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train_rnnlm.py
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train_rnnlm.py
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'''
Created on May 10, 2013
@author: sgangireddy
'''
import theano
import theano.tensor as T
import time, numpy
from vocab_create import Vocabulary
from data_provider_rnn import DataProvider
import os, sys
from learn_rates import LearningRateNewBob, LearningRateList
from learn_rates import LearningRate
from logistic_regression import LogisticRegression
from rnn import RNN
from cache import TNetsCacheSimple, TNetsCacheLastElem
from numpy.core.numeric import dtype
from utils import GlobalCfg
#import h5py
#from mlp_save import save_mlp, save_posteriors
def train_rnn(num_batches_per_bunch = 512, batch_size = 1, num_bunches_queue = 5, offset = 0, path_name = '/exports/work/inf_hcrc_cstr_udialogue/siva/data/'):
voc_list = Vocabulary(path_name + 'train')
voc_list.vocab_create()
vocab = voc_list.vocab
vocab_size = voc_list.vocab_size
dataprovider_train = DataProvider(path_name + 'train', vocab, vocab_size)
dataprovider_valid = DataProvider(path_name + 'valid', vocab, vocab_size )
dataprovider_test = DataProvider(path_name + 'test', vocab, vocab_size )
print '..building the model'
#symbolic variables for input, target vector and batch index
index = T.lscalar('index')
x = T.fvector('x')
h0 = T.fvector('h0')
y = T.ivector('y')
learning_rate = T.fscalar('learning_rate')
#theano shared variables for train, valid and test
train_set_x1 = theano.shared(numpy.empty((1,), dtype='float32'), allow_downcast = True)
train_set_y = theano.shared(numpy.empty((1), dtype = 'int32'), allow_downcast = True)
valid_set_x1 = theano.shared(numpy.empty((1,), dtype='float32'), allow_downcast = True)
valid_set_y = theano.shared(numpy.empty((1), dtype = 'int32'), allow_downcast = True)
test_set_x1 = theano.shared(numpy.empty((1,), dtype='float32'), allow_downcast = True)
test_set_y = theano.shared(numpy.empty((1), dtype = 'int32'), allow_downcast = True)
rng = numpy.random.RandomState()
classifier = RNN(rng = rng, input = x, intial_hidden = h0, n_in = vocab_size, n_hidden = int(sys.argv[1]), n_out = vocab_size)
cost = classifier.negative_log_likelihood(y)
ht1_values = numpy.ones((int(sys.argv[1]), ), dtype = 'float32')
ht1 = theano.shared(value = ht1_values, name = 'hidden_state')
#constructor for learning rate class
learnrate_schedular = LearningRateNewBob(start_rate = float(sys.argv[2]), scale_by=.5, max_epochs=9999,\
min_derror_ramp_start=.01, min_derror_stop=.01, init_error=100.)
log_likelihood = classifier.sum(y)
likelihood = classifier.likelihood(y)
#test_model
test_model = theano.function(inputs = [], outputs = [log_likelihood, likelihood], \
givens = {x: test_set_x1,
y: test_set_y,
h0: ht1})
#validation_model
validate_model = theano.function(inputs = [], outputs = [log_likelihood], \
givens = {x: valid_set_x1,
y: valid_set_y,
h0: ht1})
gradient_param = []
#calculates the gradient of cost with respect to parameters
for param in classifier.params:
gradient_param.append(T.cast(T.grad(cost, param), 'float32'))
updates = []
#updates the parameters
for param, gradient in zip(classifier.params, gradient_param):
updates.append((param, T.cast(param - learning_rate * gradient - 0.000001 * param, dtype = 'float32')))
#hidden_output = classifier.inputlayer.output
#training_model
train_model = theano.function(inputs = [learning_rate], outputs = [cost, classifier.inputlayer.output], updates = updates, \
givens = {x: train_set_x1,
y: train_set_y,
h0:ht1})
print '.....training'
best_valid_loss = numpy.inf
start_time = time.time()
while(learnrate_schedular.get_rate() != 0):
print 'learning_rate:', learnrate_schedular.get_rate()
print 'epoch_number:', learnrate_schedular.epoch
frames_showed, progress = 0, 0
start_epoch_time = time.time()
dataprovider_train.reset()
for feats_lab_tuple in dataprovider_train:
features, labels = feats_lab_tuple
if labels is None or features is None:
continue
frames_showed += features.shape[0]
for temp, i in zip(features, xrange(len(labels))):
temp_features1 = numpy.zeros(vocab_size, dtype = 'float32')
temp_features1[temp[0]] = 1
train_set_x1.set_value(numpy.asarray(temp_features1, dtype = 'float32'), borrow = True)
train_set_y.set_value(numpy.asarray([labels[i]], dtype = 'int32'), borrow = True)
out = train_model(numpy.asarray(learnrate_schedular.get_rate(), dtype = 'float32'))
ht1.set_value(numpy.asarray(out[1], dtype = 'float32'), borrow = True)
progress += 1
if progress%10000==0:
end_time_progress = time.time()
print 'PROGRESS: Processed %i bunches (%i frames), TIME: %f in seconds'\
%(progress, frames_showed,(end_time_progress-start_epoch_time))
train_set_x1.set_value(numpy.empty((1, ), dtype = 'float32'))
train_set_y.set_value(numpy.empty((1), dtype = 'int32'))
end_time_progress = time.time()
print 'PROGRESS: Processed %i bunches (%i frames), TIME: %f in seconds'\
%(progress, frames_showed,(end_time_progress-start_epoch_time))
#classifier_name = 'MLP' + str(learnrate_schedular.epoch)
#save_mlp(classifier, path+exp_name1 , classifier_name)
print 'Validating...'
valid_losses = []
log_likelihood = []
valid_frames_showed, progress = 0, 0
start_valid_time = time.time() # it is also stop of training time
dataprovider_valid.reset()
for feats_lab_tuple in dataprovider_valid:
features, labels = feats_lab_tuple
if labels is None or features is None:
continue
valid_frames_showed += features.shape[0]
for temp, i in zip(features, xrange(len(labels))):
temp_features1 = numpy.zeros(vocab_size, dtype = 'float32')
temp_features1[temp[0]] = 1
valid_set_x1.set_value(numpy.asarray(temp_features1, dtype = 'float32'), borrow = True)
valid_set_y.set_value(numpy.asarray([labels[i]], dtype = 'int32'), borrow = True)
log_likelihood.append(validate_model())
valid_set_x1.set_value(numpy.empty((1), 'float32'))
valid_set_y.set_value(numpy.empty((1), 'int32'))
progress += 1
if progress%1000==0:
end_time_valid_progress = time.time()
print 'PROGRESS: Processed %i bunches (%i frames), TIME: %f in seconds'\
%(progress, valid_frames_showed, end_time_valid_progress - start_valid_time)
end_time_valid_progress = time.time()
print 'PROGRESS: Processed %i bunches (%i frames), TIME: %f in seconds'\
%(progress, valid_frames_showed, end_time_valid_progress - start_valid_time)
entropy = (-numpy.sum(log_likelihood)/valid_frames_showed)
print entropy, numpy.sum(log_likelihood)
if entropy < best_valid_loss:
learning_rate = learnrate_schedular.get_next_rate(entropy)
best_valid_loss = entropy
else:
learnrate_schedular.rate = 0.0
end_time = time.time()
print 'The fine tuning ran for %.2fm' %((end_time-start_time)/60.)
print 'Testing...'
log_likelihood = []
likelihoods = []
test_frames_showed, progress = 0, 0
start_test_time = time.time() # it is also stop of training time
dataprovider_test.reset()
for feats_lab_tuple in dataprovider_test:
features, labels = feats_lab_tuple
if labels is None or features is None:
continue
test_frames_showed += features.shape[0]
for temp, i in zip(features, xrange(len(labels))):
temp_features1 = numpy.zeros(vocab_size, dtype = 'float32')
temp_features1[temp[0]] = 1
test_set_x1.set_value(numpy.asarray(temp_features1, dtype = 'float32'), borrow = True)
test_set_y.set_value(numpy.asarray([labels[i]], dtype = 'int32'), borrow = True)
out = test_model()
log_likelihood.append(out[0])
likelihoods.append(out[1])
progress += 1
if progress%1000==0:
end_time_test_progress = time.time()
print 'PROGRESS: Processed %i bunches (%i frames), TIME: %f in seconds'\
%(progress, test_frames_showed, end_time_test_progress - start_test_time)
end_time_test_progress = time.time()
print 'PROGRESS: Processed %i bunches (%i frames), TIME: %f in seconds'\
%(progress, test_frames_showed, end_time_test_progress - start_test_time)
#save_posteriors(log_likelihood, likelihoods, weight_path+file_name2)
print numpy.sum(log_likelihood)
train_rnn()