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Loss CCE

Loss CCE Node Documentation
The Loss CCE node in AugeLab Studio represents the categorical cross-entropy loss function for training a convolutional neural network (CNN) with Keras.
Node Overview
The Loss CCE node computes the categorical cross-entropy loss function for multi-class classification problems. It has the following properties:
Node Title: Loss CCE
Node ID: OP_NODE_AI_LOSS_CCE
Inputs
The Loss CCE node does not have any input sockets. It is a terminal node that provides the loss function for the CNN.
Outputs
The Loss CCE node outputs the categorical cross-entropy loss function, which can be used as the loss function for training the CNN.
Node Interaction
1.Drag and drop the Loss CCE node from the node library onto the canvas in AugeLab Studio.
2.Connect the output of the Loss CCE node to the Loss Function input socket of other nodes in the network.
Implementation Details
The Loss CCE node is implemented using the CategoricalCrossentropy loss function from the Keras library. The evalAi method of the node computes and returns the loss function object.
The Loss CCE node creates an instance of the CategoricalCrossentropy loss function with the from_logits=True parameter.
The loss function is configured to handle multi-class classification problems and expects the predicted values to be logits.
Usage
1.Drag and drop the Loss CCE node from the node library onto the canvas in AugeLab Studio.
2.Connect the output of the Loss CCE node to the Loss Function input socket of other nodes in the network.
3.Configure the CNN architecture and connect other nodes for data input, model compilation, and training.
4.Train the CNN using the configured loss function and observe the results.
Notes
The Loss CCE node provides the categorical cross-entropy loss function for training a CNN.
It does not require any input sockets and outputs the loss function object.
The Loss CCE node expects the Keras library to be installed.
Connect the output of the Loss CCE node to the Loss Function input socket of other nodes in the network to define the loss function for training.
The categorical cross-entropy loss function is commonly used for multi-class classification problems.
Ensure that the CNN architecture and data pipeline are properly configured to work with the categorical cross-entropy loss function.
The Loss CCE node provides the loss function with the from_logits=True parameter, assuming the predicted values are logits.
The loss function can be used during the model compilation step to define the training objective for the CNN.

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