Learner

This chapter will introduce the learner in SPAIC. Recently, SPAIC supports STCA, STBP, STDP and R-STDP algorithms. STCA and STBP use BPTT by surrogate gradient. STDP is the classical unsupervised algorithms that use synaptic plasticity. R-STDP has a reward on STDP that suitable for reinforcement learning.

self.learner = spaic.Learner(trainable=self, algorithm='STCA', alpha=0.5)
self.learner.set_optimizer('Adam', 0.001)
self.learner.set_schedule('StepLR', 0.01)

In the sample code, we use STCA learning algorithm, users need to use trainable to specify the training target. self represent the whole network. If user doesn’t want to train the whole network, can specify the target such as self.layer1 or [self.layer1, self.layer2] . And the last alpha=0.5 is a parameters of STCA learning algorithm. In SPAIC, all the parameters of algorithms should be provided at the end of the function.

In the sample code, we also use Adam optimization algorithm and StepLR learning rate scheduler. SPAIC also has some other optimization algorithms:

‘Adam’, ‘AdamW’, ‘SparseAdam’, ‘Adamx’, ‘ASGD’, ‘LBFGS’, ‘RMSprop’, ‘Rpop’, ‘SGD’,‘Adadelta’, ‘Adagrad’

and learning rate schedulers:

‘LambdaLR’, ‘StepLR’, ‘MultiStepLR’, ‘ExponentialLR’, ‘CosineAnnealingLR’, ‘ReduceLROnPlateau’, ‘CyclicLR’, ‘CosineAnnealingWarmRestarts’

Note

To RSTDPET learning algorithm, the batch_size should be 1.