def test_logistic_sgd(): logistic_sgd.sgd_optimization_mnist(n_epochs=10)
def test_logistic_sgd(): t0 = time.time() logistic_sgd.sgd_optimization_mnist(n_epochs=10) print >> sys.stderr, "test_logistic_sgd took %.3fs expected 15.2s in our buildbot" % ( time.time() - t0)
def test_logistic_sgd(): t0=time.time() logistic_sgd.sgd_optimization_mnist(n_epochs=10) print >> sys.stderr, "test_logistic_sgd took %.3fs expected 15.2s in our buildbot"%(time.time()-t0)
return logistic_labels def print_digit(digit, cols=28): for y in range(len(digit) / cols): for x in range(cols): sys.stdout.write('X' if digit[x + y*cols] else ' ') sys.stdout.write('\n') if __name__ == '__main__': arguments = docopt.docopt(__doc__) epochs = int(arguments['--epochs']) training, validation, testing = load_data(arguments['<mnist_training>'], arguments['<mnist_labels>']) sgd_classifier = sgd_optimization_mnist(training[0], training[1], validation[0], validation[1], testing[0], testing[1], n_epochs=epochs) mlp_classifier = test_mlp(training[0], training[1], validation[0], validation[1], testing[0], testing[1], n_epochs=epochs) lenet_classifier = evaluate_lenet5(training[0], training[1], validation[0], validation[1], testing[0], testing[1], n_epochs=epochs, nkerns=[4,10]) with open('sgd_classifier.pkl', 'w') as f: cPickle.dump(sgd_classifier, f) with open('mlp_classifier.pkl', 'w') as f: cPickle.dump(mlp_classifier, f) with open('lenet_classifier.pkl', 'w') as f: cPickle.dump(lenet_classifier, f) digits = load_mnist_data(arguments['<mnist_training>']) random.shuffle(digits) for digit in digits: sgd_label = sgd_classifier([digit]) mlp_label = mlp_classifier([digit])
import mlp import mlp_dropOut import mlp_dropConnect import convolutional_mlp import con_mlp_dropConnect import con_mlp_dropOut c100 = 'cifar-100-python.tar.gz' sys.stdout = open('results/cifar-100_results/lcg.out', 'w') logistic_cg.cg_optimization_mnist(mnist_pkl_gz=c100) sys.stdout = open('results/cifar-100_results/lsgd.out', 'w') logistic_sgd.sgd_optimization_mnist(dataset=c100) sys.stdout = open('results/cifar-100_results/lsgd_gau.out', 'w') logistic_sgd_gaussian.sgd_optimization_mnist(dataset=c100) sys.stdout = open('results/cifar-100_results/lsgd_bin.out', 'w') logistic_sgd_binomial.sgd_optimization_mnist(dataset=c100) sys.stdout = open('results/cifar-100_results/mlp.out', 'w') mlp.test_mlp(dataset=c100) sys.stdout = open('results/cifar-100_results/mlpO.out', 'w') # mlp_dropOut.test_mlp(p=0.8, n_hidden = 100) mlp_dropOut.test_mlp(dataset=c100) sys.stdout = open('results/cifar-100_results/mlpC.out', 'w')