docstrings

src/layers/recurrent.jl

@@ -21,7 +21,6 @@ In the forward pass, implements the function
 ```math
 h^\prime = \sigma(W_i x + W_h h + b)
 ```
-Returns a tuple `(out, state)`, where both element are given by `h'`.
 
 See [`RNN`](@ref) for a layer that processes entire sequences.
 
@@ -43,7 +42,8 @@ The arguments of the forward pass are:
 - `h`: The hidden state of the RNN. It should be a vector of size `out` or a matrix of size `out x batch_size`.
        If not provided, it is assumed to be a vector of zeros, initialized by [`initialstates`](@ref).
 
-Returns a tuple `(out, state)`, where both elements are given by the updated state `h'`.
+Returns a tuple `(output, state)`, where both elements are given by the updated state `h'`, 
+a tensor of size `out` or `out x batch_size`.
 
 # Examples
 
@@ -93,13 +93,13 @@ using Flux
 rnn = RNNCell(10 => 20)
 
 # Get the initial hidden state
-h0 = initialstates(rnn)
+state = initialstates(rnn)
 
 # Get some input data
 x = rand(Float32, 10)
 
 # Run forward
-res = rnn(x, h0)
+out, state = rnn(x, state)
 """
 initialstates(rnn::RNNCell) = zeros_like(rnn.Wh, size(rnn.Wh, 2))
 
@@ -242,7 +242,6 @@ o_t = \sigma(W_{xo} x_t + W_{ho} h_{t-1} + b_o)
 h_t = o_t \odot \tanh(c_t)
 ```
 
-The `LSTMCell` returns the new hidden state `h_t` and cell state `c_t` for a single time step.
 See also [`LSTM`](@ref) for a layer that processes entire sequences.
 
 # Arguments
@@ -263,7 +262,8 @@ The arguments of the forward pass are:
   They should be vectors of size `out` or matrices of size `out x batch_size`.
   If not provided, they are assumed to be vectors of zeros, initialized by [`initialstates`](@ref).
 
-Returns a tuple `(h′, c′)` containing the new hidden state and cell state in tensors of size  `out` or `out x batch_size`. 
+Returns a tuple `(output, state)`, where `output = h'` is the new hidden state and `state = (h', c')` is the new hidden and cell states.
+These are tensors of size `out` or `out x batch_size`. 
 
 # Examples
 
@@ -277,9 +277,9 @@ julia> c = zeros(Float32, 5); # cell state
 
 julia> x = rand(Float32, 3, 4);  # in x batch_size
 
-julia> h′, c′ = l(x, (h, c));
+julia> y, (h′, c′) = l(x, (h, c));
 
-julia> size(h′)  # out x batch_size
+julia> size(y)  # out x batch_size
 (5, 4)
 ```
 """
@@ -445,7 +445,8 @@ The arguments of the forward pass are:
 - `h`: The hidden state of the GRU. It should be a vector of size `out` or a matrix of size `out x batch_size`.
   If not provided, it is assumed to be a vector of zeros, initialized by [`initialstates`](@ref).
 
-Returns the new hidden state `h'` as an array of size `out` or `out x batch_size`.
+Returns the tuple `(output, state)`, where `output = h'` and `state = h'`.
+The new hidden state `h'` is an array of size `out` or `out x batch_size`.
 
 # Examples
 
@@ -457,7 +458,7 @@ julia> h = zeros(Float32, 5); # hidden state
 
 julia> x = rand(Float32, 3, 4);  # in x batch_size
 
-julia> h′ = g(x, h);
+julia> y, h = g(x, h);
 ```
 """
 struct GRUCell{I, H, V}
@@ -612,7 +613,8 @@ The arguments of the forward pass are:
 - `h`: The hidden state of the GRU. It should be a vector of size `out` or a matrix of size `out x batch_size`.
   If not provided, it is assumed to be a vector of zeros, initialized by [`initialstates`](@ref).
 
-Returns the new hidden state `h'` as an array of size `out` or `out x batch_size`.
+Returns the tuple `(output, state)`, where `output = h'` and `state = h'`.  
+The new hidden state `h'` is an array of size `out` or `out x batch_size`.
 """
 struct GRUv3Cell{I, H, V, HH}
   Wi::I