您好我在Ubuntu 14.04上运行了大数据流的Linux CPU版本，当我尝试保存模型时，我的内存不足。我正在使用构建卷积网络的Deep MNIST教程。你可以在这里找到它：
https://www.tensorflow.org/versions/r0.9/tutorials/mnist/pros/index.html#deep-mnist-for-experts
我更改了一些内容并尝试添加Saver来导出模型权重。但是，当我运行它时，我得到一个错误，说我失去了记忆。这对我来说没有意义，因为它可以永远训练数据，但保存它会以某种方式使用太多内存？
Extracting MNIST_data/train-images-idx3-ubyte.gz
Extracting MNIST_data/train-labels-idx1-ubyte.gz
Extracting MNIST_data/t10k-images-idx3-ubyte.gz
Extracting MNIST_data/t10k-labels-idx1-ubyte.gz
step 0, training accuracy 0.06
W tensorflow/core/framework/op_kernel.cc:909] Resource exhausted: OOM when allocating tensor with shape[10000,28,28,32]
Traceback (most recent call last):
File "/home/mgump/Lambda_Project/MNIST_TRAINER.py", line 66, in <module>
x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 555, in eval
return _eval_using_default_session(self, feed_dict, self.graph, session)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 3498, in _eval_using_default_session
return session.run(tensors, feed_dict)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 372, in run
run_metadata_ptr)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 636, in _run
feed_dict_string, options, run_metadata)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 708, in _do_run
target_list, options, run_metadata)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 728, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors.ResourceExhaustedError: OOM when allocating tensor with shape[10000,28,28,32]
[[Node: Conv2D = Conv2D[T=DT_FLOAT, data_format="NHWC", padding="SAME", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/cpu:0"](Reshape, Variable/read)]]
Caused by op u'Conv2D', defined at:
File "/home/mgump/Lambda_Project/MNIST_TRAINER.py", line 28, in <module>
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
File "/home/mgump/Lambda_Project/MNIST_TRAINER.py", line 18, in conv2d
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
File "/usr/local/lib/python2.7/dist- packages/tensorflow/python/ops/gen_nn_ops.py", line 394, in conv2d
data_format=data_format, name=name)
File "/usr/local/lib/python2.7/dist- packages/tensorflow/python/ops/op_def_library.py", line 704, in apply_op
op_def=op_def)
File "/usr/local/lib/python2.7/dist- packages/tensorflow/python/framework/ops.py", line 2260, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/usr/local/lib/python2.7/dist- packages/tensorflow/python/framework/ops.py", line 1230, in __init__
self._traceback = _extract_stack()`
这是我运行时输出的结果！非常感谢！