在 tensorflow gpu 中训练卷积神经网络时出现“Python 已停止工作”答案 |
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这是我编写的一个 tensorflow 代码，用于测试只有 1 个卷积和池化层的卷积神经网络，其中只有 512 个神经元的 1 个全连接层。 我的数据集只有 2 张图片：http://imgur.com/et1Sn1k 和 http://imgur.com/ZWxOGgO 当我训练我的网络时，窗口弹出“Python 已停止”（ss：http://imgur.com/tc5jWlA） 这是我的代码： from scipy.misc import imread import matplotlib.pyplot as plt import numpy as np import PIL.Image as Image base_image = imread("base_image.jpg") subject_image = imread("subject_image.jpg") base_image = np.resize(base_image, [1024, 768, 3]) subject_image = np.resize(subject_image, [1024, 768, 3]) images = [] images.append(base_image) images.append(subject_image) # hyper parameters epochs = 10 batch_size = 2 learning_rate = 0.01 n_classes = 2 import tensorflow as tf # Model x = tf.placeholder('float', [2, 1024, 768, 3]) y = tf.placeholder('float', [2]) weights = { "conv": tf.random_normal([10, 10, 3, 32]), "fc": tf.random_normal([-1, 512]), #7*7*64 "out": tf.Variable(tf.random_normal([512, n_classes])) } conv = tf.nn.conv2d(x, filter=weights['conv'], strides=[1, 1, 1, 1], padding="SAME") conv = tf.nn.max_pool(value=conv, ksize=[1,2,2,1], strides=[1,2,2,1], padding="SAME") fc = tf.reshape(conv, shape=[2,-1]) fc = tf.nn.relu(tf.matmul(fc, weights['fc'])) output = tf.matmul(fc, weights['out']) loss = tf.reduce_mean((output - y)**2) train = tf.train.AdamOptimizer(learning_rate).minimize(loss) sess = tf.Session() sess.run(tf.global_variables_initializer()) for i in range(epochs): sess.run(train, feed_dict={x: images, y: [0, 1]}) print(i)输出： 2017-07-09 02:29:43.688699: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE instructions, but these are available on your machine and could speed up CPU computations. 2017-07-09 02:29:43.689131: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE2 instructions, but these are available on your machine and could speed up CPU computations. 2017-07-09 02:29:43.689504: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations. 2017-07-09 02:29:43.689998: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations. 2017-07-09 02:29:43.690380: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations. 2017-07-09 02:29:43.690646: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations. 2017-07-09 02:29:43.691117: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations. 2017-07-09 02:29:43.691436: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations. 2017-07-09 02:29:44.197766: I c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\common_runtime\gpu\gpu_device.cc:940] Found device 0 with properties: name: GeForce GTX 960M major: 5 minor: 0 memoryClockRate (GHz) 1.176 pciBusID 0000:01:00.0 Total memory: 4.00GiB Free memory: 3.35GiB 2017-07-09 02:29:44.198207: I c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\common_runtime\gpu\gpu_device.cc:961] DMA: 0 2017-07-09 02:29:44.198391: I c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\common_runtime\gpu\gpu_device.cc:971] 0: Y 2017-07-09 02:29:44.198643: I c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\common_runtime\gpu\gpu_device.cc:1030] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 960M, pci bus id: 0000:01:00.0) 2017-07-09 02:29:44.881448: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\framework\op_kernel.cc:1158] Invalid argument: Dimension -1 must be >= 0 2017-07-09 02:29:44.881875: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\framework\op_kernel.cc:1158] Invalid argument: Dimension -1 must be >= 0 [[Node: random_normal_1/RandomStandardNormal = RandomStandardNormal[T=DT_INT32, dtype=DT_FLOAT, seed=0, seed2=0, _device="/job:localhost/replica:0/task:0/gpu:0"](random_normal_1/shape)]] 2017-07-09 02:29:44.882604: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\framework\op_kernel.cc:1158] Invalid argument: Dimension -1 must be >= 0 [[Node: random_normal_1/RandomStandardNormal = RandomStandardNormal[T=DT_INT32, dtype=DT_FLOAT, seed=0, seed2=0, _device="/job:localhost/replica:0/task:0/gpu:0"](random_normal_1/shape)]] 2017-07-09 02:29:45.362917: E c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\stream_executor\cuda\cuda_dnn.cc:352] Loaded runtime CuDNN library: 6021 (compatibility version 6000) but source was compiled with 5105 (compatibility version 5100). If using a binary install, upgrade your CuDNN library to match. If building from sources, make sure the library loaded at runtime matches a compatible version specified during compile configuration. 2017-07-09 02:29:45.364154: F c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\kernels\conv_ops.cc:671] Check failed: stream->parent()->GetConvolveAlgorithms(&algorithms) [Finished in 22.7s with exit code 3221226505] [shell_cmd: python -u "E:\workspace_py\convolotion_neural_net.py"] [dir: E:\workspace_py] [path: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\bin;C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\libnvvp;E:\Program Files\Python 3.5\Scripts\;E:\Program Files\Python 3.5\;C:\ProgramData\Oracle\Java\javapath;C:\Windows\system32;C:\Windows;C:\Windows\System32\Wbem;C:\Windows\System32\WindowsPowerShell\v1.0\;C:\Program Files (x86)\NVIDIA Corporation\PhysX\Common;C:\Python27\Scripts;C:\Program Files\Microsoft SQL Server\130\Tools\Binn\;E:\Program Files\MATLAB\runtime\win64;E:\Program Files\MATLAB\bin;E:\Program Files\MATLAB\polyspace\bin;C:\WINDOWS\system32;C:\WINDOWS;C:\WINDOWS\System32\Wbem;C:\WINDOWS\System32\WindowsPowerShell\v1.0\;C:\ProgramData\Oracle\Java\javapath;C:\Windows\system32;C:\Windows;C:\Windows\System32\Wbem;C:\Windows\System32\WindowsPowerShell\v1.0\;C:\Program Files (x86)\NVIDIA Corporation\PhysX\Common;C:\Python27\Scripts;C:\Program Files\Microsoft SQL Server\130\Tools\Binn\;E:\Program Files\MATLAB\runtime\win64;E:\Program Files\MATLAB\bin;E:\Program Files\MATLAB\polyspace\bin;C:\WINDOWS\system32;C:\WINDOWS;C:\WINDOWS\System32\Wbem;C:\WINDOWS\System32\WindowsPowerShell\v1.0\;C:\Users\guita\AppData\Local\Microsoft\WindowsApps;E:\Program Files\Python27\Scipts;e:\Program Files (x86)\Microsoft VS Code\bin;C:\Users\guita\AppData\Local\atom\bin]我的电脑规格（联想 Y50）： Nvidia GTX 960m 4 GB 内存， 英特尔 I7 第 4 代， 8 GB 内存 Python 3.5 + 带 GPU 的 Tensorflow |
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