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elm2mnist.py
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elm2mnist.py
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# coding: utf-8
import gzip
import cPickle
from extreme import ELMClassifier
import numpy as np
import time
import sys
def load_mnist():
f = gzip.open('../Dataset/mnist.pkl.gz', 'rb')
train_set, valid_set, test_set = cPickle.load(f)
f.close()
return train_set, valid_set, test_set
def mnist_elm(n_hidden=50, domain=[-1., 1.]):
print "hidden:", n_hidden
# initialize
train_set, valid_set, test_set = load_mnist()
train_data, train_target = train_set
valid_data, valid_target = valid_set
test_data, test_target = test_set
# size
train_size = 50000 # max 50000
valid_size = 10000 # max 10000
test_size = 10000 # max 10000
train_data, train_target = train_data[:train_size], train_target[:train_size]
valid_data, valid_target = valid_data[:valid_size], valid_target[:valid_size]
test_data, test_target = test_data[:test_size], test_target[:test_size]
# train = train + valid
train_data = np.concatenate((train_data, valid_data))
train_target = np.concatenate((train_target, valid_target))
# model
model = ELMClassifier(n_hidden = n_hidden, domain = domain)
# fit
#print "fitting ..."
model.fit(train_data, train_target)
# test
print "test score is ",
score = model.score(test_data, test_target)
print score
# valid
# print "valid score is ",
# score = model.score(valid_data, valid_target)
# print score
if __name__ == "__main__":
time1 = time.clock()
mnist_elm(15000, [-0.02, 0.02])
time2 = time.clock()
time = str(time2-time1)
print 'time %s s' % time2
sys.exit()
# mnist_elm(3000, [-0.02, 0.02])
# mnist_elm(4000, [-0.02, 0.02])
# mnist_elm(5000, [-0.02, 0.02])
# mnist_elm(6000, [-0.02, 0.02])
# mnist_elm(7000, [-0.02, 0.02])
# mnist_elm(8000, [-0.02, 0.02])
# mnist_elm(9000, [-0.02, 0.02])
# mnist_elm(10000, [-0.02, 0.02])