shorten

Dobiasd · Dec 31, 2023 · 702cb60 · 702cb60
1 parent db62540
commit 702cb60
Showing 1 changed file with 10 additions and 20 deletions.
diff --git a/include/fdeep/layers/multi_head_attention_layer.hpp b/include/fdeep/layers/multi_head_attention_layer.hpp
@@ -36,13 +36,9 @@ class multi_head_attention_layer : public layer
         const std::size_t index, const std::string& name)
     {
         assertion(index <= 2, "Invalid dense layer index.");
-
         const std::size_t index_factor = use_bias ? 2 : 1;
-
         tensor weights = weights_and_biases[index_factor * index];
-
         const std::size_t units = weights.shape().depth_;
-
         tensor biases = use_bias ?
             weights_and_biases[index_factor * index + 1] :
             tensor(tensor_shape(num_heads, units), 0);
@@ -51,32 +47,26 @@ class multi_head_attention_layer : public layer
         const auto biases_per_head = tensor_to_tensors_width_slices(biases);
         assertion(weights_per_head.size() == num_heads, "Invalid weights for number of heads.");
         assertion(biases_per_head.size() == num_heads, "Invalid biases for number of heads.");
-        const std::vector<dense_layer> dense_layers = 
-            fplus::transform(
-                [&](const std::pair<std::size_t, std::pair<tensor, tensor>>& n_and_w_with_b)
-                {
-                    return dense_layer(
-                        name + "_" + std::to_string(n_and_w_with_b.first),
-                        units,
-                        *n_and_w_with_b.second.first.as_vector(),
-                        *n_and_w_with_b.second.second.as_vector());
-                },
-                fplus::enumerate(fplus::zip(weights_per_head, biases_per_head)));
-        return dense_layers;
+        return fplus::transform(
+            [&](const std::pair<std::size_t, std::pair<tensor, tensor>>& n_and_w_with_b)
+            {
+                return dense_layer(
+                    name + "_" + std::to_string(n_and_w_with_b.first),
+                    units,
+                    *n_and_w_with_b.second.first.as_vector(),
+                    *n_and_w_with_b.second.second.as_vector());
+            },
+            fplus::enumerate(fplus::zip(weights_per_head, biases_per_head)));
     }
     dense_layer create_output_dense_layer(
         const tensors& weights_and_biases, bool use_bias, const std::string& name)
     {
         const std::size_t index_factor = use_bias ? 2 : 1;
-
         tensor weights = weights_and_biases[index_factor * 3];
-
         const std::size_t units = weights.shape().depth_;
-
         tensor biases = use_bias ?
             weights_and_biases[index_factor * 3 + 1] :
             tensor(tensor_shape(units), 0);
-
         return dense_layer(name + "_output", units, *weights.as_vector(), *biases.as_vector());
     }
     tensors extract_biases(const tensors& saved_weights, bool use_bias)