diff --git a/g3doc/tutorials/Fairness_Indicators_Example_Colab.ipynb b/g3doc/tutorials/Fairness_Indicators_Example_Colab.ipynb
index 58d397b..00a40a7 100644
--- a/g3doc/tutorials/Fairness_Indicators_Example_Colab.ipynb
+++ b/g3doc/tutorials/Fairness_Indicators_Example_Colab.ipynb
@@ -352,7 +352,7 @@
         "    hidden_units=[500, 100],\n",
         "    weight_column='weight',\n",
         "    feature_columns=[embedded_text_feature_column],\n",
-        "    optimizer=tf.keras.optimizers.Adagrad(learning_rate=0.003),\n",
+        "    optimizer=tf.keras.optimizers.legacy.Adagrad(learning_rate=0.003),\n",
         "    loss_reduction=tf.losses.Reduction.SUM,\n",
         "    n_classes=2,\n",
         "    model_dir=model_dir)\n",
diff --git a/g3doc/tutorials/Fairness_Indicators_TFCO_CelebA_Case_Study.ipynb b/g3doc/tutorials/Fairness_Indicators_TFCO_CelebA_Case_Study.ipynb
index 3d9cfaa..d32ab6a 100644
--- a/g3doc/tutorials/Fairness_Indicators_TFCO_CelebA_Case_Study.ipynb
+++ b/g3doc/tutorials/Fairness_Indicators_TFCO_CelebA_Case_Study.ipynb
@@ -819,8 +819,8 @@
         "# Create constrained optimizer and obtain train_op.\n",
         "# Separate optimizers are specified for the objective and constraints\n",
         "optimizer = tfco.ProxyLagrangianOptimizerV2(\n",
-        "      optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),\n",
-        "      constraint_optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),\n",
+        "      optimizer=tf.keras.optimizers.legacy.Adam(learning_rate=0.001),\n",
+        "      constraint_optimizer=tf.keras.optimizers.legacy.Adam(learning_rate=0.001),\n",
         "      num_constraints=problem.num_constraints)\n",
         "\n",
         "# A list of all trainable variables is also needed to use TFCO.\n",