tweaks

Author: mcabbott

src/layers/basic.jl

@@ -399,7 +399,7 @@ Parallel(
   +,
   Dense(10, 2),                         # 22 parameters
   Dense(5, 2),                          # 12 parameters
-)                   # Total: 4 arrays, 34 parameters, 392 bytes
+)                   # Total: 4 arrays, 34 parameters, 392 bytes.
 
 julia> size(model(rand(10), rand(5)))
 (2,)

src/layers/show.jl

@@ -72,13 +72,13 @@ function _big_finale(io::IO, m)
     noncnt = _childarray_sum(_->1, m) - length(ps)
     if noncnt > 0
       nonparam = underscorise(_childarray_sum(length, m) - sum(length, ps))
-      printstyled(io, " "^09, "# Total: ", length(ps), " trainable arrays, with "; color=:light_black)
-      println(io, pars, " parameters")
+      printstyled(io, " "^09, "# Total: ", length(ps), " trainable arrays, "; color=:light_black)
+      println(io, pars, " parameters,")
       printstyled(io, " "^10, "# plus ", noncnt, " non-trainable, ", nonparam, " parameters, summarysize "; color=:light_black)
-      print(io, bytes)
+      print(io, bytes, ".")
     else
       printstyled(io, " "^19, "# Total: ", length(ps), " arrays, "; color=:light_black)
-      print(io, pars, " parameters, ", bytes)
+      print(io, pars, " parameters, ", bytes, ".")
     end
   end
 end

src/utils.jl

@@ -727,8 +727,8 @@ Chain(
     BatchNorm(64, relu),                # 128 parameters, plus 128
   ),
   Dense(64, 10),                        # 650 parameters
-)         # Total: 6 trainable arrays, with 51_018 parameters
-          # plus 2 non-trainable, 128 parameters, summarysize 200.312 KiB
+)         # Total: 6 trainable arrays, 51_018 parameters,
+          # plus 2 non-trainable, 128 parameters, summarysize 200.312 KiB.
 
 julia> Flux.modules(m2)
 5-element Vector{Any}: