Class TensorFlowTrainer<T extends Output<T>>

java.lang.Object
org.tribuo.interop.tensorflow.TensorFlowTrainer<T>
All Implemented Interfaces:
com.oracle.labs.mlrg.olcut.config.Configurable, com.oracle.labs.mlrg.olcut.provenance.Provenancable<TrainerProvenance>, Trainer<T>
public final class TensorFlowTrainer<T extends Output<T>> extends Object implements Trainer<T>
Trainer for TensorFlow. Expects the underlying TensorFlow graph to have named placeholders for the inputs, ground truth outputs and a named output operation. The output operation should be before any softmax or sigmoid non-linearities to allow the use of more optimized loss functions.

This trainer only works with graphs setup for minibatches. To recover single example training just use a batch size of 1.

This trainer uses the serialisation functionality in TensorFlowUtil, or a TF checkpoint.

N.B. TensorFlow support is experimental and may change without a major version bump.

  • Constructor Details

    • TensorFlowTrainer

      public TensorFlowTrainer(Path graphPath, String outputName, GradientOptimiser optimiser, Map<String,Float> gradientParams, FeatureConverter featureConverter, OutputConverter<T> outputConverter, int trainBatchSize, int epochs, int testBatchSize, int loggingInterval) throws IOException
      Constructs a Trainer for a TensorFlow graph. Stores the model parameters inside the Tribuo model.
      Parameters:
      graphPath - Path to the graph definition on disk. Must have the necessary targets and placeholders.
      outputName - The name of the output operation.
      optimiser - The gradient optimiser.
      gradientParams - The parameters of the gradient optimiser.
      featureConverter - The example converter to convert a Tribuo Example into a Tensor.
      outputConverter - The output converter to convert a Tribuo Output into a Tensor and back. This encodes the output type.
      trainBatchSize - The minibatch size to use in training.
      epochs - The number of SGD epochs to run.
      testBatchSize - The minibatch size to use at test time.
      loggingInterval - The logging interval. Set to -1 to quiesce the loss level logging.
      Throws:
      IOException - If the graph could not be loaded from the supplied path.

    • TensorFlowTrainer

      public TensorFlowTrainer(Path graphPath, String outputName, GradientOptimiser optimiser, Map<String,Float> gradientParams, FeatureConverter featureConverter, OutputConverter<T> outputConverter, int trainBatchSize, int epochs, int testBatchSize, int loggingInterval, Path checkpointPath) throws IOException
      Constructs a Trainer for a TensorFlow graph. Stores the model parameters in a TensorFlow checkpoint.
      Parameters:
      graphPath - Path to the graph definition on disk. Must have the necessary targets and placeholders.
      outputName - The name of the output operation.
      optimiser - The gradient optimiser.
      gradientParams - The parameters of the gradient optimiser.
      featureConverter - The example converter to convert a Tribuo Example into a Tensor.
      outputConverter - The output converter to convert a Tribuo Output into a Tensor and back. This encodes the output type.
      trainBatchSize - The minibatch size to use in training.
      epochs - The number of SGD epochs to run.
      testBatchSize - The minibatch size to use at test time.
      loggingInterval - The logging interval. Set to -1 to quiesce the loss level logging.
      checkpointPath - The path to save out the TensorFlow checkpoint.
      Throws:
      IOException - If the graph could not be loaded from the supplied path.

    • TensorFlowTrainer

      public TensorFlowTrainer(org.tensorflow.proto.framework.GraphDef graphDef, String outputName, GradientOptimiser optimiser, Map<String,Float> gradientParams, FeatureConverter featureConverter, OutputConverter<T> outputConverter, int trainBatchSize, int epochs, int testBatchSize, int loggingInterval)
      Constructs a Trainer for a TensorFlow graph. Stores the model parameters inside the Tribuo model.
      Parameters:
      graphDef - The graph definition. Must have the necessary targets and placeholders.
      outputName - The name of the output operation.
      optimiser - The gradient optimiser.
      gradientParams - The parameters of the gradient optimiser.
      featureConverter - The example converter to convert a Tribuo Example into a Tensor.
      outputConverter - The output converter to convert a Tribuo Output into a Tensor and back. This encodes the output type.
      trainBatchSize - The minibatch size to use in training.
      epochs - The number of SGD epochs to run.
      testBatchSize - The minibatch size to use at test time.
      loggingInterval - The logging interval. Set to -1 to quiesce the loss level logging.

    • TensorFlowTrainer

      public TensorFlowTrainer(org.tensorflow.proto.framework.GraphDef graphDef, String outputName, GradientOptimiser optimiser, Map<String,Float> gradientParams, FeatureConverter featureConverter, OutputConverter<T> outputConverter, int trainBatchSize, int epochs, int testBatchSize, int loggingInterval, Path checkpointPath)
      Constructs a Trainer for a TensorFlow graph. Stores the model parameters in a TensorFlow checkpoint.
      Parameters:
      graphDef - The graph definition. Must have the necessary targets and placeholders.
      outputName - The name of the output operation.
      optimiser - The gradient optimiser.
      gradientParams - The parameters of the gradient optimiser.
      featureConverter - The example converter to convert a Tribuo Example into a Tensor.
      outputConverter - The output converter to convert a Tribuo Output into a Tensor and back. This encodes the output type.
      trainBatchSize - The minibatch size to use in training.
      epochs - The number of SGD epochs to run.
      testBatchSize - The minibatch size to use at test time.
      loggingInterval - The logging interval. Set to -1 to quiesce the loss level logging.
      checkpointPath - The path to save out the TensorFlow checkpoint.

    • TensorFlowTrainer

      public TensorFlowTrainer(org.tensorflow.Graph graph, String outputName, GradientOptimiser optimiser, Map<String,Float> gradientParams, FeatureConverter featureConverter, OutputConverter<T> outputConverter, int trainBatchSize, int epochs, int testBatchSize, int loggingInterval)
      Constructs a Trainer for a TensorFlow graph. Stores the model parameters inside the Tribuo model.

      The graph can be closed after the trainer is constructed. Tribuo maintains a copy of the graphdef inside the trainer.

      Parameters:
      graph - The graph definition. Must have the necessary targets and placeholders.
      outputName - The name of the output operation.
      optimiser - The gradient optimiser.
      gradientParams - The parameters of the gradient optimiser.
      featureConverter - The example converter to convert a Tribuo Example into a Tensor.
      outputConverter - The output converter to convert a Tribuo Output into a Tensor and back. This encodes the output type.
      trainBatchSize - The minibatch size to use in training.
      epochs - The number of SGD epochs to run.
      testBatchSize - The minibatch size to use at test time.
      loggingInterval - The logging interval. Set to -1 to quiesce the loss level logging.

    • TensorFlowTrainer

      public TensorFlowTrainer(org.tensorflow.Graph graph, String outputName, GradientOptimiser optimiser, Map<String,Float> gradientParams, FeatureConverter featureConverter, OutputConverter<T> outputConverter, int trainBatchSize, int epochs, int testBatchSize, int loggingInterval, Path checkpointPath)
      Constructs a Trainer for a TensorFlow graph. Stores the model parameters in a TensorFlow checkpoint.

      The graph can be closed after the trainer is constructed. Tribuo maintains a copy of the graphdef inside the trainer.

      Parameters:
      graph - The graph definition. Must have the necessary targets and placeholders.
      outputName - The name of the output operation.
      optimiser - The gradient optimiser.
      gradientParams - The parameters of the gradient optimiser.
      featureConverter - The example converter to convert a Tribuo Example into a Tensor.
      outputConverter - The output converter to convert a Tribuo Output into a Tensor and back. This encodes the output type.
      trainBatchSize - The minibatch size to use in training.
      epochs - The number of SGD epochs to run.
      testBatchSize - The minibatch size to use at test time.
      loggingInterval - The logging interval. Set to -1 to quiesce the loss level logging.
      checkpointPath - The checkpoint path, if using checkpoints.

  • Method Details

    • postConfig

      public void postConfig() throws IOException
      Used by the OLCUT configuration system, and should not be called by external code.
      Specified by:
      postConfig in interface com.oracle.labs.mlrg.olcut.config.Configurable
      Throws:
      IOException

    • train

      public TensorFlowModel<T> train(Dataset<T> examples)
      Description copied from interface: Trainer
      Trains a predictive model using the examples in the given data set.
      Specified by:
      train in interface Trainer<T extends Output<T>>
      Parameters:
      examples - the data set containing the examples.
      Returns:
      a predictive model that can be used to generate predictions for new examples.

    • train

      public TensorFlowModel<T> train(Dataset<T> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)
      Description copied from interface: Trainer
      Trains a predictive model using the examples in the given data set.
      Specified by:
      train in interface Trainer<T extends Output<T>>
      Parameters:
      examples - the data set containing the examples.
      runProvenance - Training run specific provenance (e.g., fold number).
      Returns:
      a predictive model that can be used to generate predictions for new examples.

    • toString

      public String toString()
      Overrides:
      toString in class Object

    • getInvocationCount

      public int getInvocationCount()
      Description copied from interface: Trainer
      The number of times this trainer instance has had it's train method invoked.

      This is used to determine how many times the trainer's RNG has been accessed to ensure replicability in the random number stream.

      Specified by:
      getInvocationCount in interface Trainer<T extends Output<T>>
      Returns:
      The number of train invocations.

    • getProvenance

      public TrainerProvenance getProvenance()
      Specified by:
      getProvenance in interface com.oracle.labs.mlrg.olcut.provenance.Provenancable<T extends Output<T>>