The main concept behind sampling is how you choose control stochasticity (or randomness) in selecting the next character from the probability distributions for possible characters to come. Various applications may ask for different approaches.
The purpose of controlling stochasticity
Greedy sampling
If you are trying to train an RNN for automatic text completion and correction, you will probably be better off going with a greedy sampling strategy. This simply means that, at each sampling step, you will choose the next character in the sequence based on the character that was attributed the highest probability distribution by our Softmax output. This ensures that your network will output predictions that likely correspond to...
