np.random.seed(101)
def func(x, form=0):
if form == 0:
return x + 1.0
if form == 1:
return x * x + x + 1.0
xd = np.random.randn(100)
yd = func(xd, form=1)
nn = NeuralNetwork(sizes=[1, 5, 1], activation_function="sigmoid")
y = nn.eval(1.0)
print y
gamma = np.random.randn(nn.n) * 0.2
for j in range(10000):
nn.set_from_array(gamma)
dJdgamma = np.zeros_like(gamma)
J = 0.0
for i in range(len(xd)):
xin = xd[i]
yeval = nn.eval(xin)
#print yeval
J += (yeval - yd[i])**2
dydgamma = nn.dydgamma(xin, gamma)
dJdgamma += 2.0 * (yeval - yd[i]) * dydgamma
gamma = gamma - dJdgamma / np.abs(dJdgamma) * 0.001
