Python make_input_fn示例

示例#1

文件： task.py 项目： amygdala/tensorflow-workshop

def make_experiment_fn(args):
  train_input_fn = util.make_input_fn(
      args.train_data_file,
      args.batch_size,
      args.num_skips,
      args.skip_window,
      args.vocab_size,
      num_epochs=args.num_epochs
  )
  eval_input_fn = util.make_input_fn(
      args.eval_data_file,
      args.batch_size,
      args.num_skips,
      args.skip_window,
      args.vocab_size,
      num_epochs=args.num_epochs
  )

  def experiment_fn(output_dir):
    return Experiment(
        Estimator(
            model_fn=model.make_model_fn(**args.__dict__),
            model_dir=output_dir
        ),
        train_input_fn=train_input_fn,
        eval_input_fn=eval_input_fn,
        continuous_eval_throttle_secs=args.min_eval_seconds,
        min_eval_frequency=args.min_train_eval_rate,
        # Until Experiment moves to train_and_evaluate call internally
        local_eval_frequency=args.min_train_eval_rate
    )
  return experiment_fn

示例#2

文件： task.py 项目： fluxcapacitor/pipeline

def make_experiment_fn(args):
  train_input_fn = util.make_input_fn(
      args.train_data_paths,
      args.batch_size,
      args.index_file,
      num_epochs=args.num_epochs
  )
  eval_input_fn = util.make_input_fn(
      args.eval_data_paths,
      args.batch_size,
      args.index_file,
      num_epochs=args.num_epochs
  )

  def experiment_fn(output_dir):
    return Experiment(
        Estimator(
            model_fn=model.make_model_fn(args),
            model_dir=output_dir
        ),
        train_input_fn=train_input_fn,
        eval_input_fn=eval_input_fn,
        continuous_eval_throttle_secs=args.min_eval_seconds,
        min_eval_frequency=args.min_train_eval_rate,
        # Until learn_runner is updated to use train_and_evaluate
        local_eval_frequency=args.min_train_eval_rate
    )
  return experiment_fn

示例#3

文件： task.py 项目： ccortezb/pipeline

def make_experiment_fn(args):
  train_input_fn = util.make_input_fn(
      args.train_data_paths,
      util.parse_examples,
      args.batch_size,
      num_epochs=args.num_epochs
  )
  eval_input_fn = util.make_input_fn(
      args.eval_data_paths,
      util.parse_examples,
      args.batch_size,
      num_epochs=args.num_epochs
  )

  def _experiment_fn(output_dir):
      return learn.Experiment(
          learn.Estimator(
              model_fn=model.make_model_fn(args),
              model_dir=output_dir
          ),
          train_input_fn=train_input_fn,
          eval_input_fn=eval_input_fn,
          train_steps=args.max_steps,
          eval_metrics=model.METRICS,
          continuous_eval_throttle_secs=args.min_eval_seconds,
          min_eval_frequency=args.min_train_eval_rate,
          # Until learn_runner is updated to use train_and_evaluate
          local_eval_frequency=args.min_train_eval_rate
      )
  return _experiment_fn

示例#4

def make_experiment_fn(args):
  train_input_fn = util.make_input_fn(
      args.train_data_paths,
      util.parse_examples,
      args.batch_size,
      num_epochs=args.num_epochs
  )
  eval_input_fn = util.make_input_fn(
      args.eval_data_paths,
      util.parse_examples,
      args.batch_size,
      num_epochs=args.num_epochs
  )

  def _experiment_fn(output_dir):
      return learn.Experiment(
          learn.Estimator(
              model_fn=model.make_model_fn(args),
              model_dir=output_dir
          ),
          train_input_fn=train_input_fn,
          eval_input_fn=eval_input_fn,
          train_steps=args.max_steps,
          eval_metrics=model.METRICS,
          continuous_eval_throttle_secs=args.min_eval_seconds,
          min_eval_frequency=args.min_train_eval_rate,
          # Until learn_runner is updated to use train_and_evaluate
          local_eval_frequency=args.min_train_eval_rate
      )
  return _experiment_fn

示例#5

文件： task.py 项目： feuerchop/source.ml

def make_experiment_fn(args):
    train_input_fn = util.make_input_fn(args.train_data_file,
                                        args.batch_size,
                                        args.num_skips,
                                        args.skip_window,
                                        args.vocab_size,
                                        num_epochs=args.num_epochs)
    eval_input_fn = util.make_input_fn(args.eval_data_file,
                                       args.batch_size,
                                       args.num_skips,
                                       args.skip_window,
                                       args.vocab_size,
                                       num_epochs=args.num_epochs)

    def experiment_fn(output_dir):
        return Experiment(
            Estimator(model_fn=model.make_model_fn(**args.__dict__),
                      model_dir=output_dir),
            train_input_fn=train_input_fn,
            eval_input_fn=eval_input_fn,
            continuous_eval_throttle_secs=args.min_eval_seconds,
            min_eval_frequency=args.min_train_eval_rate,
            # Until Experiment moves to train_and_evaluate call internally
            local_eval_frequency=args.min_train_eval_rate)

    return experiment_fn

示例#6

文件： task.py 项目： akansal1/jupyterhub.ml

def make_experiment_fn(args):
    train_input_fn = util.make_input_fn(args.train_data_paths,
                                        args.batch_size,
                                        args.index_file,
                                        num_epochs=args.num_epochs)
    eval_input_fn = util.make_input_fn(args.eval_data_paths,
                                       args.batch_size,
                                       args.index_file,
                                       num_epochs=args.num_epochs)

    def experiment_fn(output_dir):
        return Experiment(
            Estimator(model_fn=model.make_model_fn(args),
                      model_dir=output_dir),
            train_input_fn=train_input_fn,
            eval_input_fn=eval_input_fn,
            continuous_eval_throttle_secs=args.min_eval_seconds,
            min_eval_frequency=args.min_train_eval_rate,
            # Until learn_runner is updated to use train_and_evaluate
            local_eval_frequency=args.min_train_eval_rate)

    return experiment_fn