Merge branch 'keras-team:main' into add-affine

keras_core/__init__.py

@@ -15,5 +15,4 @@
 from keras_core.models import Functional
 from keras_core.models import Model
 from keras_core.models import Sequential
-
-__version__ = "0.1.0"
+from keras_core.version import __version__

keras_core/backend/common/global_state.py

@@ -83,7 +83,7 @@ def clear_session():
     GLOBAL_SETTINGS_TRACKER = threading.local()
 
     if backend.backend() == "tensorflow":
-        import tensorflow as tf
+        from keras_core.utils.module_utils import tensorflow as tf
 
         tf.compat.v1.reset_default_graph()
         if tf.executing_eagerly():

keras_core/initializers/constant_initalizers_test.py

@@ -0,0 +1,43 @@
+import numpy as np
+
+from keras_core import backend
+from keras_core import initializers
+from keras_core import testing
+
+
+class ConstantInitializersTest(testing.TestCase):
+    def test_zeros_initializer(self):
+        shape = (3, 3)
+
+        initializer = initializers.Zeros()
+        values = initializer(shape=shape)
+        self.assertEqual(values.shape, shape)
+        np_values = backend.convert_to_numpy(values).data
+        self.assertEqual(np_values, np.zeros(shape=shape))
+
+        self.run_class_serialization_test(initializer)
+
+    def test_ones_initializer(self):
+        shape = (3, 3)
+
+        initializer = initializers.Ones()
+        values = initializer(shape=shape)
+        self.assertEqual(values.shape, shape)
+        np_values = backend.convert_to_numpy(values).data
+        self.assertEqual(np_values, np.ones(shape=shape))
+
+        self.run_class_serialization_test(initializer)
+
+    def test_constant_initializer(self):
+        shape = (3, 3)
+        constant_value = 6.0
+
+        initializer = initializers.Constant(value=constant_value)
+        values = initializer(shape=shape)
+        self.assertEqual(values.shape, shape)
+        np_values = backend.convert_to_numpy(values).data
+        self.assertEqual(
+            np_values, np.full(shape=shape, fill_value=constant_value)
+        )
+
+        self.run_class_serialization_test(initializer)

keras_core/initializers/constant_initializers.py

@@ -30,6 +30,7 @@ def __init__(self, value=0.0):
         self.value = float(value)
 
     def __call__(self, shape, dtype=None):
+        dtype = standardize_dtype(dtype)
         return self.value * ops.ones(shape=shape, dtype=dtype)
 
     def get_config(self):

keras_core/layers/preprocessing/category_encoding.py

@@ -2,6 +2,7 @@
 from keras_core.api_export import keras_core_export
 from keras_core.layers.layer import Layer
 from keras_core.utils import backend_utils
+from keras_core.utils.module_utils import tensorflow as tf
 
 
 @keras_core_export("keras_core.layers.CategoryEncoding")
@@ -83,9 +84,7 @@ class CategoryEncoding(Layer):
     """
 
     def __init__(self, num_tokens=None, output_mode="multi_hot", **kwargs):
-        try:
-            import tensorflow as tf
-        except ImportError:
+        if not tf.available:
             raise ImportError(
                 "Layer CategoryEncoding requires TensorFlow. "
                 "Install it via `pip install tensorflow`."

keras_core/layers/preprocessing/discretization.py

@@ -4,6 +4,7 @@
 from keras_core.api_export import keras_core_export
 from keras_core.layers.layer import Layer
 from keras_core.utils import backend_utils
+from keras_core.utils.module_utils import tensorflow as tf
 
 
 @keras_core_export("keras_core.layers.Discretization")
@@ -102,9 +103,7 @@ def __init__(
         dtype=None,
         **kwargs,
     ):
-        try:
-            import tensorflow as tf
-        except ImportError:
+        if not tf.available:
             raise ImportError(
                 "Layer Discretization requires TensorFlow. "
                 "Install it via `pip install tensorflow`."
@@ -193,8 +192,6 @@ def compute_output_spec(self, inputs):
         return backend.KerasTensor(shape=inputs.shape, dtype="int32")
 
     def __call__(self, inputs):
-        import tensorflow as tf
-
         if not isinstance(inputs, (tf.Tensor, np.ndarray, backend.KerasTensor)):
             inputs = tf.convert_to_tensor(backend.convert_to_numpy(inputs))
         if not self.built:

keras_core/layers/preprocessing/hashed_crossing.py

@@ -2,6 +2,7 @@
 from keras_core.api_export import keras_core_export
 from keras_core.layers.layer import Layer
 from keras_core.utils import backend_utils
+from keras_core.utils.module_utils import tensorflow as tf
 
 
 @keras_core_export("keras_core.layers.HashedCrossing")
@@ -74,9 +75,7 @@ def __init__(
         dtype=None,
         **kwargs,
     ):
-        try:
-            import tensorflow as tf
-        except ImportError:
+        if not tf.available:
             raise ImportError(
                 "Layer HashedCrossing requires TensorFlow. "
                 "Install it via `pip install tensorflow`."

keras_core/layers/preprocessing/hashing.py

@@ -4,6 +4,7 @@
 from keras_core.api_export import keras_core_export
 from keras_core.layers.layer import Layer
 from keras_core.utils import backend_utils
+from keras_core.utils.module_utils import tensorflow as tf
 
 
 @keras_core_export("keras_core.layers.Hashing")
@@ -143,9 +144,7 @@ def __init__(
         name=None,
         **kwargs,
     ):
-        try:
-            import tensorflow as tf
-        except ImportError:
+        if not tf.available:
             raise ImportError(
                 "Layer Hashing requires TensorFlow. "
                 "Install it via `pip install tensorflow`."
@@ -183,8 +182,6 @@ def __init__(
         self.supports_jit = False
 
     def call(self, inputs):
-        import tensorflow as tf
-
         if not isinstance(inputs, (tf.Tensor, np.ndarray, list, tuple)):
             inputs = tf.convert_to_tensor(np.array(inputs))
         outputs = self.layer.call(inputs)

keras_core/layers/preprocessing/integer_lookup.py

@@ -4,6 +4,7 @@
 from keras_core.api_export import keras_core_export
 from keras_core.layers.layer import Layer
 from keras_core.utils import backend_utils
+from keras_core.utils.module_utils import tensorflow as tf
 
 
 @keras_core_export("keras_core.layers.IntegerLookup")
@@ -309,9 +310,7 @@ def __init__(
         name=None,
         **kwargs,
     ):
-        try:
-            import tensorflow as tf
-        except ImportError:
+        if not tf.available:
             raise ImportError(
                 "Layer IntegerLookup requires TensorFlow. "
                 "Install it via `pip install tensorflow`."
@@ -449,8 +448,6 @@ def set_vocabulary(self, vocabulary, idf_weights=None):
         self.layer.set_vocabulary(vocabulary, idf_weights=idf_weights)
 
     def call(self, inputs):
-        import tensorflow as tf
-
         if not isinstance(inputs, (tf.Tensor, np.ndarray, list, tuple)):
             inputs = tf.convert_to_tensor(np.array(inputs))
         outputs = self.layer.call(inputs)

keras_core/layers/preprocessing/normalization.py

@@ -6,6 +6,7 @@
 from keras_core import ops
 from keras_core.api_export import keras_core_export
 from keras_core.layers.layer import Layer
+from keras_core.utils.module_utils import tensorflow as tf
 
 
 @keras_core_export("keras_core.layers.Normalization")
@@ -216,8 +217,6 @@ def adapt(self, data):
                 data is batched, and if that assumption doesn't hold, the mean
                 and variance may be incorrectly computed.
         """
-        import tensorflow as tf
-
         if isinstance(data, np.ndarray) or backend.is_tensor(data):
             input_shape = data.shape
         elif isinstance(data, tf.data.Dataset):

keras_core/layers/preprocessing/random_crop.py

@@ -4,6 +4,7 @@
 from keras_core.api_export import keras_core_export
 from keras_core.layers.layer import Layer
 from keras_core.utils import backend_utils
+from keras_core.utils.module_utils import tensorflow as tf
 
 
 @keras_core_export("keras_core.layers.RandomCrop")
@@ -52,9 +53,7 @@ class RandomCrop(Layer):
     """
 
     def __init__(self, height, width, seed=None, name=None, **kwargs):
-        try:
-            import tensorflow as tf
-        except ImportError:
+        if not tf.available:
             raise ImportError(
                 "Layer RandomCrop requires TensorFlow. "
                 "Install it via `pip install tensorflow`."
@@ -74,8 +73,6 @@ def __init__(self, height, width, seed=None, name=None, **kwargs):
         self._allow_non_tensor_positional_args = True
 
     def call(self, inputs, training=True):
-        import tensorflow as tf
-
         if not isinstance(inputs, (tf.Tensor, np.ndarray, list, tuple)):
             inputs = tf.convert_to_tensor(backend.convert_to_numpy(inputs))
         outputs = self.layer.call(inputs, training=training)

keras_core/layers/preprocessing/random_flip.py

@@ -4,6 +4,7 @@
 from keras_core.api_export import keras_core_export
 from keras_core.layers.layer import Layer
 from keras_core.utils import backend_utils
+from keras_core.utils.module_utils import tensorflow as tf
 
 HORIZONTAL = "horizontal"
 VERTICAL = "vertical"
@@ -45,9 +46,7 @@ class RandomFlip(Layer):
     def __init__(
         self, mode=HORIZONTAL_AND_VERTICAL, seed=None, name=None, **kwargs
     ):
-        try:
-            import tensorflow as tf
-        except ImportError:
+        if not tf.available:
             raise ImportError(
                 "Layer RandomFlip requires TensorFlow. "
                 "Install it via `pip install tensorflow`."
@@ -66,8 +65,6 @@ def __init__(
         self._allow_non_tensor_positional_args = True
 
     def call(self, inputs, training=True):
-        import tensorflow as tf
-
         if not isinstance(inputs, (tf.Tensor, np.ndarray, list, tuple)):
             inputs = tf.convert_to_tensor(backend.convert_to_numpy(inputs))
         outputs = self.layer.call(inputs, training=training)

keras_core/layers/preprocessing/random_rotation.py

@@ -4,6 +4,7 @@
 from keras_core.api_export import keras_core_export
 from keras_core.layers.layer import Layer
 from keras_core.utils import backend_utils
+from keras_core.utils.module_utils import tensorflow as tf
 
 
 @keras_core_export("keras_core.layers.RandomRotation")
@@ -85,9 +86,7 @@ def __init__(
         name=None,
         **kwargs,
     ):
-        try:
-            import tensorflow as tf
-        except ImportError:
+        if not tf.available:
             raise ImportError(
                 "Layer RandomRotation requires TensorFlow. "
                 "Install it via `pip install tensorflow`."
@@ -109,8 +108,6 @@ def __init__(
         self._allow_non_tensor_positional_args = True
 
     def call(self, inputs, training=True):
-        import tensorflow as tf
-
         if not isinstance(inputs, (tf.Tensor, np.ndarray, list, tuple)):
             inputs = tf.convert_to_tensor(backend.convert_to_numpy(inputs))
         outputs = self.layer.call(inputs, training=training)

keras_core/layers/preprocessing/random_translation.py

@@ -4,6 +4,7 @@
 from keras_core.api_export import keras_core_export
 from keras_core.layers.layer import Layer
 from keras_core.utils import backend_utils
+from keras_core.utils.module_utils import tensorflow as tf
 
 
 @keras_core_export("keras_core.layers.RandomTranslation")
@@ -75,9 +76,7 @@ def __init__(
         name=None,
         **kwargs,
     ):
-        try:
-            import tensorflow as tf
-        except ImportError:
+        if not tf.available:
             raise ImportError(
                 "Layer RandomTranslation requires TensorFlow. "
                 "Install it via `pip install tensorflow`."
@@ -99,8 +98,6 @@ def __init__(
         self._allow_non_tensor_positional_args = True
 
     def call(self, inputs, training=True):
-        import tensorflow as tf
-
         if not isinstance(inputs, (tf.Tensor, np.ndarray, list, tuple)):
             inputs = tf.convert_to_tensor(backend.convert_to_numpy(inputs))
         outputs = self.layer.call(inputs, training=training)

keras_core/layers/preprocessing/random_zoom.py

@@ -4,6 +4,7 @@
 from keras_core.api_export import keras_core_export
 from keras_core.layers.layer import Layer
 from keras_core.utils import backend_utils
+from keras_core.utils.module_utils import tensorflow as tf
 
 
 @keras_core_export("keras_core.layers.RandomZoom")
@@ -97,9 +98,7 @@ def __init__(
         name=None,
         **kwargs,
     ):
-        try:
-            import tensorflow as tf
-        except ImportError:
+        if not tf.available:
             raise ImportError(
                 "Layer RandomZoom requires TensorFlow. "
                 "Install it via `pip install tensorflow`."
@@ -122,8 +121,6 @@ def __init__(
         self.supports_jit = False
 
     def call(self, inputs, training=True):
-        import tensorflow as tf
-
         if not isinstance(inputs, (tf.Tensor, np.ndarray, list, tuple)):
             inputs = tf.convert_to_tensor(backend.convert_to_numpy(inputs))
         outputs = self.layer.call(inputs, training=training)

keras_core/layers/preprocessing/string_lookup.py

@@ -4,6 +4,7 @@
 from keras_core.api_export import keras_core_export
 from keras_core.layers.layer import Layer
 from keras_core.utils import backend_utils
+from keras_core.utils.module_utils import tensorflow as tf
 
 
 @keras_core_export("keras_core.layers.StringLookup")
@@ -307,9 +308,7 @@ def __init__(
         dtype=None,
         **kwargs,
     ):
-        try:
-            import tensorflow as tf
-        except ImportError:
+        if not tf.available:
             raise ImportError(
                 "Layer StringLookup requires TensorFlow. "
                 "Install it via `pip install tensorflow`."
@@ -443,8 +442,6 @@ def set_vocabulary(self, vocabulary, idf_weights=None):
         self.layer.set_vocabulary(vocabulary, idf_weights=idf_weights)
 
     def call(self, inputs):
-        import tensorflow as tf
-
         if not isinstance(inputs, (tf.Tensor, np.ndarray, list, tuple)):
             inputs = tf.convert_to_tensor(np.array(inputs))
         outputs = self.layer.call(inputs)