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# TensorFlow Proto
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Generated Java code for TensorFlow protocol buffers, providing type-safe access to TensorFlow's structured data formats including MetaGraphDef, ConfigProto, and other core TensorFlow data structures. This package serves as a foundational dependency for TensorFlow Java API applications that need to serialize, deserialize, or manipulate TensorFlow protocol buffer messages.
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## Package Information
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- **Package Name**: org.tensorflow:proto
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- **Package Type**: maven
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- **Language**: Java
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- **Installation**: Add to your Maven pom.xml:
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```xml
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<dependency>
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<groupId>org.tensorflow</groupId>
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<artifactId>proto</artifactId>
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<version>1.15.0</version>
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</dependency>
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```
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For Gradle:
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```gradle
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implementation 'org.tensorflow:proto:1.15.0'
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```
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## Core Imports
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```java
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import org.tensorflow.framework.*;
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import org.tensorflow.example.*;
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import org.tensorflow.util.*;
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import org.tensorflow.distruntime.*;
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import com.google.protobuf.ByteString;
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```
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Common specific imports:
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```java
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import org.tensorflow.framework.TensorProto;
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import org.tensorflow.framework.GraphDef;
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import org.tensorflow.framework.NodeDef;
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import org.tensorflow.framework.ConfigProto;
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import org.tensorflow.framework.MetaGraphDef;
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import org.tensorflow.framework.DataType;
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import org.tensorflow.framework.TensorShapeProto;
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import org.tensorflow.framework.AttrValue;
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import org.tensorflow.example.Example;
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import org.tensorflow.example.Feature;
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import org.tensorflow.example.Features;
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import org.tensorflow.example.FloatList;
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import org.tensorflow.example.Int64List;
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import org.tensorflow.example.BytesList;
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import com.google.protobuf.ByteString;
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```
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## Basic Usage
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```java
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import org.tensorflow.framework.*;
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// Create a tensor proto with float values
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TensorProto tensor = TensorProto.newBuilder()
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.setDtype(DataType.DT_FLOAT)
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.setTensorShape(TensorShapeProto.newBuilder()
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.addDim(TensorShapeProto.Dim.newBuilder().setSize(3)))
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.addFloatVal(1.0f)
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.addFloatVal(2.0f)
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.addFloatVal(3.0f)
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.build();
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// Create a simple graph with one node
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GraphDef graph = GraphDef.newBuilder()
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.addNode(NodeDef.newBuilder()
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.setName("input")
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.setOp("Placeholder")
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.putAttr("dtype", AttrValue.newBuilder()
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.setType(DataType.DT_FLOAT).build()))
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.build();
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// Create training example
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Example example = Example.newBuilder()
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.setFeatures(Features.newBuilder()
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.putFeature("height", Feature.newBuilder()
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.setFloatList(FloatList.newBuilder().addValue(175.5f))
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.build())
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.putFeature("weight", Feature.newBuilder()
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.setFloatList(FloatList.newBuilder().addValue(68.2f))
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.build()))
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.build();
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```
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## Architecture
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The TensorFlow Proto package is organized around several key areas:
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- **Core Framework**: Primary data structures for graphs, tensors, operations, and configurations (`org.tensorflow.framework`)
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- **Data Examples**: Normalized formats for training and inference data (`org.tensorflow.example`)
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- **Distributed Runtime**: Service interfaces and messages for distributed TensorFlow execution (`org.tensorflow.distruntime`)
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- **Utilities**: Event logging and other utility types (`org.tensorflow.util`)
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- **Protocol Buffer Pattern**: All classes follow the standard Protocol Buffer Java pattern with immutable message objects and mutable Builder classes
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## Capabilities
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### Core Framework Types
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The fundamental data structures for TensorFlow graphs, tensors, operations, and session configuration. Essential for any application working with TensorFlow models or computation graphs.
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```java { .api }
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class TensorProto {
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DataType getDtype();
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TensorShapeProto getTensorShape();
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ByteString getTensorContent();
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List<Float> getFloatValList();
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List<Integer> getIntValList();
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// ... other type-specific accessors
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}
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class GraphDef {
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List<NodeDef> getNodeList();
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VersionDef getVersions();
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FunctionDefLibrary getLibrary();
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}
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class NodeDef {
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String getName();
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String getOp();
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List<String> getInputList();
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String getDevice();
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Map<String, AttrValue> getAttrMap();
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}
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```
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[Core Framework](./core-framework.md)
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### Data Examples and Features
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Normalized data formats for representing training examples, feature vectors, and structured data inputs for machine learning models.
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```java { .api }
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class Example {
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Features getFeatures();
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}
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class Feature {
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BytesList getBytesList();
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FloatList getFloatList();
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Int64List getInt64List();
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KindCase getKindCase();
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}
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class Features {
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Map<String, Feature> getFeatureMap();
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}
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```
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[Data Examples](./data-examples.md)
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### Model Configuration and Persistence
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Configuration objects and data structures for TensorFlow sessions, saved models, and model metadata management.
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```java { .api }
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class ConfigProto {
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Map<String, Integer> getDeviceCountMap();
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int getIntraOpParallelismThreads();
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int getInterOpParallelismThreads();
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GPUOptions getGpuOptions();
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boolean getAllowSoftPlacement();
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}
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class MetaGraphDef {
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MetaInfoDef getMetaInfoDef();
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GraphDef getGraphDef();
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SaverDef getSaverDef();
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Map<String, CollectionDef> getCollectionDefMap();
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Map<String, SignatureDef> getSignatureDefMap();
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}
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class SavedModel {
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long getSavedModelSchemaVersion();
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List<MetaGraphDef> getMetaGraphsList();
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}
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```
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[Configuration and Persistence](./config-persistence.md)
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### Distributed Runtime Services
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Service interfaces and message types for distributed TensorFlow execution across multiple devices and machines.
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```java { .api }
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// Master Service Messages
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class CreateSessionRequest {
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GraphDef getGraphDef();
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ConfigProto getConfig();
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}
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class RunStepRequest {
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String getSessionHandle();
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List<String> getFeedList();
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List<String> getFetchList();
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}
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// Worker Service Messages
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class RegisterGraphRequest {
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String getSessionHandle();
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GraphDef getGraphDef();
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}
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```
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[Distributed Runtime](./distributed-runtime.md)
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### Utility Types
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Event logging, debugging, and other utility data structures for TensorFlow operations monitoring and analysis.
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```java { .api }
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class Event {
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double getWallTime();
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long getStep();
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String getFileVersion();
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ByteString getGraphDef();
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Summary getSummary();
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WhatCase getWhatCase();
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}
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```
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[Utilities](./utilities.md)
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## Common Types
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### DataType Enum
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```java { .api }
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enum DataType {
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DT_INVALID(0),
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DT_FLOAT(1), // 32-bit float
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DT_DOUBLE(2), // 64-bit double
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DT_INT32(3), // 32-bit integer
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DT_UINT8(4), // Unsigned 8-bit integer
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DT_INT16(5), // 16-bit integer
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DT_INT8(6), // 8-bit integer
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DT_STRING(7), // String
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DT_COMPLEX64(8), // Single-precision complex
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DT_INT64(9), // 64-bit integer
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DT_BOOL(10), // Boolean
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DT_QINT8(11), // Quantized 8-bit integer
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DT_QUINT8(12), // Quantized unsigned 8-bit
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DT_QINT32(13), // Quantized 32-bit integer
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DT_BFLOAT16(14), // Brain floating point 16-bit
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DT_QINT16(15), // Quantized 16-bit integer
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DT_QUINT16(16), // Quantized unsigned 16-bit
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DT_UINT16(17), // Unsigned 16-bit integer
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DT_COMPLEX128(18), // Double-precision complex
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DT_HALF(19), // 16-bit float
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DT_RESOURCE(20), // Resource handle
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DT_VARIANT(21), // Arbitrary C++ data types
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DT_UINT32(22), // Unsigned 32-bit integer
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DT_UINT64(23), // Unsigned 64-bit integer
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// Reference types (for parameters)
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DT_FLOAT_REF(101),
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DT_DOUBLE_REF(102),
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DT_INT32_REF(103),
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DT_UINT8_REF(104),
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DT_INT16_REF(105),
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DT_INT8_REF(106),
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DT_STRING_REF(107),
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DT_COMPLEX64_REF(108),
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DT_INT64_REF(109),
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DT_BOOL_REF(110),
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DT_QINT8_REF(111),
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DT_QUINT8_REF(112),
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DT_QINT32_REF(113),
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DT_BFLOAT16_REF(114),
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DT_QINT16_REF(115),
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DT_QUINT16_REF(116),
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DT_UINT16_REF(117),
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DT_COMPLEX128_REF(118),
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DT_HALF_REF(119),
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DT_RESOURCE_REF(120),
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DT_VARIANT_REF(121),
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DT_UINT32_REF(122),
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DT_UINT64_REF(123);
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}
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```
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### AttrValue
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```java { .api }
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class AttrValue {
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ByteString getS(); // String value
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long getI(); // Integer value
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float getF(); // Float value
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boolean getB(); // Boolean value
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DataType getType(); // Type value
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TensorShapeProto getShape(); // Shape value
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TensorProto getTensor(); // Tensor value
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ListValue getList(); // List value
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ValueCase getValueCase(); // Which value is set
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}
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```
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### TensorShapeProto
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```java { .api }
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class TensorShapeProto {
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List<Dim> getDimList();
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boolean getUnknownRank();
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static class Dim {
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long getSize();
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String getName();
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}
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}
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```