dropout_rate=dropout_rate,
batch_size=batch_size,
np_rng=rng)
start_time = timeit.default_timer()
for epoch in xrange(training_epochs):
epoch_time = timeit.default_timer()
mean_cost = []
for batch_index in xrange(n_train_batches):
rbm.update(np_train_set[batch_index*batch_size:(batch_index+1)*batch_size,:], persistent=True, k=k)
sys.stdout.write("\rEpoch advancement: %d%%" % (100*float(batch_index)/n_train_batches))
sys.stdout.flush()
sys.stdout.write("\rEvaluating accuracy...")
sys.stdout.flush()
cv_time = timeit.default_timer()
acc = rbm.cv_accuracy(np_test_set)
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 scoring=='cost':
score = np.mean(mean_cost)
elif scoring=='accuracy':
score = acc
else:
raise Warning('''scoring must be cost or accuracy,
set to accuracy''')
score = acc
