i_n = 11 #no. of input nodes h_n = 21 #no. of hidden nodes 11 ~ sqrt(i_n * o_n) o_n = 1 #np. of output nodes lr = 0.15 #learning rate NN1 = NeuralNetwork(i_n, h_n, o_n, lr) #setting up neural networks NN2 = NeuralNetwork(i_n, h_n, o_n, lr) NN3 = NeuralNetwork(i_n, h_n, o_n, lr) NN4 = NeuralNetwork(i_n, h_n, o_n, lr) NN5 = NeuralNetwork(i_n, h_n, o_n, lr) NN6 = NeuralNetwork(i_n, h_n, o_n, lr) NN7 = NeuralNetwork(i_n, h_n, o_n, lr) NN8 = NeuralNetwork(i_n, h_n, o_n, lr) NN1.load('a7', 'b7') NN2.load('c7', 'd7') NN3.load('e7', 'f7') NN4.load('g7', 'h7') NN5.load('i7', 'j7') NN6.load('k7', 'l7') NN7.load('m7', 'n7') NN8.load('o7', 'p7') test_data_file = open( 'X:/Documents/Carbon Emissions Data/Data/TestingDataBasicFinal.csv', 'r') #loading test data test_data = test_data_file.readlines() test_data_file.close()
i_n = 14 #no. of input nodes h_n = 21 #no. of hidden nodes 11 ~ sqrt(i_n * o_n) o_n = 1 #np. of output nodes lr = 0.15 #learning rate epochs = 1 #no. of epochs NN1 = NeuralNetwork(i_n, h_n, o_n, lr) #setting up neural networks NN2 = NeuralNetwork(i_n, h_n, o_n, lr) NN3 = NeuralNetwork(i_n, h_n, o_n, lr) NN4 = NeuralNetwork(i_n, h_n, o_n, lr) NN5 = NeuralNetwork(i_n, h_n, o_n, lr) NN6 = NeuralNetwork(i_n, h_n, o_n, lr) NN7 = NeuralNetwork(i_n, h_n, o_n, lr) NN8 = NeuralNetwork(i_n, h_n, o_n, lr) NN1.load('a6', 'b6') NN2.load('c6', 'd6') NN3.load('e6', 'f6') NN4.load('g6', 'h6') NN5.load('i6', 'j6') NN6.load('k6', 'l6') NN7.load('m6', 'n6') NN8.load('o6', 'p6') def normalise_inputs(inputs): property_type = (inputs[0] - 2.5)/1.5 #normalising each piece of data into range [1,-1] approx built_form = (inputs[1] - 3.5)/2.5 floor_area = (inputs[2] - 149.41)/142.69 no_rooms = (inputs[3] - 15.5)/14.5 no_fire = (inputs[4] - 4.95)/5 hotwater =(inputs[5] - 5.95)/5