#!/usr/bin/python import fann connection_rate = 1 learning_rate = 0.7 num_input = 2 num_neurons_hidden = 4 num_output = 1 desired_error = 0.0001 max_iterations = 100000 iterations_between_reports = 1000 ann = fann.create(connection_rate, learning_rate, (num_input, num_neurons_hidden, num_output)) ann.train_on_file("datasets/xor.data", max_iterations, iterations_between_reports, desired_error) ann.save("xor_float.net") ann.destroy()
return 0 # initialize network parameters connection_rate = 1 learning_rate = 0.7 num_neurons_hidden = 32 desired_error = 0.000001 max_iterations = 300 iterations_between_reports = 1 # create training data, and ann object print "Creating network." train_data = fann.read_train_from_file("datasets/mushroom.train") ann = fann.create( connection_rate, learning_rate, (train_data.get_num_input(), num_neurons_hidden, train_data.get_num_output()) ) # start training the network print "Training network" ann.set_activation_function_hidden(fann.FANN_SIGMOID_SYMMETRIC_STEPWISE) ann.set_activation_function_output(fann.FANN_SIGMOID_STEPWISE) ann.set_training_algorithm(fann.FANN_TRAIN_INCREMENTAL) ann.train_on_data(train_data, max_iterations, iterations_between_reports, desired_error) # test outcome print "Testing network" test_data = fann.read_train_from_file("datasets/mushroom.test") ann.reset_MSE()