TensorFlow 1 version | View source on GitHub |
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 always create CPU (host) tensors. In order to create
tensors on other devices, use tf.identity
. (If the value
is an eager
Tensor, however, the tensor will be returned unmodified as mentioned above.)
Related Ops:
tf.convert_to_tensor
is similar but:- It has no
shape
argument. - Symbolic tensors are allowed to pass through.
- It has no
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 liketf.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.
Args | |
---|---|
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. |
Returns | |
---|---|
A Constant Tensor. |
Raises | |
---|---|
TypeError
|
if shape is incorrectly specified or unsupported. |
ValueError
|
if called on a symbolic tensor. |