我有一个具有复值的维度(B，N，K(的张量，我想把张量转换成维度(B、N，2*K(，这样每个实数都放在它的复值旁边，类似这样：
[[[ 2.51-0.49j 0.80-0.74j]
[-0.01+0.34j -2.04+0.70j]
[ 0.02-1.85j -0.38+1.66j]]
[[ 0.54+0.49j 0.28+1.75j]
[-1.52-1.72j 0.68+0.17j]
[-0.89+0.32j -1.88+0.15j]]] (2, 3, 2)
这个复张量被转换为：
[[[ 2.51 -0.49 0.80 -0.74]
[-0.01 0.34 -2.04 0.70]
[ 0.02 -1.85 -0.38 1.66]]
[[ 0.54 0.49 0.28 1.75]
[-1.52 1.72 0.68 0.17]
[-0.89 0.32 -1.88 0.15]]] (2, 3, 4)
为了便于阅读，我减少了小数位数。
这样的东西应该可以正常工作：
inp = tf.convert_to_tensor([[[ 2.51-0.49j , 0.80-0.74j],
[-0.01+0.34j, -2.04+0.70j],
[ 0.02-1.85j, -0.38+1.66j]],
[[ 0.54+0.49j, 0.28+1.75j],
[-1.52-1.72j, 0.68+0.17j],
[-0.89+0.32j, -1.88+0.15j]]])
initial_shape = tf.shape(inp)
# convert to 1 number per "last dimension"
tmp = tf.reshape(inp, (initial_shape[0], -1, 1))
# get real part
real = tf.math.real(tmp)
# get imaginary part
imag = tf.math.imag(tmp)
# concatenate real with its corresponding imaginary
to_reshape = tf.concat((real, imag), axis=-1)
# reshape to initial shape, with the last *2
tf.reshape(to_reshape, (initial_shape[0], initial_shape[1], initial_shape[2]*2))
输出：
<tf.Tensor: shape=(2, 3, 4), dtype=float64, numpy=
array([[[ 2.51, -0.49, 0.8 , -0.74],
[-0.01, 0.34, -2.04, 0.7 ],
[ 0.02, -1.85, -0.38, 1.66]],
[[ 0.54, 0.49, 0.28, 1.75],
[-1.52, -1.72, 0.68, 0.17],
[-0.89, 0.32, -1.88, 0.15]]])>