Fix failing tests & GitHub warnings

Author: bo3z

docs/advanced/model_optimization.rst

@@ -80,6 +80,7 @@ To optimize GPU FLOPs, the code is similar to above:
         baseline_model, model_attributes, GPUFLOPEstimator, scheduler,
         X_train, y_train, X_val, y_val, batch_size, epochs, optimizer, loss_fn, metric, increasing, rtol
     )
+
     # Evaluate optimized model
     y_optimized = optimized_model.predict(X_test)
     acc_optimized = accuracy_score(np.argmax(y_test, axis=1), np.argmax(y_optimized, axis=1))
@@ -109,9 +110,15 @@ Finally, optimizing Vivado DSPs is possible, given a hls4ml config:
     # Note the change from ParameterEstimator to VivadoDSPEstimator
     optimized_model = optimize_keras_for_hls4ml(
         baseline_model, model_attributes, VivadoDSPEstimator, scheduler,
-        X_train, y_train, X_val, y_val, batch_size, epochs, optimizer, loss_fn, metric, increasing, rtol
+        X_train, y_train, X_val, y_val, batch_size, epochs,
+        optimizer, loss_fn, metric, increasing, rtol
     )
 
+    # Evaluate optimized model
+    y_optimized = optimized_model.predict(X_test)
+    acc_optimized = accuracy_score(np.argmax(y_test, axis=1), np.argmax(y_optimized, axis=1))
+    print(f'Optimized Keras accuracy: {acc_optimized}')
+
 There are two more Vivado "optimizers" - VivadoFFEstimator, aimed at reducing register utilisation and VivadoMultiObjectiveEstimator, aimed at optimising BRAM and DSP utilisation.
 Note, to ensure DSPs are optimized, "unrolled" Dense multiplication must be used before synthesing HLS, by modifying the config:
 .. code-block:: Python

setup.cfg

@@ -41,8 +41,8 @@ pytest_randomly.random_seeder =
 
 [options.extras_require]
 optimization =
-    keras-surgeon@https://github.com/fastmachinelearning/keras-surgeon
-    ortools
+    keras-surgeon@git+https://github.com/fastmachinelearning/keras-surgeon.git
+    ortools==9.4.1874
     packaging
 profiling =
     matplotlib

test/pytest/generate_ci_yaml.py

@@ -32,5 +32,26 @@ def uses_example_model(test_filename):
     else:
         yml.update(new_yml)
 
+# hls4ml Optimization API
+tests = glob.glob('test_optimization/test_*.py')
+for test in tests:
+    name = test.replace('test_optimization/', '').replace('test_', '').replace('.py', '')
+    new_yml = yaml.safe_load(template.format(name, f'test_optimization/test_{name}.py', int(uses_example_model(test))))
+    if yml is None:
+        yml = new_yml
+    else:
+        yml.update(new_yml)
+
+tests = glob.glob('test_optimization/test_keras/test_*.py')
+for test in tests:
+    name = test.replace('test_optimization/test_keras/', '').replace('test_', '').replace('.py', '')
+    new_yml = yaml.safe_load(
+        template.format(name, f'test_optimization/test_keras/test_{name}.py', int(uses_example_model(test)))
+    )
+    if yml is None:
+        yml = new_yml
+    else:
+        yml.update(new_yml)
+
 yamlfile = open('pytests.yml', 'w')
 yaml.safe_dump(yml, yamlfile)