libtorch-GPU-CNN-test-MNIST-with-Batchnorm

Test add batchnorm layers.

Was modifyed this code with adding batchnorm layer between each convolution layers

https://github.com/goldsborough/examples/tree/cpp/cpp/mnist

Youtube video

https://www.youtube.com/watch?v=wLQbXEORgFA

MNIST datasets

MNIST Fashion dataset

https://github.com/zalandoresearch/fashion-mnist

Or use the old MNIST digits dataset

http://yann.lecun.com/exdb/mnist/

Without batch norm layers connected (on MNIST digits dataset)

Train Epoch: 10 [59584/60000] Loss: 0.0165
Test set: Average loss: 0.0429 | Accuracy: 0.987

With batch norm layer attached (on MNIST digits dataset)

Train Epoch: 10 [59584/60000] Loss: 0.0120
Test set: Average loss: 0.0315 | Accuracy: 0.989

Example print out

Train Epoch: 10 [59584/60000] Loss: 0.0120
Test set: Average loss: 0.0315 | Accuracy: 0.989
Print Model weights parts of conv1 weights kernels
0.0714 -0.0887 -0.2127 -0.1545 -0.0813
0.1184  0.1395  0.0606  0.0129  0.0564
-0.0033  0.1634  0.2492  0.1134  0.0322
-0.0914 -0.0334  0.0359  0.1716  0.1377
-0.1568 -0.1173 -0.1753 -0.1878 -0.0052
[ CUDAFloatType{5,5} ]

To be continue.... code under development..

Continue exploring Libtorch C++ with OpenCV towards a plane simple ResNet-34 training from scrach with custom image dataset.

The code snippet :

    under construction main.cpp
    develop backup main (copy).cpp
    ..

I will try to do a (mid level programming) of a fix plain ResNet-34 (hardcoded ResNet-34 not generic ResNet-X with bottlenecks etc). Toghether with custom data set using OpenCV for a classification of color images or video stream. Not need using torchvision for this yet.

Prepare dataset tensor from abriarity size of test.jpg input image

The read_data() function adapt the test.jpg to a tensor with shape

   Tensor [1, 3, 224, 224]

To fit as input for the future ResNet-34 classification model

Flowers datasets from kaggle

https://www.kaggle.com/alxmamaev/flowers-recognition

Resnet paper

Paper : Figure 3, resnet-34

https://arxiv.org/pdf/1512.03385.pdf

5 classes test 5 diffrent flowers (4-conv layer network)

    main 5_classes.cpp
    
    file_names_5_classes_500_jpg_img.csv

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