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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, Adam
from blocks.bricks import Activation, Tanh, Logistic, Softmax, Rectifier, Linear, MLP, Initializable, Identity
from blocks.bricks.base import application, lazy
from blocks.bricks.recurrent import BaseRecurrent, recurrent
from blocks.initialization import IsotropicGaussian, Constant
from blocks.utils import shared_floatx_zeros
from blocks.filter import VariableFilter
from blocks.roles import WEIGHT, INITIAL_STATE, add_role
from blocks.graph import ComputationGraph, apply_noise, apply_dropout
rng = RandomStreams()
class TRectifier(Activation):
@application(inputs=['input_'], outputs=['output'])
def apply(self, input_):
return tensor.switch(input_ > 1, input_, 0)
# An epoch will be composed of 'num_seqs' sequences of len 'seq_len'
# divided in chunks of lengh 'seq_div_size'
num_seqs = 10
seq_len = 1000
seq_div_size = 5
io_dim = 256
state_dim = 1024
activation = Tanh()
transition_hidden = [1024, 1024]
transition_hidden_activations = [Rectifier(), Rectifier()]
output_hidden = []
output_hidden_activations = []
weight_noise_std = 0.05
output_h_dropout = 0.0
drop_update = 0.0
l1_state = 0.00
l1_reset = 0.1
step_rule = 'momentum'
learning_rate = 0.001
momentum = 0.99
param_desc = '%s,t%s,o%s-n%s-d%s,%s-L1:%s,%s-%s' % (
repr(state_dim), repr(transition_hidden), repr(output_hidden),
repr(weight_noise_std),
repr(output_h_dropout), repr(drop_update),
repr(l1_state), repr(l1_reset),
step_rule
)
save_freq = 5
on_irc = False
# parameters for sample generation
sample_len = 100
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)
elif step_rule == 'adam':
step_rule = Adam()
else:
assert(False)
class DGSRNN(BaseRecurrent, Initializable):
def __init__(self, input_dim, state_dim, act, transition_h, tr_h_activations, **kwargs):
super(DGSRNN, self).__init__(**kwargs)
self.input_dim = input_dim
self.state_dim = state_dim
logistic = Logistic()
self.inter = MLP(dims=[input_dim + state_dim] + transition_h,
activations=tr_h_activations,
name='inter')
self.reset = MLP(dims=[transition_h[-1], state_dim],
activations=[logistic],
name='reset')
self.update = MLP(dims=[transition_h[-1], state_dim],
activations=[act],
name='update')
self.children = [self.inter, self.reset, self.update, logistic, act] + tr_h_activations
# init state
self.params = [shared_floatx_zeros((state_dim,), name='init_state')]
add_role(self.params[0], INITIAL_STATE)
def get_dim(self, name):
if name == 'state':
return self.state_dim
return super(GFGRU, self).get_dim(name)
@recurrent(sequences=['inputs', 'drop_updates_mask'], states=['state'],
outputs=['state', 'reset'], contexts=[])
def apply(self, inputs=None, drop_updates_mask=None, state=None):
inter_v = self.inter.apply(tensor.concatenate([inputs, state], axis=1))
reset_v = self.reset.apply(inter_v)
update_v = self.update.apply(inter_v)
reset_v = reset_v * drop_updates_mask
new_state = state * (1 - reset_v) + reset_v * update_v
return new_state, reset_v
@application
def initial_state(self, state_name, batch_size, *args, **kwargs):
return tensor.repeat(self.params[0][None, :],
repeats=batch_size,
axis=0)
class Model():
def __init__(self):
inp = tensor.lmatrix('bytes')
in_onehot = tensor.eq(tensor.arange(io_dim, dtype='int16').reshape((1, 1, io_dim)),
inp[:, :, None])
in_onehot.name = 'in_onehot'
dgsrnn = DGSRNN(input_dim=io_dim,
state_dim=state_dim,
act=activation,
transition_h=transition_hidden,
tr_h_activations=transition_hidden_activations,
name='dgsrnn')
prev_state = theano.shared(numpy.zeros((num_seqs, state_dim)).astype(theano.config.floatX),
name='state')
states, resets = dgsrnn.apply(inputs=in_onehot.dimshuffle(1, 0, 2),
drop_updates_mask=rng.binomial(size=(inp.shape[1], inp.shape[0], state_dim),
p=1-drop_update,
dtype=theano.config.floatX),
state=prev_state)
states = states.dimshuffle(1, 0, 2)
resets = resets.dimshuffle(1, 0, 2)
self.states = [(prev_state, states[:, -1, :])]
out_mlp = MLP(dims=[state_dim] + output_hidden + [io_dim],
activations=output_hidden_activations + [None],
name='output_mlp')
states_sh = states.reshape((inp.shape[0]*inp.shape[1], state_dim))
out = out_mlp.apply(states_sh).reshape((inp.shape[0], inp.shape[1], io_dim))
# 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 [dgsrnn, out_mlp]:
brick.weights_init = IsotropicGaussian(0.001)
brick.biases_init = Constant(0.0)
brick.initialize()
# Apply noise and dropout
cg = ComputationGraph([cost, error_rate, states, resets])
if weight_noise_std > 0:
noise_vars = VariableFilter(roles=[WEIGHT])(cg)
cg = apply_noise(cg, noise_vars, weight_noise_std)
if output_h_dropout > 0:
dv = VariableFilter(name='input_', bricks=out_mlp.linear_transformations)(cg)
print "Output H dropout on", len(dv), "vars"
cg = apply_dropout(cg, dv, output_h_dropout)
[cost_reg, error_rate_reg, states, resets] = cg.outputs
if l1_state > 0:
cost_reg = cost_reg + l1_state * abs(states).mean()
if l1_reset > 0:
cost_reg = cost_reg + l1_reset * abs(resets).mean()
self.cost = cost
self.error_rate = error_rate
self.cost_reg = cost_reg
self.error_rate_reg = error_rate_reg
self.out = out
self.pred = pred
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