from network import ANN from compare import * from train import Trainer import numpy as np x = np.array([[4, 5.5], [4.5, 1], [9, 2.5], [6,2], [10, 10]], dtype = float) y = np.array([[70], [89], [85], [75], [10]], dtype = float) N = ANN(2, 5, 1, 0.000001) x = x / np.amax(x) y = y / np.amax(y) print "y -> net(y)" print y, "\n------------\n", N.forward(x) ag = analyticalGrad(N, x, y) ng = numericalGrad (N, x, y) print "Analytical grad: ", sum(ag), ", ", ag.shape print "Numerical grad: ", sum(ng), ", ", ng.shape T = Trainer(N) T.train(x,y) ag = analyticalGrad(N, x, y) ng = numericalGrad (N, x, y) print "Analytical grad: ", sum(ag), ", ", ag.shape print "Numerical grad: ", sum(ng), ", ", ng.shape
training = np.array([ ['10000000', '10000000'], ['01000000', '01000000'], ['00100000', '00100000'], ['00010000', '00010000'], ['00001000', '00001000'], ['00000100', '00000100'], ['00000010', '00000010'], ['00000001', '00000001']]) #training = np.array([ ['10000000', '10000000']]) nn = ANN(num_features, num_labels, num_hidden) # Build training features training_features = [] for i in range (len(training)): training_features.append( np.asarray( list(training[i][0]), dtype = int )) training_features = np.asarray(training_features) # Build training labels training_labels = [] for i in range (len(training)): training_labels.append( list(np.asarray( list(training[i][1]), dtype = int ))) training_labels = np.asarray(training_labels)
