tf.constant

Creates a constant tensor from a tensor-like object.

tf.constant(
    value, dtype=None, shape=None, name='Const'
) -> Union[tf.Operation, ops._EagerTensorBase]

Used in the notebooks

Used in the guideUsed in the tutorials

If the argument dtype is not specified, then the type is inferred from the type of value.

# Constant 1-D Tensor from a python list.
tf.constant([1, 2, 3, 4, 5, 6])
<tf.Tensor: shape=(6,), dtype=int32,
    numpy=array([1, 2, 3, 4, 5, 6], dtype=int32)>
# Or a numpy array
a = np.array([[1, 2, 3], [4, 5, 6]])
tf.constant(a)
<tf.Tensor: shape=(2, 3), dtype=int64, numpy=
  array([[1, 2, 3],
         [4, 5, 6]])>

If dtype is specified, the resulting tensor values are cast to the requested dtype.

tf.constant([1, 2, 3, 4, 5, 6], dtype=tf.float64)
<tf.Tensor: shape=(6,), dtype=float64,
    numpy=array([1., 2., 3., 4., 5., 6.])>

If shape is set, the value is reshaped to match. Scalars are expanded to fill the shape:

tf.constant(0, shape=(2, 3))
  <tf.Tensor: shape=(2, 3), dtype=int32, numpy=
  array([[0, 0, 0],
         [0, 0, 0]], dtype=int32)>
tf.constant([1, 2, 3, 4, 5, 6], shape=[2, 3])
<tf.Tensor: shape=(2, 3), dtype=int32, numpy=
  array([[1, 2, 3],
         [4, 5, 6]], dtype=int32)>

tf.constant has no effect if an eager Tensor is passed as the value, it even transmits gradients:

v = tf.Variable([0.0])
with tf.GradientTape() as g:
    loss = tf.constant(v + v)
g.gradient(loss, v).numpy()
array([2.], dtype=float32)

But, since tf.constant embeds the value in the tf.Graph this fails for symbolic tensors:

with tf.compat.v1.Graph().as_default():
  i = tf.compat.v1.placeholder(shape=[None, None], dtype=tf.float32)
  t = tf.constant(i)
Traceback (most recent call last):

TypeError: ...

tf.constant will create tensors on the current device. Inputs which are already tensors maintain their placements unchanged.

  • tf.convert_to_tensor is similar but:

    • It has no shape argument.
    • Symbolic tensors are allowed to pass through.
    with tf.compat.v1.Graph().as_default():
  i = tf.compat.v1.placeholder(shape=[None, None], dtype=tf.float32)
  t = tf.convert_to_tensor(i)

  • tf.fill: differs in a few ways:

    • tf.constant supports arbitrary constants, not just uniform scalar Tensors like tf.fill.
    • tf.fill creates an Op in the graph that is expanded at runtime, so it can efficiently represent large tensors.
    • Since tf.fill does not embed the value, it can produce dynamically sized outputs.

valueA constant value (or list) of output type dtype.
dtypeThe type of the elements of the resulting tensor.
shapeOptional dimensions of resulting tensor.
nameOptional name for the tensor.

A Constant Tensor.

TypeErrorif shape is incorrectly specified or unsupported.
ValueErrorif called on a symbolic tensor.