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output_1.txt
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output_1.txt
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ubuntu@ip-172-31-6-73:~$ python ./vgg_transfer.py
Using TensorFlow backend.
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcublas.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcudnn.so.5 locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcufft.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcurand.so.8.0 locally
I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:909] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
I tensorflow/core/common_runtime/gpu/gpu_device.cc:885] Found device 0 with properties:
name: Tesla K80
major: 3 minor: 7 memoryClockRate (GHz) 0.8235
pciBusID 0000:00:1e.0
Total memory: 11.17GiB
Free memory: 11.11GiB
I tensorflow/core/common_runtime/gpu/gpu_device.cc:906] DMA: 0
I tensorflow/core/common_runtime/gpu/gpu_device.cc:916] 0: Y
I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: Tesla K80, pci bus id: 0000:00:1e.0)
____________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
====================================================================================================
input_1 (InputLayer) (None, 32, 32, 3) 0
____________________________________________________________________________________________________
block1_conv1 (Convolution2D) (None, 32, 32, 64) 1792 input_1[0][0]
____________________________________________________________________________________________________
block1_conv2 (Convolution2D) (None, 32, 32, 64) 36928 block1_conv1[0][0]
____________________________________________________________________________________________________
block1_pool (MaxPooling2D) (None, 16, 16, 64) 0 block1_conv2[0][0]
____________________________________________________________________________________________________
block2_conv1 (Convolution2D) (None, 16, 16, 128) 73856 block1_pool[0][0]
____________________________________________________________________________________________________
block2_conv2 (Convolution2D) (None, 16, 16, 128) 147584 block2_conv1[0][0]
____________________________________________________________________________________________________
block2_pool (MaxPooling2D) (None, 8, 8, 128) 0 block2_conv2[0][0]
____________________________________________________________________________________________________
block3_conv1 (Convolution2D) (None, 8, 8, 256) 295168 block2_pool[0][0]
____________________________________________________________________________________________________
block3_conv2 (Convolution2D) (None, 8, 8, 256) 590080 block3_conv1[0][0]
____________________________________________________________________________________________________
block3_conv3 (Convolution2D) (None, 8, 8, 256) 590080 block3_conv2[0][0]
____________________________________________________________________________________________________
block3_pool (MaxPooling2D) (None, 4, 4, 256) 0 block3_conv3[0][0]
____________________________________________________________________________________________________
flatten_1 (Flatten) (None, 4096) 0 block3_pool[0][0]
____________________________________________________________________________________________________
dense_1 (Dense) (None, 256) 1048832 flatten_1[0][0]
____________________________________________________________________________________________________
dropout_1 (Dropout) (None, 256) 0 dense_1[0][0]
____________________________________________________________________________________________________
dense_2 (Dense) (None, 10) 2570 dropout_1[0][0]
====================================================================================================
Total params: 2,786,890
Trainable params: 1,051,402
Non-trainable params: 1,735,488
____________________________________________________________________________________________________
Epoch 1/50
50000/50000 [==============================] - 28s - loss: 1.6163 - acc: 0.8994 - val_loss: 1.6118 - val_acc: 0.9000
Epoch 2/50
50000/50000 [==============================] - 27s - loss: 1.6120 - acc: 0.9000 - val_loss: 1.6118 - val_acc: 0.9000
Epoch 3/50
50000/50000 [==============================] - 27s - loss: 1.6118 - acc: 0.9000 - val_loss: 1.6118 - val_acc: 0.9000
Epoch 4/50
50000/50000 [==============================] - 27s - loss: 1.6119 - acc: 0.9000 - val_loss: 1.6118 - val_acc: 0.9000
Epoch 5/50
50000/50000 [==============================] - 27s - loss: 1.6118 - acc: 0.9000 - val_loss: 1.6118 - val_acc: 0.9000
Epoch 6/50
50000/50000 [==============================] - 27s - loss: 1.6119 - acc: 0.9000 - val_loss: 1.6118 - val_acc: 0.9000
Epoch 7/50
50000/50000 [==============================] - 27s - loss: 1.6119 - acc: 0.9000 - val_loss: 1.6118 - val_acc: 0.9000
Epoch 8/50
50000/50000 [==============================] - 27s - loss: 1.6118 - acc: 0.9000 - val_loss: 1.6118 - val_acc: 0.9000
Epoch 9/50
50000/50000 [==============================] - 27s - loss: 1.6119 - acc: 0.9000 - val_loss: 1.6118 - val_acc: 0.9000
Epoch 10/50
50000/50000 [==============================] - 27s - loss: 1.6118 - acc: 0.9000 - val_loss: 1.6118 - val_acc: 0.9000
Epoch 11/50
50000/50000 [==============================] - 27s - loss: 1.6119 - acc: 0.9000 - val_loss: 1.6118 - val_acc: 0.9000
Epoch 12/50
50000/50000 [==============================] - 27s - loss: 1.6113 - acc: 0.9000 - val_loss: 1.6118 - val_acc: 0.9000
Epoch 13/50
50000/50000 [==============================] - 27s - loss: 1.5924 - acc: 0.9004 - val_loss: 1.5205 - val_acc: 0.9029
Epoch 14/50
50000/50000 [==============================] - 27s - loss: 0.5626 - acc: 0.8903 - val_loss: 0.2297 - val_acc: 0.9112
Epoch 15/50
50000/50000 [==============================] - 27s - loss: 0.2803 - acc: 0.8994 - val_loss: 0.1995 - val_acc: 0.9200
Epoch 16/50
50000/50000 [==============================] - 27s - loss: 0.2598 - acc: 0.9033 - val_loss: 0.1899 - val_acc: 0.9229
Epoch 17/50
50000/50000 [==============================] - 27s - loss: 0.2483 - acc: 0.9065 - val_loss: 0.1751 - val_acc: 0.9286
Epoch 18/50
50000/50000 [==============================] - 27s - loss: 0.2403 - acc: 0.9091 - val_loss: 0.1714 - val_acc: 0.9287
Epoch 19/50
50000/50000 [==============================] - 27s - loss: 0.2333 - acc: 0.9112 - val_loss: 0.1669 - val_acc: 0.9320
Epoch 20/50
50000/50000 [==============================] - 27s - loss: 0.2290 - acc: 0.9130 - val_loss: 0.1646 - val_acc: 0.9314
Epoch 21/50
50000/50000 [==============================] - 27s - loss: 0.2233 - acc: 0.9146 - val_loss: 0.1624 - val_acc: 0.9320
Epoch 22/50
50000/50000 [==============================] - 27s - loss: 0.2207 - acc: 0.9155 - val_loss: 0.1553 - val_acc: 0.9372
Epoch 23/50
50000/50000 [==============================] - 27s - loss: 0.2177 - acc: 0.9168 - val_loss: 0.1543 - val_acc: 0.9378
Epoch 24/50
50000/50000 [==============================] - 27s - loss: 0.2140 - acc: 0.9184 - val_loss: 0.1521 - val_acc: 0.9376
Epoch 25/50
50000/50000 [==============================] - 27s - loss: 0.2106 - acc: 0.9196 - val_loss: 0.1495 - val_acc: 0.9397
Epoch 26/50
50000/50000 [==============================] - 27s - loss: 0.2087 - acc: 0.9202 - val_loss: 0.1488 - val_acc: 0.9397
Epoch 27/50
50000/50000 [==============================] - 27s - loss: 0.2057 - acc: 0.9219 - val_loss: 0.1462 - val_acc: 0.9404
Epoch 28/50
50000/50000 [==============================] - 27s - loss: 0.2038 - acc: 0.9227 - val_loss: 0.1440 - val_acc: 0.9423
Epoch 29/50
50000/50000 [==============================] - 27s - loss: 0.2012 - acc: 0.9234 - val_loss: 0.1442 - val_acc: 0.9421
Epoch 30/50
50000/50000 [==============================] - 27s - loss: 0.1993 - acc: 0.9245 - val_loss: 0.1412 - val_acc: 0.9433
Epoch 31/50
50000/50000 [==============================] - 27s - loss: 0.1973 - acc: 0.9250 - val_loss: 0.1414 - val_acc: 0.9434
Epoch 32/50
50000/50000 [==============================] - 27s - loss: 0.1962 - acc: 0.9255 - val_loss: 0.1395 - val_acc: 0.9443
Epoch 33/50
50000/50000 [==============================] - 27s - loss: 0.1946 - acc: 0.9264 - val_loss: 0.1370 - val_acc: 0.9452
Epoch 34/50
50000/50000 [==============================] - 27s - loss: 0.1927 - acc: 0.9273 - val_loss: 0.1334 - val_acc: 0.9466
Epoch 35/50
50000/50000 [==============================] - 27s - loss: 0.1909 - acc: 0.9277 - val_loss: 0.1369 - val_acc: 0.9448
Epoch 36/50
50000/50000 [==============================] - 27s - loss: 0.1899 - acc: 0.9277 - val_loss: 0.1356 - val_acc: 0.9464
Epoch 37/50
50000/50000 [==============================] - 27s - loss: 0.1880 - acc: 0.9289 - val_loss: 0.1336 - val_acc: 0.9465
Epoch 38/50
50000/50000 [==============================] - 27s - loss: 0.1872 - acc: 0.9291 - val_loss: 0.1327 - val_acc: 0.9474
Epoch 39/50
50000/50000 [==============================] - 27s - loss: 0.1859 - acc: 0.9297 - val_loss: 0.1315 - val_acc: 0.9476
Epoch 40/50
50000/50000 [==============================] - 27s - loss: 0.1845 - acc: 0.9302 - val_loss: 0.1304 - val_acc: 0.9478
Epoch 41/50
50000/50000 [==============================] - 27s - loss: 0.1828 - acc: 0.9309 - val_loss: 0.1290 - val_acc: 0.9485
Epoch 42/50
50000/50000 [==============================] - 27s - loss: 0.1814 - acc: 0.9313 - val_loss: 0.1327 - val_acc: 0.9467
Epoch 43/50
50000/50000 [==============================] - 27s - loss: 0.1802 - acc: 0.9318 - val_loss: 0.1289 - val_acc: 0.9478
Epoch 44/50
50000/50000 [==============================] - 27s - loss: 0.1792 - acc: 0.9318 - val_loss: 0.1294 - val_acc: 0.9480
Epoch 45/50
50000/50000 [==============================] - 27s - loss: 0.1779 - acc: 0.9327 - val_loss: 0.1265 - val_acc: 0.9497
Epoch 46/50
50000/50000 [==============================] - 27s - loss: 0.1763 - acc: 0.9333 - val_loss: 0.1296 - val_acc: 0.9487
Epoch 47/50
50000/50000 [==============================] - 27s - loss: 0.1764 - acc: 0.9335 - val_loss: 0.1279 - val_acc: 0.9495
Epoch 48/50
50000/50000 [==============================] - 46s - loss: 0.1751 - acc: 0.9336 - val_loss: 0.1272 - val_acc: 0.9500
Epoch 49/50
50000/50000 [==============================] - 29s - loss: 0.1750 - acc: 0.9339 - val_loss: 0.1253 - val_acc: 0.9498
Epoch 50/50
50000/50000 [==============================] - 43s - loss: 0.1728 - acc: 0.9344 - val_loss: 0.1238 - val_acc: 0.9506