diff --git a/.github/workflows/test-python-package.yml b/.github/workflows/test-python-package.yml
index fa84b3d3a..a4db18633 100644
--- a/.github/workflows/test-python-package.yml
+++ b/.github/workflows/test-python-package.yml
@@ -16,7 +16,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [3.8, 3.9, "3.10"]
+        python-version: [3.9, "3.10"]
 
     steps:
     - uses: actions/checkout@v4
diff --git a/MANIFEST.in b/MANIFEST.in
index 12480abd8..0ace6ebe9 100644
--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -1,4 +1,5 @@
 global-exclude .DS_Store
+global-exclude */__pycache__/*
 
 include *.txt
 include CODEOWNERS
diff --git a/dataprofiler/labelers/char_load_tf_model.py b/dataprofiler/labelers/char_load_tf_model.py
index b168e9234..a4a44e03a 100644
--- a/dataprofiler/labelers/char_load_tf_model.py
+++ b/dataprofiler/labelers/char_load_tf_model.py
@@ -237,7 +237,8 @@ def _construct_model(self) -> None:
         model_loc = self._parameters["model_path"]
 
         self._model: tf.keras.Model = tf.keras.models.load_model(model_loc)
-        softmax_output_layer_name = self._model.outputs[0].name.split("/")[0]
+        self._model = tf.keras.Model(self._model.inputs, self._model.outputs)
+        softmax_output_layer_name = self._model.output_names[0]
         softmax_layer_ind = cast(
             int,
             labeler_utils.get_tf_layer_index_from_name(
@@ -252,21 +253,28 @@ def _construct_model(self) -> None:
                 num_labels, activation="softmax", name="softmax_output"
             )(self._model.layers[softmax_layer_ind - 1].output)
 
-        # Output the model into a .pb file for TensorFlow
-        argmax_layer = tf.keras.backend.argmax(new_softmax_layer)
+        # Add argmax layer to get labels directly as an output
+        argmax_layer = tf.keras.ops.argmax(new_softmax_layer, axis=2)
 
         argmax_outputs = [new_softmax_layer, argmax_layer]
         self._model = tf.keras.Model(self._model.inputs, argmax_outputs)
+        self._model = tf.keras.Model(self._model.inputs, self._model.outputs)
 
         # Compile the model w/ metrics
-        softmax_output_layer_name = self._model.outputs[0].name.split("/")[0]
+        softmax_output_layer_name = self._model.output_names[0]
         losses = {softmax_output_layer_name: "categorical_crossentropy"}
 
         # use f1 score metric
         f1_score_training = labeler_utils.F1Score(
             num_classes=num_labels, average="micro"
         )
-        metrics = {softmax_output_layer_name: ["acc", f1_score_training]}
+        metrics = {
+            softmax_output_layer_name: [
+                "categorical_crossentropy",
+                "acc",
+                f1_score_training,
+            ]
+        }
 
         self._model.compile(loss=losses, optimizer="adam", metrics=metrics)
 
@@ -294,30 +302,33 @@ def _reconstruct_model(self) -> None:
         num_labels = self.num_labels
         default_ind = self.label_mapping[self._parameters["default_label"]]
 
-        # Remove the 2 output layers ('softmax', 'tf_op_layer_ArgMax')
-        for _ in range(2):
-            self._model.layers.pop()
-
         # Add the final Softmax layer to the previous spot
+        # self._model.layers[-2] to skip: original softmax
         final_softmax_layer = tf.keras.layers.Dense(
             num_labels, activation="softmax", name="softmax_output"
-        )(self._model.layers[-4].output)
+        )(self._model.layers[-2].output)
 
-        # Output the model into a .pb file for TensorFlow
-        argmax_layer = tf.keras.backend.argmax(final_softmax_layer)
+        # Add argmax layer to get labels directly as an output
+        argmax_layer = tf.keras.ops.argmax(final_softmax_layer, axis=2)
 
         argmax_outputs = [final_softmax_layer, argmax_layer]
         self._model = tf.keras.Model(self._model.inputs, argmax_outputs)
 
         # Compile the model
-        softmax_output_layer_name = self._model.outputs[0].name.split("/")[0]
+        softmax_output_layer_name = self._model.output_names[0]
         losses = {softmax_output_layer_name: "categorical_crossentropy"}
 
         # use f1 score metric
         f1_score_training = labeler_utils.F1Score(
             num_classes=num_labels, average="micro"
         )
-        metrics = {softmax_output_layer_name: ["acc", f1_score_training]}
+        metrics = {
+            softmax_output_layer_name: [
+                "categorical_crossentropy",
+                "acc",
+                f1_score_training,
+            ]
+        }
 
         self._model.compile(loss=losses, optimizer="adam", metrics=metrics)
 
@@ -370,7 +381,7 @@ def fit(
         f1_report: dict = {}
 
         self._model.reset_metrics()
-        softmax_output_layer_name = self._model.outputs[0].name.split("/")[0]
+        softmax_output_layer_name = self._model.output_names[0]
 
         start_time = time.time()
         batch_id = 0
diff --git a/dataprofiler/labelers/character_level_cnn_model.py b/dataprofiler/labelers/character_level_cnn_model.py
index 3194a2616..2cbb7051a 100644
--- a/dataprofiler/labelers/character_level_cnn_model.py
+++ b/dataprofiler/labelers/character_level_cnn_model.py
@@ -74,6 +74,133 @@ def create_glove_char(n_dims: int, source_file: str = None) -> None:
             file.write(word + " " + " ".join(str(num) for num in embd) + "\n")
 
 
+@tf.keras.utils.register_keras_serializable(package="CharacterLevelCnnModel")
+class ThreshArgMaxLayer(tf.keras.layers.Layer):
+    """Keras layer applying a thresholded argmax."""
+
+    def __init__(
+        self, threshold_: float, num_labels_: int, default_ind: int = 1, *args, **kwargs
+    ) -> None:
+        """Apply a minimum threshold to the argmax value.
+
+        When below this threshold the index will be the default.
+
+        :param num_labels: number of entities
+        :type num_labels: int
+        :param threshold: default set to 0 so all confidences pass.
+        :type threshold: float
+        :param default_ind: default index
+        :type default_ind: int
+        :return: final argmax threshold layer for the model
+        :return : tensor containing argmax thresholded integers, labels out
+        :rtype: tf.Tensor
+        """
+        super().__init__(*args, **kwargs)
+        self._threshold_ = threshold_
+        self._num_labels_ = num_labels_
+        self._default_ind = default_ind
+        thresh_init = tf.constant_initializer(threshold_)
+        self.thresh_vec = tf.Variable(
+            name="ThreshVec",
+            initial_value=thresh_init(shape=[num_labels_]),
+            trainable=False,
+        )
+
+    def get_config(self):
+        """Return a serializable config for saving the layer."""
+        config = super().get_config().copy()
+        config.update(
+            {
+                "threshold_": self._threshold_,
+                "num_labels_": self._num_labels_,
+                "default_ind": self._default_ind,
+            }
+        )
+        return config
+
+    def call(self, argmax_layer: tf.Tensor, confidence_layer: tf.Tensor) -> tf.Tensor:
+        """Apply the threshold argmax to the input tensor."""
+        threshold_at_argmax = tf.gather(self.thresh_vec, argmax_layer)
+
+        confidence_max_layer = tf.keras.backend.max(confidence_layer, axis=2)
+
+        # Check if the confidences meet the threshold minimum.
+        argmax_mask = tf.keras.backend.cast(
+            tf.keras.backend.greater_equal(confidence_max_layer, threshold_at_argmax),
+            dtype=argmax_layer.dtype,
+        )
+
+        # Create a vector the same size as the batch_size which
+        # represents the background label
+        bg_label_tf = tf.keras.backend.constant(
+            self._default_ind, dtype=argmax_layer.dtype
+        )
+
+        # Generate the final predicted output using the function:
+        final_predicted_layer = tf.add(
+            bg_label_tf,
+            tf.multiply(tf.subtract(argmax_layer, bg_label_tf), argmax_mask),
+            name="ThreshArgMax",
+        )
+        # final_predicted_layer.set_shape(argmax_layer.shape)
+        return final_predicted_layer
+
+
+@tf.keras.utils.register_keras_serializable(package="CharacterLevelCnnModel")
+class EncodingLayer(tf.keras.layers.Layer):
+    """Encodes strings to integers."""
+
+    def __init__(
+        self, max_char_encoding_id: int, max_len: int, *args, **kwargs
+    ) -> None:
+        """
+        Encode characters for the list of sentences.
+
+        :param max_char_encoding_id: Maximum integer value for encoding the
+            input
+        :type max_char_encoding_id: int
+        :param max_len: Maximum char length in a sample
+        :type max_len: int
+        """
+        super().__init__(*args, **kwargs)
+        self.max_char_encoding_id = max_char_encoding_id
+        self.max_len = max_len
+
+    def get_config(self):
+        """Return a serializable config for saving the layer."""
+        config = super().get_config().copy()
+        config.update(
+            {
+                "max_char_encoding_id": self.max_char_encoding_id,
+                "max_len": self.max_len,
+            }
+        )
+        return config
+
+    def call(self, input_str_tensor: tf.Tensor) -> tf.Tensor:
+        """
+        Encode characters for the list of sentences.
+
+        :param input_str_tensor: input list of sentences converted to tensor
+        :type input_str_tensor: tf.tensor
+        :return : tensor containing encoded list of input sentences
+        :rtype: tf.Tensor
+        """
+        # convert characters to indices
+        input_str_flatten = tf.reshape(input_str_tensor, [-1])
+        sentences_encode = tf.strings.unicode_decode(
+            input_str_flatten, input_encoding="UTF-8"
+        )
+        sentences_encode = tf.add(tf.cast(1, tf.int32), sentences_encode)
+        sentences_encode = tf.math.minimum(
+            sentences_encode, self.max_char_encoding_id + 1
+        )
+
+        # padding
+        sentences_encode_pad = sentences_encode.to_tensor(shape=[None, self.max_len])
+        return sentences_encode_pad
+
+
 class CharacterLevelCnnModel(BaseTrainableModel, metaclass=AutoSubRegistrationMeta):
     """Class for training char data labeler."""
 
@@ -280,7 +407,7 @@ def save_to_disk(self, dirpath: str) -> None:
         labels_dirpath = os.path.join(dirpath, "label_mapping.json")
         with open(labels_dirpath, "w") as fp:
             json.dump(self.label_mapping, fp)
-        self._model.save(os.path.join(dirpath))
+        self._model.save(os.path.join(dirpath, "model.keras"))
 
     @classmethod
     def load_from_disk(cls, dirpath: str) -> CharacterLevelCnnModel:
@@ -301,15 +428,7 @@ def load_from_disk(cls, dirpath: str) -> CharacterLevelCnnModel:
         with open(labels_dirpath) as fp:
             label_mapping = json.load(fp)
 
-        # use f1 score metric
-        custom_objects = {
-            "F1Score": labeler_utils.F1Score(
-                num_classes=max(label_mapping.values()) + 1, average="micro"
-            ),
-            "CharacterLevelCnnModel": cls,
-        }
-        with tf.keras.utils.custom_object_scope(custom_objects):
-            tf_model = tf.keras.models.load_model(dirpath)
+        tf_model = tf.keras.models.load_model(os.path.join(dirpath, "model.keras"))
 
         loaded_model = cls(label_mapping, parameters)
         loaded_model._model = tf_model
@@ -333,35 +452,6 @@ def load_from_disk(cls, dirpath: str) -> CharacterLevelCnnModel:
         ]
         return loaded_model
 
-    @staticmethod
-    def _char_encoding_layer(
-        input_str_tensor: tf.Tensor, max_char_encoding_id: int, max_len: int
-    ) -> tf.Tensor:
-        """
-        Encode characters for the list of sentences.
-
-        :param input_str_tensor: input list of sentences converted to tensor
-        :type input_str_tensor: tf.tensor
-        :param max_char_encoding_id: Maximum integer value for encoding the
-            input
-        :type max_char_encoding_id: int
-        :param max_len: Maximum char length in a sample
-        :type max_len: int
-        :return : tensor containing encoded list of input sentences
-        :rtype: tf.Tensor
-        """
-        # convert characters to indices
-        input_str_flatten = tf.reshape(input_str_tensor, [-1])
-        sentences_encode = tf.strings.unicode_decode(
-            input_str_flatten, input_encoding="UTF-8"
-        )
-        sentences_encode = tf.add(tf.cast(1, tf.int32), sentences_encode)
-        sentences_encode = tf.math.minimum(sentences_encode, max_char_encoding_id + 1)
-
-        # padding
-        sentences_encode_pad = sentences_encode.to_tensor(shape=[None, max_len])
-        return sentences_encode_pad
-
     @staticmethod
     def _argmax_threshold_layer(
         num_labels: int, threshold: float = 0.0, default_ind: int = 1
@@ -383,47 +473,7 @@ def _argmax_threshold_layer(
         """
         # Initialize the thresholds vector variable and create the threshold
         # matrix.
-        class ThreshArgMaxLayer(tf.keras.layers.Layer):
-            def __init__(self, threshold_: float, num_labels_: int) -> None:
-                super().__init__()
-                thresh_init = tf.constant_initializer(threshold_)
-                self.thresh_vec = tf.Variable(
-                    name="ThreshVec",
-                    initial_value=thresh_init(shape=[num_labels_]),
-                    trainable=False,
-                )
-
-            def call(
-                self, argmax_layer: tf.Tensor, confidence_layer: tf.Tensor
-            ) -> tf.Tensor:
-                threshold_at_argmax = tf.gather(self.thresh_vec, argmax_layer)
-
-                confidence_max_layer = tf.keras.backend.max(confidence_layer, axis=2)
-
-                # Check if the confidences meet the threshold minimum.
-                argmax_mask = tf.keras.backend.cast(
-                    tf.keras.backend.greater_equal(
-                        confidence_max_layer, threshold_at_argmax
-                    ),
-                    dtype=argmax_layer.dtype,
-                )
-
-                # Create a vector the same size as the batch_size which
-                # represents the background label
-                bg_label_tf = tf.keras.backend.constant(
-                    default_ind, dtype=argmax_layer.dtype
-                )
-
-                # Generate the final predicted output using the function:
-                final_predicted_layer = tf.add(
-                    bg_label_tf,
-                    tf.multiply(tf.subtract(argmax_layer, bg_label_tf), argmax_mask),
-                    name="ThreshArgMax",
-                )
-
-                return final_predicted_layer
-
-        return ThreshArgMaxLayer(threshold, num_labels)
+        return ThreshArgMaxLayer(threshold, num_labels, default_ind)
 
     def _construct_model(self) -> None:
         """
@@ -449,17 +499,13 @@ def _construct_model(self) -> None:
         max_length = self._parameters["max_length"]
         max_char_encoding_id = self._parameters["max_char_encoding_id"]
 
-        # Encoding layer
-        def encoding_function(input_str: tf.Tensor) -> tf.Tensor:
-            char_in_vector = CharacterLevelCnnModel._char_encoding_layer(
-                input_str, max_char_encoding_id, max_length
-            )
-            return char_in_vector
-
         self._model.add(tf.keras.layers.Input(shape=(None,), dtype=tf.string))
 
         self._model.add(
-            tf.keras.layers.Lambda(encoding_function, output_shape=tuple([max_length]))
+            EncodingLayer(
+                max_char_encoding_id=max_char_encoding_id,
+                max_len=max_length,
+            ),
         )
 
         # Create a pre-trained weight matrix
@@ -474,7 +520,6 @@ def encoding_function(input_str: tf.Tensor) -> tf.Tensor:
         )
         embedding_dict = build_embd_dictionary(embed_file)
 
-        input_shape = tuple([max_length])
         # Fill in the weight matrix: let pad and space be 0s
         for ascii_num in range(max_char_encoding_id):
             if chr(ascii_num) in embedding_dict:
@@ -485,7 +530,6 @@ def encoding_function(input_str: tf.Tensor) -> tf.Tensor:
                 max_char_encoding_id + 2,
                 self._parameters["dim_embed"],
                 weights=[embedding_matrix],
-                input_length=input_shape[0],
                 trainable=True,
             )
         )
@@ -502,8 +546,7 @@ def encoding_function(input_str: tf.Tensor) -> tf.Tensor:
             )
             if self._parameters["dropout"]:
                 self._model.add(tf.keras.layers.Dropout(self._parameters["dropout"]))
-            # Add batch normalization, set fused = True for compactness
-            self._model.add(tf.keras.layers.BatchNormalization(fused=False, scale=True))
+            self._model.add(tf.keras.layers.BatchNormalization(scale=True))
 
         # Add the fully connected layers
         for size in self._parameters["size_fc"]:
@@ -514,29 +557,35 @@ def encoding_function(input_str: tf.Tensor) -> tf.Tensor:
         # Add the final Softmax layer
         self._model.add(tf.keras.layers.Dense(num_labels, activation="softmax"))
 
-        # Output the model into a .pb file for TensorFlow
-        argmax_layer = tf.keras.backend.argmax(self._model.output)
+        # Add argmax layer to get labels directly as an output
+        argmax_layer = tf.keras.ops.argmax(self._model.outputs[0], axis=2)
 
         # Create confidence layers
-        final_predicted_layer = CharacterLevelCnnModel._argmax_threshold_layer(
-            num_labels, threshold=0.0, default_ind=default_ind
+        final_predicted_layer = ThreshArgMaxLayer(
+            threshold_=0.0, num_labels_=num_labels, default_ind=default_ind
         )
 
         argmax_outputs = self._model.outputs + [
             argmax_layer,
-            final_predicted_layer(argmax_layer, self._model.output),
+            final_predicted_layer(argmax_layer, self._model.outputs[0]),
         ]
         self._model = tf.keras.Model(self._model.inputs, argmax_outputs)
 
         # Compile the model
-        softmax_output_layer_name = self._model.outputs[0].name.split("/")[0]
+        softmax_output_layer_name = self._model.output_names[0]
         losses = {softmax_output_layer_name: "categorical_crossentropy"}
 
         # use f1 score metric
         f1_score_training = labeler_utils.F1Score(
             num_classes=num_labels, average="micro"
         )
-        metrics = {softmax_output_layer_name: ["acc", f1_score_training]}
+        metrics = {
+            softmax_output_layer_name: [
+                "categorical_crossentropy",
+                "acc",
+                f1_score_training,
+            ]
+        }
 
         self._model.compile(loss=losses, optimizer="adam", metrics=metrics)
 
@@ -564,22 +613,18 @@ def _reconstruct_model(self) -> None:
         num_labels = self.num_labels
         default_ind = self.label_mapping[self._parameters["default_label"]]
 
-        # Remove the 3 output layers (dense_2', 'tf_op_layer_ArgMax',
-        #                             'thresh_arg_max_layer')
-        for _ in range(3):
-            self._model.layers.pop()
-
         # Add the final Softmax layer to the previous spot
+        # self._model.layers[-3] to skip: thresh and original softmax
         final_softmax_layer = tf.keras.layers.Dense(
             num_labels, activation="softmax", name="dense_2"
-        )(self._model.layers[-4].output)
+        )(self._model.layers[-3].output)
 
-        # Output the model into a .pb file for TensorFlow
-        argmax_layer = tf.keras.backend.argmax(final_softmax_layer)
+        # Add argmax layer to get labels directly as an output
+        argmax_layer = tf.keras.ops.argmax(final_softmax_layer, axis=2)
 
         # Create confidence layers
-        final_predicted_layer = CharacterLevelCnnModel._argmax_threshold_layer(
-            num_labels, threshold=0.0, default_ind=default_ind
+        final_predicted_layer = ThreshArgMaxLayer(
+            threshold_=0.0, num_labels_=num_labels, default_ind=default_ind
         )
 
         argmax_outputs = [final_softmax_layer] + [
@@ -589,14 +634,20 @@ def _reconstruct_model(self) -> None:
         self._model = tf.keras.Model(self._model.inputs, argmax_outputs)
 
         # Compile the model
-        softmax_output_layer_name = self._model.outputs[0].name.split("/")[0]
+        softmax_output_layer_name = self._model.output_names[0]
         losses = {softmax_output_layer_name: "categorical_crossentropy"}
 
         # use f1 score metric
         f1_score_training = labeler_utils.F1Score(
             num_classes=num_labels, average="micro"
         )
-        metrics = {softmax_output_layer_name: ["acc", f1_score_training]}
+        metrics = {
+            softmax_output_layer_name: [
+                "categorical_crossentropy",
+                "acc",
+                f1_score_training,
+            ]
+        }
 
         self._model.compile(loss=losses, optimizer="adam", metrics=metrics)
         self._epoch_id = 0
@@ -648,7 +699,7 @@ def fit(
         f1_report: dict = {}
 
         self._model.reset_metrics()
-        softmax_output_layer_name = self._model.outputs[0].name.split("/")[0]
+        softmax_output_layer_name = self._model.output_names[0]
 
         start_time = time.time()
         batch_id = 0
@@ -729,7 +780,9 @@ def _validate_training(
         for x_val, y_val in val_data:
             y_val_pred.append(
                 self._model.predict(
-                    x_val, batch_size=batch_size_test, verbose=verbose_keras
+                    tf.convert_to_tensor(x_val),
+                    batch_size=batch_size_test,
+                    verbose=verbose_keras,
                 )[1]
             )
             y_val_test.append(np.argmax(y_val, axis=-1))
diff --git a/dataprofiler/labelers/data_labelers.py b/dataprofiler/labelers/data_labelers.py
index 7172e7472..a6d9932b7 100644
--- a/dataprofiler/labelers/data_labelers.py
+++ b/dataprofiler/labelers/data_labelers.py
@@ -141,11 +141,11 @@ def load_from_library(cls, name: str, trainable: bool = False) -> BaseDataLabele
         :type trainable: bool
         :return: DataLabeler class
         """
+        for labeler_name, labeler_class_obj in cls.labeler_classes.items():
+            if name == labeler_name:
+                name = labeler_class_obj._default_model_loc
         if trainable:
             return TrainableDataLabeler.load_from_library(name)
-        for _, labeler_class_obj in cls.labeler_classes.items():
-            if name in labeler_class_obj._default_model_loc:
-                return labeler_class_obj()
         return BaseDataLabeler.load_from_library(name)
 
     @classmethod
diff --git a/dataprofiler/labelers/labeler_utils.py b/dataprofiler/labelers/labeler_utils.py
index b6070ff72..3a24886f3 100644
--- a/dataprofiler/labelers/labeler_utils.py
+++ b/dataprofiler/labelers/labeler_utils.py
@@ -358,7 +358,7 @@ def __init__(
 
         def _zero_wt_init(name: str) -> tf.Variable:
             return self.add_weight(
-                name, shape=self.init_shape, initializer="zeros", dtype=self.dtype
+                name=name, shape=self.init_shape, initializer="zeros", dtype=self.dtype
             )
 
         self.true_positives = _zero_wt_init("true_positives")
@@ -435,11 +435,6 @@ def get_config(self) -> dict:
         base_config = super().get_config()
         return {**base_config, **config}
 
-    def reset_state(self) -> None:
-        """Reset state."""
-        reset_value = tf.zeros(self.init_shape, dtype=self.dtype)
-        tf.keras.backend.batch_set_value([(v, reset_value) for v in self.variables])
-
 
 @protected_register_keras_serializable()
 class F1Score(FBetaScore):
diff --git a/dataprofiler/tests/labelers/test_char_tf_load_model.py b/dataprofiler/tests/labelers/test_char_tf_load_model.py
index fbfde0c49..c6d70f740 100644
--- a/dataprofiler/tests/labelers/test_char_tf_load_model.py
+++ b/dataprofiler/tests/labelers/test_char_tf_load_model.py
@@ -272,7 +272,7 @@ def test_fit_and_predict(self, *mocks):
         )
 
         # predict after fitting on just the text
-        model.predict(data_gen[0][0])
+        model.predict([data_gen[0][0]])
 
     @mock.patch("os.makedirs", return_value=None)
     def test_validation_evaluate_and_classification_report(self, *mocks):
diff --git a/dataprofiler/tests/labelers/test_character_level_cnn_model.py b/dataprofiler/tests/labelers/test_character_level_cnn_model.py
index ad549cc53..e120a9754 100644
--- a/dataprofiler/tests/labelers/test_character_level_cnn_model.py
+++ b/dataprofiler/tests/labelers/test_character_level_cnn_model.py
@@ -9,7 +9,10 @@
 import pkg_resources
 import tensorflow as tf
 
-from dataprofiler.labelers.character_level_cnn_model import CharacterLevelCnnModel
+from dataprofiler.labelers.character_level_cnn_model import (
+    CharacterLevelCnnModel,
+    EncodingLayer,
+)
 
 _file_dir = os.path.dirname(os.path.abspath(__file__))
 _resource_labeler_dir = pkg_resources.resource_filename("resources", "labelers")
@@ -272,7 +275,7 @@ def test_fit_and_predict_with_new_labels(self):
         )
 
         # predict after fitting on just the text
-        cnn_model.predict(data_gen[0][0])
+        cnn_model.predict([data_gen[0][0]])
 
     def test_fit_and_predict_with_new_labels_set_via_method(self):
         # Initialize model
@@ -301,7 +304,7 @@ def test_fit_and_predict_with_new_labels_set_via_method(self):
         history, f1, f1_report = cnn_model.fit(data_gen, cv_gen)
 
         # test predict on just the text
-        cnn_model.predict(data_gen[0][0])
+        cnn_model.predict([data_gen[0][0]])
 
     def test_validation(self):
 
@@ -368,9 +371,8 @@ def test_input_encoding(self):
         max_char_encoding_id = 127
         max_len = 10
 
-        encode_output = cnn_model._char_encoding_layer(
-            input_str_tensor, max_char_encoding_id, max_len
-        ).numpy()[0]
+        encode_layer = EncodingLayer(max_char_encoding_id, max_len)
+        encode_output = encode_layer.call(input_str_tensor).numpy()[0]
         expected_output = [117, 102, 116, 117, 0, 0, 0, 0, 0, 0]
         self.assertCountEqual(encode_output, expected_output)
 
@@ -464,7 +466,6 @@ def test_model_construct(self):
             "dense_1",
             "dropout_5",
             "dense_2",
-            "tf_op_layer_ArgMax",
             "thresh_arg_max_layer",
         ]
         model_layers = [layer.name for layer in cnn_model._model.layers]
diff --git a/requirements-ml.txt b/requirements-ml.txt
index ff525fec1..6da08b313 100644
--- a/requirements-ml.txt
+++ b/requirements-ml.txt
@@ -1,7 +1,7 @@
 scikit-learn>=0.23.2
-keras>=2.4.3,<3.0.0
+keras>=3.0.0
 rapidfuzz>=2.6.1
-tensorflow>=2.6.4,<2.15.0; sys.platform != 'darwin'
-tensorflow>=2.6.4,<2.15.0; sys_platform == 'darwin' and platform_machine != 'arm64'
-tensorflow-macos>=2.6.4,<2.15.0; sys_platform == 'darwin' and platform_machine == 'arm64'
+tensorflow>=2.16.0; sys.platform != 'darwin'
+tensorflow>=2.16.0; sys_platform == 'darwin' and platform_machine != 'arm64'
+tensorflow-macos>=2.16.0; sys_platform == 'darwin' and platform_machine == 'arm64'
 tqdm>=4.0.0
diff --git a/resources/labelers/structured_model/keras_metadata.pb b/resources/labelers/structured_model/keras_metadata.pb
deleted file mode 100644
index dcc84a213..000000000
--- a/resources/labelers/structured_model/keras_metadata.pb
+++ /dev/null
@@ -1,29 +0,0 @@
-
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-ø	root.layer_with_weights-3"_tf_keras_layer*Á	{"name": "conv1d_1", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Conv1D", "config": {"name": "conv1d_1", "trainable": true, "dtype": "float32", "filters": 48, "kernel_size": {"class_name": "__tuple__", "items": [13]}, "strides": {"class_name": "__tuple__", "items": [1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1]}, "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 13}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 14}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["batch_normalization", 0, 0, {}]]], "shared_object_id": 15, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 3, "axes": {"-1": 48}}, "shared_object_id": 58}, "build_input_shape": {"class_name": "TensorShape", "items": [null, null, 48]}}2

-­root.layer-7"_tf_keras_layer*ƒ{"name": "dropout_1", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dropout", "config": {"name": "dropout_1", "trainable": true, "dtype": "float32", "rate": 0.073, "noise_shape": null, "seed": null}, "inbound_nodes": [[["conv1d_1", 0, 0, {}]]], "shared_object_id": 16}2

-¿		root.layer_with_weights-4"_tf_keras_layer*ˆ	{"name": "batch_normalization_1", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "BatchNormalization", "config": {"name": "batch_normalization_1", "trainable": true, "dtype": "float32", "axis": [2], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 17}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 18}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 19}, "moving_variance_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 20}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["dropout_1", 0, 0, {}]]], "shared_object_id": 21, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 3, "max_ndim": null, "min_ndim": null, "axes": {"2": 48}}, "shared_object_id": 59}, "build_input_shape": {"class_name": "TensorShape", "items": [null, null, 48]}}2

-ú	
-root.layer_with_weights-5"_tf_keras_layer*Ã	{"name": "conv1d_2", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Conv1D", "config": {"name": "conv1d_2", "trainable": true, "dtype": "float32", "filters": 48, "kernel_size": {"class_name": "__tuple__", "items": [13]}, "strides": {"class_name": "__tuple__", "items": [1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1]}, "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 22}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 23}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["batch_normalization_1", 0, 0, {}]]], "shared_object_id": 24, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 3, "axes": {"-1": 48}}, "shared_object_id": 60}, "build_input_shape": {"class_name": "TensorShape", "items": [null, null, 48]}}2

-®
root.layer-10"_tf_keras_layer*ƒ{"name": "dropout_2", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dropout", "config": {"name": "dropout_2", "trainable": true, "dtype": "float32", "rate": 0.073, "noise_shape": null, "seed": null}, "inbound_nodes": [[["conv1d_2", 0, 0, {}]]], "shared_object_id": 25}2

-¿	root.layer_with_weights-6"_tf_keras_layer*ˆ	{"name": "batch_normalization_2", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "BatchNormalization", "config": {"name": "batch_normalization_2", "trainable": true, "dtype": "float32", "axis": [2], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 26}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 27}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 28}, "moving_variance_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 29}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["dropout_2", 0, 0, {}]]], "shared_object_id": 30, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 3, "max_ndim": null, "min_ndim": null, "axes": {"2": 48}}, "shared_object_id": 61}, "build_input_shape": {"class_name": "TensorShape", "items": [null, null, 48]}}2

-ú	
root.layer_with_weights-7"_tf_keras_layer*Ã	{"name": "conv1d_3", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Conv1D", "config": {"name": "conv1d_3", "trainable": true, "dtype": "float32", "filters": 48, "kernel_size": {"class_name": "__tuple__", "items": [13]}, "strides": {"class_name": "__tuple__", "items": [1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1]}, "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 31}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 32}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["batch_normalization_2", 0, 0, {}]]], "shared_object_id": 33, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 3, "axes": {"-1": 48}}, "shared_object_id": 62}, "build_input_shape": {"class_name": "TensorShape", "items": [null, null, 48]}}2

-®
root.layer-13"_tf_keras_layer*ƒ{"name": "dropout_3", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dropout", "config": {"name": "dropout_3", "trainable": true, "dtype": "float32", "rate": 0.073, "noise_shape": null, "seed": null}, "inbound_nodes": [[["conv1d_3", 0, 0, {}]]], "shared_object_id": 34}2

-¿	root.layer_with_weights-8"_tf_keras_layer*ˆ	{"name": "batch_normalization_3", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "BatchNormalization", "config": {"name": "batch_normalization_3", "trainable": true, "dtype": "float32", "axis": [2], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 35}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 36}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 37}, "moving_variance_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 38}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["dropout_3", 0, 0, {}]]], "shared_object_id": 39, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 3, "max_ndim": null, "min_ndim": null, "axes": {"2": 48}}, "shared_object_id": 63}, "build_input_shape": {"class_name": "TensorShape", "items": [null, null, 48]}}2

-„root.layer_with_weights-9"_tf_keras_layer*Í{"name": "dense", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense", "trainable": true, "dtype": "float32", "units": 96, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 40}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 41}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["batch_normalization_3", 0, 0, {}]]], "shared_object_id": 42, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 48}}, "shared_object_id": 64}, "build_input_shape": {"class_name": "TensorShape", "items": [null, null, 48]}}2

-«
root.layer-16"_tf_keras_layer*€{"name": "dropout_4", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dropout", "config": {"name": "dropout_4", "trainable": true, "dtype": "float32", "rate": 0.073, "noise_shape": null, "seed": null}, "inbound_nodes": [[["dense", 0, 0, {}]]], "shared_object_id": 43}2

-ýroot.layer_with_weights-10"_tf_keras_layer*Å{"name": "dense_1", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 96, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 44}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 45}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["dropout_4", 0, 0, {}]]], "shared_object_id": 46, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 96}}, "shared_object_id": 65}, "build_input_shape": {"class_name": "TensorShape", "items": [null, null, 96]}}2

-­
root.layer-18"_tf_keras_layer*‚{"name": "dropout_5", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dropout", "config": {"name": "dropout_5", "trainable": true, "dtype": "float32", "rate": 0.073, "noise_shape": null, "seed": null}, "inbound_nodes": [[["dense_1", 0, 0, {}]]], "shared_object_id": 47}2

-€root.layer_with_weights-11"_tf_keras_layer*È{"name": "dense_2", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense_2", "trainable": true, "dtype": "float32", "units": 24, "activation": "softmax", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 48}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 49}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["dropout_5", 0, 0, {}]]], "shared_object_id": 50, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 96}}, "shared_object_id": 66}, "build_input_shape": {"class_name": "TensorShape", "items": [null, null, 96]}}2

-í
root.layer-20"_tf_keras_layer*Â{"name": "tf_op_layer_ArgMax", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": true, "class_name": "TensorFlowOpLayer", "config": {"name": "ArgMax", "trainable": true, "dtype": "float32", "node_def": {"name": "ArgMax", "op": "ArgMax", "input": ["dense_2/truediv", "ArgMax/dimension"], "attr": {"Tidx": {"type": "DT_INT32"}, "output_type": {"type": "DT_INT64"}, "T": {"type": "DT_FLOAT"}}}, "constants": {"1": -1}}, "inbound_nodes": [[["dense_2", 0, 0, {}]]], "shared_object_id": 51}2

-Æroot.layer_with_weights-12"_tf_keras_layer*Ž{"name": "thresh_arg_max_layer", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "ThreshArgMaxLayer", "config": {"layer was saved without config": true}}2

-º
“root.keras_api.metrics.0"_tf_keras_metric*‚
{"class_name": "Mean", "name": "loss", "dtype": "float32", "config": {"name": "loss", "dtype": "float32"}, "shared_object_id": 67}2

-Ê
”root.keras_api.metrics.1"_tf_keras_metric*’
{"class_name": "Mean", "name": "dense_2_loss", "dtype": "float32", "config": {"name": "dense_2_loss", "dtype": "float32"}, "shared_object_id": 68}2

-ã
•root.keras_api.metrics.2"_tf_keras_metric*«
{"class_name": "MeanMetricWrapper", "name": "acc", "dtype": "float32", "config": {"name": "acc", "dtype": "float32", "fn": "categorical_accuracy"}, "shared_object_id": 54}2

-––root.keras_api.metrics.3"_tf_keras_metric*Þ
{"class_name": "Custom>F1Score", "name": "dense_2_f1_score", "dtype": "float32", "config": {"name": "dense_2_f1_score", "dtype": "float32", "num_classes": 24, "average": "micro", "threshold": null}, "shared_object_id": 55}2

\ No newline at end of file
diff --git a/resources/labelers/structured_model/variables/variables.data-00000-of-00001 b/resources/labelers/structured_model/model.keras
similarity index 88%
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index 95732bf16..795d637da 100644
Binary files a/resources/labelers/structured_model/variables/variables.data-00000-of-00001 and b/resources/labelers/structured_model/model.keras differ
diff --git a/resources/labelers/structured_model/saved_model.pb b/resources/labelers/structured_model/saved_model.pb
deleted file mode 100644
index 76274cae0..000000000
Binary files a/resources/labelers/structured_model/saved_model.pb and /dev/null differ
diff --git a/resources/labelers/structured_model/variables/variables.index b/resources/labelers/structured_model/variables/variables.index
deleted file mode 100644
index 627e9a577..000000000
Binary files a/resources/labelers/structured_model/variables/variables.index and /dev/null differ
diff --git a/resources/labelers/unstructured_model/keras_metadata.pb b/resources/labelers/unstructured_model/keras_metadata.pb
deleted file mode 100644
index dcc84a213..000000000
--- a/resources/labelers/unstructured_model/keras_metadata.pb
+++ /dev/null
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-®
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-®
root.layer-13"_tf_keras_layer*ƒ{"name": "dropout_3", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dropout", "config": {"name": "dropout_3", "trainable": true, "dtype": "float32", "rate": 0.073, "noise_shape": null, "seed": null}, "inbound_nodes": [[["conv1d_3", 0, 0, {}]]], "shared_object_id": 34}2

-¿	root.layer_with_weights-8"_tf_keras_layer*ˆ	{"name": "batch_normalization_3", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "BatchNormalization", "config": {"name": "batch_normalization_3", "trainable": true, "dtype": "float32", "axis": [2], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 35}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 36}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 37}, "moving_variance_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 38}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["dropout_3", 0, 0, {}]]], "shared_object_id": 39, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 3, "max_ndim": null, "min_ndim": null, "axes": {"2": 48}}, "shared_object_id": 63}, "build_input_shape": {"class_name": "TensorShape", "items": [null, null, 48]}}2

-„root.layer_with_weights-9"_tf_keras_layer*Í{"name": "dense", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense", "trainable": true, "dtype": "float32", "units": 96, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 40}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 41}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["batch_normalization_3", 0, 0, {}]]], "shared_object_id": 42, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 48}}, "shared_object_id": 64}, "build_input_shape": {"class_name": "TensorShape", "items": [null, null, 48]}}2

-«
root.layer-16"_tf_keras_layer*€{"name": "dropout_4", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dropout", "config": {"name": "dropout_4", "trainable": true, "dtype": "float32", "rate": 0.073, "noise_shape": null, "seed": null}, "inbound_nodes": [[["dense", 0, 0, {}]]], "shared_object_id": 43}2

-ýroot.layer_with_weights-10"_tf_keras_layer*Å{"name": "dense_1", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 96, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 44}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 45}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["dropout_4", 0, 0, {}]]], "shared_object_id": 46, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 96}}, "shared_object_id": 65}, "build_input_shape": {"class_name": "TensorShape", "items": [null, null, 96]}}2

-­
root.layer-18"_tf_keras_layer*‚{"name": "dropout_5", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dropout", "config": {"name": "dropout_5", "trainable": true, "dtype": "float32", "rate": 0.073, "noise_shape": null, "seed": null}, "inbound_nodes": [[["dense_1", 0, 0, {}]]], "shared_object_id": 47}2

-€root.layer_with_weights-11"_tf_keras_layer*È{"name": "dense_2", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense_2", "trainable": true, "dtype": "float32", "units": 24, "activation": "softmax", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 48}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 49}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["dropout_5", 0, 0, {}]]], "shared_object_id": 50, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 96}}, "shared_object_id": 66}, "build_input_shape": {"class_name": "TensorShape", "items": [null, null, 96]}}2

-í
root.layer-20"_tf_keras_layer*Â{"name": "tf_op_layer_ArgMax", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": true, "class_name": "TensorFlowOpLayer", "config": {"name": "ArgMax", "trainable": true, "dtype": "float32", "node_def": {"name": "ArgMax", "op": "ArgMax", "input": ["dense_2/truediv", "ArgMax/dimension"], "attr": {"Tidx": {"type": "DT_INT32"}, "output_type": {"type": "DT_INT64"}, "T": {"type": "DT_FLOAT"}}}, "constants": {"1": -1}}, "inbound_nodes": [[["dense_2", 0, 0, {}]]], "shared_object_id": 51}2

-Æroot.layer_with_weights-12"_tf_keras_layer*Ž{"name": "thresh_arg_max_layer", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "ThreshArgMaxLayer", "config": {"layer was saved without config": true}}2

-º
“root.keras_api.metrics.0"_tf_keras_metric*‚
{"class_name": "Mean", "name": "loss", "dtype": "float32", "config": {"name": "loss", "dtype": "float32"}, "shared_object_id": 67}2

-Ê
”root.keras_api.metrics.1"_tf_keras_metric*’
{"class_name": "Mean", "name": "dense_2_loss", "dtype": "float32", "config": {"name": "dense_2_loss", "dtype": "float32"}, "shared_object_id": 68}2

-ã
•root.keras_api.metrics.2"_tf_keras_metric*«
{"class_name": "MeanMetricWrapper", "name": "acc", "dtype": "float32", "config": {"name": "acc", "dtype": "float32", "fn": "categorical_accuracy"}, "shared_object_id": 54}2

-––root.keras_api.metrics.3"_tf_keras_metric*Þ
{"class_name": "Custom>F1Score", "name": "dense_2_f1_score", "dtype": "float32", "config": {"name": "dense_2_f1_score", "dtype": "float32", "num_classes": 24, "average": "micro", "threshold": null}, "shared_object_id": 55}2

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