simpler

keras_export/convert_model.py

@@ -121,7 +121,7 @@ def replace_none_with(value, shape):
 
 
 def get_first_outbound_op(layer):
-    """determine outbound operation"""
+    """Determine primary outbound operation"""
     return layer._outbound_nodes[0].operation
 
 
@@ -138,7 +138,7 @@ def embedding_layer_names(model):
             if isinstance(layer, Embedding):
                 result.add(layer.name)
         layer_type = type(layer).__name__
-        if layer_type in ['model', 'sequential', 'functional', 'Model', 'Sequential', 'Functional']:
+        if layer_type in ['Model', 'Sequential', 'Functional']:
             result.union(embedding_layer_names(layer))
         return result
 
@@ -728,10 +728,9 @@ def get_all_weights(model, prefix):
         assert is_ascii(name)
         if name in result:
             raise ValueError('duplicate layer name ' + name)
-        if layer_type in ['model', 'sequential', 'functional', 'Model', 'Sequential', 'Functional']:
+        if layer_type in ['Model', 'Sequential', 'Functional']:
             result = merge_two_disjunct_dicts(result, get_all_weights(layer, name + '_'))
-        elif layer_type in ['TimeDistributed'] and type(layer.layer).__name__ in ['model', 'sequential', 'functional',
-                                                                                  'Model', 'Sequential', 'Functional']:
+        elif layer_type in ['TimeDistributed'] and type(layer.layer).__name__ in ['Model', 'Sequential', 'Functional']:
             inner_layer = layer.layer
             result = merge_two_disjunct_dicts(result, get_layer_weights(layer, name))
             result = merge_two_disjunct_dicts(result, get_all_weights(inner_layer, name + "_"))
@@ -762,7 +761,7 @@ def convert_sequential_to_model(model):
         model._inbound_nodes = inbound_nodes
     if type(model).__name__ == 'TimeDistributed':
         model.layer = convert_sequential_to_model(model.layer)
-    if type(model).__name__ in ['model', 'functional', 'Model', 'Functional']:
+    if type(model).__name__ in ['Model', 'Functional']:
         for i in range(len(model.layers)):
             new_layer = convert_sequential_to_model(model.layers[i])
             if new_layer == model.layers[i]: