Also the code D_1 appears which should be the same as D_01 if I'm correct.
This data seems quite random, do I maybe need to alter the hyperparameters or did I do something wrong with the training?
I am using the MIMIC-III dataset and followed the guide from the README, I used checkpoint -999 for generating the data.
WARNING:tensorflow:From /content/drive/My Drive/medGAN/medgan.py:249: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.
WARNING:tensorflow:From /content/drive/My Drive/medGAN/medgan.py:54: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.
WARNING:tensorflow:From /content/drive/My Drive/medGAN/medgan.py:59: The name tf.get_variable is deprecated. Please use tf.compat.v1.get_variable instead.
WARNING:tensorflow:From /content/drive/My Drive/medGAN/medgan.py:81: The name tf.log is deprecated. Please use tf.math.log instead.
WARNING:tensorflow:From /content/drive/My Drive/medGAN/medgan.py:144: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.
Instructions for updating:
Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.
WARNING:tensorflow:From /content/drive/My Drive/medGAN/medgan.py:259: The name tf.trainable_variables is deprecated. Please use tf.compat.v1.trainable_variables instead.
WARNING:tensorflow:From /content/drive/My Drive/medGAN/medgan.py:264: The name tf.get_collection is deprecated. Please use tf.compat.v1.get_collection instead.
WARNING:tensorflow:From /content/drive/My Drive/medGAN/medgan.py:264: The name tf.GraphKeys is deprecated. Please use tf.compat.v1.GraphKeys instead.
WARNING:tensorflow:From /content/drive/My Drive/medGAN/medgan.py:266: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead.
WARNING:tensorflow:From /content/drive/My Drive/medGAN/medgan.py:271: The name tf.global_variables_initializer is deprecated. Please use tf.compat.v1.global_variables_initializer instead.
WARNING:tensorflow:From /content/drive/My Drive/medGAN/medgan.py:274: The name tf.train.Saver is deprecated. Please use tf.compat.v1.train.Saver instead.
WARNING:tensorflow:From /content/drive/My Drive/medGAN/medgan.py:277: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.
2019-11-21 14:38:40.946110: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
2019-11-21 14:38:40.962195: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-11-21 14:38:40.963126: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties:
name: Tesla P100-PCIE-16GB major: 6 minor: 0 memoryClockRate(GHz): 1.3285
pciBusID: 0000:00:04.0
2019-11-21 14:38:40.966593: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2019-11-21 14:38:40.978076: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2019-11-21 14:38:40.985276: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2019-11-21 14:38:40.994541: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2019-11-21 14:38:41.010746: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2019-11-21 14:38:41.016653: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2019-11-21 14:38:41.043468: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2019-11-21 14:38:41.043668: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-11-21 14:38:41.044661: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-11-21 14:38:41.045570: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0
2019-11-21 14:38:41.051360: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2300000000 Hz
2019-11-21 14:38:41.051637: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1e01640 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2019-11-21 14:38:41.051672: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version
2019-11-21 14:38:41.142245: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-11-21 14:38:41.143277: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1e01d40 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2019-11-21 14:38:41.143307: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Tesla P100-PCIE-16GB, Compute Capability 6.0
2019-11-21 14:38:41.143511: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-11-21 14:38:41.144279: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties:
name: Tesla P100-PCIE-16GB major: 6 minor: 0 memoryClockRate(GHz): 1.3285
pciBusID: 0000:00:04.0
2019-11-21 14:38:41.144353: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2019-11-21 14:38:41.144390: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2019-11-21 14:38:41.144418: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2019-11-21 14:38:41.144476: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2019-11-21 14:38:41.144511: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2019-11-21 14:38:41.144535: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2019-11-21 14:38:41.144560: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2019-11-21 14:38:41.144684: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-11-21 14:38:41.145608: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-11-21 14:38:41.146383: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0
2019-11-21 14:38:41.146461: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2019-11-21 14:38:41.147990: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-11-21 14:38:41.148016: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165] 0
2019-11-21 14:38:41.148029: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0: N
2019-11-21 14:38:41.148157: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-11-21 14:38:41.149070: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-11-21 14:38:41.149855: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.
2019-11-21 14:38:41.149946: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 15216 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0)
2019-11-21 14:38:41.968358: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
Pretrain_Epoch:0, trainLoss:91.632156, validLoss:49.859711, validReverseLoss:0.000000
Pretrain_Epoch:1, trainLoss:44.162811, validLoss:38.908810, validReverseLoss:0.000000
Pretrain_Epoch:2, trainLoss:36.714924, validLoss:33.333466, validReverseLoss:0.000000
Pretrain_Epoch:3, trainLoss:31.041992, validLoss:27.854301, validReverseLoss:0.000000
Pretrain_Epoch:4, trainLoss:25.633547, validLoss:22.974136, validReverseLoss:0.000000
Pretrain_Epoch:5, trainLoss:21.065298, validLoss:19.111172, validReverseLoss:0.000000
Pretrain_Epoch:6, trainLoss:17.603550, validLoss:16.332701, validReverseLoss:0.000000
Pretrain_Epoch:7, trainLoss:15.103946, validLoss:14.306634, validReverseLoss:0.000000
Pretrain_Epoch:8, trainLoss:13.297533, validLoss:12.856835, validReverseLoss:0.000000
Pretrain_Epoch:9, trainLoss:11.967052, validLoss:11.843672, validReverseLoss:0.000000
Pretrain_Epoch:10, trainLoss:10.958341, validLoss:11.032762, validReverseLoss:0.000000
Pretrain_Epoch:11, trainLoss:10.180580, validLoss:10.419333, validReverseLoss:0.000000
Pretrain_Epoch:12, trainLoss:9.582338, validLoss:9.962563, validReverseLoss:0.000000
Pretrain_Epoch:13, trainLoss:9.112109, validLoss:9.583471, validReverseLoss:0.000000
Pretrain_Epoch:14, trainLoss:8.746560, validLoss:9.310450, validReverseLoss:0.000000
Pretrain_Epoch:15, trainLoss:8.461577, validLoss:9.054235, validReverseLoss:0.000000
Pretrain_Epoch:16, trainLoss:8.224661, validLoss:8.902073, validReverseLoss:0.000000
Pretrain_Epoch:17, trainLoss:8.023074, validLoss:8.790389, validReverseLoss:0.000000
Pretrain_Epoch:18, trainLoss:7.845893, validLoss:8.618320, validReverseLoss:0.000000
Pretrain_Epoch:19, trainLoss:7.707201, validLoss:8.549589, validReverseLoss:0.000000
Pretrain_Epoch:20, trainLoss:7.581052, validLoss:8.441903, validReverseLoss:0.000000
Pretrain_Epoch:21, trainLoss:7.483572, validLoss:8.352224, validReverseLoss:0.000000
Pretrain_Epoch:22, trainLoss:7.401034, validLoss:8.320681, validReverseLoss:0.000000
Pretrain_Epoch:23, trainLoss:7.330626, validLoss:8.256131, validReverseLoss:0.000000
Pretrain_Epoch:24, trainLoss:7.265032, validLoss:8.222884, validReverseLoss:0.000000
Pretrain_Epoch:25, trainLoss:7.214301, validLoss:8.217128, validReverseLoss:0.000000
Pretrain_Epoch:26, trainLoss:7.167799, validLoss:8.175730, validReverseLoss:0.000000
Pretrain_Epoch:27, trainLoss:7.124703, validLoss:8.146324, validReverseLoss:0.000000
Pretrain_Epoch:28, trainLoss:7.092945, validLoss:8.140231, validReverseLoss:0.000000
Pretrain_Epoch:29, trainLoss:7.054426, validLoss:8.122950, validReverseLoss:0.000000
Pretrain_Epoch:30, trainLoss:7.022406, validLoss:8.084413, validReverseLoss:0.000000
Pretrain_Epoch:31, trainLoss:7.000129, validLoss:8.087962, validReverseLoss:0.000000
Pretrain_Epoch:32, trainLoss:6.972916, validLoss:8.076344, validReverseLoss:0.000000
Pretrain_Epoch:33, trainLoss:6.951451, validLoss:8.016294, validReverseLoss:0.000000
Pretrain_Epoch:34, trainLoss:6.929442, validLoss:8.047412, validReverseLoss:0.000000
Pretrain_Epoch:35, trainLoss:6.909639, validLoss:8.004986, validReverseLoss:0.000000
Pretrain_Epoch:36, trainLoss:6.890303, validLoss:8.034472, validReverseLoss:0.000000
Pretrain_Epoch:37, trainLoss:6.873143, validLoss:8.023901, validReverseLoss:0.000000
Pretrain_Epoch:38, trainLoss:6.859607, validLoss:8.012761, validReverseLoss:0.000000
Pretrain_Epoch:39, trainLoss:6.844871, validLoss:7.967826, validReverseLoss:0.000000
Pretrain_Epoch:40, trainLoss:6.833282, validLoss:7.992737, validReverseLoss:0.000000
Pretrain_Epoch:41, trainLoss:6.814266, validLoss:7.972634, validReverseLoss:0.000000
Pretrain_Epoch:42, trainLoss:6.806368, validLoss:7.954660, validReverseLoss:0.000000
Pretrain_Epoch:43, trainLoss:6.795738, validLoss:7.969618, validReverseLoss:0.000000
Pretrain_Epoch:44, trainLoss:6.782229, validLoss:7.936687, validReverseLoss:0.000000
Pretrain_Epoch:45, trainLoss:6.770532, validLoss:7.937439, validReverseLoss:0.000000
Pretrain_Epoch:46, trainLoss:6.761350, validLoss:7.902309, validReverseLoss:0.000000
Pretrain_Epoch:47, trainLoss:6.751661, validLoss:7.930335, validReverseLoss:0.000000
Pretrain_Epoch:48, trainLoss:6.733834, validLoss:7.932473, validReverseLoss:0.000000
Pretrain_Epoch:49, trainLoss:6.720202, validLoss:7.928168, validReverseLoss:0.000000
Pretrain_Epoch:50, trainLoss:6.706732, validLoss:7.901009, validReverseLoss:0.000000
Pretrain_Epoch:51, trainLoss:6.686815, validLoss:7.904763, validReverseLoss:0.000000
Pretrain_Epoch:52, trainLoss:6.671935, validLoss:7.884416, validReverseLoss:0.000000
Pretrain_Epoch:53, trainLoss:6.656465, validLoss:7.874551, validReverseLoss:0.000000
Pretrain_Epoch:54, trainLoss:6.644230, validLoss:7.862403, validReverseLoss:0.000000
Pretrain_Epoch:55, trainLoss:6.633656, validLoss:7.845164, validReverseLoss:0.000000
Pretrain_Epoch:56, trainLoss:6.621819, validLoss:7.847596, validReverseLoss:0.000000
Pretrain_Epoch:57, trainLoss:6.614031, validLoss:7.796778, validReverseLoss:0.000000
Pretrain_Epoch:58, trainLoss:6.607875, validLoss:7.855163, validReverseLoss:0.000000
Pretrain_Epoch:59, trainLoss:6.595439, validLoss:7.801251, validReverseLoss:0.000000
Pretrain_Epoch:60, trainLoss:6.588428, validLoss:7.840598, validReverseLoss:0.000000
Pretrain_Epoch:61, trainLoss:6.586468, validLoss:7.814472, validReverseLoss:0.000000
Pretrain_Epoch:62, trainLoss:6.574622, validLoss:7.822409, validReverseLoss:0.000000
Pretrain_Epoch:63, trainLoss:6.568933, validLoss:7.769971, validReverseLoss:0.000000
Pretrain_Epoch:64, trainLoss:6.555827, validLoss:7.775706, validReverseLoss:0.000000
Pretrain_Epoch:65, trainLoss:6.543493, validLoss:7.786043, validReverseLoss:0.000000
Pretrain_Epoch:66, trainLoss:6.532283, validLoss:7.795273, validReverseLoss:0.000000
Pretrain_Epoch:67, trainLoss:6.526275, validLoss:7.776935, validReverseLoss:0.000000
Pretrain_Epoch:68, trainLoss:6.508321, validLoss:7.743046, validReverseLoss:0.000000
Pretrain_Epoch:69, trainLoss:6.500664, validLoss:7.750890, validReverseLoss:0.000000
Pretrain_Epoch:70, trainLoss:6.483753, validLoss:7.734784, validReverseLoss:0.000000
Pretrain_Epoch:71, trainLoss:6.481430, validLoss:7.731408, validReverseLoss:0.000000
Pretrain_Epoch:72, trainLoss:6.470397, validLoss:7.726825, validReverseLoss:0.000000
Pretrain_Epoch:73, trainLoss:6.464901, validLoss:7.711414, validReverseLoss:0.000000
Pretrain_Epoch:74, trainLoss:6.453043, validLoss:7.717400, validReverseLoss:0.000000
Pretrain_Epoch:75, trainLoss:6.451079, validLoss:7.713556, validReverseLoss:0.000000
Pretrain_Epoch:76, trainLoss:6.444433, validLoss:7.702054, validReverseLoss:0.000000
Pretrain_Epoch:77, trainLoss:6.439002, validLoss:7.704195, validReverseLoss:0.000000
Pretrain_Epoch:78, trainLoss:6.433498, validLoss:7.729032, validReverseLoss:0.000000
Pretrain_Epoch:79, trainLoss:6.430214, validLoss:7.669126, validReverseLoss:0.000000
Pretrain_Epoch:80, trainLoss:6.423391, validLoss:7.670120, validReverseLoss:0.000000
Pretrain_Epoch:81, trainLoss:6.419544, validLoss:7.695985, validReverseLoss:0.000000
Pretrain_Epoch:82, trainLoss:6.417687, validLoss:7.706051, validReverseLoss:0.000000
Pretrain_Epoch:83, trainLoss:6.414234, validLoss:7.694446, validReverseLoss:0.000000
Pretrain_Epoch:84, trainLoss:6.409822, validLoss:7.685117, validReverseLoss:0.000000
Pretrain_Epoch:85, trainLoss:6.404850, validLoss:7.709420, validReverseLoss:0.000000
Pretrain_Epoch:86, trainLoss:6.403021, validLoss:7.703742, validReverseLoss:0.000000
Pretrain_Epoch:87, trainLoss:6.400425, validLoss:7.689323, validReverseLoss:0.000000
Pretrain_Epoch:88, trainLoss:6.393787, validLoss:7.684198, validReverseLoss:0.000000
Pretrain_Epoch:89, trainLoss:6.391764, validLoss:7.695581, validReverseLoss:0.000000
Pretrain_Epoch:90, trainLoss:6.387015, validLoss:7.688169, validReverseLoss:0.000000
Pretrain_Epoch:91, trainLoss:6.382489, validLoss:7.684075, validReverseLoss:0.000000
Pretrain_Epoch:92, trainLoss:6.383763, validLoss:7.685220, validReverseLoss:0.000000
Pretrain_Epoch:93, trainLoss:6.379379, validLoss:7.658064, validReverseLoss:0.000000
Pretrain_Epoch:94, trainLoss:6.379478, validLoss:7.701941, validReverseLoss:0.000000
Pretrain_Epoch:95, trainLoss:6.371755, validLoss:7.676590, validReverseLoss:0.000000
Pretrain_Epoch:96, trainLoss:6.371538, validLoss:7.701460, validReverseLoss:0.000000
Pretrain_Epoch:97, trainLoss:6.366984, validLoss:7.660957, validReverseLoss:0.000000
Pretrain_Epoch:98, trainLoss:6.368827, validLoss:7.656891, validReverseLoss:0.000000
Pretrain_Epoch:99, trainLoss:6.362263, validLoss:7.688737, validReverseLoss:0.000000
2019-11-21 14:40:21.567047: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
Epoch:0, d_loss:0.049718, g_loss:7.896662, accuracy:1.000000, AUC:1.000000
Epoch:1, d_loss:0.001711, g_loss:0.247020, accuracy:1.000000, AUC:1.000000
Epoch:2, d_loss:0.001532, g_loss:0.386267, accuracy:1.000000, AUC:1.000000
Epoch:3, d_loss:0.001439, g_loss:1.053180, accuracy:1.000000, AUC:1.000000
Epoch:4, d_loss:0.001324, g_loss:1.365029, accuracy:1.000000, AUC:1.000000
Epoch:5, d_loss:0.001201, g_loss:1.210130, accuracy:1.000000, AUC:1.000000
Epoch:6, d_loss:0.001051, g_loss:0.262837, accuracy:1.000000, AUC:1.000000
Epoch:7, d_loss:0.001092, g_loss:0.137890, accuracy:1.000000, AUC:1.000000
Epoch:8, d_loss:0.001030, g_loss:0.082081, accuracy:1.000000, AUC:1.000000
Epoch:9, d_loss:0.001196, g_loss:0.063162, accuracy:1.000000, AUC:1.000000
Epoch:10, d_loss:0.001634, g_loss:0.040523, accuracy:1.000000, AUC:1.000000
Epoch:11, d_loss:0.002393, g_loss:0.016229, accuracy:1.000000, AUC:1.000000
Epoch:12, d_loss:0.003662, g_loss:0.010670, accuracy:0.999929, AUC:1.000000
Epoch:13, d_loss:0.004564, g_loss:0.006378, accuracy:1.000000, AUC:1.000000
Epoch:14, d_loss:0.277425, g_loss:0.071394, accuracy:0.950393, AUC:0.983442
Epoch:15, d_loss:0.164884, g_loss:8.783463, accuracy:0.997810, AUC:0.999962
Epoch:16, d_loss:0.001950, g_loss:0.846287, accuracy:1.000000, AUC:1.000000
Epoch:17, d_loss:0.000658, g_loss:0.279623, accuracy:1.000000, AUC:1.000000
Epoch:18, d_loss:0.000843, g_loss:1.432047, accuracy:1.000000, AUC:1.000000
Epoch:19, d_loss:0.001847, g_loss:2.704693, accuracy:1.000000, AUC:1.000000
Epoch:20, d_loss:0.002777, g_loss:1.460213, accuracy:1.000000, AUC:1.000000
Epoch:21, d_loss:0.001460, g_loss:0.216559, accuracy:1.000000, AUC:1.000000
Epoch:22, d_loss:0.001971, g_loss:0.923061, accuracy:1.000000, AUC:1.000000
Epoch:23, d_loss:0.000861, g_loss:0.489755, accuracy:1.000000, AUC:1.000000
Epoch:24, d_loss:0.001161, g_loss:0.493051, accuracy:1.000000, AUC:1.000000
Epoch:25, d_loss:0.003009, g_loss:0.438455, accuracy:0.999833, AUC:1.000000
Epoch:26, d_loss:0.090678, g_loss:10.045500, accuracy:0.999690, AUC:1.000000
Epoch:27, d_loss:0.017689, g_loss:23.799906, accuracy:0.993952, AUC:0.999743
Epoch:28, d_loss:0.027158, g_loss:9.959446, accuracy:0.998774, AUC:0.999972
Epoch:29, d_loss:0.012947, g_loss:2.434124, accuracy:0.997643, AUC:0.999999
Epoch:30, d_loss:0.012352, g_loss:6.465405, accuracy:0.999726, AUC:0.999998
Epoch:31, d_loss:0.003908, g_loss:5.487545, accuracy:0.999940, AUC:1.000000
Epoch:32, d_loss:0.003172, g_loss:4.503617, accuracy:0.999631, AUC:1.000000
Epoch:33, d_loss:0.003661, g_loss:0.963288, accuracy:0.999929, AUC:1.000000
Epoch:34, d_loss:0.003996, g_loss:1.319031, accuracy:0.999476, AUC:1.000000
Epoch:35, d_loss:0.007080, g_loss:2.475548, accuracy:0.999762, AUC:1.000000
Epoch:36, d_loss:0.004227, g_loss:1.138083, accuracy:0.999988, AUC:1.000000
Epoch:37, d_loss:0.007417, g_loss:1.839218, accuracy:0.998976, AUC:1.000000
Epoch:38, d_loss:0.003696, g_loss:4.418096, accuracy:0.999881, AUC:1.000000
Epoch:39, d_loss:0.001041, g_loss:0.143046, accuracy:1.000000, AUC:1.000000
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Epoch:59, d_loss:0.012797, g_loss:6.789739, accuracy:0.999155, AUC:0.999998
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Epoch:81, d_loss:0.042272, g_loss:8.016152, accuracy:0.943881, AUC:0.997913
Epoch:82, d_loss:0.082864, g_loss:9.334076, accuracy:0.978786, AUC:0.996849
Epoch:83, d_loss:0.047900, g_loss:8.168279, accuracy:0.995488, AUC:0.998971
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Epoch:85, d_loss:0.049577, g_loss:9.729424, accuracy:0.988476, AUC:0.999166
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Epoch:87, d_loss:0.016207, g_loss:8.466186, accuracy:0.998274, AUC:0.999874
Epoch:88, d_loss:0.026103, g_loss:6.965075, accuracy:0.993226, AUC:0.999492
Epoch:89, d_loss:0.028044, g_loss:7.550342, accuracy:0.990095, AUC:0.999086
Epoch:90, d_loss:0.041404, g_loss:8.490033, accuracy:0.996333, AUC:0.999494
Epoch:91, d_loss:0.074535, g_loss:7.640459, accuracy:0.984786, AUC:0.998377
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Epoch:93, d_loss:0.040718, g_loss:8.299964, accuracy:0.996393, AUC:0.999604
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Epoch:103, d_loss:0.095052, g_loss:8.943425, accuracy:0.990095, AUC:0.999608
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Epoch:107, d_loss:0.098120, g_loss:7.629163, accuracy:0.991012, AUC:0.998549
Epoch:108, d_loss:0.064481, g_loss:7.509043, accuracy:0.983167, AUC:0.996103
Epoch:109, d_loss:0.073878, g_loss:7.365418, accuracy:0.987226, AUC:0.997516
Epoch:110, d_loss:0.132010, g_loss:8.815269, accuracy:0.901226, AUC:0.993680
Epoch:111, d_loss:0.150023, g_loss:8.366915, accuracy:0.973667, AUC:0.994767
Epoch:112, d_loss:0.112788, g_loss:6.927614, accuracy:0.954452, AUC:0.993850
Epoch:113, d_loss:0.111835, g_loss:6.637422, accuracy:0.980083, AUC:0.995868
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Epoch:115, d_loss:0.123149, g_loss:7.737255, accuracy:0.956298, AUC:0.992462
Epoch:116, d_loss:0.072535, g_loss:7.898670, accuracy:0.981119, AUC:0.996105
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Epoch:118, d_loss:0.071433, g_loss:7.549909, accuracy:0.974417, AUC:0.993336
Epoch:119, d_loss:0.069308, g_loss:7.246675, accuracy:0.904250, AUC:0.995185
Epoch:120, d_loss:0.052142, g_loss:8.211670, accuracy:0.981238, AUC:0.997481
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Epoch:122, d_loss:0.072892, g_loss:7.892231, accuracy:0.954262, AUC:0.996144
Epoch:123, d_loss:0.104401, g_loss:7.407002, accuracy:0.978548, AUC:0.995371
Epoch:124, d_loss:0.108907, g_loss:7.377442, accuracy:0.928619, AUC:0.992313
Epoch:125, d_loss:0.073378, g_loss:7.683291, accuracy:0.945238, AUC:0.994081
Epoch:126, d_loss:0.086222, g_loss:7.942225, accuracy:0.973250, AUC:0.995601
Epoch:127, d_loss:0.047094, g_loss:7.016489, accuracy:0.908060, AUC:0.995998
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Epoch:129, d_loss:0.050422, g_loss:7.383032, accuracy:0.957190, AUC:0.992285
Epoch:130, d_loss:0.064995, g_loss:6.914652, accuracy:0.974869, AUC:0.998442
Epoch:131, d_loss:0.056859, g_loss:7.607286, accuracy:0.988143, AUC:0.998289
Epoch:132, d_loss:0.072353, g_loss:7.519107, accuracy:0.963702, AUC:0.995913
Epoch:133, d_loss:0.083625, g_loss:7.308177, accuracy:0.959179, AUC:0.990884
Epoch:134, d_loss:0.080228, g_loss:7.099856, accuracy:0.970595, AUC:0.995397
Epoch:135, d_loss:0.045459, g_loss:7.339122, accuracy:0.987369, AUC:0.998343
Epoch:136, d_loss:0.041670, g_loss:7.135579, accuracy:0.962012, AUC:0.992638
Epoch:137, d_loss:0.064690, g_loss:7.460423, accuracy:0.913679, AUC:0.991869
Epoch:138, d_loss:0.075501, g_loss:7.343169, accuracy:0.964000, AUC:0.995806
Epoch:139, d_loss:0.074825, g_loss:7.220914, accuracy:0.956071, AUC:0.992056
Epoch:140, d_loss:0.086134, g_loss:6.980189, accuracy:0.920667, AUC:0.984264
Epoch:141, d_loss:0.064323, g_loss:6.532271, accuracy:0.967738, AUC:0.993145
Epoch:142, d_loss:0.099939, g_loss:7.490509, accuracy:0.935619, AUC:0.995266
Epoch:143, d_loss:0.085324, g_loss:7.177705, accuracy:0.968917, AUC:0.994409
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Epoch:145, d_loss:0.090483, g_loss:6.811230, accuracy:0.918405, AUC:0.991903
Epoch:146, d_loss:0.058886, g_loss:7.654165, accuracy:0.981690, AUC:0.995530
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Epoch:148, d_loss:0.062865, g_loss:6.976796, accuracy:0.978071, AUC:0.996296
Epoch:149, d_loss:0.062726, g_loss:7.002886, accuracy:0.982036, AUC:0.997640
Epoch:150, d_loss:0.072673, g_loss:6.657517, accuracy:0.963262, AUC:0.992946
Epoch:151, d_loss:0.072548, g_loss:6.781895, accuracy:0.955905, AUC:0.992561
Epoch:152, d_loss:0.194857, g_loss:7.442107, accuracy:0.965381, AUC:0.995443
Epoch:153, d_loss:0.184960, g_loss:6.138431, accuracy:0.908286, AUC:0.966938
Epoch:154, d_loss:0.118356, g_loss:6.841249, accuracy:0.964726, AUC:0.994510
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Epoch:157, d_loss:0.106193, g_loss:6.947374, accuracy:0.944917, AUC:0.987109
Epoch:158, d_loss:0.119195, g_loss:6.473263, accuracy:0.927131, AUC:0.991354
Epoch:159, d_loss:0.157652, g_loss:6.693114, accuracy:0.935548, AUC:0.987378
Epoch:160, d_loss:0.110699, g_loss:7.113831, accuracy:0.919607, AUC:0.972348
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Epoch:162, d_loss:0.110306, g_loss:6.755966, accuracy:0.942857, AUC:0.989558
Epoch:163, d_loss:0.080944, g_loss:6.424718, accuracy:0.882738, AUC:0.987579
Epoch:164, d_loss:0.110314, g_loss:6.662391, accuracy:0.948595, AUC:0.988955
Epoch:165, d_loss:0.054995, g_loss:7.163562, accuracy:0.950988, AUC:0.989913
Epoch:166, d_loss:0.099998, g_loss:7.418950, accuracy:0.924190, AUC:0.990753
Epoch:167, d_loss:0.082210, g_loss:6.726200, accuracy:0.941476, AUC:0.983496
Epoch:168, d_loss:0.082125, g_loss:6.983229, accuracy:0.888083, AUC:0.974239
Epoch:169, d_loss:0.102077, g_loss:6.875807, accuracy:0.928702, AUC:0.976601
Epoch:170, d_loss:0.069189, g_loss:6.777496, accuracy:0.938560, AUC:0.986812
Epoch:171, d_loss:0.065483, g_loss:6.889385, accuracy:0.959940, AUC:0.990547
Epoch:172, d_loss:0.072823, g_loss:7.061434, accuracy:0.971905, AUC:0.995221
Epoch:173, d_loss:0.101068, g_loss:6.564700, accuracy:0.958571, AUC:0.992228
Epoch:174, d_loss:0.080232, g_loss:7.120638, accuracy:0.970548, AUC:0.994657
Epoch:175, d_loss:0.086506, g_loss:6.748380, accuracy:0.869262, AUC:0.960074
Epoch:176, d_loss:0.078628, g_loss:6.821851, accuracy:0.958155, AUC:0.995338
Epoch:177, d_loss:0.052165, g_loss:7.209396, accuracy:0.895714, AUC:0.986895
Epoch:178, d_loss:0.077500, g_loss:7.042245, accuracy:0.927393, AUC:0.995467
Epoch:179, d_loss:0.066090, g_loss:6.961568, accuracy:0.959202, AUC:0.995283
Epoch:180, d_loss:0.098178, g_loss:7.013987, accuracy:0.953929, AUC:0.987525
Epoch:181, d_loss:0.103108, g_loss:7.209926, accuracy:0.953988, AUC:0.991249
Epoch:182, d_loss:0.130179, g_loss:6.611453, accuracy:0.967845, AUC:0.992488
Epoch:183, d_loss:0.085021, g_loss:6.885527, accuracy:0.956881, AUC:0.988599
Epoch:184, d_loss:0.111372, g_loss:7.414159, accuracy:0.848238, AUC:0.988087
Epoch:185, d_loss:0.119822, g_loss:7.496799, accuracy:0.966333, AUC:0.993643
Epoch:186, d_loss:0.100551, g_loss:6.559425, accuracy:0.906440, AUC:0.968475
Epoch:187, d_loss:0.107674, g_loss:7.227368, accuracy:0.912702, AUC:0.974577
Epoch:188, d_loss:0.078038, g_loss:6.853116, accuracy:0.954345, AUC:0.984951
Epoch:189, d_loss:0.113757, g_loss:6.842789, accuracy:0.967333, AUC:0.991025
Epoch:190, d_loss:0.091532, g_loss:6.815268, accuracy:0.937226, AUC:0.982698
Epoch:191, d_loss:0.084834, g_loss:6.731766, accuracy:0.861440, AUC:0.966638
Epoch:192, d_loss:0.121470, g_loss:6.668952, accuracy:0.927833, AUC:0.993617
Epoch:193, d_loss:0.075038, g_loss:7.653888, accuracy:0.928238, AUC:0.979002
Epoch:194, d_loss:0.089716, g_loss:7.191800, accuracy:0.951274, AUC:0.981365
Epoch:195, d_loss:0.106186, g_loss:6.585323, accuracy:0.966798, AUC:0.995338
Epoch:196, d_loss:0.088910, g_loss:6.949481, accuracy:0.967524, AUC:0.992763
Epoch:197, d_loss:0.075169, g_loss:6.767637, accuracy:0.936714, AUC:0.991166
Epoch:198, d_loss:0.061336, g_loss:6.993880, accuracy:0.916774, AUC:0.990649
Epoch:199, d_loss:0.062713, g_loss:6.621217, accuracy:0.933583, AUC:0.981085
Epoch:200, d_loss:0.057506, g_loss:7.028312, accuracy:0.956667, AUC:0.988783
Epoch:201, d_loss:0.060462, g_loss:6.686753, accuracy:0.938500, AUC:0.996335
Epoch:202, d_loss:0.065382, g_loss:6.676390, accuracy:0.952571, AUC:0.990354
Epoch:203, d_loss:0.098554, g_loss:7.015841, accuracy:0.904488, AUC:0.986097
Epoch:204, d_loss:0.073371, g_loss:6.974054, accuracy:0.949810, AUC:0.992691
Epoch:205, d_loss:0.149546, g_loss:6.620938, accuracy:0.925964, AUC:0.989941
Epoch:206, d_loss:0.139987, g_loss:6.811313, accuracy:0.924274, AUC:0.985654
Epoch:207, d_loss:0.121379, g_loss:7.023602, accuracy:0.827869, AUC:0.984901
Epoch:208, d_loss:0.099283, g_loss:6.783880, accuracy:0.968988, AUC:0.992141
Epoch:209, d_loss:0.114787, g_loss:6.392585, accuracy:0.957440, AUC:0.986921
Epoch:210, d_loss:0.078560, g_loss:6.565986, accuracy:0.937131, AUC:0.981535
Epoch:211, d_loss:0.093179, g_loss:6.394118, accuracy:0.918095, AUC:0.985457
Epoch:212, d_loss:0.123011, g_loss:6.242034, accuracy:0.954798, AUC:0.985465
Epoch:213, d_loss:0.118147, g_loss:7.265682, accuracy:0.898131, AUC:0.994866
Epoch:214, d_loss:0.101319, g_loss:6.649540, accuracy:0.901595, AUC:0.972733
Epoch:215, d_loss:0.078088, g_loss:7.038384, accuracy:0.946714, AUC:0.984563
Epoch:216, d_loss:0.077952, g_loss:6.656772, accuracy:0.860655, AUC:0.978999
Epoch:217, d_loss:0.095049, g_loss:7.061019, accuracy:0.951893, AUC:0.988475
Epoch:218, d_loss:0.120595, g_loss:6.292142, accuracy:0.947952, AUC:0.985002
Epoch:219, d_loss:0.076080, g_loss:6.014092, accuracy:0.927452, AUC:0.983633
Epoch:220, d_loss:0.091767, g_loss:6.745488, accuracy:0.951238, AUC:0.989238
Epoch:221, d_loss:0.108191, g_loss:7.029654, accuracy:0.908774, AUC:0.987412
Epoch:222, d_loss:0.082262, g_loss:6.513996, accuracy:0.891393, AUC:0.978445
Epoch:223, d_loss:0.062587, g_loss:6.420957, accuracy:0.959238, AUC:0.990744
Epoch:224, d_loss:0.074162, g_loss:6.845954, accuracy:0.925750, AUC:0.990626
Epoch:225, d_loss:0.048883, g_loss:7.334310, accuracy:0.976262, AUC:0.995705
Epoch:226, d_loss:0.088293, g_loss:6.703572, accuracy:0.941024, AUC:0.984667
Epoch:227, d_loss:0.093656, g_loss:6.048128, accuracy:0.962548, AUC:0.989664
Epoch:228, d_loss:0.062120, g_loss:6.486962, accuracy:0.940369, AUC:0.992606
Epoch:229, d_loss:0.088269, g_loss:6.813138, accuracy:0.982524, AUC:0.996553
Epoch:230, d_loss:0.075885, g_loss:6.581292, accuracy:0.937036, AUC:0.993988
Epoch:231, d_loss:0.109177, g_loss:7.242789, accuracy:0.991262, AUC:0.999167
Epoch:232, d_loss:0.091512, g_loss:7.114054, accuracy:0.977690, AUC:0.995687
Epoch:233, d_loss:0.108867, g_loss:6.882621, accuracy:0.947333, AUC:0.993284
Epoch:234, d_loss:0.131202, g_loss:6.653155, accuracy:0.940821, AUC:0.980606
Epoch:235, d_loss:0.086815, g_loss:6.686411, accuracy:0.941536, AUC:0.988969
Epoch:236, d_loss:0.116578, g_loss:6.918539, accuracy:0.889226, AUC:0.994342
Epoch:237, d_loss:0.087007, g_loss:7.206471, accuracy:0.941643, AUC:0.986475
Epoch:238, d_loss:0.139618, g_loss:6.186009, accuracy:0.963500, AUC:0.994991
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/content/drive/My Drive/medGAN/mixed_binary/-999