MztestImag

...........\Anaconda3\python.exe ".........\Local\JetBrains\PyCharm Community Edition 2020.3\plugins\python-ce\helpers\pydev\pydevconsole.py" --mode=client --port=50459

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

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('/cvnn/examples/MztestImag.py', wdir='C:/Users/ndongma/PycharmProjects/cvnn/examples')
2021-02-12 16:51:29.953108: W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library 'cudart64_101.dll'; dlerror: cudart64_101.dll not found
2021-02-12 16:51:29.953642: 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.
2021-02-12 16:51:41.685420: W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library 'nvcuda.dll'; dlerror: nvcuda.dll not found
2021-02-12 16:51:41.686058: W tensorflow/stream_executor/cuda/cuda_driver.cc:312] failed call to cuInit: UNKNOWN ERROR (303)
2021-02-12 16:51:41.690284: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: ip2979
2021-02-12 16:51:41.690929: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: ip2979
2021-02-12 16:51:41.692146: 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.
2021-02-12 16:51:41.704780: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1f02753f840 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2021-02-12 16:51:41.705546: 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)        1792      
_________________________________________________________________
complex_avg_pooling2d (Compl (None, 15, 15, 32)        0         
_________________________________________________________________
complex_conv2d_1 (ComplexCon (None, 13, 13, 64)        36992     
_________________________________________________________________
complex_max_pooling2d (Compl (None, 6, 6, 64)          0         
_________________________________________________________________
complex_conv2d_2 (ComplexCon (None, 4, 4, 64)          73856     
_________________________________________________________________
complex_flatten (ComplexFlat (None, 1024)              0         
_________________________________________________________________
complex_dense (ComplexDense) (None, 64)                131200    
_________________________________________________________________
complex_dense_1 (ComplexDens (None, 10)                1300      
=================================================================
Total params: 245,140
Trainable params: 245,140
Non-trainable params: 0
_________________________________________________________________
Epoch 1/70
1563/1563 [==============================] - 134s 86ms/step - loss: 1.5229 - accuracy: 0.4441 - val_loss: 1.2799 - val_accuracy: 0.5434
Epoch 2/70
1563/1563 [==============================] - 133s 85ms/step - loss: 1.1226 - accuracy: 0.6035 - val_loss: 1.0213 - val_accuracy: 0.6388
Epoch 3/70
1563/1563 [==============================] - 133s 85ms/step - loss: 0.9412 - accuracy: 0.6688 - val_loss: 0.9652 - val_accuracy: 0.6578
Epoch 4/70
1563/1563 [==============================] - 134s 85ms/step - loss: 0.8170 - accuracy: 0.7135 - val_loss: 0.9545 - val_accuracy: 0.6726
Epoch 5/70
1563/1563 [==============================] - 134s 85ms/step - loss: 0.7261 - accuracy: 0.7454 - val_loss: 0.8778 - val_accuracy: 0.6972
Epoch 6/70
1563/1563 [==============================] - 134s 86ms/step - loss: 0.6460 - accuracy: 0.7726 - val_loss: 0.8882 - val_accuracy: 0.6973
Epoch 7/70
1563/1563 [==============================] - 134s 86ms/step - loss: 0.5726 - accuracy: 0.7988 - val_loss: 0.8791 - val_accuracy: 0.7082
Epoch 8/70
1563/1563 [==============================] - 134s 86ms/step - loss: 0.5103 - accuracy: 0.8206 - val_loss: 0.8888 - val_accuracy: 0.7174
Epoch 9/70
1563/1563 [==============================] - 136s 87ms/step - loss: 0.4430 - accuracy: 0.8435 - val_loss: 1.0111 - val_accuracy: 0.7042
Epoch 10/70
1563/1563 [==============================] - 134s 86ms/step - loss: 0.3877 - accuracy: 0.8641 - val_loss: 1.0110 - val_accuracy: 0.7074
Epoch 11/70
1563/1563 [==============================] - 134s 86ms/step - loss: 0.3328 - accuracy: 0.8815 - val_loss: 1.0689 - val_accuracy: 0.7069
Epoch 12/70
1563/1563 [==============================] - 134s 85ms/step - loss: 0.2925 - accuracy: 0.8955 - val_loss: 1.1761 - val_accuracy: 0.7040
Epoch 13/70
1563/1563 [==============================] - 134s 86ms/step - loss: 0.2504 - accuracy: 0.9110 - val_loss: 1.2678 - val_accuracy: 0.7026
Epoch 14/70
1563/1563 [==============================] - 134s 86ms/step - loss: 0.2185 - accuracy: 0.9228 - val_loss: 1.3635 - val_accuracy: 0.7085
Epoch 15/70
1563/1563 [==============================] - 134s 86ms/step - loss: 0.2011 - accuracy: 0.9286 - val_loss: 1.5278 - val_accuracy: 0.6814
Epoch 16/70
1563/1563 [==============================] - 133s 85ms/step - loss: 0.1729 - accuracy: 0.9387 - val_loss: 1.5470 - val_accuracy: 0.6991
Epoch 17/70
1563/1563 [==============================] - 133s 85ms/step - loss: 0.1629 - accuracy: 0.9431 - val_loss: 1.6510 - val_accuracy: 0.6956
Epoch 18/70
1563/1563 [==============================] - 133s 85ms/step - loss: 0.1502 - accuracy: 0.9463 - val_loss: 1.6663 - val_accuracy: 0.6929
Epoch 19/70
1563/1563 [==============================] - 133s 85ms/step - loss: 0.1467 - accuracy: 0.9503 - val_loss: 1.7682 - val_accuracy: 0.6924
Epoch 20/70
1563/1563 [==============================] - 134s 85ms/step - loss: 0.1337 - accuracy: 0.9546 - val_loss: 1.8140 - val_accuracy: 0.6983
Epoch 21/70
1563/1563 [==============================] - 133s 85ms/step - loss: 0.1312 - accuracy: 0.9544 - val_loss: 1.8555 - val_accuracy: 0.6981
Epoch 22/70
1563/1563 [==============================] - 133s 85ms/step - loss: 0.1256 - accuracy: 0.9571 - val_loss: 1.9017 - val_accuracy: 0.6986
Epoch 23/70
1563/1563 [==============================] - 133s 85ms/step - loss: 0.1203 - accuracy: 0.9590 - val_loss: 2.1725 - val_accuracy: 0.6793
Epoch 24/70
1563/1563 [==============================] - 128s 82ms/step - loss: 0.1170 - accuracy: 0.9587 - val_loss: 2.0117 - val_accuracy: 0.6946
Epoch 25/70
1563/1563 [==============================] - 129s 82ms/step - loss: 0.1122 - accuracy: 0.9617 - val_loss: 2.1793 - val_accuracy: 0.6869
Epoch 26/70
1563/1563 [==============================] - 129s 82ms/step - loss: 0.1058 - accuracy: 0.9645 - val_loss: 2.2122 - val_accuracy: 0.6915
Epoch 27/70
1563/1563 [==============================] - 130s 83ms/step - loss: 0.1127 - accuracy: 0.9621 - val_loss: 2.4154 - val_accuracy: 0.6906
Epoch 28/70
1563/1563 [==============================] - 131s 84ms/step - loss: 0.1013 - accuracy: 0.9662 - val_loss: 2.3482 - val_accuracy: 0.6967
Epoch 29/70
1563/1563 [==============================] - 129s 83ms/step - loss: 0.0969 - accuracy: 0.9682 - val_loss: 2.3620 - val_accuracy: 0.6843
Epoch 30/70
1563/1563 [==============================] - 130s 83ms/step - loss: 0.1051 - accuracy: 0.9657 - val_loss: 2.4214 - val_accuracy: 0.6936
Epoch 31/70
1563/1563 [==============================] - 132s 84ms/step - loss: 0.1064 - accuracy: 0.9662 - val_loss: 2.3700 - val_accuracy: 0.6980
Epoch 32/70
1563/1563 [==============================] - 134s 85ms/step - loss: 0.0921 - accuracy: 0.9695 - val_loss: 2.5464 - val_accuracy: 0.6884
Epoch 33/70
1563/1563 [==============================] - 133s 85ms/step - loss: 0.0932 - accuracy: 0.9694 - val_loss: 2.5824 - val_accuracy: 0.6843
Epoch 34/70
1563/1563 [==============================] - 131s 84ms/step - loss: 0.0910 - accuracy: 0.9703 - val_loss: 2.5439 - val_accuracy: 0.6866
Epoch 35/70
1563/1563 [==============================] - 129s 83ms/step - loss: 0.0981 - accuracy: 0.9686 - val_loss: 2.7195 - val_accuracy: 0.6885
Epoch 36/70
1563/1563 [==============================] - 129s 83ms/step - loss: 0.0910 - accuracy: 0.9697 - val_loss: 2.5877 - val_accuracy: 0.6895
Epoch 37/70
1563/1563 [==============================] - 131s 84ms/step - loss: 0.0908 - accuracy: 0.9710 - val_loss: 3.1192 - val_accuracy: 0.6677
Epoch 38/70
1563/1563 [==============================] - 132s 84ms/step - loss: 0.0834 - accuracy: 0.9732 - val_loss: 2.6330 - val_accuracy: 0.6865
Epoch 39/70
1563/1563 [==============================] - 133s 85ms/step - loss: 0.0887 - accuracy: 0.9711 - val_loss: 2.8336 - val_accuracy: 0.6948
Epoch 40/70
1563/1563 [==============================] - 132s 84ms/step - loss: 0.0901 - accuracy: 0.9718 - val_loss: 2.6921 - val_accuracy: 0.6865
Epoch 41/70
1563/1563 [==============================] - 130s 83ms/step - loss: 0.0837 - accuracy: 0.9737 - val_loss: 2.8364 - val_accuracy: 0.6945
Epoch 42/70
1563/1563 [==============================] - 132s 85ms/step - loss: 0.0818 - accuracy: 0.9747 - val_loss: 2.8029 - val_accuracy: 0.6927
Epoch 43/70
1563/1563 [==============================] - 130s 83ms/step - loss: 0.0829 - accuracy: 0.9735 - val_loss: 2.8953 - val_accuracy: 0.6913
Epoch 44/70
1563/1563 [==============================] - 133s 85ms/step - loss: 0.0781 - accuracy: 0.9755 - val_loss: 2.8892 - val_accuracy: 0.6877
Epoch 45/70
1563/1563 [==============================] - 133s 85ms/step - loss: 0.0899 - accuracy: 0.9728 - val_loss: 2.8496 - val_accuracy: 0.6858
Epoch 46/70
1563/1563 [==============================] - 133s 85ms/step - loss: 0.0704 - accuracy: 0.9777 - val_loss: 2.9609 - val_accuracy: 0.6816
Epoch 47/70
1563/1563 [==============================] - 131s 84ms/step - loss: 0.0796 - accuracy: 0.9749 - val_loss: 3.1546 - val_accuracy: 0.6761
Epoch 48/70
1563/1563 [==============================] - 134s 86ms/step - loss: 0.0725 - accuracy: 0.9774 - val_loss: 3.0573 - val_accuracy: 0.6951
Epoch 49/70
1563/1563 [==============================] - 130s 83ms/step - loss: 0.0800 - accuracy: 0.9763 - val_loss: 3.1093 - val_accuracy: 0.6816
Epoch 50/70
1563/1563 [==============================] - 130s 83ms/step - loss: 0.0827 - accuracy: 0.9747 - val_loss: 3.2348 - val_accuracy: 0.6881
Epoch 51/70
1563/1563 [==============================] - 130s 83ms/step - loss: 0.0743 - accuracy: 0.9781 - val_loss: 3.2316 - val_accuracy: 0.6819
Epoch 52/70
1563/1563 [==============================] - 133s 85ms/step - loss: 0.0780 - accuracy: 0.9761 - val_loss: 3.0791 - val_accuracy: 0.6865
Epoch 53/70
1563/1563 [==============================] - 132s 85ms/step - loss: 0.0770 - accuracy: 0.9770 - val_loss: 3.2702 - val_accuracy: 0.6838
Epoch 54/70
1563/1563 [==============================] - 132s 84ms/step - loss: 0.0818 - accuracy: 0.9766 - val_loss: 3.2843 - val_accuracy: 0.6859
Epoch 55/70
1563/1563 [==============================] - 133s 85ms/step - loss: 0.0648 - accuracy: 0.9805 - val_loss: 3.3683 - val_accuracy: 0.6888
Epoch 56/70
1563/1563 [==============================] - 133s 85ms/step - loss: 0.0805 - accuracy: 0.9755 - val_loss: 3.2599 - val_accuracy: 0.6860
Epoch 57/70
1563/1563 [==============================] - 130s 83ms/step - loss: 0.0730 - accuracy: 0.9783 - val_loss: 3.4671 - val_accuracy: 0.6890
Epoch 58/70
1563/1563 [==============================] - 129s 83ms/step - loss: 0.0732 - accuracy: 0.9782 - val_loss: 3.4719 - val_accuracy: 0.6874
Epoch 59/70
1563/1563 [==============================] - 130s 83ms/step - loss: 0.0720 - accuracy: 0.9780 - val_loss: 3.3444 - val_accuracy: 0.6884
Epoch 60/70
1563/1563 [==============================] - 132s 85ms/step - loss: 0.0720 - accuracy: 0.9792 - val_loss: 3.5581 - val_accuracy: 0.6879
Epoch 61/70
1563/1563 [==============================] - 137s 88ms/step - loss: 0.0729 - accuracy: 0.9788 - val_loss: 3.4022 - val_accuracy: 0.6879
Epoch 62/70
1563/1563 [==============================] - 140s 90ms/step - loss: 0.0690 - accuracy: 0.9794 - val_loss: 3.6871 - val_accuracy: 0.6773
Epoch 63/70
1563/1563 [==============================] - 131s 84ms/step - loss: 0.0747 - accuracy: 0.9792 - val_loss: 3.4398 - val_accuracy: 0.6916
Epoch 64/70
1563/1563 [==============================] - 133s 85ms/step - loss: 0.0622 - accuracy: 0.9815 - val_loss: 3.6849 - val_accuracy: 0.6831
Epoch 65/70
1563/1563 [==============================] - 131s 84ms/step - loss: 0.0680 - accuracy: 0.9801 - val_loss: 3.5431 - val_accuracy: 0.6825
Epoch 66/70
1563/1563 [==============================] - 132s 85ms/step - loss: 0.0684 - accuracy: 0.9802 - val_loss: 3.5913 - val_accuracy: 0.6904
Epoch 67/70
1563/1563 [==============================] - 129s 83ms/step - loss: 0.0686 - accuracy: 0.9804 - val_loss: 3.5921 - val_accuracy: 0.6906
Epoch 68/70
1563/1563 [==============================] - 134s 85ms/step - loss: 0.0678 - accuracy: 0.9806 - val_loss: 3.7463 - val_accuracy: 0.6886
Epoch 69/70
1563/1563 [==============================] - 133s 85ms/step - loss: 0.0678 - accuracy: 0.9804 - val_loss: 3.7811 - val_accuracy: 0.6822
Epoch 70/70
1563/1563 [==============================] - 131s 84ms/step - loss: 0.0747 - accuracy: 0.9801 - val_loss: 3.7974 - val_accuracy: 0.6956