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The study aims to understand the how's and what's of creating an architectural dataset for generative adversarial nets through the evaluation of the effects of colors and shapes in image datasets on generative adversarial nets. Throughout the paper, six generative adversarial network training sessions are conducted on DCGAN and context-encoder algorithms with three different datasets having different complexities for colors and shapes. Firstly the color and shape complexities are analyzed for datasets. For color complexity, heuristic analyze is applied and for shape complexity, gray level occurrence matrix entropy which gives the textural complexity is utilized. In the end, the complexities and the training results are evaluated. Results show that color complexity has an important role for generative adversarial networks to generate colors correctly. Regularity in shape complexity /gray level co-occurrence matrix entropy distribution facilitates the algorithm training and shape generating processes.

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