Explicitly disallow Keras 3.

.github/workflows/tox.yml

@@ -15,10 +15,10 @@ jobs:
           - Windows
           - MacOs
         py:
+          - "3.11"
           - "3.10"
           - "3.9"
           - "3.8"
-          - "3.7"
     steps:
       - name: Setup python for test ${{ matrix.py }}
         uses: actions/setup-python@v3

realbook/layers/signal.py

@@ -75,7 +75,7 @@ def __init__(
             name: Name of the layer.
             dtype: Type used in calcuation.
         """
-        super().__init__(trainable=False, name=name, dtype=dtype, dynamic=False)
+        super().__init__(trainable=False, name=name, dtype=dtype)
         self.fft_length = fft_length
         self.window_length = window_length if window_length else self.fft_length
         self.hop_length = hop_length if hop_length else self.window_length // 4
@@ -171,7 +171,7 @@ def __init__(
             name: Name of the layer.
             dtype: Type used in calcuation.
         """
-        super().__init__(trainable=False, name=name, dtype=dtype, dynamic=False)
+        super().__init__(trainable=False, name=name, dtype=dtype)
 
         # Because layer saves dtype as string???
         self.dtypes_type = dtype

setup.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 1.0.2
+current_version = 1.0.3
 commit = True
 tag = True
 
@@ -22,20 +22,22 @@ classifiers =
 	Operating System :: MacOS :: MacOS X
 	Operating System :: Microsoft :: Windows
 	Programming Language :: Python
-	Programming Language :: Python :: 3.7
 	Programming Language :: Python :: 3.8
 	Programming Language :: Python :: 3.9
 	Programming Language :: Python :: 3.10
+	Programming Language :: Python :: 3.11
 	Programming Language :: Python :: Implementation :: CPython
 
 [options]
 zip_safe = False
 packages = find:
 include_package_data = True
+# Note we don't yet support TensorFlow 2.16 or later due to Keras 3,
+# which does not support complex datatypes.
 install_requires = 
-	tensorflow>=2.4; sys_platform != 'darwin' or platform.machine != 'arm64'
-	tensorflow-macos>=2.4; sys_platform == 'darwin' and platform.machine == 'arm64'
-	tensorboard
+	tensorflow>=2.4,<2.16; sys_platform != 'darwin' or platform.machine != 'arm64'
+	# TF 2.13 finally publishes ARM wheels:
+	tensorflow>=2.13,<2.16; sys_platform == 'darwin' and platform.machine == 'arm64'
 	types-protobuf
 	numpy
 	typing_extensions
@@ -48,7 +50,7 @@ tensorboard =
 	matplotlib
 	psutil
 	nvsmi
-	librosa>=0.9,<0.10
+	librosa>=0.9
 test = 
 	coverage>=5.0.2
 	pytest>=7.1.1
@@ -57,10 +59,11 @@ test =
 	tox
 	torch
 	nnaudio
-	numpy==1.21.6
-	librosa>=0.9,<0.10
-	tensorflow>=2.4,<2.11; sys_platform != 'darwin' or platform.machine != 'arm64'
-	tensorflow-macos>=2.4,<2.11; sys_platform == 'darwin' and platform.machine == 'arm64'
+	numpy>=1.22
+	librosa>=0.9
+	tensorflow>=2.4,<2.16; sys_platform != 'darwin' or platform.machine != 'arm64'
+	# TF 2.13 finally publishes ARM wheels:
+	tensorflow>=2.13,<2.16; sys_platform == 'darwin' and platform.machine == 'arm64'
 
 [bumpversion:file:realbook/__init__.py]
 

tests/callbacks/test_debugging.py

@@ -38,7 +38,7 @@ def generate_data() -> Iterator[Tuple[List[int], List[int]]]:
         ),
     )
 
-    model = tf.keras.Sequential([tf.keras.Input(shape=(1)), tf.keras.layers.Dense(1)])
+    model = tf.keras.Sequential([tf.keras.Input(shape=(1,)), tf.keras.layers.Dense(1)])
     model.compile(loss="binary_crossentropy")
     model.fit(fake_data, steps_per_epoch=1, epochs=10, callbacks=[HangDebugCallback()])
 
@@ -70,7 +70,7 @@ def generate_data() -> Iterator[Tuple[List[int], List[int]]]:
         ),
     )
 
-    model = tf.keras.Sequential([tf.keras.Input(shape=(1)), tf.keras.layers.Dense(1)])
+    model = tf.keras.Sequential([tf.keras.Input(shape=(1,)), tf.keras.layers.Dense(1)])
     model.compile(loss="binary_crossentropy")
     model.fit(
         fake_data,
@@ -114,7 +114,7 @@ def generate_data() -> Iterator[Tuple[List[int], List[int]]]:
         ),
     )
 
-    model = tf.keras.Sequential([tf.keras.Input(shape=(1)), tf.keras.layers.Dense(1)])
+    model = tf.keras.Sequential([tf.keras.Input(shape=(1,)), tf.keras.layers.Dense(1)])
     model.compile(loss="binary_crossentropy")
     model.fit(
         fake_data,

tests/layers/test_nnaudio.py

@@ -95,7 +95,7 @@ def test_cqt_trainable_layers_change_on_training(train: bool) -> None:
 
     model.compile(loss="mse")
     if train:
-        signal = librosa.chirp(32.70, TEST_SAMPLE_RATE, length=TEST_SAMPLE_RATE * 10, linear=True)
+        signal = librosa.chirp(fmin=32.70, fmax=TEST_SAMPLE_RATE, length=TEST_SAMPLE_RATE * 10, linear=True)
         noise = np.random.rand(*signal.shape).astype(signal.dtype)
 
         model.fit(np.array([signal, noise]), np.array([1, 0]), epochs=10, verbose=0)