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]
