How to split train and test datasets using validation_split in Keras
When training a deep learning model, it’s important to evaluate its performance on unseen data, to make sure it generalizes well to new examples. To do this, you need to split your data into a training set, which is used to train the model, a validation set, which is used to evaluate the model’s performance during training and a test set, which is used to evaluate the model’s performance after the training.
In Keras, you can use the validation_split argument in the fit() function to split your data into a training set and a validation set. This argument takes a float value between 0 and 1, which represents the percentage of data that should be allocated to the validation set. The remaining data will be allocated to the training set.
When you use the validation_split argument, you don’t need to explicitly split the data beforehand. Instead, Keras will automatically split the data for you when you call the fit() function. The validation set is used to monitor the performance of the model during the training process, so you can stop the training early if the performance of the model stops improving.
It’s important to note that you should use different datasets for validation and testing. The validation dataset is used to select the best performing model among different models that have been trained with different hyperparameters or architectures, while the test set is used to evaluate the performance of the final selected model.
In summary, to split train and test datasets using validation_split in Keras, you need to use the validation_split argument in the fit() function. The validation_split argument is a float value between 0 and 1, which represents the percentage of data that should be allocated to the validation set. The remaining data will be allocated to the training set. When you use the validation_split argument, you don’t need to explicitly split the data beforehand, and it is used to monitor the performance of the model during the training process, and can be used to stop the training early if the performance of the model stops improving. It is important to note that you should use different datasets for validation and testing, and the validation dataset is used to select the best performing model among different models that have been trained with different hyperparameters or architectures, while the test set is used to evaluate the performance of the final selected model.
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