import theano
from theano import tensor
import numpy
from blocks.bricks import Tanh, Softmax, Linear, MLP, Identity, Rectifier
from blocks.bricks.lookup import LookupTable
from blocks.bricks.recurrent import LSTM
from blocks.graph import ComputationGraph, apply_dropout
class Model():
def __init__(self, config, vocab_size):
question = tensor.imatrix('question')
question_mask = tensor.imatrix('question_mask')
answer = tensor.ivector('answer')
candidates = tensor.imatrix('candidates')
candidates_mask = tensor.imatrix('candidates_mask')
bricks = []
# set time as first dimension
question = question.dimshuffle(1, 0)
question_mask = question_mask.dimshuffle(1, 0)
# Embed questions
embed = LookupTable(vocab_size, config.embed_size, name='question_embed')
bricks.append(embed)
qembed = embed.apply(question)
# Create and apply LSTM stack
curr_dim = config.embed_size
curr_hidden = qembed
hidden_list = []
for k, dim in enumerate(config.lstm_size):
lstm_in = Linear(input_dim=curr_dim, output_dim=4*dim, name='lstm_in_%d'%k)
lstm = LSTM(dim=dim, activation=Tanh(), name='lstm_%d'%k)
bricks = bricks + [lstm_in, lstm]
tmp = lstm_in.apply(curr_hidden)
hidden, _ = lstm.apply(tmp, mask=question_mask.astype(theano.config.floatX))
hidden_list.append(hidden)
if config.skip_connections:
curr_hidden = tensor.concatenate([hidden, qembed], axis=2)
curr_dim = dim + config.embed_size
else:
curr_hidden = hidden
curr_dim = dim
# Create and apply output MLP
if config.skip_connections:
out_mlp = MLP(dims=[sum(config.lstm_size)] + config.out_mlp_hidden + [config.n_entities],
activations=config.out_mlp_activations + [Identity()],
name='out_mlp')
bricks.append(out_mlp)
probs = out_mlp.apply(tensor.concatenate([h[-1,:,:] for h in hidden_list], axis=1))
else:
out_mlp = MLP(dims=[config.lstm_size[-1]] + config.out_mlp_hidden + [config.n_entities],
activations=config.out_mlp_activations + [Identity()],
name='out_mlp')
bricks.append(out_mlp)
probs = out_mlp.apply(hidden_list[-1][-1,:,:])
is_candidate = tensor.eq(tensor.arange(config.n_entities, dtype='int32')[None, None, :],
tensor.switch(candidates_mask, candidates, -tensor.ones_like(candidates))[:, :, None]).sum(axis=1)
probs = tensor.switch(is_candidate, probs, -1000 * tensor.ones_like(probs))
# Calculate prediction, cost and error rate
pred = probs.argmax(axis=1)
cost = Softmax().categorical_cross_entropy(answer, probs).mean()
error_rate = tensor.neq(answer, pred).mean()
# Apply dropout
cg = ComputationGraph([cost, error_rate])
if config.dropout > 0:
cg = apply_dropout(cg, hidden_list, config.dropout)
[cost_reg, error_rate_reg] = cg.outputs
# Other stuff
cost_reg.name = cost.name = 'cost'
error_rate_reg.name = error_rate.name = 'error_rate'
self.sgd_cost = cost_reg
self.monitor_vars = [[cost_reg], [error_rate_reg]]
self.monitor_vars_valid = [[cost], [error_rate]]
# Initialize bricks
for brick in bricks:
brick.weights_init = config.weights_init
brick.biases_init = config.biases_init
brick.initialize()
# vim: set sts=4 ts=4 sw=4 tw=0 et :
