def gen_results_for_hw(X, y):
for m in [1, 20, 200, 1000, 2000]:
results = []
plt.figure()
plt.xlabel('Iteration (every 10th saved)')
plt.ylabel('Loss')
plt.title('SVRG ojective function progression for m = {}'.format(m))
for i in range(5):
svrg = SVRG(M=m, calc_loss=True)
svrg.fit(X, y)
results.append(svrg.fit_time)
plt.plot(svrg.training_loss, label='Run {}'.format(i))
print('Run {} time: {}'.format(i, round(results[-1], 3)))
plt.legend(loc='upper right')
plt.savefig('writeup/plots/svrg/m{}.pdf'.format(m), format='pdf')
plt.close('all')
print('Avg: {}s, Std: {}s'.format(round(np.mean(results), 3),
round(np.std(results), 3)))
def gen_results(X, y, m, numruns, loss=False):
results = []
if loss:
plt.figure()
plt.xlabel('Iteration (every 10th saved)')
plt.ylabel('Loss')
plt.title(
'Objective function progression across {} runs'.format(numruns))
for i in range(numruns):
svrg = SVRG(M=m, calc_loss=loss)
svrg.fit(X, y)
results.append(svrg.fit_time)
if loss:
plt.plot(svrg.training_loss, label='Run {}'.format(i))
print('Run {} time: {}'.format(i, round(results[-1], 3)))
if loss:
plt.legend(loc='upper right')
plt.savefig('plots/svrg/loss_vs_ite_{}.pdf'.format(
strftime("%Y.%m.%d_%H.%M.%S", localtime()) + '__m_{}'.format(m),
format='pdf'))
plt.close('all')
return results