You are training an LSTM-based model on AI Platform to summarize text using the following job submission script: gcloud ai-platform jobs submit training $JOB_NAME \ --package-path $TRAINER_PACKAGE_PATH \ --module-name $MAIN_TRAINER_MODULE \ --job-dir $JOB_DIR \ --region $REGION \ --scale-tier basic \ -- \ --epochs 20 \ --batch_size=32 \ --learning_rate=0.001 \ You want to ensure that training time is minimized without significantly compromising the accuracy of your model.
What should you do?
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A. B. C. D.C.
To minimize training time while preserving model accuracy, the most suitable parameter to modify in the given job submission script would be the batch size parameter (option C).
Here's why:
Epochs determine how many times the model will pass through the training data. A higher number of epochs can lead to better accuracy, but it also increases the training time. Reducing the number of epochs may result in faster training but may compromise the accuracy of the model.
The scale tier determines the type and number of machine instances used for training. Using a more powerful machine or more machines will increase the cost but may decrease the training time. However, modifying the scale tier won't have a direct impact on the hyperparameters of the model.
Learning rate determines the step size at each iteration while moving toward a minimum of a loss function. A higher learning rate can result in faster convergence, but it may cause the model to miss the optimal solution, leading to reduced accuracy. On the other hand, a lower learning rate can lead to better accuracy but slower convergence. Therefore, changing the learning rate may require tuning to find the optimal value that balances speed and accuracy.
The batch size determines how many examples the model processes in one iteration. A larger batch size may result in faster training, but it also requires more memory, and it may lead to suboptimal convergence or model overfitting. A smaller batch size requires less memory, but it also increases the number of iterations required to cover the same amount of training data, leading to slower training. Therefore, adjusting the batch size can have a significant impact on both training time and model performance.
In conclusion, modifying the batch size is the most suitable parameter to modify to minimize training time without significantly compromising the accuracy of the model.