1. In a deep neural network identifying objects classified to a plurality of categories, a deep neural network learning method of learning, using a computer, a category-independent sub-network used commonly for said plurality of categories, comprising: a step where the computer stores first, second and third sub-networks in a storage medium; and a sub-network training step where the computer trains said first, second and third sub-networks with training data belonging to first and second categories among said plurality of categories; wherein said sub-network training step includes a deep neural network training step of the computer training a first deep neural network formed by connecting said second sub-network to an output side of said first sub-network with training data belonging to said first category, and training a second deep neural network formed by connecting said third sub-network to an output side of said first sub-network with training data belonging to said second category, and thereby realizing learning of said first and second deep neural networks, and a storing step of the computer separating, after completion of said deep neural network training step, said first sub-network from other sub-networks and storing it as said category-independent sub-network in a storage medium.