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model_deepRNN_gru.py
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model_deepRNN_gru.py
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from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams
import sys
import time
from layers import Layers
import data_engine_res
import metrics
from optimizers import *
from predict import *
def validate_options(options):
if options['ctx2out']:
warnings.warn('Feeding context to output directly seems to hurt.')
if options['dim_word'] > options['mu_dim']:
warnings.warn('dim_word should only be as large as mu_dim.')
return options
class Attention(object):
def __init__(self, channel=None):
self.rng_numpy, self.rng_theano = get_two_rngs()
self.layers = Layers()
self.predict = Predict()
self.channel = channel
def load_params(self, path, params):
# load params from disk
pp = np.load(path)
for kk, vv in params.iteritems():
if kk not in pp:
raise Warning('%s is not in the archive'%kk)
params[kk] = pp[kk]
return params
def init_params(self, options):
# all parameters
params = OrderedDict()
# embedding
params['Wemb'] = norm_weight(options['n_words'], options['dim_word'])
ctx_dim = options['ctx_dim']
# decoder: gru
params = self.layers.get_layer('gru')[0](options,params, nin=options['dim_word'],
dim=options['tu_dim'], prefix='tu_gru')
params = self.layers.get_layer('gru_cond')[0](options, params, nin=options['tu_dim'],
dim=options['mu_dim'], dimctx=ctx_dim,
prefix='mu_gru')
# readout
params = self.layers.get_layer('ff')[0](params, nin=options['mu_dim'], nout=options['n_words'],
prefix='ff_logit_gru')
return params
def build_model(self, tparams, options):
trng = RandomStreams(1234)
use_noise = theano.shared(np.float32(0.))
# description string: #words x #samples
x = tensor.matrix('x', dtype='int64')
mask = tensor.matrix('mask', dtype='float32')
# context: #samples x #annotations x dim
ctx = tensor.tensor3('ctx', dtype='float32')
mask_ctx = tensor.matrix('mask_ctx', dtype='float32')
n_timesteps = x.shape[0]
n_samples = x.shape[1]
# index into the word embedding matrix, shift it forward in time
emb = tparams['Wemb'][x.flatten()].reshape(
[n_timesteps, n_samples, options['dim_word']])
emb_shifted = tensor.zeros_like(emb)
emb_shifted = tensor.set_subtensor(emb_shifted[1:], emb[:-1])
emb = emb_shifted
ctx_ = ctx
counts = mask_ctx.sum(-1).dimshuffle(0,'x')
ctx_mean = ctx_.sum(1)/counts
# decoder
tu_gru = self.layers.get_layer('gru')[1](options,tparams, emb, mask=mask, prefix='tu_gru')
mu_gru = self.layers.get_layer('gru_cond')[1](options, tparams, tu_gru[0],
mask=mask, context=ctx_mean,
one_step=False,
trng=trng,
use_noise=use_noise,
prefix='mu_gru')
proj_h = mu_gru[0]
if options['use_dropout']:
proj_h = self.layers.dropout_layer(proj_h, use_noise, trng)
# compute word probabilities
logit = self.layers.get_layer('ff')[1](tparams, proj_h, activ='linear',
prefix='ff_logit_gru')
logit_shp = logit.shape
# (t*m, n_words)
probs = tensor.nnet.softmax(logit.reshape([logit_shp[0]*logit_shp[1],
logit_shp[2]]))
# cost
x_flat = x.flatten() # (t*m,)
cost = -tensor.log(probs[T.arange(x_flat.shape[0]), x_flat] + 1e-8)
cost = cost.reshape([x.shape[0], x.shape[1]])
cost = (cost * mask).sum(0)
extra = [probs]
return trng, use_noise, x, mask, ctx, mask_ctx, cost, extra
def pred_probs(self, whichset, f_log_probs, verbose=True):
probs = []
n_done = 0
NLL = []
L = []
if whichset == 'train':
tags = self.engine.train
iterator = self.engine.kf_train
elif whichset == 'valid':
tags = self.engine.valid
iterator = self.engine.kf_valid
elif whichset == 'test':
tags = self.engine.test
iterator = self.engine.kf_test
else:
raise NotImplementedError()
n_samples = np.sum([len(index) for index in iterator])
for index in iterator:
tag = [tags[i] for i in index]
x, mask, ctx, ctx_mask = data_engine.prepare_data(
self.engine, tag)
pred_probs = f_log_probs(x, mask, ctx, ctx_mask)
L.append(mask.sum(0).tolist())
NLL.append((-1 * pred_probs).tolist())
probs.append(pred_probs.tolist())
n_done += len(tag)
if verbose:
sys.stdout.write('\rComputing LL on %d/%d examples'%(
n_done, n_samples))
sys.stdout.flush()
print
probs = flatten_list_of_list(probs)
NLL = flatten_list_of_list(NLL)
L = flatten_list_of_list(L)
perp = 2**(np.sum(NLL) / np.sum(L) / np.log(2))
return -1 * np.mean(probs), perp
def train(self,
random_seed=1234,
reload_=False,
verbose=True,
debug=True,
save_model_dir='',
from_dir=None,
# dataset
dataset='youtube2text',
video_feature='googlenet',
K=10,
OutOf=240,
# network
dim_word=256, # word vector dimensionality
ctx_dim=-1, # context vector dimensionality, auto set
tu_dim=512,
mu_dim=1024,
n_layers_out=1,
n_layers_init=1,
encoder='none',
encoder_dim=100,
prev2out=False,
ctx2out=False,
selector=False,
n_words=100000,
maxlen=100, # maximum length of the description
use_dropout=False,
isGlobal=False,
# training
patience=10,
max_epochs=5000,
decay_c=0.,
alpha_c=0.,
alpha_entropy_r=0.,
lrate=0.01,
optimizer='adadelta',
clip_c=2.,
# minibatch
batch_size = 64,
valid_batch_size = 64,
dispFreq=100,
validFreq=10,
saveFreq=10, # save the parameters after every saveFreq updates
sampleFreq=10, # generate some samples after every sampleFreq updates
# metric
metric='blue'
):
self.rng_numpy, self.rng_theano = get_two_rngs()
model_options = locals().copy()
if 'self' in model_options:
del model_options['self']
model_options = validate_options(model_options)
with open('%smodel_options.pkl'%save_model_dir, 'wb') as f:
pkl.dump(model_options, f)
print 'Loading data'
self.engine = data_engine.Movie2Caption('attention', dataset,
video_feature,
batch_size, valid_batch_size,
maxlen, n_words,
K, OutOf)
model_options['ctx_dim'] = self.engine.ctx_dim
print 'init params'
t0 = time.time()
params = self.init_params(model_options)
# reloading
if reload_:
model_saved = from_dir+'/model_best_so_far.npz'
assert os.path.isfile(model_saved)
print "Reloading model params..."
params = load_params(model_saved, params)
tparams = init_tparams(params)
if verbose:
print tparams.keys
trng, use_noise, x, mask, ctx, mask_ctx, cost, extra = \
self.build_model(tparams, model_options)
print 'buliding sampler'
f_init, f_next = self.predict.build_sampler(self.layers, tparams, model_options, use_noise, trng)
# before any regularizer
print 'building f_log_probs'
f_log_probs = theano.function([x, mask, ctx, mask_ctx], -cost,
profile=False, on_unused_input='ignore')
cost = cost.mean()
if decay_c > 0.:
decay_c = theano.shared(np.float32(decay_c), name='decay_c')
weight_decay = 0.
for kk, vv in tparams.iteritems():
weight_decay += (vv ** 2).sum()
weight_decay *= decay_c
cost += weight_decay
print 'compute grad'
grads = tensor.grad(cost, wrt=itemlist(tparams))
if clip_c > 0.:
g2 = 0.
for g in grads:
g2 += (g**2).sum()
new_grads = []
for g in grads:
new_grads.append(tensor.switch(g2 > (clip_c**2),
g / tensor.sqrt(g2) * clip_c,
g))
grads = new_grads
lr = tensor.scalar(name='lr')
print 'build train fns'
f_grad_shared, f_update = eval(optimizer)(lr, tparams, grads,
[x, mask, ctx, mask_ctx], cost,
extra + grads)
print 'compilation took %.4f sec'%(time.time()-t0)
print 'Optimization'
history_errs = []
# reload history
if reload_:
print 'loading history error...'
history_errs = np.load(
from_dir+'model_best_so_far.npz')['history_errs'].tolist()
bad_counter = 0
processes = None
queue = None
rqueue = None
shared_params = None
uidx = 0
uidx_best_blue = 0
uidx_best_valid_err = 0
estop = False
best_p = unzip(tparams)
best_blue_valid = 0
best_valid_err = 999
for eidx in xrange(max_epochs):
n_samples = 0
train_costs = []
grads_record = []
print 'Epoch ', eidx
for idx in self.engine.kf_train:
tags = [self.engine.train[index] for index in idx]
n_samples += len(tags)
uidx += 1
use_noise.set_value(1.)
pd_start = time.time()
x, mask, ctx, ctx_mask = data_engine.prepare_data(
self.engine, tags)
pd_duration = time.time() - pd_start
if x is None:
print 'Minibatch with zero sample under length ', maxlen
continue
ud_start = time.time()
rvals = f_grad_shared(x, mask, ctx, ctx_mask)
cost = rvals[0]
probs = rvals[1]
grads = rvals[2:]
grads, NaN_keys = grad_nan_report(grads, tparams)
if len(grads_record) >= 5:
del grads_record[0]
grads_record.append(grads)
if NaN_keys != []:
print 'grads contain NaN'
import pdb; pdb.set_trace()
if np.isnan(cost) or np.isinf(cost):
print 'NaN detected in cost'
import pdb; pdb.set_trace()
# update params
f_update(lrate)
ud_duration = time.time() - ud_start
if eidx == 0:
train_error = cost
else:
train_error = train_error * 0.95 + cost * 0.05
train_costs.append(cost)
if np.mod(uidx, dispFreq) == 0:
print 'Epoch ', eidx, 'Update ', uidx, 'Train cost mean so far', \
train_error, 'fetching data time spent (sec)', np.round(pd_duration,3), \
'update time spent (sec)', np.round(ud_duration,3)
if np.mod(uidx, saveFreq) == 0:
pass
if np.mod(uidx, sampleFreq) == 0:
use_noise.set_value(0.)
print '------------- sampling from train ----------'
self.predict.sample_execute(self.engine, model_options, tparams,
f_init, f_next, x, ctx, ctx_mask, trng)
print '------------- sampling from valid ----------'
idx = self.engine.kf_valid[np.random.randint(1, len(self.engine.kf_valid) - 1)]
tags = [self.engine.valid[index] for index in idx]
x_s, mask_s, ctx_s, mask_ctx_s = data_engine.prepare_data(self.engine, tags)
self.predict.sample_execute(self.engine, model_options, tparams,
f_init, f_next, x_s, ctx_s, mask_ctx_s, trng)
# end of sample
if validFreq != -1 and np.mod(uidx, validFreq) == 0:
t0_valid = time.time()
current_params = unzip(tparams)
np.savez(save_model_dir+'model_current.npz',
history_errs=history_errs, **current_params)
use_noise.set_value(0.)
train_err = -1
train_perp = -1
valid_err = -1
valid_perp = -1
test_err = -1
test_perp = -1
if not debug:
# first compute train cost
if 0:
print 'computing cost on trainset'
train_err, train_perp = self.pred_probs(
'train', f_log_probs,
verbose=model_options['verbose'])
else:
train_err = 0.
train_perp = 0.
if 1:
print 'validating...'
valid_err, valid_perp = self.pred_probs(
'valid', f_log_probs,
verbose=model_options['verbose'],
)
else:
valid_err = 0.
valid_perp = 0.
if 1:
print 'testing...'
test_err, test_perp = self.pred_probs(
'test', f_log_probs,
verbose=model_options['verbose']
)
else:
test_err = 0.
test_perp = 0.
mean_ranking = 0
blue_t0 = time.time()
scores, processes, queue, rqueue, shared_params = \
metrics.compute_score(model_type='attention',
model_archive=current_params,
options=model_options,
engine=self.engine,
save_dir=save_model_dir,
beam=5, n_process=5,
whichset='both',
on_cpu=False,
processes=processes, queue=queue, rqueue=rqueue,
shared_params=shared_params, metric=metric,
one_time=False,
f_init=f_init, f_next=f_next, model=self.predict
)
valid_B1 = scores['valid']['Bleu_1']
valid_B2 = scores['valid']['Bleu_2']
valid_B3 = scores['valid']['Bleu_3']
valid_B4 = scores['valid']['Bleu_4']
valid_Rouge = scores['valid']['ROUGE_L']
valid_Cider = scores['valid']['CIDEr']
valid_meteor = scores['valid']['METEOR']
test_B1 = scores['test']['Bleu_1']
test_B2 = scores['test']['Bleu_2']
test_B3 = scores['test']['Bleu_3']
test_B4 = scores['test']['Bleu_4']
test_Rouge = scores['test']['ROUGE_L']
test_Cider = scores['test']['CIDEr']
test_meteor = scores['test']['METEOR']
print 'computing meteor/blue score used %.4f sec, '\
'blue score: %.1f, meteor score: %.1f'%(
time.time()-blue_t0, valid_B4, valid_meteor)
history_errs.append([eidx, uidx, train_err, train_perp,
valid_perp, test_perp,
valid_err, test_err,
valid_B1, valid_B2, valid_B3,
valid_B4, valid_meteor, valid_Rouge, valid_Cider,
test_B1, test_B2, test_B3,
test_B4, test_meteor, test_Rouge, test_Cider])
np.savetxt(save_model_dir+'train_valid_test.txt',
history_errs, fmt='%.3f')
print 'save validation results to %s'%save_model_dir
# save best model according to the best blue or meteor
if len(history_errs) > 1 and valid_B4 > np.array(history_errs)[:-1,11].max():
print 'Saving to %s...'%save_model_dir,
np.savez(
save_model_dir+'model_best_blue_or_meteor.npz',
history_errs=history_errs, **best_p)
if len(history_errs) > 1 and valid_err < np.array(history_errs)[:-1,6].min():
best_p = unzip(tparams)
bad_counter = 0
best_valid_err = valid_err
uidx_best_valid_err = uidx
print 'Saving to %s...'%save_model_dir,
np.savez(
save_model_dir+'model_best_so_far.npz',
history_errs=history_errs, **best_p)
with open('%smodel_options.pkl'%save_model_dir, 'wb') as f:
pkl.dump(model_options, f)
print 'Done'
elif len(history_errs) > 1 and valid_err >= np.array(history_errs)[:-1,6].min():
bad_counter += 1
print 'history best ',np.array(history_errs)[:,6].min()
print 'bad_counter ',bad_counter
print 'patience ',patience
if bad_counter > patience:
print 'Early Stop!'
estop = True
break
if self.channel:
self.channel.save()
print 'Train ', train_err, 'Valid ', valid_err, 'Test ', test_err, \
'best valid err so far',best_valid_err
print 'valid took %.2f sec'%(time.time() - t0_valid)
# end of validatioin
if debug:
break
if estop:
break
if debug:
break
# end for loop over minibatches
print 'This epoch has seen %d samples, train cost %.2f'%(
n_samples, np.mean(train_costs))
# end for loop over epochs
print 'Optimization ended.'
if best_p is not None:
zipp(best_p, tparams)
print 'stopped at epoch %d, minibatch %d, '\
'curent Train %.2f, current Valid %.2f, current Test %.2f '%(
eidx, uidx, np.mean(train_err), np.mean(valid_err), np.mean(test_err))
params = copy.copy(best_p)
np.savez(save_model_dir+'model_best.npz',
train_err=train_err,
valid_err=valid_err, test_err=test_err, history_errs=history_errs,
**params)
if history_errs != []:
history = np.asarray(history_errs)
best_valid_idx = history[:,6].argmin()
np.savetxt(save_model_dir+'train_valid_test.txt', history, fmt='%.4f')
print 'final best exp ', history[best_valid_idx]
return train_err, valid_err, test_err
def train_from_scratch(state, channel):
t0 = time.time()
print 'training an attention model'
model = Attention(channel)
model.train(**state.attention)
print 'training time in total %.4f sec'%(time.time()-t0)