Feed-forward Back-Propagation Network (New)
Network object which contains the parameters of the network created and trained.
Training data, in m x n matrix, where m is the features, n is different datasets
Target data, in 1 x n vector, where the value shall be the classes which correspond to each set of data in x
Iteration to run
Feed-forward Back-Propagation Network Base on Andrew Ng's Coursera Deep-Learning Specialization Course.
P = [1 2 3 4; 1 2 3 4]./2 - 1; // Simple Normalization Tc = [1 2 3 4]; T = nn_onehot(Tc); // Convert to one-hot encoding N = [2,3,4]; af = ['ann_relu','ann_softmax']; num_iterations = 200; net = initialize_parameters(N,[-0.01,0.01],af); net.mini_batch = 1; net.cost_type = 'categorical_crossentropy'; net.lr = 0.05; net.l2 = 0.01; net = nn_train(net,P,T,num_iterations); y = model_forward(P, net); [maxV, maxI] = max(y,'r') disp(maxI); | ![]() | ![]() |