def momentum(params, gradients, opts):
assert opts.momentum >= 0.0 and opts.momentum <= 1.0
updates = []
for param, gradient in zip(params, gradients):
velocity_t0 = util.zeros_in_the_shape_of(param)
velocity_t1 = opts.momentum * velocity_t0 - opts.learning_rate * gradient
updates.append((velocity_t0, velocity_t1))
updates.append((param, param + velocity_t1))
return updates
def rmsprop(params, gradients, opts):
assert opts.momentum
assert opts.momentum >= 0.0 and opts.momentum <= 1.0
updates = []
for param_t0, gradient in zip(params, gradients):
# rmsprop see slide 29 of http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf
# first the mean_sqr exponential moving average
mean_sqr_t0 = util.zeros_in_the_shape_of(param_t0)
mean_sqr_t1 = (opts.momentum * mean_sqr_t0) + ((1.0-opts.momentum) * gradient**2)
updates.append((mean_sqr_t0, mean_sqr_t1))
# update param surpressing gradient by this average
param_t1 = param_t0 - opts.learning_rate * (gradient / T.sqrt(mean_sqr_t1 + 1e-10))
updates.append((param_t0, param_t1))
return updates
