CNNClassifierTraining-v0 (experimental) (by @iaroslav-ai)
Agent selects an architecture of deep CNN classifier and training parameters such that it results in high accuracy.
CNNClassifierTraining-v0 is an unsolved environment, which means it does not have a specified reward threshold at which it's considered solved.
One step in this environment is a training of a deep network for 10 epochs, where architecture and training parameters are selected by an agent. One episode in this environment have a fixed size of 10 steps.
Training parameters that agent can adjust are learning rate, learning rate decay, momentum, batch size, l1 and l2 penalty coefficients. Agent can select up to 5 layers of CNN and up to 2 layers of fully connected layers. As a feedback, agent receives # of instances in a dataset and a validation accuracy for every step.
For CNN layers architecture selection is done with 5 x 2 matrix, sequence of rows in which corresponds to sequence of layers3 of CNN; For every row, if the first entry is > 0.5, then a layer is used with # of filters in [1 .. 128] chosen by second entry in the row, normalized to [0,1] range. Similarily, architecture of fully connected net on used on top of CNN is chosen by 2 x 2 matrix, with number of neurons in [1 ... 1024].
At the beginning of every episode, a dataset to train on is chosen at random. Datasets used are MNIST, CIFAR10, CIFAR100. Number of instances in datasets are chosen at random in range from around 100% to 5% such that adjustment of l1, l2 penalty coefficients makes more difference.
Some of the parameters of the dataset are not provided to the agent in order to make agent figure it out through experimentation during an episode.
Let the best accuracy achieved so far at every epoch be denoted as a; Then reward at every step is a + a*a. On the one hand side, this encourages fast selection of good architecture, as it improves cumulative reward over the episode. On the other hand side, improving best achieved accuracy is expected to quadratically improve cumulative reward, thus encouraging agent to find quickly architectrue and training parameters which lead to high accuracy.
As the number of labels increases, learning problem becomes more difficult for a fixed dataset size. In order to avoid for the agent to ignore more complex datasets, on which accuracy is low and concentrate on simple cases which bring bulk of reward, accuracy is normalized by the number of labels in a dataset.
This environment requires Keras with Theano or TensorFlow to run. When run on laptop gpu (GTX960M) one step takes on average 2 min.
|Algorithm||Best 100-episode performance||Submitted|