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mlpfsel2ae.py
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mlpfsel2ae.py
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from theano import tensor
import theano
import numpy
from theano.tensor.shared_randomstreams import RandomStreams
from blocks.algorithms import Momentum, AdaDelta, RMSProp
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
import ber as balanced_error_rate
step_rule_name = 'adadelta'
learning_rate = 0.1
momentum = 0.
decay_rate = 0.9
if step_rule_name == 'adadelta':
step_rule = AdaDelta(decay_rate=decay_rate)
step_rule_name = 'adadelta%s' % repr(decay_rate)
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)
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)
r_noise_std = 0.01
w_noise_std = 0.00
r_dropout = 0.0
x_dropout = 0.0
s_dropout = 0.0
i_dropout = 0.0
a_dropout = 0.0
s_l1pen = 0.02
i_l1pen = 0.00
a_l1pen = 0.000
ae_dims = [100, 100]
ae_f_noise_std = 0.02
ae_l1_pen = 0.01
ae_activations = [Tanh() for _ in ae_dims]
center_feats = True
normalize_feats = True
randomize_feats = False
train_on_valid = False
hidden_dims = []
activation_functions = [Tanh() for _ in hidden_dims] + [None]
hidden_dims_2 = []
activation_functions_2 = [Tanh() for _ in hidden_dims_2]
n_inter = 10
inter_bias = None # -5
inter_act_fun = Tanh()
dataset = 'ARCENE'
pt_freq = 10
param_desc = '%s-%s,%d,%s-%s-n%s,%s-d%s,%s,%s,%s,%s-L1:%s,%s,%s-AE%sn%sp%s-%s-%s-i%s' % (dataset,
repr(hidden_dims),
n_inter,
repr(hidden_dims_2),
repr(inter_bias),
repr(r_noise_std), repr(w_noise_std),
repr(r_dropout),
repr(x_dropout),
repr(s_dropout),
repr(i_dropout),
repr(a_dropout),
repr(s_l1pen), repr(i_l1pen), repr(a_l1pen),
repr(ae_dims), repr(ae_f_noise_std), repr(ae_l1_pen),
('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):
input_dim = ref_data.shape[1]
ref_data_sh = theano.shared(numpy.array(ref_data, dtype=numpy.float32), name='ref_data')
rng = RandomStreams()
ae_bricks = []
ae_input = ref_data_sh
ae_costs = []
for i, (idim, odim) in enumerate(zip([input_dim] + ae_dims[:-1], ae_dims)):
ae_mlp = MLP(activations=[ae_activations[i]],
dims=[idim, odim],
name='enc%i'%i)
enc = ae_mlp.apply(ae_input)
enc_n = ae_mlp.apply(ae_input + rng.normal(size=ae_input.shape, std=ae_f_noise_std))
ae_mlp_dec = MLP(activations=[ae_activations[i]],
dims=[odim, idim],
name='dec%i'%i)
dec = ae_mlp_dec.apply(enc_n)
cost = tensor.sqrt(((ae_input - dec) ** 2).sum(axis=1)).mean() + \
ae_l1_pen * abs(enc).sum(axis=1).mean()
ae_costs.append(cost)
ae_input = enc
ae_bricks = ae_bricks + [ae_mlp, ae_mlp_dec]
self.ae_costs = ae_costs
ref_data_enc = ae_input
# Construct the model
j = tensor.lvector('j')
r = ref_data_enc[j, :]
x = tensor.fmatrix('x')
y = tensor.ivector('y')
# input_dim must be nr
mlp = MLP(activations=activation_functions,
dims=[ae_dims[-1]] + hidden_dims + [n_inter], name='inter_gen')
mlp2 = MLP(activations=activation_functions_2 + [None],
dims=[n_inter] + hidden_dims_2 + [output_dim],
name='end_mlp')
inter_weights = mlp.apply(r)
if inter_bias == None:
ibias = Bias(n_inter)
ibias.biases_init = Constant(0)
ibias.initialize()
inter = ibias.apply(tensor.dot(x, inter_weights))
else:
inter = tensor.dot(x, inter_weights) - inter_bias
inter = inter_act_fun.apply(inter)
final = mlp2.apply(inter)
cost = Softmax().categorical_cross_entropy(y, final)
confidence = Softmax().apply(final)
pred = final.argmax(axis=1)
# error_rate = tensor.neq(y, pred).mean()
ber = balanced_error_rate.ber(y, pred)
# Initialize parameters
for brick in ae_bricks + [mlp, mlp2]:
brick.weights_init = IsotropicGaussian(0.01)
brick.biases_init = Constant(0.001)
brick.initialize()
# apply regularization
cg = ComputationGraph([cost, ber])
if r_dropout != 0:
# - dropout on input vector r : r_dropout
cg = apply_dropout(cg, [r], r_dropout)
if x_dropout != 0:
cg = apply_dropout(cg, [x], x_dropout)
if s_dropout != 0:
# - dropout on intermediate layers of first mlp : s_dropout
s_dropout_vars = list(set(VariableFilter(bricks=[Tanh], name='output')
(ComputationGraph([inter_weights])))
- set([inter_weights]))
cg = apply_dropout(cg, s_dropout_vars, s_dropout)
if i_dropout != 0:
# - dropout on input to second mlp : i_dropout
cg = apply_dropout(cg, [inter], i_dropout)
if a_dropout != 0:
# - dropout on hidden layers of second mlp : a_dropout
a_dropout_vars = list(set(VariableFilter(bricks=[Tanh], name='output')
(ComputationGraph([final])))
- set([inter_weights]) - set(s_dropout_vars))
cg = apply_dropout(cg, a_dropout_vars, a_dropout)
if r_noise_std != 0:
cg = apply_noise(cg, [r], r_noise_std)
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)
[cost_reg, ber_reg] = cg.outputs
if s_l1pen != 0:
s_weights = VariableFilter(bricks=mlp.linear_transformations, roles=[WEIGHT])(cg)
cost_reg = cost_reg + s_l1pen * sum(abs(w).sum() for w in s_weights)
if i_l1pen != 0:
cost_reg = cost_reg + i_l1pen * abs(inter).sum()
if a_l1pen != 0:
a_weights = VariableFilter(bricks=mlp2.linear_transformations, roles=[WEIGHT])(cg)
cost_reg = cost_reg + a_l1pen * sum(abs(w).sum() for w in a_weights)
self.cost = cost
self.cost_reg = cost_reg
self.ber = ber
self.ber_reg = ber_reg
self.pred = pred
self.confidence = confidence