Update keras.backend.placeholder to preserve shape (e.g. batch size) information when constructing a ragged placeholder.

PiperOrigin-RevId: 266043329

tensorflow/python/keras/backend.py

@@ -69,7 +69,7 @@ from tensorflow.python.ops import state_ops
 from tensorflow.python.ops import tensor_array_grad  # pylint: disable=unused-import
 from tensorflow.python.ops import tensor_array_ops
 from tensorflow.python.ops import variables as variables_module
-from tensorflow.python.ops.ragged import ragged_factory_ops
+from tensorflow.python.ops.ragged import ragged_tensor
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.util import nest
 from tensorflow.python.util import object_identity
@@ -1038,16 +1038,13 @@ def placeholder(shape=None,
       ragged_rank = 0
       for i in range(1, len(shape)):
         if shape[i] is None:
-          ragged_rank += 1
-        else:
-          break
-      value_shape = shape[(ragged_rank + 1):]
-
-      x = ragged_factory_ops.placeholder(
-          dtype=dtype,
-          ragged_rank=ragged_rank,
-          value_shape=value_shape,
-          name=name)
+          ragged_rank = i
+      type_spec = ragged_tensor.RaggedTensorSpec(
+          shape=shape, dtype=dtype, ragged_rank=ragged_rank)
+      def tensor_spec_to_placeholder(tensorspec):
+        return array_ops.placeholder(tensorspec.dtype, tensorspec.shape)
+      x = nest.map_structure(tensor_spec_to_placeholder, type_spec,
+                             expand_composites=True)
     else:
       x = array_ops.placeholder(dtype, shape=shape, name=name)
   return x

tensorflow/python/keras/utils/composite_tensor_support_test.py

@@ -510,7 +510,10 @@ class RaggedTensorInputTest(keras_parameterized.TestCase,
     # Prepare the model to test.
     input_name = get_input_name(use_dict)
     model_input = input_layer.Input(
-        shape=(None, None), ragged=True, name=input_name, dtype=dtypes.int32)
+        shape=(None, None), ragged=True, name=input_name, dtype=dtypes.int32,
+        batch_size=2)
+    self.assertIsInstance(model_input, ragged_tensor.RaggedTensor)
+    self.assertEqual(model_input.shape.as_list(), [2, None, None])
     layers = [ToDense(default_value=-1)]
     model = get_model_from_layers_with_input(layers, model_input=model_input)
     model.compile(