Merge pull request #237 from SciML/fm/types

Changing defaults to Float32

src/esn/deepesn.jl

@@ -37,7 +37,7 @@ temporal features.
   - `input_layer`: A function or an array of functions to initialize the input
     matrices for each layer. Default is `scaled_rand` for each layer.
   - `bias`: A function or an array of functions to initialize the bias vectors
-    for each layer. Default is `zeros64` for each layer.
+    for each layer. Default is `zeros32` for each layer.
   - `reservoir`: A function or an array of functions to initialize the reservoir
     matrices for each layer. Default is `rand_sparse` for each layer.
   - `reservoir_driver`: The driving system for the reservoir.
@@ -50,8 +50,6 @@ temporal features.
     Default is 0.
   - `rng`: Random number generator used for initializing weights. Default is the package's
     default random number generator.
-  - `T`: The data type for the matrices (e.g., `Float64`). Influences computational
-    efficiency and precision.
   - `matrix_type`: The type of matrix used for storing the training data.
     Default is inferred from `train_data`.
 
@@ -74,21 +72,21 @@ function DeepESN(train_data,
         res_size::Int;
         depth::Int=2,
         input_layer=fill(scaled_rand, depth),
-        bias=fill(zeros64, depth),
+        bias=fill(zeros32, depth),
         reservoir=fill(rand_sparse, depth),
         reservoir_driver=RNN(),
         nla_type=NLADefault(),
         states_type=StandardStates(),
         washout::Int=0,
         rng=Utils.default_rng(),
-        T=Float64,
         matrix_type=typeof(train_data))
     if states_type isa AbstractPaddedStates
         in_size = size(train_data, 1) + 1
         train_data = vcat(Adapt.adapt(matrix_type, ones(1, size(train_data, 2))),
             train_data)
     end
 
+    T = eltype(train_data)
     reservoir_matrix = [reservoir[i](rng, T, res_size, res_size) for i in 1:depth]
     input_matrix = [i == 1 ? input_layer[i](rng, T, res_size, in_size) :
                     input_layer[i](rng, T, res_size, res_size) for i in 1:depth]

src/esn/esn.jl

@@ -49,20 +49,20 @@ function ESN(train_data,
         res_size::Int;
         input_layer=scaled_rand,
         reservoir=rand_sparse,
-        bias=zeros64,
+        bias=zeros32,
         reservoir_driver=RNN(),
         nla_type=NLADefault(),
         states_type=StandardStates(),
         washout=0,
         rng=Utils.default_rng(),
-        T=Float32,
         matrix_type=typeof(train_data))
     if states_type isa AbstractPaddedStates
         in_size = size(train_data, 1) + 1
         train_data = vcat(Adapt.adapt(matrix_type, ones(1, size(train_data, 2))),
             train_data)
     end
 
+    T = eltype(train_data)
     reservoir_matrix = reservoir(rng, T, res_size, res_size)
     input_matrix = input_layer(rng, T, res_size, in_size)
     bias_vector = bias(rng, res_size)

src/esn/hybridesn.jl

@@ -121,7 +121,7 @@ function HybridESN(model,
         res_size::Int;
         input_layer=scaled_rand,
         reservoir=rand_sparse,
-        bias=zeros64,
+        bias=zeros32,
         reservoir_driver=RNN(),
         nla_type=NLADefault(),
         states_type=StandardStates(),

test/esn/deepesn.jl

@@ -7,14 +7,19 @@ const train_len = 400
 const predict_len = 100
 const input_data = reduce(hcat, data[1:(train_len - 1)])
 const target_data = reduce(hcat, data[2:train_len])
-const test = reduce(hcat, data[(train_len + 1):(train_len + predict_len)])
+const test_data = reduce(hcat, data[(train_len + 1):(train_len + predict_len)])
 const reg = 10e-6
-#test_types = [Float64, Float32, Float16]
 
-Random.seed!(77)
-res = rand_sparse(; radius=1.2, sparsity=0.1)
-esn = DeepESN(input_data, 1, res_size)
+test_types = [Float64, Float32, Float16]
+zeros_types = [zeros64, zeros32, zeros16]
 
-output_layer = train(esn, target_data)
-output = esn(Generative(length(test)), output_layer)
-@test mean(abs.(test .- output)) ./ mean(abs.(test)) < 0.22
+for (tidx, t) in enumerate(test_types)
+    Random.seed!(77)
+    res = rand_sparse(; radius=1.2, sparsity=0.1)
+    esn = DeepESN(t.(input_data), 1, res_size;
+        bias=fill(zeros_types[tidx], 2))
+
+    output_layer = train(esn, t.(target_data))
+    output = esn(Generative(length(test_data)), output_layer)
+    @test eltype(output) == t
+end