remove reference to Keras in test files

deel/torchlip/modules/module.py

@@ -44,7 +44,7 @@
 
 
 def _is_supported_1lip_layer(layer):
-    """Return True if the Keras layer is 1-Lipschitz. Note that in some cases, the layer
+    """Return True if the layer is 1-Lipschitz. Note that in some cases, the layer
     is 1-Lipschitz for specific set of parameters.
     """
     supported_1lip_layers = (

tests/test_compute_layer_sv.py

@@ -142,7 +142,7 @@ def train_compute_and_verifySV(
     input_shape = uft.to_framework_channel(input_shape)
 
     # tf.random.uft.set_seed(1234)
-    # create the keras model, defin opt, and compile it
+    # create the model, defin opt, and compile it
     model = uft.generate_k_lip_model(layer_type, layer_params, input_shape, k_lip_model)
 
     optimizer = uft.get_instance_framework(
@@ -166,7 +166,7 @@ def train_compute_and_verifySV(
     logdir = os.path.join("logs", uft.LIP_LAYERS, "%s" % layer_type.__name__)
     os.makedirs(logdir, exist_ok=True)
 
-    callback_list = []  # [hp.KerasCallback(logdir, hparams)]
+    callback_list = []  
     if "callbacks" in kwargs and (kwargs["callbacks"] is not None):
         callback_list = callback_list + kwargs["callbacks"]
     # train model

tests/test_condense.py

@@ -202,7 +202,7 @@ def test_model(layer_type, layer_params, k_coef_lip, input_shape):
 
     model = get_model(layer_type, layer_params, input_shape, k_coef_lip)
 
-    # create the keras model, defin opt, and compile it
+    # create the model, defin opt, and compile it
     optimizer = uft.get_instance_framework(
         uft.Adam, inst_params={"lr": 0.001, "model": model}
     )

tests/test_initializers.py

@@ -71,7 +71,7 @@ def test_initializer(layer_type, layer_params, input_shape, orthogonal_test):
     # clear session to avoid side effects from previous train
     uft.init_session()  # K.clear_session()
     input_shape = uft.to_framework_channel(input_shape)
-    # create the keras model, defin opt, and compile it
+    # create the model, defin opt, and compile it
     model = uft.generate_k_lip_model(layer_type, layer_params, input_shape)
     uft.initialize_kernel(model, 0, layer_params["kernel_initializer"])
 

tests/test_layers.py

@@ -172,7 +172,7 @@ def train_k_lip_model(
     uft.init_session()  # K.clear_session()
     np.random.seed(42)
     input_shape = uft.to_framework_channel(input_shape)
-    # create the keras model, defin opt, and compile it
+    # create the model, defin opt, and compile it
     model = uft.generate_k_lip_model(layer_type, layer_params, input_shape, k_lip_model)
 
     optimizer = uft.get_instance_framework(
@@ -195,7 +195,7 @@ def train_k_lip_model(
 
     callback_list = (
         []
-    )  # [callbacks.TensorBoard(logdir), hp.KerasCallback(logdir, hparams)]
+    ) 
     if kwargs["callbacks"] is not None:
         callback_list = callback_list + kwargs["callbacks"]
     # train model

tests/test_models.py

@@ -155,8 +155,8 @@ def assert_model_outputs(input_shape, model1, model2):
     np.testing.assert_allclose(uft.to_numpy(y2), uft.to_numpy(y1), atol=1e-5)
 
 
-def test_keras_Sequential():
-    """Assert vanilla conversion of a tf.keras.Sequential model"""
+def test_Sequential():
+    """Assert vanilla conversion of a Sequential model"""
     input_shape = uft.to_framework_channel((3, 20, 20))
     model = uft.generate_k_lip_model(
         tSequential,
@@ -203,14 +203,12 @@ def test_deel_lip_Sequential():
     reason="tModel not available",
 )
 def test_Model():
-    """Assert vanilla conversion of a tf.keras.Model model"""
+    """Assert vanilla conversion of a Model model"""
     input_shape = uft.to_framework_channel((3, 8, 8))
     dict_tensors = get_functional_tensors(input_shape)
     model = uft.get_functional_model(
         tModel, dict_tensors, functional_input_output_tensors
     )
-    # inputs, outputs = functional_input_output_tensors()
-    #    model = tf.keras.Model(inputs, outputs)
     if uft.vanilla_require_a_copy():
         dict_tensors2 = get_functional_tensors(input_shape)
         model2 = uft.get_functional_model(