this is the greedy system adopted by TensorFlow to stop memory fragmentation, but this leads to a bottleneck of GPU memory. just one method exclusively has all of the memory. At check time, no models are dropped out, and in its place the layer's output values are scaled down by an element equivalent into the dropout rate, in order to harmony for T