输入张量流的复数，keras

如何解决输入张量流的复数，keras

我有输入129维复数（短时傅立叶传输数据）的模型

我的输入示例如下所示

[-1.3352364e+01+0.0000000e+00j  7.4373883e-01-7.2833991e-16j
  2.2738211e+01+6.8436519e-16j -1.4453428e+01+3.1225023e-16j
  1.0134536e+00+7.6327833e-17j -5.8692555e+00+7.8409501e-16j
 -4.2160640e+00+3.7905386e-16j  9.3214293e+00-7.9450335e-16j
 -3.8441191e+00-2.0816682e-16j  1.5526062e+00+9.3675068e-17j
  1.8541154e-01+7.5892474e-16j -2.7615318e+00+8.3960616e-16j
  6.1850090e+00+5.5511151e-17j -7.3036003e+00-8.9511731e-16j
  5.1545906e+00-7.1212077e-16j -2.2619576e+00+0.0000000e+00j
 -4.3920875e+00+3.8857806e-16j  9.7775030e+00-1.1102230e-15j
 -4.4443369e+00-1.2054651e-16j -1.7421865e+00+3.4694470e-17j
  3.4608727e+00+8.0491169e-16j -3.6370997e+00+1.1865509e-15j
  6.7330283e-01-7.8668031e-16j  1.2871089e+00-5.2388649e-16j
  1.4200196e-01+6.2753622e-16j -2.4753497e+00-1.0234869e-15j
  2.4278961e-02-1.1839655e-15j  7.3392744e+00+4.3715032e-16j
 -8.1446323e+00+7.2164497e-16j  2.3820071e+00-4.7878368e-16j
  5.5490100e-01+9.6275480e-16j  9.6059316e-01-5.1347815e-16j
 -1.4272486e+00+4.4408921e-16j -2.2834092e-01-6.9388939e-17j
  1.3941400e-01+7.0778396e-16j  7.8855026e-01-1.2420620e-15j
 -9.3603629e-01+5.5511151e-17j -8.9871936e-02-3.2612801e-16j
  1.1856022e+00+7.0518187e-16j -1.0490714e+00+3.5735304e-16j
  4.8156497e-01+1.6393137e-16j  1.9953914e-02-1.5612511e-16j
 -1.4316249e-02-4.0680944e-16j -1.9098872e-01-2.4286129e-16j
 -6.5025851e-02-2.2204460e-16j -2.7533963e-02-2.8449465e-16j
  1.3631889e-01+6.2014686e-16j -1.9419394e-01-4.4408921e-16j
  6.0891777e-01+3.3306691e-16j -5.1358789e-01+8.8817842e-16j
  4.1886669e-01+8.2314307e-16j -1.1331944e+00-2.7061686e-16j
  1.6293223e+00-5.5511151e-17j -1.3963546e+00-3.4000580e-16j
  8.9522165e-01-7.8913150e-17j -3.3560959e-01-3.7123082e-16j
 -1.4841197e-01-3.3306691e-16j  4.5283544e-01+4.6143644e-16j
 -5.2438241e-01+3.4259110e-16j  2.5227445e-01+2.9837244e-16j
  4.0655173e-02+3.6776138e-16j -2.1195586e-01+7.9797280e-16j
  9.3151316e-02-1.4892768e-15j  4.1130298e-01-4.7853973e-16j
 -3.6802697e-01-8.8817842e-16j  2.7421236e-01+2.0098398e-16j
 -4.9323350e-01-2.8707995e-16j  7.0892453e-02+9.7838404e-16j
  1.4285556e-02+1.8735014e-16j  1.2178756e-01+1.4571677e-16j
  5.1822972e-01+1.0149809e-16j -6.0321730e-01-4.6143644e-16j
  1.4312959e-01-5.5511151e-16j -2.0424712e-01-6.2796990e-16j
  2.0290254e-01-1.1145766e-15j  7.0337042e-02+1.1796120e-16j
 -2.2669752e-01+9.4368957e-16j  4.7235081e-01-1.7347235e-16j
 -6.8114263e-01-3.7905386e-16j  7.0097405e-01+0.0000000e+00j
 -5.3555572e-01+1.1102230e-16j  2.6501888e-01-4.4408921e-16j
 -3.2118353e-01-6.2014686e-16j  3.7940162e-01-4.9266147e-16j
 -4.3286872e-01-2.2204460e-16j  7.6514846e-01-2.0122792e-16j
 -7.9566664e-01-3.1483553e-16j  1.8461785e-01+4.5102810e-17j
  4.6878424e-01-1.6479873e-17j -7.7730691e-01+5.3082538e-16j
  7.9691464e-01-3.5823718e-16j -5.1372331e-01-1.0061396e-15j
  4.5839280e-01-5.5511151e-17j -6.0186821e-01-7.5633944e-16j
  5.8818871e-01+1.0685729e-15j -4.3991232e-01+2.9143354e-16j
  1.5778032e-01-4.4408921e-16j -6.3726664e-02-1.5265567e-16j
  2.4285218e-01+1.5074914e-15j -2.8261366e-01-7.6327833e-17j
  9.2593305e-02-5.5511151e-17j -7.3957220e-02+4.5102810e-16j
  2.0222366e-01-6.5035362e-17j -2.2292452e-01-3.1225023e-17j
  1.7134936e-01+3.0357661e-18j -8.9343295e-02+2.7408631e-16j
  6.6628762e-02+1.2054651e-16j -8.4265225e-02-1.8735014e-16j
  7.4724592e-02+1.3877788e-16j -4.5830503e-02+2.0816682e-17j
  4.0348507e-02+2.3156880e-16j -4.6607938e-02-2.2204460e-16j
  3.9488845e-02+1.6653345e-16j -5.3395957e-02-4.4408921e-16j
  3.3790331e-02+4.5986944e-17j -1.1470942e-02+7.8409501e-16j
  3.6072452e-03-5.5511151e-17j -1.0854214e-02+4.8572257e-17j
  5.6150518e-02+6.8436519e-16j -5.3869747e-02+1.2836954e-16j
 -4.3637045e-03+1.3877788e-17j  2.3376349e-02-7.5980888e-16j
  2.7135586e-02-4.5986944e-17j -3.3272862e-02-1.7347235e-16j
 -1.2956693e-02-3.2612801e-16j  2.3436353e-02+1.3183898e-16j
  1.5689885e-02-7.3742516e-17j -5.3210557e-02-4.8816203e-17j
  5.6559194e-02+0.0000000e+00j]

我的模型的输出是这个

[[ 2.44907394e-01 -2.97553688e-01  2.11519375e-01 -1.90888457e-02
  -4.56364267e-02 -6.27458245e-02 -1.32896289e-01  2.92474300e-01
  -4.04089779e-01 -1.56403586e-01 -1.92916021e-01 -1.43633649e-01
  -1.57259151e-01  5.65262511e-03 -2.09377334e-01  4.94567640e-02
  -1.03674516e-01 -1.69391558e-03 -7.67782032e-02  6.16271086e-02
  -7.57082552e-02 -5.81801347e-02  5.03328927e-02 -3.21788304e-02
   1.44796409e-02 -1.82129852e-02  2.29691751e-02  4.87755574e-02
  -3.32594924e-02 -4.09342609e-02  3.63402329e-02  1.22958608e-02
  -1.94040649e-02 -8.86565819e-03  2.06985734e-02  1.35932527e-02
  -3.36496159e-03  3.11814509e-02  3.27086858e-02  8.05965438e-03
   1.59415863e-02  1.15749724e-02  8.10898468e-03 -2.60975584e-03
   5.77399507e-03  1.21091865e-02  7.61231408e-03  1.23816207e-02
   1.06919296e-02  1.21192187e-02  5.17597422e-03  8.74948129e-03
   5.39486483e-03  8.50370154e-03  3.17635015e-03  1.04431063e-03
   3.65899876e-03  2.61678174e-03  6.68763369e-03  1.77711621e-03
   7.05862418e-03  4.92045656e-03 -1.12678483e-03  5.10105863e-03
   7.67963007e-03  4.02958319e-03  1.09087341e-02  4.09850851e-03
  -7.14905933e-03 -6.37976453e-03  1.45311467e-02 -1.75617263e-03
  -2.48615816e-03  8.45167413e-03  1.35500357e-03  3.68746743e-03
   7.73085281e-03  5.56082651e-03  3.27861309e-03  1.69695169e-03
   1.68296695e-03 -7.13682547e-03  4.51812893e-03  1.05617158e-02
   9.09534469e-03  7.56881759e-03  7.15654343e-04 -3.81373987e-03
  -9.41876695e-03  1.34883039e-02  6.52562454e-03  5.85681945e-03
  -3.25944275e-04 -3.52438539e-04  5.87854534e-03  4.60745022e-03
   1.70308724e-03  4.45364043e-03  3.00474837e-03  5.36788255e-03
   4.28943709e-03  1.88645348e-03  1.65197998e-04  3.76204029e-03
   4.65429574e-03  2.02246010e-03  3.14211100e-03  3.25421616e-03
   3.42429429e-03  4.88381833e-03  4.63513285e-03  1.57951191e-03
   3.13404948e-03  2.97084078e-03  4.92273644e-03  1.47051737e-03
   2.75985897e-03  3.42904776e-03  3.48226726e-03  4.90953028e-03
   3.53986397e-03  2.55738944e-03  2.57845968e-03  3.87272611e-03
   3.58704850e-03  2.76022032e-03  3.19864228e-03  3.40151414e-03
   3.43684852e-03]]

numpy的形状相同(1,129) 但是，输出没有虚数。

为什么会发生？

我使用“ LSTM”的模型非常简单，如下所示。

我需要做一些特殊的事情来处理复数吗？

NUM_DIM = 32  
NUM_RNN = 100
model.add(LSTM(NUM_DIM,activation=None,input_shape=(NUM_RNN,1),return_sequences=True))
model.add(Dense(1,activation="linear"))  
model.compile(loss='mean_squared_error',optimizer=Adam(lr=0.01,beta_1=0.9,beta_2=0.999))
model.summary()

解决方法

机器学习框架尚无法处理复杂数据。关于复杂平面中的梯度和激活函数，仍然存在一些主要挑战（例如，请参见this article作为概述）。

在我所知道的大多数方法中，数据的实部和虚部都是简单连接在一起的，因此网络只能获取实数据。例如，网络将获得[1 + 2i,3 + 4i]而不是[1,3,2,4]。

（无论如何，您的输入数据看起来几乎都是“真实值”，虚部比实部低很多量级。如果到处都是这种情况，那么您可能只采用实部就可以逃脱...）编辑：我刚刚检查了-keras网络无论如何都只使用数据的实部，这就是为什么您仍然会得到（实值）输出的原因。