修改的 SFGAN代码执行结果--21 类 ucmerced scene classification 256*256图像

SFGAN是 64*64 EuroSAT数据集上 27000图像执行的效果很好 test样例80%

21 类 ucmerced scene classification 256*256图像 2100图像 test样例80%

修改的 SFGAN代码执行结果

/home/gis/anaconda3/envs/pytguo35/bin/python /home/gis/PycharmProjects/guo/SFGAN-master/sfgan_train_evalnew.py
trainset shape: (256, 256, 3, 1680)
testset shape: (256, 256, 3, 420)
WARNING:tensorflow:From /home/gis/PycharmProjects/guo/SFGAN-master/sfgan_train_evalnew.py:231: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.
Instructions for updating:

Future major versions of TensorFlow will allow gradients to flow
into the labels input on backprop by default.

See tf.nn.softmax_cross_entropy_with_logits_v2.

2018-12-16 12:00:38.221510: I tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
2018-12-16 12:00:38.297918: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:895] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2018-12-16 12:00:38.298185: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1105] Found device 0 with properties:
name: GeForce GTX 1080 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.683
pciBusID: 0000:01:00.0
totalMemory: 10.92GiB freeMemory: 10.76GiB
2018-12-16 12:00:38.298196: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1195] Creating TensorFlow device (/device:GPU:0) -> (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1)
Epoch 0
       Classifier train accuracy: 0.224
       Classifier test accuracy 0.38095238095238093
       Step time: 0.7341856956481934
       Epoch time: 113.11737203598022
Epoch 1
       Classifier train accuracy: 0.445
       Classifier test accuracy 0.47619047619047616
       Step time: 0.5562746524810791
       Epoch time: 111.61542010307312
Epoch 2
       Classifier train accuracy: 0.571
       Classifier test accuracy 0.5
       Step time: 0.57527756690979
       Epoch time: 112.33925247192383
Epoch 3
       Classifier train accuracy: 0.639
       Classifier test accuracy 0.6190476190476191
       Step time: 0.5988922119140625
       Epoch time: 112.94010877609253
Epoch 4
       Classifier train accuracy: 0.706
       Classifier test accuracy 0.6666666666666666
       Step time: 0.6145772933959961
       Epoch time: 113.58087730407715
Epoch 5
       Classifier train accuracy: 0.736
       Classifier test accuracy 0.6190476190476191
       Step time: 0.6346220970153809
       Epoch time: 114.21302485466003
Epoch 6
       Classifier train accuracy: 0.791
       Classifier test accuracy 0.6904761904761905
       Step time: 0.6531987190246582
       Epoch time: 114.71357917785645
Epoch 7
       Classifier train accuracy: 0.819
       Classifier test accuracy 0.7380952380952381
       Step time: 0.6749696731567383
       Epoch time: 115.62474775314331
Epoch 8
       Classifier train accuracy: 0.831
       Classifier test accuracy 0.7380952380952381
       Step time: 0.6988215446472168
       Epoch time: 116.110271692276
Epoch 9
       Classifier train accuracy: 0.851
       Classifier test accuracy 0.7619047619047619
       Step time: 0.7208564281463623
       Epoch time: 116.75750017166138
Testing...
       Classifier test accuracy 0.6640211640211641
       Step time: 0.7208564281463623
       Epoch time: 130.66029167175293

Process finished with exit code 0

修改的 SFGAN代码 执行结果--21 类 ucmerced scene classification 256*256图像

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修改的 SFGAN代码执行结果--21 类 ucmerced scene classification 256*256图像