argmax_acc = 0
for epoch in xrange(training_epochs):
epoch_time = timeit.default_timer()
mean_cost = []
for batch_index, batch in enumerate(batches):
rbm.update(batch, persistent=True, k=k)
sys.stdout.write("\rEpoch advancement: %d%%" % (100*float(batch_index)/len(batches)))
sys.stdout.flush()
# Training Logistic regression
sys.stdout.write("\rTraining softmax...")
sm_time = timeit.default_timer()
softmax_classifier = LogisticRegression(penalty='l1',
C=1.0,
solver='lbfgs',
multi_class='multinomial')
softmax_classifier.fit(rbm.propup(train_set[:,n_labels:], np.ones((len(train_set),n_hidden))),
train_labels)
sys.stdout.write('\rSoftmax trained in %f minutes.\n' % ((timeit.default_timer()-sm_time) / 60.))
sys.stdout.write("Evaluating accuracy...")
cv_time = timeit.default_timer()
acc = softmax_classifier.score(rbm.propup(test_set[:,n_labels:], np.ones((len(test_set),n_hidden))),
test_labels)
accuracies.append(acc)
sys.stdout.write('''\rEpoch %i took %f minutes,
accuracy (computed in %f minutes) is %f.\n'''
% (epoch, ((cv_time-epoch_time) / 60.),
((timeit.default_timer()-cv_time) / 60.), acc))
if do_report:
report["costs"][epoch] = np.mean(mean_cost)
report["accuracy"][epoch] = acc
if (acc>argmax_acc):
