def transform(self, X, y=None):
test_set_x, _ = dataset.shared_dataset(global_theano,
global_T,
X,
borrow=True)
# pick random test examples, with which to initialize the persistent chain
persistent_vis_chain = global_theano.shared(
np.asarray(test_set_x.get_value(borrow=True),
dtype=global_theano.config.floatX))
[presig_hids, hid_mfs, hid_samples, presig_vis,
vis_mfs, vis_samples], updates = \
global_theano.scan(
self.rbm.gibbs_vhv,
outputs_info=[None, None, None, None, None, persistent_vis_chain],
n_steps=1)
# add to updates the shared variable that takes care of our persistent
# chain :.
#updates.update({persistent_vis_chain: vis_samples[-1]})
# construct the function that implements our persistent chain.
# we generate the "mean field" activations for plotting and the actual
# samples for reinitializing the state of our persistent chain
sample_fn = global_theano.function(
[], [hid_mfs[-1], hid_samples[-1], vis_mfs[-1], vis_samples[-1]],
name='sample_fn')
ident = random.randint(0, 500)
all_hid_mfs = []
all_vis_sample = []
all_hid_sample = []
for i in range(self.n_resamples):
hid_mfs, hid_sample, vis_mfs, vis_sample = sample_fn()
all_hid_mfs.append(hid_mfs)
all_hid_sample.append(hid_sample)
all_vis_sample.append(vis_sample)
hidden_mean_field = np.mean(all_hid_mfs, axis=0)
visible_mean_field = np.mean(all_vis_sample, axis=0)
print "all_hid_mfs shape", np.shape(all_hid_mfs)
print "Hidden mean field", np.shape(hidden_mean_field)
print "Shapes", np.shape(hidden_mean_field), np.shape(all_hid_mfs)
#self.sample_all(X, all_hid_sample, all_vis_sample, ident)
#return hidden_mean_field
return visible_mean_field
def transform(self, X, y=None):
test_set_x, _ = dataset.shared_dataset(global_theano, global_T, X, borrow=True)
# pick random test examples, with which to initialize the persistent chain
persistent_vis_chain = global_theano.shared(np.asarray(test_set_x.get_value(borrow=True), dtype=global_theano.config.floatX))
[presig_hids, hid_mfs, hid_samples, presig_vis,
vis_mfs, vis_samples], updates = \
global_theano.scan(
self.rbm.gibbs_vhv,
outputs_info=[None, None, None, None, None, persistent_vis_chain],
n_steps=1)
# add to updates the shared variable that takes care of our persistent
# chain :.
#updates.update({persistent_vis_chain: vis_samples[-1]})
# construct the function that implements our persistent chain.
# we generate the "mean field" activations for plotting and the actual
# samples for reinitializing the state of our persistent chain
sample_fn = global_theano.function(
[], [hid_mfs[-1], hid_samples[-1], vis_mfs[-1], vis_samples[-1]],
name='sample_fn')
ident = random.randint(0, 500)
all_hid_mfs = []
all_vis_sample = []
all_hid_sample = []
for i in range(self.n_resamples):
hid_mfs, hid_sample, vis_mfs, vis_sample = sample_fn()
all_hid_mfs.append(hid_mfs)
all_hid_sample.append(hid_sample)
all_vis_sample.append(vis_sample)
hidden_mean_field = np.mean(all_hid_mfs, axis=0)
visible_mean_field = np.mean(all_vis_sample, axis=0)
print "all_hid_mfs shape", np.shape(all_hid_mfs)
print "Hidden mean field", np.shape(hidden_mean_field)
print "Shapes", np.shape(hidden_mean_field), np.shape(all_hid_mfs)
#self.sample_all(X, all_hid_sample, all_vis_sample, ident)
#return hidden_mean_field
return visible_mean_field
def fit(self, X, y=None):
global global_theano
global global_T
global global_RandomStreams
log.debug(u"RBM Fitting with lr={0} epochs={1} n_hidden={2}".format(
self.learning_rate, self.training_epochs, self.n_hidden))
## This prevents us from multiple importing theano which is important
## since it performs some global initialization, especially for cuda
if not global_theano:
log.debug(u"Importing Theano")
import theano
import theano.tensor as T
from theano.tensor.shared_randomstreams import RandomStreams
theano.config.warn.subtensor_merge_bug = False
global_theano = theano
global_T = T
global_RandomStreams = RandomStreams
self.rng = np.random.RandomState(123456)
self.theano_rng = global_RandomStreams(self.rng.randint(2**30))
self.n_visible = np.shape(X)[1]
#log.debug(u"RBM Featureset has {0} visible nodes".format(
#self.n_visible))
train_x, train_y = dataset.shared_dataset(global_theano,
global_T,
X,
y,
borrow=True)
self.init_objects(train_x)
self.train(train_x)
return self
def fit(self, X, y=None):
global global_theano
global global_T
global global_RandomStreams
log.debug(u"RBM Fitting with lr={0} epochs={1} n_hidden={2}".format(
self.learning_rate, self.training_epochs, self.n_hidden))
## This prevents us from multiple importing theano which is important
## since it performs some global initialization, especially for cuda
if not global_theano:
log.debug(u"Importing Theano")
import theano
import theano.tensor as T
from theano.tensor.shared_randomstreams import RandomStreams
theano.config.warn.subtensor_merge_bug = False
global_theano = theano
global_T = T
global_RandomStreams = RandomStreams
self.rng = np.random.RandomState(123456)
self.theano_rng = global_RandomStreams(self.rng.randint(2 ** 30))
self.n_visible = np.shape(X)[1]
#log.debug(u"RBM Featureset has {0} visible nodes".format(
#self.n_visible))
train_x, train_y = dataset.shared_dataset(global_theano, global_T, X, y, borrow=True)
self.init_objects(train_x)
self.train(train_x)
return self