That was what was needed for softmax in keras

notebooks/adversarial/adversarial_keras.ipynb

@@ -91,7 +91,7 @@
     "        tf.keras.layers.InputLayer((28 * 28,)),\n",
     "        tf.keras.layers.Dense(50, activation=\"relu\"),\n",
     "        tf.keras.layers.Dense(50, activation=\"relu\"),\n",
-    "        tf.keras.layers.Dense(10),\n",
+    "        tf.keras.layers.Dense(10, activation=\"softmax\"),\n",
     "    ]\n",
     ")"
    ]
@@ -257,7 +257,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.8"
+   "version": "3.11.10"
   },
   "license": {
    "full_text": "# Copyright © 2023 Gurobi Optimization, LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# =============================================================================="

src/gurobi_ml/keras/keras.py

@@ -77,7 +77,7 @@ def __init__(self, gp_model, predictor, input_vars, output_vars=None, **kwargs):
             if isinstance(step, keras.layers.Dense):
                 config = step.get_config()
                 activation = config["activation"]
-                if activation not in ("relu", "linear"):
+                if activation not in ("relu", "softmax", "linear"):
                     raise NoModel(predictor, f"Unsupported activation {activation}")
             elif isinstance(step, keras.layers.ReLU):
                 if step.negative_slope != 0.0: