diff --git a/.github/dependabot.yml b/.github/dependabot.yml
new file mode 100644
index 0000000..1d48bc6
--- /dev/null
+++ b/.github/dependabot.yml
@@ -0,0 +1,33 @@
+# Checked monthly, with at most one grouped pull request per ecosystem.
+# For now only github-actions opens any: see the comment on the pip entry.
+version: 2
+updates:
+  - package-ecosystem: github-actions
+    directory: /
+    schedule:
+      interval: monthly
+    commit-message:
+      prefix: ci
+    groups:
+      github-actions:
+        patterns: ["*"]
+
+  # pyproject.toml and docs/requirements.txt only set lower bounds, and there is
+  # no lockfile. increase-if-necessary leaves a requirement unchanged while it
+  # allows the new release, and a lower bound allows every release, so this
+  # entry opens no version-update pull requests for now. It takes effect once a
+  # pin or an upper bound is added, and such a pull request may raise the lower
+  # bound too: tensorflow>=2.15 must stay, because TensorFlow 2.15 is how
+  # Keras 2 users run this package.
+  - package-ecosystem: pip
+    directories:
+      - /
+      - /docs
+    schedule:
+      interval: monthly
+    versioning-strategy: increase-if-necessary
+    commit-message:
+      prefix: build
+    groups:
+      python:
+        patterns: ["*"]
diff --git a/.github/labeler.yml b/.github/labeler.yml
new file mode 100644
index 0000000..aa9b60f
--- /dev/null
+++ b/.github/labeler.yml
@@ -0,0 +1,63 @@
+Tests:
+- changed-files:
+  - any-glob-to-any-file: [ "tests/**/*" ]
+
+Dependencies:
+- changed-files:
+  - any-glob-to-any-file:
+    - "requirements.txt"
+    - "setup.py"
+    - "pyproject.toml"
+    - "poetry.lock"
+    - "pixi.toml"
+    - "pixi.lock"
+    - "Pipfile"
+    - "Pipfile.lock"
+    - "requirements/*.txt"
+    - "requirements/*.in"
+
+Build:
+- changed-files:
+  - any-glob-to-any-file:
+    - "Dockerfile*"
+    - "docker-compose*.yml"
+    - "Makefile"
+
+Documentation:
+- changed-files:
+  - any-glob-to-any-file:
+    - "docs/**/*"
+    - "*.md"
+    - "*.rst"
+
+Config:
+- changed-files:
+  - any-glob-to-any-file:
+    - ".pre-commit-config.yaml"
+    - "config/**/*"
+    - "settings/**/*"
+    - "*.ini"
+    - "*.cfg"
+    - "*.conf"
+
+CI/CD:
+- changed-files:
+  - any-glob-to-any-file:
+    - ".github/**/*"
+    - "tox.ini"
+    - ".coveragerc"
+
+# Add 'feature' label to any PR where the head branch name starts with `feature` or has a `feature` section in the name
+feature:
+- head-branch:
+  - '^feature'
+  - 'feature'
+
+# Add 'bugfix' label to branches with bug/fix/hotfix prefixes
+bugfix:
+- head-branch: [ '^bug', '^fix', '^hotfix' ]
+
+# Add 'breaking' label for major version bumps or breaking change commits
+breaking:
+- title: [ '^BREAKING CHANGE', 'BREAKING-CHANGE' ]
+- body: [ 'BREAKING CHANGE:', 'BREAKING-CHANGE:' ]
diff --git a/.github/workflows/labeler.yml b/.github/workflows/labeler.yml
new file mode 100644
index 0000000..bf2103b
--- /dev/null
+++ b/.github/workflows/labeler.yml
@@ -0,0 +1,12 @@
+name: "Pull Request Labeler"
+on:
+- pull_request_target
+
+jobs:
+  labeler:
+    permissions:
+      contents: read
+      pull-requests: write
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/labeler@v7.0.0
diff --git a/.github/workflows/test_python.yml b/.github/workflows/test_python.yml
index bc793c3..ac305d4 100644
--- a/.github/workflows/test_python.yml
+++ b/.github/workflows/test_python.yml
@@ -13,8 +13,18 @@ jobs:
       - uses: astral-sh/setup-uv@v10.3.0
         with:
           python-version: "3.12"
-      # Stop on syntax errors and undefined names only.
-      - run: uvx flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+      # Run the ruff release that .pre-commit-config.yaml pins, so this job and the
+      # pre-commit hooks agree when pre-commit.ci updates the pin.
+      - name: Find the ruff version
+        id: ruff
+        run: |
+          version=$(grep -A1 "astral-sh/ruff-pre-commit" .pre-commit-config.yaml | sed -n "s/^ *rev: *v//p")
+          test -n "$version"
+          echo "version=$version" >> "$GITHUB_OUTPUT"
+      - name: ruff check
+        run: uvx "ruff@${{ steps.ruff.outputs.version }}" check --output-format=github .
+      - name: ruff format
+        run: uvx "ruff@${{ steps.ruff.outputs.version }}" format --check --diff .
 
   # 0.2.3 shipped invalid metadata that pip 24.1 and later refuse to install.
   # Build the package and install it the way users do before any release.
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 1bc23a9..989a0ac 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -1,4 +1,5 @@
-# Read-only checks for now; formatting and linting hooks come with the tooling update.
+# Read-only checks first, then ruff lints (fixing what it safely can) and formats
+# the Python files with the settings in pyproject.toml.
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
     rev: v6.0.0
@@ -7,3 +8,12 @@ repos:
       - id: check-toml
       - id: check-merge-conflict
       - id: check-added-large-files
+  # The lint job in .github/workflows/test_python.yml reads this rev, so keep it
+  # on the line directly below the repo line.
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.17.0
+    hooks:
+      # Lint before formatting, as ruff recommends when fixes are on.
+      - id: ruff-check
+        args: [--fix]
+      - id: ruff-format
diff --git a/README.md b/README.md
index 8eecc9b..df4ce87 100644
--- a/README.md
+++ b/README.md
@@ -83,7 +83,9 @@ To turn a complex NumPy array into this format:
 ```python
 import numpy as np
 
-z = np.random.randn(8, 3, 3, 2) + 1j * np.random.randn(8, 3, 3, 2)  # two complex channels
+z = np.random.randn(8, 3, 3, 2) + 1j * np.random.randn(
+    8, 3, 3, 2
+)  # two complex channels
 x = np.concatenate([z.real, z.imag], axis=-1)  # shape (8, 3, 3, 4)
 ```
 
diff --git a/complexnn/__init__.py b/complexnn/__init__.py
index 4d6c1b1..9f398da 100644
--- a/complexnn/__init__.py
+++ b/complexnn/__init__.py
@@ -1,9 +1,6 @@
 #!/usr/bin/env python
-# -*- coding: utf-8 -*-
 
-from . import bn, conv, dense, init, norm, pool
-
-# from . import fft
+from . import bn, conv, conv_utils, dense, fft, init, norm, pool, utils
 from .bn import ComplexBatchNormalization as ComplexBN
 from .conv import (
     ComplexConv,
@@ -31,3 +28,36 @@
     get_realpart,
     getpart_output_shape,
 )
+
+__all__ = [
+    "bn",
+    "conv",
+    "conv_utils",
+    "dense",
+    "fft",
+    "init",
+    "norm",
+    "pool",
+    "utils",
+    "ComplexBN",
+    "ComplexConv",
+    "ComplexConv1D",
+    "ComplexConv2D",
+    "ComplexConv3D",
+    "WeightNorm_Conv",
+    "ComplexDense",
+    "ComplexIndependentFilters",
+    "ComplexInit",
+    "IndependentFilters",
+    "SqrtInit",
+    "ComplexLayerNorm",
+    "LayerNormalization",
+    "SpectralPooling1D",
+    "SpectralPooling2D",
+    "GetAbs",
+    "GetImag",
+    "GetReal",
+    "get_imagpart",
+    "get_realpart",
+    "getpart_output_shape",
+]
diff --git a/complexnn/bn.py b/complexnn/bn.py
index 0682517..713846b 100644
--- a/complexnn/bn.py
+++ b/complexnn/bn.py
@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# -*- coding: utf-8 -*-
 
 # Note: The implementation of complex Batchnorm is based on
 #       the Keras implementation of batch Normalization
@@ -12,11 +11,7 @@
 from tensorflow.keras.layers import InputSpec, Layer
 from tensorflow.keras.utils import register_keras_serializable
 
-from .utils import get_initializer
-
-from .utils import _ignore_input_shape_warning
-
-from .utils import _check_complex_axis
+from .utils import _check_complex_axis, _ignore_input_shape_warning, get_initializer
 
 
 def sqrt_init(shape, dtype=None):
@@ -336,7 +331,7 @@ def __init__(
         **kwargs,
     ):
         with _ignore_input_shape_warning(kwargs):
-            super(ComplexBatchNormalization, self).__init__(**kwargs)
+            super().__init__(**kwargs)
         self.supports_masking = True
         self.axis = axis
         self.momentum = momentum
@@ -359,8 +354,6 @@ def __init__(
         self.gamma_off_constraint = constraints.get(gamma_off_constraint)
 
     def build(self, input_shape):
-        ndim = len(input_shape)
-
         dim = input_shape[self.axis]
         if dim is None:
             raise ValueError(
@@ -583,5 +576,5 @@ def get_config(self):
             "gamma_diag_constraint": constraints.serialize(self.gamma_diag_constraint),
             "gamma_off_constraint": constraints.serialize(self.gamma_off_constraint),
         }
-        base_config = super(ComplexBatchNormalization, self).get_config()
+        base_config = super().get_config()
         return dict(list(base_config.items()) + list(config.items()))
diff --git a/complexnn/conv.py b/complexnn/conv.py
index 90d2dbf..a0598cd 100644
--- a/complexnn/conv.py
+++ b/complexnn/conv.py
@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# -*- coding: utf-8 -*-
 
 import numpy as np
 import tensorflow as tf
@@ -155,7 +154,7 @@ def conv_transpose_output_length(
 ):
     """Rearrange arguments for compatibility with conv_output_length."""
     if dilation != 1:
-        msg = f"Dilation must be 1 for transposed convolution. "
+        msg = "Dilation must be 1 for transposed convolution. "
         msg += f"Got dilation = {dilation}"
         raise ValueError(msg)
     # return conv_utils.deconv_length(
@@ -298,7 +297,7 @@ def __init__(
         **kwargs,
     ):
         with _ignore_input_shape_warning(kwargs):
-            super(ComplexConv, self).__init__(**kwargs)
+            super().__init__(**kwargs)
         self.rank = rank
         self.filters = filters
         self.kernel_size = conv_utils.normalize_tuple(kernel_size, rank, "kernel_size")
@@ -647,7 +646,7 @@ def get_config(self):
             "transposed": self.transposed,
             "epsilon": self.epsilon,
         }
-        base_config = super(ComplexConv, self).get_config()
+        base_config = super().get_config()
         return dict(list(base_config.items()) + list(config.items()))
 
 
@@ -760,7 +759,7 @@ def __init__(
         transposed=False,
         **kwargs,
     ):
-        super(ComplexConv1D, self).__init__(
+        super().__init__(
             rank=1,
             filters=filters,
             kernel_size=kernel_size,
@@ -784,7 +783,7 @@ def __init__(
         )
 
     def get_config(self):
-        config = super(ComplexConv1D, self).get_config()
+        config = super().get_config()
         config.pop("rank")
         # __init__ always passes data_format="channels_last" to ComplexConv.
         config.pop("data_format")
@@ -916,7 +915,7 @@ def __init__(
         transposed=False,
         **kwargs,
     ):
-        super(ComplexConv2D, self).__init__(
+        super().__init__(
             rank=2,
             filters=filters,
             kernel_size=kernel_size,
@@ -940,7 +939,7 @@ def __init__(
         )
 
     def get_config(self):
-        config = super(ComplexConv2D, self).get_config()
+        config = super().get_config()
         config.pop("rank")
         return config
 
@@ -1073,7 +1072,7 @@ def __init__(
         transposed=False,
         **kwargs,
     ):
-        super(ComplexConv3D, self).__init__(
+        super().__init__(
             rank=3,
             filters=filters,
             kernel_size=kernel_size,
@@ -1097,7 +1096,7 @@ def __init__(
         )
 
     def get_config(self):
-        config = super(ComplexConv3D, self).get_config()
+        config = super().get_config()
         config.pop("rank")
         return config
 
@@ -1122,7 +1121,7 @@ def __init__(
         epsilon=1e-07,
         **kwargs,
     ):
-        super(WeightNorm_Conv, self).__init__(**kwargs)
+        super().__init__(**kwargs)
         if self.rank == 1:
             self.data_format = "channels_last"
         self.gamma_initializer = sanitizedInitGet(gamma_initializer)
@@ -1132,7 +1131,7 @@ def __init__(
         self.gamma = None
 
     def build(self, input_shape):
-        super(WeightNorm_Conv, self).build(input_shape)
+        super().build(input_shape)
         if self.data_format == "channels_first":
             channel_axis = 1
         else:
@@ -1201,7 +1200,7 @@ def get_config(self):
             "gamma_constraint": constraints.serialize(self.gamma_constraint),
             "epsilon": self.epsilon,
         }
-        base_config = super(WeightNorm_Conv, self).get_config()
+        base_config = super().get_config()
         return dict(list(base_config.items()) + list(config.items()))
 
 
diff --git a/complexnn/conv_utils.py b/complexnn/conv_utils.py
index d5e2c36..9094b69 100644
--- a/complexnn/conv_utils.py
+++ b/complexnn/conv_utils.py
@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# -*- coding: utf-8 -*-
 
 # Copyright 2015 The TensorFlow Authors. All Rights Reserved.
 #
diff --git a/complexnn/dense.py b/complexnn/dense.py
index 1753037..ee923a9 100644
--- a/complexnn/dense.py
+++ b/complexnn/dense.py
@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# -*- coding: utf-8 -*-
 
 import numpy as np
 from numpy.random import RandomState
@@ -87,7 +86,7 @@ def __init__(
         if "input_shape" not in kwargs and "input_dim" in kwargs:
             kwargs["input_shape"] = (kwargs.pop("input_dim"),)
         with _ignore_input_shape_warning(kwargs):
-            super(ComplexDense, self).__init__(**kwargs)
+            super().__init__(**kwargs)
         self.units = units
         self.activation = activations.get(activation)
         self.use_bias = use_bias
@@ -180,11 +179,6 @@ def init_w_imag(shape, dtype=None):
         self.built = True
 
     def call(self, inputs):
-        input_shape = K.shape(inputs)
-        input_dim = input_shape[-1] // 2
-        real_input = inputs[:, :input_dim]
-        imag_input = inputs[:, input_dim:]
-
         cat_kernels_4_real = K.concatenate(
             [self.real_kernel, -self.imag_kernel], axis=-1
         )
@@ -230,5 +224,5 @@ def get_config(self):
             "bias_constraint": constraints.serialize(self.bias_constraint),
             "seed": self.seed,
         }
-        base_config = super(ComplexDense, self).get_config()
+        base_config = super().get_config()
         return dict(list(base_config.items()) + list(config.items()))
diff --git a/complexnn/fft.py b/complexnn/fft.py
index fe6638e..b42ffc9 100644
--- a/complexnn/fft.py
+++ b/complexnn/fft.py
@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# -*- coding: utf-8 -*-
 
 # import tensorflow.keras.engine as KE
 import numpy as np
diff --git a/complexnn/init.py b/complexnn/init.py
index fb25351..fca8436 100644
--- a/complexnn/init.py
+++ b/complexnn/init.py
@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# -*- coding: utf-8 -*-
 
 import numpy as np
 import tensorflow.keras.backend as K
diff --git a/complexnn/norm.py b/complexnn/norm.py
index b5d3576..1ebb11a 100644
--- a/complexnn/norm.py
+++ b/complexnn/norm.py
@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# -*- coding: utf-8 -*-
 
 # Implementation of Layer Normalization and Complex Layer Normalization
 
@@ -67,7 +66,7 @@ def __init__(
         self.beta_regularizer = regularizers.get(beta_regularizer)
 
         with _ignore_input_shape_warning(kwargs):
-            super(LayerNormalization, self).__init__(**kwargs)
+            super().__init__(**kwargs)
 
     def build(self, input_shape):
         self.input_spec = InputSpec(
@@ -79,13 +78,13 @@ def build(self, input_shape):
             shape=shape,
             initializer=self.gamma_init,
             regularizer=self.gamma_regularizer,
-            name="{}_gamma".format(self.name),
+            name=f"{self.name}_gamma",
         )
         self.beta = self.add_weight(
             shape=shape,
             initializer=self.beta_init,
             regularizer=self.beta_regularizer,
-            name="{}_beta".format(self.name),
+            name=f"{self.name}_beta",
         )
 
         self.built = True
@@ -103,7 +102,7 @@ def get_config(self):
             "gamma_regularizer": regularizers.serialize(self.gamma_regularizer),
             "beta_regularizer": regularizers.serialize(self.beta_regularizer),
         }
-        base_config = super(LayerNormalization, self).get_config()
+        base_config = super().get_config()
         return dict(list(base_config.items()) + list(config.items()))
 
 
@@ -143,10 +142,9 @@ def __init__(
         self.gamma_diag_constraint = constraints.get(gamma_diag_constraint)
         self.gamma_off_constraint = constraints.get(gamma_off_constraint)
         with _ignore_input_shape_warning(kwargs):
-            super(ComplexLayerNorm, self).__init__(**kwargs)
+            super().__init__(**kwargs)
 
     def build(self, input_shape):
-        ndim = len(input_shape)
         dim = input_shape[self.axis]
         if dim is None:
             raise ValueError(
@@ -301,5 +299,5 @@ def get_config(self):
             "gamma_diag_constraint": constraints.serialize(self.gamma_diag_constraint),
             "gamma_off_constraint": constraints.serialize(self.gamma_off_constraint),
         }
-        base_config = super(ComplexLayerNorm, self).get_config()
+        base_config = super().get_config()
         return dict(list(base_config.items()) + list(config.items()))
diff --git a/complexnn/pool.py b/complexnn/pool.py
index 0e638d6..198ce76 100644
--- a/complexnn/pool.py
+++ b/complexnn/pool.py
@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# -*- coding: utf-8 -*-
 
 import numpy as np
 import tensorflow.keras as KE
@@ -15,7 +14,7 @@
 @register_keras_serializable(package="complexnn")
 class SpectralPooling1D(KL.Layer):
     def __init__(self, topf=(0,), **kwargs):
-        super(SpectralPooling1D, self).__init__()
+        super().__init__()
         if "topf" in kwargs:
             self.topf = (int(kwargs["topf"][0]),)
             self.topf = (self.topf[0] // 2,)
@@ -60,7 +59,7 @@ def call(self, x, mask=None):
 @register_keras_serializable(package="complexnn")
 class SpectralPooling2D(KL.Layer):
     def __init__(self, **kwargs):
-        super(SpectralPooling2D, self).__init__()
+        super().__init__()
         if "topf" in kwargs:
             self.topf = (int(kwargs["topf"][0]), int(kwargs["topf"][1]))
             self.topf = (self.topf[0] // 2, self.topf[1] // 2)
diff --git a/complexnn/utils.py b/complexnn/utils.py
index f0cd07b..2a8027e 100644
--- a/complexnn/utils.py
+++ b/complexnn/utils.py
@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# -*- coding: utf-8 -*-
 
 import contextlib
 import re
@@ -236,7 +235,7 @@ class _ComplexPart(Layer):
 
     def build(self, input_shape):
         _check_part_shape(self, input_shape)
-        super(_ComplexPart, self).build(input_shape)
+        super().build(input_shape)
 
     def call(self, inputs):
         _check_part_shape(self, inputs.shape)
diff --git a/pyproject.toml b/pyproject.toml
index c55850e..5ce7d95 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -49,3 +49,24 @@ bugtracker = "https://github.com/JesperDramsch/keras-complex/issues"
 
 [tool.setuptools.packages.find]
 exclude = ["tests*", "docs*"]
+
+# ruff takes its target Python version from requires-python above.
+[tool.ruff]
+line-length = 88
+
+[tool.ruff.lint]
+# List the rules explicitly: newer ruff releases enable a much larger default set,
+# and the result should not change when ruff is updated.
+select = [
+    "E",  # pycodestyle errors
+    "W",  # pycodestyle warnings
+    "F",  # Pyflakes
+    "I",  # isort
+    "UP", # pyupgrade
+    "B",  # flake8-bugbear
+]
+ignore = [
+    # ruff format already wraps code at 88 columns. The lines it leaves long are
+    # prose in strings and comments, which read better unbroken.
+    "E501",
+]
diff --git a/tests/test_api.py b/tests/test_api.py
new file mode 100644
index 0000000..4528b30
--- /dev/null
+++ b/tests/test_api.py
@@ -0,0 +1,57 @@
+import unittest
+
+import complexnn
+
+# Every public name that `import complexnn` provides, submodules included.
+PUBLIC_NAMES = {
+    "bn",
+    "conv",
+    "conv_utils",
+    "dense",
+    "fft",
+    "init",
+    "norm",
+    "pool",
+    "utils",
+    "ComplexBN",
+    "ComplexConv",
+    "ComplexConv1D",
+    "ComplexConv2D",
+    "ComplexConv3D",
+    "WeightNorm_Conv",
+    "ComplexDense",
+    "ComplexIndependentFilters",
+    "ComplexInit",
+    "IndependentFilters",
+    "SqrtInit",
+    "ComplexLayerNorm",
+    "LayerNormalization",
+    "SpectralPooling1D",
+    "SpectralPooling2D",
+    "GetAbs",
+    "GetImag",
+    "GetReal",
+    "get_imagpart",
+    "get_realpart",
+    "getpart_output_shape",
+}
+
+
+class TestPublicNamespace(unittest.TestCase):
+    """The names the complexnn package exports"""
+
+    def test_public_attributes(self):
+        """The package has exactly the public attributes listed above"""
+        public = {name for name in dir(complexnn) if not name.startswith("_")}
+        self.assertEqual(public, PUBLIC_NAMES)
+
+    def test_star_import(self):
+        """`from complexnn import *` provides exactly the public names"""
+        namespace = {}
+        exec("from complexnn import *", namespace)
+        del namespace["__builtins__"]
+        self.assertEqual(set(namespace), PUBLIC_NAMES)
+
+    def test_all(self):
+        """__all__ lists each public name once"""
+        self.assertCountEqual(complexnn.__all__, PUBLIC_NAMES)
diff --git a/tests/test_bn.py b/tests/test_bn.py
index 67acb9f..2763202 100644
--- a/tests/test_bn.py
+++ b/tests/test_bn.py
@@ -1,9 +1,10 @@
 import unittest
 
-import complexnn as conn
 import numpy as np
 import tensorflow as tf
 
+import complexnn as conn
+
 
 class TestComplexBatchNormalization(unittest.TestCase):
     """Moving statistics and train/inference behaviour of ComplexBatchNormalization"""
@@ -58,3 +59,12 @@ def test_compute_output_shape(self):
         for shape in [(None, 8), (None, 4, 4, 8)]:
             with self.subTest(shape=shape):
                 self.assertEqual(tuple(layer.compute_output_shape(shape)), shape)
+
+
+class TestComplexBatchNormalizationBuild(unittest.TestCase):
+    """Input checks in ComplexBatchNormalization.build"""
+
+    def test_build_rejects_unknown_rank(self):
+        """Building for an input of unknown rank raises ValueError"""
+        with self.assertRaises(ValueError):
+            conn.ComplexBN().build(tf.TensorShape(None))
diff --git a/tests/test_conv.py b/tests/test_conv.py
index 108998d..85d945a 100644
--- a/tests/test_conv.py
+++ b/tests/test_conv.py
@@ -1,9 +1,8 @@
 import unittest
 
 import numpy as np
-import tensorflow as tf
-from tensorflow.keras.layers import Dense, Input, MaxPooling2D
-from tensorflow.keras.models import Model, Sequential
+from tensorflow.keras.layers import Input
+from tensorflow.keras.models import Model
 
 import complexnn as conn
 
@@ -57,6 +56,16 @@ def test_conv2Dtranspose(self):
         calc = model.output_shape
         self.assertEqual(true, calc)
 
+    def test_spectral_parametrization_not_implemented(self):
+        """spectral_parametrization=True raises NotImplementedError when called"""
+        for layer, shape in (
+            (conn.ComplexConv1D(2, 3, spectral_parametrization=True), (2, 8, 4)),
+            (conn.ComplexConv2D(2, 3, spectral_parametrization=True), (2, 8, 8, 4)),
+        ):
+            with self.subTest(layer=type(layer).__name__):
+                with self.assertRaises(NotImplementedError):
+                    layer(np.zeros(shape, "float32"))
+
 
 class TestConvConfig(unittest.TestCase):
     """Conv layer configs rebuild the same layer"""
diff --git a/tests/test_dense.py b/tests/test_dense.py
index 98721a9..13de554 100644
--- a/tests/test_dense.py
+++ b/tests/test_dense.py
@@ -2,8 +2,6 @@
 
 import numpy as np
 import tensorflow as tf
-from tensorflow.keras.layers import Dense, Input, MaxPooling2D
-from tensorflow.keras.models import Model, Sequential
 
 import complexnn as conn
 
@@ -19,6 +17,18 @@ def test_outputs_dense(self):
         calc = layer.compute_output_shape(input_shape)
         self.assertEqual(true, calc)
 
+    def test_call_multiplies_by_conjugate_kernel(self):
+        """The output is the complex product of the input and the conjugate kernel"""
+        # ComplexConv multiplies by its kernel, ComplexDense by the conjugate of
+        # its kernel. This test records the current behaviour.
+        rng = np.random.default_rng(0)
+        x = rng.standard_normal((4, 6)).astype("float32")  # 3 real, then 3 imaginary
+        layer = conn.ComplexDense(units=2, use_bias=False)
+        y = layer(x).numpy()
+        kernel = layer.real_kernel.numpy() + 1j * layer.imag_kernel.numpy()
+        expected = (x[:, :3] + 1j * x[:, 3:]) @ np.conj(kernel)
+        np.testing.assert_allclose(y[:, :2] + 1j * y[:, 2:], expected, atol=1e-5)
+
 
 class TestDenseConfig(unittest.TestCase):
     """ComplexDense configs rebuild the same kernel initialiser"""
diff --git a/tests/test_init.py b/tests/test_init.py
index 0695ab5..97ad44c 100644
--- a/tests/test_init.py
+++ b/tests/test_init.py
@@ -1,8 +1,9 @@
 import unittest
 
-import complexnn as conn
 import numpy as np
 
+import complexnn as conn
+
 
 def make_initializers():
     """One of each complex initialiser, each filling a (4, 6) dense kernel"""
diff --git a/tests/test_norm.py b/tests/test_norm.py
index 41544fa..e5cf01e 100644
--- a/tests/test_norm.py
+++ b/tests/test_norm.py
@@ -1,7 +1,8 @@
 import unittest
 
-import complexnn as conn
 import tensorflow as tf
+
+import complexnn as conn
 from complexnn.bn import sqrt_init
 
 
@@ -53,3 +54,12 @@ def test_compute_output_shape(self):
         for shape in [(None, 8), (None, 4, 4, 8)]:
             with self.subTest(shape=shape):
                 self.assertEqual(tuple(layer.compute_output_shape(shape)), shape)
+
+
+class TestComplexLayerNormBuild(unittest.TestCase):
+    """Input checks in ComplexLayerNorm.build"""
+
+    def test_build_rejects_unknown_rank(self):
+        """Building for an input of unknown rank raises ValueError"""
+        with self.assertRaises(ValueError):
+            conn.ComplexLayerNorm().build(tf.TensorShape(None))
diff --git a/tests/test_readme.py b/tests/test_readme.py
index 9f436cd..51c3c98 100644
--- a/tests/test_readme.py
+++ b/tests/test_readme.py
@@ -1,9 +1,7 @@
 import unittest
 
-import numpy as np
 import tensorflow as tf
-from tensorflow.keras.layers import Dense, Input, MaxPooling2D
-from tensorflow.keras.models import Model, Sequential
+from tensorflow.keras.layers import MaxPooling2D
 
 import complexnn as conn
 
diff --git a/tests/test_saving.py b/tests/test_saving.py
index 627c6f3..6dabb9e 100644
--- a/tests/test_saving.py
+++ b/tests/test_saving.py
@@ -5,11 +5,12 @@
 import unittest
 import warnings
 
-import complexnn as conn
 import numpy as np
 import tensorflow as tf
 from tensorflow.keras.utils import get_registered_name, get_registered_object
 
+import complexnn as conn
+
 # Every working layer, with an input shape it accepts. Each entry builds a new
 # layer, so tests never share an instance. Layers that take arguments set some
 # of them away from their defaults: a value that from_config does not restore
diff --git a/tests/test_sequential.py b/tests/test_sequential.py
index 4e82738..1d80e3f 100644
--- a/tests/test_sequential.py
+++ b/tests/test_sequential.py
@@ -1,9 +1,10 @@
 import unittest
 
-import complexnn as conn
 import numpy as np
 import tensorflow as tf
 
+import complexnn as conn
+
 
 class TestLayersInSequential(unittest.TestCase):
     """Each layer builds, predicts and trains inside a Sequential model (issue #23)"""
diff --git a/tests/test_train.py b/tests/test_train.py
index 75452f0..e6630a7 100644
--- a/tests/test_train.py
+++ b/tests/test_train.py
@@ -1,8 +1,7 @@
 import unittest
 
 import numpy as np
-import tensorflow as tf
-from tensorflow.keras.layers import Dense, Input, MaxPooling2D
+from tensorflow.keras.layers import Dense, Input
 from tensorflow.keras.models import Model, Sequential
 
 import complexnn as conn