Revert some mess

Author: Saransh-cpp

docs/src/saving.md

@@ -116,15 +116,15 @@ This will update the `"model-checkpoint.bson"` file every thirty seconds.
 You can get more advanced by saving a series of models throughout training, for example
 
 ```julia
-julia> @save "model-$(now()).bson" model
+@save "model-$(now()).bson" model
 ```
 
 will produce a series of models like `"model-2018-03-06T02:57:10.41.bson"`. You
 could also store the current test set loss, so that it's easy to (for example)
 revert to an older copy of the model if it starts to overfit.
 
 ```julia
-julia> @save "model-$(now()).bson" model loss = testloss()
+@save "model-$(now()).bson" model loss = testloss()
 ```
 
 Note that to resume a model's training, you might need to restore other stateful parts of your training loop. Possible examples are stateful optimizers (which usually utilize an `IdDict` to store their state), and the randomness used to partition the original data into the training and validation sets.
@@ -133,7 +133,7 @@ You can store the optimiser state alongside the model, to resume training
 exactly where you left off. BSON is smart enough to [cache values](https://github.com/JuliaIO/BSON.jl/blob/v0.3.4/src/write.jl#L71) and insert links when saving, but only if it knows everything to be saved up front. Thus models and optimizers must be saved together to have the latter work after restoring.
 
 ```julia
-julia> opt = ADAM()
-julia> @save "model-$(now()).bson" model opt
+opt = ADAM()
+@save "model-$(now()).bson" model opt
 ```