tensorflow_probability/examples/logistic_regression.py
def build_input_pipeline(x, y, batch_size):
  """Build a Dataset iterator for supervised classification.

  Args:
    x: Numpy `array` of features, indexed by the first dimension.
    y: Numpy `array` of labels, with the same first dimension as `x`.
    batch_size: Number of elements in each training batch.

  Returns:
    batch_features: `Tensor` feed  features, of shape
      `[batch_size] + x.shape[1:]`.
    batch_labels: `Tensor` feed of labels, of shape
      `[batch_size] + y.shape[1:]`.
  """
  training_dataset = tf.data.Dataset.from_tensor_slices((x, y))
  training_batches = training_dataset.repeat().batch(batch_size)
  training_iterator = tf.compat.v1.data.make_one_shot_iterator(training_batches)
  batch_features, batch_labels = training_iterator.get_next()
  return batch_features, batch_labels
def build_input_pipeline(x, y, batch_size):
  training_dataset = tf.data.Dataset.from_tensor_slices((x, y))
  training_batches = training_dataset.repeat().batch(batch_size)
  training_iterator = tf.compat.v1.data.make_one_shot_iterator(training_batches)
  batch_features, batch_labels = training_iterator.get_next()
  return batch_features, batch_labels
def build_input_pipeline ( x , y , batch_size ) : training_dataset = tf . data . Dataset . from_tensor_slices ( ( x , y ) ) training_batches = training_dataset . repeat ( ) . batch ( batch_size ) training_iterator = tf . compat . v1 . data . make_one_shot_iterator ( training_batches ) batch_features , batch_labels = training_iterator . get_next ( ) return batch_features , batch_labels
Build a Dataset iterator for supervised classification.

  Args:
    x: Numpy `array` of features, indexed by the first dimension.
    y: Numpy `array` of labels, with the same first dimension as `x`.
    batch_size: Number of elements in each training batch.

  Returns:
    batch_features: `Tensor` feed  features, of shape
      `[batch_size] + x.shape[1:]`.
    batch_labels: `Tensor` feed of labels, of shape
      `[batch_size] + y.shape[1:]`.
Build a Dataset iterator for supervised classification .