def test_Linear_with_initial():
Linear1_prev = LinearBayesianGaussian(10,
100,
LinearBayesianGaussian_init=False,
p=1)
Linear1 = LinearBayesianGaussian(10, 100, Linear1_prev, p=1)
assert (Linear1_prev.rho.weight.data.numpy() ==
Linear1_prev.rho.weight.data.numpy()).all()
def test_Linear_stack():
Linear_stack = LinearBayesianGaussian(10,
10,
LinearBayesianGaussian_init=False,
p=1)
output = Linear_stack(
torch.tensor(np.random.uniform(1, 1, (20, 10)), dtype=torch.float64))
mu_stack, rho_stack, w_stack = Linear_stack.stack()
assert ((mu_stack.shape == w_stack.shape)
and (rho_stack.data.numpy().shape[0] == 110))
def test_Linear_multi_input():
Linear1 = LinearBayesianGaussian(10,
1,
LinearBayesianGaussian_init=False,
p=1)
with torch.no_grad():
Linear1.mu.weight.copy_(
torch.tensor(np.random.uniform(2, 2, (1, 10)),
dtype=torch.float64))
Linear1.mu.bias.copy_(
torch.tensor(np.random.uniform(0, 0, (1)), dtype=torch.float64))
#Linear1.rho.weight.copy_( torch.tensor( np.random.uniform( 0, 1, (100, 10) ), dtype=torch.float64 ) )
output = Linear1(
torch.tensor(np.random.uniform(1, 1, (20, 10)), dtype=torch.float64))
#print( (output.data.numpy()[0, :] == output.data.numpy()).all() )
# The derivative of a composition of function in this case is given by all 0 because the inputs are 0
# The reparam trick add to this derivative the derivative of the loss function wrt mu that is in this case
# given by all 2*2 (all the mu are 2 and then derive a square function)
#
# print(Linear1.mu.weight.grad)
assert ((output.data.numpy()[0, :] == output.data.numpy()).all()
and output.data.numpy().shape[0] == 20)
def test_Linear_reparam_trick_be():
Linear1 = LinearBayesianGaussian(10,
100,
LinearBayesianGaussian_init=False,
p=0.5)
with torch.no_grad():
Linear1.mu.weight.copy_(
torch.tensor(np.random.uniform(2, 2, (100, 10)),
dtype=torch.float64))
Linear1.rho.weight.copy_(
torch.tensor(np.random.uniform(-20, -20, (100, 10)),
dtype=torch.float64))
output = Linear1(
torch.tensor(np.random.uniform(1, 1, (10)), dtype=torch.float64))
loss = output.sum() + (Linear1.mu.weight * Linear1.mu.weight).sum() + (
2 * Linear1.rho.weight).sum()
loss.backward()
# The derivative of a composition of function in this case is given by all 0 because the inputs are 0
# The reparam trick add to this derivative the derivative of the loss function wrt mu that is in this case
# given by all 2*2 (all the mu are 2 and then derive a square function)
#
# print((Linear1.mu.weight.grad.data.numpy() == 5).sum()/1000)
# print(Linear1.rho.weight.grad)
assert (
((Linear1.mu.weight.grad.data.numpy() == 5).sum() / 1000) < 0.6
and ((Linear1.mu.weight.grad.data.numpy() == 5).sum() / 1000) > 0.4 and
(np.abs(Linear1.rho.weight.grad.data.numpy() - 2) < np.exp(-15)).all())
def test_Linear_without_initial():
Linear1 = LinearBayesianGaussian(10,
100,
LinearBayesianGaussian_init=False,
p=1)
assert ((Linear1.mu.weight.data.numpy().shape[1] == 10
and Linear1.mu.weight.data.numpy().shape[0] == 100
and Linear1.mu.bias.data.numpy().shape[0] == 100)
and (Linear1.rho.weight.data.numpy().shape[1] == 10
and Linear1.rho.weight.data.numpy().shape[0] == 100
and Linear1.rho.bias.data.numpy().shape[0] == 100))
def test_Linear_w_values():
Linear1 = LinearBayesianGaussian(10,
100,
LinearBayesianGaussian_init=False,
p=1)
with torch.no_grad():
Linear1.mu.weight.copy_(
torch.tensor(np.random.uniform(1, 1, (100, 10)),
dtype=torch.float64))
output = Linear1(
torch.tensor(np.random.uniform(1, 1, (10)), dtype=torch.float64))
assert (output.data.numpy() > 9).all() and (output.data.numpy() < 11).all()