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import theano
from theano import tensor
import numpy
from blocks.algorithms import Momentum, AdaDelta, RMSProp
from blocks.bricks import Tanh, Softmax, Linear, MLP
from blocks.bricks.recurrent import LSTM
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
# An epoch will be composed of 'num_seqs' sequences of len 'seq_len'
# divided in chunks of lengh 'seq_div_size'
num_seqs = 20
seq_len = 5000
seq_div_size = 200
io_dim = 256
hidden_dims = [1024, 1024, 1024]
activation_function = Tanh()
i2h_all = True # input to all hidden layers or only first layer
h2o_all = True # all hiden layers to output or only last layer
w_noise_std = 0.02
i_dropout = 0.5
l1_reg = 0
step_rule = 'adadelta'
learning_rate = 0.1
momentum = 0.9
param_desc = '%s-%sIH,%sHO-n%s-d%s-l1r%s-%dx%d(%d)-%s' % (
repr(hidden_dims),
'all' if i2h_all else 'first',
'all' if h2o_all else 'last',
repr(w_noise_std),
repr(i_dropout),
repr(l1_reg),
num_seqs, seq_len, seq_div_size,
step_rule
)
save_freq = 5
# parameters for sample generation
sample_len = 1000
sample_temperature = 0.7 #0.5
sample_freq = 10
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 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'
# Construct hidden states
dims = [io_dim] + hidden_dims
hidden = [in_onehot.dimshuffle(1, 0, 2)]
bricks = []
states = []
for i in xrange(1, len(dims)):
init_state = theano.shared(numpy.zeros((num_seqs, dims[i])).astype(theano.config.floatX),
name='st0_%d'%i)
init_cell = theano.shared(numpy.zeros((num_seqs, dims[i])).astype(theano.config.floatX),
name='cell0_%d'%i)
linear = Linear(input_dim=dims[i-1], output_dim=4*dims[i],
name="lstm_in_%d"%i)
bricks.append(linear)
inter = linear.apply(hidden[-1])
if i2h_all and i > 1:
linear2 = Linear(input_dim=dims[0], output_dim=4*dims[i],
name="lstm_in0_%d"%i)
bricks.append(linear2)
inter = inter + linear2.apply(hidden[0])
inter.name = 'inter_bis_%d'%i
lstm = LSTM(dim=dims[i], activation=activation_function,
name="lstm_rec_%d"%i)
bricks.append(lstm)
new_hidden, new_cells = lstm.apply(inter,
states=init_state,
cells=init_cell)
states.append((init_state, new_hidden[-1, :, :]))
states.append((init_cell, new_cells[-1, :, :]))
hidden.append(new_hidden)
hidden = [s.dimshuffle(1, 0, 2) for s in hidden]
# Construct output from hidden states
out = None
layers = zip(dims, hidden)[1:]
if not h2o_all:
layers = [layers[-1]]
for i, (dim, state) in enumerate(layers):
top_linear = Linear(input_dim=dim, output_dim=io_dim,
name='top_linear_%d'%i)
bricks.append(top_linear)
out_i = top_linear.apply(state)
out = out_i if out is None else out + out_i
out.name = 'out_part_%d'%i
# 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 bricks:
brick.weights_init = IsotropicGaussian(0.1)
brick.biases_init = Constant(0.)
brick.initialize()
# Apply noise and dropout
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)
if i_dropout > 0:
cg = apply_dropout(cg, hidden[1:], i_dropout)
[cost_reg, error_rate_reg] = cg.outputs
# add l1 regularization
if l1_reg > 0:
l1pen = sum(abs(st).mean() for st in hidden[1:])
cost_reg = cost_reg + l1_reg * l1pen
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 = states
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