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In this paper,\nwe use a recurrent network to generate the model descriptions of neural\nnetworks and train this RNN with reinforcement learning to maximize the\nexpected accuracy of the generated architectures on a validation set. On the\nCIFAR-10 dataset, our method, starting from scratch, can design a novel network\narchitecture that rivals the best human-invented architecture in terms of test\nset accuracy. Our CIFAR-10 model achieves a test error rate of 3.65, which is\n0.09 percent better and 1.05x faster than the previous state-of-the-art model\nthat used a similar architectural scheme. On the Penn Treebank dataset, our\nmodel can compose a novel recurrent cell that outperforms the widely-used LSTM\ncell, and other state-of-the-art baselines. Our cell achieves a test set\nperplexity of 62.4 on the Penn Treebank, which is 3.6 perplexity better than\nthe previous state-of-the-art model. The cell can also be transferred to the\ncharacter language modeling task on PTB and achieves a state-of-the-art\nperplexity of 1.214.","url_abs":"http://arxiv.org/abs/1611.01578v2","url_pdf":"http://arxiv.org/pdf/1611.01578v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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