def initwb_reg(layer):
"""
Initialize weights and bias
in the range defined by the activation function (transf.inp_active)
"""
active = layer.transf.inp_active[:]
if np.isinf(active[0]):
active[0] = -100.0
if np.isinf(active[1]):
active[1] = 100.0
min = active[0] / (2 * layer.cn)
max = active[1] / (2 * layer.cn)
init_rand(layer, min, max, 'w')
if 'b' in layer.np:
init_rand(layer, min, max, 'b')
def initnw(layer):
"""
Nguyen-Widrow initialization function
"""
ci = layer.ci
cn = layer.cn
w_fix = 0.7 * cn ** (1. / ci)
w_rand = np.random.rand(cn, ci) * 2 - 1
# Normalize
if ci == 1:
w_rand = w_rand / np.abs(w_rand)
else:
w_rand = w_rand * np.sqrt(1. / np.square(w_rand).sum(axis=1).reshape(cn, 1))
w = w_fix * w_rand
b = np.array([0]) if cn == 1 else w_fix * np.linspace(-1, 1, cn) * np.sign(w[:, 0])
# Scaleble to inp_active
amin, amax = layer.transf.inp_active
amin = -1 if amin == -np.Inf else amin
amax = 1 if amax == np.Inf else amax
x = 0.5 * (amax - amin)
y = 0.5 * (amax + amin)
w = x * w
b = x * b + y
# Scaleble to inp_minmax
minmax = layer.inp_minmax.copy()
minmax[np.isneginf(minmax)] = -1
minmax[np.isinf(minmax)] = 1
x = 2. / (minmax[:, 1] - minmax[:, 0])
y = 1. - minmax[:, 1] * x
w = w * x
b += np.dot(w, y)
layer.np['w'][:] = w
layer.np['b'][:] = b
return
