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remove cruft from YellowFin

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i might just remove YellowFin itself because it isn't working for me.
This commit is contained in:
Connor Olding 2017-07-25 21:37:38 +00:00
parent 2cf38d4ece
commit e5fd937ef6
1 changed files with 17 additions and 43 deletions
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60
onn.py
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@ -183,11 +183,11 @@ class MomentumClip(Optimizer):
else:
return -self.lr * self.accum
yfalt = True # use computations from https://gist.github.com/botev/f8b32c00eafee222e47393f7f0747666
class YellowFin(Optimizer):
# paper: https://arxiv.org/abs/1706.03471
# knowyourmeme: http://cs.stanford.edu/~zjian/project/YellowFin/
# author's implementation: https://github.com/JianGoForIt/YellowFin/blob/master/tuner_utils/yellowfin.py
# code lifted: https://gist.github.com/botev/f8b32c00eafee222e47393f7f0747666
def __init__(self, lr=0.1, mu=0.0, beta=0.999, window_size=20,
debias=True, clip=1.0):
@ -226,44 +226,23 @@ class YellowFin(Optimizer):
self.mu_lpf = 0
def get_lr_mu(self):
if yfalt:
p = (np.square(self.dist_avg) * np.square(self.h_min)) / (2 * self.g_var)
w3 = p * (np.sqrt(0.25 + p / 27.0) - 0.5)
w = np.power(w3, 1/3)
y = w - p / (3 * w)
sqrt_mu1 = y + 1
p = (np.square(self.dist_avg) * np.square(self.h_min)) / (2 * self.g_var)
w3 = p * (np.sqrt(0.25 + p / 27.0) - 0.5)
w = np.power(w3, 1/3)
y = w - p / (3 * w)
sqrt_mu1 = y + 1
sqrt_h_min = np.sqrt(self.h_min)
sqrt_h_max = np.sqrt(self.h_max)
sqrt_mu2 = (sqrt_h_max - sqrt_h_min) / (sqrt_h_max + sqrt_h_min)
sqrt_h_min = np.sqrt(self.h_min)
sqrt_h_max = np.sqrt(self.h_max)
sqrt_mu2 = (sqrt_h_max - sqrt_h_min) / (sqrt_h_max + sqrt_h_min)
sqrt_mu = max(sqrt_mu1, sqrt_mu2)
if sqrt_mu2 > sqrt_mu1:
print('note: taking dr calculation. something may have exploded.')
sqrt_mu = max(sqrt_mu1, sqrt_mu2)
if sqrt_mu2 > sqrt_mu1:
print('note: taking dr calculation. something may have exploded.')
lr = np.square(1 - sqrt_mu) / self.h_min
mu = np.square(sqrt_mu)
return lr, mu
else:
const_fact = np.square(self.dist_avg) * np.square(self.h_min) / 2 / self.g_var
assert const_fact > -1e-7, "invalid factor"
coef = [-1.0, 3.0, -(3.0 + const_fact), 1.0]
roots = np.roots(coef) # note: returns a list of np.complex64.
roots = roots[np.logical_and(np.real(roots) > 0, np.real(roots) < 1)]
root = roots[np.argmin(np.imag(roots))]
assert np.absolute(root.imag) < 1e-5
real_root = np.real(root)
dr_sqrt = np.sqrt(self.h_max / self.h_min)
a, b = np.square((dr_sqrt - 1) / (dr_sqrt + 1)), np.square(real_root)
mu = max(a, b)
if a > b:
print('note: taking dr calculation. something may have exploded.')
lr_min = np.square(1 - np.sqrt(mu)) / self.h_min
#lr_max = np.square(1 + np.sqrt(mu)) / self.h_max
return lr_min, mu
lr = np.square(1 - sqrt_mu) / self.h_min
mu = np.square(sqrt_mu)
return lr, mu
def compute(self, dW, W):
if self.accum is None:
@ -278,10 +257,6 @@ class YellowFin(Optimizer):
#print("clipping gradients; norm: {:10.5f}".format(total_norm))
dW *= clip_scale
if not yfalt:
self.accum[:] = self.accum * self.mu + dW
V = -self.lr * self.accum
#fmt = 'W std: {:10.7f}e-3, dWstd: {:10.7f}e-3, V std: {:10.7f}e-3'
#print(fmt.format(np.std(W), np.std(dW) * 100, np.std(V) * 100))
@ -331,9 +306,8 @@ class YellowFin(Optimizer):
self.mu = debias * self.mu_lpf
self.lr = debias * self.lr_lpf
if yfalt:
self.accum[:] = self.accum * self.mu - self.lr * dW
V = self.accum
self.accum[:] = self.accum * self.mu - self.lr * dW
V = self.accum
self.step += 1
self.beta_t *= self.beta