Hello folks. I am new to Pytorch and so far I like it a lot more than Tensorflow. But my question was about checking the architecture of my models. I am still getting used to the API for the layers in Pytorch, and I was hoping to find a way to visually validate the architecture of my models.
In other words, I write a stacked RNN or an LSTM with multiple layers, and I would like to know that the architecture that is coded is the architecture that I actually intended, if that makes sense. Without some explicit error on tensor shape, etc., it can be hard to make sure that I used the right argument to specify the number of hidden layers that I wanted, or the number of hidden states that I am using to plug into my fully connected layer. Especially for longer models, or models with a lot of cutting and pasting, it can be hard to ensure that the model architecture is what I intended.
Would the Tensorboard visualization fulfill this purpose, or are there other tools that take the model specification and then convert it into a visual representation of the model. I think a visual check would be the easiest way for me to tell that the model looks correct.
Any suggestions would be appreciated.