def test_blob_nonlinearity(self):
# generate test data
x = randn(1, 10000) * 4.
y = exp(-(x - 2.)**2) / 2. + exp(-(x + 5.)**2 / 4.) / 4.
z = (rand(*y.shape) < y) * 1.
glm = GLM(1, BlobNonlinearity(3))
glm.weights = [[.5 + rand()]]
err = glm._check_gradient(x,
z,
parameters={
'train_weights': False,
'train_bias': False,
'train_nonlinearity': True
})
self.assertLess(err, 1e-6)
err = glm._check_gradient(x,
z,
parameters={
'train_weights': True,
'train_bias': False,
'train_nonlinearity': False
})
self.assertLess(err, 1e-6)
def test_glm_train(self):
w = asarray([[-1., 0., 1., 2.]]).T
b = 1.
x = randn(4, 100000)
p = 1. / (1. + exp(-dot(w.T, x) - b))
y = rand(*p.shape) < p
glm = GLM(4, LogisticFunction, Bernoulli)
# test gradient
err = glm._check_gradient(x, y, 1e-5, parameters={
'train_weights': False,
'train_bias': True})
self.assertLess(err, 1e-8)
err = glm._check_gradient(x, y, 1e-5, parameters={
'train_weights': True,
'train_bias': False})
self.assertLess(err, 1e-8)
err = glm._check_gradient(x, y, 1e-5)
self.assertLess(err, 1e-8)
err = glm._check_gradient(x, y, 1e-5, parameters={
'regularize_weights': 10.,
'regularize_bias': 10.})
self.assertLess(err, 1e-8)
# test training
glm.train(x, y, parameters={'verbosity': 0})
self.assertLess(max(abs(glm.weights - w)), 0.1)
self.assertLess(max(abs(glm.bias - b)), 0.1)
glm.weights = w
glm.bias = -1.
glm.train(x, y, parameters={'verbosity': 0, 'train_weights': False})
self.assertLess(max(abs(glm.weights - w)), 1e-12)
self.assertLess(max(abs(glm.bias - b)), 0.1)
glm.weights = randn(*glm.weights.shape)
glm.bias = b
glm.train(x, y, parameters={'verbosity': 0, 'train_bias': False})
self.assertLess(max(abs(glm.weights - w)), 0.1)
self.assertLess(max(abs(glm.bias - b)), 1e-12)
def test_blob_nonlinearity(self):
# generate test data
x = randn(1, 10000) * 4.
y = exp(-(x - 2.)**2) / 2. + exp(-(x + 5.)**2 / 4.) / 4.
z = (rand(*y.shape) < y) * 1.
glm = GLM(1, BlobNonlinearity(3))
glm.weights = [[.5 + rand()]]
err = glm._check_gradient(x, z,
parameters={'train_weights': False, 'train_bias': False, 'train_nonlinearity': True})
self.assertLess(err, 1e-6)
err = glm._check_gradient(x, z,
parameters={'train_weights': True, 'train_bias': False, 'train_nonlinearity': False})
self.assertLess(err, 1e-6)
def test_glm_train(self):
w = asarray([[-1., 0., 1., 2.]]).T
b = 1.
x = randn(4, 100000)
p = 1. / (1. + exp(-dot(w.T, x) - b))
y = rand(*p.shape) < p
glm = GLM(4, LogisticFunction, Bernoulli)
# test gradient
err = glm._check_gradient(x,
y,
1e-5,
parameters={
'train_weights': False,
'train_bias': True
})
self.assertLess(err, 1e-8)
err = glm._check_gradient(x,
y,
1e-5,
parameters={
'train_weights': True,
'train_bias': False
})
self.assertLess(err, 1e-8)
err = glm._check_gradient(x, y, 1e-5)
self.assertLess(err, 1e-8)
err = glm._check_gradient(x,
y,
1e-5,
parameters={
'regularize_weights': 10.,
'regularize_bias': 10.
})
self.assertLess(err, 1e-8)
# test training
glm.train(x, y, parameters={'verbosity': 0})
self.assertLess(max(abs(glm.weights - w)), 0.1)
self.assertLess(max(abs(glm.bias - b)), 0.1)
glm.weights = w
glm.bias = -1.
glm.train(x, y, parameters={'verbosity': 0, 'train_weights': False})
self.assertLess(max(abs(glm.weights - w)), 1e-12)
self.assertLess(max(abs(glm.bias - b)), 0.1)
glm.weights = randn(*glm.weights.shape)
glm.bias = b
glm.train(x, y, parameters={'verbosity': 0, 'train_bias': False})
self.assertLess(max(abs(glm.weights - w)), 0.1)
self.assertLess(max(abs(glm.bias - b)), 1e-12)
