nnadl=True) from pymri.model import FNN # create Classifier cls = FNN(type='FNN simple', input_layer_size=784, hidden_layer_size=46, output_layer_size=2, epochs=100, mini_batch_size=11, learning_rate=3.0, verbose=True) # split dataset training_data, test_data, valid_data = dataset.split_data(sizes=(0.75, 0.25)) # train and test classifier cls.train_and_test(training_data, test_data) accuracy = cls.get_accuracy() print('accuracy = %0.2f' % accuracy) ############################################################################## # # FITNESS FUCNTION # ############################################################################## from pymri.genetic_algorithm import get_prob_class # the goal ('fitness') function to be maximized
import numpy as np accuracies = np.zeros(shape=(n_times_LeavePOut,)) for i in range(n_times_LeavePOut): print('testing iteration: %d' % i) ########################################################################### # # SPLIT DATA # ########################################################################### # get training, validation and test datasets for specified roi # training_data, validation_data, test_data = ds.split_data() training_data, test_data, vd = ds.split_data(sizes=(0.75,0.25)) ########################################################################### # # CREATE MODEL # ########################################################################### # artificial neural network from pymri.model import fnn net = fnn.Network([k_features, hidden_neurons, 2]) # train and test network net.SGD(training_data, epochs, minibatch_size, eta, test_data=test_data) # record the best result accuracies[i] = net.best_score/float(len(test_data))
import numpy as np accuracies = np.zeros(shape=(n_times_LeavePOut, )) for i in range(n_times_LeavePOut): print('testing iteration: %d' % i) ########################################################################### # # SPLIT DATA # ########################################################################### # get training, validation and test datasets for specified roi # training_data, validation_data, test_data = ds.split_data() training_data, test_data, vd = ds.split_data(sizes=(0.75, 0.25)) ########################################################################### # # CREATE MODEL # ########################################################################### # artificial neural network from pymri.model import fnn net = fnn.Network([k_features, hidden_neurons, 2]) # train and test network net.SGD(training_data, epochs, minibatch_size, eta, test_data=test_data) # record the best result accuracies[i] = net.best_score / float(len(test_data))
# create Classifier cls = FNN( type='FNN simple', input_layer_size=k_features, hidden_layer_size=46, output_layer_size=2, epochs=100, # epochs=10, mini_batch_size=11, learning_rate=3.0, verbose=True ) # split dataset training_data, test_data, valid_data = dataset.split_data( sizes=(0.75, 0.25) ) # train and test classifier cls.train_and_test(training_data, test_data) accuracy = cls.get_accuracy() print('accuracy = %0.2f' % accuracy) ############################################################################## # # GENETIC ALGORITHM SETUP # ##############################################################################