我有两个被证明与以下相同的模型：
if len(m_s.layers) != len(m_m.layers):
print("number of layers are different")
for i in range(len(m_s.layers)):
weight_s = m_s.layers[i].get_weights()
weight_m = m_m.layers[i].get_weights()
if len(weight_s) > 0:
for j in range(len(weight_s)):
if (weight_s[j] == weight_m[j]).all:
print("layer %d identical" % i)
else:
print("!!!!! layer %d not the same" % i)
else:
if len(weight_m) == 0:
print("layer %d identical" % i)
else:
print("!!!!! layer %d not the same" % i)
，输出显示它们是相同的。它们是imagenet模型的片段。
layer 0 identical
layer 1 identical
layer 2 identical
layer 2 identical
layer 2 identical
layer 2 identical
layer 3 identical
layer 4 identical
layer 5 identical
layer 5 identical
layer 5 identical
layer 5 identical
layer 6 identical
layer 7 identical
layer 8 identical
layer 8 identical
layer 8 identical
layer 8 identical
layer 9 identical
layer 10 identical
layer 10 identical
layer 10 identical
layer 10 identical
layer 11 identical
layer 12 identical
layer 13 identical
layer 13 identical
layer 13 identical
layer 13 identical
layer 14 identical
layer 15 identical
layer 16 identical
layer 16 identical
layer 16 identical
layer 16 identical
layer 17 identical
layer 18 identical
layer 18 identical
layer 18 identical
layer 18 identical
layer 19 identical
layer 20 identical
layer 21 identical
layer 21 identical
layer 21 identical
layer 21 identical
layer 22 identical
layer 23 identical
layer 24 identical
layer 24 identical
layer 24 identical
layer 24 identical
layer 25 identical
layer 26 identical
layer 27 identical
layer 27 identical
layer 27 identical
layer 27 identical
layer 28 identical
layer 29 identical
layer 30 identical
layer 30 identical
layer 30 identical
layer 30 identical
layer 31 identical
layer 32 identical
layer 33 identical
layer 33 identical
layer 33 identical
layer 33 identical
layer 34 identical
layer 35 identical
layer 35 identical
layer 35 identical
layer 35 identical
layer 36 identical
layer 37 identical
layer 38 identical
layer 38 identical
layer 38 identical
layer 38 identical
layer 39 identical
layer 40 identical
layer 41 identical
layer 41 identical
layer 41 identical
layer 41 identical
layer 42 identical
layer 43 identical
layer 44 identical
layer 44 identical
layer 44 identical
layer 44 identical
layer 45 identical
layer 46 identical
layer 47 identical
layer 47 identical
layer 47 identical
layer 47 identical
layer 48 identical
layer 49 identical
layer 50 identical
layer 50 identical
layer 50 identical
layer 50 identical
layer 51 identical
但是，当我在同一台机器和同一环境中使用这两个模型来预测相同的输入数据时，输出却完全不同。
m_s.predict(data)
输出
array([[[[-2.2014694e+00, -7.4636793e+00, -3.7543521e+00, ...,
4.2393379e+00, 7.2923303e+00, -7.9203067e+00],
[-6.8980045e+00, -6.7517347e+00, 5.9752476e-01, ...,
2.2391853e+00, -2.0161586e+00, -7.5054851e+00],
[-4.4470978e+00, -4.2420959e+00, -3.9374633e+00, ...,
5.9843721e+00, 5.4481273e+00, -2.7136576e+00],
...,
[-8.2077494e+00, -5.5874801e+00, 2.2708473e+00, ...,
-2.5585687e-01, 4.0198727e+00, -4.5880938e+00],
[-7.5793233e+00, -6.3811040e+00, 3.7389126e+00, ...,
1.7169635e+00, -3.4249902e-01, -7.1873198e+00],
[-8.2512989e+00, -4.2883468e+00, -2.7908459e+00, ...,
3.9796615e+00, 4.7512245e-01, -4.5338011e+00]],
[[-5.2522459e+00, -5.2272692e+00, -3.7313356e+00, ...,
1.0820831e+00, -1.9317195e+00, -8.3177958e+00],
[-5.8229809e+00, -6.8049965e+00, -1.4538713e+00, ...,
4.0576010e+00, -1.9025326e-02, -8.2517090e+00],
[-6.1541910e+00, -2.6757658e-01, -5.4412403e+00, ...,
1.7984511e+00, 2.9016986e+00, 7.6427579e-01],
...,
[-1.1129386e+00, 7.9319181e+00, 7.7404571e-01, ...,
-1.7145084e+01, 1.5210888e+01, 1.3812095e+01],
[ 3.5752565e-01, 1.4212518e+00, -6.1826277e-01, ...,
-3.4348285e+00, 5.1942883e+00, 2.1960042e+00],
[-6.3907943e+00, -5.3237562e+00, -3.1632636e+00, ...,
2.1118989e+00, -3.8516359e+00, -6.2463970e+00]],
[[-7.2064867e+00, -3.6420932e+00, -1.6844990e+00, ...,
6.4910537e-01, -4.4807429e+00, -7.8619242e+00],
[-6.4934230e+00, -4.5477719e+00, 9.2149705e-01, ...,
4.2846882e-01, -7.4903011e-01, -9.8737726e+00],
[-7.2704558e+00, 9.5214283e-01, -2.0818310e+00, ...,
-1.6958854e-01, 1.6371614e+00, -2.7756066e+00],
...,
[-7.1980424e+00, -7.2074276e-01, 2.3514495e+00, ...,
-9.7255888e+00, 2.1547556e-01, 4.3379207e+00],
[-6.7656651e+00, 6.3100419e+00, -7.8286257e+00, ...,
-5.1035576e+00, -1.3960669e+00, 2.3991609e+00],
[-7.0669832e+00, -1.2582588e-01, -5.3176193e+00, ...,
3.4836166e+00, -2.4024684e+00, -6.0632706e+00]],
...,
[[-7.3400059e+00, -3.1168675e+00, -1.9545169e+00, ...,
1.0936095e+00, -1.5736668e+00, -9.5641651e+00],
[-2.9115820e+00, -4.7334772e-01, 2.6805878e-01, ...,
8.3148491e-01, -1.2751791e+00, -5.5142212e+00],
[ 1.2365078e+00, 1.0945862e+01, -4.9259267e+00, ...,
1.9169430e+00, 5.1151342e+00, 4.9710069e+00],
...,
[-2.2321188e+00, 8.8735223e-02, -7.6890874e+00, ...,
-3.1269640e-01, 7.3404179e+00, -7.2507386e+00],
[-2.2741010e+00, -6.5992510e-01, 4.0761769e-01, ...,
1.8645943e+00, 4.0359187e+00, -7.7996893e+00],
[ 5.5672646e-02, -1.4715804e+00, -1.9753509e+00, ...,
2.5039923e+00, -1.0506821e-01, -6.5183282e+00]],
[[-8.3111782e+00, -4.6992331e+00, -3.1351955e+00, ...,
1.8569698e+00, -1.1717710e+00, -8.5070782e+00],
[-4.7671299e+00, -2.5072317e+00, 2.9760203e+00, ...,
2.9142296e+00, 3.2271760e+00, -4.7557964e+00],
[ 5.5070686e-01, 5.3218126e-02, -2.1629403e+00, ...,
8.8359457e-01, 3.1481497e+00, -2.1769693e+00],
...,
[-3.7305963e+00, -1.2512873e+00, 2.0231385e+00, ...,
4.4094267e+00, 3.0268743e+00, -9.6763916e+00],
[-5.4271636e+00, -4.6796727e+00, 5.7922940e+00, ...,
3.6725988e+00, 5.2563481e+00, -8.1707211e+00],
[-1.2138665e-02, -3.6983132e+00, -6.4367266e+00, ...,
6.8217549e+00, 5.7782011e+00, -5.4132147e+00]],
[[-5.0323372e+00, -3.3903065e+00, -2.7963824e+00, ...,
3.9016938e+00, 1.4906535e+00, -2.1907964e+00],
[-7.7795396e+00, -5.7441168e+00, 3.4615259e+00, ...,
1.4764800e+00, -2.9045539e+00, -4.4136987e+00],
[-7.2599754e+00, -3.4636111e+00, 4.3936129e+00, ...,
1.9856967e+00, -1.0856767e+00, -5.7980385e+00],
...,
[-6.1726952e+00, -3.9608026e+00, 5.5742388e+00, ...,
4.9396091e+00, -2.8744078e+00, -8.3122082e+00],
[-1.3442982e+00, -5.5807371e+00, 4.7524319e+00, ...,
5.0170369e+00, 2.9530718e+00, -7.1846304e+00],
[-1.7616816e+00, -6.7234058e+00, -8.3512306e+00, ...,
4.1365266e+00, -2.8818092e+00, -2.9208889e+00]]]],
dtype=float32)
同时
m_m.predict(data)
输出
array([[[[ -7.836284 , -2.3029385 , -3.6463926 , ..., -1.104739 ,
12.992413 , -6.7326055 ],
[-11.714638 , -2.161682 , -2.0715065 , ..., -0.0467519 ,
6.557784 , -2.7576606 ],
[ -8.029486 , -4.068902 , -4.6803293 , ..., 7.022674 ,
7.741771 , -1.874607 ],
...,
[-11.229774 , -5.3050747 , 2.807798 , ..., 1.1340691 ,
4.3236184 , -5.2162905 ],
[-11.458603 , -6.2387724 , 0.25091058, ..., 1.0305461 ,
5.9631624 , -6.284294 ],
[ -8.663513 , -1.8256164 , -3.0079443 , ..., 5.9437366 ,
7.0928698 , -1.0781381 ]],
[[ -4.362539 , -2.8450599 , -3.1030283 , ..., -1.5129573 ,
2.2504683 , -8.414198 ],
[ -6.308961 , -4.99597 , -3.8596241 , ..., 4.2793174 ,
2.7787375 , -5.9963284 ],
[ -4.8252788 , -1.5710263 , -6.083002 , ..., 4.856139 ,
2.9387665 , 0.29977918],
...,
[ -0.8481703 , 5.348722 , 2.3885899 , ..., -19.35567 ,
13.1428795 , 12.364189 ],
[ -1.8864173 , -3.7014763 , -2.5292692 , ..., -3.6618025 ,
4.3906307 , 0.03934002],
[ -6.0526505 , -5.504422 , -3.8778243 , ..., 4.3741727 ,
1.0135782 , -5.1025114 ]],
[[ -6.7328253 , -1.5671132 , 0.16782492, ..., -2.5069456 ,
1.4343324 , -8.59162 ],
[ -7.5468965 , -5.6893063 , 0.13871288, ..., 0.22174302,
1.1608338 , -8.77916 ],
[ -5.940791 , 1.1769392 , -4.5080614 , ..., 3.5371704 ,
2.4181929 , -2.7893126 ],
...,
[ -9.490874 , -2.3575358 , 2.5908213 , ..., -18.813345 ,
-3.4546187 , 4.8375816 ],
[ -5.1123285 , 3.3766522 , -10.71935 , ..., -5.8476105 ,
-3.5569503 , 0.6331433 ],
[ -6.2075157 , 0.4942119 , -7.044799 , ..., 5.191918 ,
2.7723277 , -4.5243273 ]],
...,
[[ -7.06453 , -1.3950944 , -0.37429178, ..., -0.11883163,
0.22527158, -9.231563 ],
[ -4.0204725 , -3.6592636 , 0.15709507, ..., 1.7647433 ,
4.6479545 , -3.8798246 ],
[ 0.75817275, 9.890637 , -7.069035 , ..., 2.995041 ,
6.8453026 , 6.028713 ],
...,
[ -1.5892754 , 2.119719 , -10.078391 , ..., -2.546938 ,
6.5255003 , -6.749384 ],
[ -3.2769198 , -0.46709523, -2.1529863 , ..., 1.8028917 ,
7.2509494 , -7.5441256 ],
[ -1.2531447 , 0.96327865, -1.0863694 , ..., 2.423694 ,
-1.1047542 , -6.4944725 ]],
[[-10.218704 , -2.5448627 , -0.6002845 , ..., 0.80485874,
2.7691112 , -7.374723 ],
[ -8.354421 , -5.461962 , 5.2284613 , ..., 0.5315646 ,
5.701563 , -4.0477304 ],
[ -2.7866952 , -5.8492465 , -1.5627437 , ..., 1.9490132 ,
4.0491743 , -2.7550128 ],
...,
[ -4.5389686 , -3.2624135 , 0.7429285 , ..., 2.5953412 ,
3.8780956 , -8.652936 ],
[ -5.704813 , -3.730238 , 4.87866 , ..., 2.6826556 ,
4.8833456 , -6.8225956 ],
[ -0.16680491, -0.4325713 , -4.7689047 , ..., 8.588567 ,
6.786765 , -4.7118473 ]],
[[ -1.4958351 , 2.151188 , -4.1733856 , ..., -1.891511 ,
12.969635 , -2.5913832 ],
[ -7.6865544 , 0.5423928 , 6.2699823 , ..., -2.4558625 ,
6.1929445 , -2.7875526 ],
[ -6.995783 , 2.609788 , 5.6196365 , ..., -0.6639404 ,
5.7171726 , -3.7962272 ],
...,
[ -3.6628227 , -1.3322173 , 4.7582774 , ..., 2.122392 ,
3.1294663 , -8.338194 ],
[ -3.0116327 , -1.322252 , 4.802135 , ..., 1.9731755 ,
8.750839 , -6.989321 ],
[ 2.3386476 , -2.4584374 , -5.9336634 , ..., 0.48920852,
3.540884 , -2.9136944 ]]]], dtype=float32)
这显然不是因为浮动舍入，因为输出是完全不同的。我不明白为什么。请帮忙
答案 0 :(得分：0)
我通过逐层提取找出了原因。尽管我将其设置为不可训练，但模型中存在BatchNormalizing层，并且权重已更改。