sdc

Run1

Anaconda3\python.exeta\Local\JetBrains\PyCharm Community Edition 2020.3\plugins\python-ce\helpers\pydev\pydevconsole.py" --mode=client --port=49739

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

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]: runDocuments/SystemDesign/5refs/CVNNradioML.py', wdir=/Documents/SystemDesign/5refs')
2021-02-10 12:19:53.160670: W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library 'cudart64_101.dll'; dlerror: cudart64_101.dll not found
2021-02-10 12:19:53.161233: 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.
2021-02-10 12:19:57.055227: W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library 'nvcuda.dll'; dlerror: nvcuda.dll not found
2021-02-10 12:19:57.055775: W tensorflow/stream_executor/cuda/cuda_driver.cc:312] failed call to cuInit: UNKNOWN ERROR (303)
2021-02-10 12:19:57.059848: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: ip2979
2021-02-10 12:19:57.060548: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: ip2979
2021-02-10 12:19:57.061538: 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.
2021-02-10 12:19:57.073216: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x149bff5ffa0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2021-02-10 12:19:57.074055: 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, 64)        640       
_________________________________________________________________
complex_max_pooling2d (Compl (None, 15, 15, 64)        0         
_________________________________________________________________
complex_conv2d_1 (ComplexCon (None, 13, 13, 128)       73856     
_________________________________________________________________
complex_max_pooling2d_1 (Com (None, 6, 6, 128)         0         
_________________________________________________________________
complex_conv2d_2 (ComplexCon (None, 4, 4, 128)         147584    
_________________________________________________________________
complex_flatten (ComplexFlat (None, 2048)              0         
_________________________________________________________________
complex_dense (ComplexDense) (None, 128)               262272    
_________________________________________________________________
complex_dense_1 (ComplexDens (None, 24)                3096      
=================================================================
Total params: 487,448
Trainable params: 487,448
Non-trainable params: 0
_________________________________________________________________
Epoch 1/150
438/438 [==============================] - 60s 136ms/step - loss: 0.6356 - accuracy: 0.7037 - val_loss: 2.4896 - val_accuracy: 0.0000e+00
Epoch 2/150
438/438 [==============================] - 59s 135ms/step - loss: 0.6068 - accuracy: 0.7143 - val_loss: 3.1951 - val_accuracy: 0.0000e+00
Epoch 3/150
438/438 [==============================] - 59s 134ms/step - loss: 0.6002 - accuracy: 0.7143 - val_loss: 3.2916 - val_accuracy: 0.0000e+00
Epoch 4/150
438/438 [==============================] - 59s 134ms/step - loss: 0.5977 - accuracy: 0.7143 - val_loss: 2.5750 - val_accuracy: 0.0000e+00
Epoch 5/150
438/438 [==============================] - 59s 134ms/step - loss: 0.5898 - accuracy: 0.7146 - val_loss: 2.7857 - val_accuracy: 8.5714e-04
Epoch 6/150
438/438 [==============================] - 59s 135ms/step - loss: 0.5642 - accuracy: 0.7208 - val_loss: 2.6128 - val_accuracy: 0.0331
Epoch 7/150
438/438 [==============================] - 59s 134ms/step - loss: 0.5101 - accuracy: 0.7554 - val_loss: 2.6816 - val_accuracy: 0.1666
Epoch 8/150
438/438 [==============================] - 59s 134ms/step - loss: 0.4378 - accuracy: 0.7997 - val_loss: 4.1965 - val_accuracy: 0.1770
Epoch 9/150
438/438 [==============================] - 59s 134ms/step - loss: 0.3503 - accuracy: 0.8460 - val_loss: 3.4959 - val_accuracy: 0.1911
Epoch 10/150
438/438 [==============================] - 59s 134ms/step - loss: 0.2657 - accuracy: 0.8907 - val_loss: 4.1059 - val_accuracy: 0.1957
Epoch 11/150
438/438 [==============================] - 59s 135ms/step - loss: 0.1893 - accuracy: 0.9258 - val_loss: 4.6468 - val_accuracy: 0.2157
Epoch 12/150
438/438 [==============================] - 59s 134ms/step - loss: 0.1431 - accuracy: 0.9434 - val_loss: 4.8431 - val_accuracy: 0.2147
Epoch 13/150
438/438 [==============================] - 59s 134ms/step - loss: 0.1048 - accuracy: 0.9596 - val_loss: 4.7646 - val_accuracy: 0.2271
Epoch 14/150
438/438 [==============================] - 59s 134ms/step - loss: 0.0877 - accuracy: 0.9666 - val_loss: 6.7164 - val_accuracy: 0.2064
Epoch 15/150
438/438 [==============================] - 59s 134ms/step - loss: 0.0768 - accuracy: 0.9701 - val_loss: 7.4353 - val_accuracy: 0.1923
Epoch 16/150
438/438 [==============================] - 59s 135ms/step - loss: 0.0694 - accuracy: 0.9731 - val_loss: 6.5690 - val_accuracy: 0.1960
Epoch 17/150
438/438 [==============================] - 59s 134ms/step - loss: 0.0567 - accuracy: 0.9791 - val_loss: 6.0207 - val_accuracy: 0.2263
Epoch 18/150
438/438 [==============================] - 59s 134ms/step - loss: 0.0590 - accuracy: 0.9775 - val_loss: 8.1606 - val_accuracy: 0.2220
Epoch 19/150
438/438 [==============================] - 59s 134ms/step - loss: 0.0436 - accuracy: 0.9836 - val_loss: 8.0945 - val_accuracy: 0.1950
Epoch 20/150
438/438 [==============================] - 59s 134ms/step - loss: 0.0545 - accuracy: 0.9782 - val_loss: 6.8986 - val_accuracy: 0.2173
Epoch 21/150
438/438 [==============================] - 59s 135ms/step - loss: 0.0413 - accuracy: 0.9840 - val_loss: 7.1590 - val_accuracy: 0.2464
Epoch 22/150
438/438 [==============================] - 59s 134ms/step - loss: 0.0434 - accuracy: 0.9834 - val_loss: 8.8319 - val_accuracy: 0.2183
Epoch 23/150
438/438 [==============================] - 59s 134ms/step - loss: 0.0461 - accuracy: 0.9818 - val_loss: 6.8165 - val_accuracy: 0.2509
Epoch 24/150
438/438 [==============================] - 59s 134ms/step - loss: 0.0413 - accuracy: 0.9841 - val_loss: 7.8393 - val_accuracy: 0.2547
Epoch 25/150
438/438 [==============================] - 59s 135ms/step - loss: 0.0374 - accuracy: 0.9859 - val_loss: 9.3445 - val_accuracy: 0.2297
Epoch 26/150
438/438 [==============================] - 60s 136ms/step - loss: 0.0407 - accuracy: 0.9836 - val_loss: 8.8371 - val_accuracy: 0.2499
Epoch 27/150
438/438 [==============================] - 60s 137ms/step - loss: 0.0384 - accuracy: 0.9857 - val_loss: 8.7116 - val_accuracy: 0.2203
Epoch 28/150
438/438 [==============================] - 61s 140ms/step - loss: 0.0339 - accuracy: 0.9871 - val_loss: 8.5114 - val_accuracy: 0.2427
Epoch 29/150
438/438 [==============================] - 61s 138ms/step - loss: 0.0386 - accuracy: 0.9853 - val_loss: 8.2025 - val_accuracy: 0.2366
Epoch 30/150
438/438 [==============================] - 60s 136ms/step - loss: 0.0320 - accuracy: 0.9885 - val_loss: 7.4458 - val_accuracy: 0.2506
Epoch 31/150
438/438 [==============================] - 60s 136ms/step - loss: 0.0288 - accuracy: 0.9899 - val_loss: 9.1556 - val_accuracy: 0.2763
Epoch 32/150
438/438 [==============================] - 59s 135ms/step - loss: 0.0342 - accuracy: 0.9867 - val_loss: 9.5841 - val_accuracy: 0.2090
Epoch 33/150
438/438 [==============================] - 60s 136ms/step - loss: 0.0279 - accuracy: 0.9891 - val_loss: 10.3226 - val_accuracy: 0.2169
Epoch 34/150
438/438 [==============================] - 59s 135ms/step - loss: 0.0350 - accuracy: 0.9866 - val_loss: 8.0610 - val_accuracy: 0.2409
Epoch 35/150
438/438 [==============================] - 59s 135ms/step - loss: 0.0303 - accuracy: 0.9898 - val_loss: 10.6924 - val_accuracy: 0.1876
Epoch 36/150
438/438 [==============================] - 59s 136ms/step - loss: 0.0313 - accuracy: 0.9880 - val_loss: 7.6250 - val_accuracy: 0.2739
Epoch 37/150
438/438 [==============================] - 59s 135ms/step - loss: 0.0300 - accuracy: 0.9891 - val_loss: 8.7032 - val_accuracy: 0.2501
Epoch 38/150
438/438 [==============================] - 59s 135ms/step - loss: 0.0236 - accuracy: 0.9922 - val_loss: 9.3688 - val_accuracy: 0.2161
Epoch 39/150
438/438 [==============================] - 59s 135ms/step - loss: 0.0266 - accuracy: 0.9901 - val_loss: 10.3763 - val_accuracy: 0.2259
Epoch 40/150
438/438 [==============================] - 59s 136ms/step - loss: 0.0295 - accuracy: 0.9886 - val_loss: 10.0403 - val_accuracy: 0.2047
Epoch 41/150
438/438 [==============================] - 60s 136ms/step - loss: 0.0262 - accuracy: 0.9914 - val_loss: 11.5816 - val_accuracy: 0.2183
Epoch 42/150
438/438 [==============================] - 59s 136ms/step - loss: 0.0253 - accuracy: 0.9900 - val_loss: 11.5512 - val_accuracy: 0.2084
Epoch 43/150
438/438 [==============================] - 59s 134ms/step - loss: 0.0243 - accuracy: 0.9905 - val_loss: 11.3360 - val_accuracy: 0.2414
Epoch 44/150
438/438 [==============================] - 57s 131ms/step - loss: 0.0265 - accuracy: 0.9906 - val_loss: 10.6310 - val_accuracy: 0.2036
Epoch 45/150
438/438 [==============================] - 57s 131ms/step - loss: 0.0251 - accuracy: 0.9909 - val_loss: 10.9365 - val_accuracy: 0.1851
Epoch 46/150
438/438 [==============================] - 58s 133ms/step - loss: 0.0227 - accuracy: 0.9913 - val_loss: 12.9638 - val_accuracy: 0.1803
Epoch 47/150
438/438 [==============================] - 59s 136ms/step - loss: 0.0240 - accuracy: 0.9917 - val_loss: 10.8335 - val_accuracy: 0.1969
Epoch 48/150
438/438 [==============================] - 59s 134ms/step - loss: 0.0214 - accuracy: 0.9926 - val_loss: 11.2181 - val_accuracy: 0.2333
Epoch 49/150
438/438 [==============================] - 59s 136ms/step - loss: 0.0223 - accuracy: 0.9920 - val_loss: 9.5424 - val_accuracy: 0.2547
Epoch 50/150
438/438 [==============================] - 57s 131ms/step - loss: 0.0231 - accuracy: 0.9918 - val_loss: 12.0902 - val_accuracy: 0.2486
Epoch 51/150
438/438 [==============================] - 58s 132ms/step - loss: 0.0208 - accuracy: 0.9921 - val_loss: 11.5389 - val_accuracy: 0.2084
Epoch 52/150
438/438 [==============================] - 60s 136ms/step - loss: 0.0197 - accuracy: 0.9931 - val_loss: 11.8878 - val_accuracy: 0.2019
Epoch 53/150
438/438 [==============================] - 59s 136ms/step - loss: 0.0263 - accuracy: 0.9904 - val_loss: 10.6926 - val_accuracy: 0.2324
Epoch 54/150
438/438 [==============================] - 59s 135ms/step - loss: 0.0226 - accuracy: 0.9913 - val_loss: 11.7994 - val_accuracy: 0.2243
Epoch 55/150
438/438 [==============================] - 59s 135ms/step - loss: 0.0198 - accuracy: 0.9926 - val_loss: 14.3443 - val_accuracy: 0.1761
Epoch 56/150
438/438 [==============================] - 60s 136ms/step - loss: 0.0202 - accuracy: 0.9921 - val_loss: 12.9387 - val_accuracy: 0.2053
Epoch 57/150
438/438 [==============================] - 59s 135ms/step - loss: 0.0202 - accuracy: 0.9929 - val_loss: 14.1261 - val_accuracy: 0.1956
Epoch 58/150
438/438 [==============================] - 59s 136ms/step - loss: 0.0154 - accuracy: 0.9948 - val_loss: 13.3815 - val_accuracy: 0.2083
Epoch 59/150
438/438 [==============================] - 57s 131ms/step - loss: 0.0182 - accuracy: 0.9934 - val_loss: 14.0329 - val_accuracy: 0.2230
Epoch 60/150
438/438 [==============================] - 58s 131ms/step - loss: 0.0180 - accuracy: 0.9932 - val_loss: 11.8810 - val_accuracy: 0.2419
Epoch 61/150
438/438 [==============================] - 59s 134ms/step - loss: 0.0169 - accuracy: 0.9943 - val_loss: 13.2512 - val_accuracy: 0.2171
Epoch 62/150
438/438 [==============================] - 60s 137ms/step - loss: 0.0185 - accuracy: 0.9927 - val_loss: 11.5766 - val_accuracy: 0.2051
Epoch 63/150
438/438 [==============================] - 59s 135ms/step - loss: 0.0233 - accuracy: 0.9914 - val_loss: 12.9120 - val_accuracy: 0.2009
Epoch 64/150
438/438 [==============================] - 59s 135ms/step - loss: 0.0190 - accuracy: 0.9928 - val_loss: 13.9790 - val_accuracy: 0.2104
Epoch 65/150
438/438 [==============================] - 59s 136ms/step - loss: 0.0170 - accuracy: 0.9940 - val_loss: 12.4601 - val_accuracy: 0.2324
Epoch 66/150
438/438 [==============================] - 59s 135ms/step - loss: 0.0146 - accuracy: 0.9943 - val_loss: 14.6099 - val_accuracy: 0.2337
Epoch 67/150
438/438 [==============================] - 60s 136ms/step - loss: 0.0211 - accuracy: 0.9924 - val_loss: 11.4220 - val_accuracy: 0.2240
Epoch 68/150
438/438 [==============================] - 59s 135ms/step - loss: 0.0158 - accuracy: 0.9943 - val_loss: 13.8769 - val_accuracy: 0.2213
Epoch 69/150
438/438 [==============================] - 59s 134ms/step - loss: 0.0173 - accuracy: 0.9938 - val_loss: 13.1279 - val_accuracy: 0.2226
Epoch 70/150
438/438 [==============================] - 59s 136ms/step - loss: 0.0132 - accuracy: 0.9946 - val_loss: 15.1604 - val_accuracy: 0.2304
Epoch 71/150
438/438 [==============================] - 59s 135ms/step - loss: 0.0196 - accuracy: 0.9926 - val_loss: 14.8120 - val_accuracy: 0.1729
Epoch 72/150
438/438 [==============================] - 60s 136ms/step - loss: 0.0144 - accuracy: 0.9942 - val_loss: 15.0024 - val_accuracy: 0.2073
Epoch 73/150
438/438 [==============================] - 59s 135ms/step - loss: 0.0170 - accuracy: 0.9929 - val_loss: 16.1338 - val_accuracy: 0.1944
Epoch 74/150
438/438 [==============================] - 59s 135ms/step - loss: 0.0186 - accuracy: 0.9931 - val_loss: 16.5254 - val_accuracy: 0.1620
Epoch 75/150
438/438 [==============================] - 59s 136ms/step - loss: 0.0142 - accuracy: 0.9946 - val_loss: 13.4221 - val_accuracy: 0.2271
Epoch 76/150
438/438 [==============================] - 59s 135ms/step - loss: 0.0091 - accuracy: 0.9961 - val_loss: 18.2434 - val_accuracy: 0.2199
Epoch 77/150
438/438 [==============================] - 60s 136ms/step - loss: 0.0153 - accuracy: 0.9946 - val_loss: 14.9888 - val_accuracy: 0.2257
Epoch 78/150
438/438 [==============================] - 59s 135ms/step - loss: 0.0176 - accuracy: 0.9937 - val_loss: 17.1715 - val_accuracy: 0.1831
Epoch 79/150
438/438 [==============================] - 58s 132ms/step - loss: 0.0159 - accuracy: 0.9946 - val_loss: 16.1641 - val_accuracy: 0.2034
Epoch 80/150
438/438 [==============================] - 57s 131ms/step - loss: 0.0147 - accuracy: 0.9954 - val_loss: 14.4854 - val_accuracy: 0.2127
Epoch 81/150
438/438 [==============================] - 60s 136ms/step - loss: 0.0142 - accuracy: 0.9951 - val_loss: 13.7161 - val_accuracy: 0.2221
Epoch 82/150
438/438 [==============================] - 59s 134ms/step - loss: 0.0142 - accuracy: 0.9944 - val_loss: 15.2591 - val_accuracy: 0.2354
Epoch 83/150
438/438 [==============================] - 59s 136ms/step - loss: 0.0124 - accuracy: 0.9952 - val_loss: 16.9211 - val_accuracy: 0.2150
Epoch 84/150
438/438 [==============================] - 60s 136ms/step - loss: 0.0125 - accuracy: 0.9959 - val_loss: 19.0973 - val_accuracy: 0.2104
Epoch 85/150
438/438 [==============================] - 59s 135ms/step - loss: 0.0161 - accuracy: 0.9942 - val_loss: 17.0804 - val_accuracy: 0.2070
Epoch 86/150
438/438 [==============================] - 59s 135ms/step - loss: 0.0141 - accuracy: 0.9950 - val_loss: 16.6664 - val_accuracy: 0.2097
Epoch 87/150
438/438 [==============================] - 60s 136ms/step - loss: 0.0097 - accuracy: 0.9969 - val_loss: 15.5170 - val_accuracy: 0.2294
Epoch 88/150
438/438 [==============================] - 59s 136ms/step - loss: 0.0121 - accuracy: 0.9956 - val_loss: 15.4465 - val_accuracy: 0.2077
Epoch 89/150
438/438 [==============================] - 59s 136ms/step - loss: 0.0151 - accuracy: 0.9947 - val_loss: 17.6339 - val_accuracy: 0.2136
Epoch 90/150
438/438 [==============================] - 59s 136ms/step - loss: 0.0172 - accuracy: 0.9939 - val_loss: 16.6517 - val_accuracy: 0.1921
Epoch 91/150
438/438 [==============================] - 59s 135ms/step - loss: 0.0100 - accuracy: 0.9966 - val_loss: 18.7855 - val_accuracy: 0.1887
Epoch 92/150
438/438 [==============================] - 60s 136ms/step - loss: 0.0120 - accuracy: 0.9959 - val_loss: 15.2049 - val_accuracy: 0.2429
Epoch 93/150
438/438 [==============================] - 60s 136ms/step - loss: 0.0123 - accuracy: 0.9959 - val_loss: 16.9048 - val_accuracy: 0.2156
Epoch 94/150
438/438 [==============================] - 59s 135ms/step - loss: 0.0175 - accuracy: 0.9938 - val_loss: 16.4091 - val_accuracy: 0.1870
Epoch 95/150
438/438 [==============================] - 60s 136ms/step - loss: 0.0132 - accuracy: 0.9956 - val_loss: 16.2724 - val_accuracy: 0.1833
Epoch 96/150
438/438 [==============================] - 61s 139ms/step - loss: 0.0118 - accuracy: 0.9958 - val_loss: 19.6867 - val_accuracy: 0.2124
Epoch 97/150
438/438 [==============================] - 61s 139ms/step - loss: 0.0132 - accuracy: 0.9952 - val_loss: 17.0057 - val_accuracy: 0.2324
Epoch 98/150
438/438 [==============================] - 61s 140ms/step - loss: 0.0100 - accuracy: 0.9964 - val_loss: 17.3581 - val_accuracy: 0.2390
Epoch 99/150
438/438 [==============================] - 60s 137ms/step - loss: 0.0101 - accuracy: 0.9961 - val_loss: 18.7840 - val_accuracy: 0.2347
Epoch 100/150
438/438 [==============================] - 60s 136ms/step - loss: 0.0057 - accuracy: 0.9982 - val_loss: 21.2000 - val_accuracy: 0.2177
Epoch 101/150
438/438 [==============================] - 59s 135ms/step - loss: 0.0142 - accuracy: 0.9946 - val_loss: 21.0848 - val_accuracy: 0.2113
Epoch 102/150
438/438 [==============================] - 60s 137ms/step - loss: 0.0180 - accuracy: 0.9936 - val_loss: 18.7845 - val_accuracy: 0.2086
Epoch 103/150
438/438 [==============================] - 59s 136ms/step - loss: 0.0130 - accuracy: 0.9952 - val_loss: 16.5347 - val_accuracy: 0.2317
Epoch 104/150
438/438 [==============================] - 59s 136ms/step - loss: 0.0053 - accuracy: 0.9980 - val_loss: 19.9523 - val_accuracy: 0.2381
Epoch 105/150
438/438 [==============================] - 57s 131ms/step - loss: 0.0183 - accuracy: 0.9937 - val_loss: 16.7464 - val_accuracy: 0.2186
Epoch 106/150
438/438 [==============================] - 57s 131ms/step - loss: 0.0110 - accuracy: 0.9959 - val_loss: 18.5318 - val_accuracy: 0.1867
Epoch 107/150
438/438 [==============================] - 59s 134ms/step - loss: 0.0101 - accuracy: 0.9966 - val_loss: 22.4708 - val_accuracy: 0.2303
Epoch 108/150
438/438 [==============================] - 60s 136ms/step - loss: 0.0144 - accuracy: 0.9950 - val_loss: 19.0921 - val_accuracy: 0.1890
Epoch 109/150
438/438 [==============================] - 59s 135ms/step - loss: 0.0113 - accuracy: 0.9961 - val_loss: 20.8358 - val_accuracy: 0.1977
Epoch 110/150
438/438 [==============================] - 59s 136ms/step - loss: 0.0078 - accuracy: 0.9971 - val_loss: 22.2141 - val_accuracy: 0.2164
Epoch 111/150
438/438 [==============================] - 59s 135ms/step - loss: 0.0093 - accuracy: 0.9966 - val_loss: 20.9881 - val_accuracy: 0.2109
Epoch 112/150
438/438 [==============================] - 59s 136ms/step - loss: 0.0140 - accuracy: 0.9954 - val_loss: 20.9951 - val_accuracy: 0.2197
Epoch 113/150
438/438 [==============================] - 59s 135ms/step - loss: 0.0083 - accuracy: 0.9976 - val_loss: 24.4149 - val_accuracy: 0.1863
Epoch 114/150
438/438 [==============================] - 60s 136ms/step - loss: 0.0110 - accuracy: 0.9956 - val_loss: 20.9526 - val_accuracy: 0.1727
Epoch 115/150
438/438 [==============================] - 59s 135ms/step - loss: 0.0131 - accuracy: 0.9948 - val_loss: 19.8622 - val_accuracy: 0.2049
Epoch 116/150
438/438 [==============================] - 59s 135ms/step - loss: 0.0105 - accuracy: 0.9971 - val_loss: 20.3705 - val_accuracy: 0.2224
Epoch 117/150
438/438 [==============================] - 60s 137ms/step - loss: 0.0105 - accuracy: 0.9958 - val_loss: 21.0373 - val_accuracy: 0.1976
Epoch 118/150
438/438 [==============================] - 59s 136ms/step - loss: 0.0107 - accuracy: 0.9959 - val_loss: 20.3715 - val_accuracy: 0.2183
Epoch 119/150
438/438 [==============================] - 59s 135ms/step - loss: 0.0040 - accuracy: 0.9986 - val_loss: 25.9642 - val_accuracy: 0.2191
Epoch 120/150
438/438 [==============================] - 59s 135ms/step - loss: 0.0156 - accuracy: 0.9941 - val_loss: 19.5925 - val_accuracy: 0.1700
Epoch 121/150
438/438 [==============================] - 60s 136ms/step - loss: 0.0118 - accuracy: 0.9954 - val_loss: 21.0675 - val_accuracy: 0.2090
Epoch 122/150
438/438 [==============================] - 59s 135ms/step - loss: 0.0067 - accuracy: 0.9974 - val_loss: 25.9611 - val_accuracy: 0.2090
Epoch 123/150
438/438 [==============================] - 59s 135ms/step - loss: 0.0149 - accuracy: 0.9946 - val_loss: 19.2916 - val_accuracy: 0.2229
Epoch 124/150
438/438 [==============================] - 59s 135ms/step - loss: 0.0108 - accuracy: 0.9962 - val_loss: 19.8004 - val_accuracy: 0.2336
Epoch 125/150
438/438 [==============================] - 59s 135ms/step - loss: 0.0070 - accuracy: 0.9975 - val_loss: 21.2666 - val_accuracy: 0.1849
Epoch 126/150
438/438 [==============================] - 59s 135ms/step - loss: 0.0054 - accuracy: 0.9980 - val_loss: 25.1386 - val_accuracy: 0.2021
Epoch 127/150
438/438 [==============================] - 59s 136ms/step - loss: 0.0121 - accuracy: 0.9953 - val_loss: 20.9420 - val_accuracy: 0.2219
Epoch 128/150
438/438 [==============================] - 59s 136ms/step - loss: 0.0100 - accuracy: 0.9962 - val_loss: 25.1264 - val_accuracy: 0.1980
Epoch 129/150
438/438 [==============================] - 59s 135ms/step - loss: 0.0069 - accuracy: 0.9975 - val_loss: 23.9898 - val_accuracy: 0.2034
Epoch 130/150
438/438 [==============================] - 59s 135ms/step - loss: 0.0098 - accuracy: 0.9963 - val_loss: 21.7751 - val_accuracy: 0.2187
Epoch 131/150
438/438 [==============================] - 59s 135ms/step - loss: 0.0104 - accuracy: 0.9961 - val_loss: 18.3052 - val_accuracy: 0.2140
Epoch 132/150
438/438 [==============================] - 59s 135ms/step - loss: 0.0094 - accuracy: 0.9967 - val_loss: 25.5771 - val_accuracy: 0.1971
Epoch 133/150
438/438 [==============================] - 59s 135ms/step - loss: 0.0115 - accuracy: 0.9962 - val_loss: 18.0659 - val_accuracy: 0.2177
Epoch 134/150
438/438 [==============================] - 59s 134ms/step - loss: 0.0093 - accuracy: 0.9966 - val_loss: 21.0781 - val_accuracy: 0.2176
Epoch 135/150
438/438 [==============================] - 59s 135ms/step - loss: 0.0081 - accuracy: 0.9976 - val_loss: 21.2983 - val_accuracy: 0.2334
Epoch 136/150
438/438 [==============================] - 59s 134ms/step - loss: 0.0068 - accuracy: 0.9975 - val_loss: 24.1916 - val_accuracy: 0.2309
Epoch 137/150
438/438 [==============================] - 59s 135ms/step - loss: 0.0079 - accuracy: 0.9974 - val_loss: 21.8092 - val_accuracy: 0.2271
Epoch 138/150
438/438 [==============================] - 59s 136ms/step - loss: 0.0167 - accuracy: 0.9947 - val_loss: 25.1187 - val_accuracy: 0.2139
Epoch 139/150
438/438 [==============================] - 59s 134ms/step - loss: 0.0072 - accuracy: 0.9973 - val_loss: 20.9736 - val_accuracy: 0.2176
Epoch 140/150
438/438 [==============================] - 59s 135ms/step - loss: 0.0093 - accuracy: 0.9964 - val_loss: 23.0839 - val_accuracy: 0.2200
Epoch 141/150
438/438 [==============================] - 59s 134ms/step - loss: 0.0058 - accuracy: 0.9974 - val_loss: 21.6380 - val_accuracy: 0.2400
Epoch 142/150
438/438 [==============================] - 59s 135ms/step - loss: 0.0071 - accuracy: 0.9979 - val_loss: 22.7540 - val_accuracy: 0.2206
Epoch 143/150
438/438 [==============================] - 59s 136ms/step - loss: 0.0084 - accuracy: 0.9975 - val_loss: 21.4848 - val_accuracy: 0.2287
Epoch 144/150
438/438 [==============================] - 59s 135ms/step - loss: 0.0069 - accuracy: 0.9975 - val_loss: 23.8004 - val_accuracy: 0.2334
Epoch 145/150
438/438 [==============================] - 59s 134ms/step - loss: 0.0100 - accuracy: 0.9964 - val_loss: 21.8112 - val_accuracy: 0.1881
Epoch 146/150
438/438 [==============================] - 59s 135ms/step - loss: 0.0122 - accuracy: 0.9954 - val_loss: 22.3977 - val_accuracy: 0.2340
Epoch 147/150
438/438 [==============================] - 59s 135ms/step - loss: 0.0071 - accuracy: 0.9976 - val_loss: 29.5000 - val_accuracy: 0.1907
Epoch 148/150
438/438 [==============================] - 59s 135ms/step - loss: 0.0070 - accuracy: 0.9976 - val_loss: 27.8572 - val_accuracy: 0.1851
Epoch 149/150
438/438 [==============================] - 59s 135ms/step - loss: 0.0120 - accuracy: 0.9963 - val_loss: 22.9596 - val_accuracy: 0.2087
Epoch 150/150
438/438 [==============================] - 59s 134ms/step - loss: 0.0047 - accuracy: 0.9983 - val_loss: 31.2955 - val_accuracy: 0.2090
219/219 - 12s - loss: 31.2955 - accuracy: 0.2090
In[3]: a, b = cvnn_fit(epochs=150) 
Model: "sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
complex_conv2d_3 (ComplexCon (None, 30, 30, 64)        640       
_________________________________________________________________
complex_max_pooling2d_2 (Com (None, 15, 15, 64)        0         
_________________________________________________________________
complex_conv2d_4 (ComplexCon (None, 13, 13, 128)       73856     
_________________________________________________________________
complex_max_pooling2d_3 (Com (None, 6, 6, 128)         0         
_________________________________________________________________
complex_conv2d_5 (ComplexCon (None, 4, 4, 128)         147584    
_________________________________________________________________
complex_flatten_1 (ComplexFl (None, 2048)              0         
_________________________________________________________________
complex_dense_2 (ComplexDens (None, 128)               262272    
_________________________________________________________________
complex_dense_3 (ComplexDens (None, 24)                3096      
=================================================================
Total params: 487,448
Trainable params: 487,448
Non-trainable params: 0
_________________________________________________________________
Epoch 1/150
438/438 [==============================] - 59s 136ms/step - loss: 0.6362 - accuracy: 0.7082 - val_loss: 2.3866 - val_accuracy: 0.0000e+00
Epoch 2/150
438/438 [==============================] - 59s 134ms/step - loss: 0.6026 - accuracy: 0.7143 - val_loss: 2.4331 - val_accuracy: 0.0000e+00
Epoch 3/150
313/438 [====================>.........] - ETA: 14s - loss: 0.5968 - accuracy: 0.7172