import theano
from theano import tensor
import numpy
from blocks.algorithms import Momentum, AdaDelta, RMSProp
from blocks.bricks import 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
# 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 = 2000
seq_div_size = 100
io_dim = 256
recurrent_blocks = [
# (256, Tanh(), [2048], [Rectifier()]),
(256, Tanh(), [], []),
(256, Tanh(), [], []),
(256, Tanh(), [512], [Rectifier()]),
(256, Tanh(), [512], [Rectifier()]),
]
control_hidden = [512]
control_hidden_activations = [Tanh()]
output_hidden = [512]
output_hidden_activations = [Rectifier()]
weight_noise_std = 0.02
recurrent_dropout = 0.5
control_dropout = 0.5
step_rule = 'adadelta'
learning_rate = 0.1
momentum = 0.9
param_desc = '%s,c%s,o%s-n%s-d%s,%s-%dx%d(%d)-%s' % (
repr(map(lambda (a, b, c, d): (a, c), recurrent_blocks)),
repr(control_hidden), repr(output_hidden),
repr(weight_noise_std),
repr(recurrent_dropout), repr(control_dropout),
num_seqs, seq_len, seq_div_size,
step_rule
)
save_freq = 1
# 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)
else:
assert(False)
class GFGRU(BaseRecurrent, Initializable):
@lazy(allocation=['input_dim', 'recurrent_blocks', 'control_hidden', 'control_hidden_activations'])
def __init__(self, input_dim=None, recurrent_blocks=None, control_hidden=None, control_hidden_activations=None, **kwargs):
super(GFGRU, self).__init__(**kwargs)
self.input_dim = input_dim
self.recurrent_blocks = recurrent_blocks
self.control_hidden = control_hidden
self.control_hidden_activations = control_hidden_activations
self.children = control_hidden_activations
def _allocate(self):
for (_, a, _, b) in recurrent_blocks:
self.children.append(a)
for c in b:
self.children.append(c)
logistic = Logistic()
self.children.append(logistic)
self.hidden_total_dim = sum(x for (x, _, _, _) in self.recurrent_blocks)
control_idim = self.hidden_total_dim + self.input_dim
control_odim = len(self.recurrent_blocks) * (len(self.recurrent_blocks) + 2)
self.control = MLP(dims=[control_idim] + self.control_hidden + [control_odim],
activations=self.control_hidden_activations + [logistic],
name='control')
self.children.append(self.control)
self.blocks = []
self.params = []
self.initial_states = {}
for i, (dim, act, hdim, hact) in enumerate(self.recurrent_blocks):
idim = self.input_dim + self.hidden_total_dim
if i > 0:
idim = idim + self.recurrent_blocks[i-1][0]
rgate = MLP(dims=[self.hidden_total_dim, self.hidden_total_dim],
activations=[logistic],
name='rgate%d'%i)
idims = [idim] + hdim
if hdim == []:
inter = Identity()
else:
inter = MLP(dims=idims, activations=hact, name='inter%d'%i)
zgate = MLP(dims=[idims[-1], dim], activations=[logistic], name='zgate%d'%i)
nstate = MLP(dims=[idims[-1], dim], activations=[act], name='nstate%d'%i)
for brick in [rgate, inter, zgate, nstate]:
self.children.append(brick)
self.blocks.append((rgate, inter, zgate, nstate))
init_states = shared_floatx_zeros((self.hidden_total_dim,), name='initial_states')
self.params = [init_states]
add_role(self.params[0], INITIAL_STATE)
def get_dim(self, name):
if name == 'states':
return self.hidden_total_dim
return super(GFLSTM, self).get_dim(name)
@recurrent(sequences=['inputs'], states=['states'],
outputs=['states'], contexts=[])
def apply(self, inputs=None, states=None):
concat_states = states
states = []
offset = 0
for (dim, _, _, _) in self.recurrent_blocks:
states.append(concat_states[:, offset:offset+dim])
offset += dim
concat_input_states = tensor.concatenate([inputs, concat_states], axis=1)
control = self.control.apply(concat_input_states)
new_states = []
for i, (rgate, inter, zgate, nstate) in enumerate(self.blocks):
controls = control[:, i * (len(self.recurrent_blocks)+2):(i+1) * (len(self.recurrent_blocks)+2)]
rgate_v = rgate.apply(concat_states)
r_inputs = tensor.concatenate([s * controls[:, j][:, None] for j, s in enumerate(states)], axis=1)
r_inputs = r_inputs * (1 - rgate_v * controls[:, -1][:, None])
more_inputs = [inputs]
if i > 0:
more_inputs = more_inputs + [new_states[-1]]
inter_inputs = tensor.concatenate([r_inputs] + more_inputs, axis=1)
inter_v = inter.apply(inter_inputs)
zgate_v = zgate.apply(inter_v)
nstate_v = nstate.apply(inter_v)
nstate_v = nstate_v * (1 - zgate_v * controls[:, -2][:, None])
new_states.append(nstate_v)
return tensor.concatenate(new_states, axis=1)
@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'
gfgru = GFGRU(input_dim=io_dim,
recurrent_blocks=recurrent_blocks,
control_hidden=control_hidden,
control_hidden_activations=control_hidden_activations)
hidden_total_dim = sum(x for (x, _, _, _) in recurrent_blocks)
prev_states = theano.shared(numpy.zeros((num_seqs, hidden_total_dim)).astype(theano.config.floatX),
name='states_save')
states = gfgru.apply(in_onehot.dimshuffle(1, 0, 2),
states=prev_states).dimshuffle(1, 0, 2)
new_states = states[:, -1, :]
out_mlp = MLP(dims=[hidden_total_dim] + output_hidden + [io_dim],
activations=output_hidden_activations + [None],
name='output_mlp')
out = out_mlp.apply(states.reshape((inp.shape[0]*inp.shape[1], hidden_total_dim))).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 [gfgru, out_mlp]:
brick.weights_init = IsotropicGaussian(0.1)
brick.biases_init = Constant(0.)
brick.initialize()
# Apply noise and dropout
cg = ComputationGraph([cost, error_rate])
if weight_noise_std > 0:
noise_vars = VariableFilter(roles=[WEIGHT])(cg)
cg = apply_noise(cg, noise_vars, weight_noise_std)
# if i_dropout > 0:
# cg = apply_dropout(cg, hidden[1:], i_dropout)
[cost_reg, error_rate_reg] = cg.outputs
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
self.states = [(prev_states, new_states)]
