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Creates a constant tensor from a tensor-like object.
tf.constant( value, dtype=None, shape=None, name='Const' )
Note: All eagertf.Tensor
values are immutable (in contrast totf.Variable
). There is nothing especially constant about the value returned fromtf.constant
. This function it is not fundamentally different fromtf.convert_to_tensor
. The nametf.constant
comes from the symbolic APIs (liketf.data
or keras functional models) where thevalue
is embeded in aConst
node in thetf.Graph
.tf.constant
is useful for asserting that the value can be embedded that way.
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:
i = tf.keras.layers.Input(shape=[None, None]) t = tf.constant(i) Traceback (most recent call last): NotImplementedError: ...
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.)
tf.convert_to_tensor
is similar but: shape
argument.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.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. |
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/constant