# plot_save_loss(losses, losses_validate, '/home/hh/data/loss_csnn_2_layers_run{}.png'.format(run))
# plot_save_acc(accuracies, accuracies_validate, '/home/hh/data/acc_csnn_2_layers_run{}.png'.format(run))
# plot_save_acc_nzs_mmcs(alphas, accuracies_validate, nzs, mmcs,
# '/home/hh/data/acc_nzs_mmcs_csnn_2_layers_run{}.png'.format(run))
# plot_save_loss(losses, losses_validate, '/home/hh/data/loss_csnn_2_layers_run{}_trim.png'.format(run))
# plot_save_acc(accuracies, accuracies_validate, '/home/hh/data/acc_csnn_2_layers_run{}_trim.png'.format(run))
# plot_save_acc_nzs_mmcs(alphas, accuracies_validate, nzs, mmcs,
# '/home/hh/data/acc_nzs_mmcs_csnn_2_layers_run{}_trim.png'.format(run))
# plot_save_loss(losses, losses_validate, '/home/hh/data/loss_csnn_2_layers_run{}_r2{:.1f}.png'.format(run, r2))
# plot_save_acc(accuracies, accuracies_validate, '/home/hh/data/acc_csnn_2_layers_run{}_r2{:.1f}.png'.format(run, r2))
# plot_save_acc_nzs_mmcs(alphas, accuracies_validate, nzs, mmcs,
# '/home/hh/data/acc_nzs_mmcs_csnn_2_layers_run{}_r2{:.1f}.png'.format(run, r2))
plot_save_loss(losses, losses_validate, '/home/hh/data/loss_csnn_2_layers_run{}_r2{:.1f}_maxAlpha{:.1f}.png'.format(run, r2, maxAlpha))
plot_save_acc(accuracies, accuracies_validate, '/home/hh/data/acc_csnn_2_layers_run{}_r2{:.1f}_maxAlpha{:.1f}.png'.format(run, r2, maxAlpha))
plot_save_acc_nzs_mmcs(alphas, accuracies_validate, nzs, mmcs,
'/home/hh/data/acc_nzs_mmcs_csnn_2_layers_run{}_r2{:.1f}_maxAlpha{:.1f}.png'.format(run, r2, maxAlpha))
bestValidationAccs.append(max(accuracies_validate))
AUCs.append(aucs)
ACCs.append(accs)
print('mean and std of best validation acc in {} runs: {:.4f}, {:.4f}'
.format(runs, np.mean(np.array(bestValidationAccs)), np.std(np.array(bestValidationAccs))))
# plt.show()
AUCs = np.array(AUCs)
ACCs = np.array(ACCs)
print('average and std of AUC in {} runs'.format(runs))
# print('mean ', np.mean(AUCs, axis=0))
# print('std ', np.std(AUCs, axis=0))
# dir = '/home/hh/data/mean_std_accs_aucs_csnn_2_layers_trim.npz'
# dir = '/home/hh/data/mean_std_accs_aucs_csnn_2_layers_r2{:.1f}.npz'.format(r2)
print('loss: cross_entropy {:.4f}, penalty {:.4f}'.format(
loss_ce, loss_penalty))
# eliminate dead nodes
# if ( (epoch<200 and (epoch + 1) % (epochs / 100) == 0)
# or (epoch>=200 and (epoch + 1) % (epochs/10) == 0)):
# #_, dmu = active(torch.tensor(x_train).float(), model, alpha, r2)
# PATH = outputDir+'/csnn_2_csnn_layers_run{}_epoch{}_r2{:.1f}_maxAlpha{:.1f}_affine_false.pth'.format(run, epoch+1, r2, maxAlpha)
# torch.save({'net':model, 'alpha':alpha, 'r2':r2}, PATH)
# # model.keepNodes(dmu > 0)
plot_save_loss(losses, losses_validate,
outputDir + '/loss_run{}.png'.format(run))
plot_save_acc(accuracies, accuracies_validate,
outputDir + '/acc_run{}.png'.format(run))
plot_save_acc_nzs_mmcs(alphas, accuracies_validate, nzs, aucs,
outputDir + '/acc_nzs_mmcs_run{}.png'.format(run))
bestValidationAccs.append(max(accuracies_validate))
AUCs.append(aucs)
ACCs.append(accuracies_validate)
if ALPHAs is None: ALPHAs = alphas
AUCs = np.array(AUCs)
ACCs = np.array(ACCs)
print('mean and std of best validation acc in {} runs: {:.4f}, {:.4f}'.format(
runs, np.mean(np.array(bestValidationAccs)),
np.std(np.array(bestValidationAccs))))
dir = outputDir + '/mean_std_accs_aucs_net4.npz'
np.savez(dir,
a=np.mean(AUCs, axis=0),
b=np.std(AUCs, axis=0),
c=np.mean(ACCs, axis=0),
# model.keepNodes(dmu > 0)
# print('epoch ', epoch)
dir0 = '/home/hh/data/'
dir = '/home/hh/data/loss_csnn_4_layers_batchNorm_noPretrain_maxAlpha{:.2f}_run{}'.format(maxAlpha, run)
np.savez(dir + ".npz", a=np.array(losses), b=np.array(losses_validate), c=np.array(mmcs))
dir = '/home/hh/data/acc_csnn_4_layers_batchNorm_noPretrain_maxAlpha{:.2f}_run{}'.format(maxAlpha, run)
np.savez(dir + ".npz", a=np.array(accuracies), b=np.array(accuracies_validate))
dir = '/home/hh/data/csnn_nz_mmcs_4_layers_batchNorm_noPretrain_maxAlpha{:.2f}_run{}.npz'.format(maxAlpha, run)
np.savez(dir, a=np.array(nzs), b=np.array(mmcs))
plot_save_loss(losses, losses_validate,
'/home/hh/data/loss_csnn_4_layers_batchNorm_noPretrain_maxAlpha{:.2f}_run{}.png'.format(maxAlpha, run))
plot_save_acc(accuracies, accuracies_validate,
'/home/hh/data/acc_csnn_4_layers_batchNorm_noPretrain_maxAlpha{:.2f}_run{}.png'.format(maxAlpha, run))
plot_save_acc_nzs_mmcs(alphas, accuracies_validate, nzs, mmcs,
'/home/hh/data/acc_nzs_mmcs_csnn_4_layers_batchNorm_noPretrain_maxAlpha{:.2f}_run{}.png'.format(maxAlpha, run))
bestValidationAccs.append(max(accuracies_validate))
print('mean and std of best validation acc in {} runs: {:.4f}, {:.4f}'
.format(runs, np.mean(np.array(bestValidationAccs)), np.std(np.array(bestValidationAccs))))
# plt.show()
else:
AUCs = []
ACCs = []
epochs = []
# for i in range(10, 191, 10):
for i in range(10, 101, 10):
epochs.append(i)
# for i in range(200, 1001, 100):
# epochs.append(i)
torch.tensor(x_train).float(), model, alpha, r2)
PATH = outputDir + '/csnn_2_csnn_layers_run{}_epoch{}_r2{:.1f}_maxAlpha{:.1f}_affine_false.pth'.format(
run, epoch + 1, r2, maxAlpha)
torch.save({'net': model, 'alpha': alpha, 'r2': r2}, PATH)
# model.keepNodes(dmu > 0)
plot_save_loss(
losses, losses_validate, outputDir +
'/loss_csnn_2_csnn_layers_run{}_r2{:.1f}_maxAlpha{:.1f}_affine_false.png'
.format(run, r2, maxAlpha))
plot_save_acc(
accuracies, accuracies_validate, outputDir +
'/acc_csnn_2_csnn_layers_run{}_r2{:.1f}_maxAlpha{:.1f}_affine_false.png'
.format(run, r2, maxAlpha))
plot_save_acc_nzs_mmcs(
alphas, accuracies_validate, nzs, mmcs, outputDir +
'/acc_nzs_mmcs_csnn_2_csnn_layers_run{}_r2{:.1f}_maxAlpha{:.1f}_affine_false.png'
.format(run, r2, maxAlpha))
bestValidationAccs.append(max(accuracies_validate))
print('mean and std of best validation acc in {} runs: {:.4f}, {:.4f}'.
format(runs, np.mean(np.array(bestValidationAccs)),
np.std(np.array(bestValidationAccs))))
# plt.show()
else:
AUCs = []
ACCs = []
epochs = []
alphas = []
# for i in range(0, 191, 10):
for i in range(0, 101, 10):
# PATH = outputDir+'/csnn_2_csnn_layers_run{}_epoch{}_r2{:.1f}_maxAlpha{:.1f}_affine_false.pth'.format(run, epoch+1, r2, maxAlpha)
# torch.save({'net':model, 'alpha':alpha, 'r2':r2}, PATH)
# # model.keepNodes(dmu > 0)
# plot_save_loss(losses, losses_validate, outputDir+'/loss_run{}.png'.format(run))
plot_save_loss(
losses, losses_validate,
outputDir + '/loss_lambda{:.2f}_run{}.png'.format(LAMBDA, run))
# plot_save_acc(accuracies, accuracies_validate, outputDir+'/acc_run{}.png'.format(run))
plot_save_acc(
accuracies, accuracies_validate,
outputDir + '/acc_lambda{:.2f}_run{}.png'.format(LAMBDA, run))
plot_save_acc_nzs_mmcs(
alphas,
accuracies_validate,
nzs,
aucs,
# outputDir+'/acc_nzs_mmcs_run{}.png'.format(run))
outputDir +
'/acc_nzs_mmcs_lambda{:.2f}_run{}.png'.format(LAMBDA, run))
bestValidationAccs.append(max(accuracies_validate))
AUCs.append(aucs)
ACCs.append(accuracies_validate)
if ALPHAs is None: ALPHAs = alphas
AUCs = np.array(AUCs)
ACCs = np.array(ACCs)
print('mean and std of best validation acc in {} runs: {:.4f}, {:.4f}'.
format(runs, np.mean(np.array(bestValidationAccs)),
np.std(np.array(bestValidationAccs))))
dir = outputDir + '/mean_std_accs_aucs_net4_lambda{:.2f}.npz'.format(
LAMBDA)
_, dmu = active(torch.tensor(x_train[:, mask]).float(), model, alpha, r2)
print('fc3 non zero neurons {}'.format(np.sum(dmu>0)))
# model.keepNodes(dmu > 0)
# print('epoch ', epoch)
dir = '/home/hh/data/loss_csnn'
np.savez(dir + ".npz", a=np.array(losses), b=np.array(losses_validate), c=np.array(mmcs))
dir = '/home/hh/data/acc_csnn'
np.savez(dir + ".npz", a=np.array(accuracies), b=np.array(accuracies_validate))
dir = '/home/hh/data/csnn_nz_mmcs.npz'
np.savez(dir, a=np.array(nzs), b=np.array(mmcs))
plot_save_loss(losses, losses_validate, '/home/hh/data/loss_csnn.png')
plot_save_acc(accuracies, accuracies_validate, '/home/hh/data/acc_csnn.png')
plot_save_acc_nzs_mmcs(alphas, accuracies_validate, nzs, mmcs, '/home/hh/data/acc_nzs_mmcs_csnn.png')
bestValidationAccs.append(max(accuracies_validate))
print('mean and std of best validation acc in {} runs: {:.4f}, {:.4f}'
.format(runs, np.mean(np.array(bestValidationAccs)), np.std(np.array(bestValidationAccs))))
# plt.show()
else:
AUCs = []
for run in range(runs):
PATH = '/home/hh/data/csnn_run{}.pth'.format(run)
l = torch.load(PATH)
model = l['net']
alpha = l['alpha']
r2 = l['r2']
print('load model at run {}, alpha {:.4f}, r2 {:.4f}'.format(run, alpha, r2))