fix doctests

docs/src/guide/models/recurrence.md

@@ -169,34 +169,21 @@ Flux.update!(opt_state, model, g)
 
 Finally, the [`Recurrence`](@ref) layer can be used wrap any recurrent cell to process the entire sequence at once. For instance, a type behaving the same as the `LSTM` layer can be defined as follows:
 
-```jldoctest
-julia> rnn = Recurrence(LSTMCell(2 => 3))   # similar to LSTM(2 => 3)
-Recurrence(
-  LSTMCell(2 => 3),                     # 72 parameters
-)                   # Total: 3 arrays, 72 parameters, 448 bytes.
-
-julia> y = rnn(rand(Float32, 2, 4, 3));
+```julia
+rnn = Recurrence(LSTMCell(2 => 3))   # similar to LSTM(2 => 3)
+x = rand(Float32, 2, 4, 3)
+y = rnn(x)
 ```
 
 ## Stacking recurrent layers
 
 Recurrent layers can be stacked to form a deeper model by simply chaining them together using the [`Chain`](@ref) layer. The output of a layer is fed as input to the next layer in the chain.
 For instance, a model with two LSTM layers can be defined as follows:
 
-```jldoctest
-julia> stacked_rnn = Chain(LSTM(3 => 5), Dropout(0.5), LSTM(5 => 5))
-Chain(
-  LSTM(3 => 5),                         # 180 parameters
-  Dropout(0.5),
-  LSTM(5 => 5),                         # 220 parameters
-)                   # Total: 6 arrays, 400 parameters, 1.898 KiB.
-
-julia> x = rand(Float32, 3, 4);
-
-julia> y = stacked_rnn(x);
-
-julia> size(y)
-(5, 4)
+```julia
+stacked_rnn = Chain(LSTM(3 => 5), Dropout(0.5), LSTM(5 => 5))
+x = rand(Float32, 3, 4)
+y = stacked_rnn(x)
 ```
 
 If more fine grained control is needed, for instance to have a trainable initial hidden state, one can define a custom model as follows: 
@@ -223,6 +210,6 @@ function (m::StackedRNN)(x)
 end
 
 rnn = StackedRNN(3; num_layers=2)
-x = rand(Float32, 3, 2)
+x = rand(Float32, 3, 10)
 y = rnn(x)
 ```

src/layers/recurrent.jl

@@ -53,6 +53,9 @@ stack(out, dims = 2)
 
 ```jldoctest
 julia> rnn = Recurrence(RNNCell(2 => 3))
+Recurrence(
+  RNNCell(2 => 3, tanh),                # 18 parameters
+)                   # Total: 3 arrays, 18 parameters, 232 bytes.
 
 julia> x = rand(Float32, 2, 3, 4); # in x len x batch_size