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tf.io.encode_proto

TensorFlow 2.0 version

Defined in generated file: python/ops/gen_encode_proto_ops.py

The op serializes protobuf messages provided in the input tensors.

Aliases:

  • tf.compat.v1.io.encode_proto
  • tf.compat.v2.io.encode_proto
  • tf.contrib.proto.encode_proto
tf.io.encode_proto(
    sizes,
    values,
    field_names,
    message_type,
    descriptor_source='local://',
    name=None
)

The types of the tensors in values must match the schema for the fields specified in field_names. All the tensors in values must have a common shape prefix, batch_shape.

The sizes tensor specifies repeat counts for each field. The repeat count (last dimension) of a each tensor in values must be greater than or equal to corresponding repeat count in sizes.

A message_type name must be provided to give context for the field names. The actual message descriptor can be looked up either in the linked-in descriptor pool or a filename provided by the caller using the descriptor_source attribute.

The descriptor_source attribute selects a source of protocol descriptors to consult when looking up message_type. This may be a filename containing a serialized FileDescriptorSet message, or the special value local://, in which case only descriptors linked into the code will be searched; the filename can be on any filesystem accessible to TensorFlow.

You can build a descriptor_source file using the --descriptor_set_out and --include_imports options to the protocol compiler protoc.

The local:// database only covers descriptors linked into the code via C++ libraries, not Python imports. You can link in a proto descriptor by creating a cc_library target with alwayslink=1.

There are a few special cases in the value mapping:

Submessage and group fields must be pre-serialized as TensorFlow strings.

TensorFlow lacks support for unsigned int64s, so they must be represented as tf.int64 with the same twos-complement bit pattern (the obvious way).

Unsigned int32 values can be represented exactly with tf.int64, or with sign wrapping if the input is of type tf.int32.

Args:

  • sizes: A Tensor of type int32. Tensor of int32 with shape [batch_shape, len(field_names)].
  • values: A list of Tensor objects. List of tensors containing values for the corresponding field.
  • field_names: A list of strings. List of strings containing proto field names.
  • message_type: A string. Name of the proto message type to decode.
  • descriptor_source: An optional string. Defaults to "local://".
  • name: A name for the operation (optional).

Returns:

A Tensor of type string.