We present a deep neural network for generating a multi-depth hologram and its training strategy. The proposed network takes multiple images of different depths as inputs and calculates the complex hologram as an output, which reconstructs each input image at the corresponding depth. We design a structure of the proposed network and develop the dataset compositing method to train the network effectively. The dataset consists of multiple input intensity profiles and their propagated holograms. Rather than simply training random speckle images and their propagated holograms, we generate the training dataset by adjusting the density of the random dots or combining basic shapes to the dataset such as a circle. The proposed dataset composition method improves the quality of reconstructed images by the holograms generated by the network, called deep learning holograms (DLHs). To verify the proposed method, we numerically and optically reconstruct the DLHs. The results confirmed that the DLHs can reconstruct clear images at multiple depths similar to conventional multi-depth computer-generated holograms. To evaluate the performance of the DLH quantitatively, we compute the peak signal-to-noise ratio of the reconstructed images and analyze the reconstructed intensity patterns with various methods.