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Loss CCE
The
Loss CCE
node in AugeLab Studio represents the categorical cross-entropy loss function for training a convolutional neural network (CNN) with Keras.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
The
Loss CCE
node does not have any input sockets. It is a terminal node that provides the loss function for the CNN.The
Loss CCE
node outputs the categorical cross-entropy loss function, which can be used as the loss function for training the CNN.- 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 theLoss Function
input socket of other nodes in the network.
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 theCategoricalCrossentropy
loss function with thefrom_logits=True
parameter. - The loss function is configured to handle multi-class classification problems and expects the predicted values to be logits.
- 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 theLoss 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.
- 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 theLoss 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 thefrom_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.