Function ones

tensorflow/python/ops/array_ops.py:2534–2576

Creates a tensor with all elements set to 1. This operation returns a tensor of type `dtype` with shape `shape` and all elements set to 1. For example: ```python tf.ones([2, 3], tf.int32) # [[1, 1, 1], [1, 1, 1]] ``` Args: shape: A list of integers, a tuple of integers, or a 1

(shape, dtype=dtypes.float32, name=None)

@tf_export("ones")
def ones(shape, dtype=dtypes.float32, name=None):
  """Creates a tensor with all elements set to 1.

  This operation returns a tensor of type `dtype` with shape `shape` and all
  elements set to 1.

  For example:

  ```python
  tf.ones([2, 3], tf.int32)  # [[1, 1, 1], [1, 1, 1]]
  ```

  Args:
    shape: A list of integers, a tuple of integers, or a 1-D `Tensor` of type
      `int32`.
    dtype: The type of an element in the resulting `Tensor`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` with all elements set to 1.
  """
  dtype = dtypes.as_dtype(dtype).base_dtype
  with ops.name_scope(name, "ones", [shape]) as name:
    one = True if dtype == dtypes.bool else 1
    if not isinstance(shape, ops.Tensor):
      try:
        # Create a constant if it won't be very big. Otherwise create a fill op
        # to prevent serialized GraphDefs from becoming too large.
        output = _constant_if_small(one, shape, dtype, name)
        if output is not None:
          return output

        # Go through tensor shapes to get int64-if-needed semantics
        shape = constant_op._tensor_shape_tensor_conversion_function(
            tensor_shape.TensorShape(shape))
      except (TypeError, ValueError):
        # Happens when shape is a list with tensor elements
        shape = ops.convert_to_tensor(shape, dtype=dtypes.int32)
    if not shape._shape_tuple():
      shape = reshape(shape, [-1])  # Ensure it's a vector
    output = fill(shape, constant(one, dtype=dtype), name=name)
  assert output.dtype.base_dtype == dtype
  return output


@tf_export(v1=["placeholder"])