valid[1][:n]), (test[0][:n],
test[1][:n])
mean, std = train[0].mean(), train[0].std()
train = standardize_dataset(train, mean, std)
valid = standardize_dataset(valid, mean, std)
test = standardize_dataset(test, mean, std)
# for i in [1, 2, 5, 10, 20, 100]:
for i in [20]:
print(f"x-----> {i} hidden nodes <-----x")
net = MLP(train[0], number_to_one_hoot(train[1]), i, outtype="softmax")
# net.train(train[0], number_to_one_hoot(train[1]), 0.1, 1000)
net.earlystopping_primitive(train[0],
number_to_one_hoot(train[1]),
valid[0],
number_to_one_hoot(valid[1]),
eta=0.1,
niteration=100,
early_stop_count=2)
net.confmat(train[0], number_to_one_hoot(train[1]))
net.confmat(test[0], number_to_one_hoot(test[1]))
print()
print()
"""
Confusion matrix:
[[4827 0 15 6 13 16 23 5 15 21]
[ 1 5541 22 12 7 8 9 12 37 8]
[ 10 25 4700 60 15 5 13 38 24 18]
[ 8 24 41 4780 2 81 2 18 51 31]
[ 6 6 40 2 4659 25 13 34 12 71]
[ 23 19 18 90 4 4260 46 7 40 27]
# Data normalization
inputs = (inputs - inputs.mean(0))
inputs /= np.abs(inputs).max(0)
print(np.abs(inputs).max(0))
# Targets to one hoot
targets = number_to_one_hoot(targets, 3)
train = (inputs[::2], targets[::2])
valid = (inputs[1::4], targets[1::4])
test = (inputs[3::4], targets[3::4])
accs = []
for i in range(10):
net = MLP(train[0], train[1], 10)
net.earlystopping_primitive(train[0], train[1], valid[0], valid[1], 0.1, 10, 2)
# net.train(train[0], train[1], 0.1, 100000)
print("Train")
net.confmat(train[0], train[1])
print("Valid")
net.confmat(valid[0], valid[1])
print("Test")
acc = conf_mat_acc(net.confmat(test[0], test[1]))
accs.append(acc)
print(f"Mean accuracy: {np.mean(np.array(accs)) * 100:2.4f}")
"""
5. Number of Instances: 150 (50 in each of three classes)
6. Number of Attributes: 4 numeric, predictive attributes and the class
7. Attribute Information:
