def test_ELM_SaveLoad(self):
X = np.array([1, 2, 3, 1, 2, 3])
T = np.array([[1, 0], [1, 0], [1, 0], [0, 1], [0, 1], [0, 1]])
elm = ELM(1, 2, precision='32', norm=0.02)
elm.add_neurons(1, "lin")
elm.add_neurons(2, "tanh")
elm.train(X, T, "wc", w=(0.7, 0.3))
B1 = elm.nnet.get_B()
try:
f, fname = tempfile.mkstemp()
elm.save(fname)
elm2 = ELM(3, 3)
elm2.load(fname)
finally:
os.close(f)
self.assertEqual(elm2.nnet.inputs, 1)
self.assertEqual(elm2.nnet.outputs, 2)
self.assertEqual(elm2.classification, "wc")
self.assertIs(elm.precision, np.float32)
self.assertIs(elm2.precision, np.float64) # precision has changed
np.testing.assert_allclose(np.array([0.7, 0.3]), elm2.wc)
np.testing.assert_allclose(0.02, elm2.nnet.norm)
np.testing.assert_allclose(B1, elm2.nnet.get_B())
self.assertEqual(elm2.nnet.get_neurons()[0][1], "lin")
self.assertEqual(elm2.nnet.get_neurons()[1][1], "tanh")
def test_ELM_SaveLoad(self):
X = np.array([1, 2, 3, 1, 2, 3])
T = np.array([[1, 0], [1, 0], [1, 0], [0, 1], [0, 1], [0, 1]])
elm = ELM(1, 2, precision='32', norm=0.02)
elm.add_neurons(1, "lin")
elm.add_neurons(2, "tanh")
elm.train(X, T, "wc", w=(0.7, 0.3))
B1 = elm.nnet.get_B()
try:
f, fname = tempfile.mkstemp()
elm.save(fname)
elm2 = ELM(3, 3)
elm2.load(fname)
finally:
os.close(f)
self.assertEqual(elm2.nnet.inputs, 1)
self.assertEqual(elm2.nnet.outputs, 2)
self.assertEqual(elm2.classification, "wc")
self.assertIs(elm.precision, np.float32)
self.assertIs(elm2.precision, np.float64) # precision has changed
np.testing.assert_allclose(np.array([0.7, 0.3]), elm2.wc)
np.testing.assert_allclose(0.02, elm2.nnet.norm)
np.testing.assert_allclose(B1, elm2.nnet.get_B())
self.assertEqual(elm2.nnet.get_neurons()[0][1], "lin")
self.assertEqual(elm2.nnet.get_neurons()[1][1], "tanh")
def model_training(model_path, data_path, neurons=300):
images, labels = read_mnist.load_mnist(data_path, kind='train')
images = map(read_mnist.up_to_2D, images)
images = map(get_hog, images)
images = np.mat(np.array(images))
labels = np.mat(map(read_mnist.handle_label, labels))
elm = ELM(images.shape[1], labels.shape[1])
elm.add_neurons(neurons, 'sigm')
elm.add_neurons(neurons, 'sigm')
# elm.add_neurons(int(images.shape[1]*0.8), 'sigm')
# elm.add_neurons(int(images.shape[1]*0.6), 'tanh')
elm.train(images, labels)
elm.save(model_path)
npos = sum((p == 1))
print "\n Training results"
print "Tpos:", ntpos, " / ", npos, "TD:", ntpos / float(npos)
print "Acc: ", nhit / (float)(len(p)), "total", len(p)
# now prediction for testing set
prjinput = elmInput.project(XXtest)
prjinput_normed = mapMinMax(prjinput)
prjhidden = elmHidden1.project(prjinput_normed)
prjhidden_normed = mapMinMax(prjhidden)
youtput = elmOutput.predict(prjhidden_normed)
# training results
mse_error = elmOutput.error(youtput, YYtest)
p = ytest.squeeze()
yout = np.argmax(youtput, axis=1)
nhit = sum(yout == p)
ntpos = sum((yout == 1) & (p == 1))
ntneg = sum((yout == 0) & (p == 0))
npos = sum((p == 1))
nneg = sum((p == 0))
print "\n Testing results"
print "Tpos:", ntpos, " / ", npos, "TD:", ntpos / float(npos)
print "Tneg:", ntneg, " / ", nneg, "TN:", ntneg / float(nneg)
print "Acc: ", nhit / (float)(len(p)), "total", len(p)
print '\ saving layers'
elmInput.save("elm_input_0")
elmHidden1.save("elm_hidden_0")
elmOutput.save("elm_output_0")
