tf.constant

Creates a constant tensor from a tensor-like object.

tf.constant(     value, dtype=None, shape=None, name='Const' )

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.

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.