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[1506.01186] Cyclical Learning Rates for Training Neural Networks

Abstract: It is known that the learning rate is the most important hyper-parameter to tune for training deep neural networks. This paper describes a new method for setting the learning rate, named cyclical learning rates, which practically eliminates the need to experimentally find the best values and schedule for the global learning rates. Instead of monotonically decreasing the learning rate, this method lets the learning rate cyclically vary between reasonable boundary values.

Cycling learning rate - GitHub Pages

Cycling learning rate. 30 Jan 2018. CLR was proposed by Leslie Smith in 2015. It is an approach to LR adjustments where the value is cycled between a lower bound and upper bound. By nature, it is seen as a competitor to the adaptive LR approaches and hence used mostly with SGD.

Cyclical Learning Rates for Training Neural Networks | by ...

Cyclical Learning Rate is the main idea discussed in the paper Cyclical Learning Rates for Training Neural Networks. It is a recent variant of learning rate annealing.

Cyclical Learning Rates for Training Neural Networks – arXiv ...

It is known that the learning rate is the most important hyper-parameter to tune for training deep neural networks. This paper describes a new method for setting the learning rate, named cyclical learning rates, which practically eliminates the need to experimentally find the best values and schedule for the global learning rates. Instead of monotonically decreasing the learning rate, this method lets the learning rate cyclically vary between reasonable boundary values.

Improve Your ML Models Training. Cycling learning rates in ...

Using Cyclical Learning Rates you can dramatically reduce the number of experiments required to tune and find an optimal learning rate for your model. Now, instead of monotonically decreasing the...

Finding Good Learning Rate and The One Cycle Policy. | by ...

CIFAR -10: One Cycle for learning rate = 0.08–0.8 , batch size 512, weight decay = 1e-4 , resnet-56. As in figure , We start at learning rate 0.08 and make step of 41 epochs to reach learning rate of 0.8, then make another step of 41 epochs where we go back to learning rate 0.08.

The 1 Cycle Policy - Derek Chia

Picking the right learning rate at different iterations helps model to converge quickly. The 1cycle policy gives very fast results when training complex models. It follows the Cyclical Learning Rate (CLR) to obtain faster training time with regularization effect but with a slight modification. Specifically, it uses one cycle that is smaller than the total number of iterations/epochs and allow learning rate to decrease several orders of magnitude less than the initial learning rate for the ...

One Cycle & Cyclic Learning Rate for Keras - GitHub

OneCycle (lr_range = (0.01, 0.1), momentum_range = (0.95, 0.85), reset_on_train_begin = True, record_frq = 10) ocp_cb. test_run (1000) # plot out values of learning rate and momentum as a function of iteration (batch). 1000 is just for plotting. The actual iteration will be computed when model.fit or model.fit_generator is run.