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_neuron_one_input(self):
xv = np.array([0.5, 0.1, 0.5])
x = Input(['x'], 'x')
Wv = np.array([[0.1, 0.1, 0.2], [0.5, 0.2, 0.2]])
W = MWeight(3, 2, weights=Wv)
bv = np.array([0.3, 0.1])
b = VWeight(2, weights=bv)
net = ComputationalGraphLayer(Sigmoid(W.dot(x) + b))
out = net.forward(xv)
self.assertEqual(out.shape, (2, ))
check_out = 1.0 / (1.0 + np.exp(-Wv.dot(xv) - bv))
assert_almost_equal(out, check_out)
dJdy = net.backward(np.array([1.0, 1.0]))
self.assertEqual(dJdy.shape, (3, ))
assert_almost_equal(dJdy, np.sum(net.numeric_gradient(xv), 0))
assert_almost_equal(dJdy, (check_out * (1 - check_out)).dot(Wv))
net2 = Sequential(
LinearLayer(3, 2, weights=np.hstack([Wv, bv.reshape(2, 1)])),
SigmoidLayer)
out2 = net2.forward(xv)
assert_almost_equal(out, out2)
dJdy2 = net.backward(np.array([1.0, 1.0]))
assert_almost_equal(dJdy, dJdy2)