Hidden Layer Activation Functions
In H2o deep learning operator I can set the hidden layer size and the activation function. Is there a way to assign different activation functions for each hidden layers?
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Altair EmployeeHi @User36964,
The H2O deep learning can only define activate once for all hidden layers. If you want to add layer by layer and define the activation individually, please check out the deep learning extension or keras extension from marketplace.
https://marketplace.rapidminer.com/UpdateServer/faces/product_details.xhtml?productId=rmx_deeplearning
https://marketplace.rapidminer.com/UpdateServer/faces/product_details.xhtml?productId=rmx_keras
You can use different activation function when you add core layer, fully connected layer, convolution layer, embedding layer, etc..
Best,
YY
The H2O deep learning can only define activate once for all hidden layers. If you want to add layer by layer and define the activation individually, please check out the deep learning extension or keras extension from marketplace.
https://marketplace.rapidminer.com/UpdateServer/faces/product_details.xhtml?productId=rmx_deeplearning
https://marketplace.rapidminer.com/UpdateServer/faces/product_details.xhtml?productId=rmx_keras
You can use different activation function when you add core layer, fully connected layer, convolution layer, embedding layer, etc..
Best,
YY
The H2O deep learning can only define activate once for all hidden layers. If you want to add layer by layer and define the activation individually, please check out the deep learning extension or keras extension from marketplace.
https://marketplace.rapidminer.com/UpdateServer/faces/product_details.xhtml?productId=rmx_deeplearning
https://marketplace.rapidminer.com/UpdateServer/faces/product_details.xhtml?productId=rmx_keras
You can use different activation function when you add core layer, fully connected layer, convolution layer, embedding layer, etc..
Best,
YY