Bureaucrats, dave_user, Administrators
4,650
edits
Changes
→Building the application
==Building the application==
The starting point for the application is the model—in the form of a TensorFlow protobuf file (.pb)—described [[https://wiki.dave.eu/index.php/ML-TN-001_-_AI_at_the_edge:_comparison_of_different_embedded_platforms_-_Part_1#Reference_application_.231:_fruit_classifier|here]]. Incidentally, this is the '''same''' protobuf file used as starting point for [[ML-TN-001 001_- AI at the edge_AI_at_the_edge:this test_comparison_of_different_embedded_platforms_-_Part_2|this test]] as well.
===Training the model===
Model training is performed with the help of the Docker container provided by Vitis AI.
The model is trained for a total number of 100 epochs, with early stopping to prevent model overfitting on train data and checkpointing the weights on best val_loss. After that, a new model is created disabling all the layers only useful during training such as dropouts and batchnorms (i.e. in this case the batchnorm layers are not used).
===Pruning the model===
{{ImportantMessage|text=This operation if performed at TensorFlow level. As such, it does not make use of the Xilinx pruning tool, which is referred to in [https://www.xilinx.com/support/documentation/ip_documentation/dpu/v3_2/pg338-dpu.pdf this document], for example.}}
Weight pruning means eliminating unnecessary values in the weight tensors, practically setting the neural network parameters’ values to zero in order to remove the unnecessary connections between the layers of a neural network. This is done during the training process to allow the neural network to adapt to the changes. An immediate benefit from this work is disk compression: sparse tensors are amenable to compression. Hence, by applying simple file compression to the pruned TensorFlow checkpoint, it is possible to reduce the size of the model for its storage and/or transmission.
