Class CollectionDef.Builder

java.lang.Object
com.google.protobuf.AbstractMessageLite.Builder
com.google.protobuf.AbstractMessage.Builder<CollectionDef.Builder>
com.google.protobuf.GeneratedMessageV3.Builder<CollectionDef.Builder>
org.tensorflow.proto.CollectionDef.Builder
All Implemented Interfaces:
Message.Builder, MessageLite.Builder, MessageLiteOrBuilder, MessageOrBuilder, Cloneable, CollectionDefOrBuilder
Enclosing class:
CollectionDef
public static final class CollectionDef.Builder extends GeneratedMessageV3.Builder<CollectionDef.Builder> implements CollectionDefOrBuilder
CollectionDef should cover most collections.
To add a user-defined collection, do one of the following:
1. For simple data types, such as string, int, float:
     tf.add_to_collection("your_collection_name", your_simple_value)
   strings will be stored as bytes_list.
2. For Protobuf types, there are three ways to add them:
   1) tf.add_to_collection("your_collection_name",
        your_proto.SerializeToString())
      collection_def {
        key: "user_defined_bytes_collection"
        value {
          bytes_list {
            value: "queue_name: \"test_queue\"\n"
          }
        }
      }
 or
   2) tf.add_to_collection("your_collection_name", str(your_proto))
      collection_def {
        key: "user_defined_string_collection"
        value {
         bytes_list {
            value: "\n\ntest_queue"
          }
        }
      }
 or
   3) any_buf = any_pb2.Any()
      tf.add_to_collection("your_collection_name",
        any_buf.Pack(your_proto))
      collection_def {
        key: "user_defined_any_collection"
        value {
          any_list {
            value {
              type_url: "type.googleapis.com/tensorflow.QueueRunnerDef"
              value: "\n\ntest_queue"
            }
          }
        }
      }
3. For Python objects, implement to_proto() and from_proto(), and register
   them in the following manner:
   ops.register_proto_function("your_collection_name",
                               proto_type,
                               to_proto=YourPythonObject.to_proto,
                               from_proto=YourPythonObject.from_proto)
   These functions will be invoked to serialize and de-serialize the
   collection. For example,
   ops.register_proto_function(ops.GraphKeys.GLOBAL_VARIABLES,
                               proto_type=variable_pb2.VariableDef,
                               to_proto=Variable.to_proto,
                               from_proto=Variable.from_proto)
Protobuf type tensorflow.CollectionDef