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import theano
from theano import tensor
import numpy
from theano.tensor.shared_randomstreams import RandomStreams
from blocks.algorithms import Momentum, AdaDelta, RMSProp
from blocks.bricks import Tanh, Softmax, Linear, MLP, Initializable
from blocks.bricks.lookup import LookupTable
from blocks.bricks.recurrent import LSTM, BaseRecurrent, recurrent
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
rng = RandomStreams()
# An epoch will be composed of 'num_seqs' sequences of len 'seq_len'
# divided in chunks of lengh 'seq_div_size'
num_seqs = 50
seq_len = 2000
seq_div_size = 100
io_dim = 256
# Model structure
hidden_dims = [512, 512, 512, 512, 512]
activation_function = Tanh()
cond_cert = [0.5, 0.5, 0.5, 0.5]
block_prob = [0.1, 0.1, 0.1, 0.1]
# Regularization
w_noise_std = 0.02
# Step rule
step_rule = 'adadelta'
learning_rate = 0.1
momentum = 0.9
param_desc = '%s(x%sp%s)-n%s-%dx%d(%d)-%s' % (
repr(hidden_dims), repr(cond_cert), repr(block_prob),
repr(w_noise_std),
num_seqs, seq_len, seq_div_size,
step_rule
)
save_freq = 5
on_irc = False
# parameters for sample generation
sample_len = 200
sample_temperature = 0.7 #0.5
sample_freq = 1
if step_rule == 'rmsprop':
step_rule = RMSProp()
elif step_rule == 'adadelta':
step_rule = AdaDelta()
elif step_rule == 'momentum':
step_rule = Momentum(learning_rate=learning_rate, momentum=momentum)
else:
assert(False)
class CCHLSTM(BaseRecurrent, Initializable):
def __init__(self, io_dim, hidden_dims, cond_cert, activation=None, **kwargs):
super(CCHLSTM, self).__init__(**kwargs)
self.cond_cert = cond_cert
self.io_dim = io_dim
self.hidden_dims = hidden_dims
self.children = []
self.layers = []
self.softmax = Softmax()
self.children.append(self.softmax)
for i, d in enumerate(hidden_dims):
i0 = LookupTable(length=io_dim,
dim=4*d,
name='i0-%d'%i)
self.children.append(i0)
if i > 0:
i1 = Linear(input_dim=hidden_dims[i-1],
output_dim=4*d,
name='i1-%d'%i)
self.children.append(i1)
else:
i1 = None
lstm = LSTM(dim=d, activation=activation,
name='LSTM-%d'%i)
self.children.append(lstm)
o = Linear(input_dim=d,
output_dim=io_dim,
name='o-%d'%i)
self.children.append(o)
self.layers.append((i0, i1, lstm, o))
@recurrent(contexts=[])
def apply(self, inputs, **kwargs):
l0i, _, l0l, l0o = self.layers[0]
l0iv = l0i.apply(inputs)
new_states0, new_cells0 = l0l.apply(states=kwargs['states0'],
cells=kwargs['cells0'],
inputs=l0iv,
iterate=False)
l0ov = l0o.apply(new_states0)
pos = l0ov
ps = new_states0
passnext = tensor.ones((inputs.shape[0],))
out_sc = [new_states0, new_cells0, passnext]
for i, (cch, (i0, i1, l, o)) in enumerate(zip(self.cond_cert, self.layers[1:])):
pop = self.softmax.apply(pos)
best = pop.max(axis=1)
passnext = passnext * tensor.le(best, cch) * kwargs['pass%d'%i]
i0v = i0.apply(inputs)
i1v = i1.apply(ps)
prev_states = kwargs['states%d'%i]
prev_cells = kwargs['cells%d'%i]
new_states, new_cells = l.apply(inputs=i0v + i1v,
states=prev_states,
cells=prev_cells,
iterate=False)
new_states = tensor.switch(passnext[:, None], new_states, prev_states)
new_cells = tensor.switch(passnext[:, None], new_cells, prev_cells)
out_sc += [new_states, new_cells, passnext]
ov = o.apply(new_states)
pos = tensor.switch(passnext[:, None], pos + ov, pos)
ps = new_states
return [pos] + out_sc
def get_dim(self, name):
dims = {'pred': self.io_dim}
for i, d in enumerate(self.hidden_dims):
dims['states%d'%i] = dims['cells%d'%i] = d
if name in dims:
return dims[name]
return super(CCHLSTM, self).get_dim(name)
@apply.property('sequences')
def apply_sequences(self):
return ['inputs'] + ['pass%d'%i for i in range(len(self.hidden_dims)-1)]
@apply.property('states')
def apply_states(self):
ret = []
for i in range(len(self.hidden_dims)):
ret += ['states%d'%i, 'cells%d'%i]
return ret
@apply.property('outputs')
def apply_outputs(self):
ret = ['pred']
for i in range(len(self.hidden_dims)):
ret += ['states%d'%i, 'cells%d'%i, 'active%d'%i]
return ret
class Model():
def __init__(self):
inp = tensor.lmatrix('bytes')
# Make state vars
state_vars = {}
for i, d in enumerate(hidden_dims):
state_vars['states%d'%i] = theano.shared(numpy.zeros((num_seqs, d))
.astype(theano.config.floatX),
name='states%d'%i)
state_vars['cells%d'%i] = theano.shared(numpy.zeros((num_seqs, d))
.astype(theano.config.floatX),
name='cells%d'%i)
# Construct brick
cchlstm = CCHLSTM(io_dim=io_dim,
hidden_dims=hidden_dims,
cond_cert=cond_cert,
activation=activation_function)
# Random pass
passdict = {}
for i, p in enumerate(block_prob):
passdict['pass%d'%i] = rng.binomial(size=(inp.shape[1], inp.shape[0]), p=1-p)
# Apply it
outs = cchlstm.apply(inputs=inp.dimshuffle(1, 0),
**dict(state_vars.items() + passdict.items()))
states = []
active_prop = []
for i in range(len(hidden_dims)):
states.append((state_vars['states%d'%i], outs[3*i+1][-1, :, :]))
states.append((state_vars['cells%d'%i], outs[3*i+2][-1, :, :]))
active_prop.append(outs[3*i+3].mean())
active_prop[-1].name = 'active_prop_%d'%i
out = outs[0].dimshuffle(1, 0, 2)
# Do prediction and calculate cost
pred = out.argmax(axis=2)
cost = Softmax().categorical_cross_entropy(inp[:, 1:].flatten(),
out[:, :-1, :].reshape((inp.shape[0]*(inp.shape[1]-1),
io_dim)))
error_rate = tensor.neq(inp[:, 1:].flatten(), pred[:, :-1].flatten()).mean()
# Initialize all bricks
for brick in [cchlstm]:
brick.weights_init = IsotropicGaussian(0.1)
brick.biases_init = Constant(0.)
brick.initialize()
# Apply noise and dropoutvars
cg = ComputationGraph([cost, error_rate])
if w_noise_std > 0:
noise_vars = VariableFilter(roles=[WEIGHT])(cg)
cg = apply_noise(cg, noise_vars, w_noise_std)
[cost_reg, error_rate_reg] = cg.outputs
self.sgd_cost = cost_reg
self.monitor_vars = [[cost, cost_reg],
[error_rate, error_rate_reg],
active_prop]
cost.name = 'cost'
cost_reg.name = 'cost_reg'
error_rate.name = 'error_rate'
error_rate_reg.name = 'error_rate_reg'
self.out = out
self.pred = pred
self.states = states
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