TensorFlow 1 version View source on GitHub

Creates a constant tensor from a tensor-like object.

    value, dtype=None, shape=None, name='Const'

Used in the notebooks

Used in the guide Used 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.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:

i = tf.keras.layers.Input(shape=[None, None]) 
t = tf.constant(i) 
Traceback (most recent call last): 
ValueError: ... 
  • tf.convert_to_tensor is similar but:
    • It has no shape argument.
    • Symbolic tensors are allowed to pass through.
    i = tf.keras.layers.Input(shape=[None, None]) 
    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.


  • value: A constant value (or list) of output type dtype.
  • dtype: The type of the elements of the resulting tensor.
  • shape: Optional dimensions of resulting tensor.
  • name: Optional name for the tensor.


A Constant Tensor.


  • TypeError: if shape is incorrectly specified or unsupported.
  • ValueError: if called on a symbolic tensor.