def test_selu_activation(N=50):
from numpy_ml.neural_nets.activations import SELU
N = np.inf if N is None else N
mine = SELU()
gold = lambda z: F.selu(torch.FloatTensor(z)).numpy()
i = 0
while i < N:
n_dims = np.random.randint(1, 100)
z = random_stochastic_matrix(1, n_dims)
assert_almost_equal(mine.fn(z), gold(z))
print("PASSED")
i += 1
def test_leakyrelu_activation(N=50):
from numpy_ml.neural_nets.activations import LeakyReLU
N = np.inf if N is None else N
i = 0
while i < N:
n_dims = np.random.randint(1, 100)
z = random_stochastic_matrix(1, n_dims)
alpha = np.random.uniform(0, 10)
mine = LeakyReLU(alpha=alpha)
gold = lambda z: F.leaky_relu(torch.FloatTensor(z), alpha).numpy()
assert_almost_equal(mine.fn(z), gold(z))
print("PASSED")
i += 1
def test_gelu_activation(N=50):
from numpy_ml.neural_nets.activations import GELU
N = np.inf if N is None else N
i = 0
while i < N:
n_dims = np.random.randint(1, 100)
z = random_stochastic_matrix(1, n_dims)
approx = np.random.choice([True, False])
mine = GELU(approximate=False)
mine_approx = GELU(approximate=True)
gold = lambda z: F.gelu(torch.FloatTensor(z)).numpy()
np.testing.assert_allclose(mine.fn(z), gold(z), rtol=1e-3)
assert_almost_equal(mine.fn(z), mine_approx.fn(z))
print("PASSED")
i += 1