diff --git a/aeon/clustering/deep_learning/tests/__init__.py b/aeon/clustering/deep_learning/tests/__init__.py
deleted file mode 100644
index 8087b8988f..0000000000
--- a/aeon/clustering/deep_learning/tests/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-"""Deep learning clustering tests."""
diff --git a/aeon/clustering/deep_learning/tests/test_deep_clusterer_base.py b/aeon/clustering/deep_learning/tests/test_deep_clusterer_base.py
deleted file mode 100644
index 9e65866532..0000000000
--- a/aeon/clustering/deep_learning/tests/test_deep_clusterer_base.py
+++ /dev/null
@@ -1,43 +0,0 @@
-"""Unit tests for clusterer deep learning base class functionality."""
-
-import tempfile
-
-import numpy as np
-import pytest
-
-from aeon.clustering.dummy import DummyClusterer
-from aeon.testing.data_generation import make_example_2d_numpy_collection
-from aeon.testing.mock_estimators import MockDeepClusterer
-from aeon.utils.validation._dependencies import _check_soft_dependencies
-
-__maintainer__ = ["hadifawaz1999"]
-
-
-@pytest.mark.skipif(
-    not _check_soft_dependencies("tensorflow", severity="none"),
-    reason="skip test if required soft dependency not available",
-)
-@pytest.mark.parametrize("estimator", [None, DummyClusterer(n_clusters=2)])
-def test_base_deep_clusterer(estimator):
-    """Test base deep clusterer."""
-    with tempfile.TemporaryDirectory() as tmp:
-        last_file_name = "temp"
-        # create a dummy deep classifier
-        dummy_deep_clr = MockDeepClusterer(
-            estimator=estimator, last_file_name=last_file_name
-        )
-        # generate random data
-        X, y = make_example_2d_numpy_collection()
-        # test fit function on random data
-        dummy_deep_clr.fit(X=X)
-        # test save last model to file than delete it
-        dummy_deep_clr.save_last_model_to_file(file_path=tmp)
-
-        # test summary of model
-        assert dummy_deep_clr.summary() is not None
-        ypred = dummy_deep_clr.predict(X)
-        assert ypred is not None
-        assert len(ypred) == len(y)
-        ypred_proba = dummy_deep_clr.predict_proba(X)
-        assert ypred_proba is not None
-        assert len(ypred_proba[0]) == len(np.unique(y))
diff --git a/aeon/clustering/deep_learning/tests/test_deep_clusterer_features.py b/aeon/clustering/deep_learning/tests/test_deep_clusterer_features.py
deleted file mode 100644
index c2ad50b280..0000000000
--- a/aeon/clustering/deep_learning/tests/test_deep_clusterer_features.py
+++ /dev/null
@@ -1,25 +0,0 @@
-"""Tests whether various clusterer params work well."""
-
-import numpy as np
-import pytest
-
-from aeon.clustering.deep_learning import AEFCNClusterer, AEResNetClusterer
-from aeon.utils.validation._dependencies import _check_soft_dependencies
-
-
-@pytest.mark.skipif(
-    not _check_soft_dependencies(["tensorflow"], severity="none"),
-    reason="Tensorflow soft dependency not found.",
-)
-def test_multi_rec_fcn():
-    """Tests whether multi-rec loss works fine or not."""
-    X = np.random.random((100, 5, 2))
-    clst = AEFCNClusterer(**AEFCNClusterer._get_test_params()[0], loss="multi_rec")
-    clst.fit(X)
-    assert isinstance(clst.history["loss"][-1], float)
-
-    clst = AEResNetClusterer(
-        **AEResNetClusterer._get_test_params()[0], loss="multi_rec"
-    )
-    clst.fit(X)
-    assert isinstance(clst.history["loss"][-1], float)