def adadelta(lr, tparams, grads, x, mask, y, cost):
"""
Adadelta Optimization
"""
zipped_grads = [theano.shared(p.get_value() * numpy_floatX(0.),
name='%s_grad' % k)
for k, p in tparams.iteritems()]
running_up2 = [theano.shared(p.get_value() * numpy_floatX(0.),
name='%s_rup2' % k)
for k, p in tparams.iteritems()]
running_grads2 = [theano.shared(p.get_value() * numpy_floatX(0.),
name='%s_rgrad2' % k)
for k, p in tparams.iteritems()]
zgup = [(zg, g) for zg, g in zip(zipped_grads, grads)]
rg2up = [(rg2, 0.95 * rg2 + 0.05 * (g ** 2))
for rg2, g in zip(running_grads2, grads)]
f_grad_shared = theano.function([x, mask, y], cost, updates=zgup + rg2up,
name='adadelta_f_grad_shared')
updir = [-T.sqrt(ru2 + 1e-6) / T.sqrt(rg2 + 1e-6) * zg
for zg, ru2, rg2 in zip(zipped_grads,
running_up2,
running_grads2)]
ru2up = [(ru2, 0.95 * ru2 + 0.05 * (ud ** 2))
for ru2, ud in zip(running_up2, updir)]
param_up = [(p, p + ud) for p, ud in zip(tparams.values(), updir)]
f_update = theano.function([lr], [], updates=ru2up + param_up,
on_unused_input='ignore',
name='adadelta_f_update')
return f_grad_shared, f_update
def lstm_layer(tparams, state_below, options, prefix='lstm', mask=None):
nsteps = state_below.shape[0]
if state_below.ndim == 3:
n_samples = state_below.shape[1]
else:
n_samples = 1
assert mask is not None
def _slice(_x, n, dim):
if _x.ndim == 3:
return _x[:, :, n * dim:(n + 1) * dim]
return _x[:, n * dim:(n + 1) * dim]
def _step(m_, x_, h_, c_):
preact = T.dot(h_, tparams[_p(prefix, 'U')])
preact += x_
i = T.nnet.sigmoid(_slice(preact, 0, options['dim_proj']))
f = T.nnet.sigmoid(_slice(preact, 1, options['dim_proj']))
o = T.nnet.sigmoid(_slice(preact, 2, options['dim_proj']))
c = T.tanh(_slice(preact, 3, options['dim_proj']))
c = f * c_ + i * c
c = m_[:, None] * c + (1. - m_)[:, None] * c_
h = o * T.tanh(c)
h = m_[:, None] * h + (1. - m_)[:, None] * h_
return h, c
state_below = (T.dot(state_below, tparams[_p(prefix, 'W')]) +
tparams[_p(prefix, 'b')])
dim_proj = options['dim_proj']
rval, updates = theano.scan(_step,
sequences=[mask, state_below],
outputs_info=[T.alloc(numpy_floatX(0.),
n_samples,
dim_proj),
T.alloc(numpy_floatX(0.),
n_samples,
dim_proj)],
name=_p(prefix, '_layers'),
n_steps=nsteps)
return rval[0]
def rmsprop(lr, tparams, grads, x, mask, y, cost):
"""
Rmsprop Optimziation
"""
zipped_grads = [
theano.shared(p.get_value() * numpy_floatX(0.), name='%s_grad' % k)
for k, p in tparams.iteritems()
]
running_grads = [
theano.shared(p.get_value() * numpy_floatX(0.), name='%s_rgrad' % k)
for k, p in tparams.iteritems()
]
running_grads2 = [
theano.shared(p.get_value() * numpy_floatX(0.), name='%s_rgrad2' % k)
for k, p in tparams.iteritems()
]
zgup = [(zg, g) for zg, g in zip(zipped_grads, grads)]
rgup = [(rg, 0.95 * rg + 0.05 * g) for rg, g in zip(running_grads, grads)]
rg2up = [(rg2, 0.95 * rg2 + 0.05 * (g**2))
for rg2, g in zip(running_grads2, grads)]
f_grad_shared = theano.function([x, mask, y],
cost,
updates=zgup + rgup + rg2up,
name='rmsprop_f_grad_shared')
updir = [
theano.shared(p.get_value() * numpy_floatX(0.), name='%s_updir' % k)
for k, p in tparams.iteritems()
]
updir_new = [(ud, 0.9 * ud - 1e-4 * zg / T.sqrt(rg2 - rg**2 + 1e-4))
for ud, zg, rg, rg2 in zip(updir, zipped_grads, running_grads,
running_grads2)]
param_up = [(p, p + udn[1]) for p, udn in zip(tparams.values(), updir_new)]
f_update = theano.function([lr], [],
updates=updir_new + param_up,
on_unused_input='ignore',
name='rmsprop_f_update')
return f_grad_shared, f_update
def adadelta(lr, tparams, grads, x, mask, y, cost):
"""
Adadelta Optimization
"""
zipped_grads = [
theano.shared(p.get_value() * numpy_floatX(0.), name='%s_grad' % k)
for k, p in tparams.iteritems()
]
running_up2 = [
theano.shared(p.get_value() * numpy_floatX(0.), name='%s_rup2' % k)
for k, p in tparams.iteritems()
]
running_grads2 = [
theano.shared(p.get_value() * numpy_floatX(0.), name='%s_rgrad2' % k)
for k, p in tparams.iteritems()
]
zgup = [(zg, g) for zg, g in zip(zipped_grads, grads)]
rg2up = [(rg2, 0.95 * rg2 + 0.05 * (g**2))
for rg2, g in zip(running_grads2, grads)]
f_grad_shared = theano.function([x, mask, y],
cost,
updates=zgup + rg2up,
name='adadelta_f_grad_shared')
updir = [
-T.sqrt(ru2 + 1e-6) / T.sqrt(rg2 + 1e-6) * zg
for zg, ru2, rg2 in zip(zipped_grads, running_up2, running_grads2)
]
ru2up = [(ru2, 0.95 * ru2 + 0.05 * (ud**2))
for ru2, ud in zip(running_up2, updir)]
param_up = [(p, p + ud) for p, ud in zip(tparams.values(), updir)]
f_update = theano.function([lr], [],
updates=ru2up + param_up,
on_unused_input='ignore',
name='adadelta_f_update')
return f_grad_shared, f_update
def rmsprop(lr, tparams, grads, x, mask, y, cost):
"""
Rmsprop Optimziation
"""
zipped_grads = [theano.shared(p.get_value() * numpy_floatX(0.),
name='%s_grad' % k)
for k, p in tparams.iteritems()]
running_grads = [theano.shared(p.get_value() * numpy_floatX(0.),
name='%s_rgrad' % k)
for k, p in tparams.iteritems()]
running_grads2 = [theano.shared(p.get_value() * numpy_floatX(0.),
name='%s_rgrad2' % k)
for k, p in tparams.iteritems()]
zgup = [(zg, g) for zg, g in zip(zipped_grads, grads)]
rgup = [(rg, 0.95 * rg + 0.05 * g) for rg, g in zip(running_grads, grads)]
rg2up = [(rg2, 0.95 * rg2 + 0.05 * (g ** 2))
for rg2, g in zip(running_grads2, grads)]
f_grad_shared = theano.function([x, mask, y], cost,
updates=zgup + rgup + rg2up,
name='rmsprop_f_grad_shared')
updir = [theano.shared(p.get_value() * numpy_floatX(0.),
name='%s_updir' % k)
for k, p in tparams.iteritems()]
updir_new = [(ud, 0.9 * ud - 1e-4 * zg / T.sqrt(rg2 - rg ** 2 + 1e-4))
for ud, zg, rg, rg2 in zip(updir, zipped_grads, running_grads,
running_grads2)]
param_up = [(p, p + udn[1])
for p, udn in zip(tparams.values(), updir_new)]
f_update = theano.function([lr], [], updates=updir_new + param_up,
on_unused_input='ignore',
name='rmsprop_f_update')
return f_grad_shared, f_update
def pred_error(f_pred, prepare_data, data, iterator, verbose=False):
"""
Just compute the error
f_pred: Theano fct computing the prediction
prepare_data: usual prepare_data for that dataset.
"""
valid_err = 0
for _, valid_index in iterator:
x, mask, y = prepare_data([data[0][t] for t in valid_index],
numpy.array(data[1])[valid_index],
maxlen=None)
preds = f_pred(x, mask)
targets = numpy.array(data[1])[valid_index]
valid_err += (preds == targets).sum()
valid_err = 1. - numpy_floatX(valid_err) / len(data[0])
return valid_err
def build_model(tparams, options):
trng = RandomStreams(SEED)
# Used for dropout.
use_noise = theano.shared(numpy_floatX(0.))
x = T.matrix('x', dtype='int64')
mask = T.matrix('mask', dtype=config.floatX)
y = T.vector('y', dtype='int64')
n_timesteps = x.shape[0]
n_samples = x.shape[1]
emb = tparams['Wemb'][x.flatten()].reshape([n_timesteps,
n_samples,
options['dim_proj']])
proj = get_layer(options['encoder'])[1](tparams, emb, options,
prefix=options['encoder'],
mask=mask)
if options['encoder'] == 'lstm':
proj = (proj * mask[:, :, None]).sum(axis=0)
proj = proj / mask.sum(axis=0)[:, None]
if options['use_dropout']:
proj = dropout_layer(proj, use_noise, trng)
pred = T.nnet.softmax(T.dot(proj, tparams['U']) + tparams['b'])
f_pred_prob = theano.function([x, mask], pred, name='f_pred_prob')
f_pred = theano.function([x, mask], pred.argmax(axis=1), name='f_pred')
off = 1e-8
if pred.dtype == 'float16':
off = 1e-6
cost = -T.log(pred[T.arange(n_samples), y] + off).mean()
return use_noise, x, mask, y, f_pred_prob, f_pred, cost
def train_lstm(
dim_proj=128, # word embeding dimension and LSTM number of hidden units.
patience=10, # Number of epoch to wait before early stop if no progress
max_epochs=5000, # The maximum number of epoch to run
dispFreq=10, # Display to stdout the training progress every N updates
decay_c=0., # Weight decay for the classifier applied to the U weights.
lrate=0.0001, # Learning rate for sgd (not used for adadelta and rmsprop)
n_words=10000, # Vocabulary size
optimizer=adadelta, # sgd, adadelta and rmsprop available, sgd very hard to use, not recommanded (probably need momentum and decaying learning rate).
encoder='lstm', # TODO: can be removed must be lstm.
saveto='lstm_model.npz', # The best model will be saved there
validFreq=370, # Compute the validation error after this number of update.
saveFreq=1110, # Save the parameters after every saveFreq updates
maxlen=100, # Sequence longer then this get ignored
batch_size=16, # The batch size during training.
valid_batch_size=64, # The batch size used for validation/test set.
dataset='imdb',
# Parameter for extra option
noise_std=0.,
use_dropout=True, # if False slightly faster, but worst test error
# This frequently need a bigger model.
reload_model=None, # Path to a saved model we want to start from.
test_size=-1, # If >0, we keep only this number of test example.
):
# Model options
model_options = locals().copy()
print "model options", model_options
load_data, prepare_data = get_dataset(dataset)
print 'Loading data'
train, valid, test = load_data(n_words=n_words, valid_portion=0.05,
maxlen=maxlen)
if test_size > 0:
# The test set is sorted by size, but we want to keep random
# size example. So we must select a random selection of the
# examples.
idx = numpy.arange(len(test[0]))
numpy.random.shuffle(idx)
idx = idx[:test_size]
test = ([test[0][n] for n in idx], [test[1][n] for n in idx])
ydim = numpy.max(train[1]) + 1
model_options['ydim'] = ydim
print 'Building model'
# This create the initial parameters as numpy ndarrays.
# Dict name (string) -> numpy ndarray
params = init_params(model_options)
if reload_model:
load_params('lstm_model.npz', params)
# This create Theano Shared Variable from the parameters.
# Dict name (string) -> Theano Tensor Shared Variable
# params and tparams have different copy of the weights.
tparams = init_tparams(params)
# use_noise is for dropout
(use_noise, x, mask,
y, f_pred_prob, f_pred, cost) = build_model(tparams, model_options)
if decay_c > 0.:
decay_c = theano.shared(numpy_floatX(decay_c), name='decay_c')
weight_decay = 0.
weight_decay += (tparams['U'] ** 2).sum()
weight_decay *= decay_c
cost += weight_decay
f_cost = theano.function([x, mask, y], cost, name='f_cost')
grads = T.grad(cost, wrt=tparams.values())
f_grad = theano.function([x, mask, y], grads, name='f_grad')
lr = T.scalar(name='lr')
f_grad_shared, f_update = optimizer(lr, tparams, grads,
x, mask, y, cost)
print 'Optimization'
kf_valid = get_minibatches_idx(len(valid[0]), valid_batch_size)
kf_test = get_minibatches_idx(len(test[0]), valid_batch_size)
print "%d train examples" % len(train[0])
print "%d valid examples" % len(valid[0])
print "%d test examples" % len(test[0])
history_errs = []
best_p = None
bad_count = 0
if validFreq == -1:
validFreq = len(train[0]) / batch_size
if saveFreq == -1:
saveFreq = len(train[0]) / batch_size
uidx = 0 # the number of update done
estop = False # early stop
start_time = time.time()
try:
for eidx in xrange(max_epochs):
n_samples = 0
# Get new shuffled index for the training set.
kf = get_minibatches_idx(len(train[0]), batch_size, shuffle=True)
for _, train_index in kf:
uidx += 1
use_noise.set_value(1.)
# Select the random examples for this minibatch
y = [train[1][t] for t in train_index]
x = [train[0][t]for t in train_index]
# Get the data in numpy.ndarray format
# This swap the axis!
# Return something of shape (minibatch maxlen, n samples)
x, mask, y = prepare_data(x, y)
n_samples += x.shape[1]
cost = f_grad_shared(x, mask, y)
f_update(lrate)
if numpy.isnan(cost) or numpy.isinf(cost):
print 'NaN detected'
return 1., 1., 1.
if numpy.mod(uidx, dispFreq) == 0:
print 'Epoch ', eidx, 'Update ', uidx, 'Cost ', cost
if saveto and numpy.mod(uidx, saveFreq) == 0:
print 'Saving...',
if best_p is not None:
params = best_p
else:
params = unzip(tparams)
numpy.savez(saveto, history_errs=history_errs, **params)
pkl.dump(model_options, open('%s.pkl' % saveto, 'wb'), -1)
print 'Done'
if numpy.mod(uidx, validFreq) == 0:
use_noise.set_value(0.)
train_err = pred_error(f_pred, prepare_data, train, kf)
valid_err = pred_error(f_pred, prepare_data, valid,
kf_valid)
test_err = pred_error(f_pred, prepare_data, test, kf_test)
history_errs.append([valid_err, test_err])
if (uidx == 0 or
valid_err <= numpy.array(history_errs)[:,
0].min()):
best_p = unzip(tparams)
bad_counter = 0
print ('Train ', train_err, 'Valid ', valid_err,
'Test ', test_err)
if (len(history_errs) > patience and
valid_err >= numpy.array(history_errs)[:-patience,
0].min()):
bad_counter += 1
if bad_counter > patience:
print 'Early Stop!'
estop = True
break
print 'Seen %d samples' % n_samples
if estop:
break
except KeyboardInterrupt:
print "Training interupted"
end_time = time.time()
if best_p is not None:
zipp(best_p, tparams)
else:
best_p = unzip(tparams)
use_noise.set_value(0.)
kf_train_sorted = get_minibatches_idx(len(train[0]), batch_size)
train_err = pred_error(f_pred, prepare_data, train, kf_train_sorted)
valid_err = pred_error(f_pred, prepare_data, valid, kf_valid)
test_err = pred_error(f_pred, prepare_data, test, kf_test)
print 'Train ', train_err, 'Valid ', valid_err, 'Test ', test_err
if saveto:
numpy.savez(saveto, train_err=train_err,
valid_err=valid_err, test_err=test_err,
history_errs=history_errs, **best_p)
print 'The code run for %d epochs, with %f sec/epochs' % (
(eidx + 1), (end_time - start_time) / (1. * (eidx + 1)))
print >> sys.stderr, ('Training took %.1fs' %
(end_time - start_time))
return train_err, valid_err, test_err
