def make_ip(alg, s_config, h_config, n):
if alg == 'hack':
rval = E_Step_Scan(
clip_reflections=True,
rho=0.5,
h_new_coeff_schedule=[h_config] * n,
s_new_coeff_schedule=[s_config] * n,
)
elif alg == 'cg':
rval = E_Step_CG_Scan(h_new_coeff_schedule=[h_config] * n,
s_max_iters=[s_config] * n)
else:
assert False
global model
rval.register_model(model)
return rval
def make_ip(alg, s_config, h_config, n):
if alg == 'hack':
rval = E_Step_Scan(
clip_reflections = True,
rho = 0.5,
h_new_coeff_schedule = [ h_config ] * n,
s_new_coeff_schedule = [ s_config ] * n,
)
elif alg == 'cg':
rval = E_Step_CG_Scan(
h_new_coeff_schedule = [ h_config ] * n,
s_max_iters = [ s_config ] * n)
else:
assert False
global model
rval.register_model(model)
return rval
init_bias_hid=init_bias_hid,
init_mu=init_mu,
init_alpha=init_alpha,
init_B=init_B,
irange=None,
min_B=.01,
max_B=1e6,
min_alpha=.01,
max_alpha=1e6,
e_step=None,
m_step=Grad_M_Step())
#this initial E step is just to help out the
#BatchGradientInference object
model.e_step = E_Step_Scan(clip_reflections=True,
rho=0.5,
h_new_coeff_schedule=[.1] * OVERKILL,
s_new_coeff_schedule=[.3] * OVERKILL)
model.e_step.register_model(model)
import theano.tensor as T
from theano import function
def get_needed_steps(ip, X, target, tol):
V = T.matrix()
history = ip.infer(V, return_history=True)
kls = [ip.truncated_KL(V, history_elem).mean() for history_elem in history]
W[:,i] = means[i]/norms[i]
init_mu = np.asarray(norms)
model = checked_call(S3C,
dict(nvis = X.shape[1],
nhid = len(means),
init_bias_hid = inverse_sigmoid_numpy(np.asarray([ (y==i).mean() for i in xrange(y.max()+1)])),
irange = 0.,
min_B = .1,
max_B = 1e6,
min_alpha = .1,
max_alpha = 1e6,
m_step = checked_call(Grad_M_Step, dict(learning_rate = 1.)),
init_mu = init_mu,
init_alpha = init_mu * 10.,
init_B = init_beta,
e_step = E_Step_Scan(
h_new_coeff_schedule = [ .1 ] * 50,
s_new_coeff_schedule = [ .1 ] * 50,
clip_reflections = 1,
rho = 0.5
))
)
model.W.set_value(W)
model.dataset_yaml_src = '!obj:pylearn2.datasets.mnist.MNIST { which_set : "train", center : 0 }'
serial.save('/u/goodfeli/galatea/pddbm/config/mnist/s3c_hack.pkl',model)
