def buildIrisNetwork(all_inputs, all_targets): net = NeuralNet() net.init_layers(4, [6], 3) net.randomize_network() net.set_halt_on_extremes(True) # Set to constrain beginning weights to -.5 to .5 # Just to show we can #net.set_random_constraint(.5) net.set_learnrate(.1) net.set_all_inputs(all_inputs) net.set_all_targets(all_targets) length = len(all_inputs) learn_end_point = int(length * .5) net.set_learn_range(0, learn_end_point) net.set_test_range(learn_end_point + 1, length-1) net.layers[0].set_activation_type('tanh') net.layers[1].set_activation_type('tanh') net.layers[2].set_activation_type('threshold') return net
def performNN(all_extracted_features, all_targets): from pyneurgen.neuralnet import NeuralNet #from pyneurgen.nodes import BiasNode, Connection net = NeuralNet() net.init_layers(len(all_extracted_features[0]), [2], 1) net.randomize_network() net.set_halt_on_extremes(True) # Set to constrain beginning weights to -5 to 5 # Just to show we can #net.set_random_constraint(.5) net.set_learnrate(.001) net.set_all_inputs(all_extracted_features) net.set_all_targets(all_targets) length = len(all_extracted_features) learn_end_point = int(length * .8) net.set_learn_range(0, learn_end_point) net.set_test_range(learn_end_point + 1, length - 1) net.layers[1].set_activation_type('tanh') net.learn(epochs=150, show_epoch_results=True, random_testing=True) mse = net.test() print mse
def buildIrisNetwork(all_inputs, all_targets): net = NeuralNet() net.init_layers(4, [6], 3) net.randomize_network() net.set_halt_on_extremes(True) # Set to constrain beginning weights to -.5 to .5 # Just to show we can #net.set_random_constraint(.5) net.set_learnrate(.1) net.set_all_inputs(all_inputs) net.set_all_targets(all_targets) length = len(all_inputs) learn_end_point = int(length * .5) net.set_learn_range(0, learn_end_point) net.set_test_range(learn_end_point + 1, length - 1) net.layers[0].set_activation_type('tanh') net.layers[1].set_activation_type('tanh') net.layers[2].set_activation_type('threshold') return net
net.randomize_network() net.set_halt_on_extremes(True) # Set to constrain beginning weights to -.5 to .5 # Just to show we can #net.set_random_constraint(.5) net.set_learnrate(.1) net.set_all_inputs(all_inputs) net.set_all_targets(all_targets) length = len(all_inputs) learn_end_point = int(length * .5) net.set_learn_range(0, learn_end_point) net.set_test_range(learn_end_point + 1, length - 1) net.layers[0].set_activation_type('tanh') net.layers[1].set_activation_type('tanh') net.layers[2].set_activation_type('tanh') iterations = 500 trn_errors = [] tst_errors = [] tst_perc_errors = [] ################################ # left off: was checking whether normal GA is learning same as # learn epochs=1. need to check percent error validation is all # correct. Write tests!!!! ############################ for i in range(iterations):
net.randomize_network() net.set_halt_on_extremes(True) # Set to constrain beginning weights to -.5 to .5 # Just to show we can net.set_random_constraint(.5) net.set_learnrate(.1) net.set_all_inputs(all_inputs) net.set_all_targets(all_targets) net.set_learn_range(indexes_learn) net.get_learn_range() net.set_test_range(indexes_test) net.get_test_range() net.layers[0].set_activation_type('tanh') net.layers[1].set_activation_type('tanh') net.layers[2].set_activation_type('tanh') net.layers[3].set_activation_type('tanh') ###training network net.learn(epochs=1200, show_epoch_results=True, random_testing=True) mse = net.test() #extract predicted velues all_learn = [item[1][0] for item in net.get_learn_data()] learn_positions = [item[0][3] for item in net.get_learn_data()]
y_train = scaler_y.fit_transform(y_train) y_test = scaler_y.transform(y_test) x_input = np.concatenate( (x_train, x_test, np.zeros((1, np.shape(x_train)[1])))) y_input = np.concatenate((y_train, y_test, np.zeros((1, 1)))) #elaboracao do modelo de rede neural com os parametros definidos fit1 = NeuralNet() fit1.init_layers(input_nodes, [hidden_nodes], output_nodes, ElmanSimpleRecurrent()) fit1.randomize_network() fit1.layers[1].set_activation_type('sigmoid') fit1.set_learnrate(0.05) fit1.set_all_inputs(x_input) fit1.set_all_targets(y_input) fit1.set_learn_range(0, i) fit1.set_test_range(i, i + 1) fit1.learn(epochs=100, show_epoch_results=True, random_testing=False) mse = fit1.test() all_mse.append(mse) print("test set MSE = ", np.round(mse, 6)) target = [item[0][0] for item in fit1.test_targets_activations] target = scaler_y.inverse_transform( np.array(target).reshape((len(target), 1))) pred = [item[1][0] for item in fit1.test_targets_activations] pred = scaler_y.inverse_transform(np.array(pred).reshape((len(pred), 1))) real_y_test.append(target[0][0]) predicted_y_test.append(pred[0][0]) filehandler = open('objects/elman/el_' + str(i) + '.obj', 'w') pickle.dump(fit1, filehandler) filehandler.close()
net.randomize_network() net.set_halt_on_extremes(True) # Set to constrain beginning weights to -.5 to .5 # Just to show we can net.set_random_constraint(.5) net.set_learnrate(.1) net.set_all_inputs(all_inputs) net.set_all_targets(all_targets) length = len(all_inputs) learn_end_point = int(length * .8) net.set_learn_range(0, learn_end_point) net.set_test_range(learn_end_point + 1, length - 1) net.layers[1].set_activation_type('tanh') net.learn(epochs=125, show_epoch_results=True, random_testing=False) mse = net.test() test_positions = [item[0][1] * 1000.0 for item in net.get_test_data()] all_targets1 = [item[0][0] for item in net.test_targets_activations] allactuals = [item[1][0] for item in net.test_targets_activations] fig = plt.figure() ax1 = fig.add_subplot(311)
input_nodes = 1 hidden_nodes = 5 output_nodes = 1 output_order = 20 incoming_weight_from_output = .5 input_order = 20 incoming_weight_from_input = .5 net = NeuralNet() net.init_layers( input_nodes, [hidden_nodes], output_nodes, NARXRecurrent(output_order, incoming_weight_from_output, input_order, incoming_weight_from_input)) net.randomize_network() X = np.linspace(0, 10.0, num=10001) Y = simpleWeierstrassTimeSeries(X) Y = Y.reshape(-1, 1) net.set_all_inputs(Y[:-1]) net.set_all_targets(Y[1:]) net.set_learn_range(0, 8000) net.set_test_range(8000, 9999) print net.test() net.learn(epochs=5) print net.test()
def serNeural(sDay,nAhead,x0,hWeek): nLin = sDay.shape[0] + nAhead nFit = sDay.shape[0] if int(x0['obs_time']) <= 14 else int(x0['obs_time']) predS = getHistory(sDay,nAhead,x0,hWeek) weekS = [x.isocalendar()[1] for x in sDay.index] population = [[float(i),sDay['y'][i],float(i%7),weekS[i]] for i in range(sDay.shape[0])] all_inputs = [] all_targets = [] factorY = sDay['y'].mean() factorT = 1.0 / float(len(population))*factorY factorD = 1./7.*factorY factorW = 1./52.*factorY factorS = 4.*sDay['y'].std() factorH = factorY/sDay['hist'].mean() def population_gen(population): pop_sort = [item for item in population] # random.shuffle(pop_sort) for item in pop_sort: yield item for t,y,y1,y2 in population_gen(population): #all_inputs.append([t*factorT,(.5-random.random())*factorS+factorY,y1*factorD,y2*factorW]) all_inputs.append([y1*factorD,(.5-random.random())*factorS+factorY,y2*factorW]) all_targets.append([y]) if False: plt.plot([x[0] for x in all_inputs],'-',label='targets0') plt.plot([x[1] for x in all_inputs],'-',label='targets1') plt.plot([x[2] for x in all_inputs],'-',label='targets2') # plt.plot([x[3] for x in all_inputs],'-',label='targets3') plt.plot([x[0] for x in all_targets],'-',label='actuals') plt.legend(loc='lower left', numpoints=1) plt.show() net = NeuralNet() net.init_layers(3,[10],1,NARXRecurrent(3,.6,2,.4)) net.randomize_network() net.set_random_constraint(.5) net.set_learnrate(.1) net.set_all_inputs(all_inputs) net.set_all_targets(all_targets) #predS['pred'] = [item[0][0] for item in net.test_targets_activations] length = len(all_inputs) learn_end_point = int(length * .8) # random.sample(all_inputs,10) net.set_learn_range(0, learn_end_point) net.set_test_range(learn_end_point + 1, length - 1) net.layers[1].set_activation_type('tanh') net.learn(epochs=125,show_epoch_results=True,random_testing=False) mse = net.test() #net.save(os.environ['LAV_DIR'] + "/out/train/net.txt") test_positions = [item[0][0] for item in net.get_test_data()] all_targets1 = [item[0][0] for item in net.test_targets_activations] all_actuals = [item[1][0] for item in net.test_targets_activations] # This is quick and dirty, but it will show the results plt.subplot(3, 1, 1) plt.plot([i for i in sDay['y']],'-') plt.title("Population") plt.grid(True) plt.subplot(3, 1, 2) plt.plot(test_positions, all_targets1, 'b-', label='targets') plt.plot(test_positions, all_actuals, 'r-', label='actuals') plt.grid(True) plt.legend(loc='lower left', numpoints=1) plt.title("Test Target Points vs Actual Points") plt.subplot(3, 1, 3) plt.plot(range(1, len(net.accum_mse) + 1, 1), net.accum_mse) plt.xlabel('epochs') plt.ylabel('mean squared error') plt.grid(True) plt.title("Mean Squared Error by Epoch") plt.show()
net.set_random_constraint(.5) net.set_learnrate(.1) # net.set_all_inputs(training_data[:, 1]) # this results in [a, b, ..., z] not [[a], [b], ..., [z]] # net.set_all_targets(training_data[:, 2]) net.set_all_inputs([[row[1]] for row in selected_data]) # wanting [[a], [b], ..., [z]] net.set_all_targets([[row[2]] for row in selected_data]) length = len(selected_data) learn_end_point = int( (end_validate_idx - start_training_idx) / pick_every * .8) # validation net.set_learn_range(0, (end_validate_idx - start_training_idx) / pick_every) net.set_test_range((end_validate_idx - start_training_idx) / pick_every, (end_test_idx - start_training_idx) / pick_every) net.layers[1].set_activation_type('tanh') # train net.learn(epochs=125, show_epoch_results=True) mse = net.test() # test and generate charts all_real = [item[0] for item in net.test_targets_activations] all_targets = [item[1] for item in net.test_targets_activations] plot(selected_data_time[(end_validate_idx - start_training_idx) / pick_every:(end_test_idx - start_training_idx) / pick_every], all_targets, 'bo',
print() print() g = ges.population[ges.fitness_list.best_member()] program = g.local_bnf['program'] saved_model = g.local_bnf['<saved_name>'][0] # We will create a brand new model net = NeuralNet() net.load(saved_model) net.set_all_inputs(all_inputs) net.set_all_targets(all_targets) test_start_point = int(pop_len * .8) + 1 net.set_test_range(test_start_point, pop_len - 1) mse = net.test() print("The selected model has the following characteristics") print("Activation Type:", net.layers[1].nodes[1].get_activation_type()) print("Hidden Nodes:", len(net.layers[1].nodes), ' + 1 bias node') print("Learn Rate:", net.get_learnrate()) print("Epochs:", net.get_epochs()) test_positions = [item[0][0] * pop_len for item in net.get_test_data()] all_targets1 = [item[0][0] for item in net.test_targets_activations] allactuals = [item[1][0] for item in net.test_targets_activations] # This is quick and dirty, but it will show the results fig = figure()
print print g = ges.population[ges.fitness_list.best_member()] program = g.local_bnf['program'] saved_model = g.local_bnf['<saved_name>'][0] # We will create a brand new model net = NeuralNet() net.load(saved_model) net.set_all_inputs(all_inputs) net.set_all_targets(all_targets) test_start_point = int(pop_len * .8) + 1 net.set_test_range(test_start_point, pop_len - 1) mse = net.test() print "The selected model has the following characteristics" print "Activation Type:", net.layers[1].nodes[1].get_activation_type() print "Hidden Nodes:", len(net.layers[1].nodes), ' + 1 bias node' print "Learn Rate:", net.get_learnrate() print "Epochs:", net.get_epochs() test_positions = [item[0][0] * pop_len for item in net.get_test_data()] all_targets1 = [item[0][0] for item in net.test_targets_activations] allactuals = [item[1][0] for item in net.test_targets_activations] # This is quick and dirty, but it will show the results fig = figure()
fit1 = NeuralNet() fit1.init_layers( input_nodes, [hidden_nodes], output_nodes, NARXRecurrent(output_order, incoming_weight_from_output, input_order, incoming_weight_from_input)) fit1.randomize_network() fit1.layers[1].set_activation_type('sigmoid') fit1.set_learnrate(0.35) fit1.set_all_inputs(x) fit1.set_all_targets(y) length = len(x) learn_end_point = int(length * 0.85) fit1.set_learn_range(0, learn_end_point) fit1.set_test_range(learn_end_point + 1, length - 1) fit1.learn(epochs=10, show_epoch_results=True, random_testing=False) mse = fit1.test() print("MSE for test set: ", round(mse, 6)) plt.figure(figsize=(15, 6)) plt.plot(np.arange(len(fit1.accum_mse)), fit1.accum_mse) plt.xlabel('Epochs') plt.ylabel('Mean Squared Error') plt.savefig('../figs/fig9.png') yhat = [i[1][0] for i in fit1.test_targets_activations] yhat = scaler.inverse_transform(np.array(yhat).reshape((len(yhat), 1))) yhat = yhat.flatten()