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To generate output data format that exactly corresponds with 'keras.datasets.cifar100.load_data()' using a personal dataset, you need to follow these steps:

1. Prepare your personal dataset: This involves collecting and preparing your personal dataset in a format that is compatible with Keras. Your personal dataset should contain your image files and corresponding labels.

2. Load your personal dataset: You need to load your personal dataset using Keras. This can be done using a similar function as 'keras.datasets.cifar100.load_data()', depending on the format of your personal dataset.

3. Normalize the dataset: To ensure consistency with the CIFAR-100 dataset, you need to normalize your personal dataset. This involves scaling the values of your images to be between 0 and 1.

4. Reshape the dataset: You need to reshape your personal dataset to match the format of the CIFAR-100 dataset. This involves reshaping the image data to be (numsamples, 32, 32, 3) and the label data to be (numsamples,).

5. Split the dataset: Finally, you need to split your personal dataset into training and testing sets, just as in the CIFAR-100 dataset.

Once you have completed these steps, your personal dataset will be in the same format as the CIFAR-100 dataset, and you can use it in the same way as you would use the CIFAR-100 dataset in your machine learning models.