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optimisers.py
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optimisers.py
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from __future__ import (division, absolute_import,
print_function, unicode_literals)
import numpy
import theano
import theano.tensor as tensor
profile=False
def itemlist(tparams):
return [v for k, v in tparams.items()]
# name(hyperp, tparams, grads, inputs (list), cost) = f_grad_shared, f_update
def adam(lr, tparams, grads, inp, cost):
gshared = [theano.shared(p.get_value() * 0.,
name='%s_grad' % k)
for k, p in tparams.items()]
gsup = [(gs, g) for gs, g in zip(gshared, grads)]
f_grad_shared = theano.function(inp, cost,
updates=gsup, profile=profile,
allow_input_downcast=True)
lr0 = 0.0002
b1 = 0.1
b2 = 0.001
e = 1e-8
updates = []
i = theano.shared(numpy.float32(0.))
i_t = i + 1.
fix1 = 1. - b1**(i_t)
fix2 = 1. - b2**(i_t)
lr_t = lr0 * (tensor.sqrt(fix2) / fix1)
for p, g in zip(tparams.values(), gshared):
m = theano.shared(p.get_value() * 0.)
v = theano.shared(p.get_value() * 0.)
m_t = (b1 * g) + ((1. - b1) * m)
v_t = (b2 * tensor.sqr(g)) + ((1. - b2) * v)
g_t = m_t / (tensor.sqrt(v_t) + e)
p_t = p - (lr_t * g_t)
updates.append((m, m_t))
updates.append((v, v_t))
updates.append((p, p_t))
updates.append((i, i_t))
f_update = theano.function([lr], [], updates=updates,
on_unused_input='ignore', profile=profile,
allow_input_downcast=True)
return f_grad_shared, f_update
def adadelta(lr, tparams, grads, inp, cost):
zipped_grads = [theano.shared(p.get_value() * numpy.float32(0.),
name='%s_grad' % k)
for k, p in tparams.items()]
running_up2 = [theano.shared(p.get_value() * numpy.float32(0.),
name='%s_rup2' % k)
for k, p in tparams.items()]
running_grads2 = [theano.shared(p.get_value() * numpy.float32(0.),
name='%s_rgrad2' % k)
for k, p in tparams.items()]
zgup = [(zg, g) for zg, g in zip(zipped_grads, grads)]
rg2up = [(rg2, 0.95 * rg2 + 0.05 * (g ** 2))
for rg2, g in zip(running_grads2, grads)]
f_grad_shared = theano.function(inp, cost, updates=zgup+rg2up,
profile=profile,
allow_input_downcast=True)
updir = [-tensor.sqrt(ru2 + 1e-6) / tensor.sqrt(rg2 + 1e-6) * zg
for zg, ru2, rg2 in
zip(zipped_grads, running_up2, running_grads2)]
ru2up = [(ru2, 0.95 * ru2 + 0.05 * (ud ** 2))
for ru2, ud in zip(running_up2, updir)]
param_up = [(p, p + ud) for p, ud in zip(itemlist(tparams), updir)]
f_update = theano.function([lr], [], updates=ru2up+param_up,
on_unused_input='ignore', profile=profile,
allow_input_downcast=True)
return f_grad_shared, f_update
def rmsprop(lr, tparams, grads, inp, cost):
zipped_grads = [theano.shared(p.get_value() * numpy.float32(0.),
name='%s_grad' % k)
for k, p in tparams.items()]
running_grads = [theano.shared(p.get_value() * numpy.float32(0.),
name='%s_rgrad' % k)
for k, p in tparams.items()]
running_grads2 = [theano.shared(p.get_value() * numpy.float32(0.),
name='%s_rgrad2' % k)
for k, p in tparams.items()]
zgup = [(zg, g) for zg, g in zip(zipped_grads, grads)]
rgup = [(rg, 0.95 * rg + 0.05 * g) for rg, g in zip(running_grads, grads)]
rg2up = [(rg2, 0.95 * rg2 + 0.05 * (g ** 2))
for rg2, g in zip(running_grads2, grads)]
f_grad_shared = theano.function(inp, cost, updates=zgup+rgup+rg2up,
profile=profile,
allow_input_downcast=True)
updir = [theano.shared(p.get_value() * numpy.float32(0.),
name='%s_updir' % k)
for k, p in tparams.iteritems()]
updir_new = [(ud, 0.9 * ud - 1e-4 * zg / tensor.sqrt(rg2 - rg ** 2 + 1e-4))
for ud, zg, rg, rg2 in zip(updir, zipped_grads, running_grads,
running_grads2)]
param_up = [(p, p + udn[1])
for p, udn in zip(itemlist(tparams), updir_new)]
f_update = theano.function([lr], [], updates=updir_new+param_up,
on_unused_input='ignore', profile=profile,
allow_input_downcast=True)
return f_grad_shared, f_update
def sgd(lr, tparams, grads, x, mask, y, cost):
gshared = [theano.shared(p.get_value() * 0., name='%s_grad' % k)
for k, p in tparams.items()]
gsup = [(gs, g) for gs, g in zip(gshared, grads)]
f_grad_shared = theano.function([x, mask, y], cost, updates=gsup,
profile=profile,
allow_input_downcast=True)
pup = [(p, p - lr * g) for p, g in zip(itemlist(tparams), gshared)]
f_update = theano.function([lr], [], updates=pup, profile=profile,
allow_input_downcast=True)
return f_grad_shared, f_update