From 79ac7f977a07683e6e850f756eb279e862f68090 Mon Sep 17 00:00:00 2001
From: Dario Izzo <darioizzo@gmail.com>
Date: Tue, 31 Oct 2023 19:12:27 +0100
Subject: [PATCH] WIP

---
 src/model/ffnn.cpp  | 20 ++++++++++++++++----
 test/model_ffnn.cpp |  3 ++-
 2 files changed, 18 insertions(+), 5 deletions(-)

diff --git a/src/model/ffnn.cpp b/src/model/ffnn.cpp
index 4ef1c60da..a0e2c6420 100644
--- a/src/model/ffnn.cpp
+++ b/src/model/ffnn.cpp
@@ -70,18 +70,30 @@ std::vector<expression> compute_layer(std::uint32_t layer_id, const std::vector<
     auto n_neurons_curr_layer = n_neurons[layer_id];
 
     std::vector<expression> retval(n_neurons_curr_layer, 0_dbl);
-    fmt::print("nneurons: {}", n_neurons_prev_layer);
+    fmt::print("net_wb: {}\n", net_wb.size());
     std::cout << std::endl;
 
     for (std::uint32_t i = 0u; i < n_neurons_curr_layer; ++i) {
         for (std::uint32_t j = 0u; j < n_neurons_prev_layer; ++j) {
-            fmt::print("layer, i, j: {}, {}, {}", layer_id, i, j);
+            fmt::print("layer, i, j, idx: {}, {}, {}\n", layer_id, i, j, flattenw(i, j, n_neurons, layer_id));
             std::cout << std::endl;
-            retval[i] += net_wb[flattenw(i, j, n_neurons, layer_id)] * inputs[j];
+            retval[i] += 1_dbl;//net_wb[flattenw(i, j, n_neurons, layer_id)] * inputs[j];
         }
-        retval[i] += net_wb[flattenb(i, n_neurons, layer_id, n_net_w)];
+        fmt::print("idxb {}\n", flattenb(i, n_neurons, layer_id, n_net_w));
+        std::cout << std::endl;
+
+        retval[i]+= 1_dbl; //net_wb[flattenb(i, n_neurons, layer_id, n_net_w)];
+
+        fmt::print("\n{}\n", retval[i]);
+        fmt::print("Here1");
+
+        std::cout << std::endl;
         retval[i] = activation(retval[i]);
+        fmt::print("Here2");
+        std::cout << std::endl;
     }
+    fmt::print("Here3");
+
     return retval;
 }
 } // namespace detail
diff --git a/test/model_ffnn.cpp b/test/model_ffnn.cpp
index b09c6a113..5ac59511e 100644
--- a/test/model_ffnn.cpp
+++ b/test/model_ffnn.cpp
@@ -20,8 +20,9 @@ using namespace heyoka;
 
 TEST_CASE("impl")
 {
+    auto linear = [](expression ret) -> expression { return ret; };
     auto [x] = make_vars("x");
-    auto my_net = model::ffnn_impl({x}, 2, {2, 2}, {heyoka::tanh, heyoka::tanh, [](auto ret) { return ret; }},
+    auto my_net = model::ffnn_impl({x}, 2, {2, 2}, {heyoka::tanh, heyoka::tanh, heyoka::tanh},
                                    {1_dbl, 2_dbl, 3_dbl, 4_dbl, 5_dbl, 6_dbl, 7_dbl, 8_dbl, 9_dbl, 0_dbl, 1_dbl, 2_dbl,
                                     3_dbl, 4_dbl, 5_dbl, 6_dbl});
 }
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