1. tiny-dnn is a header-only deep learning framework for C++ that aims to be easy to introduce, have simple syntax, and support extensible backends. 2. It allows defining neural networks concisely using modern C++ features and supports common network types like MLPs and CNNs through simple syntax similar to Keras and TensorFlow. 3. The framework has optional performance-oriented backends like AVX and NNPACK to accelerate computation on different hardware, and supports functions for model serialization, basic training, and more through additional modules.

1. deep learning with c++
an introduction to tiny-dnn
by Taiga Nomi
embedded software engineer, Osaka, Japan

2. deep learning
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Facial recognition
Image understanding
Finance
Game playing
Translation
Robotics
Drug discovery
Text recognition
Video processing
Text generation

3. Deep learning
- Learning a complicated function from many data
- Composed of trainable, simple mathematical functions
Input OutputTrainable Building
Blocks
- text
- audio
- image
- video
- ...
- text
- audio
- image
- video
- ...

4. deep learning framework

6. A modern deep learning framework for C++ programmers

7. 1400 stars
500 forks
35 contributors
100 clones/day

8. “A Modern Deep Learning module” by Edgar Riba
“Deep Learning with Quantization for Semantic Saliency Detection” by Yida Wang
https://summerofcode.withgoogle.com/archive/

9. 1.Easy to introduce
2.Simple syntax
3.Extensible backends

10. 1.Easy to introduce
- Just put the following line into your cpp
tiny-dnn is header only - No installation
tiny-dnn is dependency-free - No prerequisites
#include <tiny_dnn/tiny_dnn.h>

11. 1.Easy to introduce
- You can bring Deep Learning to any target you have a C++ compiler
- Officially supported (by CI builds):
- Windows (msvc2013 32/64bit, msvc2015 32/64bit)
- Linux (gcc4.9, clang3.5)
- OSX(LLVM 7.3)
- tiny-dnn might run on other compiler that support C++11

12. 1.Easy to introduce
- Caffe model converter is also available
- TensorFlow converter - coming soon!
- Close the gap between researcher and engineer

13. 1.Easy to introduce
2.Simple syntax
3.Extensible backends

14. 2.Simple syntax
Example: Multi layer perceptron

15. Caffe prototxt
input: "data"
input_shape {
dim: 1
dim: 1
dim: 1
dim: 20
}
layer {
name: "ip1"
type: "InnerProduct"
inner_product_param {
num_output: 100
}
bottom: "ip1"
top: "ip2"
}
layer {
name: "a1"
type: "TanH"
bottom: "ip1"
top: "ip1"
}
layer {
name: "ip2"
type: "InnerProduct"
inner_product_param {
num_output: 10
}
bottom: "ip1"
top: "out"
}
layer {
name: "a1"
type: "TanH"
bottom: "out"
top: "out"
}

16. Tensorflow
w1 = tf.Variable(tf.random_normal([10, 100]))
w2 = tf.Variable(tf.random_normal([100, 20]))
b1 = tf.Variable(tf.random_normal([100]))
b2 = tf.Variable(tf.random_normal([20]))
layer1 = tf.add(tf.matmul(x, w1), b1)
layer1 = tf.nn.relu(layer1)
layer2 = tf.add(tf.matmul(x, w2), b2)
layer2 = tf.nn.relu(layer2)

17. Keras
model = Sequential([
Dense(100, input_dim=10),
Activation('relu'),
Dense(20),
Activation('relu'),
])

18. tiny-dnn
network<sequential> net;
net << dense<relu>(10, 100)
<< dense<relu>(100, 20);

19. tiny-dnn, another solution
auto net = make_mlp<relu>({10, 100, 20});
- modern C++ enable us to keep code simple
- type inference, initializer list

20. 2.Simple syntax
Example: Convolutional Neural Networks

21. Caffe prototxt
name: "LeNet"
layer {
name: "data"
type: "Input"
top: "data"
input_param { shape: { dim: 64
dim: 1 dim: 28 dim: 28 } }
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 20
kernel_size: 5
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 50
kernel_size: 5
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "conv2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "ip1"
type: "InnerProduct"
bottom: "pool2"
top: "ip1"
param {
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 500
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "ip1"
top: "ip1"
}
layer {
name: "ip2"
type: "InnerProduct"
bottom: "ip1"
top: "ip2"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 10
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "ip1"
top: "ip1"
}
layer {
name: "ip2"
type: "InnerProduct"
bottom: "ip1"
top: "ip2"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 10
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "prob"
type: "Softmax"
bottom: "ip2"
top: "prob"
}

22. Tensorflow
x = tf.Variable(tf.random_normal([-1, 28, 28, 1]))
wc1 = tf.Variable(tf.random_normal([5, 5, 1, 32]))
wc2 = tf.Variable(tf.random_normal([5, 5, 32, 64]))
wd1 = tf.Variable(tf.random_normal([7*7*64, 1024]))
wout = tf.Variable(tf.random_normal([1024, n_classes]))
bc1 = tf.Variable(tf.random_normal([32]))
bc2 = tf.Variable(tf.random_normal([64]))
bd1 = tf.Variable(tf.random_normal([1024]))
bout = tf.Variable(tf.random_normal([n_classes]))
conv1 = conv2d(x, wc1, bc1)
conv1 = maxpool2d(conv1, k=2)
conv1 = tf.nn.relu(conv1)
conv2 = conv2d(conv1, wc2, bc2)
conv2 = maxpool2d(conv2, k=2)
conv2 = tf.nn.relu(conv2)
fc1 = tf.reshape(conv2, [-1, wd1.get_shape().as_list()[0]])
fc1 = tf.add(tf.matmul(fc1, wd1), bd1)
fc1 = tf.nn.relu(fc1)
fc1 = tf.nn.dropout(fc1, dropout)
out = tf.add(tf.matmul(fc1, wout), bout)

23. Keras
model = Sequential([
Convolution2D(32, 5, 5, input_shape=[28,28,5]),
MaxPooling2D(pool_size=2),
Activation('relu'),
Convolution2D(64, 5, 5),
MaxPooling2D(pool_size=2),
Activation('relu'),
Dense(1024),
Dropout(0.5),
Dense(10),
])

24. tiny-dnn
network<sequential> net;
net << conv<>(28, 28, 5, 1, 32)
<< max_pool<relu>(24, 24, 2)
<< conv<>(12, 12, 5, 32, 64)
<< max_pool<relu>(8, 8, 64, 2)
<< fc<relu>(4*4*64, 1024)
<< dropout(1024, 0.5f)
<< fc<>(1024, 10);

25. 1.Easy to introduce
2.Simple syntax
3.Extensible backends

26. 3.Extensible backends
Common scenario1:
“We have a good GPU machine to train networks, but
we need to deploy trained model into mobile device”
Common scenario2:
“We need to write platform-specific code to get
production-level performance... but it’s painful to
understand whole framework”

27. 3.Extensible backends
Some performance critical layers have backend engine
Layer API
backend::internal
pure-c++ code
backend::avx
avx-optimized code …
backend::nnpack
x86/ARM
backend::opencl
GPU
Optional

28. 3.Extensible backends
// select an engine explicitly
net << conv<>(28, 28, 5, 1, 32, backend::avx)
<< ...;
// switch them seamlessly
net[0]->set_backend_type(backend::opencl);

29. Model serialization (binary/json)
Regression training
Basic image processing
Layer freezing
Graph visualization
Multi-thread execution
Double precision support
Basic functionality

30. Caffe importer (requires protobuf)
OpenMP support
Intel TBB support
NNPACK backend (same to caffe2)
libdnn backend (same to caffe-opencl)Extra modules
(requires 3rd-party libraries)

31. Future plans

32. - GPU integration
- GPU backend is still experimental
- cudnn backend
- More mobile-oriented
- iOS/Android examples
- Quantized operation for less RAM
- TensorFlow Importer
- Performance profiling tools
- OpenVX support
We need your help!

33. User chat for QA:
https://gitter.im/tiny-dnn
Official documents:
http://tiny-dnn.readthedocs.io/en/latest/
For users

34. Join our developer chat:
https://gitter.im/tiny-dnn/developers
or
Check out docs, and our issues marked as “contributions welcome”:
https://github.com/tiny-dnn/tiny-dnn/blob/master/docs/developer_gui
des/How-to-contribute.md
https://github.com/tiny-dnn/tiny-dnn/labels/contributions%20welcome
For developers

35. code: github.com/tiny-dnn/tiny-dnn
slide: https://goo.gl/Se2rzu