# custom layer

Tensorflow insights - part 5: Custom model - 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.

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Tensorflow insights - part 4: Custom model

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.

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