tf.broadcast_to

Broadcast an array for a compatible shape.

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Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.broadcast_to

tf.broadcast_to(
    input, shape, name=None
)

Broadcasting is the process of making arrays to have compatible shapes for arithmetic operations. Two shapes are compatible if for each dimension pair they are either equal or one of them is one. When trying to broadcast a Tensor to a shape, it starts with the trailing dimensions, and works its way forward.

For example,

x = tf.constant([1, 2, 3])
y = tf.broadcast_to(x, [3, 3])
print(y)
tf.Tensor(
    [[1 2 3]
     [1 2 3]
     [1 2 3]], shape=(3, 3), dtype=int32)

In the above example, the input Tensor with the shape of [1, 3] is broadcasted to output Tensor with shape of [3, 3].

When doing broadcasted operations such as multiplying a tensor by a scalar, broadcasting (usually) confers some time or space benefit, as the broadcasted tensor is never materialized.

However, broadcast_to does not carry with it any such benefits. The newly-created tensor takes the full memory of the broadcasted shape. (In a graph context, broadcast_to might be fused to subsequent operation and then be optimized away, however.)

Args
input	A Tensor. A Tensor to broadcast.
shape	A Tensor. Must be one of the following types: int32, int64. An 1-D int Tensor. The shape of the desired output.
name	A name for the operation (optional).

Returns
A Tensor. Has the same type as input.