Python linear_regression_zero_init Examples

Example #1

File: estimator_test.py Project: Immexxx/tensorflow

def linear_model_fn_with_model_fn_ops(features, labels, mode):
  """Same as linear_model_fn, but returns `ModelFnOps`."""
  assert mode in (model_fn.ModeKeys.TRAIN, model_fn.ModeKeys.EVAL,
                  model_fn.ModeKeys.INFER)
  prediction, loss = (models.linear_regression_zero_init(features, labels))
  train_op = optimizers.optimize_loss(
      loss, variables.get_global_step(), optimizer='Adagrad', learning_rate=0.1)
  return model_fn.ModelFnOps(
      mode=mode, predictions=prediction, loss=loss, train_op=train_op)

Example #2

File: estimator_test.py Project: GPrathap/OpenBCIPython

def linear_model_fn_with_model_fn_ops(features, labels, mode):
  """Same as linear_model_fn, but returns `ModelFnOps`."""
  assert mode in (model_fn.ModeKeys.TRAIN, model_fn.ModeKeys.EVAL,
                  model_fn.ModeKeys.INFER)
  prediction, loss = (models.linear_regression_zero_init(features, labels))
  train_op = optimizers.optimize_loss(
      loss, variables.get_global_step(), optimizer='Adagrad', learning_rate=0.1)
  return model_fn.ModelFnOps(
      mode=mode, predictions=prediction, loss=loss, train_op=train_op)

Example #3

File: estimator_test.py Project: Immexxx/tensorflow

def linear_model_fn(features, labels, mode):
  features = extract(features, 'input')
  labels = extract(labels, 'labels')
  assert mode in (model_fn.ModeKeys.TRAIN, model_fn.ModeKeys.EVAL,
                  model_fn.ModeKeys.INFER)
  if isinstance(features, dict):
    (_, features), = features.items()
  prediction, loss = (models.linear_regression_zero_init(features, labels))
  train_op = optimizers.optimize_loss(
      loss, variables.get_global_step(), optimizer='Adagrad', learning_rate=0.1)
  return prediction, loss, train_op

Example #4

File: estimator_test.py Project: GPrathap/OpenBCIPython

def linear_model_fn(features, labels, mode):
  features = extract(features, 'input')
  labels = extract(labels, 'labels')
  assert mode in (model_fn.ModeKeys.TRAIN, model_fn.ModeKeys.EVAL,
                  model_fn.ModeKeys.INFER)
  if isinstance(features, dict):
    (_, features), = features.items()
  prediction, loss = (models.linear_regression_zero_init(features, labels))
  train_op = optimizers.optimize_loss(
      loss, variables.get_global_step(), optimizer='Adagrad', learning_rate=0.1)
  return prediction, loss, train_op

Example #5

File: estimator_test.py Project: kanuleader/Compile-tensorflow-in-window

def linear_model_params_fn(features, labels, mode, params):
    features = extract(features, 'input')
    labels = extract(labels, 'labels')

    assert mode in (model_fn.ModeKeys.TRAIN, model_fn.ModeKeys.EVAL,
                    model_fn.ModeKeys.INFER)
    prediction, loss = (models.linear_regression_zero_init(features, labels))
    train_op = optimizers.optimize_loss(loss,
                                        variables.get_global_step(),
                                        optimizer='Adagrad',
                                        learning_rate=params['learning_rate'])
    return prediction, loss, train_op

Example #6

File: estimator_test.py Project: Immexxx/tensorflow

def linear_model_params_fn(features, labels, mode, params):
  features = extract(features, 'input')
  labels = extract(labels, 'labels')

  assert mode in (model_fn.ModeKeys.TRAIN, model_fn.ModeKeys.EVAL,
                  model_fn.ModeKeys.INFER)
  prediction, loss = (models.linear_regression_zero_init(features, labels))
  train_op = optimizers.optimize_loss(
      loss,
      variables.get_global_step(),
      optimizer='Adagrad',
      learning_rate=params['learning_rate'])
  return prediction, loss, train_op