research/GDP_2019/adult_tutorial.py [127:148]:
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      model_fn=nn_model_fn, model_dir=FLAGS.model_dir)

  # Create tf.Estimator input functions for the training and test data.
  eval_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn(
      x={'x': test_data}, y=test_labels, num_epochs=1, shuffle=False)

  # Training loop.
  steps_per_epoch = num_examples // sampling_batch
  test_accuracy_list = []
  for epoch in range(1, FLAGS.epochs + 1):
    for _ in range(steps_per_epoch):
      whether = np.random.random_sample(num_examples) > (
          1 - sampling_batch / num_examples)
      subsampling = [i for i in np.arange(num_examples) if whether[i]]
      global microbatches
      microbatches = len(subsampling)

      train_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn(
          x={'x': train_data[subsampling]},
          y=train_labels[subsampling],
          batch_size=len(subsampling),
          num_epochs=1,
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research/GDP_2019/imdb_tutorial.py [126:148]:
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      model_fn=nn_model_fn, model_dir=FLAGS.model_dir)

  # Create tf.Estimator input functions for the training and test data.
  eval_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn(
      x={'x': test_data}, y=test_labels, num_epochs=1, shuffle=False)

  # Training loop.
  steps_per_epoch = num_examples // sampling_batch
  test_accuracy_list = []

  for epoch in range(1, FLAGS.epochs + 1):
    for _ in range(steps_per_epoch):
      whether = np.random.random_sample(num_examples) > (
          1 - sampling_batch / num_examples)
      subsampling = [i for i in np.arange(num_examples) if whether[i]]
      global microbatches
      microbatches = len(subsampling)

      train_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn(
          x={'x': train_data[subsampling]},
          y=train_labels[subsampling],
          batch_size=len(subsampling),
          num_epochs=1,
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