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Training Parameters
The
Training Parameters
node in AugeLab Studio represents the parameters used for training a deep learning model.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.The
Training Parameters
node does not require any inputs.The
Training Parameters
node outputs a dictionary containing the training parameters.- 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.
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.
- 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.
- 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 theData Preparation
node and theModel 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.