Is it possible to make a 'forked path' neural network? The 2019 Stack Overflow Developer Survey Results Are InNeural Net for regression task on images only learning mean of training dataAdjusting weights in an convolutional neural networkMultiple Output Layers in Neural Networks in Deep Q LearningHow do I provide input and output for such a network structure in kerasKeras retrieve value of node before activation functionNeed help understanding the structure of this convoluted neural networkWhat principle is behind semantic segmenation with CNNs?How can I find out what class each of the columns in the probabilities output correspond to using Keras for a multi-class classification problem?Does it make sense to train a convolutional neural network on lo-res, use on hi-res pictures?1x1 convolutions, equivalence with fully connected layer
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Is it possible to make a 'forked path' neural network?
The 2019 Stack Overflow Developer Survey Results Are InNeural Net for regression task on images only learning mean of training dataAdjusting weights in an convolutional neural networkMultiple Output Layers in Neural Networks in Deep Q LearningHow do I provide input and output for such a network structure in kerasKeras retrieve value of node before activation functionNeed help understanding the structure of this convoluted neural networkWhat principle is behind semantic segmenation with CNNs?How can I find out what class each of the columns in the probabilities output correspond to using Keras for a multi-class classification problem?Does it make sense to train a convolutional neural network on lo-res, use on hi-res pictures?1x1 convolutions, equivalence with fully connected layer
$begingroup$
I want to make a network, specifically a CNN for image recognition, that takes an input, processes it the same way for several layers, and then at some point splits before coming to two different outputs. Is it possible to create a network such as this? It would look something like this:
Input ─> Conv ─> Pool ─> Conv ─> Pool ──> Dense ─> Output 1
|
└──> Dense ─> Output 2
I.E. it splits off after the second pooling layer into separate fully connected layers. Of course, it has to train to both outputs, so that it is producing minimal error on both separate outputs using these common convolutional layers. Also, I am using Python Keras, and it would help if there was some way to do this using Keras in some way. Thank you!
machine-learning neural-network keras cnn image-recognition
edited 5 hours ago
Simon Larsson
750114
asked 5 hours ago
Fred EFred E
11
New contributor
Fred E is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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add a comment |
$begingroup$
I want to make a network, specifically a CNN for image recognition, that takes an input, processes it the same way for several layers, and then at some point splits before coming to two different outputs. Is it possible to create a network such as this? It would look something like this:
Input ─> Conv ─> Pool ─> Conv ─> Pool ──> Dense ─> Output 1
|
└──> Dense ─> Output 2
I.E. it splits off after the second pooling layer into separate fully connected layers. Of course, it has to train to both outputs, so that it is producing minimal error on both separate outputs using these common convolutional layers. Also, I am using Python Keras, and it would help if there was some way to do this using Keras in some way. Thank you!
machine-learning neural-network keras cnn image-recognition
edited 5 hours ago
Simon Larsson
750114
asked 5 hours ago
Fred EFred E
11
New contributor
Fred E is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
$begingroup$
Welcome to SE.DataScience! Your question has been answered on stackoverflow.
$endgroup$
– Esmailian
5 hours ago
add a comment |
$begingroup$
I want to make a network, specifically a CNN for image recognition, that takes an input, processes it the same way for several layers, and then at some point splits before coming to two different outputs. Is it possible to create a network such as this? It would look something like this:
Input ─> Conv ─> Pool ─> Conv ─> Pool ──> Dense ─> Output 1
|
└──> Dense ─> Output 2
I.E. it splits off after the second pooling layer into separate fully connected layers. Of course, it has to train to both outputs, so that it is producing minimal error on both separate outputs using these common convolutional layers. Also, I am using Python Keras, and it would help if there was some way to do this using Keras in some way. Thank you!
machine-learning neural-network keras cnn image-recognition
edited 5 hours ago
Simon Larsson
750114
asked 5 hours ago
Fred EFred E
11
New contributor
Fred E is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I want to make a network, specifically a CNN for image recognition, that takes an input, processes it the same way for several layers, and then at some point splits before coming to two different outputs. Is it possible to create a network such as this? It would look something like this:
Input ─> Conv ─> Pool ─> Conv ─> Pool ──> Dense ─> Output 1
|
└──> Dense ─> Output 2
I.E. it splits off after the second pooling layer into separate fully connected layers. Of course, it has to train to both outputs, so that it is producing minimal error on both separate outputs using these common convolutional layers. Also, I am using Python Keras, and it would help if there was some way to do this using Keras in some way. Thank you!
machine-learning neural-network keras cnn image-recognition
machine-learning neural-network keras cnn image-recognition
edited 5 hours ago
Simon Larsson
750114
asked 5 hours ago
Fred EFred E
11
New contributor
Fred E is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
edited 5 hours ago
Simon Larsson
750114
asked 5 hours ago
Fred EFred E
11
New contributor
Fred E is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
edited 5 hours ago
Simon Larsson
750114
edited 5 hours ago
Simon Larsson
750114
edited 5 hours ago
Simon Larsson
750114
750114
asked 5 hours ago
Fred EFred E
11
New contributor
Fred E is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 5 hours ago
Fred EFred E
11
asked 5 hours ago
Fred EFred E
11
11
New contributor
Fred E is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Fred E is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Fred E is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$begingroup$
Welcome to SE.DataScience! Your question has been answered on stackoverflow.
$endgroup$
– Esmailian
5 hours ago
add a comment |
$begingroup$
Welcome to SE.DataScience! Your question has been answered on stackoverflow.
$endgroup$
– Esmailian
5 hours ago
$begingroup$
Welcome to SE.DataScience! Your question has been answered on stackoverflow.
$endgroup$
– Esmailian
5 hours ago
$begingroup$
Welcome to SE.DataScience! Your question has been answered on stackoverflow.
$endgroup$
– Esmailian
5 hours ago
add a comment |
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