diff --git a/tests/autoqkeras_test.py b/tests/autoqkeras_test.py
index 8d0f5239..43d2983c 100644
--- a/tests/autoqkeras_test.py
+++ b/tests/autoqkeras_test.py
@@ -29,11 +29,16 @@
 from tensorflow.keras.layers import Dropout
 from tensorflow.keras.layers import Input
 from tensorflow.keras.models import Model
-from tensorflow.keras.optimizers import Adam
+# TODO: Update to new optimizer API
+from tensorflow.keras.optimizers.legacy import Adam
 from tensorflow.keras.utils import to_categorical
 
 from qkeras.autoqkeras import AutoQKerasScheduler
 
+np.random.seed(42)
+tf.random.set_seed(42)
+tf.config.experimental.enable_op_determinism()
+
 
 def dense_model():
   """Creates test dense model."""
@@ -52,13 +57,20 @@ def dense_model():
   x = Activation("softmax", name="softmax")(x)
 
   model = Model(inputs=x_in, outputs=x)
-  return model
 
+  # Manually set the weights for each layer. Needed for test determinism.
+  for layer in model.layers:
+    if isinstance(layer, Dense):
+      weights_shape = layer.get_weights()[0].shape
+      bias_shape = layer.get_weights()[1].shape
+      weights = np.random.RandomState(42).randn(*weights_shape)
+      bias = np.random.RandomState(42).randn(*bias_shape)
+      layer.set_weights([weights, bias])
+
+  return model
 
 def test_autoqkeras():
   """Tests AutoQKeras scheduler."""
-  np.random.seed(42)
-  tf.random.set_seed(42)
 
   x_train, y_train = load_iris(return_X_y=True)
 
@@ -104,7 +116,7 @@ def test_autoqkeras():
 
   model = dense_model()
   model.summary()
-  optimizer = Adam(lr=0.01)
+  optimizer = Adam(learning_rate=0.015)
   model.compile(optimizer=optimizer, loss="categorical_crossentropy",
                 metrics=["acc"])
 
@@ -140,14 +152,12 @@ def test_autoqkeras():
 
   qmodel = autoqk.get_best_model()
 
-  optimizer = Adam(lr=0.01)
+  optimizer = Adam(learning_rate=0.015)
   qmodel.compile(optimizer=optimizer, loss="categorical_crossentropy",
                  metrics=["acc"])
-  history = qmodel.fit(x_train, y_train, epochs=5, batch_size=150,
+  _ = qmodel.fit(x_train, y_train, epochs=5, batch_size=150,
                        validation_split=0.1)
 
-  quantized_acc = history.history["acc"][-1]
-
 if __name__ == "__main__":
   pytest.main([__file__])