unifying docstrings layout

src/cells/fastrnn_cell.jl

@@ -1,6 +1,6 @@
 #https://arxiv.org/abs/1901.02358
 @doc raw"""
-    FastRNNCell((input_size => hidden_size), [activation];
+    FastRNNCell(input_size => hidden_size, [activation];
         init_kernel = glorot_uniform,
         init_recurrent_kernel = glorot_uniform,
         bias = true)
@@ -84,8 +84,8 @@ function Base.show(io::IO, fastrnn::FastRNNCell)
 end
 
 @doc raw"""
-    FastRNN((input_size => hidden_size), [activation];
-    return_state = false, kwargs...)
+    FastRNN(input_size => hidden_size, [activation];
+        return_state = false, kwargs...)
 
 [Fast recurrent neural network](https://arxiv.org/abs/1901.02358).
 See [`FastRNNCell`](@ref) for a layer that processes a single sequences.
@@ -150,7 +150,7 @@ function Base.show(io::IO, fastrnn::FastRNN)
 end
 
 @doc raw"""
-    FastGRNNCell((input_size => hidden_size), [activation];
+    FastGRNNCell(input_size => hidden_size, [activation];
         init_kernel = glorot_uniform,
         init_recurrent_kernel = glorot_uniform,
         bias = true)
@@ -240,7 +240,7 @@ function Base.show(io::IO, fastgrnn::FastGRNNCell)
 end
 
 @doc raw"""
-    FastGRNN((input_size => hidden_size), [activation];
+    FastGRNN(input_size => hidden_size, [activation];
     return_state = false, kwargs...)
 
 [Fast recurrent neural network](https://arxiv.org/abs/1901.02358).

src/cells/indrnn_cell.jl

@@ -1,7 +1,7 @@
 #https://arxiv.org/pdf/1803.04831
 
 @doc raw"""
-    IndRNNCell((input_size => hidden_size), σ=relu;
+    IndRNNCell(input_size => hidden_size, σ=relu;
         init_kernel = glorot_uniform,
         init_recurrent_kernel = glorot_uniform,
         bias = true)
@@ -72,7 +72,7 @@ function Base.show(io::IO, indrnn::IndRNNCell)
 end
 
 @doc raw"""
-    IndRNN((input_size, hidden_size), σ = tanh;
+    IndRNN(input_size, hidden_size, σ = tanh;
         return_state = false, kwargs...)
 
 [Independently recurrent network](https://arxiv.org/pdf/1803.04831).

src/cells/lightru_cell.jl

@@ -1,7 +1,7 @@
 #https://www.mdpi.com/2079-9292/13/16/3204
 
 @doc raw"""
-    LightRUCell((input_size => hidden_size);
+    LightRUCell(input_size => hidden_size;
         init_kernel = glorot_uniform,
         init_recurrent_kernel = glorot_uniform,
         bias = true)
@@ -79,7 +79,7 @@ function Base.show(io::IO, lightru::LightRUCell)
 end
 
 @doc raw"""
-    LightRU((input_size => hidden_size);
+    LightRU(input_size => hidden_size;
         return_state = false, kwargs...)
 
 [Light recurrent unit network](https://www.mdpi.com/2079-9292/13/16/3204).

src/cells/ligru_cell.jl

@@ -1,6 +1,6 @@
 #https://arxiv.org/pdf/1803.10225
 @doc raw"""
-    LiGRUCell((input_size => hidden_size);
+    LiGRUCell(input_size => hidden_size;
         init_kernel = glorot_uniform,
         init_recurrent_kernel = glorot_uniform,
         bias = true)
@@ -79,7 +79,7 @@ function Base.show(io::IO, ligru::LiGRUCell)
 end
 
 @doc raw"""
-    LiGRU((input_size => hidden_size);
+    LiGRU(input_size => hidden_size;
         return_state = false, kwargs...)
 
 [Light gated recurrent network](https://arxiv.org/pdf/1803.10225).

src/cells/mgu_cell.jl

@@ -1,6 +1,6 @@
 #https://arxiv.org/pdf/1603.09420
 @doc raw"""
-    MGUCell((input_size => hidden_size);
+    MGUCell(input_size => hidden_size;
         init_kernel = glorot_uniform,
         init_recurrent_kernel = glorot_uniform,
         bias = true)
@@ -77,7 +77,7 @@ function Base.show(io::IO, mgu::MGUCell)
 end
 
 @doc raw"""
-    MGU((input_size => hidden_size);
+    MGU(input_size => hidden_size;
         return_state = false, kwargs...)
 
 [Minimal gated unit network](https://arxiv.org/pdf/1603.09420).

src/cells/mut_cell.jl

@@ -1,6 +1,6 @@
 #https://proceedings.mlr.press/v37/jozefowicz15.pdf
 @doc raw"""
-    MUT1Cell((input_size => hidden_size);
+    MUT1Cell(input_size => hidden_size;
         init_kernel = glorot_uniform,
         init_recurrent_kernel = glorot_uniform,
         bias = true)
@@ -81,7 +81,9 @@ function Base.show(io::IO, mut::MUT1Cell)
 end
 
 @doc raw"""
-    MUT1((input_size => hidden_size); kwargs...)
+    MUT1(input_size => hidden_size;
+        return_state=false,
+        kwargs...)
 
 [Mutated unit 1 network](https://proceedings.mlr.press/v37/jozefowicz15.pdf).
 See [`MUT1Cell`](@ref) for a layer that processes a single sequence.
@@ -92,6 +94,7 @@ See [`MUT1Cell`](@ref) for a layer that processes a single sequence.
 - `init_kernel`: initializer for the input to hidden weights
 - `init_recurrent_kernel`: initializer for the hidden to hidden weights
 - `bias`: include a bias or not. Default is `true`
+- `return_state`: Option to return the last state together with the output. Default is `false`.
 
 # Equations
 ```math
@@ -145,7 +148,7 @@ function Base.show(io::IO, mut::MUT1)
 end
 
 @doc raw"""
-    MUT2Cell((input_size => hidden_size);
+    MUT2Cell(input_size => hidden_size;
         init_kernel = glorot_uniform,
         init_recurrent_kernel = glorot_uniform,
         bias = true)
@@ -225,7 +228,9 @@ function Base.show(io::IO, mut::MUT2Cell)
 end
 
 @doc raw"""
-    MUT2Cell((input_size => hidden_size); kwargs...)
+    MUT2Cell(input_size => hidden_size;
+        return_state=false,
+        kwargs...)
 
 [Mutated unit 2 network](https://proceedings.mlr.press/v37/jozefowicz15.pdf).
 See [`MUT2Cell`](@ref) for a layer that processes a single sequence.
@@ -236,6 +241,7 @@ See [`MUT2Cell`](@ref) for a layer that processes a single sequence.
 - `init_kernel`: initializer for the input to hidden weights
 - `init_recurrent_kernel`: initializer for the hidden to hidden weights
 - `bias`: include a bias or not. Default is `true`
+- `return_state`: Option to return the last state together with the output. Default is `false`.
 
 # Equations
 ```math
@@ -289,7 +295,7 @@ function Base.show(io::IO, mut::MUT2)
 end
 
 @doc raw"""
-    MUT3Cell((input_size => hidden_size);
+    MUT3Cell(input_size => hidden_size;
         init_kernel = glorot_uniform,
         init_recurrent_kernel = glorot_uniform,
         bias = true)
@@ -368,7 +374,8 @@ function Base.show(io::IO, mut::MUT3Cell)
 end
 
 @doc raw"""
-    MUT3((input_size => hidden_size); kwargs...)
+    MUT3(input_size => hidden_size;
+    return_state = false, kwargs...)
 
 [Mutated unit 3 network](https://proceedings.mlr.press/v37/jozefowicz15.pdf).
 See [`MUT3Cell`](@ref) for a layer that processes a single sequence.
@@ -379,6 +386,7 @@ See [`MUT3Cell`](@ref) for a layer that processes a single sequence.
 - `init_kernel`: initializer for the input to hidden weights
 - `init_recurrent_kernel`: initializer for the hidden to hidden weights
 - `bias`: include a bias or not. Default is `true`
+- `return_state`: Option to return the last state together with the output. Default is `false`.
 
 # Equations
 ```math

src/cells/nas_cell.jl

@@ -24,7 +24,7 @@
 # limitations under the License.
 
 @doc raw"""
-    NASCell((input_size => hidden_size);
+    NASCell(input_size => hidden_size;
         init_kernel = glorot_uniform,
         init_recurrent_kernel = glorot_uniform,
         bias = true)
@@ -145,14 +145,17 @@ function Base.show(io::IO, nas::NASCell)
 end
 
 @doc raw"""
-    NAS((input_size => hidden_size)::Pair; kwargs...)
+    NAS(input_size => hidden_size;
+        return_state = false,
+        kwargs...)
 
 
 [Neural Architecture Search unit](https://arxiv.org/pdf/1611.01578).
 See [`NASCell`](@ref) for a layer that processes a single sequence.
 
 # Arguments
 
+- `return_state`: Option to return the last state together with the output. Default is `false`.
 - `input_size => hidden_size`: input and inner dimension of the layer
 - `init_kernel`: initializer for the input to hidden weights
 - `init_recurrent_kernel`: initializer for the hidden to hidden weights

src/cells/peepholelstm_cell.jl

@@ -1,6 +1,6 @@
 #https://www.jmlr.org/papers/volume3/gers02a/gers02a.pdf
 @doc raw"""
-    PeepholeLSTMCell((input_size => hidden_size);
+    PeepholeLSTMCell(input_size => hidden_size;
         init_kernel = glorot_uniform,
         init_recurrent_kernel = glorot_uniform,
         bias = true)
@@ -78,13 +78,16 @@ function Base.show(io::IO, lstm::PeepholeLSTMCell)
 end
 
 @doc raw"""
-    PeepholeLSTM((input_size => hidden_size); kwargs...)
+    PeepholeLSTM(input_size => hidden_size;
+        return_state=false,
+        kwargs...)
 
 [Peephole long short term memory network](https://www.jmlr.org/papers/volume3/gers02a/gers02a.pdf).
 See [`PeepholeLSTMCell`](@ref) for a layer that processes a single sequence.
 
 # Arguments
 
+- `return_state`: Option to return the last state together with the output. Default is `false`.
 - `input_size => hidden_size`: input and inner dimension of the layer
 - `init_kernel`: initializer for the input to hidden weights
 - `init_recurrent_kernel`: initializer for the hidden to hidden weights

src/cells/ran_cell.jl

@@ -1,6 +1,6 @@
 #https://arxiv.org/pdf/1705.07393
 @doc raw"""
-    RANCell((input_size => hidden_size)::Pair;
+    RANCell(input_size => hidden_size;
         init_kernel = glorot_uniform,
         init_recurrent_kernel = glorot_uniform,
         bias = true)
@@ -80,13 +80,15 @@ function Base.show(io::IO, ran::RANCell)
 end
 
 @doc raw"""
-    RAN(input_size => hidden_size; kwargs...)
+    RAN(input_size => hidden_size;
+        return_state = false, kwargs...)
 
 [Recurrent Additive Network cell](https://arxiv.org/pdf/1705.07393).
 See [`RANCell`](@ref) for a layer that processes a single sequence.
 
 # Arguments
 
+- `return_state`: Option to return the last state together with the output. Default is `false`.
 - `input_size => hidden_size`: input and inner dimension of the layer
 - `init_kernel`: initializer for the input to hidden weights
 - `init_recurrent_kernel`: initializer for the hidden to hidden weights

src/cells/rhn_cell.jl

@@ -2,7 +2,7 @@
 #https://github.com/jzilly/RecurrentHighwayNetworks/blob/master/rhn.py#L138C1-L180C60
 
 """
-    RHNCellUnit((input_size => hidden_size)::Pair;
+    RHNCellUnit(input_size => hidden_size;
         init_kernel = glorot_uniform,
         bias = true)
 """
@@ -44,8 +44,8 @@ function Base.show(io::IO, rhn::RHNCellUnit)
 end
 
 @doc raw"""
-    RHNCell((input_size => hidden_size), depth=3;
-        couple_carry::Bool = true,
+    RHNCell(input_size => hidden_size, [depth];
+        couple_carry = true,
         cell_kwargs...)
 
 [Recurrent highway network](https://arxiv.org/pdf/1607.03474).
@@ -139,14 +139,17 @@ end
 
 # TODO fix implementation here
 @doc raw"""
-    RHN((input_size => hidden_size), depth=3; kwargs...)
+    RHN(input_size => hidden_size, [depth];
+        return_state = false,
+        kwargs...)
 
 [Recurrent highway network](https://arxiv.org/pdf/1607.03474).
 See [`RHNCellUnit`](@ref) for a the unit component of this layer.
 See [`RHNCell`](@ref) for a layer that processes a single sequence.
 
 # Arguments
 
+- `return_state`: Option to return the last state together with the output. Default is `false`.
 - `input_size => hidden_size`: input and inner dimension of the layer
 - `depth`: depth of the recurrence. Default is 3
 - `couple_carry`: couples the carry gate and the transform gate. Default `true`

src/cells/scrn_cell.jl

@@ -1,7 +1,7 @@
 #https://arxiv.org/pdf/1412.7753
 
 @doc raw"""
-    SCRNCell((input_size => hidden_size);
+    SCRNCell(input_size => hidden_size;
         init_kernel = glorot_uniform,
         init_recurrent_kernel = glorot_uniform,
         bias = true,
@@ -87,17 +87,19 @@ function Base.show(io::IO, scrn::SCRNCell)
 end
 
 @doc raw"""
-    SCRN((input_size => hidden_size);
+    SCRN(input_size => hidden_size;
         init_kernel = glorot_uniform,
         init_recurrent_kernel = glorot_uniform,
         bias = true,
-        alpha = 0.0)
+        alpha = 0.0,
+        return_state = false)
 
 [Structurally contraint recurrent unit](https://arxiv.org/pdf/1412.7753).
 See [`SCRNCell`](@ref) for a layer that processes a single sequence.
 
 # Arguments
 
+- `return_state`: Option to return the last state together with the output. Default is `false`.
 - `input_size => hidden_size`: input and inner dimension of the layer
 - `init_kernel`: initializer for the input to hidden weights
 - `init_recurrent_kernel`: initializer for the hidden to hidden weights

src/generics.jl

@@ -31,20 +31,20 @@ function (rlayer::AbstractRecurrentLayer{false})(inp::AbstractArray,
         state::Union{AbstractVecOrMat, Tuple{AbstractVecOrMat, AbstractVecOrMat}})
     @assert ndims(inp) == 2 || ndims(inp) == 3
     @assert typeof(state)==typeof(initialstates(rlayer)) """\n
-      The layer $rlayer is calling states not supported by its
-      forward method. Check if this is a single or double return
-      recurrent layer, and adjust your inputs accordingly.
-  """
+       The layer $rlayer is calling states not supported by its
+       forward method. Check if this is a single or double return
+       recurrent layer, and adjust your inputs accordingly.
+    """
     return first(scan(rlayer.cell, inp, state))
 end
 
 function (rlayer::AbstractRecurrentLayer{true})(inp::AbstractArray,
         state::Union{AbstractVecOrMat, Tuple{AbstractVecOrMat, AbstractVecOrMat}})
     @assert ndims(inp) == 2 || ndims(inp) == 3
     @assert typeof(state)==typeof(initialstates(rlayer)) """\n
-      The layer $rlayer is calling states not supported by its
-      forward method. Check if this is a single or double return
-      recurrent layer, and adjust your inputs accordingly.
-  """
+       The layer $rlayer is calling states not supported by its
+       forward method. Check if this is a single or double return
+       recurrent layer, and adjust your inputs accordingly.
+    """
     return scan(rlayer.cell, inp, state)
 end