This is a TensorFlow implementation of the M-SVAG and SVAG optimization algorithms described in the paper Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients
Install via
pip install git+https://github.com/lballes/msvag.git
msvag requires a TensorFlow installation (the code has been tested for versions 1.4 and higher), but this is not currently enforced in the setup.py to allow for either the CPU or the GPU version.
The msvag module contains the two classes MSVAGOptimizer and SVAGOptimizer, which inherit from tf.train.Optimizer and can be used as direct drop-in replacement for TensorFlow's built-in optimizers.
from msvag import MSVAGOptimizer
loss = ...
opt = MSVAGOptimizer(learning_rate=0.1, beta=0.9)
step = opt.minimize(loss)
with tf.Session() as sess:
sess.run([loss, step])
SVAG and M-SVAG have two hyper-parameters: a learning rate (learning_rate) and a moving average constant (beta). The default value beta=0.9 should work for most problems.
We give a short description of the two algorithms, ignoring various details. Please refer to the paper for a complete description.
M-SVAG and SVAG maintain exponential moving averages of past stochastic gradients and their element-wise square
m = beta*m + (1-beta)*g
v = beta*v + (1-beta)*g**2
and obtain an estimate of the stochastic gradient variance via
s = (v-m**2)/(1-rho)
where rho is a scalar factor (see paper). We then compute variance adaptation factors
gamma = m**2/(m**2 + rho*s) # for M-SVAG
gamma = m**2/(m**2 + s) # for SVAG
and update
theta = theta - learning_rate*gamma*m # for M-SVAG
theta = theta - learning_rate*gamma*g # for SVAG
If you have any questions or suggestions regarding this implementation, please open an issue in lballes/msvag. Apart from that, we welcome any feedback regarding the performance of (M-)SVAG on your training problems (mail to lballes@tue.mpg.de).
If you use (M-)SVAG for your research, please cite the paper.