for epoch in range(training_epochs):
 mean_cost = []
cost = rbm.get_cost_updates(lr=learning_rate, k=k)
print ('Training epoch %d, cost is ' % epoch, cost)

cost_plot(mean_cost_list)
end_time = time.clock()

pretraining_time = (end_time - start_time)

print(('Training took %f minutes' % (pretraining_time / 60.)))

return rbm

def cost_plot(mean_cost):
import pylab as plt

plt.plot(mean_cost, ',')
plt.xlabel('epoch')
plt.ylabel('mean cost')
plt.savefig('result.png')