tiny-dnn
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A quick introduction to tiny-dnn
How-Tos
Integrate with your application
Train network with your original dataset
Layers
Changing from v0.0.1
Adding a new layer
tiny-dnn
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How-Tos
construct the network model
sequential model
graph model
train the model
regression
classification
train graph model
“freeze” layers
use/evaluate trained model
predict a value
evaluate accuracy
calculate the loss
visualize the model
visualize graph networks
visualize each layer activations
visualize convolution kernels
io
save and load the model
import caffe’s model
reading data
reading images
get/set the properties
traverse layers
get layer types
get weight vector
change the weight initialization
change the seed value
tune the performance
profile
change the number of threads while training
handle errors
catch application exceptions
Integrate with your application
Step1/3: Include tiny_dnn.h in your application
Step2/3: Enable C++11 options
Visual Studio(2013-)
gcc(4.8-)/clang(3.3-)
Step3/3: Add include path of tiny-dnn to your build system
Train network with your original dataset
1. using opencv (image file => vec_t)
2. using mnisten (image file => idx format)
3. from levelDB (caffe style => [vec_t, label_t])
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