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).

===Prune the model===

In this particular case, a good compromise between compression and accuracy drop, is to prune only the two dense layers of the model, which have a high number of parameters, with a pruning schedule that start at epoch 0, ends at 1/3 of the total number of epochs (i.e. 100 epochs), starting with an initial sparsity of 50% and ending with a final sparsity of 80%, with a pruning frequency of 5 steps (i.e. the model is pruned every 5 steps during the training phase).

The weights sparsity of the model, after applying pruning:

Vitis-AI Profiler is an application-level tool that could help to optimize the whole AI application. The main purpose of this tool is to help to detect bottlenecks of the whole AI application by profiling the pre-processing functions and the post-processing functions together with DPU kernels' running status. There are two components of this tool named vaitrace, which runs on the target device and takes the responsibility for data collection, and vaiprofiler, which runs on a PC or local server and takes the responsibility for analyzation and visualization of the collected data.

<pre>