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train.py
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train.py
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from __future__ import print_function
import logging
import numpy
import os
import pickle
import cPickle;
from collections import Counter
from theano import tensor
from theano import function
from toolz import merge
from blocks.algorithms import (GradientDescent, StepClipping, AdaDelta,
CompositeRule)
from blocks.extensions import FinishAfter, Printing
from blocks.extensions.monitoring import TrainingDataMonitoring
from blocks.filter import VariableFilter
from blocks.graph import ComputationGraph, apply_noise, apply_dropout
from blocks.initialization import IsotropicGaussian, Orthogonal, Constant
from blocks.main_loop import MainLoop
from blocks.model import Model
#from blocks.search import BeamSearch
from search import BeamSearch;
from blocks.select import Selector
from blocks.serialization import load_parameter_values
from checkpoint import CheckpointNMT, LoadNMT
from model import BidirectionalEncoder, Decoder, topicalq_transformer
from sampling import BleuValidator, Sampler, SamplingBase
from stream import (get_tr_stream_with_topicalq,get_tr_stream_unsorted, get_dev_stream_with_topicalq,
_ensure_special_tokens)
try:
from blocks.extras.extensions.plot import Plot
BOKEH_AVAILABLE = True
except ImportError:
BOKEH_AVAILABLE = False
logger = logging.getLogger(__name__)
def main(mode, config, use_bokeh=False):
# Construct model
logger.info('Building RNN encoder-decoder')
encoder = BidirectionalEncoder(
config['src_vocab_size'], config['enc_embed'], config['enc_nhids'])
decoder = Decoder(
config['trg_vocab_size'], config['dec_embed'], config['dec_nhids'],
config['enc_nhids'] * 2,config['topical_embedding_dim'])
topical_transformer=topicalq_transformer(config['topical_vocab_size'],config['topical_embedding_dim'], config['enc_nhids'],config['topical_word_num'],config['batch_size']);
if mode == "train":
# Create Theano variables
logger.info('Creating theano variables')
source_sentence = tensor.lmatrix('source')
source_sentence_mask = tensor.matrix('source_mask')
target_sentence = tensor.lmatrix('target')
target_sentence_mask = tensor.matrix('target_mask')
sampling_input = tensor.lmatrix('input')
source_topical_word=tensor.lmatrix('source_topical')
source_topical_mask=tensor.matrix('source_topical_mask')
# Get training and development set streams
tr_stream = get_tr_stream_with_topicalq(**config)
dev_stream = get_dev_stream_with_topicalq(**config)
topic_embedding=topical_transformer.apply(source_topical_word);
# Get cost of the model
representation=encoder.apply(source_sentence, source_sentence_mask);
tw_representation=topical_transformer.look_up.apply(source_topical_word.T);
content_embedding=representation[0,:,(representation.shape[2]/2):];
cost = decoder.cost(
representation,source_sentence_mask,tw_representation,
source_topical_mask, target_sentence, target_sentence_mask,topic_embedding,content_embedding);
logger.info('Creating computational graph')
cg = ComputationGraph(cost)
# Initialize model
logger.info('Initializing model')
encoder.weights_init = decoder.weights_init = IsotropicGaussian(
config['weight_scale'])
encoder.biases_init = decoder.biases_init = Constant(0)
encoder.push_initialization_config()
decoder.push_initialization_config()
encoder.bidir.prototype.weights_init = Orthogonal()
decoder.transition.weights_init = Orthogonal()
encoder.initialize()
decoder.initialize()
topical_transformer.weights_init=IsotropicGaussian(
config['weight_scale']);
topical_transformer.biases_init=Constant(0);
topical_transformer.push_allocation_config();#don't know whether the initialize is for
topical_transformer.look_up.weights_init=Orthogonal();
topical_transformer.transformer.weights_init=Orthogonal();
topical_transformer.initialize();
word_topical_embedding=cPickle.load(open(config['topical_embeddings'], 'rb'));
np_word_topical_embedding=numpy.array(word_topical_embedding,dtype='float32');
topical_transformer.look_up.W.set_value(np_word_topical_embedding);
topical_transformer.look_up.W.tag.role=[];
# apply dropout for regularization
if config['dropout'] < 1.0:
# dropout is applied to the output of maxout in ghog
logger.info('Applying dropout')
dropout_inputs = [x for x in cg.intermediary_variables
if x.name == 'maxout_apply_output']
cg = apply_dropout(cg, dropout_inputs, config['dropout'])
# Apply weight noise for regularization
if config['weight_noise_ff'] > 0.0:
logger.info('Applying weight noise to ff layers')
enc_params = Selector(encoder.lookup).get_params().values()
enc_params += Selector(encoder.fwd_fork).get_params().values()
enc_params += Selector(encoder.back_fork).get_params().values()
dec_params = Selector(
decoder.sequence_generator.readout).get_params().values()
dec_params += Selector(
decoder.sequence_generator.fork).get_params().values()
dec_params += Selector(decoder.state_init).get_params().values()
cg = apply_noise(
cg, enc_params+dec_params, config['weight_noise_ff'])
# Print shapes
shapes = [param.get_value().shape for param in cg.parameters]
logger.info("Parameter shapes: ")
for shape, count in Counter(shapes).most_common():
logger.info(' {:15}: {}'.format(shape, count))
logger.info("Total number of parameters: {}".format(len(shapes)))
# Print parameter names
enc_dec_param_dict = merge(Selector(encoder).get_parameters(),
Selector(decoder).get_parameters())
logger.info("Parameter names: ")
for name, value in enc_dec_param_dict.items():
logger.info(' {:15}: {}'.format(value.get_value().shape, name))
logger.info("Total number of parameters: {}"
.format(len(enc_dec_param_dict)))
# Set up training model
logger.info("Building model")
training_model = Model(cost)
# Set extensions
logger.info("Initializing extensions")
extensions = [
FinishAfter(after_n_batches=config['finish_after']),
TrainingDataMonitoring([cost], after_batch=True),
Printing(after_batch=True),
CheckpointNMT(config['saveto'],
every_n_batches=config['save_freq'])
]
'''
# Set up beam search and sampling computation graphs if necessary
if config['hook_samples'] >= 1 or config['bleu_script'] is not None:
logger.info("Building sampling model")
sampling_representation = encoder.apply(
sampling_input, tensor.ones(sampling_input.shape))
generated = decoder.generate(
sampling_input, sampling_representation)
search_model = Model(generated)
_, samples = VariableFilter(
bricks=[decoder.sequence_generator], name="outputs")(
ComputationGraph(generated[1]))
# Add sampling
if config['hook_samples'] >= 1:
logger.info("Building sampler")
extensions.append(
Sampler(model=search_model, data_stream=tr_stream,
hook_samples=config['hook_samples'],
every_n_batches=config['sampling_freq'],
src_vocab_size=config['src_vocab_size']))
# Add early stopping based on bleu
if config['bleu_script'] is not None:
logger.info("Building bleu validator")
extensions.append(
BleuValidator(sampling_input, samples=samples, config=config,
model=search_model, data_stream=dev_stream,
normalize=config['normalized_bleu'],
every_n_batches=config['bleu_val_freq']))
'''
# Reload model if necessary
if config['reload']:
extensions.append(LoadNMT(config['saveto']))
# Plot cost in bokeh if necessary
if use_bokeh and BOKEH_AVAILABLE:
extensions.append(
Plot('Cs-En', channels=[['decoder_cost_cost']],
after_batch=True))
# Set up training algorithm
logger.info("Initializing training algorithm")
algorithm = GradientDescent(
cost=cost, parameters=cg.parameters,on_unused_sources='warn',
step_rule=CompositeRule([StepClipping(config['step_clipping']),
eval(config['step_rule'])()])
)
# Initialize main loop
logger.info("Initializing main loop")
main_loop = MainLoop(
model=training_model,
algorithm=algorithm,
data_stream=tr_stream,
extensions=extensions
)
# Train!
main_loop.run()
elif mode == 'translate':
# Create Theano variables
logger.info('Creating theano variables')
source_sentence = tensor.lmatrix('source')
source_topical_word=tensor.lmatrix('source_topical')
# Get test set stream
test_stream = get_dev_stream_with_topicalq(
config['test_set'], config['src_vocab'],
config['src_vocab_size'],config['topical_test_set'],config['topical_vocab'],config['topical_vocab_size'],config['unk_id'])
ftrans = open(config['test_set'] + '.trans.out', 'w')
# Helper utilities
sutils = SamplingBase()
unk_idx = config['unk_id']
src_eos_idx = config['src_vocab_size'] - 1
trg_eos_idx = config['trg_vocab_size'] - 1
# Get beam search
logger.info("Building sampling model")
topic_embedding=topical_transformer.apply(source_topical_word);
representation=encoder.apply(source_sentence, tensor.ones(source_sentence.shape));
tw_representation=topical_transformer.look_up.apply(source_topical_word.T);
content_embedding=representation[0,:,(representation.shape[2]/2):];
generated = decoder.generate(source_sentence,representation, tw_representation,topical_embedding=topic_embedding,content_embedding=content_embedding);
_, samples = VariableFilter(
bricks=[decoder.sequence_generator], name="outputs")(
ComputationGraph(generated[1])) # generated[1] is next_outputs
beam_search = BeamSearch(samples=samples)
logger.info("Loading the model..")
model = Model(generated)
loader = LoadNMT(config['saveto'])
loader.set_model_parameters(model, loader.load_parameters())
# Get target vocabulary
trg_vocab = _ensure_special_tokens(
pickle.load(open(config['trg_vocab'], 'rb')), bos_idx=0,
eos_idx=trg_eos_idx, unk_idx=unk_idx)
trg_ivocab = {v: k for k, v in trg_vocab.items()}
logger.info("Started translation: ")
total_cost = 0.0
for i, line in enumerate(test_stream.get_epoch_iterator()):
seq = sutils._oov_to_unk(
line[0], config['src_vocab_size'], unk_idx)
seq2 = line[1];
input_ = numpy.tile(seq, (config['beam_size'], 1))
input_topical=numpy.tile(seq2,(config['beam_size'],1))
# draw sample, checking to ensure we don't get an empty string back
trans, costs = \
beam_search.search(
input_values={source_sentence: input_,source_topical_word:input_topical},
max_length=10*len(seq), eol_symbol=src_eos_idx,
ignore_first_eol=True)
'''
# normalize costs according to the sequence lengths
if config['normalized_bleu']:
lengths = numpy.array([len(s) for s in trans])
costs = costs / lengths
'''
#best = numpy.argsort(costs)[0]
best=numpy.argsort(costs)[0:config['beam_size']];
for b in best:
try:
total_cost += costs[b]
trans_out = trans[b]
# convert idx to words
trans_out = sutils._idx_to_word(trans_out, trg_ivocab)
except ValueError:
logger.info(
"Can NOT find a translation for line: {}".format(i+1))
trans_out = '<UNK>'
print(trans_out, file=ftrans)
if i != 0 and i % 100 == 0:
logger.info(
"Translated {} lines of test set...".format(i))
logger.info("Total cost of the test: {}".format(total_cost))
ftrans.close()
elif mode == 'rerank':
# Create Theano variables
ftrans = open(config['val_set'] + '.scores.out', 'w')
logger.info('Creating theano variables')
source_sentence = tensor.lmatrix('source')
source_sentence_mask = tensor.matrix('source_mask')
target_sentence = tensor.lmatrix('target')
target_sentence_mask = tensor.matrix('target_mask')
config['src_data']=config['val_set']
config['trg_data']=config['val_set_grndtruth']
config['batch_size']=1;
config['sort_k_batches']=1;
test_stream=get_tr_stream_unsorted(**config);
logger.info("Building sampling model")
representations= encoder.apply(
source_sentence, source_sentence_mask)
costs = decoder.cost(representations, source_sentence_mask,
target_sentence, target_sentence_mask)
logger.info("Loading the model..")
model = Model(costs)
loader = LoadNMT(config['saveto'])
loader.set_model_parameters(model, loader.load_parameters())
costs_computer = function([source_sentence,source_sentence_mask,
target_sentence,
target_sentence_mask],costs)
iterator = test_stream.get_epoch_iterator()
scores = []
for i, (src, src_mask, trg, trg_mask) in enumerate(iterator):
costs = costs_computer(*[src, src_mask, trg, trg_mask])
cost = costs.sum()
print(i, cost)
scores.append(cost)
ftrans.write(str(cost)+"\n");
ftrans.close();