import load_MNIST as ms import load_heart_disease as hd import load_MISR as misr import load_Adult as ad import neural_net as nn import neural_net_ext as nne import randff as rff import randff_ext as rffe import numpy as np from sklearn import cross_validation train_data, test_data = ad.load_Adult_wrapper() m = [100, 150, 200, 250, 300, 350, 400, 450, 500] train_data = np.array(train_data) m_matrix = np.zeros(9) for i in range(0, 9): net = nn.Network([108, m[i], 2], nn.Quad, nn.Cos, nn.Sin, False) m_matrix[i] = net.train_network(train_data, 20, 10, 0.1, 0.1, test_data) print m_matrix
np.random.shuffle(data) train_data = data[:400] test_data = data[400:] n = nn.Network([9, 20, 2], nn.Entropy, nn.Cos, nn.Sin, False) n.train_network(train_data, 30, 10, 0.1, 10, test_data) if False: """ test of neural_net Adult dataset modified neural net with cosine/sine activation functions in hidden layers and softmax in the output layer No bias term for any score apart from the one in the first layer """ data_train, data_test = ad.load_Adult_wrapper() net = nn.Network([108, 500, 2], nn.Entropy, nn.Cos, nn.Sin, False) net.train_network(data_train, 30, 10, 0.1, 1, data_test) if True: """ test of neural_net_ext MISR dataset modified neural net with three layer + mean pooling structure: hidden cos/sin || mean pooling || hidden cos/sin || output layer no bias term beyond first layer """ data = misr.load_MISR() np.random.shuffle(data) train_data = data[:700]