def test_three_hiddenlayer_three_input_two_output(self):
net = FeedforwardNetwork(ninput=3,
noutput=2,
nhidden=[2, 3, 4],
activ_func=[
Logistic(),
HyperbolicTangent(),
Identity(),
Logistic()
])
for i in range(5):
params = np.random.normal(size=(net.nparams, ))
x = np.random.normal(size=(net.ninput, ))
def call_x(y):
return net(y, params)
def call_params(theta):
return net(x, theta)
dx_numeric = nd.Jacobian(call_x)(x)
dparams_numeric = nd.Jacobian(call_params)(params)
dx, dparams = net.derivatives(x, params)
assert_array_almost_equal(dx, dx_numeric)
assert_array_almost_equal(dparams, dparams_numeric)
def test_two_hiddenlayer_three_input_two_output(self):
net = FeedforwardNetwork(ninput=3,
noutput=2,
nhidden=[2, 3],
activ_func=Logistic())
params = np.array([
1.111, 1.112, 11.13, 1.121, 0.1122, 1123, 1.21, 1.22, 0.2111,
0.2112, 2.121, 2.122, 0.2131, 0.2132, 2.21, 2.22, 0.223, 3.111,
0.3112, 3.113, 0.3121, 0.3122, 0.3123, 0.321, 3.22
])
x = [0.1, 2.2, 0.34]
def call_x(y):
return net(y, params)
def call_params(theta):
return net(x, theta)
dx_numeric = nd.Jacobian(call_x)(x)
dparams_numeric = nd.Jacobian(call_params)(params)
dx, dparams = net.derivatives(x, params)
assert_array_almost_equal(dx, dx_numeric)
assert_array_almost_equal(dparams, dparams_numeric)