# DNN Inception
This program is ported by C# from examples\dnn_inception_ex.cpp.
## How to use?
## 1. Build
1. Open command prompt and change to <DnnInception_dir>
1. Type the following command
````
dotnet build -c Release
````
2. Copy ***DlibDotNet.dll***, ***DlibDotNetNative.dll*** and ***DlibDotNetNativeDnn.dll*** to output directory; <DnnInception_dir>\bin\Release\netcoreapp2.0.
**NOTE**
- You should build ***DlibDotNetNative.dll*** and ***DlibDotNetNativeDnn.dll*** with CUDA.
- If you want to run at Linux and MacOS, you should build the **DlibDotNet** at first.
Please refer the [Tutorial for Linux](https://github.com/takuya-takeuchi/DlibDotNet/wiki/Tutorial-for-Linux) or [Tutorial for MacOS](https://github.com/takuya-takeuchi/DlibDotNet/wiki/Tutorial-for-MacOS).
## 2. Download demo data
Download test data from the following urls.
- http://yann.lecun.com/exdb/mnist/
- train-images-idx3-ubyte.gz
- train-labels-idx1-ubyte.gz
- t10k-images-idx3-ubyte.gz
- t10k-labels-idx1-ubyte.gz
And extract them and copy to extracted files to <DnnInception_dir>.
## 3. Run
````
cd <DnnInception_dir>
dotnet run -c Release .
The net has 43 layers in it.
layer<0> loss_multiclass_log
layer<1> fc (num_outputs=10) learning_rate_mult=1 weight_decay_mult=1 bias_learning_rate_mult=1 bias_weight_decay_mult=0
layer<2> relu
layer<3> fc (num_outputs=32) learning_rate_mult=1 weight_decay_mult=1 bias_learning_rate_mult=1 bias_weight_decay_mult=0
layer<4> max_pool (nr=2, nc=2, stride_y=2, stride_x=2, padding_y=0, padding_x=0)
layer<5> concat (1001,1002,1003)
layer<6> tag1001
layer<7> relu
layer<8> con (num_filters=4, nr=1, nc=1, stride_y=1, stride_x=1, padding_y=0, padding_x=0) learning_rate_mult=1 weight_decay_mult=1 bias_learning_rate_mult=1 bias_weight_decay_mult=0
layer<9> skip1000
layer<10> tag1002
layer<11> relu
layer<12> con (num_filters=4, nr=3, nc=3, stride_y=1, stride_x=1, padding_y=1, padding_x=1) learning_rate_mult=1 weight_decay_mult=1 bias_learning_rate_mult=1 bias_weight_decay_mult=0
layer<13> skip1000
layer<14> tag1003
layer<15> relu
layer<16> con (num_filters=4, nr=1, nc=1, stride_y=1, stride_x=1, padding_y=0, padding_x=0) learning_rate_mult=1 weight_decay_mult=1 bias_learning_rate_mult=1 bias_weight_decay_mult=0
layer<17> max_pool (nr=3, nc=3, stride_y=1, stride_x=1, padding_y=1, padding_x=1)
layer<18> tag1000
layer<19> max_pool (nr=2, nc=2, stride_y=2, stride_x=2, padding_y=0, padding_x=0)
layer<20> concat (1001,1002,1003,1004)
layer<21> tag1001
layer<22> relu
layer<23> con (num_filters=10, nr=1, nc=1, stride_y=1, stride_x=1, padding_y=0, padding_x=0) learning_rate_mult=1 weight_decay_mult=1 bias_learning_rate_mult=1 bias_weight_decay_mult=0
layer<24> skip1000
layer<25> tag1002
layer<26> relu
layer<27> con (num_filters=10, nr=3, nc=3, stride_y=1, stride_x=1, padding_y=1, padding_x=1) learning_rate_mult=1 weight_decay_mult=1 bias_learning_rate_mult=1 bias_weight_decay_mult=0
layer<28> relu
layer<29> con (num_filters=16, nr=1, nc=1, stride_y=1, stride_x=1, padding_y=0, padding_x=0) learning_rate_mult=1 weight_decay_mult=1 bias_learning_rate_mult=1 bias_weight_decay_mult=0
layer<30> skip1000
layer<31> tag1003
layer<32> relu
layer<33> con (num_filters=10, nr=5, nc=5, stride_y=1, stride_x=1, padding_y=2, padding_x=2) learning_rate_mult=1 weight_decay_mult=1 bias_learning_rate_mult=1 bias_weight_decay_mult=0
layer<34> relu
layer<35> con (num_filters=16, nr=1, nc=1, stride_y=1, stride_x=1, padding_y=0, padding_x=0) learning_rate_mult=1 weight_decay_mult=1 bias_learning_rate_mult=1 bias_weight_decay_mult=0
layer<36> skip1000
layer<37> tag1004
layer<38> relu
layer<39> con (num_filters=10, nr=1, nc=1, stride_y=1, stride_x=1, padding_y=0, padding_x=0) learning_rate_mult=1 weight_decay_mult=1 bias_learning_rate_mult=1 bias_weight_decay_mult=0
layer<40> max_pool (nr=3, nc=3, stride_y=1, stride_x=1, padding_y=1, padding_x=1)
layer<41> tag1000
layer<42> input<matrix>
Traning NN...
Epoch: 1 learning rate: 0.01 average loss: 0.480985 steps without apparent progress: 8
Epoch: 2 learning rate: 0.01 average loss: 0.105669 steps without apparent progress: 15
Epoch: 3 learning rate: 0.01 average loss: 0.0740526 steps without apparent progress: 318
Epoch: 4 learning rate: 0.01 average loss: 0.0604944 steps without apparent progress: 9
Saved state to inception_sync
Epoch: 5 learning rate: 0.01 average loss: 0.0520312 steps without apparent progress: 326
Epoch: 6 learning rate: 0.01 average loss: 0.0459913 steps without apparent progress: 350
Epoch: 7 learning rate: 0.01 average loss: 0.0413119 steps without apparent progress: 10
Epoch: 8 learning rate: 0.01 average loss: 0.0379306 steps without apparent progress: 26
Saved state to inception_sync_
Epoch: 9 learning rate: 0.01 average loss: 0.0350026 steps without apparent progress: 542
Epoch: 10 learning rate: 0.01 average loss: 0.0325879 steps without apparent progress: 11
Epoch: 11 learning rate: 0.01 average loss: 0.0306371 steps without apparent progress: 550
Epoch: 12 learning rate: 0.01 average loss: 0.0287543 steps without apparent progress: 517
Epoch: 13 learning rate: 0.01 average loss: 0.0271211 steps without apparent progress: 12
Saved state to inception_sync
Epoch: 14 learning rate: 0.01 average loss: 0.0258299 steps without apparent progress: 540
Epoch: 15 learning rate: 0.01 average loss: 0.0246587 steps without apparent progress: 518
Epoch: 16 learning rate: 0.01 average loss: 0.0235673 steps without apparent progress: 522
Epoch: 17 learning rate: 0.01 average loss: 0.0224451 steps without apparent progress: 558
Epoch: 18 learning rate: 0.01 average loss: 0.0216577 steps without apparent progress: 545
Saved state to inception_sync_
Epoch: 19 learning rate: 0.01 average loss: 0.0207857 steps without apparent progress: 566
Epoch: 20 learning rate: 0.01 average loss: 0.0197219 steps without apparent progress: 979
Epoch: 21 learning rate: 0.01 average loss: 0.0189983 steps without apparent progress: 989
Epoch: 22 learning rate: 0.01 average loss: 0.0183556 steps without apparent progress: 1037
Saved state to inception_sync
Epoch: 23 learning rate: 0.01 average loss: 0.0176799 steps without apparent progress: 986
Epoch: 24 learning rate: 0.01 average loss: 0.0169129 steps without apparent progress: 970
Epoch: 25 learning rate: 0.01 average loss: 0.0161669 steps without apparent progress: 979
Epoch: 26 learning rate: 0.01 average loss: 0.0154576 steps without apparent progress: 983
Epoch: 27 learning rate: 0.01 average loss: 0.0146988 steps without apparent progress: 985
Saved state to inception_sync_
Epoch: 28 learning rate: 0.01 average loss: 0.0141198 steps without apparent progress: 984
Epoch: 29 learning rate: 0.01 average loss: 0.0135091 steps without apparent progress: 984
Epoch: 30 learning rate: 0.01 average loss: 0.0130994 steps without apparent progress: 991
Epoch: 31 learning rate: 0.01 average loss: 0.0125059 steps without apparent progress: 979
Epoch: 32 learning rate: 0.01 average loss: 0.012067 steps without apparent progress: 983
Saved state to inception_sync
Epoch: 33 learning rate: 0.01 average loss: 0.0114886 steps without apparent progress: 998
Epoch: 34 learning rate: 0.01 average loss: 0.0109174 steps without apparent progress: 560
Epoch: 35 learning rate: 0.01 average loss: 0.0106421 steps without apparent progress: 986
Epoch: 36 learning rate: 0.01 average loss: 0.0103991 steps without apparent progress: 1060
Saved state to inception_sync_
Epoch: 37 learning rate: 0.01 average loss: 0.0100768 steps without apparent progress: 1061
Epoch: 38 learnin
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DlibDotNet:针对Windows,MacOS和Linux用C ++和C#编写的Dlib .NET包装器
共1544个文件
cs:546个
h:169个
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DlibDotNet 适用于Windows,MacOS和Linux的用C ++和C#编写的Dlib包装器 DlibDotNet 包 操作系统 x86 x64 臂 的ARM64 努吉特 DlibDotNet(CPU) 视窗 ✓ ✓ -- -- Linux -- ✓ -- -- OSX -- ✓ -- -- 适用于CUDA 9.2的DlibDotNet 视窗 -- ✓ -- -- Linux -- ✓ -- -- OSX -- -- -- -- 适用于CUDA 10.0的DlibDotNet 视窗 -- ✓ -- -- Linux -- ✓ -- -- OSX -- -- -- -- 适用于CUDA 10.1的DlibDotNet 视窗 -- ✓ -- -- Linux -- ✓ -- -- OSX -- -- -- -- 适用于CUDA 10.2的DlibDotNet 视窗 -- ✓ -- -- Linux -- ✓ -- -- OSX
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DlibDotNet:针对Windows,MacOS和Linux用C ++和C#编写的Dlib .NET包装器 (1544个子文件)
Package.appxmanifest 2KB
Package.appxmanifest 1KB
Dockerfile.bak 1KB
Dockerfile.bak 1KB
Dockerfile.bak 888B
Dockerfile.bak 885B
Dockerfile.bak 286B
Dockerfile.bak 286B
ExecuteTest.bat 932B
Publish.bat 809B
CreateAllPackage.bat 395B
BuildNuspec.Pre.bat 127B
Initialize.bat 39B
Lenna.bmp 257KB
Lenna.bmp 257KB
Lenna.bmp 257KB
Lenna_mini.bmp 65KB
packages.config 403B
packages.config 403B
packages.config 284B
packages.config 284B
packages.config 210B
packages.config 210B
App.config 189B
App.config 189B
App.config 189B
App.config 184B
App.config 184B
App.config 184B
App.config 184B
packages.config 165B
main.cpp 2KB
custom_drawable_window.cpp 2KB
action_mediator.cpp 411B
custom_multithreaded_object.cpp 350B
LossBase.cpp 332B
optimization_solve_qp2_using_smo.cpp 45B
histogram_intersection_kernel.cpp 42B
LossMulticlassLogPerPixelBase.cpp 42B
LossMulticlassLogPerPixel.cpp 38B
op_std_vect_to_mat_value.cpp 37B
detection_template_tools.cpp 37B
shape_predictor_trainer.cpp 36B
render_face_detections.cpp 35B
random_color_transform.cpp 35B
image_dataset_metadata.cpp 35B
matrix_math_functions.cpp 34B
frontal_face_detector.cpp 34B
full_object_detection.cpp 34B
threads_kernel_shared.cpp 34B
LossMulticlassLogBase.cpp 34B
multithreaded_object.cpp 33B
max_cost_assignment.cpp 32B
correlation_tracker.cpp 32B
box_overlap_testing.cpp 32B
radial_basis_kernel.cpp 32B
menu_item_separator.cpp 32B
op_std_vect_to_mat.cpp 31B
matrix_expressions.cpp 31B
equalize_histogram.cpp 31B
perspective_window.cpp 31B
load_image_dataset.cpp 31B
misc_api_kernel_1.cpp 30B
op_array2d_to_mat.cpp 30B
scan_fhog_pyramid.cpp 30B
scan_fhog_pyramid.cpp 30B
spatial_filtering.cpp 30B
polynomial_kernel.cpp 30B
scan_fhog_pyramid.cpp 30B
scan_fhog_pyramid.cpp 30B
scrollable_region.cpp 30B
vector_normalizer.cpp 30B
gui_core_kernel_1.cpp 30B
LossMulticlassLog.cpp 30B
point_transforms.cpp 29B
matrix_utilities.cpp 29B
draw_surf_points.cpp 29B
spectral_cluster.cpp 29B
chinese_whispers.cpp 29B
hessian_pyramid.cpp 28B
shape_predictor.cpp 28B
object_detector.cpp 28B
hough_transform.cpp 28B
feature_ranking.cpp 28B
random_cropper.cpp 27B
sigmoid_kernel.cpp 27B
svm_nu_trainer.cpp 27B
canvas_drawing.cpp 27B
menu_item_text.cpp 27B
LossMetricBase.cpp 27B
rand_kernel_1.cpp 26B
matrix_common.cpp 26B
custom_logger.cpp 26B
ostringstream.cpp 26B
segment_image.cpp 26B
image_pyramid.cpp 26B
edge_detector.cpp 26B
interpolation.cpp 26B
batch_trainer.cpp 26B
linear_kernel.cpp 26B
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