Python main示例

示例#1

import sys
import learn_yz_x_ss

print('Usage: python [this_script.py] [n_labels] [seed]')
n_labels = int(sys.argv[1])
print('n_labels:', n_labels)
seed = int(sys.argv[2])
print('seed:', seed)

if n_labels not in (100, 600, 1000, 3000):
    print(
        'WARNING: for MNIST, n_labels should be in (100,600,1000,3000), otherwise the number of datapoints might not be a multiple of the number of minibatches.')
if n_labels == 100:
    learn_yz_x_ss.main(3000, n_labels, dataset='mnist_2layer', n_z=50, n_hidden=(300,), seed=seed, alpha=0.1,
                       n_minibatches=100, comment='')
else:
    learn_yz_x_ss.main(3000, n_labels, dataset='mnist_2layer', n_z=50, n_hidden=(500,), seed=seed, alpha=0.1,
                       n_minibatches=200, comment='')

示例#2

文件： exp_ssl.py 项目： SandervanL/deep-semi-supervised-learning

import learn_yz_x_ss
import sys

learn_yz_x_ss.main(n_passes=3000,
                   n_labeled=int(sys.argv[1]),
                   dataset='mnist_2layer',
                   n_z=50,
                   n_hidden=tuple([int(sys.argv[2])] * int(sys.argv[3])),
                   seed=int(sys.argv[4]),
                   alpha=0.1,
                   comment='')

示例#3

文件： exp_ssl.py 项目： 2020zyc/nips14-ssl

import learn_yz_x_ss
import sys
learn_yz_x_ss.main(n_passes=3000, n_labeled=int(sys.argv[1]), dataset='mnist_2layer', n_z=50, n_hidden=tuple([int(sys.argv[2])]*int(sys.argv[3])), seed=int(sys.argv[4]), alpha=0.1, comment='')

示例#4

文件： run_2layer_ssl.py 项目： 2020zyc/nips14-ssl

import sys
import learn_yz_x_ss

print 'Usage: python [this_script.py] [n_labels] [seed]'
n_labels = int(sys.argv[1])
print 'n_labels:', n_labels
seed = int(sys.argv[2])
print 'seed:', seed

if n_labels not in (100,600,1000,3000):
    print 'WARNING: for MNIST, n_labels should be in (100,600,1000,3000), otherwise the number of datapoints might not be a multiple of the number of minibatches.'
if n_labels == 100:
    learn_yz_x_ss.main(3000, n_labels, dataset='mnist_2layer', n_z=50, n_hidden=(300,), seed=seed, alpha=0.1, n_minibatches=100, comment='')
else:
    learn_yz_x_ss.main(3000, n_labels, dataset='mnist_2layer', n_z=50, n_hidden=(500,), seed=seed, alpha=0.1, n_minibatches=200, comment='')