Add keras_core.operations.convert_to_numpy (keras-team#378)

For downstream use cases, nice to be able to have this in the ops layer,
instead of being forced to reach into backend.

keras_core/operations/__init__.py

@@ -4,7 +4,6 @@
 
 from keras_core.backend import cast
 from keras_core.backend import cond
-from keras_core.backend import convert_to_tensor
 from keras_core.backend import is_tensor
 from keras_core.backend import name_scope
 from keras_core.backend import random

keras_core/operations/core.py

@@ -279,3 +279,21 @@ def cast(x, dtype):
     if any_symbolic_tensors((x,)):
         return backend.KerasTensor(shape=x.shape, dtype=dtype)
     return backend.core.cast(x, dtype)
+
+
+@keras_core_export("keras_core.operations.convert_to_tensor")
+def convert_to_tensor(x, dtype=None):
+    """Convert a NumPy array to a tensor."""
+    return backend.convert_to_tensor(x, dtype=dtype)
+
+
+@keras_core_export("keras_core.operations.convert_to_numpy")
+def convert_to_numpy(x):
+    """Convert a tensor to a NumPy array."""
+    if any_symbolic_tensors((x,)):
+        raise ValueError(
+            "A symbolic tensor (usually the result of applying layers or "
+            "operations to a `keras.Input`), cannot be converted to a numpy "
+            "array. There is no concrete value for the input."
+        )
+    return backend.convert_to_numpy(x)

keras_core/operations/core_test.py

@@ -231,3 +231,13 @@ def test_shape(self):
 
         x = KerasTensor((None, 3, None, 1))
         self.assertAllEqual(core.shape(x), (None, 3, None, 1))
+
+    def test_convert_to_tensor(self):
+        x = np.ones((2,))
+        x = ops.convert_to_tensor(x)
+        x = ops.convert_to_numpy(x)
+        self.assertAllEqual(x, (1, 1))
+        self.assertIsInstance(x, np.ndarray)
+
+        with self.assertRaises(ValueError):
+            ops.convert_to_numpy(KerasTensor((2,)))