error happened when to train a mapping from input with 100 neurons to output with 4 neurons

[RyanCV]

The network defined as follows:
input size = 100, output size = 4

layers.af{1} = [];
layers.sz{1} = [input_size 1 1];
layers.typ{1} = defs.TYPES.INPUT;

layers.af{end+1} = ReLU(defs, []);
layers.sz{end+1} = [input_size 1 1];
layers.typ{end+1} = defs.TYPES.FULLY_CONNECTED;

layers.af{end+1} = ReLU(defs, []);
layers.sz{end+1} = [output_size 1 1];
layers.typ{end+1} = defs.TYPES.FULLY_CONNECTED;

if defs.plotOn
    nnShow(23, layers, defs);
end

Error in

Error using  - 
Matrix dimensions must agree.

Error in squaredErrorCostFun (line 2)
    J = (Y.v(:,:,t)-A.v(:,:,t)).^2;

Error in ReLU/cost (line 65)
            J = squaredErrorCostFun(Y, A, m, t);

Error in nnCostFunctionCNN (line 29)
J = nn.l.af{nn.N_l}.cost(Y, nn.A{nn.N_l}, m, 1) + J_s;

Error in
Train_proposal>@(nn,r,newRandGen)nnCostFunctionCNN(nn,r,newRandGen)

Error in gradientDescentAdaDelta (line 69)
    [J, dJdW, dJdB] = feval(f, nn, r, true);

Error in Train_proposal (line 158)
nn = gradientDescentAdaDelta(costFunc, nn, defs, [], [], [], [], 'Training
Entire Network');

[joncox123]

Its hard to say exactly without seeing your exact code and data formats. However, it seems like the dimensionality of your label matrix (Y) could be incorrect. Check the shape of Y and try permuting it so that it matches that of the final output layer.

[RyanCV]

Thanks for your help, it solves the problem.

[RyanCV]

@joncox123 by the way, where can I find '~/mnist_full.mat' in order to run your example?

[RyanCV]

@joncox123 , if I want to design a network only has one hidden layer (including one Fully_connected layer + ReLU), and the output layer is only Fully_connected layer, is the following design right??

layers.af{1} = [];
layers.sz{1} = [input_size 1 1];
layers.typ{1} = defs.TYPES.INPUT;  (input layer)

layers.af{end+1} = ReLU(defs, defs.COSTS.SQUARED_ERROR);
layers.sz{end+1} = [input_size 1 1];
layers.typ{end+1} = defs.TYPES.FULLY_CONNECTED; (hidden layer 1)

layers.af{end+1} = ReLU(defs, defs.COSTS.SQUARED_ERROR); (**for this line, if I use [], there will be error??** )
layers.sz{end+1} = [output_size 1 1];
layers.typ{end+1} = defs.TYPES.FULLY_CONNECTED; (**output layer, how to set it only has fully connected layer??**)

X = varObj(X,defs, defs.TYPES.INPUT);
Y = varObj(Y,defs, defs.TYPES.OUTPUT);

Thanks.