You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: If the above regularization methods are applied individually, they aresomewhat effective at producing more interpretable images; Figure 3 shows the effects of each individual hyperparameter.However, preliminary experiments uncovered that their combinedeffect produces better visualizations. To pick a reasonable set ofhyperparameters for all methods at once, we ran a randomhyperparameter search of 300 possible combinations and settled on fourthat complement each other well. The four selected combinations arelisted in Table 1 and optimized images using each are shown for the “Gorilla” class output unit in Figure 4. Of the four, some show highfrequency information, others low frequency; some contain densepixel data, and others contain only sparse outlines of importantregions.We found the version in the lower-left quadrant to be the best single set of hyperparameters, but often greater intuition canbe gleaned by considering all four at once.Figure 5 shows the optimization results computed for a selection of units on all layers. A single image for every filter of all five convolutional layers is shown in Supplementary Figure S1. Nine images for each filter of all layers, including each of the 1000 ImageNet output classes, can be viewed at http://yosinski.com/deepvis.

Question: How many hyperparameter combinations were used for the random hyperparameter search?
300 sets of possible hyperparameter combinations then choose four of them that complement each other well.