#! /usr/bin/env python3
# -*- coding: utf-8 -*-
from mnist.mnist import get_training_set, get_test_set
from nylearn.logistic import LogisticRegression
from nylearn.dataset import Dataset
from nylearn.train import ConjugateGradientDescent as cg
from nylearn.train import MinibatchGradientDescent as mbgd
from nylearn.train import momentum, decay
tr_x, tr_y = get_training_set(60000)
te_x, te_y = get_test_set(10000)
training_set = Dataset(tr_x[:50000] / 256, tr_y[:50000])
validation_set = Dataset(tr_x[50000:] / 256, tr_y[50000:])
test_set = Dataset(te_x / 256, te_y)
m = momentum(0.9, 0.99, end=100)
d = decay(0.3, 20)
lg = LogisticRegression(tr_x.shape[1], 10, 0.01)
tn = mbgd(lg)
last = tn.train(
training_set, maxiter=200, batch_size=500, eta=0.1, validation_set=validation_set, momentum=m, adjust_eta=d
)
print("training set error: {}, test set error: {}".format(lg.errors(training_set), lg.errors(test_set)))
lg.save("mnist/lg")
lg.theta = last
print("last theta test set error: {}".format(lg.errors(test_set)))
#! /usr/bin/env python3
# -*- coding: utf-8 -*-
import numpy as np
from mnist.mnist import get_training_set, get_test_set
from nylearn.lenet import LeNet
from nylearn.dataset import Dataset
from nylearn.train import ConjugateGradientDescent as cg
from nylearn.train import MinibatchGradientDescent as mbgd
from nylearn.train import momentum, decay
tr_x, tr_y = get_training_set(60000)
trd_x, trd_y = get_training_set(60000, distortion='elastic')
te_x, te_y = get_test_set(10000)
training_set = Dataset(np.vstack([tr_x[:40000], trd_x])/256,
np.hstack([tr_y[:40000], trd_y]))
validation_set = Dataset(tr_x[40000:]/256, tr_y[40000:])
test_set = Dataset(te_x/256, te_y)
m = momentum(0.9, 0.99, end=100)
d = decay(0.5, 10)
convnn = LeNet(28, [5, 25], [200], 10, lamda=0.01)
tn = mbgd(convnn)
last = tn.train(training_set, maxiter=200, batch_size=100, eta=0.1,
validation_set=validation_set, momentum=m, adjust_eta=d)
print('validation set error: {}, test set error: {}'.format(
convnn.errors(validation_set), convnn.errors(test_set)))
convnn.save('mnist/conv-28-5-25-200-10')
convnn.theta = last
print('last theta test set error: {}'.format(convnn.errors(test_set)))
#! /usr/bin/env python3
# -*- coding: utf-8 -*-
from mnist.mnist import get_training_set, get_test_set
from nylearn.nnet import MLP
from nylearn.dataset import Dataset
from nylearn.train import ConjugateGradientDescent as cg
from nylearn.train import MinibatchGradientDescent as mbgd
from nylearn.train import momentum, decay
tr_x, tr_y = get_training_set(60000)
te_x, te_y = get_test_set(10000)
training_set = Dataset(tr_x[:50000]/256, tr_y[:50000])
validation_set = Dataset(tr_x[50000:]/256, tr_y[50000:])
test_set = Dataset(te_x/256, te_y)
m = momentum(0.5, 0.99, end=400)
d = decay(0.99, 10)
nn = MLP(tr_x.shape[1], [500], 10, lamda=0.01)
tn = mbgd(nn)
last = tn.train(training_set, maxiter=600, batch_size=50, eta=0.01,
validation_set=validation_set, momentum=m, adjust_eta=d)
print('validation set error: {}, test set error: {}'.format(
nn.errors(validation_set), nn.errors(test_set)))
nn.save('mnist/nn-500-10')
nn.theta = last
print('last theta test set error: {}'.format(nn.errors(test_set)))