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output_2.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: 2,786,890
Non-trainable params: 0
____________________________________________________________________________________________________
Epoch 1/50
50000/50000 [==============================] - 64s - loss: 1.6197 - acc: 0.8991 - val_loss: 1.6118 - val_acc: 0.9000
Epoch 2/50
50000/50000 [==============================] - 66s - loss: 0.3935 - acc: 0.9037 - val_loss: 0.1906 - val_acc: 0.9267
Epoch 3/50
50000/50000 [==============================] - 76s - loss: 0.2029 - acc: 0.9222 - val_loss: 0.1569 - val_acc: 0.9394
Epoch 4/50
50000/50000 [==============================] - 63s - loss: 0.1784 - acc: 0.9315 - val_loss: 0.1427 - val_acc: 0.9438
Epoch 5/50
50000/50000 [==============================] - 63s - loss: 0.1632 - acc: 0.9377 - val_loss: 0.1325 - val_acc: 0.9480
Epoch 6/50
50000/50000 [==============================] - 63s - loss: 0.1536 - acc: 0.9413 - val_loss: 0.1236 - val_acc: 0.9515
Epoch 7/50
50000/50000 [==============================] - 63s - loss: 0.1450 - acc: 0.9446 - val_loss: 0.1184 - val_acc: 0.9530
Epoch 8/50
50000/50000 [==============================] - 63s - loss: 0.1384 - acc: 0.9469 - val_loss: 0.1132 - val_acc: 0.9556
Epoch 9/50
50000/50000 [==============================] - 63s - loss: 0.1334 - acc: 0.9494 - val_loss: 0.1150 - val_acc: 0.9551
Epoch 10/50
50000/50000 [==============================] - 63s - loss: 0.1286 - acc: 0.9509 - val_loss: 0.1064 - val_acc: 0.9582
Epoch 11/50
50000/50000 [==============================] - 63s - loss: 0.1248 - acc: 0.9529 - val_loss: 0.1042 - val_acc: 0.9591
Epoch 12/50
50000/50000 [==============================] - 63s - loss: 0.1199 - acc: 0.9543 - val_loss: 0.1010 - val_acc: 0.9608
Epoch 13/50
50000/50000 [==============================] - 63s - loss: 0.1170 - acc: 0.9556 - val_loss: 0.1002 - val_acc: 0.9605
Epoch 14/50
50000/50000 [==============================] - 63s - loss: 0.1150 - acc: 0.9562 - val_loss: 0.0947 - val_acc: 0.9632
Epoch 15/50
50000/50000 [==============================] - 63s - loss: 0.1111 - acc: 0.9577 - val_loss: 0.0936 - val_acc: 0.9634
Epoch 16/50
50000/50000 [==============================] - 63s - loss: 0.1093 - acc: 0.9587 - val_loss: 0.0931 - val_acc: 0.9630
Epoch 17/50
50000/50000 [==============================] - 63s - loss: 0.1071 - acc: 0.9593 - val_loss: 0.0918 - val_acc: 0.9643
Epoch 18/50
50000/50000 [==============================] - 63s - loss: 0.1051 - acc: 0.9602 - val_loss: 0.0890 - val_acc: 0.9650
Epoch 19/50
50000/50000 [==============================] - 63s - loss: 0.1023 - acc: 0.9612 - val_loss: 0.0904 - val_acc: 0.9643
Epoch 20/50
50000/50000 [==============================] - 63s - loss: 0.0998 - acc: 0.9620 - val_loss: 0.0868 - val_acc: 0.9662
Epoch 21/50
50000/50000 [==============================] - 63s - loss: 0.0984 - acc: 0.9628 - val_loss: 0.0853 - val_acc: 0.9668
Epoch 22/50
50000/50000 [==============================] - 63s - loss: 0.0968 - acc: 0.9636 - val_loss: 0.0867 - val_acc: 0.9658
Epoch 23/50
50000/50000 [==============================] - 63s - loss: 0.0954 - acc: 0.9640 - val_loss: 0.0835 - val_acc: 0.9682
Epoch 24/50
50000/50000 [==============================] - 63s - loss: 0.0934 - acc: 0.9646 - val_loss: 0.0839 - val_acc: 0.9668
Epoch 25/50
50000/50000 [==============================] - 63s - loss: 0.0913 - acc: 0.9656 - val_loss: 0.0824 - val_acc: 0.9682
Epoch 26/50
50000/50000 [==============================] - 63s - loss: 0.0906 - acc: 0.9658 - val_loss: 0.0803 - val_acc: 0.9687
Epoch 27/50
50000/50000 [==============================] - 63s - loss: 0.0880 - acc: 0.9666 - val_loss: 0.0785 - val_acc: 0.9697
Epoch 28/50
50000/50000 [==============================] - 63s - loss: 0.0871 - acc: 0.9671 - val_loss: 0.0797 - val_acc: 0.9693
Epoch 29/50
50000/50000 [==============================] - 63s - loss: 0.0856 - acc: 0.9677 - val_loss: 0.0765 - val_acc: 0.9706
Epoch 30/50
50000/50000 [==============================] - 63s - loss: 0.0846 - acc: 0.9679 - val_loss: 0.0820 - val_acc: 0.9681
Epoch 31/50
50000/50000 [==============================] - 63s - loss: 0.0833 - acc: 0.9686 - val_loss: 0.0755 - val_acc: 0.9709
Epoch 32/50
50000/50000 [==============================] - 63s - loss: 0.0815 - acc: 0.9690 - val_loss: 0.0774 - val_acc: 0.9701
Epoch 33/50
50000/50000 [==============================] - 63s - loss: 0.0803 - acc: 0.9698 - val_loss: 0.0755 - val_acc: 0.9704
Epoch 34/50
50000/50000 [==============================] - 63s - loss: 0.0799 - acc: 0.9698 - val_loss: 0.0791 - val_acc: 0.9690
Epoch 35/50
50000/50000 [==============================] - 63s - loss: 0.0774 - acc: 0.9708 - val_loss: 0.0745 - val_acc: 0.9713
Epoch 36/50
50000/50000 [==============================] - 63s - loss: 0.0778 - acc: 0.9704 - val_loss: 0.0758 - val_acc: 0.9705
Epoch 37/50
50000/50000 [==============================] - 63s - loss: 0.0763 - acc: 0.9711 - val_loss: 0.0724 - val_acc: 0.9725
Epoch 38/50
50000/50000 [==============================] - 63s - loss: 0.0757 - acc: 0.9714 - val_loss: 0.0742 - val_acc: 0.9718
Epoch 39/50
50000/50000 [==============================] - 63s - loss: 0.0743 - acc: 0.9719 - val_loss: 0.0720 - val_acc: 0.9726
Epoch 40/50
50000/50000 [==============================] - 63s - loss: 0.0728 - acc: 0.9725 - val_loss: 0.0710 - val_acc: 0.9732
Epoch 41/50
50000/50000 [==============================] - 63s - loss: 0.0725 - acc: 0.9726 - val_loss: 0.0711 - val_acc: 0.9726
Epoch 42/50
50000/50000 [==============================] - 63s - loss: 0.0712 - acc: 0.9730 - val_loss: 0.0707 - val_acc: 0.9733
Epoch 43/50
50000/50000 [==============================] - 63s - loss: 0.0702 - acc: 0.9736 - val_loss: 0.0729 - val_acc: 0.9728
Epoch 44/50
50000/50000 [==============================] - 63s - loss: 0.0690 - acc: 0.9742 - val_loss: 0.0697 - val_acc: 0.9734
Epoch 45/50
50000/50000 [==============================] - 63s - loss: 0.0687 - acc: 0.9742 - val_loss: 0.0723 - val_acc: 0.9726
Epoch 46/50
50000/50000 [==============================] - 63s - loss: 0.0669 - acc: 0.9747 - val_loss: 0.0688 - val_acc: 0.9737
Epoch 47/50
50000/50000 [==============================] - 63s - loss: 0.0664 - acc: 0.9749 - val_loss: 0.0701 - val_acc: 0.9734
Epoch 48/50
50000/50000 [==============================] - 63s - loss: 0.0655 - acc: 0.9755 - val_loss: 0.0689 - val_acc: 0.9736
Epoch 49/50
50000/50000 [==============================] - 63s - loss: 0.0650 - acc: 0.9756 - val_loss: 0.0688 - val_acc: 0.9743
Epoch 50/50
50000/50000 [==============================] - 63s - loss: 0.0645 - acc: 0.9757 - val_loss: 0.0675 - val_acc: 0.9741