PS D:\> virtualenv --system-site-packages D:\tensorflow Using base prefix 'd:\\python35' New python executable in D:\tensorflow\Scripts\python.exe Installing setuptools, pip, wheel...done.
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\dso_loader.cc:128] successfu lly opened CUDA library cublas64_80.dll locally I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\dso_loader.cc:128] successfu lly opened CUDA library cudnn64_5.dll locally I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\dso_loader.cc:128] successfu lly opened CUDA library cufft64_80.dll locally I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\dso_loader.cc:128] successfu lly opened CUDA library nvcuda.dll locally I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\dso_loader.cc:128] successfu lly opened CUDA library curand64_80.dll locally Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes. Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes. Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes. Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes. Extracting data\train-images-idx3-ubyte.gz Extracting data\train-labels-idx1-ubyte.gz Extracting data\t10k-images-idx3-ubyte.gz Extracting data\t10k-labels-idx1-ubyte.gz I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:885] F ound device 0 with properties: name: GeForce GTX 960 major: 5 minor: 2 memoryClockRate (GHz) 1.253 pciBusID 0000:01:00.0 Total memory: 2.00GiB Free memory: 1.64GiB I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:906] D MA: 0 I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:916] 0 : Y I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:975] C reating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 960, pci bus id: 0000:01:00.0) E c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:586] C ould not identify NUMA node of /job:localhost/replica:0/task:0/gpu:0, defaulting to 0. Your kernel may not have been bu ilt with NUMA support. Initialized! Step 0 (epoch 0.00), 50.9 ms Minibatch loss: 8.334, learning rate: 0.010000 Minibatch error: 85.9% Validation error: 84.6% Step 100 (epoch 0.12), 12.1 ms Minibatch loss: 3.226, learning rate: 0.010000 Minibatch error: 4.7% Validation error: 7.3% Step 200 (epoch 0.23), 12.0 ms Minibatch loss: 3.404, learning rate: 0.010000 Minibatch error: 10.9% (省略) Minibatch loss: 1.609, learning rate: 0.006302 Minibatch error: 0.0% Validation error: 1.0% Test error: 0.8%
PS D:\tensorflow\models\image\imagenet> python .\classify_image.py I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\dso_loader.cc:128] successfully opened CUDA library cublas64_8 0.dll locally I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\dso_loader.cc:128] successfully opened CUDA library cudnn64_5. dll locally I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\dso_loader.cc:128] successfully opened CUDA library cufft64_80 .dll locally I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\dso_loader.cc:128] successfully opened CUDA library nvcuda.dll locally I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\dso_loader.cc:128] successfully opened CUDA library curand64_8 0.dll locally >> Downloading inception-2015-12-05.tgz 100.0% Successfully downloaded inception-2015-12-05.tgz 88931400 bytes. I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:885] Found device 0 with properties: name: GeForce GTX 960 major: 5 minor: 2 memoryClockRate (GHz) 1.253 pciBusID 0000:01:00.0 Total memory: 2.00GiB Free memory: 1.64GiB I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:906] DMA: 0 I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:916] 0: Y I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 960, pci bus id: 0000:01:00.0) E c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:586] Could not identify NUMA node of /jo b:localhost/replica:0/task:0/gpu:0, defaulting to 0. Your kernel may not have been built with NUMA support. W c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\framework\op_def_util.cc:332] Op BatchNormWithGlobalNormalization is depr ecated. It will cease to work in GraphDef version 9. Use tf.nn.batch_normalization(). W c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\bfc_allocator.cc:217] Ran out of memory trying to allocate 1.91GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available. giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca (score = 0.89233) indri, indris, Indri indri, Indri brevicaudatus (score = 0.00859) lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens (score = 0.00264) custard apple (score = 0.00141) earthstar (score = 0.00107)
PS D:\tensorflow\models\image\imagenet> python .\classify_image.py --image_file M:\fuji.jpg I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\dso_loader.cc:128] successfully opened CUDA library cublas64_8 0.dll locally I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\dso_loader.cc:128] successfully opened CUDA library cudnn64_5. dll locally I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\dso_loader.cc:128] successfully opened CUDA library cufft64_80 .dll locally I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\dso_loader.cc:128] successfully opened CUDA library nvcuda.dll locally I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\dso_loader.cc:128] successfully opened CUDA library curand64_8 0.dll locally I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:885] Found device 0 with properties: name: GeForce GTX 960 major: 5 minor: 2 memoryClockRate (GHz) 1.253 pciBusID 0000:01:00.0 Total memory: 2.00GiB Free memory: 1.64GiB I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:906] DMA: 0 I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:916] 0: Y I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 960, pci bus id: 0000:01:00.0) E c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:586] Could not identify NUMA node of /jo b:localhost/replica:0/task:0/gpu:0, defaulting to 0. Your kernel may not have been built with NUMA support. W c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\framework\op_def_util.cc:332] Op BatchNormWithGlobalNormalization is depr ecated. It will cease to work in GraphDef version 9. Use tf.nn.batch_normalization(). W c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\bfc_allocator.cc:217] Ran out of memory trying to allocate 1.91GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available. volcano (score = 0.91087) fire screen, fireguard (score = 0.00192) alp (score = 0.00162) lakeside, lakeshore (score = 0.00130) geyser (score = 0.00077)
W c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\bfc_allocator.cc:217] Ran out of memory trying to allocate 1.91GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.