View source on GitHub
|
Creates a constant tensor.
tf.compat.v1.constant(
value, dtype=None, shape=None, name='Const', verify_shape=False
) -> Union[tf.Operation, ops._EagerTensorBase]
The resulting tensor is populated with values of type dtype, as
specified by arguments value and (optionally) shape (see examples
below).
The argument value can be a constant value, or a list of values of type
dtype. If value is a list, then the length of the list must be less
than or equal to the number of elements implied by the shape argument (if
specified). In the case where the list length is less than the number of
elements specified by shape, the last element in the list will be used
to fill the remaining entries.
The argument shape is optional. If present, it specifies the dimensions of
the resulting tensor. If not present, the shape of value is used.
If the argument dtype is not specified, then the type is inferred from
the type of value.
For example:
# Constant 1-D Tensor populated with value list.
tensor = tf.constant([1, 2, 3, 4, 5, 6, 7]) => [1 2 3 4 5 6 7]
# Constant 2-D tensor populated with scalar value -1.
tensor = tf.constant(-1.0, shape=[2, 3]) => [[-1. -1. -1.]
[-1. -1. -1.]]
tf.constant differs from tf.fill in a few ways:
tf.constantsupports arbitrary constants, not just uniform scalar Tensors liketf.fill.tf.constantcreates aConstnode in the computation graph with the exact value at graph construction time. On the other hand,tf.fillcreates an Op in the graph that is expanded at runtime.- Because
tf.constantonly embeds constant values in the graph, it does not support dynamic shapes based on other runtime Tensors, whereastf.filldoes.
Returns | |
|---|---|
| A Constant Tensor. |
Raises | |
|---|---|
TypeError
|
if shape is incorrectly specified or unsupported. |
View source on GitHub