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Fixed documentation error.

This commit is contained in:
Michael Green 2019-03-27 20:22:01 +01:00
parent ab46da11c7
commit d68866a238
1 changed files with 1 additions and 11 deletions
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docs/src/models/recurrence.md
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@ -77,7 +77,7 @@ If you use the `RNN(10, 5)` constructor – as opposed to `RNNCell` – you'll s
```julia
julia> RNN(10, 5)
Recur(RNNCell(Dense(15, 5)))
Recur(RNNCell(Dense(10, 5)))
```
## Sequences
@ -114,13 +114,3 @@ truncate!(m)
Calling `truncate!` wipes the slate clean, so we can call the model with more inputs without building up an expensive gradient computation.
`truncate!` makes sense when you are working with multiple chunks of a large sequence, but we may also want to work with a set of independent sequences. In this case the hidden state should be completely reset to its original value, throwing away any accumulated information. `reset!` does this for you.
In general, when training with recurrent layers in your model, you'll want to call `reset!` or `truncate!` for each loss calculation:
```julia
function loss(x,y)
l = Flux.mse(m(x), y)
Flux.reset!(m)
return l
end
```