cvnn10dBRadioML2018

C:\...\Anaconda3\python.exe ".........JetBrains\PyCharm Community Edition 2020.3\plugins\python-ce\helpers\pydev\pydevconsole.py" --mode=client --port=54909

import sys; print('Python %s on %s' % (sys.version, sys.platform))
sys.path.extend(['...........SystemDesign', ......./SystemDesign'])

Python 3.8.5 (default, Sep  3 2020, 21:29:08) [MSC v.1916 64 bit (AMD64)]
Type 'copyright', 'credits' or 'license' for more information
IPython 7.19.0 -- An enhanced Interactive Python. Type '?' for help.
PyDev console: using IPython 7.19.0

Python 3.8.5 (default, Sep  3 2020, 21:29:08) [MSC v.1916 64 bit (AMD64)] on win32
In[2]: runfile('................./5refs/CVNN10dBradioML2018.py', wdir='C:/Users/ndongma/Documents/SystemDesign/5refs')
2021-02-18 15:35:55.739861: W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library 'cudart64_101.dll'; dlerror: cudart64_101.dll not found
2021-02-18 15:35:55.740477: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
2021-02-18 15:36:02.898502: W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library 'nvcuda.dll'; dlerror: nvcuda.dll not found
2021-02-18 15:36:02.899066: W tensorflow/stream_executor/cuda/cuda_driver.cc:312] failed call to cuInit: UNKNOWN ERROR (303)
2021-02-18 15:36:02.903345: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: ip2979
2021-02-18 15:36:02.903963: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: ip2979
2021-02-18 15:36:02.904696: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN)to use the following CPU instructions in performance-critical operations:  AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2021-02-18 15:36:02.913676: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x26a18ef64c0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2021-02-18 15:36:02.914754: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
complex_conv2d (ComplexConv2 (None, 30, 30, 32)        320       
_________________________________________________________________
complex_max_pooling2d (Compl (None, 15, 15, 32)        0         
_________________________________________________________________
complex_conv2d_1 (ComplexCon (None, 13, 13, 64)        18496     
_________________________________________________________________
complex_max_pooling2d_1 (Com (None, 6, 6, 64)          0         
_________________________________________________________________
complex_conv2d_2 (ComplexCon (None, 4, 4, 64)          36928     
_________________________________________________________________
complex_flatten (ComplexFlat (None, 1024)              0         
_________________________________________________________________
complex_dense (ComplexDense) (None, 64)                65600     
_________________________________________________________________
complex_dense_1 (ComplexDens (None, 24)                1560      
=================================================================
Total params: 122,904
Trainable params: 122,904
Non-trainable params: 0
_________________________________________________________________
Epoch 1/150
1563/1563 [==============================] - 104s 66ms/step - loss: 1.8540 - accuracy: 0.2667 - val_loss: 10.4121 - val_accuracy: 0.0000e+00
Epoch 2/150
1563/1563 [==============================] - 104s 66ms/step - loss: 1.4237 - accuracy: 0.4123 - val_loss: 11.6570 - val_accuracy: 0.0000e+00
Epoch 3/150
1563/1563 [==============================] - 104s 67ms/step - loss: 1.2519 - accuracy: 0.4714 - val_loss: 11.4573 - val_accuracy: 0.0000e+00
Epoch 4/150
1563/1563 [==============================] - 104s 66ms/step - loss: 1.2051 - accuracy: 0.4870 - val_loss: 13.3360 - val_accuracy: 0.0000e+00
Epoch 5/150
1563/1563 [==============================] - 104s 66ms/step - loss: 1.1693 - accuracy: 0.4964 - val_loss: 13.0194 - val_accuracy: 0.0000e+00
Epoch 6/150
1563/1563 [==============================] - 104s 67ms/step - loss: 1.1404 - accuracy: 0.5090 - val_loss: 13.8312 - val_accuracy: 0.0000e+00
Epoch 7/150
1563/1563 [==============================] - 104s 66ms/step - loss: 1.1146 - accuracy: 0.5171 - val_loss: 13.9626 - val_accuracy: 0.0000e+00
Epoch 8/150
1563/1563 [==============================] - 103s 66ms/step - loss: 1.0840 - accuracy: 0.5308 - val_loss: 15.2444 - val_accuracy: 0.0000e+00
Epoch 9/150
1563/1563 [==============================] - 104s 67ms/step - loss: 1.0483 - accuracy: 0.5430 - val_loss: 15.6613 - val_accuracy: 1.0000e-04
Epoch 10/150
1563/1563 [==============================] - 104s 67ms/step - loss: 1.0130 - accuracy: 0.5604 - val_loss: 17.7254 - val_accuracy: 0.0000e+00
Epoch 11/150
1563/1563 [==============================] - 104s 66ms/step - loss: 0.9994 - accuracy: 0.5647 - val_loss: 17.1886 - val_accuracy: 1.0000e-04
Epoch 12/150
1563/1563 [==============================] - 104s 66ms/step - loss: 0.9670 - accuracy: 0.5827 - val_loss: 17.7842 - val_accuracy: 0.0000e+00
Epoch 13/150
1563/1563 [==============================] - 103s 66ms/step - loss: 0.9486 - accuracy: 0.5890 - val_loss: 18.0878 - val_accuracy: 2.0000e-04
Epoch 14/150
1563/1563 [==============================] - 103s 66ms/step - loss: 0.9320 - accuracy: 0.5974 - val_loss: 17.9758 - val_accuracy: 1.0000e-04
Epoch 15/150
1563/1563 [==============================] - 103s 66ms/step - loss: 0.9077 - accuracy: 0.6078 - val_loss: 18.6362 - val_accuracy: 6.0000e-04
Epoch 16/150
1563/1563 [==============================] - 102s 66ms/step - loss: 0.8991 - accuracy: 0.6137 - val_loss: 17.7966 - val_accuracy: 0.0013
Epoch 17/150
1563/1563 [==============================] - 103s 66ms/step - loss: 0.8735 - accuracy: 0.6245 - val_loss: 19.0962 - val_accuracy: 5.0000e-04
Epoch 18/150
1563/1563 [==============================] - 103s 66ms/step - loss: 0.8543 - accuracy: 0.6327 - val_loss: 21.0454 - val_accuracy: 2.0000e-04
Epoch 19/150
1563/1563 [==============================] - 104s 66ms/step - loss: 0.8366 - accuracy: 0.6445 - val_loss: 20.5377 - val_accuracy: 0.0015
Epoch 20/150
1563/1563 [==============================] - 110s 70ms/step - loss: 0.8145 - accuracy: 0.6544 - val_loss: 22.9499 - val_accuracy: 1.0000e-04
Epoch 21/150
1563/1563 [==============================] - 105s 67ms/step - loss: 0.7941 - accuracy: 0.6640 - val_loss: 21.4425 - val_accuracy: 0.0055
Epoch 22/150
1563/1563 [==============================] - 104s 66ms/step - loss: 0.7775 - accuracy: 0.6721 - val_loss: 22.9635 - val_accuracy: 9.0000e-04
Epoch 23/150
1563/1563 [==============================] - 105s 67ms/step - loss: 0.7611 - accuracy: 0.6818 - val_loss: 24.1593 - val_accuracy: 0.0051
Epoch 24/150
1563/1563 [==============================] - 105s 67ms/step - loss: 0.7349 - accuracy: 0.6937 - val_loss: 22.8880 - val_accuracy: 0.0059
Epoch 25/150
1563/1563 [==============================] - 104s 67ms/step - loss: 0.7174 - accuracy: 0.7006 - val_loss: 24.8198 - val_accuracy: 0.0066
Epoch 26/150
1563/1563 [==============================] - 104s 67ms/step - loss: 0.6947 - accuracy: 0.7120 - val_loss: 24.8221 - val_accuracy: 0.0036
Epoch 27/150
1563/1563 [==============================] - 103s 66ms/step - loss: 0.6691 - accuracy: 0.7217 - val_loss: 24.8728 - val_accuracy: 0.0042
Epoch 28/150
1563/1563 [==============================] - 105s 67ms/step - loss: 0.6469 - accuracy: 0.7337 - val_loss: 25.7219 - val_accuracy: 0.0044
Epoch 29/150
1563/1563 [==============================] - 105s 67ms/step - loss: 0.6359 - accuracy: 0.7374 - val_loss: 25.0975 - val_accuracy: 0.0071
Epoch 30/150
1563/1563 [==============================] - 104s 67ms/step - loss: 0.6067 - accuracy: 0.7504 - val_loss: 26.6746 - val_accuracy: 0.0086
Epoch 31/150
1563/1563 [==============================] - 104s 67ms/step - loss: 0.5890 - accuracy: 0.7596 - val_loss: 27.7736 - val_accuracy: 0.0053
Epoch 32/150
1563/1563 [==============================] - 108s 69ms/step - loss: 0.5611 - accuracy: 0.7727 - val_loss: 29.0676 - val_accuracy: 0.0096
Epoch 33/150
1563/1563 [==============================] - 105s 67ms/step - loss: 0.5425 - accuracy: 0.7808 - val_loss: 27.8539 - val_accuracy: 0.0128
Epoch 34/150
1563/1563 [==============================] - 104s 67ms/step - loss: 0.5183 - accuracy: 0.7912 - val_loss: 28.6431 - val_accuracy: 0.0049
Epoch 35/150
1563/1563 [==============================] - 105s 67ms/step - loss: 0.5054 - accuracy: 0.7964 - val_loss: 30.5384 - val_accuracy: 0.0110
Epoch 36/150
1563/1563 [==============================] - 105s 67ms/step - loss: 0.4869 - accuracy: 0.8034 - val_loss: 29.9604 - val_accuracy: 0.0057
Epoch 37/150
1563/1563 [==============================] - 108s 69ms/step - loss: 0.4657 - accuracy: 0.8150 - val_loss: 31.6767 - val_accuracy: 0.0112
Epoch 38/150
1563/1563 [==============================] - 111s 71ms/step - loss: 0.4443 - accuracy: 0.8231 - val_loss: 31.2316 - val_accuracy: 0.0095
Epoch 39/150
1563/1563 [==============================] - 114s 73ms/step - loss: 0.4270 - accuracy: 0.8309 - val_loss: 30.5924 - val_accuracy: 0.0181
Epoch 40/150
1563/1563 [==============================] - 117s 75ms/step - loss: 0.4164 - accuracy: 0.8362 - val_loss: 34.1432 - val_accuracy: 0.0076
Epoch 41/150
1563/1563 [==============================] - 119s 76ms/step - loss: 0.3918 - accuracy: 0.8451 - val_loss: 34.5374 - val_accuracy: 0.0074
Epoch 42/150
1563/1563 [==============================] - 119s 76ms/step - loss: 0.3889 - accuracy: 0.8478 - val_loss: 36.5796 - val_accuracy: 0.0124
Epoch 43/150
1563/1563 [==============================] - 119s 76ms/step - loss: 0.3692 - accuracy: 0.8573 - val_loss: 36.4488 - val_accuracy: 0.0101
Epoch 44/150
1563/1563 [==============================] - 118s 75ms/step - loss: 0.3454 - accuracy: 0.8648 - val_loss: 36.7198 - val_accuracy: 0.0105
Epoch 45/150
1563/1563 [==============================] - 117s 75ms/step - loss: 0.3446 - accuracy: 0.8663 - val_loss: 36.5892 - val_accuracy: 0.0102
Epoch 46/150
1563/1563 [==============================] - 107s 68ms/step - loss: 0.3260 - accuracy: 0.8739 - val_loss: 37.7424 - val_accuracy: 0.0140
Epoch 47/150
1563/1563 [==============================] - 105s 67ms/step - loss: 0.3128 - accuracy: 0.8806 - val_loss: 38.9625 - val_accuracy: 0.0111
Epoch 48/150
1563/1563 [==============================] - 106s 68ms/step - loss: 0.3091 - accuracy: 0.8829 - val_loss: 39.5381 - val_accuracy: 0.0070
Epoch 49/150
1563/1563 [==============================] - 105s 67ms/step - loss: 0.2886 - accuracy: 0.8884 - val_loss: 43.5679 - val_accuracy: 0.0101
Epoch 50/150
1563/1563 [==============================] - 105s 67ms/step - loss: 0.2831 - accuracy: 0.8919 - val_loss: 41.7548 - val_accuracy: 0.0129
Epoch 51/150
1563/1563 [==============================] - 106s 68ms/step - loss: 0.2793 - accuracy: 0.8930 - val_loss: 43.8515 - val_accuracy: 0.0122
Epoch 52/150
1563/1563 [==============================] - 105s 67ms/step - loss: 0.2675 - accuracy: 0.8994 - val_loss: 43.7282 - val_accuracy: 0.0116
Epoch 53/150
1563/1563 [==============================] - 105s 67ms/step - loss: 0.2451 - accuracy: 0.9082 - val_loss: 44.2137 - val_accuracy: 0.0096
Epoch 54/150
1563/1563 [==============================] - 105s 67ms/step - loss: 0.2557 - accuracy: 0.9040 - val_loss: 47.5989 - val_accuracy: 0.0127
Epoch 55/150
1563/1563 [==============================] - 105s 67ms/step - loss: 0.2334 - accuracy: 0.9114 - val_loss: 46.3780 - val_accuracy: 0.0117
Epoch 56/150
1563/1563 [==============================] - 105s 67ms/step - loss: 0.2330 - accuracy: 0.9139 - val_loss: 47.5233 - val_accuracy: 0.0108
Epoch 57/150
1563/1563 [==============================] - 107s 68ms/step - loss: 0.2252 - accuracy: 0.9135 - val_loss: 49.4422 - val_accuracy: 0.0113
Epoch 58/150
1563/1563 [==============================] - 108s 69ms/step - loss: 0.2215 - accuracy: 0.9192 - val_loss: 49.9722 - val_accuracy: 0.0129
Epoch 59/150
1563/1563 [==============================] - 107s 69ms/step - loss: 0.2019 - accuracy: 0.9239 - val_loss: 53.8186 - val_accuracy: 0.0073
Epoch 60/150
1563/1563 [==============================] - 105s 67ms/step - loss: 0.2188 - accuracy: 0.9194 - val_loss: 49.2864 - val_accuracy: 0.0104
Epoch 61/150
1563/1563 [==============================] - 109s 70ms/step - loss: 0.1962 - accuracy: 0.9298 - val_loss: 53.2832 - val_accuracy: 0.0099
Epoch 62/150
1563/1563 [==============================] - 119s 76ms/step - loss: 0.1994 - accuracy: 0.9268 - val_loss: 54.0371 - val_accuracy: 0.0074
Epoch 63/150
1563/1563 [==============================] - 121s 77ms/step - loss: 0.1828 - accuracy: 0.9325 - val_loss: 55.5071 - val_accuracy: 0.0141
Epoch 64/150
1563/1563 [==============================] - 121s 77ms/step - loss: 0.1888 - accuracy: 0.9301 - val_loss: 54.4933 - val_accuracy: 0.0093
Epoch 65/150
1563/1563 [==============================] - 121s 77ms/step - loss: 0.1751 - accuracy: 0.9360 - val_loss: 56.4463 - val_accuracy: 0.0073
Epoch 66/150
1563/1563 [==============================] - 120s 77ms/step - loss: 0.1820 - accuracy: 0.9334 - val_loss: 54.6490 - val_accuracy: 0.0134
Epoch 67/150
1563/1563 [==============================] - 120s 77ms/step - loss: 0.1783 - accuracy: 0.9352 - val_loss: 59.2141 - val_accuracy: 0.0169
Epoch 68/150
1563/1563 [==============================] - 119s 76ms/step - loss: 0.1687 - accuracy: 0.9405 - val_loss: 58.4350 - val_accuracy: 0.0074
Epoch 69/150
1563/1563 [==============================] - 118s 76ms/step - loss: 0.1753 - accuracy: 0.9365 - val_loss: 57.4712 - val_accuracy: 0.0145
Epoch 70/150
1563/1563 [==============================] - 119s 76ms/step - loss: 0.1730 - accuracy: 0.9369 - val_loss: 57.4008 - val_accuracy: 0.0138
Epoch 71/150
1563/1563 [==============================] - 116s 74ms/step - loss: 0.1574 - accuracy: 0.9423 - val_loss: 58.5548 - val_accuracy: 0.0102
Epoch 72/150
1563/1563 [==============================] - 116s 74ms/step - loss: 0.1741 - accuracy: 0.9381 - val_loss: 64.3633 - val_accuracy: 0.0142
Epoch 73/150
1563/1563 [==============================] - 116s 74ms/step - loss: 0.1512 - accuracy: 0.9453 - val_loss: 63.5861 - val_accuracy: 0.0119
Epoch 74/150
1563/1563 [==============================] - 116s 74ms/step - loss: 0.1591 - accuracy: 0.9419 - val_loss: 61.6292 - val_accuracy: 0.0084
Epoch 75/150
1563/1563 [==============================] - 115s 74ms/step - loss: 0.1665 - accuracy: 0.9406 - val_loss: 64.1435 - val_accuracy: 0.0103
Epoch 76/150
1563/1563 [==============================] - 115s 73ms/step - loss: 0.1480 - accuracy: 0.9474 - val_loss: 65.0358 - val_accuracy: 0.0129
Epoch 77/150
1563/1563 [==============================] - 115s 73ms/step - loss: 0.1392 - accuracy: 0.9510 - val_loss: 63.1277 - val_accuracy: 0.0127
Epoch 78/150
1563/1563 [==============================] - 116s 74ms/step - loss: 0.1560 - accuracy: 0.9455 - val_loss: 67.5044 - val_accuracy: 0.0105
Epoch 79/150
1563/1563 [==============================] - 115s 74ms/step - loss: 0.1588 - accuracy: 0.9444 - val_loss: 63.6638 - val_accuracy: 0.0141
Epoch 80/150
1563/1563 [==============================] - 116s 74ms/step - loss: 0.1448 - accuracy: 0.9481 - val_loss: 69.5775 - val_accuracy: 0.0148
Epoch 81/150
1563/1563 [==============================] - 114s 73ms/step - loss: 0.1484 - accuracy: 0.9475 - val_loss: 66.2697 - val_accuracy: 0.0164
Epoch 82/150
1563/1563 [==============================] - 114s 73ms/step - loss: 0.1298 - accuracy: 0.9548 - val_loss: 70.7204 - val_accuracy: 0.0113
Epoch 83/150
1563/1563 [==============================] - 115s 73ms/step - loss: 0.1572 - accuracy: 0.9455 - val_loss: 70.7535 - val_accuracy: 0.0122
Epoch 84/150
1563/1563 [==============================] - 109s 70ms/step - loss: 0.1404 - accuracy: 0.9507 - val_loss: 69.1139 - val_accuracy: 0.0177
Epoch 85/150
1563/1563 [==============================] - 111s 71ms/step - loss: 0.1415 - accuracy: 0.9519 - val_loss: 76.3973 - val_accuracy: 0.0104
Epoch 86/150
1563/1563 [==============================] - 103s 66ms/step - loss: 0.1443 - accuracy: 0.9510 - val_loss: 70.3573 - val_accuracy: 0.0151
Epoch 87/150
1563/1563 [==============================] - 103s 66ms/step - loss: 0.1316 - accuracy: 0.9541 - val_loss: 75.5037 - val_accuracy: 0.0145
Epoch 88/150
1563/1563 [==============================] - 104s 66ms/step - loss: 0.1432 - accuracy: 0.9518 - val_loss: 78.6419 - val_accuracy: 0.0125
Epoch 89/150
1563/1563 [==============================] - 103s 66ms/step - loss: 0.1262 - accuracy: 0.9567 - val_loss: 72.7579 - val_accuracy: 0.0127
Epoch 90/150
1563/1563 [==============================] - 103s 66ms/step - loss: 0.1333 - accuracy: 0.9539 - val_loss: 72.3495 - val_accuracy: 0.0143
Epoch 91/150
1563/1563 [==============================] - 103s 66ms/step - loss: 0.1329 - accuracy: 0.9539 - val_loss: 76.5033 - val_accuracy: 0.0110
Epoch 92/150
1563/1563 [==============================] - 103s 66ms/step - loss: 0.1260 - accuracy: 0.9563 - val_loss: 74.3116 - val_accuracy: 0.0114
Epoch 93/150
1563/1563 [==============================] - 103s 66ms/step - loss: 0.1427 - accuracy: 0.9516 - val_loss: 77.6267 - val_accuracy: 0.0118
Epoch 94/150
1563/1563 [==============================] - 103s 66ms/step - loss: 0.1256 - accuracy: 0.9576 - val_loss: 77.4523 - val_accuracy: 0.0150
Epoch 95/150
1563/1563 [==============================] - 109s 70ms/step - loss: 0.1282 - accuracy: 0.9558 - val_loss: 76.3116 - val_accuracy: 0.0139
Epoch 96/150
1563/1563 [==============================] - 109s 70ms/step - loss: 0.1129 - accuracy: 0.9621 - val_loss: 81.3491 - val_accuracy: 0.0119
Epoch 97/150
1563/1563 [==============================] - 114s 73ms/step - loss: 0.1343 - accuracy: 0.9551 - val_loss: 84.8593 - val_accuracy: 0.0122
Epoch 98/150
1563/1563 [==============================] - 114s 73ms/step - loss: 0.1329 - accuracy: 0.9552 - val_loss: 80.4936 - val_accuracy: 0.0151
Epoch 99/150
1563/1563 [==============================] - 114s 73ms/step - loss: 0.1298 - accuracy: 0.9569 - val_loss: 85.1404 - val_accuracy: 0.0143
Epoch 100/150
1563/1563 [==============================] - 113s 72ms/step - loss: 0.1084 - accuracy: 0.9629 - val_loss: 88.8719 - val_accuracy: 0.0149
Epoch 101/150
1563/1563 [==============================] - 112s 72ms/step - loss: 0.1228 - accuracy: 0.9580 - val_loss: 81.9993 - val_accuracy: 0.0104
Epoch 102/150
1563/1563 [==============================] - 112s 72ms/step - loss: 0.1296 - accuracy: 0.9557 - val_loss: 85.1525 - val_accuracy: 0.0113
Epoch 103/150
1563/1563 [==============================] - 111s 71ms/step - loss: 0.1130 - accuracy: 0.9625 - val_loss: 83.3725 - val_accuracy: 0.0142
Epoch 104/150
1563/1563 [==============================] - 110s 71ms/step - loss: 0.1123 - accuracy: 0.9615 - val_loss: 86.0816 - val_accuracy: 0.0116
Epoch 105/150
1563/1563 [==============================] - 114s 73ms/step - loss: 0.1247 - accuracy: 0.9570 - val_loss: 87.8485 - val_accuracy: 0.0123
Epoch 106/150
1563/1563 [==============================] - 116s 75ms/step - loss: 0.1072 - accuracy: 0.9643 - val_loss: 92.2803 - val_accuracy: 0.0086
Epoch 107/150
1563/1563 [==============================] - 118s 75ms/step - loss: 0.1271 - accuracy: 0.9581 - val_loss: 91.8779 - val_accuracy: 0.0119
Epoch 108/150
1563/1563 [==============================] - 117s 75ms/step - loss: 0.1048 - accuracy: 0.9648 - val_loss: 90.8189 - val_accuracy: 0.0086
Epoch 109/150
1563/1563 [==============================] - 118s 75ms/step - loss: 0.1104 - accuracy: 0.9631 - val_loss: 90.6274 - val_accuracy: 0.0147
Epoch 110/150
1563/1563 [==============================] - 108s 69ms/step - loss: 0.1087 - accuracy: 0.9639 - val_loss: 99.4203 - val_accuracy: 0.0100
Epoch 111/150
1563/1563 [==============================] - 104s 67ms/step - loss: 0.1293 - accuracy: 0.9585 - val_loss: 89.9093 - val_accuracy: 0.0127
Epoch 112/150
1563/1563 [==============================] - 104s 67ms/step - loss: 0.1081 - accuracy: 0.9648 - val_loss: 93.3944 - val_accuracy: 0.0160
Epoch 113/150
1563/1563 [==============================] - 110s 70ms/step - loss: 0.0966 - accuracy: 0.9681 - val_loss: 96.6603 - val_accuracy: 0.0110
Epoch 114/150
1563/1563 [==============================] - 118s 76ms/step - loss: 0.1197 - accuracy: 0.9607 - val_loss: 94.2283 - val_accuracy: 0.0135
Epoch 115/150
1563/1563 [==============================] - 126s 80ms/step - loss: 0.1119 - accuracy: 0.9627 - val_loss: 92.9651 - val_accuracy: 0.0128
Epoch 116/150
1563/1563 [==============================] - 127s 81ms/step - loss: 0.1110 - accuracy: 0.9631 - val_loss: 96.5933 - val_accuracy: 0.0133
Epoch 117/150
1563/1563 [==============================] - 121s 78ms/step - loss: 0.1093 - accuracy: 0.9648 - val_loss: 99.8761 - val_accuracy: 0.0144
Epoch 118/150
1563/1563 [==============================] - 113s 72ms/step - loss: 0.1119 - accuracy: 0.9635 - val_loss: 93.4622 - val_accuracy: 0.0119
Epoch 119/150
1563/1563 [==============================] - 113s 72ms/step - loss: 0.1122 - accuracy: 0.9628 - val_loss: 100.6695 - val_accuracy: 0.0092
Epoch 120/150
1563/1563 [==============================] - 113s 72ms/step - loss: 0.0982 - accuracy: 0.9671 - val_loss: 100.3566 - val_accuracy: 0.0131
Epoch 121/150
1563/1563 [==============================] - 112s 71ms/step - loss: 0.1080 - accuracy: 0.9648 - val_loss: 104.7265 - val_accuracy: 0.0122
Epoch 122/150
1563/1563 [==============================] - 110s 70ms/step - loss: 0.1088 - accuracy: 0.9640 - val_loss: 108.3300 - val_accuracy: 0.0055
Epoch 123/150
1563/1563 [==============================] - 111s 71ms/step - loss: 0.1090 - accuracy: 0.9640 - val_loss: 107.4884 - val_accuracy: 0.0078
Epoch 124/150
1563/1563 [==============================] - 107s 68ms/step - loss: 0.1095 - accuracy: 0.9656 - val_loss: 101.9690 - val_accuracy: 0.0100
Epoch 125/150
1563/1563 [==============================] - 107s 68ms/step - loss: 0.1071 - accuracy: 0.9652 - val_loss: 102.2603 - val_accuracy: 0.0111
Epoch 126/150
1563/1563 [==============================] - 113s 72ms/step - loss: 0.1082 - accuracy: 0.9635 - val_loss: 107.7489 - val_accuracy: 0.0079
Epoch 127/150
1563/1563 [==============================] - 112s 72ms/step - loss: 0.0964 - accuracy: 0.9680 - val_loss: 101.4497 - val_accuracy: 0.0100
Epoch 128/150
1563/1563 [==============================] - 113s 72ms/step - loss: 0.1136 - accuracy: 0.9637 - val_loss: 99.2518 - val_accuracy: 0.0150
Epoch 129/150
1563/1563 [==============================] - 113s 72ms/step - loss: 0.1046 - accuracy: 0.9662 - val_loss: 102.3373 - val_accuracy: 0.0105
Epoch 130/150
1563/1563 [==============================] - 111s 71ms/step - loss: 0.1038 - accuracy: 0.9657 - val_loss: 109.9996 - val_accuracy: 0.0105
Epoch 131/150
1563/1563 [==============================] - 112s 72ms/step - loss: 0.0991 - accuracy: 0.9674 - val_loss: 108.7128 - val_accuracy: 0.0128
Epoch 132/150
1563/1563 [==============================] - 108s 69ms/step - loss: 0.1087 - accuracy: 0.9659 - val_loss: 115.1295 - val_accuracy: 0.0097
Epoch 133/150
1563/1563 [==============================] - 113s 72ms/step - loss: 0.0927 - accuracy: 0.9696 - val_loss: 117.2692 - val_accuracy: 0.0114
Epoch 134/150
1563/1563 [==============================] - 113s 72ms/step - loss: 0.1059 - accuracy: 0.9667 - val_loss: 106.5951 - val_accuracy: 0.0127
Epoch 135/150
1563/1563 [==============================] - 113s 72ms/step - loss: 0.0922 - accuracy: 0.9696 - val_loss: 109.2908 - val_accuracy: 0.0128
Epoch 136/150
1563/1563 [==============================] - 113s 73ms/step - loss: 0.1036 - accuracy: 0.9669 - val_loss: 107.0613 - val_accuracy: 0.0107
Epoch 137/150
1563/1563 [==============================] - 112s 72ms/step - loss: 0.0968 - accuracy: 0.9696 - val_loss: 113.0483 - val_accuracy: 0.0162
Epoch 138/150
1563/1563 [==============================] - 113s 73ms/step - loss: 0.1134 - accuracy: 0.9641 - val_loss: 110.4524 - val_accuracy: 0.0142
Epoch 139/150
1563/1563 [==============================] - 113s 73ms/step - loss: 0.1030 - accuracy: 0.9679 - val_loss: 120.9530 - val_accuracy: 0.0095
Epoch 140/150
1563/1563 [==============================] - 113s 73ms/step - loss: 0.0889 - accuracy: 0.9707 - val_loss: 114.4035 - val_accuracy: 0.0089
Epoch 141/150
1563/1563 [==============================] - 113s 72ms/step - loss: 0.1037 - accuracy: 0.9673 - val_loss: 114.0952 - val_accuracy: 0.0119
Epoch 142/150
1563/1563 [==============================] - 113s 73ms/step - loss: 0.1016 - accuracy: 0.9686 - val_loss: 118.3095 - val_accuracy: 0.0114
Epoch 143/150
1563/1563 [==============================] - 112s 72ms/step - loss: 0.0952 - accuracy: 0.9696 - val_loss: 110.5978 - val_accuracy: 0.0111
Epoch 144/150
1563/1563 [==============================] - 113s 72ms/step - loss: 0.1032 - accuracy: 0.9675 - val_loss: 119.8202 - val_accuracy: 0.0128
Epoch 145/150
1563/1563 [==============================] - 112s 72ms/step - loss: 0.0890 - accuracy: 0.9718 - val_loss: 120.5380 - val_accuracy: 0.0111
Epoch 146/150
1563/1563 [==============================] - 104s 67ms/step - loss: 0.1098 - accuracy: 0.9672 - val_loss: 123.2203 - val_accuracy: 0.0103
Epoch 147/150
1563/1563 [==============================] - 105s 67ms/step - loss: 0.0857 - accuracy: 0.9729 - val_loss: 121.2522 - val_accuracy: 0.0096
Epoch 148/150
1563/1563 [==============================] - 104s 67ms/step - loss: 0.1019 - accuracy: 0.9696 - val_loss: 119.6316 - val_accuracy: 0.0145
Epoch 149/150
1563/1563 [==============================] - 104s 67ms/step - loss: 0.0865 - accuracy: 0.9727 - val_loss: 120.6233 - val_accuracy: 0.0094
Epoch 150/150
1563/1563 [==============================] - 105s 67ms/step - loss: 0.0952 - accuracy: 0.9689 - val_loss: 127.3407 - val_accuracy: 0.0150
313/313 - 9s - loss: 127.3407 - accuracy: 0.0150