A search framework for finding effective architectural building blocks for deep convolutional neural networks is disclosed. The search framework described herein utilizes a building block which incorporates branch and skip connections. At least some operations of the architecture of the building block are undefined and treated as hyperparameters which can be automatically selected and optimized for a particular task. The search framework uses random search over the reduced search space to generate a building block and repeats the building block multiple times to create a deep convolutional neural network.
: MetaQNN is a meta-modeling algorithm based on reinforcement learning that automatically generates high performing cnn architectures for a given learning task. The learning agent sequentially chooses neural network layers with layer types (e.g. convolutional, pooling, fully connected, termination) and their corresponding hyperparameters via @math -greedy policy until it reaches a termination state. The generated architecture is trained and validated with a held-out validation set. The validation performance is used as a reward to update the Q-learning network to generate better architectures over time. While this work extends the architecture generation ability of @cite_24 to more layer types, it still suffers from similar drawbacks resulting from the use of reinforcement learning which requires many samples to accurately learn a good policy.
@cite_24
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Neural networks are powerful and flexible models that work well for many difficult learning tasks in image, speech and natural language understanding. Despite their success, neural networks are still hard to design. In this paper, we use a recurrent network to generate the model descriptions of neural networks and train this RNN with reinforcement learning to maximize the expected accuracy of the generated architectures on a validation set. On the CIFAR-10 dataset, our method, starting from scratch, can design a novel network architecture that rivals the best human-invented architecture in terms of test set accuracy. Our CIFAR-10 model achieves a test error rate of 3.65, which is 0.09 percent better and 1.05x faster than the previous state-of-the-art model that used a similar architectural scheme. On the Penn Treebank dataset, our model can compose a novel recurrent cell that outperforms the widely-used LSTM cell, and other state-of-the-art baselines. Our cell achieves a test set perplexity of 62.4 on the Penn Treebank, which is 3.6 perplexity better than the previous state-of-the-art model. The cell can also be transferred to the character language modeling task on PTB and achieves a state-of-the-art perplexity of 1.214.