def run(self):
print "--- Loading data"
self.jobman.save()
if not os.path.exists("files"):
os.mkdir("files")
bs = self.jobman.state["bs"]
params = self.cae_emotions.params.values() + self.cae_identity.params.values() + self.logrege.params.values()
self.cost = self.init_costs()
# Computing gradient of the sum of all costs
# on all the parameters of the model
# (except the convolutional part)
self.updates = dict((p, p - self.jobman.state["lr"] * g) for p, g in zip(params, T.grad(self.cost, params)))
k = T.iscalar()
# Compiling optimisation theano function
self.optimize = theano.function(
[k],
self.total_cost_list,
updates=self.updates,
givens={self.x: self.shared_x[k : k + 1], self.y: self.shared_y[k : k + 1]},
)
self.jobman.state["valid_emotion"] = 0
self.jobman.state["valid_identity"] = 0
self.jobman.state["valid_both"] = 0
print "--- Training"
sys.stdout.flush()
bs = self.jobman.state["bs"]
costs = []
# self.evalreg()
for i in range(self.jobman.state["epochs"]):
costs = []
# For each epoch we take 50% of labeled data
# and 50% of unlabeled data shuffled in random
# order.
x, y = shuffle_array(
self.data.get_train_unlabeled(i, shared_array=False), self.data.get_train_labeled(i, shared_array=False)
)
self.shared_x.set_value(x)
self.shared_y.set_value(y)
# We evaluate the representations using an SVM
# with 'evalfreq' frequency.
if (i + 1) % self.jobman.state["evalfreq"] == 0:
he = self.cae_emotions.get_hidden(self.x)
hi = self.cae_identity.get_hidden(self.x)
self.svm(he, "emotion")
self.svm(hi, "identity")
self.svm(T.concatenate([he, hi], axis=1), "both")
self.cae_emotions.save(i, "emot")
self.cae_identity.save(i, "iden")
self.logrege.save(i, "logreg")
mi.view_data(self.cae_emotions.params["W"].get_value(borrow=True).T, "files/emot_" + str(i))
mi.view_data(self.cae_identity.params["W"].get_value(borrow=True).T, "files/iden_" + str(i))
self.jobman.save()
total = self.shared_x.get_value(borrow=True).shape[0] / bs
# We loop over the samples of the current split
# to tune the parameters of the model.
for j in range(total):
costs.append(self.optimize(j))
if j % self.jobman.state["freq"] == 0:
# self.evalreg()
print i, j, zip(self.total_cost_str, numpy.mean(numpy.array(costs), axis=0))
sys.stdout.flush()
self.jobman.state["cft"] = i
self.svm()
def run(self):
print '--- Loading data'
self.jobman.save()
if not os.path.exists('files'):
os.mkdir('files')
bs = self.jobman.state['bs']
params = self.cae.params.values() + self.logreg.params.values()
cost = self.getcosts()
# Computing gradient of the sum of all costs
# on all the parameters of the model
# (except the convolutional part)
updates = dict ( (p,p - self.jobman.state['lr']*g) for p,g in zip(params,T.grad(cost,params)) )
k = T.iscalar()
# Compiling optimisation theano function
optimize = theano.function([k],self.costs,updates=updates,givens = { self.x:self.shared_x[k:k+1],
self.y:self.shared_y[k:k+1] })
self.jobman.state['valid_discriminant'] = 0
self.jobman.state['valid_nuisance'] = 0
self.jobman.state['valid_both'] = 0
print '--- Training'
sys.stdout.flush()
bs = self.jobman.state['bs']
costs = []
h = self.cae.get_hidden(self.x)
h_d = h[:,:self.featsplit]
h_o = h[:,self.featsplit:]
for i in range(self.jobman.state['epochs']):
costs = []
# For each epoch we take 50% of labeled data
# and 50% of unlabeled data shuffled in random
# order.
self.data.get_train_labeled(i)
# We evaluate the representations using an SVM
# with 'evalfreq' frequency.
if (i+1)%self.jobman.state['evalfreq'] == 0:
self.svm(h_d,'discriminant')
self.svm(h_o,'nuisance')
self.svm(h,'both')
self.cae.save(i)
self.logreg.save(i,'logreg')
mi.view_data(self.cae.params['W'].get_value(borrow=True).T,'files/cae_'+str(i))
self.jobman.save()
total = self.shared_x.get_value(borrow=True).shape[0]/bs
# We loop over the samples of the current split
# to tune the parameters of the model.
for j in range(total):
costs.append(optimize(j))
if j%self.jobman.state['freq'] == 0:
#self.evalreg()
print i, j,zip(self.costs_str,numpy.mean(numpy.array(costs),axis=0))
sys.stdout.flush()
self.jobman.state['cft']=i
self.svm(h_d,'discriminant')
self.svm(h_o,'nuisance')
self.svm(h,'both')
def run(self):
print '--- Loading data'
self.jobman.save()
if not os.path.exists('files'):
os.mkdir('files')
bs = self.jobman.state['bs']
params = self.cae_emotions.params.values(
) + self.cae_identity.params.values() + self.logrege.params.values()
self.cost = self.init_costs()
# Computing gradient of the sum of all costs
# on all the parameters of the model
# (except the convolutional part)
self.updates = dict((p, p - self.jobman.state['lr'] * g)
for p, g in zip(params, T.grad(self.cost, params)))
k = T.iscalar()
# Compiling optimisation theano function
self.optimize = theano.function([k],
self.total_cost_list,
updates=self.updates,
givens={
self.x: self.shared_x[k:k + 1],
self.y: self.shared_y[k:k + 1]
})
self.jobman.state['valid_emotion'] = 0
self.jobman.state['valid_identity'] = 0
self.jobman.state['valid_both'] = 0
print '--- Training'
sys.stdout.flush()
bs = self.jobman.state['bs']
costs = []
#self.evalreg()
for i in range(self.jobman.state['epochs']):
costs = []
# For each epoch we take 50% of labeled data
# and 50% of unlabeled data shuffled in random
# order.
x, y = shuffle_array(
self.data.get_train_unlabeled(i, shared_array=False),
self.data.get_train_labeled(i, shared_array=False))
self.shared_x.set_value(x)
self.shared_y.set_value(y)
# We evaluate the representations using an SVM
# with 'evalfreq' frequency.
if (i + 1) % self.jobman.state['evalfreq'] == 0:
he = self.cae_emotions.get_hidden(self.x)
hi = self.cae_identity.get_hidden(self.x)
self.svm(he, 'emotion')
self.svm(hi, 'identity')
self.svm(T.concatenate([he, hi], axis=1), 'both')
self.cae_emotions.save(i, 'emot')
self.cae_identity.save(i, 'iden')
self.logrege.save(i, 'logreg')
mi.view_data(
self.cae_emotions.params['W'].get_value(borrow=True).T,
'files/emot_' + str(i))
mi.view_data(
self.cae_identity.params['W'].get_value(borrow=True).T,
'files/iden_' + str(i))
self.jobman.save()
total = self.shared_x.get_value(borrow=True).shape[0] / bs
# We loop over the samples of the current split
# to tune the parameters of the model.
for j in range(total):
costs.append(self.optimize(j))
if j % self.jobman.state['freq'] == 0:
#self.evalreg()
print i, j, zip(self.total_cost_str,
numpy.mean(numpy.array(costs), axis=0))
sys.stdout.flush()
self.jobman.state['cft'] = i
self.svm()
def run(self):
print '--- Loading data'
self.jobman.save()
if not os.path.exists('files'):
os.mkdir('files')
bs = self.jobman.state['bs']
params = self.cae.params.values() + self.logreg.params.values()
cost = self.getcosts()
# Computing gradient of the sum of all costs
# on all the parameters of the model
# (except the convolutional part)
updates = dict((p, p - self.jobman.state['lr'] * g)
for p, g in zip(params, T.grad(cost, params)))
k = T.iscalar()
# Compiling optimisation theano function
optimize = theano.function([k],
self.costs,
updates=updates,
givens={
self.x: self.shared_x[k:k + 1],
self.y: self.shared_y[k:k + 1]
})
self.jobman.state['valid_discriminant'] = 0
self.jobman.state['valid_nuisance'] = 0
self.jobman.state['valid_both'] = 0
print '--- Training'
sys.stdout.flush()
bs = self.jobman.state['bs']
costs = []
h = self.cae.get_hidden(self.x)
h_d = h[:, :self.featsplit]
h_o = h[:, self.featsplit:]
for i in range(self.jobman.state['epochs']):
costs = []
# For each epoch we take 50% of labeled data
# and 50% of unlabeled data shuffled in random
# order.
self.data.get_train_labeled(i)
# We evaluate the representations using an SVM
# with 'evalfreq' frequency.
if (i + 1) % self.jobman.state['evalfreq'] == 0:
self.svm(h_d, 'discriminant')
self.svm(h_o, 'nuisance')
self.svm(h, 'both')
self.cae.save(i)
self.logreg.save(i, 'logreg')
mi.view_data(self.cae.params['W'].get_value(borrow=True).T,
'files/cae_' + str(i))
self.jobman.save()
total = self.shared_x.get_value(borrow=True).shape[0] / bs
# We loop over the samples of the current split
# to tune the parameters of the model.
for j in range(total):
costs.append(optimize(j))
if j % self.jobman.state['freq'] == 0:
#self.evalreg()
print i, j, zip(self.costs_str,
numpy.mean(numpy.array(costs), axis=0))
sys.stdout.flush()
self.jobman.state['cft'] = i
self.svm(h_d, 'discriminant')
self.svm(h_o, 'nuisance')
self.svm(h, 'both')
