DL models contain a high number of layers. Layers have many parameters, such as their name, type, weight dimensions, layer-type-specific parameters, input, and output tensor names. While typical feedforward network structures do not have cycles, the Recurrent Neural Network (RNN) and other network structures have cycles and other topologies. So, the ability to visualize the structure of a DL model is important, both for researchers devising new networks to solve problems, and for practitioners using new networks.
Caffe2 model visualization
Visualization using Caffe2 net_drawer
Caffe2 ships with a simple visualization tool written in Python named net_drawer. This Python script can be found in your Caffe2 installation directory...
