Tensorflow 张量重塑并用零填充

Question

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，但据我了解，将 Tensor 转换为 ndarray 需要在 GPU 和 CPU 之间来回切换。

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()？

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]]

Answer 1

据我所知，没有内置运算符可以执行此操作（如果形状不匹配，tf.reshape() 会报错）。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])

Answer 2

Tensorflow 现在提供 pad 函数，它以多种方式对张量执行填充（例如 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)

上述文档中的示例：

# '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]]

Tensorflow 张量重塑并用零填充

Tensorflow Tensor reshape and pad with zeros

python

numpy

tensorflow