Learn By Example 309 | How to split train and test datasets in a Deep Leaning Model in Keras?
Splitting a dataset into a training and a test set is a crucial step when building a deep learning model. The training set is used to train the model and the test set is used to evaluate the model’s performance on unseen data.
In Keras, splitting a dataset into a training and a test set can be done using the “train_test_split” function from the “sklearn.model_selection” library. This function takes in the input data and the target data, and splits it into training and test sets with a specified ratio.
First, you will need to import the “train_test_split” function from the “sklearn.model_selection” library.
Next, you will need to specify the ratio of the data you want to allocate for the training set and for the test set. This is usually done by specifying the “test_size” parameter. For example, if you want to allocate 80% of the data for training and 20% for testing, you would set the “test_size” parameter to 0.2.
Then, you will need to pass in your input data and target data to the “train_test_split” function, along with the “test_size” parameter. The function will then return four arrays, two for the input data (training and test) and two for the target data (training and test).
Finally, you can use the returned arrays to train and test your model. The training data and target data will be used to train the model and the test data and target data will be used to evaluate the model’s performance on unseen data.
It’s important to note that the splitting process should be done randomly, to ensure that the model is not trained and tested on the same dataset and to minimize the chances of overfitting. This is a general description of how to split train and test datasets in a deep learning model in Keras, and is not an exhaustive guide.
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