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import theano
from theano import tensor
import numpy
from blocks.bricks import Softmax, Linear
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
class Model():
def __init__(self, config):
inp = tensor.imatrix('bytes')
in_onehot = tensor.eq(tensor.arange(config.io_dim, dtype='int16').reshape((1, 1, config.io_dim)),
inp[:, :, None])
in_onehot.name = 'in_onehot'
# Construct hidden states
dims = [config.io_dim] + config.hidden_dims
hidden = [in_onehot.dimshuffle(1, 0, 2)]
bricks = []
states = []
for i in xrange(1, len(dims)):
init_state = theano.shared(numpy.zeros((config.num_seqs, dims[i])).astype(theano.config.floatX),
name='st0_%d'%i)
init_cell = theano.shared(numpy.zeros((config.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 config.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=config.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 config.h2o_all:
layers = [layers[-1]]
for i, (dim, state) in enumerate(layers):
top_linear = Linear(input_dim=dim, output_dim=config.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),
config.io_dim))).mean()
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 config.w_noise_std > 0:
noise_vars = VariableFilter(roles=[WEIGHT])(cg)
cg = apply_noise(cg, noise_vars, config.w_noise_std)
if config.i_dropout > 0:
cg = apply_dropout(cg, hidden[1:], config.i_dropout)
[cost_reg, error_rate_reg] = cg.outputs
# add l1 regularization
if config.l1_reg > 0:
l1pen = sum(abs(st).mean() for st in hidden[1:])
cost_reg = cost_reg + config.l1_reg * l1pen
cost_reg += 1e-10 # so that it is not the same Theano variable
error_rate_reg += 1e-10
# put stuff into self that is usefull for training or extensions
self.sgd_cost = cost_reg
cost.name = 'cost'
cost_reg.name = 'cost_reg'
error_rate.name = 'error_rate'
error_rate_reg.name = 'error_rate_reg'
self.monitor_vars = [[cost, cost_reg],
[error_rate, error_rate_reg]]
self.out = out
self.pred = pred
self.states = states
# vim: set sts=4 ts=4 sw=4 tw=0 et :
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