This is an implementation of a simple modification to the deep-neural decision forest [Kontschieder et al.] usng TensorFlow. The modification allows the joint optimization of decision nodes and leaf nodes which speeds up the training (haven't compared yet).

Deep Neural Deicision Forest, ICCV 2015, proposed a great way to incorporate a neural network with a decision forest. During the optimization (training), the terminal (leaf) node has to be updated after each epoch.

This alternating optimization scheme is usually slower than joint optimization since other variable that is not being optimized slows down the optimization.

This code is just a proof-of-concept that

1. one can train both decision nodes and leaf nodes $\pi$ jointly using parametric formulation of leaf node.

2. one can implement the idea in a symbolic math library very easily.

The leaf node probability can be parametrized using a $softmax(W_{leaf})$. i.e. let a vector $W_{leaf} \in \mathbb{R}^N$ where N is the number of classes.

Then taking the soft max operation on W_{leaf} would give us

$$ softmax(W_{leaf}) = \frac{e^{-w_i}}{\sum_j e^{-w_j}} $$

which is always in a simplex. Thus, without any constraint, we can parametrize the leaf nodes and can compute the gradient of $L$ w.r.t $W_{leaf}$. This allows us to jointly optimize both leaf nodes and decision nodes.

I tested a simple (3 convolution + 2 fc) network for the experiment.

On the MNIST, the simple Deep-NDF got 99.5% accuracy on the test set after 100 epochs of training. After just 10 epochs, it reaches 99.1% and after 100 epochs, it reaches 99.5%

The following is the number of epoch and training accuracy after each epoch.

0 0.955829326923
1 0.979166666667
2 0.982572115385
3 0.988080929487
4 0.988181089744
5 0.988481570513
6 0.987980769231
7 0.989583333333
8 0.991185897436
9 0.991586538462
10 0.991987179487
11 0.992888621795
12 0.993088942308
13 0.992988782051
14 0.992988782051
15 0.992588141026
16 0.993289262821
17 0.99358974359
18 0.992387820513
19 0.993790064103
20 0.994090544872
21 0.993289262821
22 0.993489583333
23 0.99358974359
24 0.993990384615
25 0.993689903846
26 0.99469150641
27 0.994491185897
28 0.994090544872
29 0.994090544872
30 0.99469150641
31 0.994090544872
32 0.994791666667
33 0.993790064103
34 0.994190705128
35 0.994591346154
36 0.993990384615
37 0.995092147436
38 0.994391025641
39 0.993389423077
40 0.994991987179
41 0.994991987179
42 0.994991987179
43 0.994491185897
44 0.995192307692
45 0.995192307692
46 0.994791666667
47 0.995092147436
48 0.994991987179
49 0.994290865385
50 0.994591346154
51 0.994791666667
52 0.995092147436
53 0.995492788462
54 0.994591346154
55 0.995092147436
56 0.994190705128
57 0.99469150641
58 0.99469150641
59 0.994090544872
60 0.994290865385
61 0.994891826923
62 0.994791666667
63 0.994491185897
64 0.994591346154
65 0.994290865385
66 0.99469150641
67 0.994391025641
68 0.994791666667
69 0.99469150641
70 0.994791666667
71 0.994591346154
72 0.994891826923
73 0.994791666667
74 0.995192307692
75 0.995392628205
76 0.995392628205
77 0.995292467949
78 0.994791666667
79 0.995092147436
80 0.995392628205
81 0.994891826923
82 0.995092147436
83 0.994891826923
84 0.995092147436
85 0.995092147436
86 0.995292467949
87 0.994891826923
88 0.995693108974
89 0.994391025641
90 0.994591346154
91 0.995592948718
92 0.995292467949
93 0.995192307692
94 0.994791666667
95 0.995192307692
96 0.995092147436
97 0.994591346154
98 0.995292467949
99 0.995392628205

SDL Reading Group Slides

[Kontschieder et al.] Deep Neural Decision Forests, ICCV 2015

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Copyright (c) 2016 Christopher B. Choy ([email protected])

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