def test_neural_tree_network_copy(self): net = NeuralTreeNet(3, empty=False) net.append(NeuralTreeNode(1, activation='identity'), inputs=[3]) net2 = net.copy() X = numpy.random.randn(2, 3) self.assertEqualArray(net.predict(X), net2.predict(X))
def test_neural_tree_network(self): net = NeuralTreeNet(3, empty=False) X = numpy.random.randn(2, 3) got = net.predict(X) exp = X.sum(axis=1) self.assertEqual(exp.reshape((-1, 1)), got[:, -1:]) rep = repr(net) self.assertEqual(rep, 'NeuralTreeNet(3)') net.clear() self.assertEqual(len(net), 0)
def test_neural_tree_network_append(self): net = NeuralTreeNet(3, empty=False) self.assertRaise(lambda: net.append( NeuralTreeNode(2, activation='identity'), inputs=[3])) net.append(NeuralTreeNode(1, activation='identity'), inputs=[3]) self.assertEqual(net.size_, 5) last_node = net.nodes[-1] X = numpy.random.randn(2, 3) got = net.predict(X) exp = X.sum(axis=1) * last_node.input_weights[0] + last_node.bias self.assertEqual(exp.reshape((-1, 1)), got[:, -1:]) rep = repr(net) self.assertEqual(rep, 'NeuralTreeNet(3)')
def test_neural_net_gradient_regression_2(self): X = numpy.abs(numpy.random.randn(10, 2)) w1 = numpy.array([-0.5, 0.8, -0.6]) noise = numpy.random.randn(X.shape[0]) / 10 noise[0] = 0 noise[1] = 0.07 X[1, 0] = 0.7 X[1, 1] = -0.5 y = w1[0] + X[:, 0] * w1[1] + X[:, 1] * w1[2] + noise for act in [ 'relu', 'sigmoid', 'identity', 'leakyrelu', 'sigmoid4', 'expit' ]: with self.subTest(act=act): neu = NeuralTreeNode(w1[1:], bias=w1[0], activation=act) loss1 = neu.loss(X, y) pred1 = neu.predict(X) if act == 'relu': self.assertEqualArray(pred1[1:2], numpy.array([0.36])) pred11 = neu.predict(X) self.assertEqualArray(pred11[1:2], numpy.array([0.36])) net = NeuralTreeNet(X.shape[1], empty=True) net.append(neu, numpy.arange(0, 2)) ide = NeuralTreeNode(numpy.array([1], dtype=X.dtype), bias=numpy.array([0], dtype=X.dtype), activation='identity') net.append(ide, numpy.arange(2, 3)) pred2 = net.predict(X) loss2 = net.loss(X, y) self.assertEqualArray(pred1, pred2[:, -1]) self.assertEqualArray(pred2[:, -2], pred2[:, -1]) self.assertEqualArray(pred2[:, 2], pred2[:, 3]) self.assertEqualArray(loss1, loss2) for p in range(0, 5): grad1 = neu.gradient(X[p], y[p]) grad2 = net.gradient(X[p], y[p]) self.assertEqualArray(grad1, grad2[:3])