Optimizer · Apache SINGA
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EditOptimizer
SINGA supports various popular optimizers including stochastic gradient descent
with momentum, Adam, RMSProp, and AdaGrad, etc. For each of the optimizer, it
supports to use a decay schedular to schedule the learning rate to be applied in
different epochs. The optimizers and the decay schedulers are included in
singa/opt.py.
Create an optimizer
SGD with momentum
# define hyperparameter learning rate
lr = 0.001
# define hyperparameter momentum
momentum = 0.9
# define hyperparameter weight decay
weight_decay = 0.0001
from singa import opt
sgd = opt.SGD(lr=lr, momentum=momentum, weight_decay=weight_decay)
RMSProp
# define hyperparameter learning rate
lr = 0.001
# define hyperparameter rho
rho = 0.9
# define hyperparameter epsilon
epsilon = 1e-8
# define hyperparameter weight decay
weight_decay = 0.0001
from singa import opt
sgd = opt.RMSProp(lr=lr, rho=rho, epsilon=epsilon, weight_decay=weight_decay)
AdaGrad
# define hyperparameter learning rate
lr = 0.001
# define hyperparameter epsilon
epsilon = 1e-8
# define hyperparameter weight decay
weight_decay = 0.0001
from singa import opt
sgd = opt.AdaGrad(lr=lr, epsilon=epsilon, weight_decay=weight_decay)
Adam
# define hyperparameter learning rate
lr = 0.001
# define hyperparameter beta 1
beta_1= 0.9
# define hyperparameter beta 2
beta_1= 0.999
# define hyperparameter epsilon
epsilon = 1e-8
# define hyperparameter weight decay
weight_decay = 0.0001
from singa import opt
sgd = opt.Adam(lr=lr, beta_1=beta_1, beta_2=beta_2, epsilon=epsilon, weight_decay=weight_decay)
Create a Decay Scheduler
from singa import opt
# define initial learning rate
lr_init = 0.001
# define the rate of decay in the decay scheduler
decay_rate = 0.95
# define whether the learning rate schedule is a staircase shape
staircase=True
# define the decay step of the decay scheduler (in this example the lr is decreased at every 2 steps)
decay_steps = 2
# create the decay scheduler, the schedule of lr becomes lr_init * (decay_rate ^ (step // decay_steps) )
lr = opt.ExponentialDecay(0.1, 2, 0.5, True)
# Use the lr to create an optimizer
sgd = opt.SGD(lr=lr, momentum=0.9, weight_decay=0.0001)
Use the optimizer in Model API
When we create the model, we need to attach the optimizer to the model.
# create a CNN using the Model API
model = CNN()
# initialize optimizer and attach it to the model
sgd = opt.SGD(lr=0.005, momentum=0.9, weight_decay=1e-5)
model.set_optimizer(sgd)
Then, when we call the model, it runs the train_one_batch method that utilizes
the optimizer.
Hence, an example of an iterative loop to optimize the model is:
for b in range(num_train_batch):
# generate the next mini-batch
x, y = ...
# Copy the data into input tensors
tx.copy_from_numpy(x)
ty.copy_from_numpy(y)
# Training with one batch
out, loss = model(tx, ty)
Last updated on 4/7/2020
← AutogradModel →
Create an optimizer
Create a Decay Scheduler
Use the optimizer in Model API
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