Merge pull request #116 from JuliaDecisionFocusedLearning/frankwolfe-tests

Replace Agnostic with Adaptive in FrankWolfe tests

Author: BatyLeo

examples/tutorial.jl

@@ -113,14 +113,15 @@ Thanks to this smoothing, we can now train our model with a standard gradient op
 
 encoder = deepcopy(initial_encoder)
 opt = Flux.Adam();
+opt_state = Flux.setup(opt, encoder)
 losses = Float64[]
 for epoch in 1:100
     l = 0.0
     for (x, y) in zip(X_train, Y_train)
-        grads = gradient(Flux.params(encoder)) do
-            l += loss(encoder(x), y; directions=queen_directions)
+        grads = Flux.gradient(encoder) do m
+            l += loss(m(x), y; directions=queen_directions)
         end
-        Flux.update!(opt, Flux.params(encoder), grads)
+        Flux.update!(opt_state, encoder, grads[1])
     end
     push!(losses, l)
 end;

test/argmax.jl

@@ -116,7 +116,7 @@ end
             one_hot_argmax;
             Ω=half_square_norm,
             Ω_grad=identity_kw,
-            frank_wolfe_kwargs=(; max_iteration=10, line_search=FrankWolfe.Agnostic()),
+            frank_wolfe_kwargs=(; max_iteration=10, line_search=FrankWolfe.Adaptive()),
         ),
         loss=mse_kw,
         error_function=hamming_distance,
@@ -198,7 +198,7 @@ end
                 one_hot_argmax;
                 Ω=half_square_norm,
                 Ω_grad=identity_kw,
-                frank_wolfe_kwargs=(; max_iteration=10, line_search=FrankWolfe.Agnostic()),
+                frank_wolfe_kwargs=(; max_iteration=10, line_search=FrankWolfe.Adaptive()),
             ),
         ),
         error_function=hamming_distance,
@@ -263,7 +263,7 @@ end
                 one_hot_argmax;
                 Ω=half_square_norm,
                 Ω_grad=identity_kw,
-                frank_wolfe_kwargs=(; max_iteration=10, line_search=FrankWolfe.Agnostic()),
+                frank_wolfe_kwargs=(; max_iteration=10, line_search=FrankWolfe.Adaptive()),
             ),
             cost,
         ),

test/paths.jl

@@ -101,7 +101,7 @@ end
             shortest_path_maximizer;
             Ω=half_square_norm,
             Ω_grad=identity_kw,
-            frank_wolfe_kwargs=(; max_iteration=10, line_search=FrankWolfe.Agnostic()),
+            frank_wolfe_kwargs=(; max_iteration=10, line_search=FrankWolfe.Adaptive()),
         ),
         loss=mse_kw,
         error_function=mse_kw,
@@ -177,7 +177,7 @@ end
                 shortest_path_maximizer;
                 Ω=half_square_norm,
                 Ω_grad=identity_kw,
-                frank_wolfe_kwargs=(; max_iteration=10, line_search=FrankWolfe.Agnostic()),
+                frank_wolfe_kwargs=(; max_iteration=10, line_search=FrankWolfe.Adaptive()),
             ),
         ),
         error_function=mse_kw,
@@ -247,7 +247,7 @@ end
                 shortest_path_maximizer;
                 Ω=half_square_norm,
                 Ω_grad=identity_kw,
-                frank_wolfe_kwargs=(; max_iteration=10, line_search=FrankWolfe.Agnostic()),
+                frank_wolfe_kwargs=(; max_iteration=10, line_search=FrankWolfe.Adaptive()),
             ),
             cost,
         ),

test/ranking.jl

@@ -101,7 +101,7 @@ end
             ranking;
             Ω=half_square_norm,
             Ω_grad=identity_kw,
-            frank_wolfe_kwargs=(; max_iteration=10, line_search=FrankWolfe.Agnostic()),
+            frank_wolfe_kwargs=(; max_iteration=10, line_search=FrankWolfe.Adaptive()),
         ),
         loss=mse_kw,
         error_function=hamming_distance,
@@ -170,7 +170,7 @@ end
                 ranking;
                 Ω=half_square_norm,
                 Ω_grad=identity_kw,
-                frank_wolfe_kwargs=(; max_iteration=10, line_search=FrankWolfe.Agnostic()),
+                frank_wolfe_kwargs=(; max_iteration=10, line_search=FrankWolfe.Adaptive()),
             ),
         ),
         error_function=hamming_distance,
@@ -303,7 +303,7 @@ end
                 ranking;
                 Ω=half_square_norm,
                 Ω_grad=identity_kw,
-                frank_wolfe_kwargs=(; max_iteration=10, line_search=FrankWolfe.Agnostic()),
+                frank_wolfe_kwargs=(; max_iteration=10, line_search=FrankWolfe.Adaptive()),
             ),
             cost,
         ),