def test_activation_set():
# Default values
act = Activation()
# Ensure that changing default values result in changed function calls
act.set(function='tanh')
assert act.function == act._tanh
act.set(function='relu')
assert act.function == act._relu
act.set(function='leakyrelu')
assert act.function == act._leakyrelu
# Check wrong string error is handled correctly
caught = False
try:
act.set(function='this_is_not_an_allowed_string')
except ValueError as e:
caught = True
assert caught == True
# Ensure alpha values are set correctly
alpha = 0.867
act.set(alpha=alpha)
assert act.alpha == pytest.approx(alpha)
def test_activation_functions():
# Ensure the correct values are calculated by the member functions
# We compare against sklearn functions
from sklearn.neural_network._base import tanh, relu
from scipy.special import expit as sigmoid
N = 100
act = Activation(function='sigmoid')
x = np.random.uniform(-10.0, 10.0, size=(N, 1))
assert act(x) == pytest.approx(sigmoid(x))
act.set(function='tanh')
x = np.random.uniform(-10.0, 10.0, size=(N, 1))
assert act(x) == pytest.approx(tanh(x))
act.set(function='relu')
x = np.random.uniform(-10.0, 10.0, size=(N, 1))
assert act(x) == pytest.approx(relu(x))
alpha = 2.5082958
act.set(function='leakyrelu', alpha=alpha)
x = np.random.uniform(-10.0, 10.0, size=(N, 1))
assert act(x) == pytest.approx((x >= 0.0) * x + (x < 0.0) * alpha * x)
