tensorflow_probability/python/math/sparse.py
def dense_to_sparse(x, ignore_value=None, name=None):
  """Converts dense `Tensor` to `SparseTensor`, dropping `ignore_value` cells.

  Args:
    x: A `Tensor`.
    ignore_value: Entries in `x` equal to this value will be
      absent from the return `SparseTensor`. If `None`, default value of
      `x` dtype will be used (e.g. '' for `str`, 0 for `int`).
    name: Python `str` prefix for ops created by this function.

  Returns:
    sparse_x: A `tf.SparseTensor` with the same shape as `x`.

  Raises:
    ValueError: when `x`'s rank is `None`.
  """
  # Copied (with modifications) from:
  # tensorflow/contrib/layers/python/ops/sparse_ops.py.
  with tf.compat.v1.name_scope(name, 'dense_to_sparse', [x, ignore_value]):
    x = tf.convert_to_tensor(value=x, name='x')
    if ignore_value is None:
      if x.dtype.base_dtype == tf.string:
        # Exception due to TF strings are converted to numpy objects by default.
        ignore_value = ''
      else:
        ignore_value = x.dtype.as_numpy_dtype(0)
      ignore_value = tf.cast(ignore_value, x.dtype, name='ignore_value')
    indices = tf.where(tf.not_equal(x, ignore_value), name='indices')
    return tf.SparseTensor(
        indices=indices,
        values=tf.gather_nd(x, indices, name='values'),
        dense_shape=tf.shape(input=x, out_type=tf.int64, name='dense_shape'))
def dense_to_sparse(x, ignore_value=None, name=None):
  """Converts dense `Tensor` to `SparseTensor`, dropping `ignore_value` cells.

  Args:
    x: A `Tensor`.
    ignore_value: Entries in `x` equal to this value will be
      absent from the return `SparseTensor`. If `None`, default value of
      `x` dtype will be used (e.g. '' for `str`, 0 for `int`).
    name: Python `str` prefix for ops created by this function.

  Returns:
    sparse_x: A `tf.SparseTensor` with the same shape as `x`.

  Raises:
    ValueError: when `x`'s rank is `None`.
  """
  # Copied (with modifications) from:
  # tensorflow/contrib/layers/python/ops/sparse_ops.py.
  with tf.compat.v1.name_scope(name, 'dense_to_sparse', [x, ignore_value]):
    x = tf.convert_to_tensor(value=x, name='x')
    if ignore_value is None:
      if x.dtype.base_dtype == tf.string:
        # Exception due to TF strings are converted to numpy objects by default.
        ignore_value = ''
      else:
        ignore_value = x.dtype.as_numpy_dtype(0)
      ignore_value = tf.cast(ignore_value, x.dtype, name='ignore_value')
    indices = tf.where(tf.not_equal(x, ignore_value), name='indices')
    return tf.SparseTensor(
        indices=indices,
        values=tf.gather_nd(x, indices, name='values'),
        dense_shape=tf.shape(input=x, out_type=tf.int64, name='dense_shape'))
Converts dense `Tensor` to `SparseTensor`, dropping `ignore_value` cells.

  Args:
    x: A `Tensor`.
    ignore_value: Entries in `x` equal to this value will be
      absent from the return `SparseTensor`. If `None`, default value of
      `x` dtype will be used (e.g. '' for `str`, 0 for `int`).
    name: Python `str` prefix for ops created by this function.

  Returns:
    sparse_x: A `tf.SparseTensor` with the same shape as `x`.

  Raises:
    ValueError: when `x`'s rank is `None`.