# deep learning

Tensorflow insights - part 5: Custom model - VGG - continue

In the last part, we have shown how to use the custom model to implement the VGG network. However, one problem that remained is we cannot use model.summary() to see the output shape of each layer. In addition, we also cannot get the shape of filters. Although we know how the VGG is constructed, overcoming this problem will help the end-users - who only use our checkpoint files to investigate the model. In particular, it is very important for us to get the output shape of each layer/block when using the file test.py.


Tensorflow insights - part 4: Custom model - VGG

In this post, we will use the Tensorflow custom model to efficiently implement the VGG architecture so we can easily experiment with many variants of the network. The network architecture is deeper and will help us to increase the final performance.


Tensorflow insights - part 2: Basic techniques to improve the performance of a neural network

In the previous post, we have talked about the core components of Tensorflow to train a model and how to use a dataset. In this post, we will continue to work on that dataset and show some basic techniques to improve the performance of a neural network. From the state of the previous code, the new code will be added right on it.