MyTestComplex

C:\\AppData\Local\JetBrains\PyCharm Community Edition 2020.3\plugins\python-ce\helpers\pydev\pydevconsole.py" --mode=client --port=51450

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

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(/PycharmProjects/cvnn/examples/MyTestComplex.py', wdir='C:armProjects/cvnn/examples')
2021-02-15 12:31:07.998340: W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library 'cudart64_101.dll'; dlerror: cudart64_101.dll not found
2021-02-15 12:31:07.998928: 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.
2021-02-15 12:31:18.991559: W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library 'nvcuda.dll'; dlerror: nvcuda.dll not found
2021-02-15 12:31:18.992101: W tensorflow/stream_executor/cuda/cuda_driver.cc:312] failed call to cuInit: UNKNOWN ERROR (303)
2021-02-15 12:31:18.997768: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: ip2979
2021-02-15 12:31:18.998568: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: ip2979
2021-02-15 12:31:18.999505: 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.
2021-02-15 12:31:19.010674: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1b6d6ed38c0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2021-02-15 12:31:19.011428: 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 1/50
1563/1563 [==============================] - 143s 91ms/step - loss: 1.5110 - accuracy: 0.4488 - val_loss: 1.2889 - val_accuracy: 0.5366
Epoch 2/50
1563/1563 [==============================] - 140s 89ms/step - loss: 1.1109 - accuracy: 0.6088 - val_loss: 1.0252 - val_accuracy: 0.6405
Epoch 3/50
1563/1563 [==============================] - 140s 89ms/step - loss: 0.9422 - accuracy: 0.6695 - val_loss: 0.9887 - val_accuracy: 0.6490
Epoch 4/50
1563/1563 [==============================] - 140s 90ms/step - loss: 0.8216 - accuracy: 0.7118 - val_loss: 0.9612 - val_accuracy: 0.6705
Epoch 5/50
1563/1563 [==============================] - 140s 90ms/step - loss: 0.7277 - accuracy: 0.7441 - val_loss: 0.8866 - val_accuracy: 0.6936
Epoch 6/50
1563/1563 [==============================] - 140s 90ms/step - loss: 0.6409 - accuracy: 0.7767 - val_loss: 0.9238 - val_accuracy: 0.6909
Epoch 7/50
1563/1563 [==============================] - 140s 90ms/step - loss: 0.5603 - accuracy: 0.8029 - val_loss: 0.9498 - val_accuracy: 0.6981
Epoch 8/50
1563/1563 [==============================] - 144s 92ms/step - loss: 0.4865 - accuracy: 0.8275 - val_loss: 1.0241 - val_accuracy: 0.6928
Epoch 9/50
1563/1563 [==============================] - 141s 90ms/step - loss: 0.4191 - accuracy: 0.8506 - val_loss: 1.0685 - val_accuracy: 0.6888
Epoch 10/50
1563/1563 [==============================] - 141s 91ms/step - loss: 0.3565 - accuracy: 0.8714 - val_loss: 1.2009 - val_accuracy: 0.6870
Epoch 11/50
1563/1563 [==============================] - 142s 91ms/step - loss: 0.3047 - accuracy: 0.8907 - val_loss: 1.2294 - val_accuracy: 0.6857
Epoch 12/50
1563/1563 [==============================] - 141s 90ms/step - loss: 0.2645 - accuracy: 0.9049 - val_loss: 1.3471 - val_accuracy: 0.6866
Epoch 13/50
1563/1563 [==============================] - 142s 91ms/step - loss: 0.2339 - accuracy: 0.9165 - val_loss: 1.4194 - val_accuracy: 0.6832
Epoch 14/50
1563/1563 [==============================] - 142s 91ms/step - loss: 0.2004 - accuracy: 0.9287 - val_loss: 1.5005 - val_accuracy: 0.6914
Epoch 15/50
1563/1563 [==============================] - 142s 91ms/step - loss: 0.1870 - accuracy: 0.9336 - val_loss: 1.7081 - val_accuracy: 0.6856
Epoch 16/50
1563/1563 [==============================] - 143s 92ms/step - loss: 0.1687 - accuracy: 0.9410 - val_loss: 1.7442 - val_accuracy: 0.6844
Epoch 17/50
1563/1563 [==============================] - 136s 87ms/step - loss: 0.1544 - accuracy: 0.9462 - val_loss: 1.7740 - val_accuracy: 0.6841
Epoch 18/50
1563/1563 [==============================] - 131s 84ms/step - loss: 0.1510 - accuracy: 0.9479 - val_loss: 1.8155 - val_accuracy: 0.6783
Epoch 19/50
1563/1563 [==============================] - 131s 84ms/step - loss: 0.1490 - accuracy: 0.9475 - val_loss: 1.9241 - val_accuracy: 0.6802
Epoch 20/50
1563/1563 [==============================] - 130s 83ms/step - loss: 0.1335 - accuracy: 0.9541 - val_loss: 2.0245 - val_accuracy: 0.6829
Epoch 21/50
1563/1563 [==============================] - 131s 84ms/step - loss: 0.1371 - accuracy: 0.9532 - val_loss: 2.1338 - val_accuracy: 0.6723
Epoch 22/50
1563/1563 [==============================] - 133s 85ms/step - loss: 0.1235 - accuracy: 0.9585 - val_loss: 2.0892 - val_accuracy: 0.6801
Epoch 23/50
1563/1563 [==============================] - 131s 84ms/step - loss: 0.1229 - accuracy: 0.9590 - val_loss: 2.2426 - val_accuracy: 0.6836
Epoch 24/50
1563/1563 [==============================] - 132s 84ms/step - loss: 0.1241 - accuracy: 0.9588 - val_loss: 2.2278 - val_accuracy: 0.6710
Epoch 25/50
1563/1563 [==============================] - 132s 85ms/step - loss: 0.1174 - accuracy: 0.9611 - val_loss: 2.3759 - val_accuracy: 0.6754
Epoch 26/50
1563/1563 [==============================] - 132s 84ms/step - loss: 0.1142 - accuracy: 0.9624 - val_loss: 2.3872 - val_accuracy: 0.6822
Epoch 27/50
1563/1563 [==============================] - 131s 84ms/step - loss: 0.1127 - accuracy: 0.9626 - val_loss: 2.5138 - val_accuracy: 0.6815
Epoch 28/50
1563/1563 [==============================] - 131s 84ms/step - loss: 0.1135 - accuracy: 0.9631 - val_loss: 2.4889 - val_accuracy: 0.6842
Epoch 29/50
1563/1563 [==============================] - 132s 84ms/step - loss: 0.1098 - accuracy: 0.9634 - val_loss: 2.5979 - val_accuracy: 0.6754
Epoch 30/50
1563/1563 [==============================] - 131s 84ms/step - loss: 0.1056 - accuracy: 0.9663 - val_loss: 2.7030 - val_accuracy: 0.6693
Epoch 31/50
1563/1563 [==============================] - 131s 84ms/step - loss: 0.1082 - accuracy: 0.9654 - val_loss: 2.7626 - val_accuracy: 0.6807
Epoch 32/50
1563/1563 [==============================] - 133s 85ms/step - loss: 0.1049 - accuracy: 0.9673 - val_loss: 2.7668 - val_accuracy: 0.6727
Epoch 33/50
1563/1563 [==============================] - 131s 84ms/step - loss: 0.1079 - accuracy: 0.9659 - val_loss: 2.7229 - val_accuracy: 0.6784
Epoch 34/50
1563/1563 [==============================] - 131s 84ms/step - loss: 0.1044 - accuracy: 0.9675 - val_loss: 2.8055 - val_accuracy: 0.6718
Epoch 35/50
1563/1563 [==============================] - 130s 83ms/step - loss: 0.0977 - accuracy: 0.9691 - val_loss: 3.0105 - val_accuracy: 0.6742
Epoch 36/50
1563/1563 [==============================] - 130s 83ms/step - loss: 0.0946 - accuracy: 0.9699 - val_loss: 3.0860 - val_accuracy: 0.6650
Epoch 37/50
1563/1563 [==============================] - 130s 83ms/step - loss: 0.0982 - accuracy: 0.9687 - val_loss: 3.0617 - val_accuracy: 0.6721
Epoch 38/50
1563/1563 [==============================] - 130s 83ms/step - loss: 0.1032 - accuracy: 0.9684 - val_loss: 3.0260 - val_accuracy: 0.6762
Epoch 39/50
1563/1563 [==============================] - 130s 83ms/step - loss: 0.0969 - accuracy: 0.9706 - val_loss: 3.0526 - val_accuracy: 0.6770
Epoch 40/50
1563/1563 [==============================] - 130s 83ms/step - loss: 0.0867 - accuracy: 0.9726 - val_loss: 3.1313 - val_accuracy: 0.6656
Epoch 41/50
1563/1563 [==============================] - 131s 84ms/step - loss: 0.0986 - accuracy: 0.9693 - val_loss: 3.1024 - val_accuracy: 0.6779
Epoch 42/50
1563/1563 [==============================] - 131s 84ms/step - loss: 0.0853 - accuracy: 0.9742 - val_loss: 3.2359 - val_accuracy: 0.6696
Epoch 43/50
1563/1563 [==============================] - 131s 84ms/step - loss: 0.0948 - accuracy: 0.9717 - val_loss: 3.2210 - val_accuracy: 0.6707
Epoch 44/50
1563/1563 [==============================] - 131s 84ms/step - loss: 0.1017 - accuracy: 0.9692 - val_loss: 3.2260 - val_accuracy: 0.6797
Epoch 45/50
1563/1563 [==============================] - 131s 84ms/step - loss: 0.0848 - accuracy: 0.9747 - val_loss: 3.3114 - val_accuracy: 0.6718
Epoch 46/50
1563/1563 [==============================] - 131s 84ms/step - loss: 0.0831 - accuracy: 0.9748 - val_loss: 3.2722 - val_accuracy: 0.6844
Epoch 47/50
1563/1563 [==============================] - 131s 84ms/step - loss: 0.0887 - accuracy: 0.9733 - val_loss: 3.2204 - val_accuracy: 0.6780
Epoch 48/50
1563/1563 [==============================] - 131s 84ms/step - loss: 0.0868 - accuracy: 0.9740 - val_loss: 3.2955 - val_accuracy: 0.6651
Epoch 49/50
1563/1563 [==============================] - 131s 84ms/step - loss: 0.0838 - accuracy: 0.9751 - val_loss: 3.2689 - val_accuracy: 0.6721
Epoch 50/50
1563/1563 [==============================] - 131s 84ms/step - loss: 0.0873 - accuracy: 0.9741 - val_loss: 3.3897 - val_accuracy: 0.6672