From bfc9feb17d0758619810ae26b50ace447e462a88 Mon Sep 17 00:00:00 2001
From: Francois Chollet <francois.chollet@gmail.com>
Date: Mon, 17 Jul 2023 10:39:56 -0700
Subject: [PATCH] Trainable summary touch ups

---
 keras_core/ops/numpy.py           | 17 ++---------------
 keras_core/utils/summary_utils.py | 17 ++++++++++-------
 2 files changed, 12 insertions(+), 22 deletions(-)

diff --git a/keras_core/ops/numpy.py b/keras_core/ops/numpy.py
index f1c161806..666578acc 100644
--- a/keras_core/ops/numpy.py
+++ b/keras_core/ops/numpy.py
@@ -146,21 +146,8 @@ from keras_core.ops.operation_utils import reduce_shape
 
 
 def broadcast_shapes(shape1, shape2):
-    """Broadcast input shapes to a unified shape.
-    
-    Convert to list for mutability.
-
-    Args:
-        shape1: A tuple or list of integers.
-        shape2: A tuple or list of integers.
-
-    Returns:
-        output_shape (list of int or None): The broadcasted shape.
-
-    Example:
-    >>> broadcast_shapes((5, 3), (1, 3))
-    [5, 3]
-    """
+    # Broadcast input shapes to a unified shape.
+    # Convert to list for mutability.
     shape1 = list(shape1)
     shape2 = list(shape2)
     origin_shape1 = shape1
diff --git a/keras_core/utils/summary_utils.py b/keras_core/utils/summary_utils.py
index dfeba89a8..8717d4d40 100644
--- a/keras_core/utils/summary_utils.py
+++ b/keras_core/utils/summary_utils.py
@@ -192,8 +192,8 @@ def print_summary(
             relevant_nodes += v
 
     if show_trainable:
-        default_line_length += 8
-        positions = [p * 0.88 for p in positions] + [1.0]
+        default_line_length += 12
+        positions = [p * 0.90 for p in positions] + [1.0]
         header.append("Trainable")
         alignment.append("center")
 
@@ -262,11 +262,14 @@ def print_summary(
         if not sequential_like:
             fields.append(get_connections(layer))
         if show_trainable:
-            fields.append(
-                bold_text("Y", color=34)
-                if layer.trainable
-                else bold_text("N", color=9)
-            )
+            if layer.weights:
+                fields.append(
+                    bold_text("Y", color=34)
+                    if layer.trainable
+                    else bold_text("N", color=9)
+                )
+            else:
+                fields.append(bold_text("-"))
         return fields
 
     def print_layer(layer, nested_level=0):