我正在从预训练的VGG19 ConvNet中提取给定图像的中间层输出。我希望如果我两次给出相同的图像，我应该得到相同的输出。但是，我没有得到相同的输出。为什么会发生这种情况以及如何解决呢？
其他详细信息： 我正在关注this paper。他们使用VGG19 ConvNet并从某个中间层（VGG22表示第二次卷积之前的第二层）中提取特征，以用于超分辨图像和地真图像。然后，他们计算这两个特征集之间的均方误差，并将其用作损失参数。现在，我的期望是，如果我只给地面真理图像两次，则均方误差应该为零。但是这没有发生吗？我在不同的迭代中获得了不同的特征值，但是具有相同的图像。我还注意到，当我再次运行该程序时，会得到相同的一组值。以下代码供参考：
import numpy
from keras import backend as K
from keras.applications.vgg19 import VGG19, preprocess_input
from keras.preprocessing.image import img_to_array, load_img
model = VGG19()
vgg22_layer_output = K.function([model.layers[0].input], [model.layers[5].output])
# image_matrix is a 224x224x3 matrix for an RGB-image.
hr_image_obj = load_img(hr_image_path)
hr_image_matrix = img_to_array(hr_image_obj)
cropped_hr_image = hr_image_matrix[0:224, 0:224, :]
expanded_image = numpy.expand_dims(cropped_hr_image, axis=0)
preprocessed_image = preprocess_input(expanded_image)
features1 = vgg22_layer_output ([preprocessed_image])[0]
features2 = vgg22_layer_output ([preprocessed_image])[0]
在这里，我期望的是features1 = features2，不是
结果：
功能1：
array([[[[2.15184002e+01, 1.81470230e+02, 0.00000000e+00, ...,
0.00000000e+00, 1.98130661e+02, 0.00000000e+00],
[2.27056488e+02, 0.00000000e+00, 0.00000000e+00, ...,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
[1.54923904e+02, 0.00000000e+00, 0.00000000e+00, ...,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
...,
[2.29082489e+02, 2.58140778e+02, 0.00000000e+00, ...,
3.18900665e+02, 0.00000000e+00, 0.00000000e+00],
[1.58660873e+02, 1.24280603e+03, 0.00000000e+00, ...,
2.76672821e+02, 0.00000000e+00, 0.00000000e+00],
[2.66982513e+02, 4.27661194e+02, 0.00000000e+00, ...,
4.57434418e+02, 0.00000000e+00, 0.00000000e+00]],
[[0.00000000e+00, 0.00000000e+00, 1.71959274e+02, ...,
0.00000000e+00, 1.25863232e+03, 0.00000000e+00],
[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
3.51934662e+02, 4.45714081e+02, 0.00000000e+00],
[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
3.73108368e+02, 7.51479004e+02, 0.00000000e+00],
...,
[0.00000000e+00, 3.06031370e+00, 0.00000000e+00, ...,
3.09630096e+02, 2.15055069e+02, 1.91232590e+02],
[0.00000000e+00, 1.33151245e+03, 0.00000000e+00, ...,
2.78728699e+02, 2.91452618e+01, 4.12124878e+02],
[1.13750778e+02, 3.04266022e+02, 0.00000000e+00, ...,
4.93073273e+02, 0.00000000e+00, 1.25463562e+02]],
[[0.00000000e+00, 2.36886551e+02, 1.87017990e+02, ...,
0.00000000e+00, 5.56484497e+02, 0.00000000e+00],
[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
1.29744125e+02, 5.47009888e+02, 0.00000000e+00],
[2.10977726e+01, 0.00000000e+00, 5.83388855e+02, ...,
3.78568268e+02, 1.76858459e+03, 0.00000000e+00],
...,
[0.00000000e+00, 2.26063950e+02, 0.00000000e+00, ...,
1.74201874e+02, 1.10421577e+02, 2.92625153e+02],
[0.00000000e+00, 1.49054639e+03, 1.73763367e+02, ...,
3.43214760e+01, 1.41045761e+02, 5.26752502e+02],
[1.79130356e+02, 4.18553101e+02, 1.12429085e+02, ...,
2.08473053e+02, 0.00000000e+00, 1.46159897e+02]],
...,
[[0.00000000e+00, 0.00000000e+00, 6.14884460e+02, ...,
4.48683044e+02, 2.60172217e+03, 0.00000000e+00],
[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
1.05306360e+03, 5.45696045e+02, 0.00000000e+00],
[5.33453941e+01, 0.00000000e+00, 6.09368164e+02, ...,
7.00016541e+02, 0.00000000e+00, 0.00000000e+00],
...,
[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
2.49793106e+02, 0.00000000e+00, 0.00000000e+00],
[0.00000000e+00, 0.00000000e+00, 3.44778638e+03, ...,
1.97339310e+02, 0.00000000e+00, 0.00000000e+00],
[1.27069351e+02, 0.00000000e+00, 0.00000000e+00, ...,
1.85339737e+02, 0.00000000e+00, 0.00000000e+00]],
[[0.00000000e+00, 0.00000000e+00, 4.90521271e+02, ...,
4.68645844e+02, 3.26934399e+03, 0.00000000e+00],
[2.26508102e+01, 0.00000000e+00, 7.08834915e+01, ...,
1.11953967e+03, 1.10590857e+03, 0.00000000e+00],
[1.11061287e+02, 0.00000000e+00, 8.05527405e+02, ...,
8.03228516e+02, 2.84233459e+02, 0.00000000e+00],
...,
[0.00000000e+00, 0.00000000e+00, 1.10313757e+03, ...,
5.78258667e+02, 1.47924316e+03, 0.00000000e+00],
[0.00000000e+00, 0.00000000e+00, 1.59146082e+03, ...,
7.10267578e+02, 6.43671143e+02, 0.00000000e+00],
[3.27744568e+02, 0.00000000e+00, 0.00000000e+00, ...,
4.53388458e+02, 0.00000000e+00, 0.00000000e+00]],
[[0.00000000e+00, 0.00000000e+00, 1.12306348e+03, ...,
1.63393646e+02, 3.52517969e+03, 0.00000000e+00],
[0.00000000e+00, 0.00000000e+00, 6.36935806e+01, ...,
4.52494598e+02, 1.94326257e+03, 0.00000000e+00],
[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
2.83666046e+02, 4.89346985e+02, 0.00000000e+00],
...,
[0.00000000e+00, 0.00000000e+00, 1.02328314e+03, ...,
2.65413391e+02, 2.64639990e+03, 0.00000000e+00],
[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
4.30894745e+02, 1.33343530e+03, 0.00000000e+00],
[7.57115707e+01, 0.00000000e+00, 0.00000000e+00, ...,
2.14354630e+02, 0.00000000e+00, 0.00000000e+00]]]],
dtype=float32)
功能2：
array([[[[2.44103737e+01, 3.35516052e+02, 0.00000000e+00, ...,
0.00000000e+00, 2.06830643e+02, 0.00000000e+00],
[4.71717712e+02, 0.00000000e+00, 0.00000000e+00, ...,
2.63770996e+02, 0.00000000e+00, 0.00000000e+00],
[3.93549591e+02, 0.00000000e+00, 0.00000000e+00, ...,
1.77212814e+02, 0.00000000e+00, 0.00000000e+00],
...,
[5.33919487e+01, 0.00000000e+00, 0.00000000e+00, ...,
1.85940536e+02, 0.00000000e+00, 0.00000000e+00],
[0.00000000e+00, 2.96363708e+02, 0.00000000e+00, ...,
1.09057648e+02, 0.00000000e+00, 0.00000000e+00],
[2.27105503e+01, 8.29022141e+01, 0.00000000e+00, ...,
1.38949188e+02, 0.00000000e+00, 0.00000000e+00]],
[[0.00000000e+00, 0.00000000e+00, 2.47062546e+02, ...,
0.00000000e+00, 1.66465466e+03, 0.00000000e+00],
[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
6.74320862e+02, 4.15592712e+02, 0.00000000e+00],
[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
8.65957825e+02, 8.59399170e+02, 0.00000000e+00],
...,
[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
1.66129944e+02, 0.00000000e+00, 0.00000000e+00],
[0.00000000e+00, 2.76259674e+02, 0.00000000e+00, ...,
8.00474930e+01, 0.00000000e+00, 1.08291901e+02],
[0.00000000e+00, 2.20606117e+01, 0.00000000e+00, ...,
1.28005768e+02, 0.00000000e+00, 3.49725151e+01]],
[[0.00000000e+00, 2.14503006e+02, 8.82690811e+01, ...,
0.00000000e+00, 5.60968628e+02, 0.00000000e+00],
[3.28399010e+01, 0.00000000e+00, 0.00000000e+00, ...,
3.34213745e+02, 2.90819824e+02, 0.00000000e+00],
[8.66472626e+01, 0.00000000e+00, 1.10250635e+03, ...,
6.37486572e+02, 1.67822144e+03, 0.00000000e+00],
...,
[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
5.90463066e+01, 0.00000000e+00, 9.77452278e+00],
[0.00000000e+00, 3.39350586e+02, 4.62688398e+00, ...,
1.32679808e+00, 0.00000000e+00, 1.65987671e+02],
[2.47563610e+01, 7.48269196e+01, 1.33592939e+01, ...,
6.36582108e+01, 0.00000000e+00, 5.70933228e+01]],
...,
[[0.00000000e+00, 0.00000000e+00, 6.27470215e+02, ...,
2.55267532e+02, 2.27369629e+03, 0.00000000e+00],
[1.52827530e+02, 0.00000000e+00, 0.00000000e+00, ...,
1.20087329e+03, 0.00000000e+00, 0.00000000e+00],
[1.33066071e+02, 0.00000000e+00, 5.95311890e+02, ...,
7.66817871e+02, 0.00000000e+00, 0.00000000e+00],
...,
[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
4.81101898e+02, 0.00000000e+00, 0.00000000e+00],
[0.00000000e+00, 0.00000000e+00, 3.99484155e+03, ...,
5.40802429e+02, 0.00000000e+00, 0.00000000e+00],
[1.93494095e+02, 0.00000000e+00, 1.16481377e+02, ...,
3.75594208e+02, 0.00000000e+00, 0.00000000e+00]],
[[0.00000000e+00, 0.00000000e+00, 5.08203369e+02, ...,
3.65947357e+02, 2.66369580e+03, 0.00000000e+00],
[2.29821182e+02, 0.00000000e+00, 3.83578918e+02, ...,
1.37410413e+03, 1.28806320e+02, 0.00000000e+00],
[1.89210968e+02, 0.00000000e+00, 9.40994324e+02, ...,
8.16117615e+02, 0.00000000e+00, 0.00000000e+00],
...,
[0.00000000e+00, 0.00000000e+00, 1.34960962e+03, ...,
1.03916003e+03, 6.58975891e+02, 0.00000000e+00],
[6.77491531e+01, 0.00000000e+00, 2.07465186e+03, ...,
1.13461414e+03, 0.00000000e+00, 0.00000000e+00],
[2.96653259e+02, 0.00000000e+00, 0.00000000e+00, ...,
6.33178528e+02, 0.00000000e+00, 0.00000000e+00]],
[[0.00000000e+00, 0.00000000e+00, 1.20268628e+03, ...,
6.86023560e+01, 2.83282886e+03, 0.00000000e+00],
[0.00000000e+00, 0.00000000e+00, 3.50556335e+02, ...,
8.04942566e+02, 7.94925537e+02, 0.00000000e+00],
[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
3.74615967e+02, 0.00000000e+00, 0.00000000e+00],
...,
[0.00000000e+00, 0.00000000e+00, 1.17975757e+03, ...,
6.35223450e+02, 1.62643567e+03, 0.00000000e+00],
[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
9.20189697e+02, 4.05781097e+02, 0.00000000e+00],
[1.26037315e+02, 0.00000000e+00, 0.00000000e+00, ...,
4.17285614e+02, 0.00000000e+00, 0.00000000e+00]]]],
dtype=float32)
图像取自here。路径：v1.3/Code/Ours/Images_GroundTruth/BSD200/335094.png
编辑1 ：添加了其他代码和结果