def test_calculate_gradient(self):
sys.stdout.write(
'FNN_layer -> Performing calculate_gradient test ... ')
sys.stdout.flush()
l = FullConnLayer(input_dim=9,
output_dim=4,
activation_function=AFct.Identity,
initial_weights=0.001 *
numx.arange(9 * 4).reshape(9, 4),
initial_bias=0.0,
initial_offset=0.5,
connections=None)
l.forward_propagate(numx.arange(9).reshape(1, 9))
l._get_deltas(
numx.arange(4).reshape(1, 4), None, None, 0.0, 0.0, None, 0.0)
l._backward_propagate()
dw, db = l._calculate_gradient()
targetW = numx.array([[0., -0.5, -1., -1.5], [0., 0.5, 1., 1.5],
[0., 1.5, 3., 4.5], [0., 2.5, 5., 7.5],
[0., 3.5, 7., 10.5], [0., 4.5, 9., 13.5],
[0., 5.5, 11., 16.5], [0., 6.5, 13., 19.5],
[0., 7.5, 15., 22.5]])
assert numx.all(numx.abs(dw - targetW) < self.epsilon)
targetb = numx.array([0., 1., 2., 3.])
assert numx.all(numx.abs(db - targetb) < self.epsilon)
l = FullConnLayer(input_dim=9,
output_dim=4,
activation_function=AFct.SoftMax,
initial_weights=0.001 *
numx.arange(9 * 4).reshape(9, 4),
initial_bias=0.0,
initial_offset=0.5,
connections=None)
l.forward_propagate(numx.arange(9).reshape(1, 9))
l._get_deltas(
numx.arange(4).reshape(1, 4), None, None, 0.0, 0.0, None, 0.0)
l._backward_propagate()
dw, db = l._calculate_gradient()
targetW = numx.array(
[[0.1834263, 0.0663258, -0.05845731, -0.1912948],
[-0.1834263, -0.0663258, 0.05845731, 0.1912948],
[-0.55027891, -0.19897739, 0.17537192, 0.57388439],
[-0.91713152, -0.33162899, 0.29228653, 0.95647398],
[-1.28398412, -0.46428059, 0.40920114, 1.33906357],
[-1.65083673, -0.59693218, 0.52611575, 1.72165316],
[-2.01768934, -0.72958378, 0.64303037, 2.10424275],
[-2.38454194, -0.86223538, 0.75994498, 2.48683234],
[-2.75139455, -0.99488697, 0.87685959, 2.86942193]])
targetb = numx.array([-0.36685261, -0.1326516, 0.11691461, 0.38258959])
assert numx.all(numx.abs(dw - targetW) < self.epsilon)
assert numx.all(numx.abs(db - targetb) < self.epsilon)
print('successfully passed!')
sys.stdout.flush()
def test_backward_propagate(self):
sys.stdout.write(
'FNN_layer -> Performing backward_propagate test ... ')
sys.stdout.flush()
l = FullConnLayer(input_dim=9,
output_dim=4,
activation_function=AFct.Identity,
initial_weights=0.001 *
numx.arange(9 * 4).reshape(9, 4),
initial_bias=0.0,
initial_offset=0.5,
connections=None)
l.forward_propagate(numx.arange(9).reshape(1, 9))
l._get_deltas(
numx.arange(4).reshape(1, 4), None, None, 0.0, 0.0, None, 0.0)
res = l._backward_propagate()
target = numx.array(
[[0.014, 0.038, 0.062, 0.086, 0.11, 0.134, 0.158, 0.182, 0.206]])
assert numx.all(numx.abs(res - target) < self.epsilon)
l = FullConnLayer(input_dim=9,
output_dim=4,
activation_function=AFct.SoftMax,
initial_weights=0.001 *
numx.arange(9 * 4).reshape(9, 4),
initial_bias=0.0,
initial_offset=0.5,
connections=None)
l.forward_propagate(numx.arange(9).reshape(1, 9))
l._get_deltas(
numx.arange(4).reshape(1, 4), None, None, 0.0, 0.0, None, 0.0)
res = l._backward_propagate()
target = numx.array([[
0.00124895, 0.00124895, 0.00124895, 0.00124895, 0.00124895,
0.00124895, 0.00124895, 0.00124895, 0.00124895
]])
assert numx.all(numx.abs(res - target) < self.epsilon)
print('successfully passed!')
sys.stdout.flush()