我正在尝试将结果从 / 复制到tf1.15 / 2.4.1。这是我期望产生类似结果的 2 个示例。第一个例子 usingtf.layers.conv2d()和第二个 using tf.keras.layers.Conv2D()。我试过使用kernel_initializer=some_tf_initializer(seed=seed)，也试过不使用内核初始化程序，还是一样。==================================================== =========================
TF1
import tensorflow as tf
import numpy as np
if __name__ == '__main__':
seed = 1
np.random.seed(seed)
tf.set_random_seed(seed)
session = tf.InteractiveSession()
initializer = tf.initializers.orthogonal(1.0, seed)
x = np.random.random((2, 84, 84, 1))
xp = tf.placeholder(x.dtype, x.shape)
result = tf.layers.conv2d(xp, 32, 8, 4, kernel_initializer=initializer)
session.run(tf.global_variables_initializer())
print(f'tf.layers.conv2d() output:\n{session.run([result], {xp: x})}\n{100 * "="}')
print(f'x:\n{x}')
结果是
tf.layers.conv2d() output:
[array([[[[-4.92592295e-01, 5.84946930e-01, 6.45957303e-01, ...,
3.65328020e-01, 4.03869755e-01, -1.06886940e+00],
[ 5.37549724e-02, 5.47611947e-01, 3.91425858e-01, ...,
1.20702194e-01, 9.21035825e-02, -1.10599980e+00],
[-4.68301348e-01, 8.54547125e-01, 2.99738041e-01, ...,
-7.47656723e-02, -3.29838560e-01, -6.43979360e-01],
...,
[-3.61929553e-01, 5.70854964e-01, 1.49096153e-01, ...,
4.05001572e-01, -5.70764458e-01, -8.50054931e-01],
[-5.23792632e-01, 5.79328891e-01, 4.36402398e-01, ...,
4.66264733e-01, -8.00687780e-02, -1.05017933e+00],
[-9.59200260e-01, 9.77023018e-01, 7.22006476e-01, ...,
3.29199395e-01, -6.48041695e-02, -9.65927090e-01]],
[[-3.05192775e-01, 5.62304495e-01, 2.66985564e-01, ...,
4.01866526e-01, -5.27853938e-01, -1.36294176e+00],
[-3.33627733e-01, 8.70909716e-01, -1.90685411e-01, ...,
5.82781510e-01, -9.54447222e-04, -5.93152081e-01],
[ 1.05312097e-01, 1.34995604e+00, 4.18403345e-01, ...,
7.29398371e-01, -1.52729852e-01, -1.44087877e+00],
...,
[-2.65877698e-01, 1.30702457e+00, 2.12075863e-01, ...,
4.02235128e-01, -4.83213829e-02, -9.84811507e-01],
[-2.33065665e-01, 8.06240360e-01, 2.04250988e-01, ...,
5.11907848e-01, -2.97176027e-01, -5.89994050e-01],
[-6.06616196e-01, 7.52128441e-01, 6.05931022e-01, ...,
8.25940123e-01, -8.11965401e-01, -1.38039418e+00]],
[[-1.14268620e-02, 9.44562211e-01, 7.30843200e-01, ...,
7.71483717e-01, -3.62299259e-01, -5.19022458e-01],
[-1.25248650e-01, 8.31288483e-01, 3.01203507e-01, ...,
6.10087249e-02, -1.37678504e-01, -1.12305468e+00],
[-2.37495350e-01, 5.80913482e-01, 2.48314393e-01, ...,
6.06333851e-01, -3.30380816e-01, -1.00515721e+00],
...,
[ 1.95822525e-01, 9.85462620e-01, 9.30753642e-02, ...,
5.07316387e-01, -2.72563623e-01, -3.44717273e-01],
[-7.58518148e-01, 7.18211754e-01, 3.56392246e-01, ...,
5.41345861e-01, -2.84327606e-01, -5.78967512e-01],
[-9.36528091e-01, 4.51066108e-01, 3.60469945e-01, ...,
6.06252149e-01, -9.82606865e-02, -1.12673980e+00]],
...,
[[-3.23906425e-01, 4.89133765e-01, 5.51718357e-01, ...,
-1.33718077e-01, 1.23015200e-03, -5.54514943e-01],
[-5.04556877e-01, 7.36618066e-01, 2.02579467e-01, ...,
-3.74142562e-01, 6.00494771e-03, -1.31111289e+00],
[-4.16236220e-01, 5.98196128e-01, 6.97848461e-01, ...,
6.23122747e-01, -1.07252840e-01, -4.83301913e-01],
...,
[ 1.06692401e-01, 1.73388947e+00, 3.22292122e-01, ...,
-1.75720933e-01, -2.96764992e-01, -1.06044579e+00],
[-5.62972482e-01, 1.36246077e+00, 9.13179694e-01, ...,
6.32824517e-01, -4.73498606e-01, -9.47552266e-01],
[-5.11697389e-01, 6.66774542e-01, 3.82947609e-01, ...,
5.67110785e-01, -7.11579248e-03, -7.77461939e-01]],
[[-1.11410557e-01, 1.10325702e+00, 5.50558807e-01, ...,
5.51697720e-01, -3.75442814e-01, -4.68653786e-01],
[-3.26447172e-01, 1.19972335e+00, 3.82313314e-01, ...,
2.20346417e-01, -3.39678412e-01, -8.66913575e-01],
[-3.98474415e-01, 7.14095329e-01, 8.04559140e-02, ...,
1.19488448e-01, -2.52660129e-01, -9.91879947e-01],
...,
[-5.70385227e-01, 8.52797253e-01, 4.66251675e-01, ...,
-3.24400644e-01, 3.20423484e-03, -1.39903236e+00],
[-2.07675682e-01, 6.29120258e-01, 6.79053796e-01, ...,
1.07336154e-01, 1.87886060e-01, -3.86794041e-01],
[-2.67700849e-02, 4.49128504e-01, 3.40760390e-01, ...,
6.36000637e-01, -3.77890278e-01, -3.30207391e-01]],
[[-3.10700139e-01, 8.18823907e-01, 3.67007768e-03, ...,
4.90558846e-01, -8.36860336e-01, -1.03869365e+00],
[-4.88181365e-01, 8.00080028e-01, 3.56097390e-01, ...,
4.76738039e-01, -3.42056012e-01, -5.30593649e-01],
[-4.60314405e-01, 7.07439805e-01, 4.22496599e-01, ...,
5.05574451e-01, -1.24705048e-01, -2.87646769e-01],
...,
[-2.57950491e-01, 1.20126622e+00, 5.56477074e-02, ...,
6.11127028e-01, -1.93377726e-01, -1.07765356e+00],
[-6.50455755e-01, 1.29336304e+00, 8.44637456e-01, ...,
1.81637465e-01, -2.88107846e-01, -4.49444781e-01],
[-4.21331048e-01, 3.20901642e-01, 8.83963968e-01, ...,
8.65426315e-01, -4.92428131e-01, -8.49800993e-01]]],
[[[-5.16387221e-01, 6.99757393e-01, 7.85561831e-01, ...,
1.03644383e-01, -3.70012470e-01, -5.32289379e-01],
[-7.05045607e-02, 6.10474017e-01, 3.49680420e-01, ...,
7.92201453e-01, -5.39308419e-01, -3.42154387e-01],
[-4.22583634e-01, 8.01291482e-01, 1.48925846e-01, ...,
7.68131504e-01, -7.38695500e-01, -1.09516989e+00],
...,
[-7.26389962e-01, 6.26188865e-01, 5.42793169e-01, ...,
3.52803865e-01, -3.46388144e-01, -7.60162696e-01],
[-9.04680334e-02, 1.75359623e-01, 8.00145598e-01, ...,
2.51379785e-01, -2.79905131e-01, -8.72223633e-01],
[-4.20990087e-01, 3.09422635e-01, 4.11481559e-01, ...,
3.69259084e-02, -1.60866470e-01, -5.05859647e-01]],
[[-4.38932989e-01, 9.72496331e-01, 2.50341307e-01, ...,
2.83248632e-01, -3.26580435e-01, -1.03773839e+00],
[-8.19208455e-01, 8.96083502e-01, 4.24397325e-01, ...,
-5.64658536e-02, -6.09974865e-01, -1.23144756e+00],
[-9.45132686e-01, 9.13248242e-01, 8.86299832e-01, ...,
2.15502049e-01, -5.58705913e-01, -2.25369791e-01],
...,
[-8.56722975e-01, 7.94114365e-01, 5.81065297e-01, ...,
4.96281428e-01, -1.03129758e+00, -1.05092158e+00],
[-2.82233182e-02, 9.48533077e-01, 6.68768609e-01, ...,
5.78499983e-01, -6.95651561e-01, -9.76414384e-01],
[-8.06302266e-01, 1.05147010e+00, 4.22578075e-01, ...,
2.94475123e-01, 1.74168406e-01, -1.14952206e+00]],
[[-3.06847178e-01, 9.29719098e-01, 2.68849689e-01, ...,
3.96790007e-01, -3.59629268e-01, -9.25076133e-01],
[-2.38372207e-01, 5.78896860e-01, 2.17575482e-01, ...,
-6.97642069e-02, -2.63976905e-01, -9.10845525e-01],
[-5.40468423e-01, 1.20147694e+00, 4.04620974e-01, ...,
1.76649501e-01, 1.46967870e-01, -6.74134783e-01],
...,
[-6.27551970e-01, 5.05884731e-01, 2.10479188e-01, ...,
3.09780840e-01, 1.46682047e-01, -1.07307698e+00],
[-6.44653887e-01, 8.90779216e-01, 3.82747674e-01, ...,
1.72155001e-01, -4.48190676e-01, -8.62522695e-01],
[-1.50221285e-01, 9.35658435e-01, 3.59703911e-01, ...,
-8.34232530e-02, -1.89649715e-01, -7.73705172e-01]],
...,
[[-3.47146995e-01, 6.34597952e-02, 4.73544353e-01, ...,
-8.20012972e-02, -1.78752555e-01, -1.36296041e+00],
[-8.95298221e-01, 8.66479595e-01, 5.61457267e-01, ...,
2.22826074e-01, -4.86448503e-01, -8.56547191e-01],
[-8.62118822e-01, 4.83077034e-01, 3.60908309e-02, ...,
3.29259093e-01, -4.08073639e-02, -6.45683881e-01],
...,
[-5.01558985e-01, 4.62753228e-01, 4.01966678e-01, ...,
6.35652593e-01, -7.45519465e-02, -5.39741380e-01],
[ 1.29982837e-01, 7.31941977e-01, -2.05750614e-01, ...,
3.16325608e-01, -3.28495177e-01, -1.09927128e+00],
[-4.90335504e-01, 4.94757973e-01, 9.40801327e-02, ...,
5.66634378e-02, -7.30613447e-01, -7.25730750e-01]],
[[-2.75066335e-01, 7.35209409e-01, 5.99839688e-01, ...,
6.71254510e-02, 7.97677772e-02, -7.72461196e-01],
[-1.58777502e-01, 7.51857910e-01, 3.80584693e-01, ...,
-1.01868390e-01, -7.12568409e-02, -6.42932271e-01],
[-1.45951405e-01, 1.03692197e+00, 4.91333873e-01, ...,
2.98796942e-01, -5.46416283e-01, -1.04881221e+00],
...,
[-9.75652891e-03, 8.28500896e-01, 3.48450207e-01, ...,
4.07241092e-01, -2.34265134e-01, -5.27081486e-01],
[-5.65917326e-01, 7.37496827e-01, 1.65005917e-01, ...,
6.61606291e-01, -2.20420580e-01, -1.11307865e+00],
[-2.21167732e-01, 4.83734785e-01, 4.59793140e-01, ...,
4.19304500e-01, -2.96987262e-01, -2.01353157e-01]],
[[-6.33535626e-01, 1.11787318e+00, 6.41380845e-01, ...,
1.12652086e-01, -1.99377096e-02, -6.69645437e-01],
[-1.05662073e-01, 3.87718948e-01, 4.30142659e-01, ...,
3.27637078e-01, -3.59568870e-01, -9.63431155e-01],
[-1.26793132e-01, 1.29664648e+00, 3.28922837e-01, ...,
3.21313848e-01, -7.53446525e-01, -7.93674733e-01],
...,
[-5.32937597e-01, 1.09915270e+00, 6.23443352e-01, ...,
9.96585500e-01, -6.21343220e-01, -1.01232184e+00],
[ 5.05322966e-02, 1.18874480e+00, 4.57358272e-01, ...,
4.80935716e-01, -2.04122013e-01, -1.13864994e+00],
[-1.03084733e-01, 1.14916096e+00, 2.73508528e-01, ...,
6.76093153e-01, -3.34324702e-01, -1.28436283e+00]]]])]
====================================================================================================
x:
[[[[4.17022005e-01]
[7.20324493e-01]
[1.14374817e-04]
...
[6.23672207e-01]
[7.50942434e-01]
[3.48898342e-01]]
[[2.69927892e-01]
[8.95886218e-01]
[4.28091190e-01]
...
[1.85762022e-02]
[7.00221437e-02]
[4.86345111e-01]]
[[6.06329462e-01]
[5.68851437e-01]
[3.17362409e-01]
...
[9.18601778e-01]
[4.02024891e-04]
[9.76759149e-01]]
...
[[5.89549934e-01]
[3.89137609e-01]
[5.05975232e-01]
...
[4.35888475e-01]
[7.89075202e-01]
[4.66467704e-01]]
[[6.73554921e-01]
[8.84836452e-01]
[9.38138449e-01]
...
[7.93970466e-01]
[2.13784215e-01]
[6.41105035e-01]]
[[7.31134736e-01]
[9.50619892e-02]
[7.00729238e-02]
...
[9.95522026e-01]
[4.81429517e-01]
[8.37812754e-01]]]
[[[6.03655452e-01]
[6.64374944e-01]
[2.72461392e-01]
...
[1.14069927e-01]
[2.93705095e-01]
[8.78904978e-03]]
[[2.53263696e-01]
[8.37712781e-01]
[8.07756027e-01]
...
[7.37152445e-01]
[6.00521471e-01]
[7.37999367e-01]]
[[3.75760828e-01]
[9.11106703e-01]
[8.72308594e-01]
...
[9.80268932e-01]
[1.13198035e-01]
[4.65678949e-01]]
...
[[4.94241516e-02]
[6.34450548e-01]
[8.93053413e-01]
...
[7.97760317e-01]
[3.18871974e-01]
[6.47314782e-01]]
[[4.65136696e-01]
[5.07669096e-01]
[4.23295851e-01]
...
[2.98177206e-01]
[5.32380132e-01]
[6.12348886e-01]]
[[2.58528146e-01]
[5.20561003e-02]
[7.82628170e-01]
...
[8.26242775e-03]
[7.43071396e-01]
[3.29652868e-01]]]]
TF2
import tensorflow as tf
import numpy as np
from tensorflow.keras.layers import Conv2D
if __name__ == '__main__':
seed = 1
np.random.seed(seed)
tf.random.set_seed(seed)
x = np.random.random((2, 84, 84, 1))
initializer = tf.initializers.Orthogonal(1.0, seed)
print(f'tf.keras.layers.Conv2D() output:\n{Conv2D(32, 8, 4, kernel_initializer=initializer)(x)}\n{100 * "="}')
print(f'x:\n{x}')
结果是
tf.keras.layers.Conv2D() output:
[[[[-5.41361034e-01 3.66543412e-01 -1.51497812e-03 ... -5.08555949e-01
-2.81920075e-01 2.03241315e-02]
[-5.59882522e-01 4.86802310e-01 -6.99946359e-02 ... -2.40252942e-01
-3.65630835e-01 -3.15424293e-01]
[-9.42213356e-01 2.15654269e-01 -1.55082747e-01 ... -4.04906332e-01
-2.01114044e-01 7.04537705e-02]
...
[-3.46490562e-01 3.19557160e-01 -2.92775959e-01 ... -4.28674221e-01
2.79810458e-01 4.20070797e-01]
[-3.45766425e-01 1.90711677e-01 -1.57374702e-02 ... -5.71854293e-01
4.97741327e-02 -1.16687842e-01]
[-9.33221936e-01 -1.68129373e-02 1.63893417e-01 ... -4.79621142e-01
-2.14530781e-01 5.61090052e-01]]
[[-7.58794546e-01 5.37562370e-01 -4.74378794e-01 ... -5.63471198e-01
-2.08135564e-02 3.88803691e-01]
[-4.21465009e-01 1.65287077e-01 -4.43225324e-01 ... -1.72018170e-01
-7.92833045e-02 3.92330065e-02]
[-4.81306076e-01 3.66574019e-01 -3.64440233e-01 ... -5.52142262e-01
-2.36726597e-01 -3.12441528e-01]
...
[-8.76132190e-01 3.22705477e-01 -1.90438583e-01 ... -6.52492762e-01
-6.05543517e-02 3.59102860e-02]
[-7.17206061e-01 1.32312387e-01 -4.24121648e-01 ... 1.82857260e-01
1.84026435e-01 -1.85313985e-01]
[-1.20653558e+00 4.54987772e-02 -2.89574206e-01 ... -3.28905612e-01
3.50747108e-02 1.20641785e-02]]
[[-5.34970939e-01 7.13559866e-01 -4.85820472e-01 ... -4.59470123e-01
3.37272674e-01 1.63189009e-01]
[-7.05033600e-01 3.56267929e-01 -4.30038758e-02 ... -5.83572984e-01
-1.40957370e-01 -2.53099173e-01]
[-5.99712431e-01 7.10705340e-01 -3.39112192e-01 ... -4.14105803e-01
1.87255502e-01 -3.33267421e-01]
...
[-5.80564499e-01 4.36445504e-01 3.36502224e-01 ... 1.45576835e-01
3.74677926e-01 -3.91695678e-01]
[-7.22398818e-01 3.15642595e-01 -2.88131565e-01 ... -3.45985293e-01
2.34268203e-01 3.29410911e-01]
[-7.46637762e-01 2.26648629e-01 -1.73913926e-01 ... -1.78611025e-01
7.35538006e-02 2.55084813e-01]]
...
[[-5.49425662e-01 -3.49880159e-02 4.77346703e-02 ... -4.04859513e-01
3.71269658e-02 -1.85562521e-02]
[-8.26125443e-01 1.29296139e-01 -2.88902789e-01 ... -8.50088000e-01
-5.04021049e-01 -4.97736454e-01]
[-7.37619460e-01 5.00219822e-01 7.76109993e-02 ... -1.41733378e-01
2.81554639e-01 3.69791657e-01]
...
[-3.06938559e-01 4.47962463e-01 -2.05980629e-01 ... -8.48388433e-01
-1.59557834e-01 3.17021906e-02]
[-3.87109697e-01 9.46836114e-01 -2.71658182e-01 ... -6.23931110e-01
-2.16598317e-01 -1.88935712e-01]
[-1.31429803e+00 3.86178017e-01 -6.20340466e-01 ... -3.68769616e-02
1.81194678e-01 -1.06644318e-01]]
[[-7.55118549e-01 -1.59330755e-01 -1.55888349e-01 ... -8.46641809e-02
5.47275543e-02 -3.14989388e-01]
[-7.29794323e-01 1.63924411e-01 5.99410906e-02 ... 1.31567806e-01
-4.67930377e-01 2.95255750e-01]
[-6.91133916e-01 5.20268269e-02 -7.29867145e-02 ... -1.88851178e-01
4.07196075e-01 4.01087821e-01]
...
[-5.79473913e-01 7.94014513e-01 -7.01249093e-02 ... -6.76721931e-01
-1.01779945e-01 -5.97994268e-01]
[-6.84648752e-01 1.01270474e-01 -4.07643348e-01 ... 3.85033749e-02
8.26148912e-02 5.69675528e-02]
[-4.62479711e-01 4.19429392e-01 1.77284703e-04 ... -1.42741054e-01
3.54655176e-01 2.56564021e-01]]
[[-2.46302426e-01 7.49420345e-01 8.12041853e-03 ... -3.32499892e-01
1.33527100e-01 6.35570288e-02]
[-9.58327591e-01 1.98065817e-01 1.93995520e-01 ... -2.94785231e-01
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