You can see that it follows much the same pattern as LeNet CNN for MNIST - a few (ok, more than a few!) convolutional layers followed by a few fully connected layers.
Maybe you could call it a "follow on" or perhaps an ANN pattern?:
Conv -> Conv -> Reshape/Flatten -> FC -> FC -> FC
(disregarding activation and such)
...which is really the lesson of the LeNet MNIST CNN - at least, that's my takeaway.
Comments
From the post:
So I guess he didn't use the MNIST CNN model.
However, if you look at the code:
https://github.com/SullyChen/Autopilot-TensorFlow/blob/maste...
You can see that it follows much the same pattern as LeNet CNN for MNIST - a few (ok, more than a few!) convolutional layers followed by a few fully connected layers.
Maybe you could call it a "follow on" or perhaps an ANN pattern?:
Conv -> Conv -> Reshape/Flatten -> FC -> FC -> FC
(disregarding activation and such)
...which is really the lesson of the LeNet MNIST CNN - at least, that's my takeaway.
You're right, that does look similar... I expected this to be based on some type of RNN!