Python Tensorflow 张量重塑并用零填充
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Tensorflow Tensor reshape and pad with zeros
提问by Aidan Gomez
Is there a way to reshape a tensor and pad any overflow with zeros? I know ndarray.reshape does this, but as I understand it, converting a Tensor to an ndarray would require flip-flopping between the GPU and CPU.
有没有办法重塑张量并用零填充任何溢出？我知道 ndarray.reshape 会这样做，但据我所知，将 Tensor 转换为 ndarray 需要在 GPU 和 CPU 之间进行触发器。
Tensorflow's reshape() documentation says the TensorShapes need to have the same number of elements, so perhaps the best way would be a pad() and then reshape()?
Tensorflow 的 reshape() 文档说 TensorShapes 需要具有相同数量的元素，所以也许最好的方法是 pad() 然后 reshape()？
I'm trying to achieve:
我正在努力实现：
a = tf.Tensor([[1,2],[3,4]])
tf.reshape(a, [2,3])
a => [[1, 2, 3],
[4, 0 ,0]]
采纳答案by DMTishler
Tensorflow now offers the pad function which performs padding on a tensor in a number of ways(like opencv2's padding function for arrays): https://www.tensorflow.org/api_docs/python/tf/pad
Tensorflow 现在提供 pad 函数，它以多种方式对张量执行填充（如 opencv2 的数组填充函数）：https://www.tensorflow.org/api_docs/python/tf/pad
tf.pad(tensor, paddings, mode='CONSTANT', name=None)
example from the docs above:
上面文档中的示例：
# 't' is [[1, 2, 3], [4, 5, 6]].
# 'paddings' is [[1, 1,], [2, 2]].
# rank of 't' is 2.
pad(t, paddings, "CONSTANT") ==> [[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 2, 3, 0, 0],
[0, 0, 4, 5, 6, 0, 0],
[0, 0, 0, 0, 0, 0, 0]]
pad(t, paddings, "REFLECT") ==> [[6, 5, 4, 5, 6, 5, 4],
[3, 2, 1, 2, 3, 2, 1],
[6, 5, 4, 5, 6, 5, 4],
[3, 2, 1, 2, 3, 2, 1]]
pad(t, paddings, "SYMMETRIC") ==> [[2, 1, 1, 2, 3, 3, 2],
[2, 1, 1, 2, 3, 3, 2],
[5, 4, 4, 5, 6, 6, 5],
[5, 4, 4, 5, 6, 6, 5]]
回答by mrry
As far as I know, there's no built-in operator that does this (tf.reshape()will give you an error if the shapes do not match). However, you can achieve the same result with a few different operators:
据我所知，没有内置的运算符可以执行此操作（tf.reshape()如果形状不匹配，则会出现错误）。但是，您可以使用几个不同的运算符获得相同的结果：
a = tf.constant([[1, 2], [3, 4]])
# Reshape `a` as a vector. -1 means "set this dimension automatically".
a_as_vector = tf.reshape(a, [-1])
# Create another vector containing zeroes to pad `a` to (2 * 3) elements.
zero_padding = tf.zeros([2 * 3] - tf.shape(a_as_vector), dtype=a.dtype)
# Concatenate `a_as_vector` with the padding.
a_padded = tf.concat([a_as_vector, zero_padding], 0)
# Reshape the padded vector to the desired shape.
result = tf.reshape(a_padded, [2, 3])
