Repository: aeon-toolkit/aeon
Pull request #2335 (2024-11-11 00:47:49)

[MNT] Automated `pre-commit` hook update
Automated weekly update to `.pre-commit-config.yaml` hook versions.


Patch:
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 6fed7804..9276240b 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -29,7 +29,7 @@ repos:
         args: [ "--create", "--python-folders", "aeon" ]
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.7.2
+    rev: v0.7.3
     hooks:
       - id: ruff
         args: [ "--fix"]
@@ -61,7 +61,7 @@ repos:
         language_version: python3
 
   - repo: https://github.com/nbQA-dev/nbQA
-    rev: 1.8.7
+    rev: 1.9.1
     hooks:
       - id: nbqa-isort
         additional_dependencies: [ isort==5.13.2 ]
diff --git a/aeon/classification/deep_learning/__init__.py b/aeon/classification/deep_learning/__init__.py
index a7c1c73f..5fac7505 100644
--- a/aeon/classification/deep_learning/__init__.py
+++ b/aeon/classification/deep_learning/__init__.py
@@ -12,8 +12,10 @@ __all__ = [
     "TapNetClassifier",
     "LITETimeClassifier",
     "IndividualLITEClassifier",
+    "DisjointCNNClassifier",
 ]
 from aeon.classification.deep_learning._cnn import TimeCNNClassifier
+from aeon.classification.deep_learning._disjoint_cnn import DisjointCNNClassifier
 from aeon.classification.deep_learning._encoder import EncoderClassifier
 from aeon.classification.deep_learning._fcn import FCNClassifier
 from aeon.classification.deep_learning._inception_time import (
diff --git a/aeon/classification/deep_learning/_cnn.py b/aeon/classification/deep_learning/_cnn.py
index 5b87cab9..d099fb04 100644
--- a/aeon/classification/deep_learning/_cnn.py
+++ b/aeon/classification/deep_learning/_cnn.py
@@ -61,12 +61,19 @@ class TimeCNNClassifier(BaseDeepClassifier):
         The number of samples per gradient update.
     verbose : boolean, default = False
         Whether to output extra information.
-    loss : string, default = "mean_squared_error"
-        Fit parameter for the keras model.
-    optimizer : keras.optimizer, default = keras.optimizers.Adam()
-    metrics : list of strings, default = ["accuracy"]
-    callbacks : keras.callbacks, default = model_checkpoint
-        To save best model on training loss.
+    loss : str, default = "mean_squared_error"
+        The name of the keras training loss.
+    optimizer : keras.optimizer, default = tf.keras.optimizers.Adam()
+        The keras optimizer used for training.
+    metrics : str or list[str], default="accuracy"
+        The evaluation metrics to use during training. If
+        a single string metric is provided, it will be
+        used as the only metric. If a list of metrics are
+        provided, all will be used for evaluation.
+    callbacks : keras callback or list of callbacks,
+        default = None
+        The default list of callbacks are set to
+        ModelCheckpoint.
     file_path : file_path for the best model
         Only used if checkpoint is used as callback.
     save_best_model : bool, default = False
@@ -131,7 +138,7 @@ class TimeCNNClassifier(BaseDeepClassifier):
         init_file_name="init_model",
         verbose=False,
         loss="mean_squared_error",
-        metrics=None,
+        metrics="accuracy",
         random_state=None,
         use_bias=True,
         optimizer=None,
@@ -201,18 +208,13 @@ class TimeCNNClassifier(BaseDeepClassifier):
         import numpy as np
         import tensorflow as tf
 
-        if self.metrics is None:
-            metrics = ["accuracy"]
-        else:
-            metrics = self.metrics
-
         rng = check_random_state(self.random_state)
         self.random_state_ = rng.randint(0, np.iinfo(np.int32).max)
         tf.keras.utils.set_random_seed(self.random_state_)
         input_layer, output_layer = self._network.build_network(input_shape, **kwargs)
 
         output_layer = tf.keras.layers.Dense(
-            units=n_classes, activation=self.activation, use_bias=self.use_bias
+            units=n_classes, activation=self.activation
         )(output_layer)
 
         self.optimizer_ = (
@@ -223,7 +225,7 @@ class TimeCNNClassifier(BaseDeepClassifier):
         model.compile(
             loss=self.loss,
             optimizer=self.optimizer_,
-            metrics=metrics,
+            metrics=self._metrics,
         )
 
         return model
@@ -249,6 +251,11 @@ class TimeCNNClassifier(BaseDeepClassifier):
         # Transpose to conform to Keras input style.
         X = X.transpose(0, 2, 1)
 
+        if isinstance(self.metrics, list):
+            self._metrics = self.metrics
+        elif isinstance(self.metrics, str):
+            self._metrics = [self.metrics]
+
         self.input_shape = X.shape[1:]
         self.training_model_ = self.build_model(self.input_shape, self.n_classes_)
 
diff --git a/aeon/classification/deep_learning/_disjoint_cnn.py b/aeon/classification/deep_learning/_disjoint_cnn.py
new file mode 100644
index 00000000..dfe85703
--- /dev/null
+++ b/aeon/classification/deep_learning/_disjoint_cnn.py
@@ -0,0 +1,416 @@
+"""DisjointCNN classifier."""
+
+__maintainer__ = ["hadifawaz1999"]
+__all__ = ["DisjointCNNClassifier"]
+
+import gc
+import os
+import time
+from copy import deepcopy
+
+from sklearn.utils import check_random_state
+
+from aeon.classification.deep_learning.base import BaseDeepClassifier
+from aeon.networks import DisjointCNNNetwork
+
+
+class DisjointCNNClassifier(BaseDeepClassifier):
+    """Disjoint Convolutional Neural Netowkr classifier.
+
+    Adapted from the implementation used in [1]_.
+
+    Parameters
+    ----------
+    n_layers : int, default = 4
+        Number of 1+1D Convolution layers.
+    n_filters : int or list of int, default = 64
+        Number of filters used in convolution layers. If
+        input is set to a list, the lenght should be the same
+        as `n_layers`, if input is int the a list of the same
+        element is created of length `n_layers`.
+    kernel_size : int or list of int, default = [8, 5, 5, 3]
+        Size of convolution kernel. If
+        input is set to a list, the lenght should be the same
+        as `n_layers`, if input is int the a list of the same
+        element is created of length `n_layers`.
+    dilation_rate : int or list of int, default = 1
+        The dilation rate for convolution. If
+        input is set to a list, the lenght should be the same
+        as `n_layers`, if input is int the a list of the same
+        element is created of length `n_layers`.
+    strides : int or list of int, default = 1
+        The strides of the convolution filter. If
+        input is set to a list, the lenght should be the same
+        as `n_layers`, if input is int the a list of the same
+        element is created of length `n_layers`.
+    padding : str or list of str, default = "same"
+        The type of padding used for convolution. If
+        input is set to a list, the lenght should be the same
+        as `n_layers`, if input is int the a list of the same
+        element is created of length `n_layers`.
+    activation : str or list of str, default = "elu"
+        Activation used after the convolution. If
+        input is set to a list, the lenght should be the same
+        as `n_layers`, if input is int the a list of the same
+        element is created of length `n_layers`.
+    use_bias : bool or list of bool, default = True
+        Whether or not ot use bias in convolution. If
+        input is set to a list, the lenght should be the same
+        as `n_layers`, if input is int the a list of the same
+        element is created of length `n_layers`.
+    kernel_initializer: str or list of str, default = "he_uniform"
+        The initialization method of convolution layers. If
+        input is set to a list, the lenght should be the same
+        as `n_layers`, if input is int the a list of the same
+        element is created of length `n_layers`.
+    pool_size: int, default = 5
+        The size of the one max pool layer at the end of
+        the model, default = 5.
+    pool_strides: int, default = None
+        The strides used for the one max pool layer at
+        the end of the model, default = None.
+    pool_padding: str, default = "valid"
+        The padding method for the one max pool layer at
+        the end of the model, default = "valid".
+    hidden_fc_units: int, default = 128
+        The number of fully connected units.
+    activation_fc: str, default = "relu"
+        The activation of the fully connected layer.
+    n_epochs : int, default = 2000
+        The number of epochs to train the model.
+    batch_size : int, default = 16
+        The number of samples per gradient update.
+    use_mini_batch_size : bool, default = False
+        Whether or not to use the mini batch size formula.
+    random_state : int, RandomState instance or None, default=None
+        If `int`, random_state is the seed used by the random number generator;
+        If `RandomState` instance, random_state is the random number generator;
+        If `None`, the random number generator is the `RandomState` instance used
+        by `np.random`.
+        Seeded random number generation can only be guaranteed on CPU processing,
+        GPU processing will be non-deterministic.
+    verbose : boolean, default = False
+        Whether to output extra information.
+    loss : str, default = "categorical_crossentropy"
+        The name of the keras training loss.
+    metrics : str or list[str], default="accuracy"
+        The evaluation metrics to use during training. If
+        a single string metric is provided, it will be
+        used as the only metric. If a list of metrics are
+        provided, all will be used for evaluation.
+    optimizer : keras.optimizer, default = tf.keras.optimizers.Adam()
+        The keras optimizer used for training.
+    file_path : str, default = "./"
+        File path to save best model.
+    save_best_model : bool, default = False
+        Whether or not to save the best model, if the
+        modelcheckpoint callback is used by default,
+        this condition, if True, will prevent the
+        automatic deletion of the best saved model from
+        file and the user can choose the file name.
+    save_last_model : bool, default = False
+        Whether or not to save the last model, last
+        epoch trained, using the base class method
+        save_last_model_to_file.
+    save_init_model : bool, default = False
+        Whether to save the initialization of the  model.
+    best_file_name : str, default = "best_model"
+        The name of the file of the best model, if
+        save_best_model is set to False, this parameter
+        is discarded.
+    last_file_name : str, default = "last_model"
+        The name of the file of the last model, if
+        save_last_model is set to False, this parameter
+        is discarded.
+    init_file_name : str, default = "init_model"
+        The name of the file of the init model, if
+        save_init_model is set to False,
+        this parameter is discarded.
+    callbacks : keras callback or list of callbacks,
+        default = None
+        The default list of callbacks are set to
+        ModelCheckpoint and ReduceLROnPlateau.
+
+    Notes
+    -----
+    Adapted from the implementation from:
+    https://github.com/Navidfoumani/Disjoint-CNN
+
+    References
+    ----------
+    .. [1] Foumani, Seyed Navid Mohammadi, Chang Wei Tan, and Mahsa Salehi.
+    "Disjoint-cnn for multivariate time series classification."
+    2021 International Conference on Data Mining Workshops
+    (ICDMW). IEEE, 2021.
+
+    Examples
+    --------
+    >>> from aeon.classification.deep_learning import DisjointCNNClassifier
+    >>> from aeon.datasets import load_unit_test
+    >>> X_train, y_train = load_unit_test(split="train")
+    >>> X_test, y_test = load_unit_test(split="test")
+    >>> disjoint_cnn = DisjointCNNClassifier(n_epochs=20,
+    ... batch_size=4)  # doctest: +SKIP
+    >>> disjoint_cnn.fit(X_train, y_train)  # doctest: +SKIP
+    DisjointCNNClassifier(...)
+    """
+
+    def __init__(
+        self,
+        n_layers=4,
+        n_filters=64,
+        kernel_size=None,
+        dilation_rate=1,
+        strides=1,
+        padding="same",
+        activation="elu",
+        use_bias=True,
+        kernel_initializer="he_uniform",
+        pool_size=5,
+        pool_strides=None,
+        pool_padding="valid",
+        hidden_fc_units=128,
+        activation_fc="relu",
+        n_epochs=2000,
+        batch_size=16,
+        use_mini_batch_size=False,
+        random_state=None,
+        verbose=False,
+        loss="categorical_crossentropy",
+        metrics="accuracy",
+        optimizer=None,
+        file_path="./",
+        save_best_model=False,
+        save_last_model=False,
+        save_init_model=False,
+        best_file_name="best_model",
+        last_file_name="last_model",
+        init_file_name="init_model",
+        callbacks=None,
+    ):
+        self.n_layers = n_layers
+        self.n_filters = n_filters
+        self.kernel_size = kernel_size
+        self.dilation_rate = dilation_rate
+        self.strides = strides
+        self.padding = padding
+        self.activation = activation
+        self.use_bias = use_bias
+        self.kernel_initializer = kernel_initializer
+        self.pool_size = pool_size
+        self.pool_strides = pool_strides
+        self.pool_padding = pool_padding
+        self.hidden_fc_units = hidden_fc_units
+        self.activation_fc = activation_fc
+
+        self.callbacks = callbacks
+        self.n_epochs = n_epochs
+        self.use_mini_batch_size = use_mini_batch_size
+        self.verbose = verbose
+        self.loss = loss
+        self.metrics = metrics
+        self.optimizer = optimizer
+
+        self.file_path = file_path
+        self.save_best_model = save_best_model
+        self.save_last_model = save_last_model
+        self.save_init_model = save_init_model
+        self.best_file_name = best_file_name
+        self.init_file_name = init_file_name
+
+        self.history = None
+
+        super().__init__(
+            batch_size=batch_size,
+            random_state=random_state,
+            last_file_name=last_file_name,
+        )
+
+        self._network = DisjointCNNNetwork(
+            n_layers=self.n_layers,
+            n_filters=self.n_filters,
+            kernel_size=self.kernel_size,
+            dilation_rate=self.dilation_rate,
+            strides=self.strides,
+            padding=self.padding,
+            activation=self.activation,
+            use_bias=self.use_bias,
+            kernel_initializer=self.kernel_initializer,
+            pool_size=self.pool_size,
+            pool_strides=self.pool_strides,
+            pool_padding=self.pool_padding,
+            hidden_fc_units=self.hidden_fc_units,
+            activation_fc=self.activation_fc,
+        )
+
+    def build_model(self, input_shape, n_classes, **kwargs):
+        """Construct a compiled, un-trained, keras model that is ready for training.
+
+        In aeon, time series are stored in numpy arrays of shape (d,m), where d
+        is the number of dimensions, m is the series length. Keras/tensorflow assume
+        data is in shape (m,d). This method also assumes (m,d). Transpose should
+        happen in fit.
+
+        Parameters
+        ----------
+        input_shape : tuple
+            The shape of the data fed into the input layer, should be (m, d).
+        n_classes : int
+            The number of classes, which becomes the size of the output layer.
+
+        Returns
+        -------
+        output : a compiled Keras Model
+        """
+        import numpy as np
+        import tensorflow as tf
+
+        rng = check_random_state(self.random_state)
+        self.random_state_ = rng.randint(0, np.iinfo(np.int32).max)
+        tf.keras.utils.set_random_seed(self.random_state_)
+        input_layer, output_layer = self._network.build_network(input_shape, **kwargs)
+
+        output_layer = tf.keras.layers.Dense(units=n_classes, activation="softmax")(
+            output_layer
+        )
+
+        self.optimizer_ = (
+            tf.keras.optimizers.Adam() if self.optimizer is None else self.optimizer
+        )
+
+        model = tf.keras.models.Model(inputs=input_layer, outputs=output_layer)
+        model.compile(
+            loss=self.loss,
+            optimizer=self.optimizer_,
+            metrics=self._metrics,
+        )
+
+        return model
+
+    def _fit(self, X, y):
+        """Fit the classifier on the training set (X, y).
+
+        Parameters
+        ----------
+        X : np.ndarray
+            The training input samples of shape (n_cases, n_channels, n_timepoints)
+        y : np.ndarray
+            The training data class labels of shape (n_cases,).
+
+        Returns
+        -------
+        self : object
+        """
+        import tensorflow as tf
+
+        y_onehot = self.convert_y_to_keras(y)
+        # Transpose to conform to Keras input style.
+        X = X.transpose(0, 2, 1)
+
+        if isinstance(self.metrics, list):
+            self._metrics = self.metrics
+        elif isinstance(self.metrics, str):
+            self._metrics = [self.metrics]
+
+        self.input_shape = X.shape[1:]
+        self.training_model_ = self.build_model(self.input_shape, self.n_classes_)
+
+        if self.save_init_model:
+            self.training_model_.save(self.file_path + self.init_file_name + ".keras")
+
+        if self.verbose:
+            self.training_model_.summary()
+
+        if self.use_mini_batch_size:
+            mini_batch_size = min(self.batch_size, X.shape[0] // 10)
+        else:
+            mini_batch_size = self.batch_size
+
+        self.file_name_ = (
+            self.best_file_name if self.save_best_model else str(time.time_ns())
+        )
+
+        if self.callbacks is None:
+            self.callbacks_ = [
+                tf.keras.callbacks.ReduceLROnPlateau(
+                    monitor="loss", factor=0.5, patience=50, min_lr=0.0001
+                ),
+                tf.keras.callbacks.ModelCheckpoint(
+                    filepath=self.file_path + self.file_name_ + ".keras",
+                    monitor="loss",
+                    save_best_only=True,
+                ),
+            ]
+        else:
+            self.callbacks_ = self._get_model_checkpoint_callback(
+                callbacks=self.callbacks,
+                file_path=self.file_path,
+                file_name=self.file_name_,
+            )
+
+        self.history = self.training_model_.fit(
+            X,
+            y_onehot,
+            batch_size=mini_batch_size,
+            epochs=self.n_epochs,
+            verbose=self.verbose,
+            callbacks=self.callbacks_,
+        )
+
+        try:
+            self.model_ = tf.keras.models.load_model(
+                self.file_path + self.file_name_ + ".keras", compile=False
+            )
+            if not self.save_best_model:
+                os.remove(self.file_path + self.file_name_ + ".keras")
+        except FileNotFoundError:
+            self.model_ = deepcopy(self.training_model_)
+
+        if self.save_last_model:
+            self.save_last_model_to_file(file_path=self.file_path)
+
+        gc.collect()
+        return self
+
+    @classmethod
+    def _get_test_params(cls, parameter_set="default"):
+        """Return testing parameter settings for the estimator.
+
+        Parameters
+        ----------
+        parameter_set : str, default = "default"
+            Name of the set of test parameters to return, for use in tests. If no
+            special parameters are defined for a value, will return "default" set.
+            For classifiers, a "default" set of parameters should be provided for
+            general testing, and a "results_comparison" set for comparing against
+            previously recorded results if the general set does not produce suitable
+            probabilities to compare against.
+
+        Returns
+        -------
+        params : dict or list of dict, default = {}
+            Parameters to create testing instances of the class.
+            Each dict are parameters to construct an "interesting" test instance, i.e.,
+            `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
+        """
+        param1 = {
+            "n_epochs": 3,
+            "batch_size": 4,
+            "use_bias": False,
+            "n_layers": 2,
+            "n_filters": 2,
+            "kernel_size": [2, 2],
+        }
+        param2 = {
+            "n_epochs": 3,
+            "batch_size": 4,
+            "use_bias": False,
+            "n_layers": 2,
+            "n_filters": 2,
+            "kernel_size": [2, 2],
+            "verbose": True,
+            "metrics": ["accuracy"],
+            "use_mini_batch_size": True,
+        }
+
+        return [param1, param2]
diff --git a/aeon/classification/deep_learning/_encoder.py b/aeon/classification/deep_learning/_encoder.py
index 1773d1ea..cb44688d 100644
--- a/aeon/classification/deep_learning/_encoder.py
+++ b/aeon/classification/deep_learning/_encoder.py
@@ -40,8 +40,29 @@ class EncoderClassifier(BaseDeepClassifier):
     fc_units : int, default = 256
         Specifying the number of units in the hidden fully
         connected layer used in the EncoderNetwork.
+    verbose : boolean, default = False
+        Whether to output extra information.
+    loss : str, default = "categorical_crossentropy"
+        The name of the keras training loss.
+    metrics : str or list[str], default="accuracy"
+        The evaluation metrics to use during training. If
+        a single string metric is provided, it will be
+        used as the only metric. If a list of metrics are
+        provided, all will be used for evaluation.
+    optimizer : keras.optimizer, default = tf.keras.optimizers.Adam()
+        The keras optimizer used for training.
+    callbacks : keras callback or list of callbacks,
+        default = None
+        The default list of callbacks are set to
+        ModelCheckpoint.
     file_path : str, default = "./"
         File path when saving model_Checkpoint callback.
+    n_epochs : int, default = 2000
+        The number of epochs to train the model.
+    batch_size : int, default = 16
+        The number of samples per gradient update.
+    use_mini_batch_size : bool, default = False
+        Whether or not to use the mini batch size formula.
     save_best_model : bool, default = False
         Whether or not to save the best model, if the
         modelcheckpoint callback is used by default,
@@ -87,10 +108,6 @@ class EncoderClassifier(BaseDeepClassifier):
 
     """
 
-    _tags = {
-        "python_dependencies": ["tensorflow"],
-    }
-
     def __init__(
         self,
         n_epochs=100,
@@ -113,7 +130,7 @@ class EncoderClassifier(BaseDeepClassifier):
         init_file_name="init_model",
         verbose=False,
         loss="categorical_crossentropy",
-        metrics=None,
+        metrics="accuracy",
         random_state=None,
         use_bias=True,
         optimizer=None,
@@ -182,18 +199,13 @@ class EncoderClassifier(BaseDeepClassifier):
         import numpy as np
         import tensorflow as tf
 
-        if self.metrics is None:
-            metrics = ["accuracy"]
-        else:
-            metrics = self.metrics
-
         rng = check_random_state(self.random_state)
         self.random_state_ = rng.randint(0, np.iinfo(np.int32).max)
         tf.keras.utils.set_random_seed(self.random_state_)
         input_layer, output_layer = self._network.build_network(input_shape, **kwargs)
 
         output_layer = tf.keras.layers.Dense(
-            units=n_classes, activation=self.activation, use_bias=self.use_bias
+            units=n_classes, activation=self.activation
         )(output_layer)
 
         self.optimizer_ = (
@@ -206,7 +218,7 @@ class EncoderClassifier(BaseDeepClassifier):
         model.compile(
             loss=self.loss,
             optimizer=self.optimizer_,
-            metrics=metrics,
+            metrics=self._metrics,
         )
 
         return model
@@ -231,6 +243,11 @@ class EncoderClassifier(BaseDeepClassifier):
         # Transpose to conform to Keras input style.
         X = X.transpose(0, 2, 1)
 
+        if isinstance(self.metrics, list):
+            self._metrics = self.metrics
+        elif isinstance(self.metrics, str):
+            self._metrics = [self.metrics]
+
         self.input_shape = X.shape[1:]
         self.training_model_ = self.build_model(self.input_shape, self.n_classes_)
 
diff --git a/aeon/classification/deep_learning/_fcn.py b/aeon/classification/deep_learning/_fcn.py
index e79ea652..ec1e4fc1 100644
--- a/aeon/classification/deep_learning/_fcn.py
+++ b/aeon/classification/deep_learning/_fcn.py
@@ -52,11 +52,15 @@ class FCNClassifier(BaseDeepClassifier):
         GPU processing will be non-deterministic.
     verbose : boolean, default = False
         Whether to output extra information.
-    loss : string, default = "mean_squared_error"
-        Fit parameter for the keras model.
-    metrics : list of strings, default = ["accuracy"]
-    optimizer : keras.optimizers object, default = Adam(lr=0.01)
-        Specify the optimizer and the learning rate to be used.
+    loss : str, default = "categorical_crossentropy"
+        The name of the keras training loss.
+    metrics : str or list[str], default="accuracy"
+        The evaluation metrics to use during training. If
+        a single string metric is provided, it will be
+        used as the only metric. If a list of metrics are
+        provided, all will be used for evaluation.
+    optimizer : keras.optimizer, default = tf.keras.optimizers.Adam()
+        The keras optimizer used for training.
     file_path : str, default = "./"
         File path to save best model.
     save_best_model : bool, default = False
@@ -83,7 +87,10 @@ class FCNClassifier(BaseDeepClassifier):
         The name of the file of the init model, if
         save_init_model is set to False,
         this parameter is discarded.
-    callbacks : keras.callbacks, default = None
+    callbacks : keras callback or list of callbacks,
+        default = None
+        The default list of callbacks are set to
+        ModelCheckpoint and ReduceLROnPlateau.
 
     Notes
     -----
@@ -128,7 +135,7 @@ class FCNClassifier(BaseDeepClassifier):
         callbacks=None,
         verbose=False,
         loss="categorical_crossentropy",
-        metrics=None,
+        metrics="accuracy",
         random_state=None,
         use_bias=True,
         optimizer=None,
@@ -198,19 +205,14 @@ class FCNClassifier(BaseDeepClassifier):
         import numpy as np
         import tensorflow as tf
 
-        if self.metrics is None:
-            metrics = ["accuracy"]
-        else:
-            metrics = self.metrics
-
         rng = check_random_state(self.random_state)
         self.random_state_ = rng.randint(0, np.iinfo(np.int32).max)
         tf.keras.utils.set_random_seed(self.random_state_)
         input_layer, output_layer = self._network.build_network(input_shape, **kwargs)
 
-        output_layer = tf.keras.layers.Dense(
-            units=n_classes, activation="softmax", use_bias=self.use_bias
-        )(output_layer)
+        output_layer = tf.keras.layers.Dense(units=n_classes, activation="softmax")(
+            output_layer
+        )
 
         self.optimizer_ = (
             tf.keras.optimizers.Adam() if self.optimizer is None else self.optimizer
@@ -220,7 +222,7 @@ class FCNClassifier(BaseDeepClassifier):
         model.compile(
             loss=self.loss,
             optimizer=self.optimizer_,
-            metrics=metrics,
+            metrics=self._metrics,
         )
 
         return model
@@ -245,6 +247,11 @@ class FCNClassifier(BaseDeepClassifier):
         # Transpose to conform to Keras input style.
         X = X.transpose(0, 2, 1)
 
+        if isinstance(self.metrics, list):
+            self._metrics = self.metrics
+        elif isinstance(self.metrics, str):
+            self._metrics = [self.metrics]
+
         self.input_shape = X.shape[1:]
         self.training_model_ = self.build_model(self.input_shape, self.n_classes_)
 
diff --git a/aeon/classification/deep_learning/_inception_time.py b/aeon/classification/deep_learning/_inception_time.py
index d56f3659..00c4e479 100644
--- a/aeon/classification/deep_learning/_inception_time.py
+++ b/aeon/classification/deep_learning/_inception_time.py
@@ -23,50 +23,50 @@ class InceptionTimeClassifier(BaseClassifier):
 
     Parameters
     ----------
-    n_classifiers       : int, default = 5,
+    n_classifiers : int, default = 5,
         the number of Inception models used for the
         Ensemble in order to create
         InceptionTime.
-    depth               : int, default = 6,
+    depth : int, default = 6,
             the number of inception modules used
-    n_filters          : int or list of int32, default = 32,
+    n_filters : int or list of int32, default = 32,
         the number of filters used in one inception
         module, if not a list,
         the same number of filters is used in
         all inception modules
-    n_conv_per_layer   : int or list of int, default = 3,
+    n_conv_per_layer : int or list of int, default = 3,
         the number of convolution layers in each inception
         module, if not a list,
         the same number of convolution layers is used
         in all inception modules
-    kernel_size         : int or list of int, default = 40,
+    kernel_size : int or list of int, default = 40,
         the head kernel size used for each inception
         module, if not a list,
         the same is used in all inception modules
-    use_max_pooling     : bool or list of bool, default = True,
+    use_max_pooling : bool or list of bool, default = True,
         conditioning whether or not to use max pooling layer
         in inception modules,if not a list,
         the same is used in all inception modules
-    max_pool_size       : int or list of int, default = 3,
+    max_pool_size : int or list of int, default = 3,
         the size of the max pooling layer, if not a list,
         the same is used in all inception modules
-    strides             : int or list of int, default = 1,
+    strides : int or list of int, default = 1,
         the strides of kernels in convolution layers for each
         inception module, if not a list,
         the same is used in all inception modules
-    dilation_rate       : int or list of int, default = 1,
+    dilation_rate : int or list of int, default = 1,
         the dilation rate of convolutions in each inception
         module, if not a list,
         the same is used in all inception modules
-    padding             : str or list of str, default = "same",
+    padding : str or list of str, default = "same",
         the type of padding used for convoltuon for each
         inception module, if not a list,
         the same is used in all inception modules
-    activation          : str or list of str, default = "relu",
+    activation : str or list of str, default = "relu",
         the activation function used in each inception
         module, if not a list,
         the same is used in all inception modules
-    use_bias            : bool or list of bool, default = False,
+    use_bias : bool or list of bool, default = False,
         conditioning whether or not convolutions should
         use bias values in each inception
         module, if not a list,
@@ -89,8 +89,10 @@ class InceptionTimeClassifier(BaseClassifier):
         formula Wang et al.
     n_epochs : int, default = 1500
         the number of epochs to train the model.
-    callbacks : callable or None, default = ReduceOnPlateau and ModelCheckpoint
-        list of tf.keras.callbacks.Callback objects.
+    callbacks : keras callback or list of callbacks,
+        default = None
+        The default list of callbacks are set to
+        ModelCheckpoint and ReduceLROnPlateau.
     file_path : str, default = "./"
         file_path when saving model_Checkpoint callback
     save_best_model : bool, default = False
@@ -99,17 +101,17 @@ class InceptionTimeClassifier(BaseClassifier):
         this condition, if True, will prevent the
         automatic deletion of the best saved model from
         file and the user can choose the file name
-    save_last_model     : bool, default = False
+    save_last_model : bool, default = False
         Whether or not to save the last model, last
         epoch trained, using the base class method
         save_last_model_to_file
     save_init_model : bool, default = False
         Whether to save the initialization of the  model.
-    best_file_name      : str, default = "best_model"
+    best_file_name : str, default = "best_model"
         The name of the file of the best model, if
         save_best_model is set to False, this parameter
         is discarded
-    last_file_name      : str, default = "last_model"
+    last_file_name : str, default = "last_model"
         The name of the file of the last model, if
         save_last_model is set to False, this parameter
         is discarded
@@ -123,12 +125,17 @@ class InceptionTimeClassifier(BaseClassifier):
         by `np.random`.
         Seeded random number generation can only be guaranteed on CPU processing,
         GPU processing will be non-deterministic.
-    verbose             : boolean, default = False
+    verbose : boolean, default = False
         whether to output extra information
-    optimizer           : keras optimizer, default = Adam
-    loss                : keras loss, default = categorical_crossentropy
-    metrics             : keras metrics, default = None,
-        will be set to accuracy as default if None
+    optimizer : keras.optimizer, default = tf.keras.optimizers.Adam()
+        The keras optimizer used for training.
+    loss : str, default = "categorical_crossentropy"
+        The name of the keras training loss.
+    metrics : str or list[str], default="accuracy"
+        The evaluation metrics to use during training. If
+        a single string metric is provided, it will be
+        used as the only metric. If a list of metrics are
+        provided, all will be used for evaluation.
 
     Notes
     -----
@@ -199,7 +206,7 @@ class InceptionTimeClassifier(BaseClassifier):
         random_state=None,
         verbose=False,
         loss="categorical_crossentropy",
-        metrics=None,
+        metrics="accuracy",
         optimizer=None,
     ):
         self.n_classifiers = n_classifiers
@@ -382,80 +389,81 @@ class IndividualInceptionClassifier(BaseDeepClassifier):
 
     Parameters
     ----------
-        depth               : int, default = 6,
+        depth : int, default = 6,
             the number of inception modules used
-        n_filters          : int or list of int32, default = 32,
+        n_filters : int or list of int32, default = 32,
             the number of filters used in one inception module, if not a list,
             the same number of filters is used in all inception modules
-        n_conv_per_layer   : int or list of int, default = 3,
+        n_conv_per_layer : int or list of int, default = 3,
             the number of convolution layers in each inception module, if not a list,
             the same number of convolution layers is used in all inception modules
-        kernel_size         : int or list of int, default = 40,
+        kernel_size : int or list of int, default = 40,
             the head kernel size used for each inception module, if not a list,
             the same is used in all inception modules
-        use_max_pooling     : bool or list of bool, default = True,
+        use_max_pooling : bool or list of bool, default = True,
             conditioning whether or not to use max pooling layer
             in inception modules,if not a list,
             the same is used in all inception modules
-        max_pool_size       : int or list of int, default = 3,
+        max_pool_size : int or list of int, default = 3,
             the size of the max pooling layer, if not a list,
             the same is used in all inception modules
-        strides             : int or list of int, default = 1,
+        strides : int or list of int, default = 1,
             the strides of kernels in convolution layers for
             each inception module, if not a list,
             the same is used in all inception modules
-        dilation_rate       : int or list of int, default = 1,
+        dilation_rate : int or list of int, default = 1,
             the dilation rate of convolutions in each inception module, if not a list,
             the same is used in all inception modules
-        padding             : str or list of str, default = "same",
+        padding : str or list of str, default = "same",
             the type of padding used for convoltuon for each
             inception module, if not a list,
             the same is used in all inception modules
-        activation          : str or list of str, default = "relu",
+        activation : str or list of str, default = "relu",
             the activation function used in each inception module, if not a list,
             the same is used in all inception modules
-        use_bias            : bool or list of bool, default = False,
+        use_bias : bool or list of bool, default = False,
             conditioning whether or not convolutions should
             use bias values in each inception
             module, if not a list,
             the same is used in all inception modules
-        use_residual        : bool, default = True,
+        use_residual : bool, default = True,
             condition whether or not to use residual connections all over Inception
-        use_bottleneck      : bool, default = True,
+        use_bottleneck : bool, default = True,
             confition whether or not to use bottlenecks all over Inception
-        bottleneck_size     : int, default = 32,
+        bottleneck_size : int, default = 32,
             the bottleneck size in case use_bottleneck = True
-        use_custom_filters  : bool, default = False,
+        use_custom_filters : bool, default = False,
             condition on whether or not to use custom filters
             in the first inception module
-        batch_size          : int, default = 64
+        batch_size : int, default = 64
             the number of samples per gradient update.
         use_mini_batch_size : bool, default = False
             condition on using the mini batch size formula Wang et al.
-        n_epochs           : int, default = 1500
+        n_epochs : int, default = 1500
             the number of epochs to train the model.
-        callbacks           : callable or None, default
-        ReduceOnPlateau and ModelCheckpoint
-            list of tf.keras.callbacks.Callback objects.
-        file_path           : str, default = "./"
+        callbacks : keras callback or list of callbacks,
+            default = None
+            The default list of callbacks are set to
+            ModelCheckpoint and ReduceLROnPlateau.
+        file_path : str, default = "./"
             file_path when saving model_Checkpoint callback
-        save_best_model     : bool, default = False
+        save_best_model : bool, default = False
             Whether or not to save the best model, if the
             modelcheckpoint callback is used by default,
             this condition, if True, will prevent the
             automatic deletion of the best saved model from
             file and the user can choose the file name
-        save_last_model     : bool, default = False
+        save_last_model : bool, default = False
             Whether or not to save the last model, last
             epoch trained, using the base class method
             save_last_model_to_file
         save_init_model : bool, default = False
             Whether to save the initialization of the  model.
-        best_file_name      : str, default = "best_model"
+        best_file_name : str, default = "best_model"
             The name of the file of the best model, if
             save_best_model is set to False, this parameter
             is discarded.
-        last_file_name      : str, default = "last_model"
+        last_file_name : str, default = "last_model"
             The name of the file of the last model, if
             save_last_model is set to False, this parameter
             is discarded.
@@ -469,12 +477,17 @@ class IndividualInceptionClassifier(BaseDeepClassifier):
             by `np.random`.
             Seeded random number generation can only be guaranteed on CPU processing,
             GPU processing will be non-deterministic.
-        verbose             : boolean, default = False
+        verbose : boolean, default = False
             whether to output extra information
-        optimizer           : keras optimizer, default = Adam
-        loss                : keras loss, default = categorical_crossentropy
-        metrics             : keras metrics, default = None, will be set
-        to accuracy as default if None
+        optimizer : keras.optimizer, default = tf.keras.optimizers.Adam()
+            The keras optimizer used for training.
+        loss : str, default = "categorical_crossentropy"
+            The name of the keras training loss.
+        metrics : str or list[str], default="accuracy"
+            The evaluation metrics to use during training. If
+            a single string metric is provided, it will be
+            used as the only metric. If a list of metrics are
+            provided, all will be used for evaluation.
 
     Notes
     -----
@@ -534,7 +547,7 @@ class IndividualInceptionClassifier(BaseDeepClassifier):
         random_state=None,
         verbose=False,
         loss="categorical_crossentropy",
-        metrics=None,
+        metrics="accuracy",
         optimizer=None,
     ):
         # predefined
@@ -624,11 +637,6 @@ class IndividualInceptionClassifier(BaseDeepClassifier):
 
         model = tf.keras.models.Model(inputs=input_layer, outputs=output_layer)
 
-        if self.metrics is None:
-            metrics = ["accuracy"]
-        else:
-            metrics = self.metrics
-
         self.optimizer_ = (
             tf.keras.optimizers.Adam() if self.optimizer is None else self.optimizer
         )
@@ -636,7 +644,7 @@ class IndividualInceptionClassifier(BaseDeepClassifier):
         model.compile(
             loss=self.loss,
             optimizer=self.optimizer_,
-            metrics=metrics,
+            metrics=self._metrics,
         )
 
         return model
@@ -665,6 +673,11 @@ class IndividualInceptionClassifier(BaseDeepClassifier):
         # Transpose to conform to Keras input style.
         X = X.transpose(0, 2, 1)
 
+        if isinstance(self.metrics, list):
+            self._metrics = self.metrics
+        elif isinstance(self.metrics, str):
+            self._metrics = [self.metrics]
+
         # ignore the number of instances, X.shape[0],
         # just want the shape of each instance
         self.input_shape = X.shape[1:]
diff --git a/aeon/classification/deep_learning/_lite_time.py b/aeon/classification/deep_learning/_lite_time.py
index b5339793..ac1594ec 100644
--- a/aeon/classification/deep_learning/_lite_time.py
+++ b/aeon/classification/deep_learning/_lite_time.py
@@ -54,8 +54,10 @@ class LITETimeClassifier(BaseClassifier):
         formula Wang et al.
     n_epochs : int, default = 1500
         the number of epochs to train the model.
-    callbacks : callable or None, default = ReduceOnPlateau and ModelCheckpoint
-        list of tf.keras.callbacks.Callback objects.
+    callbacks : keras callback or list of callbacks,
+        default = None
+        The default list of callbacks are set to
+        ModelCheckpoint and ReduceLROnPlateau.
     file_path : str, default = "./"
         file_path when saving model_Checkpoint callback
     save_best_model : bool, default = False
@@ -90,10 +92,15 @@ class LITETimeClassifier(BaseClassifier):
         GPU processing will be non-deterministic.
     verbose : boolean, default = False
         whether to output extra information
-    optimizer : keras optimizer, default = Adam
-    loss : keras loss, default = categorical_crossentropy
-    metrics : keras metrics, default = None,
-        will be set to accuracy as default if None
+    optimizer : keras.optimizer, default = tf.keras.optimizers.Adam()
+        The keras optimizer used for training.
+    loss : str, default = "categorical_crossentropy"
+        The name of the keras training loss.
+    metrics : str or list[str], default="accuracy"
+        The evaluation metrics to use during training. If
+        a single string metric is provided, it will be
+        used as the only metric. If a list of metrics are
+        provided, all will be used for evaluation.
 
     References
     ----------
@@ -150,7 +157,7 @@ class LITETimeClassifier(BaseClassifier):
         random_state=None,
         verbose=False,
         loss="categorical_crossentropy",
-        metrics=None,
+        metrics="accuracy",
         optimizer=None,
     ):
         self.n_classifiers = n_classifiers
@@ -349,8 +356,10 @@ class IndividualLITEClassifier(BaseDeepClassifier):
         formula Wang et al.
     n_epochs : int, default = 1500
         the number of epochs to train the model.
-    callbacks : callable or None, default = ReduceOnPlateau and ModelCheckpoint
-        list of tf.keras.callbacks.Callback objects.
+    callbacks : keras callback or list of callbacks,
+        default = None
+        The default list of callbacks are set to
+        ModelCheckpoint and ReduceLROnPlateau.
     file_path : str, default = "./"
         file_path when saving model_Checkpoint callback
     save_best_model : bool, default = False
@@ -385,10 +394,15 @@ class IndividualLITEClassifier(BaseDeepClassifier):
         GPU processing will be non-deterministic.
     verbose : boolean, default = False
         whether to output extra information
-    optimizer : keras optimizer, default = Adam
-    loss : keras loss, default = categorical_crossentropy
-    metrics : keras metrics, default = None,
-        will be set to accuracy as default if None
+    optimizer : keras.optimizer, default = tf.keras.optimizers.Adam()
+        The keras optimizer used for training.
+    loss : str, default = "categorical_crossentropy"
+        The name of the keras training loss.
+    metrics : str or list[str], default="accuracy"
+        The evaluation metrics to use during training. If
+        a single string metric is provided, it will be
+        used as the only metric. If a list of metrics are
+        provided, all will be used for evaluation.
 
     References
     ----------
@@ -436,7 +450,7 @@ class IndividualLITEClassifier(BaseDeepClassifier):
         random_state=None,
         verbose=False,
         loss="categorical_crossentropy",
-        metrics=None,
+        metrics="accuracy",
         optimizer=None,
     ):
         self.use_litemv = use_litemv
@@ -506,11 +520,6 @@ class IndividualLITEClassifier(BaseDeepClassifier):
 
         model = tf.keras.models.Model(inputs=input_layer, outputs=output_layer)
 
-        if self.metrics is None:
-            metrics = ["accuracy"]
-        else:
-            metrics = self.metrics
-
         self.optimizer_ = (
             tf.keras.optimizers.Adam() if self.optimizer is None else self.optimizer
         )
@@ -518,7 +527,7 @@ class IndividualLITEClassifier(BaseDeepClassifier):
         model.compile(
             loss=self.loss,
             optimizer=self.optimizer_,
-            metrics=metrics,
+            metrics=self._metrics,
         )
 
         return model
@@ -546,6 +555,11 @@ class IndividualLITEClassifier(BaseDeepClassifier):
         # Transpose to conform to Keras input style.
         X = X.transpose(0, 2, 1)
 
+        if isinstance(self.metrics, list):
+            self._metrics = self.metrics
+        elif isinstance(self.metrics, str):
+            self._metrics = [self.metrics]
+
         # ignore the number of instances, X.shape[0],
         # just want the shape of each instance
         self.input_shape = X.shape[1:]
diff --git a/aeon/classification/deep_learning/_mlp.py b/aeon/classification/deep_learning/_mlp.py
index 0f0d538c..6768ba48 100644
--- a/aeon/classification/deep_learning/_mlp.py
+++ b/aeon/classification/deep_learning/_mlp.py
@@ -21,13 +21,18 @@ class MLPClassifier(BaseDeepClassifier):
 
     Parameters
     ----------
+    use_bias : bool, default = True
+        Condition on whether or not to use bias values for dense layers.
     n_epochs : int, default = 2000
         the number of epochs to train the model
     batch_size : int, default = 16
         the number of samples per gradient update.
     use_mini_batch_size : boolean, default = False
         Condition on using the mini batch size formula
-    callbacks : callable or None, default
+    callbacks : keras callback or list of callbacks,
+        default = None
+        The default list of callbacks are set to
+        ModelCheckpoint and ReduceLROnPlateau.
     random_state : int, RandomState instance or None, default=None
         If `int`, random_state is the seed used by the random number generator;
         If `RandomState` instance, random_state is the random number generator;
@@ -37,8 +42,8 @@ class MLPClassifier(BaseDeepClassifier):
         GPU processing will be non-deterministic.
     verbose : boolean, default = False
         whether to output extra information
-    loss : string, default="mean_squared_error"
-        fit parameter for the keras model
+    loss : str, default = "categorical_crossentropy"
+        The name of the keras training loss.
     file_path : str, default = "./"
         file_path when saving model_Checkpoint callback
     save_best_model : bool, default = False
@@ -64,14 +69,17 @@ class MLPClassifier(BaseDeepClassifier):
     init_file_name : str, default = "init_model"
         The name of the file of the init model, if save_init_model is set to False,
         this parameter is discarded.
-    optimizer : keras.optimizer, default=keras.optimizers.Adadelta(),
-    metrics : list of strings, default=["accuracy"],
+    optimizer : keras.optimizer, default = tf.keras.optimizers.Adam()
+        The keras optimizer used for training.
+    metrics : str or list[str], default="accuracy"
+        The evaluation metrics to use during training. If
+        a single string metric is provided, it will be
+        used as the only metric. If a list of metrics are
+        provided, all will be used for evaluation.
     activation : string or a tf callable, default="sigmoid"
         Activation function used in the output linear layer.
         List of available activation functions:
         https://keras.io/api/layers/activations/
-    use_bias : boolean, default = True
-        whether the layer uses a bias vector.
 
     Notes
     -----
@@ -96,13 +104,14 @@ class MLPClassifier(BaseDeepClassifier):
 
     def __init__(
         self,
+        use_bias=True,
         n_epochs=2000,
         batch_size=16,
         use_mini_batch_size=False,
         callbacks=None,
         verbose=False,
         loss="categorical_crossentropy",
-        metrics=None,
+        metrics="accuracy",
         file_path="./",
         save_best_model=False,
         save_last_model=False,
@@ -112,7 +121,6 @@ class MLPClassifier(BaseDeepClassifier):
         init_file_name="init_model",
         random_state=None,
         activation="sigmoid",
-        use_bias=True,
         optimizer=None,
     ):
         self.callbacks = callbacks
@@ -139,7 +147,7 @@ class MLPClassifier(BaseDeepClassifier):
             last_file_name=last_file_name,
         )
 
-        self._network = MLPNetwork()
+        self._network = MLPNetwork(use_bias=self.use_bias)
 
     def build_model(self, input_shape, n_classes, **kwargs):
         """Construct a compiled, un-trained, keras model that is ready for training.
@@ -164,19 +172,14 @@ class MLPClassifier(BaseDeepClassifier):
         import tensorflow as tf
         from tensorflow import keras
 
-        if self.metrics is None:
-            metrics = ["accuracy"]
-        else:
-            metrics = self.metrics
-
         rng = check_random_state(self.random_state)
         self.random_state_ = rng.randint(0, np.iinfo(np.int32).max)
         tf.keras.utils.set_random_seed(self.random_state_)
         input_layer, output_layer = self._network.build_network(input_shape, **kwargs)
 
-        output_layer = keras.layers.Dense(
-            units=n_classes, activation="softmax", use_bias=self.use_bias
-        )(output_layer)
+        output_layer = keras.layers.Dense(units=n_classes, activation="softmax")(
+            output_layer
+        )
 
         self.optimizer_ = (
             keras.optimizers.Adadelta() if self.optimizer is None else self.optimizer
@@ -186,7 +189,7 @@ class MLPClassifier(BaseDeepClassifier):
         model.compile(
             loss=self.loss,
             optimizer=self.optimizer_,
-            metrics=metrics,
+            metrics=self._metrics,
         )
         return model
 
@@ -210,6 +213,11 @@ class MLPClassifier(BaseDeepClassifier):
         # Transpose to conform to Keras input style.
         X = X.transpose(0, 2, 1)
 
+        if isinstance(self.metrics, list):
+            self._metrics = self.metrics
+        elif isinstance(self.metrics, str):
+            self._metrics = [self.metrics]
+
         self.input_shape = X.shape[1:]
         self.training_model_ = self.build_model(self.input_shape, self.n_classes_)
 
diff --git a/aeon/classification/deep_learning/_resnet.py b/aeon/classification/deep_learning/_resnet.py
index fe754548..a0edf757 100644
--- a/aeon/classification/deep_learning/_resnet.py
+++ b/aeon/classification/deep_learning/_resnet.py
@@ -88,10 +88,15 @@ class ResNetClassifier(BaseDeepClassifier):
         this parameter is discarded.
     verbose : boolean, default = False
         whether to output extra information
-    loss : string, default = "mean_squared_error"
-        fit parameter for the keras model.
-    optimizer : keras.optimizer, default = keras.optimizers.Adam()
-    metrics : list of strings, default = ["accuracy"]
+    loss : str, default = "categorical_crossentropy"
+        The name of the keras training loss.
+    optimizer : keras.optimizer, default = tf.keras.optimizers.Adam()
+        The keras optimizer used for training.
+    metrics : str or list[str], default="accuracy"
+        The evaluation metrics to use during training. If
+        a single string metric is provided, it will be
+        used as the only metric. If a list of metrics are
+        provided, all will be used for evaluation.
 
     Notes
     -----
@@ -129,7 +134,7 @@ class ResNetClassifier(BaseDeepClassifier):
         callbacks=None,
         verbose=False,
         loss="categorical_crossentropy",
-        metrics=None,
+        metrics="accuracy",
         batch_size=64,
         use_mini_batch_size=False,
         random_state=None,
@@ -213,25 +218,20 @@ class ResNetClassifier(BaseDeepClassifier):
             else self.optimizer
         )
 
-        if self.metrics is None:
-            metrics = ["accuracy"]
-        else:
-            metrics = self.metrics
-
         rng = check_random_state(self.random_state)
         self.random_state_ = rng.randint(0, np.iinfo(np.int32).max)
         tf.keras.utils.set_random_seed(self.random_state_)
         input_layer, output_layer = self._network.build_network(input_shape, **kwargs)
 
-        output_layer = tf.keras.layers.Dense(
-            units=n_classes, activation="softmax", use_bias=self.use_bias
-        )(output_layer)
+        output_layer = tf.keras.layers.Dense(units=n_classes, activation="softmax")(
+            output_layer
+        )
 
         model = tf.keras.models.Model(inputs=input_layer, outputs=output_layer)
         model.compile(
             loss=self.loss,
             optimizer=self.optimizer_,
-            metrics=metrics,
+            metrics=self._metrics,
         )
 
         return model
@@ -256,6 +256,11 @@ class ResNetClassifier(BaseDeepClassifier):
         # Transpose to conform to Keras input style.
         X = X.transpose(0, 2, 1)
 
+        if isinstance(self.metrics, list):
+            self._metrics = self.metrics
+        elif isinstance(self.metrics, str):
+            self._metrics = [self.metrics]
+
         self.input_shape = X.shape[1:]
         self.training_model_ = self.build_model(self.input_shape, self.n_classes_)
 
diff --git a/aeon/clustering/deep_learning/__init__.py b/aeon/clustering/deep_learning/__init__.py
index 700bea56..e40eb924 100644
--- a/aeon/clustering/deep_learning/__init__.py
+++ b/aeon/clustering/deep_learning/__init__.py
@@ -4,11 +4,13 @@ __all__ = [
     "BaseDeepClusterer",
     "AEFCNClusterer",
     "AEResNetClusterer",
+    "AEDRNNClusterer",
     "AEAttentionBiGRUClusterer",
     "AEBiGRUClusterer",
 ]
 from aeon.clustering.deep_learning._ae_abgru import AEAttentionBiGRUClusterer
 from aeon.clustering.deep_learning._ae_bgru import AEBiGRUClusterer
+from aeon.clustering.deep_learning._ae_drnn import AEDRNNClusterer
 from aeon.clustering.deep_learning._ae_fcn import AEFCNClusterer
 from aeon.clustering.deep_learning._ae_resnet import AEResNetClusterer
 from aeon.clustering.deep_learning.base import BaseDeepClusterer
diff --git a/aeon/clustering/deep_learning/_ae_drnn.py b/aeon/clustering/deep_learning/_ae_drnn.py
new file mode 100644
index 00000000..61777ba7
--- /dev/null
+++ b/aeon/clustering/deep_learning/_ae_drnn.py
@@ -0,0 +1,355 @@
+"""Deep Learning Auto-Encoder using DRNN Network."""
+
+__maintainer__ = []
+__all__ = ["AEDRNNClusterer"]
+
+import gc
+import os
+import time
+from copy import deepcopy
+
+from sklearn.utils import check_random_state
+
+from aeon.clustering import DummyClusterer
+from aeon.clustering.deep_learning.base import BaseDeepClusterer
+from aeon.networks import AEDRNNNetwork
+from aeon.utils.validation._dependencies import _check_soft_dependencies
+
+if _check_soft_dependencies(["tensorflow"], severity="none"):
+    from aeon.networks._ae_drnn import _TensorDilation
+
+
+class AEDRNNClusterer(BaseDeepClusterer):
+    """Auto-Encoder based Dilated Recurrent Neural Network (DRNN).
+
+    Parameters
+    ----------
+    n_clusters : int, default=None
+        Number of clusters for the deep learnign model.
+    clustering_algorithm : str, default="deprecated"
+        Please use the 'estimator' parameter.
+    estimator : aeon clusterer, default=None
+        An aeon estimator to be built using the transformed data.
+        Defaults to aeon TimeSeriesKMeans() with euclidean distance
+        and mean averaging method and n_clusters set to 2.
+    clustering_params : dict, default=None
+        Please use 'estimator' parameter.
+    latent_space_dim : int, default=128
+        Dimension of the latent space of the auto-encoder.
+    temporal_latent_space : bool, default = False
+        Flag to choose whether the latent space is an MTS or Euclidean space.
+    n_layers_encoder : int, default = 3
+        Number of layers in the encoder.
+    n_layers_decoder : int, default = 3
+        Number of layers in the decoder.
+    dilation_rate_encoder : int or list of int, default = 1
+        The dilation rate for the encoder.
+    dilation_rate_decoder : int or list of int, default = 1
+        The dilation rate for the decoder.
+    activation_encoder : str or list of str, default = "relu"
+        Activation used after DRNN Layer in the encoder.
+    activation_decoder : str or list of str, default = "relu"
+        Activation used after DRNN Layer in the decoder.
+    n_units_encoder : list or int, default = None
+        Number of Units in each DRNN Layer of the encoder.
+    n_units_decoder : list or int, default = None
+        Number of Units in each DRNN Layer of the decoder.
+    n_epochs : int, default = 2000
+        The number of epochs to train the model.
+    batch_size : int, default = 16
+        The number of samples per gradient update.
+    use_mini_batch_size : bool, default = True,
+        Whether or not to use the mini batch size formula.
+    random_state : int, RandomState instance or None, default=None
+        If `int`, random_state is the seed used by the random number generator;
+        If `RandomState` instance, random_state is the random number generator;
+        If `None`, the random number generator is the `RandomState` instance used
+        by `np.random`.
+        Seeded random number generation can only be guaranteed on CPU processing,
+        GPU processing will be non-deterministic.
+    verbose : boolean, default = False
+        Whether to output extra information.
+    loss : string, default="mean_squared_error"
+        Fit parameter for the keras model.
+    metrics : keras metrics, default = ["mean_squared_error"]
+        will be set to mean_squared_error as default if None
+    optimizer : keras.optimizers object, default = Adam(lr=0.01)
+        Specify the optimizer and the learning rate to be used.
+    file_path : str, default = "./"
+        File path to save best model.
+    save_best_model : bool, default = False
+        Whether or not to save the best model, if the
+        modelcheckpoint callback is used by default,
+        this condition, if True, will prevent the
+        automatic deletion of the best saved model from
+        file and the user can choose the file name.
+    save_last_model : bool, default = False
+        Whether or not to save the last model, last
+        epoch trained, using the base class method
+        save_last_model_to_file.
+    best_file_name : str, default = "best_model"
+        The name of the file of the best model, if
+        save_best_model is set to False, this parameter
+        is discarded.
+    last_file_name : str, default = "last_model"
+        The name of the file of the last model, if
+        save_last_model is set to False, this parameter
+        is discarded.
+    callbacks : keras.callbacks, default = None
+        List of keras callbacks.
+
+    Examples
+    --------
+    >>> from aeon.clustering.deep_learning import AEDRNNClusterer
+    >>> from aeon.datasets import load_unit_test
+    >>> X_train, y_train = load_unit_test(split="train")
+    >>> X_test, y_test = load_unit_test(split="test")
+    >>> from aeon.clustering import DummyClusterer
+    >>> _clst = DummyClusterer(n_clusters=2)
+    >>> aefcn = AEDRNNClusterer(estimator = _clst,
+    ... n_epochs=20,batch_size=4)  # doctest: +SKIP
+    >>> aefcn.fit(X_train)  # doctest: +SKIP
+    AEDRNNClusterer(...)
+    """
+
+    def __init__(
+        self,
+        n_clusters=None,
+        estimator=None,
+        clustering_algorithm="deprecated",
+        clustering_params=None,
+        latent_space_dim=128,
+        temporal_latent_space=False,
+        n_layers_encoder=3,
+        n_layers_decoder=3,
+        dilation_rate_encoder=1,
+        dilation_rate_decoder=1,
+        n_units_encoder=None,
+        n_units_decoder=None,
+        activation_encoder="relu",
+        activation_decoder="relu",
+        n_epochs=2000,
+        batch_size=32,
+        use_mini_batch_size=False,
+        random_state=None,
+        verbose=False,
+        loss="mse",
+        metrics=None,
+        optimizer="Adam",
+        file_path="./",
+        save_best_model=False,
+        save_last_model=False,
+        best_file_name="best_model",
+        last_file_name="last_file",
+        callbacks=None,
+    ):
+        self.latent_space_dim = latent_space_dim
+        self.temporal_latent_space = temporal_latent_space
+        self.n_layers_encoder = n_layers_encoder
+        self.n_layers_decoder = n_layers_decoder
+        self.activation_encoder = activation_encoder
+        self.activation_decoder = activation_decoder
+        self.dilation_rate_encoder = dilation_rate_encoder
+        self.dilation_rate_decoder = dilation_rate_decoder
+        self.n_units_encoder = n_units_encoder
+        self.n_units_decoder = n_units_decoder
+        self.optimizer = optimizer
+        self.loss = loss
+        self.metrics = metrics
+        self.verbose = verbose
+        self.use_mini_batch_size = use_mini_batch_size
+        self.callbacks = callbacks
+        self.file_path = file_path
+        self.n_epochs = n_epochs
+        self.save_best_model = save_best_model
+        self.save_last_model = save_last_model
+        self.best_file_name = best_file_name
+        self.random_state = random_state
+
+        super().__init__(
+            n_clusters=n_clusters,
+            estimator=estimator,
+            clustering_algorithm=clustering_algorithm,
+            clustering_params=clustering_params,
+            batch_size=batch_size,
+            last_file_name=last_file_name,
+        )
+
+        self._network = AEDRNNNetwork(
+            latent_space_dim=self.latent_space_dim,
+            temporal_latent_space=self.temporal_latent_space,
+            n_layers_encoder=self.n_layers_encoder,
+            n_layers_decoder=self.n_layers_decoder,
+            dilation_rate_encoder=self.dilation_rate_encoder,
+            dilation_rate_decoder=self.dilation_rate_decoder,
+            activation_encoder=self.activation_encoder,
+            activation_decoder=self.activation_decoder,
+            n_units_encoder=self.n_units_encoder,
+            n_units_decoder=self.n_units_decoder,
+        )
+
+    def build_model(self, input_shape, **kwargs):
+        """Construct a compiled, un-trained, keras model that is ready for training.
+
+        In aeon, time series are stored in numpy arrays of shape
+        (n_channels,n_timepoints). Keras/tensorflow assume
+        data is in shape (n_timepoints,n_channels). This method also assumes
+        (n_timepoints,n_channels). Transpose should happen in fit.
+
+        Parameters
+        ----------
+        input_shape : tuple
+            The shape of the data fed into the input layer, should be
+            (n_timepoints,n_channels).
+
+        Returns
+        -------
+        output : a compiled Keras Model.
+        """
+        import numpy as np
+        import tensorflow as tf
+
+        rng = check_random_state(self.random_state)
+        self.random_state_ = rng.randint(0, np.iinfo(np.int32).max)
+        tf.keras.utils.set_random_seed(self.random_state_)
+        encoder, decoder = self._network.build_network(input_shape, **kwargs)
+
+        input_layer = tf.keras.layers.Input(input_shape, name="input layer")
+        encoder_output = encoder(input_layer)
+        decoder_output = decoder(encoder_output)
+        output_layer = tf.keras.layers.Reshape(
+            target_shape=input_shape, name="outputlayer"
+        )(decoder_output)
+
+        model = tf.keras.models.Model(inputs=input_layer, outputs=output_layer)
+
+        self.optimizer_ = (
+            tf.keras.optimizers.Adam() if self.optimizer is None else self.optimizer
+        )
+
+        if self.metrics is None:
+            self._metrics = ["mean_squared_error"]
+        elif isinstance(self.metrics, list):
+            self._metrics = self.metrics
+        elif isinstance(self.metrics, str):
+            self._metrics = [self.metrics]
+        else:
+            raise ValueError("Metrics should be a list, string, or None.")
+
+        model.compile(optimizer=self.optimizer_, loss=self.loss, metrics=self._metrics)
+
+        return model
+
+    def _fit(self, X):
+        """Fit the classifier on the training set (X, y).
+
+        Parameters
+        ----------
+        X : np.ndarray of shape = (n_cases (n), n_channels (d), n_timepoints (m))
+            The training input samples.
+
+        Returns
+        -------
+        self : object
+        """
+        import tensorflow as tf
+
+        # Transpose to conform to Keras input style.
+        X = X.transpose(0, 2, 1)
+
+        self.input_shape = X.shape[1:]
+        self.training_model_ = self.build_model(self.input_shape)
+
+        if self.verbose:
+            self.training_model_.summary()
+
+        if self.use_mini_batch_size:
+            mini_batch_size = min(self.batch_size, X.shape[0] // 10)
+        else:
+            mini_batch_size = self.batch_size
+
+        self.file_name_ = (
+            self.best_file_name if self.save_best_model else str(time.time_ns())
+        )
+
+        if self.callbacks is None:
+            self.callbacks_ = [
+                tf.keras.callbacks.ReduceLROnPlateau(
+                    monitor="loss", factor=0.5, patience=50, min_lr=0.0001
+                ),
+                tf.keras.callbacks.ModelCheckpoint(
+                    filepath=self.file_path + self.file_name_ + ".keras",
+                    monitor="loss",
+                    save_best_only=True,
+                ),
+            ]
+        else:
+            self.callbacks_ = self._get_model_checkpoint_callback(
+                callbacks=self.callbacks,
+                file_path=self.file_path,
+                file_name=self.file_name_,
+            )
+
+        self.history = self.training_model_.fit(
+            X,
+            X,
+            batch_size=mini_batch_size,
+            epochs=self.n_epochs,
+            verbose=self.verbose,
+            callbacks=self.callbacks_,
+        )
+
+        try:
+            self.model_ = tf.keras.models.load_model(
+                self.file_path + self.file_name_ + ".keras",
+                compile=False,
+                custom_objects={"_TensorDilation": _TensorDilation},
+            )
+            if not self.save_best_model:
+                os.remove(self.file_path + self.file_name_ + ".keras")
+        except FileNotFoundError:
+            self.model_ = deepcopy(self.training_model_)
+
+        self._fit_clustering(X=X)
+
+        gc.collect()
+
+        return self
+
+    def _score(self, X, y=None):
+        # Transpose to conform to Keras input style.
+        X = X.transpose(0, 2, 1)
+        latent_space = self.model_.layers[1].predict(X)
+        return self._estimator.score(latent_space)
+
+    @classmethod
+    def _get_test_params(cls, parameter_set="default"):
+        """Return testing parameter settings for the estimator.
+
+        Parameters
+        ----------
+        parameter_set : str, default="default"
+            Name of the set of test parameters to return, for use in tests. If no
+            special parameters are defined for a value, will return `"default"` set.
+            For classifiers, a "default" set of parameters should be provided for
+            general testing, and a "results_comparison" set for comparing against
+            previously recorded results if the general set does not produce suitable
+            probabilities to compare against.
+
+        Returns
+        -------
+        params : dict or list of dict, default={}
+            Parameters to create testing instances of the class.
+            Each dict are parameters to construct an "interesting" test instance, i.e.,
+            `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
+            `create_test_instance` uses the first (or only) dictionary in `params`.
+        """
+        param1 = {
+            "estimator": DummyClusterer(n_clusters=2),
+            "n_epochs": 1,
+            "batch_size": 4,
+            "n_layers_encoder": 1,
+            "n_layers_decoder": 1,
+        }
+
+        return [param1]
diff --git a/aeon/networks/__init__.py b/aeon/networks/__init__.py
index 72f2a2bf..20983717 100644
--- a/aeon/networks/__init__.py
+++ b/aeon/networks/__init__.py
@@ -19,6 +19,7 @@ __all__ = [
     "AEBiGRUNetwork",
     "AEDRNNNetwork",
     "AEBiGRUNetwork",
+    "DisjointCNNNetwork",
 ]
 from aeon.networks._ae_abgru import AEAttentionBiGRUNetwork
 from aeon.networks._ae_bgru import AEBiGRUNetwork
@@ -28,6 +29,7 @@ from aeon.networks._ae_fcn import AEFCNNetwork
 from aeon.networks._ae_resnet import AEResNetNetwork
 from aeon.networks._cnn import TimeCNNNetwork
 from aeon.networks._dcnn import DCNNNetwork
+from aeon.networks._disjoint_cnn import DisjointCNNNetwork
 from aeon.networks._encoder import EncoderNetwork
 from aeon.networks._fcn import FCNNetwork
 from aeon.networks._inception import InceptionNetwork
diff --git a/aeon/networks/_disjoint_cnn.py b/aeon/networks/_disjoint_cnn.py
new file mode 100644
index 00000000..126f0ad6
--- /dev/null
+++ b/aeon/networks/_disjoint_cnn.py
@@ -0,0 +1,305 @@
+"""Disjoint Convolutional Neural Network (DisjointCNNNetwork)."""
+
+__maintainer__ = ["hadifawaz1999"]
+
+
+from aeon.networks.base import BaseDeepLearningNetwork
+
+
+class DisjointCNNNetwork(BaseDeepLearningNetwork):
+    """Establish the network structure for a DisjointCNN Network.
+
+    The model is proposed in [1]_ to apply convolutions
+    specifically for multivariate series, temporal-spatial
+    phases using 1+1D Convolution layers.
+
+    Parameters
+    ----------
+    n_layers : int, default = 4
+        Number of 1+1D Convolution layers.
+    n_filters : int or list of int, default = 64
+        Number of filters used in convolution layers. If
+        input is set to a list, the lenght should be the same
+        as `n_layers`, if input is int the a list of the same
+        element is created of length `n_layers`.
+    kernel_size : int or list of int, default = [8, 5, 5, 3]
+        Size of convolution kernel. If
+        input is set to a list, the lenght should be the same
+        as `n_layers`, if input is int the a list of the same
+        element is created of length `n_layers`.
+    dilation_rate : int or list of int, default = 1
+        The dilation rate for convolution. If
+        input is set to a list, the lenght should be the same
+        as `n_layers`, if input is int the a list of the same
+        element is created of length `n_layers`.
+    strides : int or list of int, default = 1
+        The strides of the convolution filter. If
+        input is set to a list, the lenght should be the same
+        as `n_layers`, if input is int the a list of the same
+        element is created of length `n_layers`.
+    padding : str or list of str, default = "same"
+        The type of padding used for convolution. If
+        input is set to a list, the lenght should be the same
+        as `n_layers`, if input is int the a list of the same
+        element is created of length `n_layers`.
+    activation : str or list of str, default = "elu"
+        Activation used after the convolution. If
+        input is set to a list, the lenght should be the same
+        as `n_layers`, if input is int the a list of the same
+        element is created of length `n_layers`.
+    use_bias : bool or list of bool, default = True
+        Whether or not ot use bias in convolution. If
+        input is set to a list, the lenght should be the same
+        as `n_layers`, if input is int the a list of the same
+        element is created of length `n_layers`.
+    kernel_initializer: str or list of str, default = "he_uniform"
+        The initialization method of convolution layers. If
+        input is set to a list, the lenght should be the same
+        as `n_layers`, if input is int the a list of the same
+        element is created of length `n_layers`.
+    pool_size: int, default = 5
+        The size of the one max pool layer at the end of
+        the model, default = 5.
+    pool_strides: int, default = None
+        The strides used for the one max pool layer at
+        the end of the model, default = None.
+    pool_padding: str, default = "valid"
+        The padding method for the one max pool layer at
+        the end of the model, default = "valid".
+    hidden_fc_units: int, default = 128
+        The number of fully connected units.
+    activation_fc: str, default = "relu"
+        The activation of the fully connected layer.
+
+    Notes
+    -----
+    The code is adapted from:
+    https://github.com/Navidfoumani/Disjoint-CNN
+
+    References
+    ----------
+    .. [1] Foumani, Seyed Navid Mohammadi, Chang Wei Tan, and Mahsa Salehi.
+    "Disjoint-cnn for multivariate time series classification."
+    2021 International Conference on Data Mining Workshops
+    (ICDMW). IEEE, 2021.
+    """
+
+    def __init__(
+        self,
+        n_layers=4,
+        n_filters=64,
+        kernel_size=None,
+        dilation_rate=1,
+        strides=1,
+        padding="same",
+        activation="elu",
+        use_bias=True,
+        kernel_initializer="he_uniform",
+        pool_size=5,
+        pool_strides=None,
+        pool_padding="valid",
+        hidden_fc_units=128,
+        activation_fc="relu",
+    ):
+        self.n_layers = n_layers
+        self.n_filters = n_filters
+        self.kernel_size = kernel_size
+        self.dilation_rate = dilation_rate
+        self.strides = strides
+        self.padding = padding
+        self.activation = activation
+        self.use_bias = use_bias
+        self.kernel_initializer = kernel_initializer
+        self.pool_size = pool_size
+        self.pool_strides = pool_strides
+        self.pool_padding = pool_padding
+        self.hidden_fc_units = hidden_fc_units
+        self.activation_fc = activation_fc
+
+        super().__init__()
+
+    def build_network(self, input_shape, **kwargs):
+        """Construct a network and return its input and output layers.
+
+        Parameters
+        ----------
+        input_shape : tuple
+          shape = (n_timepoints (m), n_channels (d)), the shape of the data fed
+          into the input layer.
+
+        Returns
+        -------
+        input_layer : a keras layer
+        output_layer : a keras layer
+        """
+        import tensorflow as tf
+
+        self._kernel_size_ = (
+            [8, 5, 5, 3] if self.kernel_size is None else self.kernel_size
+        )
+
+        if isinstance(self._kernel_size_, list):
+            if len(self._kernel_size_) != self.n_layers:
+                raise ValueError(
+                    f"Kernel sizes {len(self._kernel_size_)} should be"
+                    f" the same as number of layers but is"
+                    f" not: {self.n_layers}"
+                )
+            self._kernel_size = self._kernel_size_
+        else:
+            self._kernel_size = [self._kernel_size_] * self.n_layers
+
+        if isinstance(self.n_filters, list):
+            if len(self.n_filters) != self.n_layers:
+                raise ValueError(
+                    f"Number of filters {len(self.n_filters)} should be"
+                    f" the same as number of layers but is"
+                    f" not: {self.n_layers}"
+                )
+            self._n_filters = self.n_filters
+        else:
+            self._n_filters = [self.n_filters] * self.n_layers
+
+        if isinstance(self.dilation_rate, list):
+            if len(self.dilation_rate) != self.n_layers:
+                raise ValueError(
+                    f"Number of dilations {len(self.dilation_rate)} should be"
+                    f" the same as number of layers but is"
+                    f" not: {self.n_layers}"
+                )
+            self._dilation_rate = self.dilation_rate
+        else:
+            self._dilation_rate = [self.dilation_rate] * self.n_layers
+
+        if isinstance(self.strides, list):
+            if len(self.strides) != self.n_layers:
+                raise ValueError(
+                    f"Number of strides {len(self.strides)} should be"
+                    f" the same as number of layers but is"
+                    f" not: {self.n_layers}"
+                )
+            self._strides = self.strides
+        else:
+            self._strides = [self.strides] * self.n_layers
+
+        if isinstance(self.padding, list):
+            if len(self.padding) != self.n_layers:
+                raise ValueError(
+                    f"Number of paddings {len(self.padding)} should be"
+                    f" the same as number of layers but is"
+                    f" not: {self.n_layers}"
+                )
+            self._padding = self.padding
+        else:
+            self._padding = [self.padding] * self.n_layers
+
+        if isinstance(self.activation, list):
+            if len(self.activation) != self.n_layers:
+                raise ValueError(
+                    f"Number of activations {len(self.activation)} should be"
+                    f" the same as number of layers but is"
+                    f" not: {self.n_layers}"
+                )
+            self._activation = self.activation
+        else:
+            self._activation = [self.activation] * self.n_layers
+
+        if isinstance(self.use_bias, list):
+            if len(self.use_bias) != self.n_layers:
+                raise ValueError(
+                    f"Number of biases {len(self.use_bias)} should be"
+                    f" the same as number of layers but is"
+                    f" not: {self.n_layers}"
+                )
+            self._use_bias = self.use_bias
+        else:
+            self._use_bias = [self.use_bias] * self.n_layers
+
+        if isinstance(self.kernel_initializer, list):
+            if len(self.kernel_initializer) != self.n_layers:
+                raise ValueError(
+                    f"Number of Kernel initializers {len(self.kernel_initializer)}"
+                    f" should be"
+                    f" the same as number of layers but is"
+                    f" not: {self.n_layers}"
+                )
+            self._kernel_initializer = self.kernel_initializer
+        else:
+            self._kernel_initializer = [self.kernel_initializer] * self.n_layers
+
+        input_layer = tf.keras.layers.Input(input_shape)
+        reshape_layer = tf.keras.layers.Reshape(
+            target_shape=(input_shape[0], input_shape[1], 1)
+        )(input_layer)
+
+        x = reshape_layer
+
+        for i in range(self.n_layers):
+            x = self._one_plus_one_d_convolution_layer(
+                input_tensor=x,
+                n_filters=self._n_filters[i],
+                kernel_size=self._kernel_size[i],
+                dilation_rate=self._dilation_rate[i],
+                strides=self._strides[i],
+                padding=self._padding[i],
+                use_bias=self._use_bias[i],
+                activation=self._activation[i],
+                kernel_initializer=self._kernel_initializer[i],
+            )
+
+        max_pool_layer = tf.keras.layers.MaxPooling2D(
+            pool_size=(self.pool_size, 1),
+            strides=self.pool_strides,
+            padding=self.pool_padding,
+        )(x)
+
+        gap = tf.keras.layers.GlobalAveragePooling2D()(max_pool_layer)
+
+        projection_head = tf.keras.layers.Dense(
+            self.hidden_fc_units, activation=self.activation_fc
+        )(gap)
+
+        return input_layer, projection_head
+
+    def _one_plus_one_d_convolution_layer(
+        self,
+        input_tensor,
+        n_filters,
+        kernel_size,
+        dilation_rate,
+        strides,
+        padding,
+        use_bias,
+        activation,
+        kernel_initializer,
+    ):
+        import tensorflow as tf
+
+        temporal_conv = tf.keras.layers.Conv2D(
+            n_filters,
+            (kernel_size, 1),
+            padding=padding,
+            kernel_initializer=kernel_initializer,
+            dilation_rate=dilation_rate,
+            use_bias=use_bias,
+            strides=strides,
+        )(input_tensor)
+
+        temporal_conv = tf.keras.layers.BatchNormalization()(temporal_conv)
+        temporal_conv = tf.keras.layers.Activation(activation=activation)(temporal_conv)
+
+        temporal_conv_output_channels = int(temporal_conv.shape[2])
+
+        spatial_conv = tf.keras.layers.Conv2D(
+            n_filters,
+            (1, temporal_conv_output_channels),
+            padding="valid",
+            kernel_initializer=kernel_initializer,
+        )(temporal_conv)
+
+        spatial_conv = tf.keras.layers.BatchNormalization()(spatial_conv)
+        spatial_conv = tf.keras.layers.Activation(activation=activation)(spatial_conv)
+
+        spatial_conv = tf.keras.layers.Permute((1, 3, 2))(spatial_conv)
+
+        return spatial_conv
diff --git a/aeon/networks/_mlp.py b/aeon/networks/_mlp.py
index 84b1570f..bb1c090c 100644
--- a/aeon/networks/_mlp.py
+++ b/aeon/networks/_mlp.py
@@ -11,6 +11,11 @@ class MLPNetwork(BaseDeepLearningNetwork):
 
     Adapted from the implementation used in [1]_
 
+    Parameters
+    ----------
+    use_bias : bool, default = True
+        Condition on whether or not to use bias values for dense layers.
+
     Notes
     -----
     Adapted from the implementation from source code
@@ -24,7 +29,10 @@ class MLPNetwork(BaseDeepLearningNetwork):
 
     def __init__(
         self,
+        use_bias=True,
     ):
+        self.use_bias = use_bias
+
         super().__init__()
 
     def build_network(self, input_shape, **kwargs):
@@ -47,13 +55,19 @@ class MLPNetwork(BaseDeepLearningNetwork):
         input_layer_flattened = keras.layers.Flatten()(input_layer)
 
         layer_1 = keras.layers.Dropout(0.1)(input_layer_flattened)
-        layer_1 = keras.layers.Dense(500, activation="relu")(layer_1)
+        layer_1 = keras.layers.Dense(500, activation="relu", use_bias=self.use_bias)(
+            layer_1
+        )
 
         layer_2 = keras.layers.Dropout(0.2)(layer_1)
-        layer_2 = keras.layers.Dense(500, activation="relu")(layer_2)
+        layer_2 = keras.layers.Dense(500, activation="relu", use_bias=self.use_bias)(
+            layer_2
+        )
 
         layer_3 = keras.layers.Dropout(0.2)(layer_2)
-        layer_3 = keras.layers.Dense(500, activation="relu")(layer_3)
+        layer_3 = keras.layers.Dense(500, activation="relu", use_bias=self.use_bias)(
+            layer_3
+        )
 
         output_layer = keras.layers.Dropout(0.3)(layer_3)
 
diff --git a/aeon/regression/deep_learning/__init__.py b/aeon/regression/deep_learning/__init__.py
index 0ea28dfe..f6e79061 100644
--- a/aeon/regression/deep_learning/__init__.py
+++ b/aeon/regression/deep_learning/__init__.py
@@ -12,9 +12,11 @@ __all__ = [
     "LITETimeRegressor",
     "EncoderRegressor",
     "MLPRegressor",
+    "DisjointCNNRegressor",
 ]
 
 from aeon.regression.deep_learning._cnn import TimeCNNRegressor
+from aeon.regression.deep_learning._disjoint_cnn import DisjointCNNRegressor
 from aeon.regression.deep_learning._encoder import EncoderRegressor
 from aeon.regression.deep_learning._fcn import FCNRegressor
 from aeon.regression.deep_learning._inception_time import (
diff --git a/aeon/regression/deep_learning/_cnn.py b/aeon/regression/deep_learning/_cnn.py
index c636c700..351e3964 100644
--- a/aeon/regression/deep_learning/_cnn.py
+++ b/aeon/regression/deep_learning/_cnn.py
@@ -21,35 +21,35 @@ class TimeCNNRegressor(BaseDeepRegressor):
 
     Parameters
     ----------
-    n_layers        : int, default = 2,
+    n_layers : int, default = 2,
         the number of convolution layers in the network
-    kernel_size    : int or list of int, default = 7,
+    kernel_size : int or list of int, default = 7,
         kernel size of convolution layers, if not a list, the same kernel size
         is used for all layer, len(list) should be n_layers
-    n_filters       : int or list of int, default = [6, 12],
+    n_filters : int or list of int, default = [6, 12],
         number of filters for each convolution layer, if not a list, the same n_filters
         is used in all layers.
-    avg_pool_size   : int or list of int, default = 3,
+    avg_pool_size : int or list of int, default = 3,
         the size of the average pooling layer, if not a list, the same
         max pooling size is used
         for all convolution layer
-    output_activation   : str, default = "linear",
+    output_activation : str, default = "linear",
         the output activation for the regressor
-    activation      : str or list of str, default = "sigmoid",
+    activation : str or list of str, default = "sigmoid",
         keras activation function used in the model for each layer,
         if not a list, the same
         activation is used for all layers
-    padding         : str or list of str, default = 'valid',
+    padding : str or list of str, default = 'valid',
         the method of padding in convolution layers, if not a list,
         the same padding used
         for all convolution layers
-    strides         : int or list of int, default = 1,
+    strides : int or list of int, default = 1,
         the strides of kernels in the convolution and max pooling layers,
         if not a list, the same strides are used for all layers
-    dilation_rate   : int or list of int, default = 1,
+    dilation_rate : int or list of int, default = 1,
         the dilation rate of the convolution layers, if not a list,
         the same dilation rate is used all over the network
-    use_bias        : bool or list of bool, default = True,
+    use_bias : bool or list of bool, default = True,
         condition on whether or not to use bias values for convolution layers,
         if not a list, the same condition is used for all layers
     random_state : int, RandomState instance or None, default=None
@@ -59,40 +59,43 @@ class TimeCNNRegressor(BaseDeepRegressor):
         by `np.random`.
         Seeded random number generation can only be guaranteed on CPU processing,
         GPU processing will be non-deterministic.
-    n_epochs       : int, default = 2000
+    n_epochs : int, default = 2000
         the number of epochs to train the model
-    batch_size      : int, default = 16
+    batch_size : int, default = 16
         the number of samples per gradient update.
-    verbose         : boolean, default = False
+    verbose : boolean, default = False
         whether to output extra information
-    loss            : string, default="mean_squared_error"
-        fit parameter for the keras model
-    optimizer       : keras.optimizer, default=keras.optimizers.Adam(),
-    metrics         : str or list of str, default="mean_squared_error"
+    loss : str, default = "mean_squared_error"
+        The name of the keras training loss.
+    optimizer : keras.optimizer, default = tf.keras.optimizers.Adam()
+        The keras optimizer used for training.
+    metrics : str or list[str], default="mean_squared_error"
         The evaluation metrics to use during training. If
         a single string metric is provided, it will be
         used as the only metric. If a list of metrics are
         provided, all will be used for evaluation.
-    callbacks       : keras.callbacks, default=model_checkpoint to save best
-                      model on training loss
-    file_path       : file_path for the best model (if checkpoint is used as callback)
-    save_best_model     : bool, default = False
+    callbacks : keras callback or list of callbacks,
+        default = None
+        The default list of callbacks are set to
+        ModelCheckpoint.
+    file_path : file_path for the best model (if checkpoint is used as callback)
+    save_best_model : bool, default = False
         Whether or not to save the best model, if the
         modelcheckpoint callback is used by default,
         this condition, if True, will prevent the
         automatic deletion of the best saved model from
         file and the user can choose the file name
-    save_last_model     : bool, default = False
+    save_last_model : bool, default = False
         Whether or not to save the last model, last
         epoch trained, using the base class method
         save_last_model_to_file
     save_init_model : bool, default = False
         Whether to save the initialization of the  model.
-    best_file_name      : str, default = "best_model"
+    best_file_name : str, default = "best_model"
         The name of the file of the best model, if
         save_best_model is set to False, this parameter
         is discarded
-    last_file_name      : str, default = "last_model"
+    last_file_name : str, default = "last_model"
         The name of the file of the last model, if
         save_last_model is set to False, this parameter
         is discarded
@@ -143,7 +146,7 @@ class TimeCNNRegressor(BaseDeepRegressor):
         last_file_name="last_model",
         init_file_name="init_model",
         verbose=False,
-        loss="mse",
+        loss="mean_squared_error",
         output_activation="linear",
         metrics="mean_squared_error",
         random_state=None,
@@ -255,10 +258,11 @@ class TimeCNNRegressor(BaseDeepRegressor):
         # Transpose to conform to Keras input style.
         X = X.transpose(0, 2, 1)
 
-        if isinstance(self.metrics, str):
-            self._metrics = [self.metrics]
-        else:
+        if isinstance(self.metrics, list):
             self._metrics = self.metrics
+        elif isinstance(self.metrics, str):
+            self._metrics = [self.metrics]
+
         self.input_shape = X.shape[1:]
         self.training_model_ = self.build_model(self.input_shape)
 
diff --git a/aeon/regression/deep_learning/_disjoint_cnn.py b/aeon/regression/deep_learning/_disjoint_cnn.py
new file mode 100644
index 00000000..cc2b0cb3
--- /dev/null
+++ b/aeon/regression/deep_learning/_disjoint_cnn.py
@@ -0,0 +1,419 @@
+"""DisjointCNN regressor."""
+
+__maintainer__ = ["hadifawaz1999"]
+__all__ = ["DisjointCNNRegressor"]
+
+import gc
+import os
+import time
+from copy import deepcopy
+
+from sklearn.utils import check_random_state
+
+from aeon.networks import DisjointCNNNetwork
+from aeon.regression.deep_learning.base import BaseDeepRegressor
+
+
+class DisjointCNNRegressor(BaseDeepRegressor):
+    """Disjoint Convolutional Neural Netowkr regressor.
+
+    Adapted from the implementation used in [1]_.
+
+    Parameters
+    ----------
+    n_layers : int, default = 4
+        Number of 1+1D Convolution layers.
+    n_filters : int or list of int, default = 64
+        Number of filters used in convolution layers. If
+        input is set to a list, the lenght should be the same
+        as `n_layers`, if input is int the a list of the same
+        element is created of length `n_layers`.
+    kernel_size : int or list of int, default = [8, 5, 5, 3]
+        Size of convolution kernel. If
+        input is set to a list, the lenght should be the same
+        as `n_layers`, if input is int the a list of the same
+        element is created of length `n_layers`.
+    dilation_rate : int or list of int, default = 1
+        The dilation rate for convolution. If
+        input is set to a list, the lenght should be the same
+        as `n_layers`, if input is int the a list of the same
+        element is created of length `n_layers`.
+    strides : int or list of int, default = 1
+        The strides of the convolution filter. If
+        input is set to a list, the lenght should be the same
+        as `n_layers`, if input is int the a list of the same
+        element is created of length `n_layers`.
+    padding : str or list of str, default = "same"
+        The type of padding used for convolution. If
+        input is set to a list, the lenght should be the same
+        as `n_layers`, if input is int the a list of the same
+        element is created of length `n_layers`.
+    activation : str or list of str, default = "elu"
+        Activation used after the convolution. If
+        input is set to a list, the lenght should be the same
+        as `n_layers`, if input is int the a list of the same
+        element is created of length `n_layers`.
+    use_bias : bool or list of bool, default = True
+        Whether or not ot use bias in convolution. If
+        input is set to a list, the lenght should be the same
+        as `n_layers`, if input is int the a list of the same
+        element is created of length `n_layers`.
+    kernel_initializer: str or list of str, default = "he_uniform"
+        The initialization method of convolution layers. If
+        input is set to a list, the lenght should be the same
+        as `n_layers`, if input is int the a list of the same
+        element is created of length `n_layers`.
+    pool_size: int, default = 5
+        The size of the one max pool layer at the end of
+        the model, default = 5.
+    pool_strides: int, default = None
+        The strides used for the one max pool layer at
+        the end of the model, default = None.
+    pool_padding: str, default = "valid"
+        The padding method for the one max pool layer at
+        the end of the model, default = "valid".
+    hidden_fc_units: int, default = 128
+        The number of fully connected units.
+    activation_fc: str, default = "relu"
+        The activation of the fully connected layer.
+    n_epochs : int, default = 2000
+        The number of epochs to train the model.
+    batch_size : int, default = 16
+        The number of samples per gradient update.
+    use_mini_batch_size : bool, default = False
+        Whether or not to use the mini batch size formula.
+    random_state : int, RandomState instance or None, default=None
+        If `int`, random_state is the seed used by the random number generator;
+        If `RandomState` instance, random_state is the random number generator;
+        If `None`, the random number generator is the `RandomState` instance used
+        by `np.random`.
+        Seeded random number generation can only be guaranteed on CPU processing,
+        GPU processing will be non-deterministic.
+    verbose : boolean, default = False
+        Whether to output extra information.
+    output_activation : str, default = "linear",
+        the output activation of the regressor.
+    loss : str, default = "mean_squared_error"
+        The name of the keras training loss.
+    metrics : str or list[str], default="mean_squared_error"
+        The evaluation metrics to use during training. If
+        a single string metric is provided, it will be
+        used as the only metric. If a list of metrics are
+        provided, all will be used for evaluation.
+    optimizer : keras.optimizer, default = tf.keras.optimizers.Adam()
+        The keras optimizer used for training.
+    file_path : str, default = "./"
+        File path to save best model.
+    save_best_model : bool, default = False
+        Whether or not to save the best model, if the
+        modelcheckpoint callback is used by default,
+        this condition, if True, will prevent the
+        automatic deletion of the best saved model from
+        file and the user can choose the file name.
+    save_last_model : bool, default = False
+        Whether or not to save the last model, last
+        epoch trained, using the base class method
+        save_last_model_to_file.
+    save_init_model : bool, default = False
+        Whether to save the initialization of the  model.
+    best_file_name : str, default = "best_model"
+        The name of the file of the best model, if
+        save_best_model is set to False, this parameter
+        is discarded.
+    last_file_name : str, default = "last_model"
+        The name of the file of the last model, if
+        save_last_model is set to False, this parameter
+        is discarded.
+    init_file_name : str, default = "init_model"
+        The name of the file of the init model, if
+        save_init_model is set to False,
+        this parameter is discarded.
+    callbacks : keras callback or list of callbacks,
+        default = None
+        The default list of callbacks are set to
+        ModelCheckpoint and ReduceLROnPlateau.
+
+    Notes
+    -----
+    Adapted from the implementation from:
+    https://github.com/Navidfoumani/Disjoint-CNN
+
+    References
+    ----------
+    .. [1] Foumani, Seyed Navid Mohammadi, Chang Wei Tan, and Mahsa Salehi.
+    "Disjoint-cnn for multivariate time series classification."
+    2021 International Conference on Data Mining Workshops
+    (ICDMW). IEEE, 2021.
+
+    Examples
+    --------
+    >>> from aeon.regression.deep_learning import DisjointCNNRegressor
+    >>> from aeon.datasets import load_unit_test
+    >>> X_train, y_train = load_unit_test(split="train")
+    >>> X_test, y_test = load_unit_test(split="test")
+    >>> disjoint_cnn = DisjointCNNRegressor(n_epochs=20,
+    ... batch_size=4)  # doctest: +SKIP
+    >>> disjoint_cnn.fit(X_train, y_train)  # doctest: +SKIP
+    DisjointCNNRegressor(...)
+    """
+
+    def __init__(
+        self,
+        n_layers=4,
+        n_filters=64,
+        kernel_size=None,
+        dilation_rate=1,
+        strides=1,
+        padding="same",
+        activation="elu",
+        use_bias=True,
+        kernel_initializer="he_uniform",
+        pool_size=5,
+        pool_strides=None,
+        pool_padding="valid",
+        hidden_fc_units=128,
+        activation_fc="relu",
+        n_epochs=2000,
+        batch_size=16,
+        use_mini_batch_size=False,
+        random_state=None,
+        verbose=False,
+        output_activation="linear",
+        loss="mean_squared_error",
+        metrics="mean_squared_error",
+        optimizer=None,
+        file_path="./",
+        save_best_model=False,
+        save_last_model=False,
+        save_init_model=False,
+        best_file_name="best_model",
+        last_file_name="last_model",
+        init_file_name="init_model",
+        callbacks=None,
+    ):
+        self.n_layers = n_layers
+        self.n_filters = n_filters
+        self.kernel_size = kernel_size
+        self.dilation_rate = dilation_rate
+        self.strides = strides
+        self.padding = padding
+        self.activation = activation
+        self.use_bias = use_bias
+        self.kernel_initializer = kernel_initializer
+        self.pool_size = pool_size
+        self.pool_strides = pool_strides
+        self.pool_padding = pool_padding
+        self.hidden_fc_units = hidden_fc_units
+        self.activation_fc = activation_fc
+
+        self.random_state = random_state
+        self.callbacks = callbacks
+        self.n_epochs = n_epochs
+        self.use_mini_batch_size = use_mini_batch_size
+        self.verbose = verbose
+        self.output_activation = output_activation
+        self.loss = loss
+        self.metrics = metrics
+        self.optimizer = optimizer
+
+        self.file_path = file_path
+        self.save_best_model = save_best_model
+        self.save_last_model = save_last_model
+        self.save_init_model = save_init_model
+        self.best_file_name = best_file_name
+        self.init_file_name = init_file_name
+
+        self.history = None
+
+        super().__init__(
+            batch_size=batch_size,
+            last_file_name=last_file_name,
+        )
+
+        self._network = DisjointCNNNetwork(
+            n_layers=self.n_layers,
+            n_filters=self.n_filters,
+            kernel_size=self.kernel_size,
+            dilation_rate=self.dilation_rate,
+            strides=self.strides,
+            padding=self.padding,
+            activation=self.activation,
+            use_bias=self.use_bias,
+            kernel_initializer=self.kernel_initializer,
+            pool_size=self.pool_size,
+            pool_strides=self.pool_strides,
+            pool_padding=self.pool_padding,
+            hidden_fc_units=self.hidden_fc_units,
+            activation_fc=self.activation_fc,
+        )
+
+    def build_model(self, input_shape, **kwargs):
+        """Construct a compiled, un-trained, keras model that is ready for training.
+
+        In aeon, time series are stored in numpy arrays of shape (d,m), where d
+        is the number of dimensions, m is the series length. Keras/tensorflow assume
+        data is in shape (m,d). This method also assumes (m,d). Transpose should
+        happen in fit.
+
+        Parameters
+        ----------
+        input_shape : tuple
+            The shape of the data fed into the input layer, should be (m, d).
+        n_classes : int
+            The number of classes, which becomes the size of the output layer.
+
+        Returns
+        -------
+        output : a compiled Keras Model
+        """
+        import numpy as np
+        import tensorflow as tf
+
+        rng = check_random_state(self.random_state)
+        self.random_state_ = rng.randint(0, np.iinfo(np.int32).max)
+        tf.keras.utils.set_random_seed(self.random_state_)
+        input_layer, output_layer = self._network.build_network(input_shape, **kwargs)
+
+        output_layer = tf.keras.layers.Dense(
+            units=1, activation=self.output_activation
+        )(output_layer)
+
+        self.optimizer_ = (
+            tf.keras.optimizers.Adam() if self.optimizer is None else self.optimizer
+        )
+
+        model = tf.keras.models.Model(inputs=input_layer, outputs=output_layer)
+        model.compile(
+            loss=self.loss,
+            optimizer=self.optimizer_,
+            metrics=self._metrics,
+        )
+
+        return model
+
+    def _fit(self, X, y):
+        """Fit the regressor on the training set (X, y).
+
+        Parameters
+        ----------
+        X : np.ndarray
+            The training input samples of shape (n_cases, n_channels, n_timepoints)
+        y : np.ndarray
+            The training data class labels of shape (n_cases,).
+
+        Returns
+        -------
+        self : object
+        """
+        import tensorflow as tf
+
+        # Transpose to conform to Keras input style.
+        X = X.transpose(0, 2, 1)
+
+        if isinstance(self.metrics, list):
+            self._metrics = self.metrics
+        elif isinstance(self.metrics, str):
+            self._metrics = [self.metrics]
+
+        self.input_shape = X.shape[1:]
+        self.training_model_ = self.build_model(self.input_shape)
+
+        if self.save_init_model:
+            self.training_model_.save(self.file_path + self.init_file_name + ".keras")
+
+        if self.verbose:
+            self.training_model_.summary()
+
+        if self.use_mini_batch_size:
+            mini_batch_size = min(self.batch_size, X.shape[0] // 10)
+        else:
+            mini_batch_size = self.batch_size
+
+        self.file_name_ = (
+            self.best_file_name if self.save_best_model else str(time.time_ns())
+        )
+
+        if self.callbacks is None:
+            self.callbacks_ = [
+                tf.keras.callbacks.ReduceLROnPlateau(
+                    monitor="loss", factor=0.5, patience=50, min_lr=0.0001
+                ),
+                tf.keras.callbacks.ModelCheckpoint(
+                    filepath=self.file_path + self.file_name_ + ".keras",
+                    monitor="loss",
+                    save_best_only=True,
+                ),
+            ]
+        else:
+            self.callbacks_ = self._get_model_checkpoint_callback(
+                callbacks=self.callbacks,
+                file_path=self.file_path,
+                file_name=self.file_name_,
+            )
+
+        self.history = self.training_model_.fit(
+            X,
+            y,
+            batch_size=mini_batch_size,
+            epochs=self.n_epochs,
+            verbose=self.verbose,
+            callbacks=self.callbacks_,
+        )
+
+        try:
+            self.model_ = tf.keras.models.load_model(
+                self.file_path + self.file_name_ + ".keras", compile=False
+            )
+            if not self.save_best_model:
+                os.remove(self.file_path + self.file_name_ + ".keras")
+        except FileNotFoundError:
+            self.model_ = deepcopy(self.training_model_)
+
+        if self.save_last_model:
+            self.save_last_model_to_file(file_path=self.file_path)
+
+        gc.collect()
+        return self
+
+    @classmethod
+    def _get_test_params(cls, parameter_set="default"):
+        """Return testing parameter settings for the estimator.
+
+        Parameters
+        ----------
+        parameter_set : str, default = "default"
+            Name of the set of test parameters to return, for use in tests. If no
+            special parameters are defined for a value, will return "default" set.
+            For classifiers, a "default" set of parameters should be provided for
+            general testing, and a "results_comparison" set for comparing against
+            previously recorded results if the general set does not produce suitable
+            probabilities to compare against.
+
+        Returns
+        -------
+        params : dict or list of dict, default = {}
+            Parameters to create testing instances of the class.
+            Each dict are parameters to construct an "interesting" test instance, i.e.,
+            `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
+        """
+        param1 = {
+            "n_epochs": 3,
+            "batch_size": 4,
+            "use_bias": False,
+            "n_layers": 2,
+            "n_filters": 2,
+            "kernel_size": [2, 2],
+        }
+        param2 = {
+            "n_epochs": 3,
+            "batch_size": 4,
+            "use_bias": False,
+            "n_layers": 2,
+            "n_filters": 2,
+            "kernel_size": [2, 2],
+            "verbose": True,
+            "metrics": ["mse"],
+            "use_mini_batch_size": True,
+        }
+
+        return [param1, param2]
diff --git a/aeon/regression/deep_learning/_encoder.py b/aeon/regression/deep_learning/_encoder.py
index 2183d5ee..fd3bf855 100644
--- a/aeon/regression/deep_learning/_encoder.py
+++ b/aeon/regression/deep_learning/_encoder.py
@@ -78,19 +78,23 @@ class EncoderRegressor(BaseDeepRegressor):
         by `np.random`.
         Seeded random number generation can only be guaranteed on CPU processing,
         GPU processing will be non-deterministic.
-    loss:
-        The loss function to use for training.
-    metrics: str or list of str, default="mean_squared_error"
+    loss : str, default = "mean_squared_error"
+        The name of the keras training loss.
+    metrics : str or list[str], default="mean_squared_error"
         The evaluation metrics to use during training. If
         a single string metric is provided, it will be
         used as the only metric. If a list of metrics are
         provided, all will be used for evaluation.
     use_bias:
         Whether to use bias in the dense layers.
-    optimizer:
-        The optimizer to use for training.
+    optimizer : keras.optimizer, default = tf.keras.optimizers.Adam()
+        The keras optimizer used for training.
     verbose:
         Whether to print progress messages during training.
+    callbacks : keras callback or list of callbacks,
+        default = None
+        The default list of callbacks are set to
+        ModelCheckpoint.
 
     Notes
     -----
@@ -105,10 +109,6 @@ class EncoderRegressor(BaseDeepRegressor):
 
     """
 
-    _tags = {
-        "python_dependencies": ["tensorflow"],
-    }
-
     def __init__(
         self,
         n_epochs=100,
@@ -204,7 +204,7 @@ class EncoderRegressor(BaseDeepRegressor):
         input_layer, output_layer = self._network.build_network(input_shape, **kwargs)
 
         output_layer = tf.keras.layers.Dense(
-            units=1, activation=self.output_activation, use_bias=self.use_bias
+            units=1, activation=self.output_activation
         )(output_layer)
 
         self.optimizer_ = (
@@ -241,10 +241,11 @@ class EncoderRegressor(BaseDeepRegressor):
         # Transpose X to conform to Keras input style
         X = X.transpose(0, 2, 1)
 
-        if isinstance(self.metrics, str):
-            self._metrics = [self.metrics]
-        else:
+        if isinstance(self.metrics, list):
             self._metrics = self.metrics
+        elif isinstance(self.metrics, str):
+            self._metrics = [self.metrics]
+
         self.input_shape = X.shape[1:]
         self.training_model_ = self.build_model(self.input_shape)
 
diff --git a/aeon/regression/deep_learning/_fcn.py b/aeon/regression/deep_learning/_fcn.py
index 361bc2ee..a6905580 100644
--- a/aeon/regression/deep_learning/_fcn.py
+++ b/aeon/regression/deep_learning/_fcn.py
@@ -21,25 +21,25 @@ class FCNRegressor(BaseDeepRegressor):
 
     Parameters
     ----------
-    n_layers        : int, default = 3
+    n_layers : int, default = 3
         number of convolution layers
-    n_filters       : int or list of int, default = [128,256,128]
+    n_filters : int or list of int, default = [128,256,128]
         number of filters used in convolution layers
-    kernel_size     : int or list of int, default = [8,5,3]
+    kernel_size : int or list of int, default = [8,5,3]
         size of convolution kernel
-    dilation_rate   : int or list of int, default = 1
+    dilation_rate : int or list of int, default = 1
         the dilation rate for convolution
-    strides         : int or list of int, default = 1
+    strides : int or list of int, default = 1
         the strides of the convolution filter
-    padding         : str or list of str, default = "same"
+    padding : str or list of str, default = "same"
         the type of padding used for convolution
-    activation      : str or list of str, default = "relu"
+    activation : str or list of str, default = "relu"
         activation used after the convolution
-    use_bias        : bool or list of bool, default = True
+    use_bias : bool or list of bool, default = True
         whether or not ot use bias in convolution
-    n_epochs        : int, default = 2000
+    n_epochs : int, default = 2000
         the number of epochs to train the model
-    batch_size      : int, default = 16
+    batch_size : int, default = 16
         the number of samples per gradient update.
     use_mini_batch_size : bool, default = False,
         whether or not to use the mini batch size formula
@@ -50,45 +50,48 @@ class FCNRegressor(BaseDeepRegressor):
         by `np.random`.
         Seeded random number generation can only be guaranteed on CPU processing,
         GPU processing will be non-deterministic.
-    verbose         : boolean, default = False
+    verbose : boolean, default = False
         whether to output extra information
     output_activation   : str, default = "linear",
         the output activation of the regressor
-    loss            : string, default="mean_squared_error"
-        fit parameter for the keras model
-    metrics         : list of strings, default="mean_squared_error",
+    loss : str, default = "mean_squared_error"
+        The name of the keras training loss.
+    metrics : str or list[str], default="mean_squared_error"
         The evaluation metrics to use during training. If
         a single string metric is provided, it will be
         used as the only metric. If a list of metrics are
         provided, all will be used for evaluation.
-    optimizer       : keras.optimizers object, default = Adam(lr=0.01)
-        specify the optimizer and the learning rate to be used.
-    file_path       : str, default = "./"
+    optimizer : keras.optimizer, default = tf.keras.optimizers.Adam()
+        The keras optimizer used for training.
+    file_path : str, default = "./"
         file path to save best model
-    save_best_model     : bool, default = False
+    save_best_model : bool, default = False
         Whether or not to save the best model, if the
         modelcheckpoint callback is used by default,
         this condition, if True, will prevent the
         automatic deletion of the best saved model from
         file and the user can choose the file name
-    save_last_model     : bool, default = False
+    save_last_model : bool, default = False
         Whether or not to save the last model, last
         epoch trained, using the base class method
         save_last_model_to_file
     save_init_model : bool, default = False
         Whether to save the initialization of the  model.
-    best_file_name      : str, default = "best_model"
+    best_file_name : str, default = "best_model"
         The name of the file of the best model, if
         save_best_model is set to False, this parameter
         is discarded
-    last_file_name      : str, default = "last_model"
+    last_file_name : str, default = "last_model"
         The name of the file of the last model, if
         save_last_model is set to False, this parameter
         is discarded
     init_file_name : str, default = "init_model"
         The name of the file of the init model, if save_init_model is set to False,
         this parameter is discarded.
-    callbacks       : keras.callbacks, default = None
+    callbacks : keras callback or list of callbacks,
+        default = None
+        The default list of callbacks are set to
+        ModelCheckpoint and ReduceLROnPlateau.
 
     Notes
     -----
@@ -134,7 +137,7 @@ class FCNRegressor(BaseDeepRegressor):
         callbacks=None,
         verbose=False,
         output_activation="linear",
-        loss="mse",
+        loss="mean_squared_error",
         metrics="mean_squared_error",
         random_state=None,
         use_bias=True,
@@ -240,10 +243,11 @@ class FCNRegressor(BaseDeepRegressor):
 
         # Transpose to conform to Keras input style.
         X = X.transpose(0, 2, 1)
-        if isinstance(self.metrics, str):
-            self._metrics = [self.metrics]
-        else:
+
+        if isinstance(self.metrics, list):
             self._metrics = self.metrics
+        elif isinstance(self.metrics, str):
+            self._metrics = [self.metrics]
 
         self.input_shape = X.shape[1:]
         self.training_model_ = self.build_model(self.input_shape)
diff --git a/aeon/regression/deep_learning/_inception_time.py b/aeon/regression/deep_learning/_inception_time.py
index 5e6c6a56..96e8a383 100644
--- a/aeon/regression/deep_learning/_inception_time.py
+++ b/aeon/regression/deep_learning/_inception_time.py
@@ -24,96 +24,97 @@ class InceptionTimeRegressor(BaseRegressor):
 
     Parameters
     ----------
-        n_regressors       : int, default = 5,
+        n_regressors : int, default = 5,
             the number of Inception models used for the
             Ensemble in order to create
             InceptionTime.
-        depth               : int, default = 6,
+        depth : int, default = 6,
             the number of inception modules used
-        n_filters          : int or list of int32, default = 32,
+        n_filters : int or list of int32, default = 32,
             the number of filters used in one inception
             module, if not a list,
             the same number of filters is used in
             all inception modules
-        n_conv_per_layer   : int or list of int, default = 3,
+        n_conv_per_layer : int or list of int, default = 3,
             the number of convolution layers in each inception
             module, if not a list,
             the same number of convolution layers is used
             in all inception modules
-        kernel_size         : int or list of int, default = 40,
+        kernel_size : int or list of int, default = 40,
             the head kernel size used for each inception
             module, if not a list,
             the same is used in all inception modules
-        use_max_pooling     : bool or list of bool, default = True,
+        use_max_pooling : bool or list of bool, default = True,
             conditioning whether or not to use max pooling layer
             in inception modules,if not a list,
             the same is used in all inception modules
-        max_pool_size       : int or list of int, default = 3,
+        max_pool_size : int or list of int, default = 3,
             the size of the max pooling layer, if not a list,
             the same is used in all inception modules
-        strides             : int or list of int, default = 1,
+        strides : int or list of int, default = 1,
             the strides of kernels in convolution layers for each
             inception module, if not a list,
             the same is used in all inception modules
-        dilation_rate       : int or list of int, default = 1,
+        dilation_rate : int or list of int, default = 1,
             the dilation rate of convolutions in each inception
             module, if not a list,
             the same is used in all inception modules
-        padding             : str or list of str, default = "same",
+        padding : str or list of str, default = "same",
             the type of padding used for convoltuon for each
             inception module, if not a list,
             the same is used in all inception modules
-        activation          : str or list of str, default = "relu",
+        activation : str or list of str, default = "relu",
             the activation function used in each inception
             module, if not a list,
             the same is used in all inception modules
-        use_bias            : bool or list of bool, default = False,
+        use_bias : bool or list of bool, default = False,
             condition whether or not convolutions should
             use bias values in each inception
             module, if not a list,
             the same is used in all inception modules
-        use_residual        : bool, default = True,
+        use_residual : bool, default = True,
             condition whether or not to use residual
             connections all over Inception
-        use_bottleneck      : bool, default = True,
+        use_bottleneck : bool, default = True,
             condition whether or not to use bottlenecks
             all over Inception
-        bottleneck_size     : int, default = 32,
+        bottleneck_size : int, default = 32,
             the bottleneck size in case use_bottleneck = True
-        use_custom_filters  : bool, default = False,
+        use_custom_filters : bool, default = False,
             condition on whether or not to use custom
             filters in the first inception module
-        output_activation   : str, default = "linear",
+        output_activation : str, default = "linear",
             the output activation for the regressor
-        batch_size          : int, default = 64
+        batch_size : int, default = 64
             the number of samples per gradient update.
         use_mini_batch_size : bool, default = False
             condition on using the mini batch size
             formula Wang et al.
-        n_epochs           : int, default = 1500
+        n_epochs : int, default = 1500
             the number of epochs to train the model.
-        callbacks           : callable or None, default
-        ReduceOnPlateau and ModelCheckpoint
-            list of tf.keras.callbacks.Callback objects.
-        file_path           : str, default = './'
+        callbacks : keras callback or list of callbacks,
+            default = None
+            The default list of callbacks are set to
+            ModelCheckpoint and ReduceLROnPlateau.
+        file_path : str, default = './'
             file_path when saving model_Checkpoint callback
-        save_best_model     : bool, default = False
+        save_best_model : bool, default = False
             Whether or not to save the best model, if the
             modelcheckpoint callback is used by default,
             this condition, if True, will prevent the
             automatic deletion of the best saved model from
             file and the user can choose the file name
-        save_last_model     : bool, default = False
+        save_last_model : bool, default = False
             Whether or not to save the last model, last
             epoch trained, using the base class method
             save_last_model_to_file
         save_init_model : bool, default = False
             Whether to save the initialization of the  model.
-        best_file_name      : str, default = "best_model"
+        best_file_name : str, default = "best_model"
             The name of the file of the best model, if
             save_best_model is set to False, this parameter
             is discarded
-        last_file_name      : str, default = "last_model"
+        last_file_name : str, default = "last_model"
             The name of the file of the last model, if
             save_last_model is set to False, this parameter
             is discarded
@@ -127,12 +128,17 @@ class InceptionTimeRegressor(BaseRegressor):
             by `np.random`.
             Seeded random number generation can only be guaranteed on CPU processing,
             GPU processing will be non-deterministic.
-        verbose             : boolean, default = False
+        verbose : boolean, default = False
             whether to output extra information
-        optimizer           : keras optimizer, default = Adam
-        loss                : keras loss,
-                              default = mean_squared_error
-        will be set to accuracy as default if None
+        optimizer : keras.optimizer, default = tf.keras.optimizers.Adam()
+            The keras optimizer used for training.
+        loss : str, default = "mean_squared_error"
+            The name of the keras training loss.
+        metrics : str or list[str], default="mean_squared_error"
+            The evaluation metrics to use during training. If
+            a single string metric is provided, it will be
+            used as the only metric. If a list of metrics are
+            provided, all will be used for evaluation.
 
     Notes
     -----
@@ -203,7 +209,8 @@ class InceptionTimeRegressor(BaseRegressor):
         callbacks=None,
         random_state=None,
         verbose=False,
-        loss="mse",
+        loss="mean_squared_error",
+        metrics="mean_squared_error",
         optimizer=None,
     ):
         self.n_regressors = n_regressors
@@ -241,6 +248,7 @@ class InceptionTimeRegressor(BaseRegressor):
         self.verbose = verbose
         self.use_mini_batch_size = use_mini_batch_size
         self.loss = loss
+        self.metrics = metrics
         self.optimizer = optimizer
 
         self.regressors_ = []
@@ -294,6 +302,7 @@ class InceptionTimeRegressor(BaseRegressor):
                 n_epochs=self.n_epochs,
                 callbacks=self.callbacks,
                 loss=self.loss,
+                metrics=self.metrics,
                 optimizer=self.optimizer,
                 random_state=rng.randint(0, np.iinfo(np.int32).max),
                 verbose=self.verbose,
@@ -366,82 +375,83 @@ class IndividualInceptionRegressor(BaseDeepRegressor):
 
     Parameters
     ----------
-        depth               : int, default = 6,
+        depth : int, default = 6,
             the number of inception modules used
-        n_filters          : int or list of int32, default = 32,
+        n_filters : int or list of int32, default = 32,
             the number of filters used in one inception module, if not a list,
             the same number of filters is used in all inception modules
-        n_conv_per_layer   : int or list of int, default = 3,
+        n_conv_per_layer : int or list of int, default = 3,
             the number of convolution layers in each inception module, if not a list,
             the same number of convolution layers is used in all inception modules
-        kernel_size         : int or list of int, default = 40,
+        kernel_size : int or list of int, default = 40,
             the head kernel size used for each inception module, if not a list,
             the same is used in all inception modules
-        use_max_pooling     : bool or list of bool, default = True,
+        use_max_pooling : bool or list of bool, default = True,
             condition whether or not to use max pooling layer
             in inception modules,if not a list,
             the same is used in all inception modules
-        max_pool_size       : int or list of int, default = 3,
+        max_pool_size : int or list of int, default = 3,
             the size of the max pooling layer, if not a list,
             the same is used in all inception modules
-        strides             : int or list of int, default = 1,
+        strides : int or list of int, default = 1,
             the strides of kernels in convolution layers for
             each inception module, if not a list,
             the same is used in all inception modules
-        dilation_rate       : int or list of int, default = 1,
+        dilation_rate : int or list of int, default = 1,
             the dilation rate of convolutions in each inception module, if not a list,
             the same is used in all inception modules
-        padding             : str or list of str, default = "same",
+        padding : str or list of str, default = "same",
             the type of padding used for convoltuon for each
             inception module, if not a list,
             the same is used in all inception modules
-        activation          : str or list of str, default = "relu",
+        activation : str or list of str, default = "relu",
             the activation function used in each inception module, if not a list,
             the same is used in all inception modules
-        use_bias            : bool or list of bool, default = False,
+        use_bias : bool or list of bool, default = False,
             condition whether or not convolutions should
             use bias values in each inception
             module, if not a list,
             the same is used in all inception modules
-        use_residual        : bool, default = True,
+        use_residual : bool, default = True,
             condition whether or not to use residual connections all over Inception
-        use_bottleneck      : bool, default = True,
+        use_bottleneck : bool, default = True,
             condition whether or not to use bottlesnecks all over Inception
-        bottleneck_size     : int, default = 32,
+        bottleneck_size : int, default = 32,
             the bottleneck size in case use_bottleneck = True
-        use_custom_filters  : bool, default = False,
+        use_custom_filters : bool, default = False,
             condition on whether or not to use custom filters
             in the first inception module
-        output_activation   : str, default = "linear",
+        output_activation : str, default = "linear",
             the output activation of the regressor
-        batch_size          : int, default = 64
+        batch_size : int, default = 64
             the number of samples per gradient update.
         use_mini_batch_size : bool, default = False
             condition on using the mini batch size formula Wang et al.
-        n_epochs           : int, default = 1500
+        n_epochs : int, default = 1500
             the number of epochs to train the model.
-        callbacks           : callable or None, default
-        ReduceOnPlateau and ModelCheckpoint
-            list of tf.keras.callbacks.Callback objects.
-        file_path           : str, default = './'
+        callbacks : keras callback or list of callbacks,
+            default = None
+            The default list of callbacks are set to
+            ModelCheckpoint and ReduceLROnPlateau.
+        file_path : str, default = './'
             file_path when saving model_Checkpoint callback
-        save_best_model     : bool, default = False
+        save_best_model : bool, default = False
             Whether or not to save the best model, if the
             modelcheckpoint callback is used by default,
             this condition, if True, will prevent the
             automatic deletion of the best saved model from
             file and the user can choose the file name
-        save_last_model     : bool, default = False
+        save_last_model : bool, default = False
             Whether or not to save the last model, last
             epoch trained, using the base class method
             save_last_model_to_file
         save_init_model : bool, default = False
             Whether to save the initialization of the  model.
-        best_file_name      : str, default = "best_model"
+        best_file_name : str, default = "best_model"
             The name of the file of the best model, if
             save_best_model is set to False, this parameter
             is discarded
-        last_file_name      : str, default = "last_model"
+        last_file_name : str, default = "last_model"
             The name of the file of the last model, if
             save_last_model is set to False, this parameter
             is discarded
@@ -455,11 +465,17 @@ class IndividualInceptionRegressor(BaseDeepRegressor):
             by `np.random`.
             Seeded random number generation can only be guaranteed on CPU processing,
             GPU processing will be non-deterministic.
-        verbose             : boolean, default = False
+        verbose : boolean, default = False
             whether to output extra information
-        optimizer           : keras optimizer, default = Adam
-        loss                : keras loss, default = mean_squared_error
-        to accuracy as default if None
+        optimizer : keras.optimizer, default = tf.keras.optimizers.Adam()
+            The keras optimizer used for training.
+        loss : str, default = "mean_squared_error"
+            The name of the keras training loss.
+        metrics : str or list[str], default="mean_squared_error"
+            The evaluation metrics to use during training. If
+            a single string metric is provided, it will be
+            used as the only metric. If a list of metrics are
+            provided, all will be used for evaluation.
 
     Notes
     -----
@@ -520,7 +536,8 @@ class IndividualInceptionRegressor(BaseDeepRegressor):
         callbacks=None,
         random_state=None,
         verbose=False,
-        loss="mse",
+        loss="mean_squared_error",
+        metrics="mean_squared_error",
         optimizer=None,
     ):
         # predefined
@@ -555,6 +572,7 @@ class IndividualInceptionRegressor(BaseDeepRegressor):
         self.verbose = verbose
         self.use_mini_batch_size = use_mini_batch_size
         self.loss = loss
+        self.metrics = metrics
         self.optimizer = optimizer
 
         super().__init__(batch_size=batch_size, last_file_name=last_file_name)
@@ -609,10 +627,7 @@ class IndividualInceptionRegressor(BaseDeepRegressor):
             tf.keras.optimizers.Adam() if self.optimizer is None else self.optimizer
         )
 
-        model.compile(
-            loss=self.loss,
-            optimizer=self.optimizer_,
-        )
+        model.compile(loss=self.loss, optimizer=self.optimizer_, metrics=self._metrics)
 
         return model
 
@@ -638,6 +653,11 @@ class IndividualInceptionRegressor(BaseDeepRegressor):
         # Transpose to conform to Keras input style.
         X = X.transpose(0, 2, 1)
 
+        if isinstance(self.metrics, list):
+            self._metrics = self.metrics
+        elif isinstance(self.metrics, str):
+            self._metrics = [self.metrics]
+
         # ignore the number of instances, X.shape[0],
         # just want the shape of each instance
         self.input_shape_ = X.shape[1:]
diff --git a/aeon/regression/deep_learning/_lite_time.py b/aeon/regression/deep_learning/_lite_time.py
index 88d88ffc..4be6bca8 100644
--- a/aeon/regression/deep_learning/_lite_time.py
+++ b/aeon/regression/deep_learning/_lite_time.py
@@ -55,8 +55,10 @@ class LITETimeRegressor(BaseRegressor):
         formula Wang et al.
     n_epochs : int, default = 1500
         the number of epochs to train the model.
-    callbacks : callable or None, default = ReduceOnPlateau and ModelCheckpoint
-        list of tf.keras.callbacks.Callback objects.
+    callbacks : keras callback or list of callbacks,
+        default = None
+        The default list of callbacks are set to
+        ModelCheckpoint and ReduceLROnPlateau.
     file_path : str, default = "./"
         file_path when saving model_Checkpoint callback
     save_best_model : bool, default = False
@@ -91,10 +93,15 @@ class LITETimeRegressor(BaseRegressor):
         GPU processing will be non-deterministic.
     verbose : boolean, default = False
         whether to output extra information
-    optimizer : keras optimizer, default = Adam
-    loss : keras loss, default = "mean_squared_error"
-    metrics : keras metrics, default = mean_squared_error,
-        will be set to mean_squared_error as default if None
+    loss : str, default = "mean_squared_error"
+        The name of the keras training loss.
+    metrics : str or list[str], default="mean_squared_error"
+        The evaluation metrics to use during training. If
+        a single string metric is provided, it will be
+        used as the only metric. If a list of metrics are
+        provided, all will be used for evaluation.
+    optimizer : keras.optimizer, default = tf.keras.optimizers.Adam()
+        The keras optimizer used for training.
 
     Notes
     -----
@@ -152,7 +159,7 @@ class LITETimeRegressor(BaseRegressor):
         random_state=None,
         verbose=False,
         loss="mean_squared_error",
-        metrics=None,
+        metrics="mean_squared_error",
         optimizer=None,
     ):
         self.n_regressors = n_regressors
@@ -333,8 +340,10 @@ class IndividualLITERegressor(BaseDeepRegressor):
         formula Wang et al.
     n_epochs : int, default = 1500
         the number of epochs to train the model.
-    callbacks : callable or None, default = ReduceOnPlateau and ModelCheckpoint
-        list of tf.keras.callbacks.Callback objects.
+    callbacks : keras callback or list of callbacks,
+        default = None
+        The default list of callbacks are set to
+        ModelCheckpoint and ReduceLROnPlateau.
     file_path : str, default = "./"
         file_path when saving model_Checkpoint callback
     save_best_model : bool, default = False
@@ -369,10 +378,15 @@ class IndividualLITERegressor(BaseDeepRegressor):
         GPU processing will be non-deterministic.
     verbose : boolean, default = False
         whether to output extra information
-    optimizer : keras optimizer, default = Adam
-    loss : keras loss, default = 'mean_squared_error'
-    metrics : keras metrics, default = mean_squared_error,
-        will be set to mean_squared_error as default if None
+    loss : str, default = "mean_squared_error"
+        The name of the keras training loss.
+    metrics : str or list[str], default="mean_squared_error"
+        The evaluation metrics to use during training. If
+        a single string metric is provided, it will be
+        used as the only metric. If a list of metrics are
+        provided, all will be used for evaluation.
+    optimizer : keras.optimizer, default = tf.keras.optimizers.Adam()
+        The keras optimizer used for training.
 
     Notes
     -----
@@ -421,7 +435,7 @@ class IndividualLITERegressor(BaseDeepRegressor):
         random_state=None,
         verbose=False,
         loss="mean_squared_error",
-        metrics=None,
+        metrics="mean_squared_error",
         optimizer=None,
     ):
         self.use_litemv = use_litemv
@@ -489,11 +503,6 @@ class IndividualLITERegressor(BaseDeepRegressor):
 
         model = tf.keras.models.Model(inputs=input_layer, outputs=output_layer)
 
-        if self.metrics is None:
-            metrics = ["mean_squared_error"]
-        else:
-            metrics = self.metrics
-
         self.optimizer_ = (
             tf.keras.optimizers.Adam() if self.optimizer is None else self.optimizer
         )
@@ -501,7 +510,7 @@ class IndividualLITERegressor(BaseDeepRegressor):
         model.compile(
             loss=self.loss,
             optimizer=self.optimizer_,
-            metrics=metrics,
+            metrics=self._metrics,
         )
 
         return model
@@ -527,6 +536,11 @@ class IndividualLITERegressor(BaseDeepRegressor):
         # Transpose to conform to Keras input style.
         X = X.transpose(0, 2, 1)
 
+        if isinstance(self.metrics, list):
+            self._metrics = self.metrics
+        elif isinstance(self.metrics, str):
+            self._metrics = [self.metrics]
+
         # ignore the number of instances, X.shape[0],
         # just want the shape of each instance
         self.input_shape = X.shape[1:]
diff --git a/aeon/regression/deep_learning/_mlp.py b/aeon/regression/deep_learning/_mlp.py
index d593eb7b..9a616238 100644
--- a/aeon/regression/deep_learning/_mlp.py
+++ b/aeon/regression/deep_learning/_mlp.py
@@ -21,16 +21,21 @@ class MLPRegressor(BaseDeepRegressor):
 
     Parameters
     ----------
+    use_bias : bool, default = True
+        Condition on whether or not to use bias values for dense layers.
     n_epochs : int, default = 2000
         the number of epochs to train the model
     batch_size : int, default = 16
         the number of samples per gradient update.
-    callbacks : callable or None, default
+    callbacks : keras callback or list of callbacks,
+        default = None
+        The default list of callbacks are set to
+        ModelCheckpoint and ReduceLROnPlateau.
     verbose : boolean, default = False
         whether to output extra information
-    loss : string, default="mean_squared_error"
-        fit parameter for the keras model
-    metrics : list of strings, default="mean_squared_error"
+    loss : str, default = "mean_squared_error"
+        The name of the keras training loss.
+    metrics : str or list[str], default="mean_squared_error"
         The evaluation metrics to use during training. If
         a single string metric is provided, it will be
         used as the only metric. If a list of metrics are
@@ -72,10 +77,9 @@ class MLPRegressor(BaseDeepRegressor):
         List of available activation functions:
         https://keras.io/api/layers/activations/
     output_activation : str = "linear"
-        Activation for the last layer in a Regressor
-    use_bias : boolean, default = True
-        whether the layer uses a bias vector.
-    optimizer : keras.optimizer, default=keras.optimizers.Adadelta()
+        Activation for the last layer in a Regressor.
+    optimizer : keras.optimizer, default = tf.keras.optimizers.Adam()
+        The keras optimizer used for training.
 
 
     References
@@ -96,11 +100,12 @@ class MLPRegressor(BaseDeepRegressor):
 
     def __init__(
         self,
+        use_bias=True,
         n_epochs=2000,
         batch_size=16,
         callbacks=None,
         verbose=False,
-        loss="mse",
+        loss="mean_squared_error",
         metrics="mean_squared_error",
         file_path="./",
         save_best_model=False,
@@ -112,7 +117,6 @@ class MLPRegressor(BaseDeepRegressor):
         random_state=None,
         activation="relu",
         output_activation="linear",
-        use_bias=True,
         optimizer=None,
     ):
         self.callbacks = callbacks
@@ -139,7 +143,7 @@ class MLPRegressor(BaseDeepRegressor):
             last_file_name=last_file_name,
         )
 
-        self._network = MLPNetwork()
+        self._network = MLPNetwork(use_bias=self.use_bias)
 
     def build_model(self, input_shape, **kwargs):
         """Construct a compiled, un-trained, keras model that is ready for training.
@@ -168,9 +172,9 @@ class MLPRegressor(BaseDeepRegressor):
 
         input_layer, output_layer = self._network.build_network(input_shape, **kwargs)
 
-        output_layer = keras.layers.Dense(
-            units=1, activation=self.output_activation, use_bias=self.use_bias
-        )(output_layer)
+        output_layer = keras.layers.Dense(units=1, activation=self.output_activation)(
+            output_layer
+        )
 
         self.optimizer_ = (
             keras.optimizers.Adadelta() if self.optimizer is None else self.optimizer
@@ -203,10 +207,11 @@ class MLPRegressor(BaseDeepRegressor):
         # Transpose to conform to Keras input style.
         X = X.transpose(0, 2, 1)
 
-        if isinstance(self.metrics, str):
-            self._metrics = [self.metrics]
-        else:
+        if isinstance(self.metrics, list):
             self._metrics = self.metrics
+        elif isinstance(self.metrics, str):
+            self._metrics = [self.metrics]
+
         self.input_shape = X.shape[1:]
 
         self.training_model_ = self.build_model(self.input_shape)
diff --git a/aeon/regression/deep_learning/_resnet.py b/aeon/regression/deep_learning/_resnet.py
index 7592d4e6..7f89a18a 100644
--- a/aeon/regression/deep_learning/_resnet.py
+++ b/aeon/regression/deep_learning/_resnet.py
@@ -24,31 +24,31 @@ class ResNetRegressor(BaseDeepRegressor):
     ----------
         n_residual_blocks : int, default = 3
             the number of residual blocks of ResNet's model
-        n_conv_per_residual_block   : int, default = 3,
+        n_conv_per_residual_block : int, default = 3,
             the number of convolution blocks in each residual block
-        n_filters                   : int or list of int, default = [128, 64, 64],
+        n_filters : int or list of int, default = [128, 64, 64],
             the number of convolution filters for all the convolution layers in the same
             residual block, if not a list, the same number of filters is used in all
             convolutions of all residual blocks.
-        kernel_sizes                : int or list of int, default = [8, 5, 3],
+        kernel_sizes : int or list of int, default = [8, 5, 3],
             the kernel size of all the convolution layers in one residual block, if not
             a list, the same kernel size is used in all convolution layers
-        strides                     : int or list of int, default = 1,
+        strides : int or list of int, default = 1,
             the strides of convolution kernels in each of the
             convolution layers in one residual block, if not
             a list, the same kernel size is used in all convolution layers
-        dilation_rate               : int or list of int, default = 1,
+        dilation_rate : int or list of int, default = 1,
             the dilation rate of the convolution layers in one residual block, if not
             a list, the same kernel size is used in all convolution layers
-        padding                     : str or list of str, default = 'padding',
+        padding : str or list of str, default = 'padding',
             the type of padding used in the convolution layers
             in one residual block, if not
             a list, the same kernel size is used in all convolution layers
-        activation                  : str or list of str, default = 'relu',
+        activation : str or list of str, default = 'relu',
             keras activation used in the convolution layers
             in one residual block, if not
             a list, the same kernel size is used in all convolution layers
-        output_activation   : str, default = "linear",
+        output_activation : str, default = "linear",
             the output activation for the regressor
         use_bias : bool or list of bool, default = True,
             condition on whether or not to use bias values in
@@ -60,9 +60,10 @@ class ResNetRegressor(BaseDeepRegressor):
             the number of samples per gradient update.
         use_mini_batch_size : bool, default = False
             condition on using the mini batch size formula Wang et al.
-        callbacks : callable or None, default
-        ReduceOnPlateau and ModelCheckpoint
-            list of tf.keras.callbacks.Callback objects.
+        callbacks : keras callback or list of callbacks,
+            default = None
+            The default list of callbacks are set to
+            ModelCheckpoint and ReduceLROnPlateau.
         random_state : int, RandomState instance or None, default=None
             If `int`, random_state is the seed used by the random number generator;
             If `RandomState` instance, random_state is the random number generator;
@@ -70,37 +71,38 @@ class ResNetRegressor(BaseDeepRegressor):
             by `np.random`.
             Seeded random number generation can only be guaranteed on CPU processing,
             GPU processing will be non-deterministic.
-        file_path                   : str, default = './'
+        file_path : str, default = './'
             file_path when saving model_Checkpoint callback
-        save_best_model     : bool, default = False
+        save_best_model : bool, default = False
             Whether or not to save the best model, if the
             modelcheckpoint callback is used by default,
             this condition, if True, will prevent the
             automatic deletion of the best saved model from
             file and the user can choose the file name
-        save_last_model     : bool, default = False
+        save_last_model : bool, default = False
             Whether or not to save the last model, last
             epoch trained, using the base class method
             save_last_model_to_file
         save_init_model : bool, default = False
             Whether to save the initialization of the  model.
-        best_file_name      : str, default = "best_model"
+        best_file_name : str, default = "best_model"
             The name of the file of the best model, if
             save_best_model is set to False, this parameter
             is discarded
-        last_file_name      : str, default = "last_model"
+        last_file_name : str, default = "last_model"
             The name of the file of the last model, if
             save_last_model is set to False, this parameter
             is discarded
         init_file_name : str, default = "init_model"
             The name of the file of the init model, if save_init_model is set to False,
             this parameter is discarded.
-        verbose                     : boolean, default = False
+        verbose : boolean, default = False
             whether to output extra information
-        loss                        : string, default="mean_squared_error"
-            fit parameter for the keras model
-        optimizer                   : keras.optimizer, default=keras.optimizers.Adam(),
-        metrics                     : list of strings, default="mean_squared_error",
+        loss : str, default = "mean_squared_error"
+            The name of the keras training loss.
+        optimizer : keras.optimizer, default = tf.keras.optimizers.Adam()
+            The keras optimizer used for training.
+        metrics : str or list[str], default="mean_squared_error"
             The evaluation metrics to use during training. If
             a single string metric is provided, it will be
             used as the only metric. If a list of metrics are
@@ -143,7 +145,7 @@ class ResNetRegressor(BaseDeepRegressor):
         n_epochs=1500,
         callbacks=None,
         verbose=False,
-        loss="mse",
+        loss="mean_squared_error",
         output_activation="linear",
         metrics="mean_squared_error",
         batch_size=64,
@@ -263,10 +265,11 @@ class ResNetRegressor(BaseDeepRegressor):
         # Transpose to conform to Keras input style.
         X = X.transpose(0, 2, 1)
 
-        if isinstance(self.metrics, str):
-            self._metrics = [self.metrics]
-        else:
+        if isinstance(self.metrics, list):
             self._metrics = self.metrics
+        elif isinstance(self.metrics, str):
+            self._metrics = [self.metrics]
+
         self.input_shape = X.shape[1:]
         self.training_model_ = self.build_model(self.input_shape)
 
diff --git a/docs/api_reference/classification.rst b/docs/api_reference/classification.rst
index 9f5482fe..55865112 100644
--- a/docs/api_reference/classification.rst
+++ b/docs/api_reference/classification.rst
@@ -44,6 +44,7 @@ Deep learning
     LITETimeClassifier
     MLPClassifier
     ResNetClassifier
+    DisjointCNNClassifier
     TapNetClassifier
 
 Dictionary-based
diff --git a/docs/api_reference/networks.rst b/docs/api_reference/networks.rst
index 66741bcb..63ff3b6a 100644
--- a/docs/api_reference/networks.rst
+++ b/docs/api_reference/networks.rst
@@ -25,3 +25,4 @@ Deep learning networks
     AEResNetNetwork
     LITENetwork
     AEBiGRUNetwork
+    DisjointCNNNetwork
diff --git a/docs/api_reference/regression.rst b/docs/api_reference/regression.rst
index 0f4b6823..4c211d9e 100644
--- a/docs/api_reference/regression.rst
+++ b/docs/api_reference/regression.rst
@@ -64,6 +64,7 @@ Deep learning
     ResNetRegressor
     TapNetRegressor
     MLPRegressor
+    DisjointCNNRegressor
 
 Distance-based
 --------------


Test patch:
diff --git a/aeon/networks/tests/test_all_networks.py b/aeon/networks/tests/test_all_networks.py
index 106a5b8b..eeb8c5d6 100644
--- a/aeon/networks/tests/test_all_networks.py
+++ b/aeon/networks/tests/test_all_networks.py
@@ -115,6 +115,9 @@ def test_all_networks_params(network):
         if network.__name__ == "LITENetwork":
             continue
 
+        if network.__name__ == "MLPNetwork":
+            continue
+
         # Here we use 'None' string as default to differentiate with None values
         attr = getattr(my_network, attrname, "None")
         if attr != "None":
diff --git a/aeon/networks/tests/test_disjoint_cnn.py b/aeon/networks/tests/test_disjoint_cnn.py
new file mode 100644
index 00000000..c40eb4be
--- /dev/null
+++ b/aeon/networks/tests/test_disjoint_cnn.py
@@ -0,0 +1,22 @@
+"""Tests for the DisjointCNN Network."""
+
+import pytest
+
+from aeon.networks import DisjointCNNNetwork
+from aeon.utils.validation._dependencies import _check_soft_dependencies
+
+
+@pytest.mark.skipif(
+    not _check_soft_dependencies(["tensorflow"], severity="none"),
+    reason="Tensorflow soft dependency unavailable.",
+)
+def test_disjoint_cnn_netowkr_kernel_initializer():
+    """Test DisjointCNN for different kernel_initializer per layer."""
+    input_layer, output_layer = DisjointCNNNetwork(
+        n_layers=2,
+        kernel_initializer=["he_uniform", "glorot_uniform"],
+        kernel_size=[2, 2],
+    ).build_network(input_shape=((10, 2)))
+
+    assert len(output_layer.shape) == 2
+    assert len(input_layer.shape) == 3