diff --git a/keras/src/callbacks/reduce_lr_on_plateau.py b/keras/src/callbacks/reduce_lr_on_plateau.py
index 04c1076f2e1..63e7a94bf45 100644
--- a/keras/src/callbacks/reduce_lr_on_plateau.py
+++ b/keras/src/callbacks/reduce_lr_on_plateau.py
@@ -138,7 +138,7 @@ def on_epoch_end(self, epoch, logs=None):
                         self.model.optimizer.learning_rate = new_lr
                         if self.verbose > 0:
                             io_utils.print_msg(
-                                f"\nEpoch {epoch +1}: "
+                                f"\nEpoch {epoch + 1}: "
                                 "ReduceLROnPlateau reducing "
                                 f"learning rate to {new_lr}."
                             )
diff --git a/keras/src/layers/convolutional/conv2d.py b/keras/src/layers/convolutional/conv2d.py
index ab51c40e67a..662de235b37 100644
--- a/keras/src/layers/convolutional/conv2d.py
+++ b/keras/src/layers/convolutional/conv2d.py
@@ -7,9 +7,9 @@ class Conv2D(BaseConv):
     """2D convolution layer.
 
     This layer creates a convolution kernel that is convolved with the layer
-    input over a 2D spatial (or temporal) dimension (height and width) to 
-    produce a tensor of outputs. If `use_bias` is True, a bias vector is created 
-    and added to the outputs. Finally, if `activation` is not `None`, it is 
+    input over a 2D spatial (or temporal) dimension (height and width) to
+    produce a tensor of outputs. If `use_bias` is True, a bias vector is created
+    and added to the outputs. Finally, if `activation` is not `None`, it is
     applied to the outputs as well.
 
     Args:
diff --git a/keras/src/layers/convolutional/conv3d.py b/keras/src/layers/convolutional/conv3d.py
index 1fc533cfdc3..e6ed74fed49 100644
--- a/keras/src/layers/convolutional/conv3d.py
+++ b/keras/src/layers/convolutional/conv3d.py
@@ -7,9 +7,9 @@ class Conv3D(BaseConv):
     """3D convolution layer.
 
     This layer creates a convolution kernel that is convolved with the layer
-    input over a 3D spatial (or temporal) dimension (width,height and depth) to 
-    produce a tensor of outputs. If `use_bias` is True, a bias vector is created 
-    and added to the outputs. Finally, if `activation` is not `None`, it is 
+    input over a 3D spatial (or temporal) dimension (width,height and depth) to
+    produce a tensor of outputs. If `use_bias` is True, a bias vector is created
+    and added to the outputs. Finally, if `activation` is not `None`, it is
     applied to the outputs as well.
 
     Args: