def test_Softmax():
x = np.array([[1., 2., 3.],
[1., 4., 9.]])
SOFTMAX = activations.get('Softmax')
M = SOFTMAX.ForwardPropagation(x)
N = SOFTMAX.BackwardPropagation(grads=np.array([[1., 1.]]))
# print('\n', M)
# print('\n', N)
assert round(M[0][0], 3) == round(9.00305732e-02, 3)
assert round(M[0][1], 3) == round(2.44728471e-01, 3)
assert round(M[0][2], 3) == round(6.65240956e-01, 3)
assert round(M[1][0], 3) == round(3.33106430e-04, 3)
assert round(M[1][1], 3) == round(6.69062149e-03, 3)
assert round(M[1][2], 3) == round(9.92976272e-01, 3)
def test_Tanh():
TANH = activations.get('Tanh')
M = TANH.ForwardPropagation(np.array([[3, -1.], [0, 2.]]))
N = TANH.BackwardPropagation(np.array([[1., -2.], [-7., 0.2]]))
# print('\n', M)
# print('\n', N)
assert round(M[0][0],
3) == 0.995 and round(M[0][1],
3) == -0.762 and round(M[1][0],
3) == 0 and round(M[1][1],
3) == 0.964
assert round(N[0][0],
3) == round(-8, 3) and round(N[0][1],
3) == round(0, 3) and round(N[1][0],
3) == round(
-7, 3) and round(N[1][1],
3) == round(-0.6, 3)
def test_sigmoid():
SIGMOID = activations.get('Sigmoid')
M = SIGMOID.ForwardPropagation(np.array([[3, -1], [0, 2]]))
N = SIGMOID.BackwardPropagation(np.array([[1., -2.], [-7, 0.2]]))
# print('\n', M)
# print('\n', N)
assert round(M[0][0],
3) == 0.953 and round(M[0][1],
3) == 0.269 and round(M[1][0],
3) == 0.5 and round(M[1][1],
3) == 0.881
assert round(N[0][0],
3) == round(-6, 3) and round(N[0][1],
3) == round(4, 3) and round(N[1][0],
3) == round(0, 3) and round(N[1][1],
3) == round(-0.4,
3)
def test_Softplus():
SOFTPLUS = activations.get('Softplus')
M = SOFTPLUS.ForwardPropagation(np.array([[3, -1.], [0, 2.]]))
N = SOFTPLUS.BackwardPropagation(np.array([[1., -2.], [-7., 0.2]]))
# print('\n', M)
# print('\n', N)
assert round(M[0][0],
3) == 3.049 and round(M[0][1],
3) == 0.313 and round(M[1][0],
3) == 0.693 and round(M[1][1],
3) == 2.127
assert round(N[0][0],
3) == round(0.04742587, 3) and round(N[0][1],
3) == round(-1.46211716, 3) and round(N[1][0],
3) == round(-3.5,
3) and round(
N[1][1],
3) == round(0.02384058, 3)
def test_Relu():
RELU = activations.get('Relu')
M = RELU.ForwardPropagation(np.array([3., -1.]))
N = RELU.BackwardPropagation(np.array([1., -2.]))
assert M[0] == 3 and M[1] == 0
assert N[0] == 1 and N[1] == 0
def test_linear():
LINEAR = activations.get('linear')
M = LINEAR.ForwardPropagation(np.array([[3, -1], [0, 2]]))
N = LINEAR.BackwardPropagation(np.array([[1., -2.], [-7, 0.2]]))
print('\n', M)
print('\n', N)
def test_None():
NONE = activations.get(None)
M = NONE.ForwardPropagation(np.array([[3, -1], [0, 2]]))
N = NONE.BackwardPropagation(np.array([[1., -2.], [-7, 0.2]]))
print('\n', M)
print('\n', N)