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Softmax Layer

Softmax Layer Node Documentation
The Softmax Layer node in AugeLab Studio represents the Softmax activation layer for deep learning models.
Node Overview
The Softmax Layer node allows you to add a Softmax activation layer to a deep learning model. Softmax is a popular activation function that normalizes the outputs of a model to represent a probability distribution.
Inputs
The Softmax Layer node does not require any inputs.
Outputs
The Softmax Layer node outputs the Softmax activation layer.
Node Interaction
1.Drag and drop the Softmax Layer node from the node library onto the canvas in AugeLab Studio.
2.Configure the node properties:
Axis: Specify the axis along which the Softmax activation should be applied. The default value of 1 applies the Softmax activation to each row independently.
3.The Softmax activation layer will be created based on the specified configuration.
4.Connect the output Softmax activation layer to the appropriate nodes for building the deep learning model, such as the Add Layer node or the Model Building node.
Implementation Details
The Softmax Layer node is implemented as a subclass of the NodeCNN base class. It overrides the getKerasLayer method to create the Softmax activation layer.
The node reads the input value for the axis.
The Softmax activation layer is created using the specified configuration.
The created layer is returned as the output.
Usage
1.Drag and drop the Softmax Layer node from the node library onto the canvas in AugeLab Studio.
2.Configure the node properties:
Axis: Specify the axis along which the Softmax activation should be applied. The default value of 1 applies the Softmax activation to each row independently.
3.The Softmax activation layer will be created based on the specified configuration.
4.Connect the output Softmax activation layer to the appropriate nodes for building the deep learning model, such as the Add Layer node or the Model Building node.
5.Continue building the deep learning model by adding more layers or connecting other nodes as needed.
Notes
The Softmax Layer node allows you to add a Softmax activation layer to a deep learning model.
The Softmax activation function normalizes the outputs of a model to represent a probability distribution.
The axis parameter specifies the axis along which the Softmax activation should be applied. By default, the Softmax activation is applied independently to each row (axis=1).
The Softmax activation is commonly used in multi-class classification problems, where the model needs to predict the probability distribution over multiple classes.
Connect the output Softmax activation layer to other nodes for building the deep learning model, such as the Add Layer node or the Model Building node.
Combine the Softmax activation layer with other layers and techniques to create powerful and expressive deep learning models.
The Softmax Layer node is suitable for various deep learning tasks, including image classification, object detection, and natural language processing.

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