问题描述
我正在尝试进行简单的卷积运算,但是具有复杂的数字:
I'm trying to run a simple convolution but with complex numbers:
r = np.random.random([1,10,10,10])
i = np.random.random([1,10,10,10])
x = tf.complex(r,i)
conv_layer = tf.layers.conv2d(
inputs=x,
filters=10,
kernel_size=[3,3],
kernel_initializer=utils.truncated_normal_complex(),
activation=tf.nn.sigmoid)
但是我收到此错误:
TypeError: Value passed to parameter 'input' has DataType complex128 not in list of allowed values: float16, float32
有人知道如何在Tensorflow中实现这种卷积吗?
Does anyone know how to implement such a convolution in Tensorflow?
我需要实现自定义操作,还是这里有更好的选择?
Will I need to implement a custom op, or is there some better option here?
令人沮丧的是,可能进行复杂的矩阵乘法,例如可以正常运行:
Frustratingly, complex matrix multiplication is possible, e.g. the following runs fine:
def r():
return np.random.random([10,10])
A = tf.complex(r(),r())
B = tf.complex(r(),r())
C = tf.multiply(A,B)
sess.run(C)
所以我想,没有任何真正的原因卷积不起作用(因为卷积本质上只是矩阵乘法).
So there's no real reason convolution shouldn't work, I would think (as convolution is essentially just matrix multiplication).
谢谢
推荐答案
所有复数值特征均分为笛卡尔(实数,虚数)或极坐标(模数,角度)表示.没有人真正尝试使用纯粹复杂的单个功能.我希望证明自己是错的!
All complex-valued features are split into either Cartesian (real, imaginary) or polar (modulus, angle) representations. Nobody is really trying to use a single feature that is purely complex; I would love to be proven wrong!
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