Deep learning with C++ - an introduction to tiny-dnn
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deep learning with c++ an introduction to tiny-dnn by Taiga Nomi embedded software engineer, Osaka, Japan
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Transcript of Deep learning with C++ - an introduction to tiny-dnn
deep learning with c++an introduction to tiny-dnn
by Taiga Nomiembedded software engineer, Osaka, Japan
deep learning
Icons made by Freepik from www.flaticon.com is licensed by CC 3.0 BY
Facial recognition
Image understanding
Finance
Game playing
Translation
Robotics
Drug discovery
Text recognition
Video processing
Text generation
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
- ...
deep learning framework
A modern deep learning framework for C++ programmers
1400 stars 500 forks 35 contributors 100 clones/day
“A Modern Deep Learning module” by Edgar Riba
“Deep Learning with Quantization for Semantic Saliency Detection” by Yida Wang
https://summerofcode.withgoogle.com/archive/
1.Easy to introduce
2.Simple syntax
3.Extensible backends
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>
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
1.Easy to introduce- Caffe model converter is also available
- TensorFlow converter - coming soon!
- Close the gap between researcher and engineer
1.Easy to introduce
2.Simple syntax
3.Extensible backends
2.Simple syntaxExample: Multi layer perceptron
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"}
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)
Keras
model = Sequential([ Dense(100, input_dim=10), Activation('relu'), Dense(20), Activation('relu'),])
tiny-dnn
network<sequential> net;net << dense<relu>(10, 100) << dense<relu>(100, 20);
tiny-dnn, another solution
auto net = make_mlp<relu>({10, 100, 20});
- modern C++ enable us to keep code simple- type inference, initializer list
2.Simple syntaxExample: Convolutional Neural Networks
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 { 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: "prob" type: "Softmax" bottom: "ip2" top: "prob"}
}}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 { 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: "prob" type: "Softmax" bottom: "ip2" top: "prob"}
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: "prob" type: "Softmax" bottom: "ip2" top: "prob"}
}}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"}
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)
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),])
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);
1.Easy to introduce
2.Simple syntax
3.Extensible backends
3.Extensible backendsCommon 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”
3.Extensible backendsSome 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
3.Extensible backends
// select an engine explicitlynet << conv<>(28, 28, 5, 1, 32, backend::avx) << ...;
// switch them seamlesslynet[0]->set_backend_type(backend::opencl);
Model serialization (binary/json)
Regression training
Basic image processing
Layer freezing
Graph visualization
Multi-thread execution
Double precision support
Basic functionality
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)
Future plans
- 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!
User chat for QA:
https://gitter.im/tiny-dnn
Official documents:
http://tiny-dnn.readthedocs.io/en/latest/
For users
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_guides/How-to-contribute.mdhttps://github.com/tiny-dnn/tiny-dnn/labels/contributions%20welcome
For developers
code: github.com/tiny-dnn/tiny-dnnslide: https://goo.gl/Se2rzu
