Method __init__

tensorflow/python/framework/sparse_tensor.py:115–143

Creates a `SparseTensor`. Args: indices: A 2-D int64 tensor of shape `[N, ndims]`. values: A 1-D tensor of any type and shape `[N]`. dense_shape: A 1-D int64 tensor of shape `[ndims]`.

(self, indices, values, dense_shape)

        dense_shape=sparse_tensor_value.dense_shape)

  def __init__(self, indices, values, dense_shape):
    """Creates a `SparseTensor`.

    Args:
      indices: A 2-D int64 tensor of shape `[N, ndims]`.
      values: A 1-D tensor of any type and shape `[N]`.
      dense_shape: A 1-D int64 tensor of shape `[ndims]`.
    """
    with ops.name_scope(None, "SparseTensor", [indices, values, dense_shape]):
      indices = ops.convert_to_tensor(
          indices, name="indices", dtype=dtypes.int64)
      # TODO(touts): Consider adding mutable_values() when 'values'
      # is a VariableOp and updating users of SparseTensor.
      values = ops.internal_convert_to_tensor(values, name="values")
      dense_shape = ops.convert_to_tensor(
          dense_shape, name="dense_shape", dtype=dtypes.int64)
    self._indices = indices
    self._values = values
    self._dense_shape = dense_shape

    indices_shape = indices.shape.with_rank(2)
    values_shape = values.shape.with_rank(1)
    dense_shape_shape = dense_shape.shape.with_rank(1)

    # Assert number of rows in indices match the number of elements in values.
    indices_shape.dims[0].merge_with(values_shape.dims[0])
    # Assert number of columns in indices matches the number of elements in
    # dense_shape.
    indices_shape.dims[1].merge_with(dense_shape_shape.dims[0])

  def get_shape(self):
    """Get the `TensorShape` representing the shape of the dense tensor.