def test_sample(self):
sys.stdout.write(
'GaussianRectVarianceRBM -> Performing sample test ...')
numx.random.seed(420)
input_dim = 8
hidden_dim = 10
batchsize = 3
rbm = Model.GaussianRectVarianceRBM(input_dim, hidden_dim)
data = numx.random.rand(batchsize, input_dim)
h = rbm.probability_h_given_v(data)
assert numx.all(h >= 0.0)
assert numx.all(h.shape == (batchsize, hidden_dim))
h = rbm.sample_h(h)
assert numx.all(h >= 0.0)
assert numx.all(h <= rbm.max_act)
assert numx.all(h.shape == (batchsize, hidden_dim))
v = rbm.probability_v_given_h(h)
assert numx.all(v.shape == (batchsize, input_dim))
v = rbm.sample_v(v)
assert numx.all(v.shape == (batchsize, input_dim))
sys.stdout.flush()
print(' successfully passed!')
sys.stdout.flush()
pass
def test_get_parameters(self):
print('GaussianRectVarianceRBM -> Performing get_parameters test ...')
numx.random.seed(42)
input_dim = 2
hidden_dim = 2
rbm = Model.GaussianRectVarianceRBM(input_dim, hidden_dim)
assert len(rbm.get_parameters()) == 4
print('successfully passed!')
sys.stdout.flush()
def test_calculate_gradients(self):
sys.stdout.write(
'GaussianRectVarianceRBM -> Performing calculate_gradients test ...'
)
numx.random.seed(42)
input_dim = 2
hidden_dim = 2
rbm = Model.GaussianRectVarianceRBM(input_dim, hidden_dim)
assert len(
rbm.calculate_gradients(numx.array([[0.98, -0.56], [-0.3, 0.8]]),
numx.array([[0, 1], [1, 1]]))) == 4
print(' successfully passed!')
sys.stdout.flush()
def test__calculate_sigma_gradient(self):
sys.stdout.write(
'GaussianRectVarianceRBM -> Performing calculate_sigma_gradient test ...'
)
numx.random.seed(42)
input_dim = 2
hidden_dim = 2
rbm = Model.GaussianRectVarianceRBM(input_dim, hidden_dim)
deltaSigma = rbm._calculate_sigma_gradient(
numx.array([[0.98, -0.56], [-0.3, 0.8]]),
numx.array([[0, 1], [1, 1]]))
target = numx.array([[-0.63545491, -0.07162506]])
assert numx.all(numx.abs(target - deltaSigma) < self.epsilon)
print(' successfully passed!')
sys.stdout.flush()
def test___init__(self):
sys.stdout.write('GaussianRectVarianceRBM -> Performing init test ...')
numx.random.seed(42)
input_dim = 8
hidden_dim = 10
batchsize = 3
rbm = Model.GaussianRectVarianceRBM(input_dim, hidden_dim)
assert numx.all(rbm.w.shape == (input_dim, hidden_dim))
assert numx.all(rbm.bv.shape == (1, input_dim))
assert numx.all(rbm.bh.shape == (1, hidden_dim))
assert numx.all(rbm.input_dim == input_dim)
assert numx.all(rbm.output_dim == hidden_dim)
print(' successfully passed!')
sys.stdout.flush()
pass
