org.tensorflow.op.core

Class EncodeProto

java.lang.Object

org.tensorflow.op.PrimitiveOp

org.tensorflow.op.core.EncodeProto

All Implemented Interfaces:

Op, Operand<String>

@Operator
public final class EncodeProto
extends PrimitiveOp
implements Operand<String>

The op serializes protobuf messages provided in the input tensors.

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`.

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	EncodeProto.Options Optional attributes for EncodeProto

Field Summary

Fields inherited from class org.tensorflow.op.PrimitiveOp

operation

Method Summary

Modifier and Type	Method and Description
Output<String>	asOutput() Returns the symbolic handle of a tensor.
Output<String>	bytes() Tensor of serialized protos with shape `batch_shape`.
static EncodeProto	create(Scope scope, Operand<Integer> sizes, Iterable<Operand<?>> values, List<String> fieldNames, String messageType, EncodeProto.Options... options) Factory method to create a class to wrap a new EncodeProto operation to the graph.
static EncodeProto.Options	descriptorSource(String descriptorSource)

Methods inherited from class org.tensorflow.op.PrimitiveOp

equals, hashCode, toString

Methods inherited from class java.lang.Object

clone, finalize, getClass, notify, notifyAll, wait, wait, wait

Method Detail

create

public static EncodeProto create(Scope scope,
                                 Operand<Integer> sizes,
                                 Iterable<Operand<?>> values,
                                 List<String> fieldNames,
                                 String messageType,
                                 EncodeProto.Options... options)

Factory method to create a class to wrap a new EncodeProto operation to the graph.

Parameters:

scope - current graph scope

sizes - Tensor of int32 with shape `[batch_shape, len(field_names)]`.

values - List of tensors containing values for the corresponding field.

fieldNames - List of strings containing proto field names.

messageType - Name of the proto message type to decode.

options - carries optional attributes values

Returns:

a new instance of EncodeProto

descriptorSource

public static EncodeProto.Options descriptorSource(String descriptorSource)

Parameters:

descriptorSource -

bytes

public Output<String> bytes()

Tensor of serialized protos with shape `batch_shape`.

asOutput

public Output<String> asOutput()

Description copied from interface: Operand

Returns the symbolic handle of a tensor.

Inputs to TensorFlow operations are outputs of another TensorFlow operation. This method is used to obtain a symbolic handle that represents the computation of the input.

Specified by:

asOutput in interface Operand<String>

See Also:

OperationBuilder.addInput(Output)