more consistent terminology

docs/src/training/training.md

@@ -2,35 +2,34 @@
 
 To actually train a model we need three things:
 
-* A *model loss function*, that evaluates how well a model is doing given some input data.
+* A *objective function*, that evaluates how well a model is doing given some input data.
-* A collection of data points that will be provided to the loss function.
+* A collection of data points that will be provided to the objective function.
 * An [optimiser](optimisers.md) that will update the model parameters appropriately.
 
 With these we can call `Flux.train!`:
 
 ```julia
-Flux.train!(modelLoss, data, opt)
+Flux.train!(objective, data, opt)
 ```
 
 There are plenty of examples in the [model zoo](https://github.com/FluxML/model-zoo).
 
 ## Loss Functions
 
-The `loss` that we defined in [basics](../models/basics.md) is completely valid for training. We can also define a loss in terms of some model:
+The objective function must return a number representing how far the model is from its target – the *loss* of the model. The `loss` function that we defined in [basics](../models/basics.md) will work as an objective. We can also define an objective in terms of some model:
 
 ```julia
 m = Chain(
   Dense(784, 32, σ),
   Dense(32, 10), softmax)
 
-# Model loss function
 loss(x, y) = Flux.mse(m(x), y)
 
 # later
 Flux.train!(loss, data, opt)
 ```
 
-The loss will almost always be defined in terms of some *cost function* that measures the distance of the prediction `m(x)` from the target `y`. Flux has several of these built in, like `mse` for mean squared error or `crossentropy` for cross entropy loss, but you can calculate it however you want.
+The objective will almost always be defined in terms of some *cost function* that measures the distance of the prediction `m(x)` from the target `y`. Flux has several of these built in, like `mse` for mean squared error or `crossentropy` for cross entropy loss, but you can calculate it however you want.
 
 ## Datasets
 
@@ -63,7 +62,7 @@ data = zip(xs, ys)
 `train!` takes an additional argument, `cb`, that's used for callbacks so that you can observe the training process. For example:
 
 ```julia
-train!(loss, data, opt, cb = () -> println("training"))
+train!(objective, data, opt, cb = () -> println("training"))
 ```
 
 Callbacks are called for every batch of training data. You can slow this down using `Flux.throttle(f, timeout)` which prevents `f` from being called more than once every `timeout` seconds.
@@ -74,6 +73,6 @@ A more typical callback might look like this:
 test_x, test_y = # ... create single batch of test data ...
 evalcb() = @show(loss(test_x, test_y))
 
-Flux.train!(loss, data, opt,
+Flux.train!(objective, data, opt,
             cb = throttle(evalcb, 5))
 ```