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ladder.py
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ladder.py
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import logging
import numpy as np
from collections import OrderedDict
import theano
import theano.tensor as T
from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams
from blocks.bricks.cost import SquaredError
from blocks.bricks.cost import CategoricalCrossEntropy, MisclassificationRate
from blocks.roles import PARAMETER, WEIGHT, BIAS, add_role
from blocks.graph import add_annotation, Annotation
from utils import shared_param, AttributeDict, apply_act
from blocks.bricks import Linear
from utils import Glorot, BNPARAM
logger = logging.getLogger('main.model')
floatX = theano.config.floatX
class LadderAE():
def __init__(self):
self.input_dim = 784
# self.denoising_cost_x = (500.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0)
# self.denoising_cost_x = (4000.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0)
self.denoising_cost_x = (1000, 10, 0.1, 0.1, 0.1, 0.1, 0.1)
self.noise_std = (0.3,) * 7
# self.noise_std = (0.55, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0)
self.default_lr = 0.002
self.shareds = OrderedDict()
self.rstream = RandomStreams(seed=1)
self.rng = np.random.RandomState(seed=1)
self.layers = [(0, (('fc', 784), 'relu')),
(1, (('fc', 1000), 'relu')),
(2, (('fc', 500), 'relu')),
(3, (('fc', 250), 'relu')),
(4, (('fc', 250), 'relu')),
(5, (('fc', 250), 'relu')),
(6, (('fc', 10), 'softmax'))]
def counter(self):
name = 'counter'
p = self.shareds.get(name)
update = []
if p is None:
p_max_val = np.float32(10)
p = self.shared(np.float32(1), name, role=BNPARAM)
p_max = self.shared(p_max_val, name + '_max', role=BNPARAM)
update = [(p, T.clip(p + np.float32(1),
np.float32(0),
p_max)),
(p_max, p_max_val)]
return (p, update)
def annotate_bn(self, var, id, var_type, mb_size, size):
var_shape = np.array((1, size))
out_dim = np.prod(var_shape) / np.prod(var_shape[0])
# Flatten the var - shared variable updating is not trivial otherwise,
# as theano seems to believe a row vector is a matrix and will complain
# about the updates
orig_shape = var.shape
var = var.flatten()
# Here we add the name and role, the variables will later be identified
# by these values
var.name = id + '_%s_clean' % var_type
add_role(var, BNPARAM)
shared_var = self.shared(np.zeros(out_dim),
name='shared_%s' % var.name, role=None)
# Update running average estimates. When the counter is reset to 1, it
# will clear its memory
cntr, c_up = self.counter()
one = np.float32(1)
run_avg = lambda new, old: one / cntr * new + (one - one / cntr) * old
if var_type == 'mean':
new_value = run_avg(var, shared_var)
elif var_type == 'var':
mb_size = T.cast(mb_size, 'float32')
new_value = run_avg(mb_size / (mb_size - one) * var, shared_var)
else:
raise NotImplemented('Unknown batch norm var %s' % var_type)
def annotate_update(update, tag_to):
a = Annotation()
for (var, up) in update:
a.updates[var] = up
add_annotation(tag_to, a)
# Add the counter update to the annotated update if it is the first
# instance of a counter
annotate_update([(shared_var, new_value)] + c_up, var)
return var.reshape(orig_shape)
def shared(self, init, name, cast_float32=True, role=PARAMETER, **kwargs):
p = self.shareds.get(name)
if p is None:
p = shared_param(init, name, cast_float32, role, **kwargs)
self.shareds[name] = p
return p
def new_activation_dict(self):
return AttributeDict({'z': {}, 'h': {}, 's': {}, 'm': {}})
def encoder(self, input_, noise_std):
z = input_
d = self.new_activation_dict()
z = z + (self.rstream.normal(size=z.shape).astype(floatX) *
noise_std[0])
d.z[0] = z
h = z
d.h[0] = h
prev_dim = self.input_dim
for i, (spec, act_f) in self.layers[1:]:
layer_type, dim = spec
noise = noise_std[i] if i < len(noise_std) else 0.
z, m, s, h = self.f(h, prev_dim, layer_type, dim,
i, act_f, noise)
self.layer_dims[i] = dim
d.z[i] = z
d.s[i] = s
d.m[i] = m
d.h[i] = h
prev_dim = dim
return d
def decoder(self, clean, corr, batch_size):
get_unlabeled = lambda x: x[batch_size:] if x is not None else x
est = self.new_activation_dict()
costs = AttributeDict()
costs.denois = AttributeDict()
for i, ((_, spec), act_f) in self.layers[::-1]:
z_corr = get_unlabeled(corr.z[i])
z_clean = get_unlabeled(clean.z[i])
z_clean_s = get_unlabeled(clean.s.get(i))
z_clean_m = get_unlabeled(clean.m.get(i))
# It's the last layer
if i == len(self.layers) - 1:
fspec = (None, None)
ver = get_unlabeled(corr.h[i])
ver_dim = self.layer_dims[i]
top_g = True
else:
fspec = self.layers[i + 1][1][0]
ver = est.z.get(i + 1)
ver_dim = self.layer_dims.get(i + 1)
top_g = False
z_est = self.g(z_lat=z_corr,
z_ver=ver,
in_dims=ver_dim,
out_dims=self.layer_dims[i],
num=i,
fspec=fspec,
top_g=top_g)
# For semi-supervised version
if z_clean_s:
z_est_norm = (z_est - z_clean_m) / z_clean_s
else:
z_est_norm = z_est
z_est_norm = z_est
se = SquaredError('denois' + str(i))
costs.denois[i] = se.apply(z_est_norm.flatten(2),
z_clean.flatten(2)) \
/ np.prod(self.layer_dims[i], dtype=floatX)
costs.denois[i].name = 'denois' + str(i)
# Store references for later use
est.z[i] = z_est
est.h[i] = apply_act(z_est, act_f)
est.s[i] = None
est.m[i] = None
return est, costs
def apply(self, input_lb, input_un, target):
batch_size = input_lb.shape[0]
get_labeled = lambda x: x[:batch_size] if x is not None else x
input = T.concatenate([input_lb, input_un], axis=0)
self.layer_dims = {0: self.input_dim}
self.lr = self.shared(self.default_lr, 'learning_rate', role=None)
top = len(self.layers) - 1
clean = self.encoder(input, noise_std=[0])
corr = self.encoder(input, noise_std=self.noise_std)
ests, costs = self.decoder(clean, corr, batch_size)
# Costs
y = target.flatten()
costs.class_clean = CategoricalCrossEntropy().apply(
y, get_labeled(clean.h[top]))
costs.class_clean.name = 'CE_clean'
costs.class_corr = CategoricalCrossEntropy().apply(
y, get_labeled(corr.h[top]))
costs.class_corr.name = 'CE_corr'
costs.total = costs.class_corr * 1.0
for i in range(len(self.layers)):
costs.total += costs.denois[i] * self.denoising_cost_x[i]
costs.total.name = 'Total_cost'
self.costs = costs
# Classification error
mr = MisclassificationRate()
self.error = mr.apply(y, get_labeled(clean.h[top])) * np.float32(100.)
self.error.name = 'Error_rate'
def rand_init(self, in_dim, out_dim):
return self.rng.randn(in_dim, out_dim) / np.sqrt(in_dim)
def apply_layer(self, layer_type, input_, in_dim, out_dim, layer_name):
# Since we pass this path twice (clean and corr encoder), we
# want to make sure that parameters of both layers are shared.
layer = self.shareds.get(layer_name)
if layer is None:
if layer_type == 'fc':
linear = Linear(use_bias=False,
name=layer_name,
input_dim=in_dim,
output_dim=out_dim,
seed=1)
linear.weights_init = Glorot(self.rng, in_dim, out_dim)
linear.initialize()
layer = linear
self.shareds[layer_name] = layer
return layer.apply(input_)
def f(self, h, in_dim, layer_type, dim, num, act_f, noise_std):
layer_name = 'f_' + str(num) + '_'
z = self.apply_layer(layer_type, h, in_dim, dim, layer_name)
m = s = None
m = z.mean(0, keepdims=True)
s = z.var(0, keepdims=True)
# if noise_std == 0:
# m = self.annotate_bn(m, layer_name + 'bn', 'mean',
# z.shape[0], dim)
# s = self.annotate_bn(s, layer_name + 'bn', 'var',
# z.shape[0], dim)
z = (z - m) / T.sqrt(s + np.float32(1e-10))
z_lat = z + self.rstream.normal(size=z.shape).astype(
floatX) * noise_std
z = z_lat
# Add bias
if act_f != 'linear':
z += self.shared(0.0 * np.ones(dim), layer_name + 'b',
role=BIAS)
# Add Gamma parameter if necessary. (Not needed for all act_f)
if (act_f in ['sigmoid', 'tanh', 'softmax']):
c = self.shared(1.0 * np.ones(dim), layer_name + 'c',
role=WEIGHT)
z *= c
h = apply_act(z, act_f)
return z_lat, m, s, h
def g(self, z_lat, z_ver, in_dims, out_dims, num, fspec, top_g):
f_layer_type, dims = fspec
layer_name = 'g_' + str(num) + '_'
in_dim = np.prod(dtype=floatX, a=in_dims)
out_dim = np.prod(dtype=floatX, a=out_dims)
if top_g:
u = z_ver
else:
u = self.apply_layer(f_layer_type, z_ver,
in_dim, out_dim, layer_name)
u -= u.mean(0, keepdims=True)
u /= T.sqrt(u.var(0, keepdims=True) + np.float32(1e-10))
z_lat = z_lat.flatten(2)
bi = lambda inits, name: self.shared(inits * np.ones(out_dim),
layer_name + name, role=BIAS)
wi = lambda inits, name: self.shared(inits * np.ones(out_dim),
layer_name + name, role=WEIGHT)
type_ = 'wierd'
if type_ == 'wierd':
sigval = (bi(0., 'c1') +
wi(1., 'c2') * z_lat +
wi(0., 'c3') * u +
wi(0., 'c4') * z_lat * u)
sigval = T.nnet.sigmoid(sigval)
z_est = (bi(0., 'a1') +
wi(1., 'a2') * z_lat +
wi(0., 'a3') * u +
wi(0., 'a4') * z_lat * u +
wi(1., 'b1') * sigval)
elif type_ == 'simple':
# if num != 6:
# z_lat = z_lat * 0.0
# wu = wi(1., 'a3') * u
# else:
wu = wi(0., 'a3') * u
wz = wi(1., 'a2') * z_lat
wzu = wi(0., 'a4') * z_lat * u
z_est = (bi(0., 'a1') +
wz +
wu +
wzu)
elif type_ == 'yoshua':
wz = wi(1., 'a2') * z_lat
wu = wi(0., 'a3') * u
b = wi(1., 'b1')
batch_size = u[:, 0:1].shape
srng = T.shared_randomstreams.RandomStreams(
self.rng.randint(999999))
mask = srng.binomial(n=1, p=0.5, size=batch_size)
mask = T.addbroadcast(mask, 1)
z_est = (mask * wz + (1 - mask) * wu) + b
if (type(out_dims) == tuple and
len(out_dims) > 1.0 and z_est.ndim < 4):
z_est = z_est.reshape((z_est.shape[0],) + out_dims)
return z_est