Comments (1)
Hi @jhoh10, my GPU only has 2GB and at most 1.8GB is available for training. I am afraid that the memory size cannot be reduced. But you may move some calculations to be calculated by the CPU and use the general memory instead.
The bottleneck is in the fully connected layers. In most of my project that using the VGG, when I found the GPU memory issue, I will add with tf.device('/cpu:0'):
to the largest layer or even the second largest layer.
Hope that is it is helpful.
from tensorflow-vgg.
Related Issues (20)
- The vgg16.npy can't be downed. HOT 4
- Relu layer
- _pickle.UnpicklingError HOT 1
- About NPY file
- why there is no activation function behind the convolution layer? HOT 1
- what about more channels
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- about fc_layer
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from tensorflow-vgg.