[英]Memory issues with VGG16 on Tensorflow
我一直在尝试使用具有Tensorflow后端的VGG16 Keras模型来工作,以便对“行星:从 Kaggle的太空竞赛中了解亚马逊”进行图像分类。 不幸的是,当试图使模型运行时,即使在具有60 GB内存的AWS g.2.8大型AWS上运行时,我始终遇到内存问题。
问题的回溯如下:
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) (None, 224, 224, 3) 0
_________________________________________________________________
block1_conv1 (Conv2D) (None, 224, 224, 64) 1792
_________________________________________________________________
block1_conv2 (Conv2D) (None, 224, 224, 64) 36928
_________________________________________________________________
block1_pool (MaxPooling2D) (None, 112, 112, 64) 0
_________________________________________________________________
block2_conv1 (Conv2D) (None, 112, 112, 128) 73856
_________________________________________________________________
block2_conv2 (Conv2D) (None, 112, 112, 128) 147584
_________________________________________________________________
block2_pool (MaxPooling2D) (None, 56, 56, 128) 0
_________________________________________________________________
block3_conv1 (Conv2D) (None, 56, 56, 256) 295168
_________________________________________________________________
block3_conv2 (Conv2D) (None, 56, 56, 256) 590080
_________________________________________________________________
block3_conv3 (Conv2D) (None, 56, 56, 256) 590080
_________________________________________________________________
block3_pool (MaxPooling2D) (None, 28, 28, 256) 0
_________________________________________________________________
block4_conv1 (Conv2D) (None, 28, 28, 512) 1180160
_________________________________________________________________
block4_conv2 (Conv2D) (None, 28, 28, 512) 2359808
_________________________________________________________________
block4_conv3 (Conv2D) (None, 28, 28, 512) 2359808
_________________________________________________________________
block4_pool (MaxPooling2D) (None, 14, 14, 512) 0
_________________________________________________________________
block5_conv1 (Conv2D) (None, 14, 14, 512) 2359808
_________________________________________________________________
block5_conv2 (Conv2D) (None, 14, 14, 512) 2359808
_________________________________________________________________
block5_conv3 (Conv2D) (None, 14, 14, 512) 2359808
_________________________________________________________________
block5_pool (MaxPooling2D) (None, 7, 7, 512) 0
_________________________________________________________________
sequential_1 (Sequential) (None, 1) 6423041
=================================================================
Total params: 21,137,729.0
Trainable params: 21,137,729.0
Non-trainable params: 0.0
_________________________________________________________________
Traceback (most recent call last):
File "VGG16_Kg_Kernel.py", line 160, in <module>
train_datagen.fit(x_train)
File "/home/ec2-user/src/anaconda3/lib/python3.5/site-packages/keras/preprocessing/image.py", line 648, in fit
x = np.copy(x)
File "/home/ec2-user/src/anaconda3/lib/python3.5/site-packages/numpy/lib/function_base.py", line 1497, in copy
return array(a, order=order, copy=True)
MemoryError
完整的打印输出可以在这里找到: https : //github.com/jvk/VGG16/blob/master/error_text.txt
从打印结果来看,GPU似乎正在运行,但是可能运行不正常。
数据包含〜100K 11.6 KB图像。 我用来运行模型的代码可以在这里找到: https : //github.com/jvk/VGG16/blob/master/VGG16_Kg_Kernel.py
如果需要更多信息,请告诉我。谢谢!
简而言之,就是您的GPU内存不足,而不是RAM。 G2具有4GB图形处理器,并且VGG16和Tensorflow似乎存在问题。
我已经使用Theano后端运行了相同的程序,没有任何问题。 我建议尝试一下。
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