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mlpfsel5.py
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mlpfsel5.py
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from theano import tensor
import theano
import numpy
from blocks.algorithms import Momentum, AdaDelta, RMSProp, Adam
from blocks.bricks import Rectifier, MLP, Softmax, Tanh, Bias
from blocks.initialization import IsotropicGaussian, Constant
from blocks.filter import VariableFilter
from blocks.roles import WEIGHT
from blocks.graph import ComputationGraph, apply_noise, apply_dropout
from datastream import RandomTransposeIt
step_rule_name = 'rmsprop'
learning_rate = 0.1
momentum = 0.9
if step_rule_name == 'adadelta':
step_rule = AdaDelta()
elif step_rule_name == 'rmsprop':
step_rule = RMSProp()
elif step_rule_name == 'momentum':
step_rule_name = "mom%s,%s" % (repr(learning_rate), repr(momentum))
step_rule = Momentum(learning_rate=learning_rate, momentum=momentum)
elif step_rule_name == 'adam':
step_rule = Adam()
else:
raise ValueError("No such step rule: " + step_rule_name)
ibatchsize = None
iter_scheme = RandomTransposeIt(ibatchsize, False, None, False)
valid_iter_scheme = RandomTransposeIt(ibatchsize, False, None, False)
w_noise_std = 0.03
r_dropout = 0.0
s_dropout = 0.0
i_dropout = 0.5
nparts = 10
part_r_proba = 0.4
reconstruction_penalty = 1
center_feats = True
normalize_feats = True
randomize_feats = False
train_on_valid = False
hidden_dims_0 = [5]
activation_functions_0 = [Tanh() for _ in hidden_dims_0]
hidden_dims_1 = []
activation_functions_1 = [Tanh() for _ in hidden_dims_1] + [None]
hidden_dims_2 = []
activation_functions_2 = [Tanh() for _ in hidden_dims_2]
n_inter = 8
inter_act_fun = Tanh()
dataset = 'ARCENE'
pt_freq = 10
param_desc = '%s-%s,%s,%d,%s-%dp%s-n%s-d%s,%s,%s-p%s-%s-%s-i%s' % (dataset,
repr(hidden_dims_0),
repr(hidden_dims_1),
n_inter,
repr(hidden_dims_2),
nparts,
repr(part_r_proba),
repr(w_noise_std),
repr(r_dropout), repr(s_dropout), repr(i_dropout),
repr(reconstruction_penalty),
('C' if center_feats else'') +
('N' if normalize_feats else '') +
('W' if train_on_valid else '') +
('R' if randomize_feats else ''),
step_rule_name,
repr(ibatchsize))
class Model(object):
def __init__(self, ref_data, output_dim):
ref_data_sh = theano.shared(numpy.array(ref_data, dtype=numpy.float32), name='ref_data')
# Construct the model
j = tensor.lvector('j')
x = tensor.fmatrix('x')
y = tensor.ivector('y')
last_outputs = []
s_dropout_vars = []
r_dropout_vars = []
i_dropout_vars = []
penalties = []
for i in range(nparts):
fs = numpy.random.binomial(1, part_r_proba, size=(ref_data.shape[1],))
input_dim = int(fs.sum())
fs_sh = theano.shared(fs)
r = ref_data_sh[j, :][:, fs_sh.nonzero()[0]]
mlp0 = MLP(activations=activation_functions_0,
dims=[input_dim] + hidden_dims_0, name='enc%d'%i)
mlp0r = MLP(activations=[None], dims=[hidden_dims_0[-1], input_dim], name='dec%d'%i)
mlp1 = MLP(activations=activation_functions_1,
dims=[hidden_dims_0[-1]] + hidden_dims_1 + [n_inter], name='inter_gen_%d'%i)
mlp2 = MLP(activations=activation_functions_2 + [None],
dims=[n_inter] + hidden_dims_2 + [output_dim],
name='end_mlp_%d'%i)
encod = mlp0.apply(r)
rprime = mlp0r.apply(encod)
inter_weights = mlp1.apply(encod)
ibias = Bias(n_inter, name='inter_bias_%d'%i)
inter = ibias.apply(tensor.dot(x, inter_weights))
inter = inter_act_fun.apply(inter)
out = mlp2.apply(inter)
penalties.append(tensor.sqrt(((rprime - r)**2).sum(axis=1)).mean()[None])
last_outputs.append(out)
r_dropout_vars.append(r)
s_dropout_vars = s_dropout_vars + (
VariableFilter(bricks=[Tanh], name='output')
(ComputationGraph([inter_weights]))
)
i_dropout_vars.append(inter)
# Initialize parameters
for brick in [mlp0, mlp0r, mlp1, mlp2, ibias]:
brick.weights_init = IsotropicGaussian(0.01)
brick.biases_init = Constant(0.001)
brick.initialize()
final = tensor.concatenate([x[:, :, None] for x in last_outputs], axis=2).mean(axis=2)
cost = Softmax().categorical_cross_entropy(y, final)
confidence = Softmax().apply(final)
pred = final.argmax(axis=1)
error_rate = tensor.neq(y, pred).mean()
# apply regularization
cg = ComputationGraph([cost, error_rate])
if w_noise_std != 0:
# - apply noise on weight variables
weight_vars = VariableFilter(roles=[WEIGHT])(cg)
cg = apply_noise(cg, weight_vars, w_noise_std)
if s_dropout != 0:
cg = apply_dropout(cg, s_dropout_vars, s_dropout)
if r_dropout != 0:
cg = apply_dropout(cg, r_dropout_vars, r_dropout)
if i_dropout != 0:
cg = apply_dropout(cg, i_dropout_vars, i_dropout)
[cost_reg, error_rate_reg] = cg.outputs
cost_reg = cost_reg + reconstruction_penalty * tensor.concatenate(penalties, axis=0).sum()
self.cost = cost
self.cost_reg = cost_reg
self.error_rate = error_rate
self.error_rate_reg = error_rate_reg
self.pred = pred
self.confidence = confidence