If using Sigmoid instead of Relu, training takes much more time, but, it seems we get similar performance for sae total time. However, feature maps are strange, it seems many of them are discarded. Even extending to 10k iters, this does not changes. It appears as if, when using sigmoid, we need lest volumetric complexity in the layers for the DNN to work
If using Sigmoid instead of Relu, training takes much more time, but, it seems we get similar performance for sae total time. However, feature maps are strange, it seems many of them are discarded. Even extending to 10k iters, this does not changes. It appears as if, when using sigmoid, we need lest volumetric complexity in the layers for the DNN to work

eigendreams/TensorFlow-Tutorials