Fix extra summary column when show_trainable=True (#435)

Silly bug, we were literally just adding the trainable field twice

keras_core/utils/summary_utils.py

@@ -215,6 +215,26 @@ def print_summary(
 
     table = rich.table.Table(*columns, width=line_length, show_lines=True)
 
+    def get_connections(layer):
+        connections = ""
+        for node in layer._inbound_nodes:
+            if relevant_nodes and node not in relevant_nodes:
+                # node is not part of the current network
+                continue
+            for kt in node.input_tensors:
+                keras_history = kt._keras_history
+                inbound_layer = keras_history.operation
+                node_index = highlight_number(keras_history.node_index)
+                tensor_index = highlight_number(keras_history.tensor_index)
+                if connections:
+                    connections += ", "
+                connections += (
+                    f"{inbound_layer.name}[{node_index}][{tensor_index}]"
+                )
+        if not connections:
+            connections = "-"
+        return connections
+
     def get_layer_fields(layer, prefix=""):
         output_shape = format_layer_shape(layer)
         name = prefix + layer.name
@@ -230,6 +250,8 @@ def get_layer_fields(layer, prefix=""):
             params = highlight_number(f"{layer.count_params():,}")
 
         fields = [name, output_shape, params]
+        if not sequential_like:
+            fields.append(get_connections(layer))
         if show_trainable:
             fields.append(
                 bold_text("Y", color=34)
@@ -238,37 +260,13 @@ def get_layer_fields(layer, prefix=""):
             )
         return fields
 
-    def get_connections(layer):
-        connections = ""
-        for node in layer._inbound_nodes:
-            if relevant_nodes and node not in relevant_nodes:
-                # node is not part of the current network
-                continue
-            for kt in node.input_tensors:
-                keras_history = kt._keras_history
-                inbound_layer = keras_history.operation
-                node_index = highlight_number(keras_history.node_index)
-                tensor_index = highlight_number(keras_history.tensor_index)
-                if connections:
-                    connections += ", "
-                connections += (
-                    f"{inbound_layer.name}[{node_index}][{tensor_index}]"
-                )
-        if not connections:
-            connections = "-"
-        return connections
-
     def print_layer(layer, nested_level=0):
         if nested_level:
             prefix = "   " * nested_level + "└" + " "
         else:
             prefix = ""
 
         fields = get_layer_fields(layer, prefix=prefix)
-        if not sequential_like:
-            fields.append(get_connections(layer))
-        if show_trainable:
-            fields.append("Y" if layer.trainable else "N")
 
         rows = [fields]
         if expand_nested and hasattr(layer, "layers") and layer.layers: