[keras/optimizers/legacy/optimizer_v2.py] Backtick keywords in docstr…

…ing ; [keras/optimizers/legacy_learning_rate_decay.py] Remove "Linear default"
keras-team · Apr 20, 2023 · 0ebca04 · 0ebca04
1 parent 1a57052
commit 0ebca04
Show file tree

Hide file tree

Showing 2 changed files with 3 additions and 3 deletions.
diff --git a/keras/optimizers/legacy/optimizer_v2.py b/keras/optimizers/legacy/optimizer_v2.py
@@ -692,12 +692,12 @@ def apply_gradients(
 
         Args:
           grads_and_vars: List of (gradient, variable) pairs.
-          name: Optional name for the returned operation. When None, uses the
+          name: Optional name for the returned operation. When `None`, uses the
             name passed to the `Optimizer` constructor. Defaults to `None`.
           experimental_aggregate_gradients: Whether to sum gradients from
             different replicas in the presence of `tf.distribute.Strategy`. If
             False, it's user responsibility to aggregate the gradients. Default
-            to True.
+            to `True`.
 
         Returns:
           An `Operation` that applies the specified gradients. The `iterations`

diff --git a/keras/optimizers/legacy_learning_rate_decay.py b/keras/optimizers/legacy_learning_rate_decay.py
@@ -264,7 +264,7 @@ def polynomial_decay(
       end_learning_rate: A scalar `float32` or `float64` `Tensor` or a Python
         number.  The minimal end learning rate.
       power: A scalar `float32` or `float64` `Tensor` or a Python number.  The
-        power of the polynomial. Linear is default. Defaults to `1.0`.
+        power of the polynomial. Defaults to `1.0`.
       cycle: A boolean, whether it should cycle beyond decay_steps. Defaults to
         `False`.
       name: String. Optional name of the operation. Defaults to