def test_calc_delta(self):
l1 = SoftMaxLayer()
n = Sequential([l1])
x = np.array([15.0, 10.0, 2.0])
y = n.forward(x)
self.assertEqual(y.shape, (3, ))
nll = NegativeLogLikelihoodLoss()
t = np.array([0.0, 0.0, 1.0])
self.assertEqual(y.shape, t.shape)
J1 = nll.loss(y, t)
self.assertEqual(J1.shape, (3, ))
assert_almost_equal(J1, [0.0, 0.0, 13.0067176], decimal=5)
cel = CrossEntropyLoss()
t = np.array([0.0, 0.0, 1.0])
J2 = cel.loss(x, t)
self.assertEqual(J2.shape, (3, ))
assert_almost_equal(J2, [0.0, 0.0, 13.0067176], decimal=5)
delta_in = -nll.dJdy_gradient(y, t)
assert_almost_equal(delta_in, [0.0, 0.0, 445395.349996])
delta_out1 = n.backward(delta_in)
assert_almost_equal(delta_out1, [-0.9933049, -0.0066928, 0.9999978],
decimal=5)
#
delta_out2 = -cel.dJdy_gradient(x, t)
assert_almost_equal(delta_out2, [-0.9933049, -0.0066928, 0.9999978],
decimal=5)
def test_variable_dict(self):
xv = np.array([0.5, 0.1, 0.5])
yv = np.array([0.2, 0.4, 0.5])
valin = ['x', 'y']
x = Input(valin, 'x')
y = Input(valin, 'y')
xyv = [xv, yv]
Wxv = np.array([[2.1, 3.1, 2.2], [2.2, 3.2, 4.2], [2.2, 5.2, 4.2]])
Wyv = np.array([[2.1, 2.1, 2.2], [1.6, 1.2, 6.2], [2.1, 3.1, 2.2]])
Wx = MWeight(3, 3, weights=Wxv)
Wy = MWeight(3, 3, weights=Wyv)
net = ComputationalGraphLayer(Sigmoid(Wx.dot(x)) + Tanh(Wy.dot(y)))
netDict = Sequential(VariableDictLayer(valin), net)
out = net.forward(xyv)
self.assertEqual(out.shape, (3, ))
assert_almost_equal(out, sigmoid(Wxv.dot(xv)) + np.tanh(Wyv.dot(yv)))
dJdy = net.backward(np.array([1.0, 1.0, 1.0]))
self.assertEqual(len(dJdy), 2)
for ind, key in enumerate(dJdy):
self.assertEqual(dJdy[ind].shape, xyv[ind].shape)
assert_almost_equal(dJdy[ind],
np.sum(net.numeric_gradient(xyv)[ind], 0))
auxdict = {'x': 0, 'y': 1}
out = netDict.forward({'x': xv, 'y': yv})
self.assertEqual(out.shape, (3, ))
assert_almost_equal(out, sigmoid(Wxv.dot(xv)) + np.tanh(Wyv.dot(yv)))
dJdy = netDict.backward(np.array([1.0, 1.0, 1.0]))
self.assertEqual(len(dJdy), 2)
for key in dJdy:
self.assertEqual(dJdy[key].shape, xyv[auxdict[key]].shape)
assert_almost_equal(
dJdy[key], np.sum(net.numeric_gradient(xyv)[auxdict[key]], 0))
def test_SigmoidLayer(self):
l1 = SigmoidLayer()
n = Sequential([l1])
y = n.forward(np.array([0]))
self.assertEqual(y.shape, (1, ))
assert_array_equal(y, np.array([0.5]))
d = n.backward(np.array([1]))
self.assertEqual(d.shape, (1, ))
assert_array_equal(d, np.array([0.25]))
def test_LinearLayer(self):
l1 = LinearLayer(5, 6, 'ones')
n = Sequential([l1])
y = n.forward(np.array([2.0, 1.0, 2.0, 3.0, 4.0]))
self.assertEqual(y.shape, (6, ))
assert_array_equal(y, np.array([
13.0,
13.0,
13.0,
13.0,
13.0,
13.0,
]))
l2 = LinearLayer(6, 2, 'ones')
n.add(l2)
y = n.forward(np.array([2.0, 1.0, 2.0, 3.0, 4.0]))
self.assertEqual(y.shape, (2, ))
assert_array_equal(y, np.array([79.0, 79.0]))
d = n.backward(np.array([2.0, 3.0]))
self.assertEqual(d.shape, (5, ))
assert_array_equal(d, np.array([30., 30., 30., 30., 30.]))