Broadcast an array for a compatible shape.
Compat aliases for migration
See Migration guide for more details.
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.
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
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.
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.)
| || A |
| || A |
| ||A name for the operation (optional).|
| A |
© 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.