tf.compat.v1.constant

Creates a constant tensor.

tf.compat.v1.constant(
    value, dtype=None, shape=None, name='Const', verify_shape=False
)

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.constant supports arbitrary constants, not just uniform scalar Tensors like tf.fill.
• tf.constant creates a Const node in the computation graph with the exact value at graph construction time. On the other hand, tf.fill creates an Op in the graph that is expanded at runtime.
• Because tf.constant only embeds constant values in the graph, it does not support dynamic shapes based on other runtime Tensors, whereas tf.fill does.

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.
verify_shape	Boolean that enables verification of a shape of values.

Returns
A Constant Tensor.

Raises
TypeError	if shape is incorrectly specified or unsupported.