Issue
I'm trying to train an CNN autoencoder model on a pretty big dataset so I'm using tf.data.Dataset. I've been trying to zip the train images to themselves so that I can use the images themselves as targets. I tried doing is like this:
train_dataset_target = tf.keras.preprocessing.image_dataset_from_directory(BASE_TRAIN
,label_mode=None,batch_size=64, image_size = (96, 96))
train_dataset_images = tf.keras.preprocessing.image_dataset_from_directory(BASE_TRAIN
,label_mode=None,batch_size=64, image_size = (96, 96))
train_dataset = tf.data.Dataset.zip((train_dataset_images, train_dataset_target))
And also like this:
train_dataset_target = tf.keras.preprocessing.image_dataset_from_directory(BASE_TRAIN
,label_mode=None,batch_size=64, image_size = (96, 96))
train_dataset = tf.data.Dataset.zip((train_dataset_target, train_dataset_target))
This, however, resulted in a huge loss both times so I visualized couple of examples to see what's the deal. As it turned out, images are not correctly mapped and I'm not sure how to fix that. Any tips?
Edit: As it was requested, here is that print(next(iter(train_dataset.take(1)))) produces:
(<tf.Tensor: shape=(64, 96, 96, 3), dtype=float32, numpy=
array([[[[ 23.918022 , 26.30344 , 14.074273 ],
[ 24.361654 , 26.74707 , 14.517903 ],
[ 23.715603 , 26.10102 , 13.871853 ],
...,
[ 43.131763 , 26.131763 , 16.131763 ],
[ 46.355305 , 29.355307 , 19.355307 ],
[ 41.15631 , 26.578156 , 16.763157 ]],
[[ 21.554363 , 22.716959 , 4.4814453 ],
[ 19.994629 , 21.350586 , 2.463379 ],
[ 23.771809 , 25.584797 , 5.157226 ],
...,
[ 38.281742 , 24.3125 , 15. ],
[ 38.958496 , 25.802246 , 16.489746 ],
[ 42.488426 , 29.332176 , 20.27195 ]],
[[ 44.6021 , 44.6021 , 18.098955 ],
[ 46.815266 , 46.815266 , 19.887207 ],
[ 52.252987 , 52.252987 , 24.32059 ],
...,
[ 40.21609 , 30.359264 , 20.325459 ],
[ 39.332844 , 29.796873 , 20.796873 ],
[ 38.908302 , 29.908302 , 20.908302 ]],
...,
[[ 64.59651 , 71.6835 , 29.318817 ],
[ 59.916687 , 66.978226 , 25.360334 ],
[ 53.713356 , 59.82009 , 21.497223 ],
...,
[109.53851 , 106.53851 , 89.53851 ],
[115.37259 , 112.37259 , 95.37259 ],
[112.60654 , 109.60654 , 92.60654 ]],
[[ 89.68636 , 93.06136 , 60.84261 ],
[ 95.50537 , 98.39258 , 67.149414 ],
[ 95.82389 , 98.35514 , 67.82389 ],
...,
[134.62599 , 133.46974 , 113.313484 ],
[132.64404 , 131.4878 , 111.33154 ],
[132.15625 , 131. , 110.84375 ]],
[[128.258 , 128.258 , 102.25799 ],
[127.007034 , 127.007034 , 101.007034 ],
[125.637 , 125.637 , 99.637 ],
...,
[138.79515 , 136.79515 , 115.02434 ],
[139.58838 , 137.58838 , 115.817566 ],
[140.47763 , 138.47763 , 115.71452 ]]],
[[[ 13.833333 , 13.833333 , 13.833333 ],
[ 13.666666 , 13.666666 , 13.666666 ],
[ 13.666666 , 13.666666 , 13.666666 ],
...,
[ 17.742188 , 17.742188 , 17.742188 ],
[ 17.265625 , 17.265625 , 17.265625 ],
[ 19.481771 , 20.325521 , 19.903646 ]],
[[ 9. , 9. , 9. ],
[ 9. , 9. , 9. ],
[ 9. , 9. , 9. ],
...,
[ 17.5 , 17.5 , 17.5 ],
[ 17.632812 , 17.632812 , 17.632812 ],
[ 18.789062 , 19.632812 , 19.210938 ]],
[[ 11. , 11. , 11. ],
[ 11.9557295 , 11.9557295 , 11.9557295 ],
[ 12. , 12. , 12. ],
...,
[ 21.851562 , 21.851562 , 21.851562 ],
[ 22. , 22. , 22. ],
[ 21.507812 , 22.351562 , 21.929688 ]],
...,
[[ 1.1668091 , 6.018424 , 9.518485 ],
[ 0.46620274, 4.3542995 , 10.023627 ],
[ 0.31514263, 2.497552 , 10.648639 ],
...,
[ 93.25734 , 64.57512 , 36.960796 ],
[ 92.95539 , 63.400787 , 37.25782 ],
[ 89.5597 , 59.393135 , 32.39338 ]],
[[ 85.71094 , 97.21094 , 120.21094 ],
[ 87.10156 , 98.60156 , 121.60156 ],
[ 91.5625 , 103.0625 , 126.0625 ],
...,
[117.33594 , 110.83594 , 110.05469 ],
[118.39844 , 111.63281 , 112.765625 ],
[120.47656 , 112.97656 , 114.47656 ]],
[[ 92.33331 , 108.16666 , 124.80724 ],
[ 91.765594 , 104.72133 , 122.18745 ],
[ 88.73424 , 101.71601 , 119.10401 ],
...,
[175.3202 , 184.82018 , 163.5025 ],
[178.28647 , 187.78644 , 166.72919 ],
[190.29189 , 199.79185 , 179.13564 ]]],
[[[136.4796 , 147.02127 , 149.02127 ],
[135.67708 , 146.21875 , 148.21875 ],
[135.45009 , 145.99174 , 147.99174 ],
...,
[239.9041 , 146.93533 , 171.61241 ],
[241.89714 , 145.9323 , 169.0664 ],
[242.08333 , 145.45833 , 168. ]],
[[130.625 , 137.5 , 140.875 ],
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...,
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[242.125 , 151.25 , 172.14583 ]],
[[154.07074 , 159.07074 , 165.07074 ],
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...,
[248.61504 , 165.64627 , 187.32335 ],
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[248.42708 , 162.0104 , 181.0104 ]],
...,
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...,
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[243.9404 , 223.47725 , 51.194305 ]],
[[ 3.5 , 5.5 , 1.25 ],
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[ 4.7539062 , 6.7539062 , 1.875 ],
...,
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[228.58456 , 190.94785 , 32.588474 ]],
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[ 48.45112 , 50.45112 , 45.45112 ],
...,
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[214.19675 , 154.57162 , 13.123365 ]]],
...,
[[[198.8639 , 199.8639 , 191.8639 ],
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...,
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[187. , 188. , 180. ]],
[[197.25203 , 198.95515 , 188.84578 ],
[197. , 198.70312 , 188.59375 ],
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...,
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[187.90625 , 188.90625 , 180.90625 ],
[187. , 188. , 180. ]],
[[197. , 199. , 188. ],
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...,
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[187.90625 , 188.90625 , 180.90625 ],
[187. , 188. , 180. ]],
...,
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...,
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[[180.14787 , 183.14787 , 172.14787 ],
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...,
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[186.69792 , 188.69792 , 177.69792 ]],
[[188.86389 , 191.86389 , 180.86389 ],
[187.64581 , 190.64581 , 179.64581 ],
[187.02422 , 190.02422 , 179.02422 ],
...,
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[186.69792 , 188.69792 , 177.69792 ]]],
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...,
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[147. , 134. , 118. ],
[145.64583 , 132.64583 , 116.645836 ]],
[[183.60417 , 176.49902 , 46.0625 ],
[169.80664 , 163.30371 , 34.68164 ],
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...,
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[157.51465 , 144.19922 , 129.14551 ],
[156.59473 , 143.59473 , 127.501945 ]],
[[185.28125 , 178.28125 , 45.28125 ],
[179.07292 , 172.07292 , 39.479168 ],
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...,
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[172.25185 , 157.36642 , 135.63358 ]],
...,
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...,
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...,
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...,
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...,
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...,
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[[ 11.03125 , 0.984375 , 0. ],
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[ 6.6917315 , 9.645344 , 12.80965 ],
...,
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[ 16.9375 , 15.9375 , 21.96875 ]],
...,
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...,
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...,
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...,
[ 69.27084 , 36.81771 , 27.453133 ],
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[ 64. , 34. , 23. ]]]],
dtype=float32)>, <tf.Tensor: shape=(64, 96, 96, 3),
dtype=float32, numpy=
array([[[[5.00000000e+00, 4.00000000e+00, 2.00000000e+00],
[5.00000000e+00, 4.00000000e+00, 2.00000000e+00],
[5.81250000e+00, 4.81250000e+00, 2.81250000e+00],
...,
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...,
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...,
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...,
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...,
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...,
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...,
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...,
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...,
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...,
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...,
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...,
[1.01921906e+02, 8.36423416e+01, 6.13484421e+01],
[1.07153809e+02, 8.54440918e+01, 7.07697754e+01],
[1.07190506e+02, 8.31874924e+01, 7.24651718e+01]],
[[1.11863800e+02, 9.73638000e+01, 7.72563477e+01],
[1.14675293e+02, 9.57586288e+01, 7.70659180e+01],
[1.14547661e+02, 9.08393326e+01, 7.20328217e+01],
...,
[1.10582077e+02, 9.21505585e+01, 6.88656998e+01],
[1.15131348e+02, 9.38037949e+01, 7.66209335e+01],
[1.16233017e+02, 9.29361420e+01, 7.89693222e+01]],
...,
[[3.34529076e+01, 1.27744722e+01, 0.00000000e+00],
[1.00004509e+02, 6.81486435e+01, 2.02915249e+01],
[1.59042236e+02, 1.13147179e+02, 3.63206062e+01],
...,
[1.95015884e+02, 1.39584366e+02, 1.22070408e+01],
[1.90469879e+02, 1.32253387e+02, 1.25335159e+01],
[1.83808060e+02, 1.25580246e+02, 1.97625771e+01]],
[[3.33319511e+01, 1.35767422e+01, 0.00000000e+00],
[1.08526611e+02, 7.80554199e+01, 2.51909180e+01],
[1.80072845e+02, 1.35266113e+02, 4.96700821e+01],
...,
[1.85928131e+02, 1.29474991e+02, 8.25048065e+00],
[1.79291992e+02, 1.19676025e+02, 6.69653320e+00],
[1.69351471e+02, 1.09841049e+02, 1.23887711e+01]],
[[3.36822929e+01, 1.39270840e+01, 0.00000000e+00],
[1.13921875e+02, 8.40312500e+01, 2.80937500e+01],
[1.97880219e+02, 1.54838547e+02, 5.89791641e+01],
...,
[1.77796829e+02, 1.20062439e+02, 6.04162598e+00],
[1.64437500e+02, 1.03890625e+02, 0.00000000e+00],
[1.46265594e+02, 8.61145477e+01, 0.00000000e+00]]],
[[[1.05939857e+02, 7.22836075e+01, 5.32836113e+01],
[1.07194580e+02, 7.39687500e+01, 5.38977051e+01],
[1.07669106e+02, 7.45260391e+01, 5.18996582e+01],
...,
[1.22373611e+02, 8.03892365e+01, 7.67173615e+01],
[1.18265625e+02, 7.36093750e+01, 7.29375000e+01],
[1.16455963e+02, 6.99687195e+01, 7.16562500e+01]],
[[1.08517090e+02, 6.81979141e+01, 4.99720879e+01],
[1.06988770e+02, 6.89943848e+01, 4.87331543e+01],
[1.08728104e+02, 7.07281036e+01, 4.77572403e+01],
...,
[1.22051018e+02, 7.42978287e+01, 7.22155609e+01],
[1.21400635e+02, 7.20568848e+01, 7.50268555e+01],
[1.23591896e+02, 7.35952377e+01, 7.65952377e+01]],
[[1.17069092e+02, 7.01458359e+01, 5.41458321e+01],
[1.14718750e+02, 6.91291504e+01, 5.12072754e+01],
[1.12810791e+02, 6.87083359e+01, 4.95052071e+01],
...,
[1.28756989e+02, 7.81597366e+01, 7.71597366e+01],
[1.27769775e+02, 7.34604492e+01, 7.62302246e+01],
[1.30496201e+02, 7.54962006e+01, 8.00691376e+01]],
...,
[[1.39930908e+02, 8.13684082e+01, 9.22902832e+01],
[1.34566650e+02, 7.49531250e+01, 8.62854004e+01],
[1.28079834e+02, 6.75815430e+01, 7.92360840e+01],
...,
[1.24175446e+02, 8.33073654e+01, 9.20643539e+01],
[1.25202148e+02, 8.41281738e+01, 9.38698730e+01],
[1.24568748e+02, 8.34423599e+01, 9.34347229e+01]],
[[1.23842285e+02, 8.04531250e+01, 8.70416641e+01],
[1.24983154e+02, 7.87644043e+01, 8.57019043e+01],
[1.26321777e+02, 7.91134415e+01, 8.53321915e+01],
...,
[1.30121902e+02, 1.18215645e+02, 1.22215645e+02],
[1.30850586e+02, 1.18944336e+02, 1.22944336e+02],
[1.36539169e+02, 1.24632927e+02, 1.28632919e+02]],
[[1.53647705e+02, 1.28350830e+02, 1.37694580e+02],
[1.53113525e+02, 1.27606445e+02, 1.36950195e+02],
[1.59272629e+02, 1.33466721e+02, 1.42878174e+02],
...,
[1.32164215e+02, 1.36432617e+02, 1.37134354e+02],
[1.31789795e+02, 1.35288330e+02, 1.36375000e+02],
[1.34850952e+02, 1.38153503e+02, 1.39338165e+02]]],
[[[1.90000000e+01, 1.50000000e+01, 1.60000000e+01],
[1.90000000e+01, 1.50000000e+01, 1.60000000e+01],
[1.92968750e+01, 1.54947920e+01, 1.64947910e+01],
...,
[2.25937538e+01, 1.67916718e+01, 1.86927128e+01],
[2.20000000e+01, 1.60000000e+01, 1.80000000e+01],
[2.20000000e+01, 1.60000000e+01, 1.80000000e+01]],
[[2.05000000e+01, 1.75000000e+01, 1.85000000e+01],
[2.05000000e+01, 1.75000000e+01, 1.85000000e+01],
[2.03515625e+01, 1.75494785e+01, 1.85000000e+01],
...,
[2.34947948e+01, 1.76927128e+01, 1.95937538e+01],
[2.30000000e+01, 1.70000000e+01, 1.90000000e+01],
[2.30000000e+01, 1.70000000e+01, 1.90000000e+01]],
[[2.10000000e+01, 2.00000000e+01, 2.06666660e+01],
[2.10000000e+01, 2.00000000e+01, 2.06666660e+01],
[2.05546875e+01, 1.96866322e+01, 2.02708340e+01],
...,
[2.43298626e+01, 1.84947948e+01, 2.04453144e+01],
[2.40000000e+01, 1.80000000e+01, 2.00000000e+01],
[2.40000000e+01, 1.80000000e+01, 2.00000000e+01]],
...,
[[1.69000031e+02, 1.74000031e+02, 1.67166687e+02],
[1.69000031e+02, 1.74000031e+02, 1.67166687e+02],
[1.71144135e+02, 1.76144135e+02, 1.69310791e+02],
...,
[1.35512199e+01, 2.40989590e+01, 2.23020897e+01],
[1.36666718e+01, 2.40000000e+01, 2.25000076e+01],
[1.36666718e+01, 2.40000000e+01, 2.25000076e+01]],
[[1.76500000e+02, 1.81500000e+02, 1.74500000e+02],
[1.76500000e+02, 1.81500000e+02, 1.74500000e+02],
[1.78677094e+02, 1.83677094e+02, 1.76677094e+02],
...,
[1.52031231e+01, 2.48515625e+01, 2.37031231e+01],
[1.55000000e+01, 2.50000000e+01, 2.40000000e+01],
[1.55000000e+01, 2.50000000e+01, 2.40000000e+01]],
[[1.82000000e+02, 1.87000000e+02, 1.80000000e+02],
[1.82000000e+02, 1.87000000e+02, 1.80000000e+02],
[1.84078125e+02, 1.89078125e+02, 1.82078125e+02],
...,
[1.65052052e+01, 2.56041641e+01, 2.46041641e+01],
[1.70000000e+01, 2.60000000e+01, 2.50000000e+01],
[1.70000000e+01, 2.60000000e+01, 2.50000000e+01]]]],
dtype=float32)>)
Solution
Maybe set the shuffle parameter of tf.keras.preprocessing.image_dataset_from_directory to False for train_dataset_target and train_dataset_images and call shuffle after zipping:
train_dataset = tf.data.Dataset.zip((train_dataset_target, train_dataset_target)).shuffle(some_buffer_size)
Answered By - AloneTogether
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