Forward pass output of a pertained network changes without back propagation
I am using Chainer's pertained model vgg (here named net). Every time I run the following code, I get a different result:
img = Image.open("/Users/macintosh/Desktop/Code/Ger.jpg")
img = Variable(vgg.prepare(img))
img = img.reshape((1,) + img.shape)
print(net(img,layers=['prob'])['prob'])
I have checked vgg.prepare() several times but its output is the same, and there is no random initialization here (net is a pre-trained vgg network). So why is this happening?
python neural-network pre-trained-model chainer vgg-net
asked Nov 15 '18 at 17:09
saman jahangirisaman jahangiri
377
add a comment |
I am using Chainer's pertained model vgg (here named net). Every time I run the following code, I get a different result:
img = Image.open("/Users/macintosh/Desktop/Code/Ger.jpg")
img = Variable(vgg.prepare(img))
img = img.reshape((1,) + img.shape)
print(net(img,layers=['prob'])['prob'])
I have checked vgg.prepare() several times but its output is the same, and there is no random initialization here (net is a pre-trained vgg network). So why is this happening?
python neural-network pre-trained-model chainer vgg-net
asked Nov 15 '18 at 17:09
saman jahangirisaman jahangiri
377
add a comment |
I am using Chainer's pertained model vgg (here named net). Every time I run the following code, I get a different result:
img = Image.open("/Users/macintosh/Desktop/Code/Ger.jpg")
img = Variable(vgg.prepare(img))
img = img.reshape((1,) + img.shape)
print(net(img,layers=['prob'])['prob'])
I have checked vgg.prepare() several times but its output is the same, and there is no random initialization here (net is a pre-trained vgg network). So why is this happening?
python neural-network pre-trained-model chainer vgg-net
asked Nov 15 '18 at 17:09
saman jahangirisaman jahangiri
377
I am using Chainer's pertained model vgg (here named net). Every time I run the following code, I get a different result:
img = Image.open("/Users/macintosh/Desktop/Code/Ger.jpg")
img = Variable(vgg.prepare(img))
img = img.reshape((1,) + img.shape)
print(net(img,layers=['prob'])['prob'])
I have checked vgg.prepare() several times but its output is the same, and there is no random initialization here (net is a pre-trained vgg network). So why is this happening?
python neural-network pre-trained-model chainer vgg-net
python neural-network pre-trained-model chainer vgg-net
asked Nov 15 '18 at 17:09
saman jahangirisaman jahangiri
377
asked Nov 15 '18 at 17:09
saman jahangirisaman jahangiri
377
asked Nov 15 '18 at 17:09
saman jahangirisaman jahangiri
377
asked Nov 15 '18 at 17:09
saman jahangirisaman jahangiri
377
asked Nov 15 '18 at 17:09
saman jahangirisaman jahangiri
377
377
add a comment |
add a comment |
1 Answer
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As you can see VGG implementation, it has dropout function. I think this causes the randomness.
When you want to forward the computation in evaluation mode (instead of training mode), you can set chainer config 'train' to False as follows:
with chainer.no_backprop_mode(), chainer.using_config('train', False):
result = net(img,layers=['prob'])['prob']
when train flag is False, dropout is not executed (and some other function behaviors also change, e.g., BatchNormalization uses trained statistics).
answered Nov 16 '18 at 4:12
corochanncorochann
1,2051619
add a comment |
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1 Answer
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1 Answer
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active
oldest
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As you can see VGG implementation, it has dropout function. I think this causes the randomness.
When you want to forward the computation in evaluation mode (instead of training mode), you can set chainer config 'train' to False as follows:
with chainer.no_backprop_mode(), chainer.using_config('train', False):
result = net(img,layers=['prob'])['prob']
when train flag is False, dropout is not executed (and some other function behaviors also change, e.g., BatchNormalization uses trained statistics).
answered Nov 16 '18 at 4:12
corochanncorochann
1,2051619
add a comment |
As you can see VGG implementation, it has dropout function. I think this causes the randomness.
When you want to forward the computation in evaluation mode (instead of training mode), you can set chainer config 'train' to False as follows:
with chainer.no_backprop_mode(), chainer.using_config('train', False):
result = net(img,layers=['prob'])['prob']
when train flag is False, dropout is not executed (and some other function behaviors also change, e.g., BatchNormalization uses trained statistics).
answered Nov 16 '18 at 4:12
corochanncorochann
1,2051619
add a comment |
As you can see VGG implementation, it has dropout function. I think this causes the randomness.
When you want to forward the computation in evaluation mode (instead of training mode), you can set chainer config 'train' to False as follows:
with chainer.no_backprop_mode(), chainer.using_config('train', False):
result = net(img,layers=['prob'])['prob']
when train flag is False, dropout is not executed (and some other function behaviors also change, e.g., BatchNormalization uses trained statistics).
answered Nov 16 '18 at 4:12
corochanncorochann
1,2051619
As you can see VGG implementation, it has dropout function. I think this causes the randomness.
When you want to forward the computation in evaluation mode (instead of training mode), you can set chainer config 'train' to False as follows:
with chainer.no_backprop_mode(), chainer.using_config('train', False):
result = net(img,layers=['prob'])['prob']
when train flag is False, dropout is not executed (and some other function behaviors also change, e.g., BatchNormalization uses trained statistics).
answered Nov 16 '18 at 4:12
corochanncorochann
1,2051619
answered Nov 16 '18 at 4:12
corochanncorochann
1,2051619
answered Nov 16 '18 at 4:12
corochanncorochann
1,2051619
answered Nov 16 '18 at 4:12
corochanncorochann
1,2051619
1,2051619
add a comment |
add a comment |
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