import theano
from theano import tensor
import numpy
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
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 = 2000
seq_div_size = 100
io_dim = 256
recurrent_blocks = [
# (256, Tanh(), [2048], [Rectifier()]),
# (512, Rectifier(), [1024], [Rectifier()]),
(512, Tanh(), [1024], [Rectifier()]),
(512, Tanh(), [1024], [Rectifier()]),
# (2, Tanh(), [2], [Rectifier()]),
# (2, Tanh(), [], []),
]
control_hidden = [1024]
control_hidden_activations = [Rectifier()]
output_hidden = [1024]
output_hidden_activations = [Rectifier()]
weight_noise_std = 0.05
recurrent_h_dropout = 0
control_h_dropout = 0
output_h_dropout = 0.5
step_rule = 'adam'
learning_rate = 0.1
momentum = 0.99
param_desc = '%s,c%s,o%s-n%s-d%s,%s,%s-%s' % (
repr(map(lambda (a, b, c, d): (a, c), recurrent_blocks)),
repr(control_hidden), repr(output_hidden),
repr(weight_noise_std),
repr(recurrent_h_dropout), repr(control_h_dropout), repr(output_h_dropout),
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 GFGRU(BaseRecurrent, Initializable):
def __init__(self, input_dim, recurrent_blocks, control_hidden, control_hidden_activations, **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
# setup children
self.children = control_hidden_activations
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 block
self.cblocklen = len(self.recurrent_blocks) + 2
control_idim = self.hidden_total_dim + self.input_dim
control_odim = len(self.recurrent_blocks) * self.cblocklen
self.control = MLP(dims=[control_idim] + self.control_hidden + [control_odim],
activations=self.control_hidden_activations + [logistic],
name='control')
self.children.append(self.control)
# recurrent blocks
self.blocks = []
self.params = []
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]
idims = [idim] + hdim
if hdim == []:
inter = Identity()
else:
inter = MLP(dims=idims, activations=hact, name='inter%d'%i)
rgate = MLP(dims=[idims[-1], dim], activations=[logistic], name='rgate%d'%i)
nstate = MLP(dims=[idims[-1], dim], activations=[act], name='nstate%d'%i)
for brick in [inter, rgate, nstate]:
self.children.append(brick)
self.blocks.append((inter, rgate, nstate))
# init state zeros
self.init_states_names = []
self.init_states_dict = {}
self.params = []
for i, (dim, _, _, _) in enumerate(self.recurrent_blocks):
name = 'init_state_%d'%i
svar = shared_floatx_zeros((dim,), name=name)
add_role(svar, INITIAL_STATE)
self.init_states_names.append(name)
self.init_states_dict[name] = svar
self.params.append(svar)
def get_dim(self, name):
if name in self.init_states_dict:
return self.init_states_dict[name].shape.eval()
return super(GFGRU, self).get_dim(name)
def recurrent_h_dropout_vars(self, cg):
ret = []
for (inter, rgate, nstate) in self.blocks:
ret = ret + VariableFilter(name='input_',
bricks=inter.linear_transformations + rgate.linear_transformations + nstate.linear_transformations
)(cg)
return ret
def control_h_dropout_vars(self, cg):
return VariableFilter(name='input_', bricks=self.control.linear_transformations)(cg)
@recurrent(sequences=['inputs'], contexts=[])
def apply(self, inputs=None, **kwargs):
states = [kwargs[i] for i in self.init_states_names]
concat_states = tensor.concatenate(states, axis=1)
concat_input_states = tensor.concatenate([inputs, concat_states], axis=1)
control_v = self.control.apply(concat_input_states)
new_states = []
for i, (inter, rgate, nstate) in enumerate(self.blocks):
controls = control_v[:, i * self.cblocklen:(i+1) * self.cblocklen]
r_inputs = tensor.concatenate([s * controls[:, j][:, None] for j, s in enumerate(states)], axis=1)
more_inputs = [inputs]
if i > 0:
more_inputs.append(new_states[-1])
inter_inputs = tensor.concatenate([r_inputs] + more_inputs, axis=1)
inter_v = inter.apply(inter_inputs)
rgate_v = rgate.apply(inter_v)
nstate_v = nstate.apply(inter_v)
rctl = controls[:, -1][:, None] * rgate_v
uctl = controls[:, -2][:, None]
nstate_v = uctl * nstate_v + (1 - rctl) * states[i]
new_states.append(nstate_v)
return new_states
@apply.property('states')
def apply_states(self):
return self.init_states_names
@apply.property('outputs')
def apply_outputs(self):
return self.init_states_names
@application
def initial_state(self, state_name, batch_size, *args, **kwargs):
return tensor.repeat(self.init_states_dict[state_name][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_dict = {}
for i, (dim, _, _, _) in enumerate(recurrent_blocks):
prev_state = theano.shared(numpy.zeros((num_seqs, dim)).astype(theano.config.floatX),
name='states_save')
prev_states_dict['init_state_%d'%i] = prev_state
states = [x.dimshuffle(1, 0, 2) for x in gfgru.apply(in_onehot.dimshuffle(1, 0, 2), **prev_states_dict)]
self.states = []
for i, _ in enumerate(recurrent_blocks):
self.states.append((prev_states_dict['init_state_%d'%i], states[i][:, -1, :]))
states_concat = tensor.concatenate(states, axis=2)
out_mlp = MLP(dims=[hidden_total_dim] + output_hidden + [io_dim],
activations=output_hidden_activations + [None],
name='output_mlp')
states_sh = states_concat.reshape((inp.shape[0]*inp.shape[1], hidden_total_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 [gfgru, out_mlp]:
brick.weights_init = IsotropicGaussian(0.01)
brick.biases_init = Constant(0.001)
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 recurrent_h_dropout > 0:
dv = gfgru.recurrent_h_dropout_vars(cg)
print "Recurrent H dropout on", len(dv), "vars"
cg = apply_dropout(cg, dv, recurrent_h_dropout)
if control_h_dropout > 0:
dv = gfgru.control_h_dropout_vars(cg)
print "Control H dropout on", len(dv), "vars"
cg = apply_dropout(cg, dv, control_h_dropout)
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] = 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
