How do I use numpy.concatenate()?

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neural-network

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I am very new to GAN and machine learning overall, anyway this line is not working and I don't know why:

Xd = numpy.concatenate((image_batch, generated_images))

image_batch looks like this:

[[[[-1.         -1.         -0.99215686 ... -1.         -1.
    -1.        ]
   [-1.         -1.         -0.99215686 ... -0.99215686 -1.
    -1.        ]
   [-1.         -1.         -0.99215686 ... -0.9843137  -0.99215686
    -1.        ]
   ...
   [-1.         -1.         -1.         ... -0.84313726 -0.8509804
    -0.85882354]
   [-1.         -1.         -1.         ... -0.9372549  -0.9529412
    -0.9529412 ]
   [-1.         -1.         -1.         ... -0.99215686 -1.
    -1.        ]]

  [[-0.9843137  -0.9764706  -0.96862745 ... -1.         -0.99215686
    -0.9843137 ]
   [-0.9843137  -0.9764706  -0.96862745 ... -0.9843137  -0.99215686
    -0.9843137 ]
   [-0.9764706  -0.9764706  -0.96862745 ... -0.96862745 -0.9843137
    -0.99215686]
   ...
   [-1.         -1.         -1.         ... -0.75686276 -0.77254903
    -0.78039217]
   [-1.         -1.         -1.         ... -0.8666667  -0.90588236
    -0.92941177]
   [-1.         -1.         -1.         ... -0.92156863 -0.9764706
    -1.        ]]

  [[-0.8980392  -0.8901961  -0.88235295 ... -0.99215686 -0.9843137
    -0.9764706 ]
   [-0.8980392  -0.8901961  -0.88235295 ... -0.9764706  -0.9843137
    -0.9764706 ]
   [-0.8901961  -0.8901961  -0.88235295 ... -0.9607843  -0.9764706
    -0.9843137 ]
   ...
   [-1.         -1.         -1.         ... -0.654902   -0.67058825
    -0.6784314 ]
   [-1.         -1.         -1.         ... -0.7647059  -0.8117647
    -0.8352941 ]
   [-1.         -1.         -1.         ... -0.81960785 -0.88235295
    -0.9137255 ]]]]

and generated_images like this:

[[[[ 4.23198326e-05  3.00442276e-04 -1.81058873e-04]
   [ 1.45811282e-04  3.19001469e-04 -5.63107751e-05]
   [ 2.64693721e-04  1.66193859e-04 -1.39604657e-04]
   ...
   [-6.86520070e-04  5.22632385e-04  5.16742817e-04]
   [-1.71413078e-04  3.02032888e-04  4.35194233e-04]
   [-5.06673474e-04  6.82787504e-04  1.78376344e-04]]

  [[ 6.45761684e-05  8.61766166e-05 -6.29937422e-05]
   [ 2.98403582e-04  6.86934000e-05  4.29495230e-05]
   [ 4.09575558e-04 -4.51698754e-04  4.19436947e-05]
   ...
   [-9.55444353e-04  5.43228874e-04  1.13315729e-03]
   [-4.64068027e-04  4.53829765e-04  6.01236941e-04]
   [-8.01370596e-04  6.68600784e-04  2.78927822e-04]]

  [[ 1.68935439e-04  1.33932399e-05  1.74131943e-04]
   [ 1.84323857e-04  4.10580367e-04  3.57588753e-04]
   [ 2.85821239e-04 -1.35206705e-04  3.16827151e-04]
   ...
   [-7.73848209e-04  2.02491228e-03 -4.51121850e-05]
   [-4.27678955e-04  1.60137843e-03 -2.11666484e-04]
   [-6.78208133e-04  1.14948954e-03 -9.94570655e-05]]

  ...

  [[-4.19918069e-04 -1.32954505e-03  2.47726566e-03]
   [ 1.10020614e-04 -7.01685902e-04  2.04097223e-03]
   [ 4.61560732e-04 -1.73690103e-04  1.79996120e-03]
   ...
   [ 1.10731961e-03 -1.98406796e-03  3.62820574e-04]
   [ 9.58909630e-04 -1.13258348e-03  2.37658562e-04]
   [ 9.03887034e-04 -6.93049049e-04 -3.79532954e-04]]

  [[ 2.94119072e-05 -1.02064828e-03  2.08724616e-03]
   [ 4.25078993e-04 -1.15254743e-03  2.18772772e-03]
   [ 4.50835330e-04 -1.38088432e-03  1.82284403e-03]
   ...
   [ 1.06123753e-03 -1.31258322e-03  1.31079473e-03]
   [ 9.41374048e-04 -9.39883583e-04  5.85307193e-04]
   [ 6.50550472e-04 -7.27863458e-04 -2.13365493e-04]]

  [[ 3.37545469e-04 -1.10758666e-03  1.32661499e-03]
   [ 6.59163459e-04 -9.21792001e-04  1.55565923e-03]
   [ 5.12639701e-04 -6.35552977e-04  9.16730613e-04]
   ...
   [ 1.27078302e-03 -6.56231248e-04  1.26750499e-03]
   [ 8.59004795e-04 -8.31177284e-04  5.39227622e-04]
   [ 5.79519896e-04 -1.06042309e-03  1.16935298e-04]]]]

What I have tried:

I tried changing the code slightly to this:

Xd = np.concatenate((image_batch, generated_images), axis=1)

this gave me the following error:

ValueError: all the input array dimensions for the concatenation axis must match exactly, but along dimension 1, the array at index 0 has size 3 and the array at index 1 has size 16384