TensorFlow中的函数与PyTorch中的expand（）相同？

Question

Let's say I have a 2 x 3 matrix and I want to create a 6 x 2 x 3 matrix where each element in the first dimension is the original 2 x 3 matrix.

In PyTorch, I can do this:

import torch
from torch.autograd import Variable
import numpy as np

x = np.array([[1, 2, 3], [4, 5, 6]])
x = Variable(torch.from_numpy(x))

# y is the desired result
y = x.unsqueeze(0).expand(6, 2, 3)

What is the equivalent way to do this in TensorFlow? I know unsqueeze() is equivalent to tf.expand_dims() but I don't TensorFlow has anything equivalent to expand() . I'm thinking of using tf.concat on a list of the 1 x 2 x 3 tensors but am not sure if this is the best way to do it.

Answer 1

The equivalent function for pytorch expand is tensorflow tf.broadcast_to

Docs: https://www.tensorflow.org/api_docs/python/tf/broadcast_to

Answer 2

Tensorflow automatically broadcasts, so in general you don't need to do any of this. Suppose you have a y' of shape 6x2x3 and your x is of shape 2x3 , then you can already do y'*x or y'+x will already behave as if you had expanded it. But if for some other reason you really need to do it, then the command in tensorflow is tile :

y = tf.tile(tf.reshape(x, (1,2,3)), multiples=(6,1,1))

Docs: https://www.tensorflow.org/api_docs/python/tf/tile