Changes

[[File:Train Loss.png|thumb|center|500px|Plot of model's loss during training phase]]

Weight pruning means eliminating unnecessary values in the weight tensors, practically setting the neural network parameters’ values to zero in order to remove the unnecessary connections between the layers of a neural network. This is done during the training process to allow the neural network to adapt to the changes. An immediate benefit from this work is disk compression: sparse tensors are amenable to compression. Hence, by applying simple file compression to the pruned TensorFlow checkpoint, it is possible to reduce the size of the model for its storage and/or transmission.