Fix message formatting

keras/src/backend/tensorflow/nn.py

@@ -353,7 +353,7 @@ def depthwise_conv(
     if num_spatial_dims > 2:
         raise ValueError(
             "`inputs` rank must be 3 (1D conv) or 4 (2D conv). Received: "
-            "{inputs.ndim}."
+            f"{inputs.ndim}."
         )
     # Because we use `tf.nn.depthwise_conv2d` for both 1D and 2D convs, we set
     # `tf_data_format` using 2D conv format.

keras/src/backend/tensorflow/numpy.py

@@ -1999,7 +1999,7 @@ def unravel_index(x, shape):
 
     if None in shape:
         raise ValueError(
-            "`shape` argument cannot contain `None`. Received: shape={shape}"
+            f"`shape` argument cannot contain `None`. Received: shape={shape}"
         )
 
     if x.ndim == 1:

keras/src/layers/attention/grouped_query_attention.py

@@ -464,7 +464,8 @@ def compute_output_shape(
             raise ValueError(
                 "The last dimension of `query_shape` and `value_shape` "
                 f"must be equal, but are {query_shape[-1]}, {value_shape[-1]}. "
-                "Received: query_shape={query_shape}, value_shape={value_shape}"
+                f"Received: query_shape={query_shape}, "
+                f"value_shape={value_shape}"
             )
 
         if value_shape[1:-1] != key_shape[1:-1]:

keras/src/layers/core/lambda_layer.py

@@ -170,7 +170,7 @@ def _serialize_function_to_config(self, fn):
     def _raise_for_lambda_deserialization(arg_name, safe_mode):
         if safe_mode:
             raise ValueError(
-                "The `{arg_name}` of this `Lambda` layer is a Python lambda. "
+                f"The `{arg_name}` of this `Lambda` layer is a Python lambda. "
                 "Deserializing it is unsafe. If you trust the source of the "
                 "config artifact, you can override this error "
                 "by passing `safe_mode=False` "

keras/src/layers/preprocessing/category_encoding.py

@@ -157,7 +157,7 @@ def call(self, inputs, count_weights=None):
             if self.output_mode != "count":
                 raise ValueError(
                     "`count_weights` is not used when `output_mode` is not "
-                    "`'count'`. Received `count_weights={count_weights}`."
+                    f"`'count'`. Received `count_weights={count_weights}`."
                 )
             count_weights = self.backend.convert_to_tensor(
                 count_weights, dtype=self.compute_dtype

keras/src/ops/linalg.py

@@ -665,7 +665,7 @@ def _assert_1d(*arrays):
     for a in arrays:
         if a.ndim < 1:
             raise ValueError(
-                "Expected input to have rank >= 1. Received scalar input {a}."
+                f"Expected input to have rank >= 1. Received scalar input {a}."
             )
 
 
@@ -674,7 +674,7 @@ def _assert_2d(*arrays):
         if a.ndim < 2:
             raise ValueError(
                 "Expected input to have rank >= 2. "
-                "Received input with shape {a.shape}."
+                f"Received input with shape {a.shape}."
             )
 
 

keras/src/ops/numpy.py

@@ -2283,7 +2283,7 @@ def compute_output_spec(self, x):
         if len(x_shape) < 2:
             raise ValueError(
                 "`diagonal` requires an array of at least two dimensions, but "
-                "`x` is of shape {x.shape}."
+                f"`x` is of shape {x.shape}."
             )
 
         shape_2d = [x_shape[self.axis1], x_shape[self.axis2]]

keras/src/utils/dataset_utils.py

@@ -406,7 +406,7 @@ def _rescale_dataset_split_sizes(left_size, right_size, total_length):
     if left_size + right_size > total_length:
         raise ValueError(
             "The sum of `left_size` and `right_size` should "
-            "be smaller than the {total_length}. "
+            f"be smaller than the {total_length}. "
             f"Received: left_size + right_size = {left_size + right_size}"
             f"and total_length = {total_length}"
         )