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Training Parameters

Training Parameters Node Documentation
The Training Parameters node in AugeLab Studio represents the parameters used for training a deep learning model.
Node Overview
The Training Parameters node allows you to specify the parameters for training a deep learning model, such as the batch size and the number of epochs.
Inputs
The Training Parameters node does not require any inputs.
Outputs
The Training Parameters node outputs a dictionary containing the training parameters.
Node Interaction
1.Drag and drop the Training Parameters node from the node library onto the canvas in AugeLab Studio.
2.Configure the node properties:
Batch Size: Specify the number of samples per gradient update. This value determines the number of samples that will be propagated through the network at once.
Epochs: Specify the number of times the entire training dataset will be passed through the network during training.
3.The training parameters dictionary will be created based on the specified configuration.
4.Connect the output training parameters dictionary to the appropriate nodes for training the deep learning model, such as the Model Training node.
Implementation Details
The Training Parameters node is implemented as a subclass of the NodeCNN base class. It overrides the evalAi method to create and output the training parameters dictionary.
The node reads the input values for the batch size and the number of epochs.
The training parameters dictionary is created using the specified configuration.
The created dictionary is returned as the output.
Usage
1.Drag and drop the Training Parameters node from the node library onto the canvas in AugeLab Studio.
2.Configure the node properties:
Batch Size: Specify the number of samples per gradient update.
Epochs: Specify the number of times the entire training dataset will be passed through the network during training.
3.The training parameters dictionary will be created based on the specified configuration.
4.Connect the output training parameters dictionary to the appropriate nodes for training the deep learning model, such as the Model Training node.
5.Continue building and training the deep learning model by connecting other nodes as needed.
Notes
The Training Parameters node allows you to specify the parameters for training a deep learning model.
The batch size determines the number of samples that will be propagated through the network at once during training.
The number of epochs defines the number of times the entire training dataset will be passed through the network during training.
The training parameters are important for controlling the learning process of a deep learning model.
Adjusting the batch size and the number of epochs can have an impact on the training time and the model's performance.
Connect the output training parameters dictionary to other nodes for training the deep learning model, such as the Model Training node.
The Training Parameters node is typically used in combination with other nodes in the training workflow, such as the Data Preparation node and the Model Building node.
Experiment with different batch sizes and epochs to find the optimal training configuration for your specific deep learning task.
The Training Parameters node provides flexibility in defining the training parameters to meet the requirements of your deep learning project.

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