Exponential linear units were introduced to alleviate the disadvantages of
ReLU and LeakyReLU units, namely to push the mean activation closer to zero while
still saturating to a negative value which increases robustness against noise if the unit is
in an off state (i.e. the input is very negative).
The formula is f(x) = alpha * (exp(x) - 1) for x < 0 and f(x) = x for x >= 0.
For the exact details see the corresponding paper.
Corresponds to the
Keras ELU Layer.
Options
Popular Successors
- Keras Network Learner21 %
- Keras ELU Layer18 %
- Keras Dense Layer9 %
- Keras Leaky ReLU Layer6 %
DL Network Executor3 %- Keras Network Executor3 %
- Keras Network Writer3 %
- Keras Softmax Layer3 %
- Keras Thresholded ReLU Layer3 %
- Keras Convolution 1D Layer3 %
Keras Separable Convolution 1D Layer3 %- Keras Activation Layer3 %
- Keras Dropout Layer3 %
- Keras Embedding Layer3 %
Keras Batch Normalization Layer3 %- Keras Batch Normalization Layer3 %
- Keras CuDNN LSTM Layer3 %
Keras LSTM Layer3 %- Keras LSTM Layer3 %
- Keras Simple RNN Layer3 %
Views
- This node has no views
Workflows
- No workflows found
Links
Developers
You want to see the source code for this node? Click the following button and we’ll use our super-powers to find it for you.