def _test_jacobian(self, input_dim, hidden_dim, param_dim):
jacobian = torch.zeros(input_dim, input_dim)
arn = AutoRegressiveNN(input_dim, [hidden_dim], param_dims=[param_dim])
def nonzero(x):
return torch.sign(torch.abs(x))
for output_index in range(param_dim):
for j in range(input_dim):
for k in range(input_dim):
x = torch.randn(1, input_dim)
epsilon_vector = torch.zeros(1, input_dim)
epsilon_vector[0, j] = self.epsilon
delta = (arn(x + 0.5 * epsilon_vector) - arn(x - 0.5 * epsilon_vector)) / self.epsilon
jacobian[j, k] = float(delta[0, output_index, k])
permutation = arn.get_permutation()
permuted_jacobian = jacobian.clone()
for j in range(input_dim):
for k in range(input_dim):
permuted_jacobian[j, k] = jacobian[permutation[j], permutation[k]]
lower_sum = torch.sum(torch.tril(nonzero(permuted_jacobian), diagonal=0))
assert lower_sum == float(0.0)
def _test_jacobian(self, input_dim, hidden_dim, multiplier):
jacobian = torch.zeros(input_dim, input_dim)
arn = AutoRegressiveNN(input_dim, hidden_dim, multiplier)
def nonzero(x):
return torch.sign(torch.abs(x))
for output_index in range(multiplier):
for j in range(input_dim):
for k in range(input_dim):
x = Variable(torch.randn(1, input_dim))
epsilon_vector = torch.zeros(1, input_dim)
epsilon_vector[0, j] = self.epsilon
delta = (arn(x + Variable(epsilon_vector)) -
arn(x)) / self.epsilon
jacobian[j, k] = float(
delta[0, k +
output_index * input_dim].data.cpu().numpy()[0])
permutation = arn.get_permutation()
permuted_jacobian = jacobian.clone()
for j in range(input_dim):
for k in range(input_dim):
permuted_jacobian[j, k] = jacobian[permutation[j],
permutation[k]]
lower_sum = torch.sum(
torch.tril(nonzero(permuted_jacobian), diagonal=0))
self.assertTrue(lower_sum == float(0.0))
def _test_jacobian(self, input_dim, hidden_dim, multiplier):
jacobian = torch.zeros(input_dim, input_dim)
arn = AutoRegressiveNN(input_dim, hidden_dim, multiplier)
def nonzero(x):
return torch.sign(torch.abs(x))
for output_index in range(multiplier):
for j in range(input_dim):
for k in range(input_dim):
x = torch.randn(1, input_dim)
epsilon_vector = torch.zeros(1, input_dim)
epsilon_vector[0, j] = self.epsilon
delta = (arn(x + epsilon_vector) - arn(x)) / self.epsilon
jacobian[j, k] = float(delta[0, k + output_index * input_dim])
permutation = arn.get_permutation()
permuted_jacobian = jacobian.clone()
for j in range(input_dim):
for k in range(input_dim):
permuted_jacobian[j, k] = jacobian[permutation[j], permutation[k]]
lower_sum = torch.sum(torch.tril(nonzero(permuted_jacobian), diagonal=0))
assert lower_sum == float(0.0)
