Is it possible to infer more than one parameter from Convolution neural network
I have a question and I am not sure if it's a smart one. But I've been reading quite a lot about convolution neural networks. And so far I understand that the output layer could for example be a softmax layer for a classification problem or you could do regression in order to get a quantitative value. But I was wondering if it is possible to infer more than one parameter. For example, if I have a data and my output label is both price of the house and size of the house. I know it is not a smart example. But I just want to know if it's possible to predict two different output values in the same output layer in the convolution neural network. Or do I need to have two different convolution neural network where one predicts the size of the house and the one predicts price of the house. And how can we combine these two predictions then. And if we can do it in one convolution neural network, then how can we do that?
conv-neural-network
asked Nov 13 '18 at 22:53
Shafa HaiderShafa Haider
669
add a comment |
I have a question and I am not sure if it's a smart one. But I've been reading quite a lot about convolution neural networks. And so far I understand that the output layer could for example be a softmax layer for a classification problem or you could do regression in order to get a quantitative value. But I was wondering if it is possible to infer more than one parameter. For example, if I have a data and my output label is both price of the house and size of the house. I know it is not a smart example. But I just want to know if it's possible to predict two different output values in the same output layer in the convolution neural network. Or do I need to have two different convolution neural network where one predicts the size of the house and the one predicts price of the house. And how can we combine these two predictions then. And if we can do it in one convolution neural network, then how can we do that?
conv-neural-network
asked Nov 13 '18 at 22:53
Shafa HaiderShafa Haider
669
add a comment |
I have a question and I am not sure if it's a smart one. But I've been reading quite a lot about convolution neural networks. And so far I understand that the output layer could for example be a softmax layer for a classification problem or you could do regression in order to get a quantitative value. But I was wondering if it is possible to infer more than one parameter. For example, if I have a data and my output label is both price of the house and size of the house. I know it is not a smart example. But I just want to know if it's possible to predict two different output values in the same output layer in the convolution neural network. Or do I need to have two different convolution neural network where one predicts the size of the house and the one predicts price of the house. And how can we combine these two predictions then. And if we can do it in one convolution neural network, then how can we do that?
conv-neural-network
asked Nov 13 '18 at 22:53
Shafa HaiderShafa Haider
669
I have a question and I am not sure if it's a smart one. But I've been reading quite a lot about convolution neural networks. And so far I understand that the output layer could for example be a softmax layer for a classification problem or you could do regression in order to get a quantitative value. But I was wondering if it is possible to infer more than one parameter. For example, if I have a data and my output label is both price of the house and size of the house. I know it is not a smart example. But I just want to know if it's possible to predict two different output values in the same output layer in the convolution neural network. Or do I need to have two different convolution neural network where one predicts the size of the house and the one predicts price of the house. And how can we combine these two predictions then. And if we can do it in one convolution neural network, then how can we do that?
conv-neural-network
conv-neural-network
asked Nov 13 '18 at 22:53
Shafa HaiderShafa Haider
669
asked Nov 13 '18 at 22:53
Shafa HaiderShafa Haider
669
asked Nov 13 '18 at 22:53
Shafa HaiderShafa Haider
669
asked Nov 13 '18 at 22:53
Shafa HaiderShafa Haider
669
asked Nov 13 '18 at 22:53
Shafa HaiderShafa Haider
669
669
add a comment |
add a comment |
1 Answer
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In your mentioned cases, the output layer is most likely a dense layer, not a convolutional one. But that's beside the point, if you want multiple outputs, then multiple output layers are often trained. So the same convolutional network can go to two separate output layers, which can be trained independently. Then you've one neural network, with two outputs. The convolutional part is often received by transfer learning, and are often frozen layers that can no longer be trained. Have a look at the figures of this paper, this shows how it can be done.
answered Nov 13 '18 at 23:00
T. KelherT. Kelher
33126
Thank you. That makes much more sense. So just like we can have multiple input branches, we can have multiple output branches.
– Shafa Haider
Nov 13 '18 at 23:05
1
yes, you can even have a an input leading to a few convolutional layers, those leading to two seperate convolutional networks, each with their own output layer. The sky is the limit.
– T. Kelher
Nov 13 '18 at 23:10
add a comment |
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In your mentioned cases, the output layer is most likely a dense layer, not a convolutional one. But that's beside the point, if you want multiple outputs, then multiple output layers are often trained. So the same convolutional network can go to two separate output layers, which can be trained independently. Then you've one neural network, with two outputs. The convolutional part is often received by transfer learning, and are often frozen layers that can no longer be trained. Have a look at the figures of this paper, this shows how it can be done.
answered Nov 13 '18 at 23:00
T. KelherT. Kelher
33126
Thank you. That makes much more sense. So just like we can have multiple input branches, we can have multiple output branches.
– Shafa Haider
Nov 13 '18 at 23:05
1
yes, you can even have a an input leading to a few convolutional layers, those leading to two seperate convolutional networks, each with their own output layer. The sky is the limit.
– T. Kelher
Nov 13 '18 at 23:10
add a comment |
In your mentioned cases, the output layer is most likely a dense layer, not a convolutional one. But that's beside the point, if you want multiple outputs, then multiple output layers are often trained. So the same convolutional network can go to two separate output layers, which can be trained independently. Then you've one neural network, with two outputs. The convolutional part is often received by transfer learning, and are often frozen layers that can no longer be trained. Have a look at the figures of this paper, this shows how it can be done.
answered Nov 13 '18 at 23:00
T. KelherT. Kelher
33126
Thank you. That makes much more sense. So just like we can have multiple input branches, we can have multiple output branches.
– Shafa Haider
Nov 13 '18 at 23:05
1
yes, you can even have a an input leading to a few convolutional layers, those leading to two seperate convolutional networks, each with their own output layer. The sky is the limit.
– T. Kelher
Nov 13 '18 at 23:10
add a comment |
In your mentioned cases, the output layer is most likely a dense layer, not a convolutional one. But that's beside the point, if you want multiple outputs, then multiple output layers are often trained. So the same convolutional network can go to two separate output layers, which can be trained independently. Then you've one neural network, with two outputs. The convolutional part is often received by transfer learning, and are often frozen layers that can no longer be trained. Have a look at the figures of this paper, this shows how it can be done.
answered Nov 13 '18 at 23:00
T. KelherT. Kelher
33126
In your mentioned cases, the output layer is most likely a dense layer, not a convolutional one. But that's beside the point, if you want multiple outputs, then multiple output layers are often trained. So the same convolutional network can go to two separate output layers, which can be trained independently. Then you've one neural network, with two outputs. The convolutional part is often received by transfer learning, and are often frozen layers that can no longer be trained. Have a look at the figures of this paper, this shows how it can be done.
answered Nov 13 '18 at 23:00
T. KelherT. Kelher
33126
answered Nov 13 '18 at 23:00
T. KelherT. Kelher
33126
answered Nov 13 '18 at 23:00
T. KelherT. Kelher
33126
answered Nov 13 '18 at 23:00
T. KelherT. Kelher
33126
33126
Thank you. That makes much more sense. So just like we can have multiple input branches, we can have multiple output branches.
– Shafa Haider
Nov 13 '18 at 23:05
1
yes, you can even have a an input leading to a few convolutional layers, those leading to two seperate convolutional networks, each with their own output layer. The sky is the limit.
– T. Kelher
Nov 13 '18 at 23:10
add a comment |
Thank you. That makes much more sense. So just like we can have multiple input branches, we can have multiple output branches.
– Shafa Haider
Nov 13 '18 at 23:05
1
yes, you can even have a an input leading to a few convolutional layers, those leading to two seperate convolutional networks, each with their own output layer. The sky is the limit.
– T. Kelher
Nov 13 '18 at 23:10
Thank you. That makes much more sense. So just like we can have multiple input branches, we can have multiple output branches.
– Shafa Haider
Nov 13 '18 at 23:05
Thank you. That makes much more sense. So just like we can have multiple input branches, we can have multiple output branches.
– Shafa Haider
Nov 13 '18 at 23:05
1
1
yes, you can even have a an input leading to a few convolutional layers, those leading to two seperate convolutional networks, each with their own output layer. The sky is the limit.
– T. Kelher
Nov 13 '18 at 23:10
yes, you can even have a an input leading to a few convolutional layers, those leading to two seperate convolutional networks, each with their own output layer. The sky is the limit.
– T. Kelher
Nov 13 '18 at 23:10
add a comment |
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