Minor simplifications

README.md

@@ -6,12 +6,15 @@ Keras Core is a new multi-backend implementation of the Keras API, with support
 
 Keras Core is intend to work as a drop-in replacement for `tf.keras` (when using the TensorFlow backend).
 
+In addition, Keras models can consume datasets in any format, regardless of the backend you're using:
+you can train your models with your existing tf.data.Dataset pipelines or Torch DataLoaders.
+
 ## Why use Keras Core?
 
 - Write custom components (e.g. layers, models, metrics) that you can move across framework boundaries.
 - Make your code future-proof by avoiding framework lock-in.
-- As a JAX user: get access to a fully-featured modeling and training library.
-- As a PyTorch user: get access to the real Keras, at last!
+- As a PyTorch user: get access to power of Keras, at last!
+- As a JAX user: get access to a fully-featured, battle-tested modeling and training library.
 
 ## Credits
 

keras_core/layers/merging/dot.py

@@ -1,6 +1,7 @@
 from keras_core import operations as ops
 from keras_core.api_export import keras_core_export
 from keras_core.layers.merging.base_merge import Merge
+from keras_core.utils.numerical_utils import normalize
 
 
 def batch_dot(x, y, axes=None):
@@ -195,32 +196,6 @@ def batch_dot(x, y, axes=None):
     return result
 
 
-def l2_normalize(x, axis=None, epsilon=1e-9):
-    """Normalizes along dimension `axis` using an L2 norm.
-
-    For a 1-D tensor with `axis = 0`, computes
-
-    `output = x / sqrt(max(sum(x**2), epsilon))`
-
-    For `x` with more dimensions, independently normalizes each
-    1-D slice along dimension `axis`.
-
-    Args:
-        x: Input tensor.
-        axis: Dimension along which to normalize. A scalar or a
-            vector of integers.
-        epsilon: A lower bound value for the norm. Will use `sqrt(epsilon)`
-            as the divisor if `norm < sqrt(epsilon)`.
-
-    Returns:
-        A normalized tensor with the same shape as input.
-    """
-
-    square_sum = ops.sum(ops.square(x), axis=axis, keepdims=True)
-    x_inv_norm = 1.0 / ops.sqrt(ops.maximum(square_sum, epsilon))
-    return ops.multiply(x, x_inv_norm)
-
-
 @keras_core_export("keras_core.layers.Dot")
 class Dot(Merge):
     """Computes element-wise dot product of two tensors.
@@ -340,8 +315,8 @@ def _merge_function(self, inputs):
                     axes.append(self.axes[i])
 
         if self.normalize:
-            x1 = l2_normalize(x1, axis=axes[0])
-            x2 = l2_normalize(x2, axis=axes[1])
+            x1 = normalize(x1, axis=axes[0])
+            x2 = normalize(x2, axis=axes[1])
         output = batch_dot(x1, x2, axes)
         return output
 

keras_core/layers/normalization/spectral_normalization.py

@@ -1,8 +1,10 @@
+from keras_core import backend
 from keras_core import initializers
 from keras_core import operations as ops
 from keras_core.api_export import keras_core_export
 from keras_core.layers import Wrapper
 from keras_core.layers.input_spec import InputSpec
+from keras_core.utils.numerical_utils import normalize
 
 
 @keras_core_export("keras_core.layers.SpectralNormalization")
@@ -96,10 +98,10 @@ def normalize_weights(self):
         # check for zeroes weights
         if not all([w == 0.0 for w in weights]):
             for _ in range(self.power_iterations):
-                vector_v = self._l2_normalize(
-                    ops.matmul(vector_u, ops.transpose(weights))
+                vector_v = normalize(
+                    ops.matmul(vector_u, ops.transpose(weights)), axis=None
                 )
-                vector_u = self._l2_normalize(ops.matmul(vector_v, weights))
+                vector_u = normalize(ops.matmul(vector_v, weights), axis=None)
             # vector_u = tf.stop_gradient(vector_u)
             # vector_v = tf.stop_gradient(vector_v)
             sigma = ops.matmul(
@@ -113,11 +115,6 @@ def normalize_weights(self):
                 )
             )
 
-    def _l2_normalize(self, x):
-        square_sum = ops.sum(ops.square(x), keepdims=True)
-        x_inv_norm = 1 / ops.sqrt(ops.maximum(square_sum, 1e-12))
-        return ops.multiply(x, x_inv_norm)
-
     def get_config(self):
         config = {"power_iterations": self.power_iterations}
         base_config = super().get_config()

pip_build.py

@@ -25,10 +25,7 @@
 package = "keras_core"
 build_directory = "tmp_build_dir"
 dist_directory = "dist"
-to_copy = [
-    "setup.py",
-    "README.md"
-]
+to_copy = ["setup.py", "README.md"]
 
 
 def ignore_files(_, filenames):