Bump tested versions to TensorFlow 2.21.0 / Keras 3.14.0

Update CI pin and the README's documented tested versions, and accept
the renamed Threshold layer config keys (threshold_value -> threshold,
value -> default_value) so models exported from Keras 3.14 load.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

Author: Dobiasd

.github/workflows/ci.yml

@@ -13,7 +13,7 @@ jobs:
           sudo apt-get install libblas-dev liblapack-dev libatlas-base-dev gfortran
           # python libs
           sudo pip3 install --upgrade pip
-          sudo pip3 install numpy scipy h5py "tensorflow==2.18.0" "keras==3.8.0"
+          sudo pip3 install numpy scipy h5py "tensorflow==2.21.0" "keras==3.14.0"
           echo "Python version:"
           python3 --version
           echo "Version numbers of TensorFlow and Keras:"

README.md

@@ -137,8 +137,8 @@ Requirements and Installation
 
 - A **C++14**-compatible compiler: Compilers from these versions on are fine: GCC 4.9, Clang 3.7 (libc++ 3.7) and Visual C++ 2015
 - Python 3.9 or higher
-- TensorFlow 2.18.0
-- Keras 3.8.0
+- TensorFlow 2.21.0
+- Keras 3.14.0
 
 (These are the tested versions, but somewhat older ones might work too.)
 

include/fdeep/import_model.hpp

@@ -1037,8 +1037,14 @@ namespace internal {
         const get_param_f&, const nlohmann::json& data,
         const std::string& name)
     {
-        float_type threshold = data["config"]["threshold_value"];
-        float_type default_value = data["config"]["value"];
+        // Keras renamed these config keys: threshold_value -> threshold, value -> default_value.
+        const auto& config = data["config"];
+        float_type threshold = json_obj_has_member(config, "threshold")
+            ? static_cast<float_type>(config["threshold"])
+            : static_cast<float_type>(config["threshold_value"]);
+        float_type default_value = json_obj_has_member(config, "default_value")
+            ? static_cast<float_type>(config["default_value"])
+            : static_cast<float_type>(config["value"]);
         return std::make_shared<threshold_layer>(name, threshold, default_value);
     }