,ts+AcInd),gE,Q,1,hints); Error in adaptwb (line 37) [out1,out2,out3] = adapt_network(in1,in2,in3,in4); Error in network/adapt (line 108) [net,Ac,tr] = feval(net.adaptFcn,net,Pd,T,Ai); Error in D_Train_Net_Adapt (line 14) net = adapt(net,X,T);
Expert Answer
Prashant Kumar
PhD Expert
Answered Aug 25, 2026
% You are trying to design a classifier that classifies 101-dimensional vectors into one of 133 classes. using a net with 101 hidden nodes.
% What kind of input data is that and what kind of target classes are those?
% This is such a highly unusual and unlikely scenario that I suggest you illustrate your problem with a MATLAB example dataset.
% The documentation commands "help patternnet" and "doc patternnet" both yield examples using the iris_dataset containing fifty 4 dimensional vectors from each of 3 classes
% The help and doc commands for adapt indicate it was meant for timeseries, not classification. Nevertheless, it will be interesting to see how well it does.
% Removing ending semicolons will print the results of any command
close all, clear all, clc [ x, t] = iris_dataset; [ I N ] = size(x) % [ 4 150 ] [ O N ] = size(t) % [ 3 150 ] % TCI TrueClassIndices: 1, 2 and 3 TCI = vec2ind(t); H = 10 % default No. of hidden nodes % NET TRAINING net1 = patternnet(H); rng(4151941) % my RNG initialization [ net1 tr y1 e1 ] = train(net1,x,t); % PCI PredictedClassIndices: 1, 2 and 3 PCI1 = vec2ind(y1); Errors1 = [ PCI1 ~= TCI ]; % 0s and 1s Nerrs1 = sum(Errors1) % 4 PctErr1 = 100*Nerrs1/N % 2.67 % NET ADAPTATION net2 = patternnet(H); rng(4151941) % my RNG initialization [ net2 y2 e2 xf af ar] = adapt(net2,x,t); % PCI PredictedClassIndices: 1, 2 and 3 PCI2 = vec2ind(y2); Errors2 = [ PCI2 ~= TCI ]; % 0s and 1s Nerrs2 = sum(Errors2) % 91 PctErr2 = 100*Nerrs2/N % 60.7
100% Run Guarantee
3-Hour Fast-Track Delivery
Need a Custom Version or Complete Simulation for This Problem?
Our 500+ PhD engineers build, debug, and optimize working MATLAB scripts and Simulink (.slx) models tailored to your exact assignment rubrics with zero plagiarism.
Tested on MATLAB R2024b / R2026a
Turnitin 0% Plagiarism Report
Free 7-Day Revisions Guarantee
Have a different question? Ask here
Related MATLAB Questions & Solutions
Browse All →
Explore similar technical troubleshooting questions and verified MATLAB solutions:
Ready-to-Run MATLAB & Simulink Projects
Browse All Projects →