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Nn models sets8/4/2023 ![]() ![]() Setting a schedule to adjust your learning rate during trainingĪnother commonly employed technique, known as learning rate annealing, recommends starting with a relatively high learning rate and then gradually lowering the learning rate during training. This technique was proposed by Leslie Smith in Cyclical Learning Rates for Training Neural Networks and evangelized by Jeremy Howard in fast.ai's course. You should set the range of your learning rate bounds for this experiment such that you observe all three phases, making the optimal range trivial to identify. Remember, the best learning rate is associated with the steepest drop in loss, so we're mainly interested in analyzing the slope of the plot. Increasing the learning rate further will cause an increase in the loss as the parameter updates cause the loss to "bounce around" and even diverge from the minima. ![]() When entering the optimal learning rate zone, you'll observe a quick drop in the loss function. This gradual increase can be on either a linear or exponential scale.įor learning rates which are too low, the loss may decrease, but at a very shallow rate. We can observe this by performing a simple experiment where we gradually increase the learning rate after each mini batch, recording the loss at each increment. Ultimately, we'd like a learning rate which results is a steep decrease in the network's loss. A systematic approach towards finding the optimal learning rate I hope you'll see in the next section that this is quite an easy task. the defaults set by your deep learning library) may provide decent results, you can often improve the performance or speed up training by searching for an optimal learning rate. The optimal learning rate will be dependent on the topology of your loss landscape, which is in turn dependent on both your model architecture and your dataset. The images below are from a paper, Visualizing the Loss Landscape of Neural Nets, which shows how residual connections in a network can yield an smoother loss topology. This loss landscape can look quite different, even for very similar network architectures. The loss landscape of a neural network (visualized below) is a function of the network's parameter values quantifying the "error" associated with using a specific configuration of parameter values when performing inference (prediction) on a given dataset. (Humor yourself by reading through that thread after finishing this post.) (i just wanted to make sure that people understand that this is a joke.)- Andrej Karpathy November 24, 2016 So how do we find the optimal learning rate?ģe-4 is the best learning rate for Adam, hands down.- Andrej Karpathy November 24, 2016 I'll visualize these cases below - if you find these visuals hard to interpret, I'd recommend reading (at least) the first section in my post on gradient descent. However, if your learning rate is set too high, it can cause undesirable divergent behavior in your loss function. If your learning rate is set too low, training will progress very slowly as you are making very tiny updates to the weights in your network. As a reminder, this parameter scales the magnitude of our weight updates in order to minimize the network's loss function. One of the key hyperparameters to set in order to train a neural network is the learning rate for gradient descent. In previous posts, I've discussed how we can train neural networks using backpropagation with gradient descent. ![]()
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