def test___init__(self):
sys.stdout.write('FNN_layer -> Performing init test ... ')
sys.stdout.flush()
l = FullConnLayer(input_dim=9,
output_dim=4,
activation_function=AFct.Sigmoid,
initial_weights='AUTO',
initial_bias='AUTO',
initial_offset='AUTO',
connections=generate_2d_connection_matrix(
3, 3, 2, 2, 1, 1, False))
assert numx.all(l.weights.shape == (9, 4))
assert numx.all(l.bias.shape == (1, 4))
assert numx.all(l.offset.shape == (1, 9))
assert numx.all(l.connections.shape == (9, 4))
assert numx.all(l.activation_function == AFct.Sigmoid)
l = FullConnLayer(input_dim=9,
output_dim=4,
activation_function=AFct.Rectifier,
initial_weights=1.0,
initial_bias=2.0,
initial_offset=3.0,
connections=None)
assert numx.all(l.weights.shape == (9, 4))
assert numx.all(
numx.abs(l.bias - numx.ones((1, 4)) * 2.0) < self.epsilon)
assert numx.all(
numx.abs(l.offset - numx.ones((1, 9)) * 3.0) < self.epsilon)
assert numx.all(l.connections is None)
assert numx.all(l.activation_function == AFct.Rectifier)
l = FullConnLayer(input_dim=9,
output_dim=4,
activation_function=AFct.SoftMax,
initial_weights=numx.ones((9, 4)) * 1.0,
initial_bias=numx.ones((1, 4)) * 2.0,
initial_offset=numx.ones((1, 9)) * 3.0,
connections=None)
assert numx.all(
numx.abs(l.weights - numx.ones((9, 4)) * 1.0) < self.epsilon)
assert numx.all(
numx.abs(l.bias - numx.ones((1, 4)) * 2.0) < self.epsilon)
assert numx.all(
numx.abs(l.offset - numx.ones((1, 9)) * 3.0) < self.epsilon)
assert numx.all(l.activation_function == AFct.SoftMax)
print('successfully passed!')
sys.stdout.flush()
def test_get_parameters(self):
sys.stdout.write('FNN_layer -> Performing get_parameters test ... ')
sys.stdout.flush()
l = FullConnLayer(input_dim=9,
output_dim=4,
activation_function=AFct.Sigmoid,
initial_weights='AUTO',
initial_bias='AUTO',
initial_offset='AUTO',
connections=generate_2d_connection_matrix(
3, 3, 2, 2, 1, 1, False))
w, b = l.get_parameters()
assert numx.all(l.weights.shape == (9, 4))
assert numx.all(l.bias.shape == (1, 4))
assert numx.all(numx.abs(l.weights - w) < self.epsilon)
assert numx.all(numx.abs(l.bias - b) < self.epsilon)
print('successfully passed!')
sys.stdout.flush()
def test_update_parameters(self):
sys.stdout.write('FNN_layer -> Performing update_parameters test ... ')
sys.stdout.flush()
l = FullConnLayer(input_dim=9,
output_dim=4,
activation_function=AFct.Sigmoid,
initial_weights=0,
initial_bias=0,
initial_offset=0,
connections=generate_2d_connection_matrix(
3, 3, 2, 2, 1, 1, False))
assert numx.all(
numx.abs(l.weights - numx.zeros((9, 4))) < self.epsilon)
assert numx.all(numx.abs(l.bias - numx.zeros((1, 4))) < self.epsilon)
l.update_parameters([-numx.ones((9, 4)), -numx.ones((1, 4))])
assert numx.all(numx.abs(l.weights - numx.ones((9, 4))) < self.epsilon)
assert numx.all(numx.abs(l.bias - numx.ones((1, 4))) < self.epsilon)
print('successfully passed!')
sys.stdout.flush()
def check(self, data, delta, act1, act2, act3, reg_sparseness,
desired_sparseness, cost_sparseness, reg_targets,
desired_targets, cost_targets, full):
connections = None
if full is False:
connections = generate_2d_connection_matrix(
6, 6, 3, 3, 2, 2, False)
model1 = FullConnLayer(6 * 6,
4 * 4,
activation_function=act1,
initial_weights='AUTO',
initial_bias=0.0,
initial_offset=0.0,
connections=connections,
dtype=numx.float64)
model2 = FullConnLayer(4 * 4,
5 * 5,
activation_function=act2,
initial_weights='AUTO',
initial_bias=0.0,
initial_offset=0.5,
dtype=numx.float64)
model3 = FullConnLayer(5 * 5,
6 * 6,
activation_function=act3,
initial_weights='AUTO',
initial_bias=0.0,
initial_offset=0.5,
dtype=numx.float64)
model = MODEL.Model([model1, model2, model3])
trainer = TRAINER.GDTrainer(model)
_, _, maxw, maxb = model.finit_differences(
delta, data, desired_targets, cost_targets, reg_targets,
desired_sparseness, cost_sparseness, reg_sparseness)
return numx.max([maxw, maxb])
