d = DBN([28*28, 500,500, 2000], 10, 0.1)
# Loads the Images and Labels from the MNIST database
images = MNIST.load_images('images')
labels = MNIST.load_labels('labels')
# Gets the training set of images
img = images[0:60000]
lbl = labels[0:60000]
# Loads the test images from the MNIST database
tst_img = MNIST.load_images('test_images')
tst_lbl = MNIST.load_labels('test_labels')
# Pre trains the DBN
d.pre_train(img,5,50)
# Executes a 100 iterations of label training
# Attempts to classify and prints the error rate
for i in xrange(0, 100):
d.train_labels(img, lbl, 50, 50)
tst_class = d.classify(tst_img,10)
print 'Error over test data: {0}'.format(1 - (tst_class*tst_lbl).mean() * dbn.number_labels)
# Tests the DBN on test images
tst_class = d.classify(tst_img,20)
# Calculates the error
err_test = 1 - ((tst_class * tst_lbl).mean() * 10)
# Prints the error rate
# Gets the training set of images
img = images[0:60000]
lbl = labels[0:60000]
# Permutes the data to ensure random batches
train_tuples = zip(img, lbl)
train_tuples = np.random.permutation(train_tuples)
(img, lbl) = zip(*train_tuples)
# Loads the test images from the MNIST database
tst_img = MNIST.load_images('test_images')
tst_lbl = MNIST.load_labels('test_labels')
# Pre trains the DBN
d.pre_train(img, 5, 50)
# Executes a 100 iterations of label training
# Attempts to classify and prints the error rate
for i in xrange(0, 100):
d.train_labels(img, lbl, 50, 50)
tst_class = d.classify(tst_img, 10)
print 'Error over test data: {0}'.format(1 - (tst_class * tst_lbl).mean() *
dbn.number_labels)
# Tests the DBN on test images
tst_class = d.classify(tst_img, 20)
# Calculates the error
err_test = 1 - ((tst_class * tst_lbl).mean() * 10)
# Prints the error rate
from data import MNIST
from dbn import DBN
import numpy as np
d = DBN([28*28, 500,500, 1000], 10,0.1)
images = MNIST.load_images('images')
labels = MNIST.load_labels('labels')
img = images[0:56000]
lbl = labels[0:56000]
tst_img = images[56000:60000]
tst_lbl = labels[56000:60000]
d.pre_train(img,30)
d.train_labels(img, lbl,200)
#print d.sample(img[0:1],0)
#print d.sample(img[1:2],0)
#print 'layer 2'
#print d.sample(img[0:1],1)
#print d.sample(img[1:2],1)
#print 'layer 2'
#print d.sample(img[0:1],2)
#print d.sample(img[1:2],2)
#print 'layer 3'
#print d.sample(img[0:1],3)[0]
#print d.sample(img[1:2],3)[0]
#print 'labels'
print np.around(d.classify(img, 20), 2)[0:20]
print lbl[0:20]
from data import MNIST
from dbn import DBN
import numpy as np
d = DBN([28*28, 500, 2000], 10,0.1)
images = MNIST.load_images('test_images')
labels = MNIST.load_labels('test_labels')
img = images[0:100]
lbl = labels[0:100]
tst_img = images[9000:95000]
tst_lbl = labels[9000:95000]
d.pre_train(img,30,50)
for i in xrange(0, 100):
d.train_labels(img, lbl, 3, 50)
tst_class = d.classify(tst_img,20)
print 'Error kurwa: {0}'.format( 1 - ((tst_class * tst_lbl).mean()) * 10)
#print d.sample(img[0:1],0)
#print d.sample(img[1:2],0)
#print 'layer 2'
#print d.sample(img[0:1],1)
#print d.sample(img[1:2],1)
#print 'layer 2'
#print d.sample(img[0:1],2)
#print d.sample(img[1:2],2)
#print 'layer 3'
#print d.sample(img[0:1],3)[0]
#print d.sample(img[1:2],3)[0]
