def test_classification():
num_class = 10
print 'initialize Neural Network.'
nn_obj = dp.obj_load('./learn-classification-batch.pkl')
print 'read test data.'
test_data = dp.obj_load_gzip('../../mnist/mnist-test_data.pkl.gz')
print 'read test label.'
test_label = dp.obj_load_gzip('../../mnist/mnist-test_label.pkl.gz')
print 'data size : ', len(test_data)
print 'label size : ', len(test_label)
data_num = len(test_data)
prediction_error = []
prediction_recog = []
print '--start--'
print '@@ Test Character Recognition @@'
for j in range(0, data_num):
prg.show_progressxxx(j+1, data_num)
num_recog, list_recog = nn_obj.test(test_data[j], test_label[j])
if test_label[j][num_recog] == 0:
prediction_error.append((test_label[j], num_recog))
prg.end_progress()
count = len(prediction_error)
for item in prediction_error:
print "Truth:", np.argmax(item[0]), ", --> But predict:", item[1]
print ''
print 'error : ', count, '/', data_num, '(', count * 1.0 / data_num * 100.0, '%)'
return
import dump as dp
import progress as prg
start = time.time()
# XORの入出力データ
input_data = [[0., 0.], [0., 1.], [ 1., 0.], [ 1., 1.]]
teach_data = [ [0.], [1.], [1.], [0.]]
data_num = len(input_data)
print '--start--'
print '@@ Show before learning @@'
neuro_obj = dp.obj_load('./default-br.dump')
for i in range(0, data_num):
print '------Input[%d]------' % i
neuro_obj.test(input_data[i], teach_data[i])
neuro_obj.show_element('input')
neuro_obj.show_element('output')
neuro_obj.show_element('ttlerr')
print ''
print '@@ Show after learning @@'
neuro_obj = dp.obj_load('./learn-br.dump')
for i in range(0, data_num):
print '------Input[%d]------' % i
neuro_obj.test(input_data[i], teach_data[i])
neuro_obj.show_element('input')
neuro_obj.show_element('output')
import numpy as np
import Mln as mln
import dump as dp
import progress as prg
import image as img
from sklearn.datasets import load_digits
from sklearn.cross_validation import train_test_split
from sklearn.preprocessing import LabelBinarizer
from sklearn.metrics import confusion_matrix, classification_report
teach_data = [0, 0, 0, 1, 0, 0, 0, 0, 0, 0] # dummy
print '--start--'
print '@@ Show after learning @@'
neuro_obj = dp.obj_load('./learn-cr.dump')
def recognition_digit_image(fname, digit = 100):
im = cv2.imread(fname)
im = img.change_size_with_size(im, 28, 28)
im = img.change_grayscale(im)
im = 255 - im
input_data = im
input_data = input_data.astype(np.float64)
input_data = im / im.max()
input_data = np.reshape(input_data, (1, 28*28))
neuro_obj.test(input_data, teach_data)
output = neuro_obj.get_output()
if digit >=0 and digit <= 9:
print "judged:", neuro_obj.get_max_output_index(),
import Mln as mln
import dump as dp
import progress as prg
start = time.time()
# XORの入出力データ
input_data = [[0.0, 0.0], [0.0, 1.0], [1.0, 0.0], [1.0, 1.0]]
teach_data = [[0.0], [1.0], [1.0], [0.0]]
data_num = len(input_data)
print "--start--"
print "@@ Show before learning @@"
neuro_obj = dp.obj_load("./default-br.dump")
for i in range(0, data_num):
print "------Input[%d]------" % i
neuro_obj.test(input_data[i], teach_data[i])
neuro_obj.show_element("input")
neuro_obj.show_element("output")
neuro_obj.show_element("ttlerr")
print ""
print "@@ Show after learning @@"
neuro_obj = dp.obj_load("./learn-br.dump")
for i in range(0, data_num):
print "------Input[%d]------" % i
neuro_obj.test(input_data[i], teach_data[i])
neuro_obj.show_element("input")
neuro_obj.show_element("output")
print "judged:", neuro_obj.get_max_output_index()
# if you wanna show image then you delete comment-out fllowing 3 lines.
im = cv2.resize(im, (28*10, 28*10))
cv2.imshow("input_data", im)
cv2.waitKey(0)
cv2.destroyAllWindows()
teach_data = [0, 0, 0, 1, 0, 0, 0, 0, 0, 0] # dummy
print '--start--'
print '@@ Show after learning @@'
#neuro_obj = dp.obj_load('./learn-classification.pkl') # online training's pkl
neuro_obj = dp.obj_load('./learn-classification-batch.pkl') # batch training's pkl
recognition_digit_image("../../hand-writing-number/0.png", 0)
recognition_digit_image("../../hand-writing-number/1.png", 1)
recognition_digit_image("../../hand-writing-number/2.png", 2)
recognition_digit_image("../../hand-writing-number/3.png", 3)
recognition_digit_image("../../hand-writing-number/4.png", 4)
recognition_digit_image("../../hand-writing-number/5.png", 5)
recognition_digit_image("../../hand-writing-number/6.png", 6)
recognition_digit_image("../../hand-writing-number/7.png", 7)
recognition_digit_image("../../hand-writing-number/8.png", 8)
recognition_digit_image("../../hand-writing-number/9.png", 9)
# judging '9' is success.
recognition_digit_image("../../hand-writing-number/number.png")
else : print "judged(miss) :", neuro_obj.get_max_output_index()
else:
print "judged:", neuro_obj.get_max_output_index()
# if you wanna show image then you delete comment-out fllowing 3 lines.
# cv2.imshow("input_data", im)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
teach_data = [0, 0, 0, 1, 0, 0, 0, 0, 0, 0] # dummy
print '--start--'
print '@@ Show after learning @@'
neuro_obj = dp.obj_load('./learn-classification.pkl') # online training's pkl
#neuro_obj = dp.obj_load('./learn-classification-batch.pkl') # batch training's pkl
recognition_digit_image("../../hand-writing-number/0.png", 0)
recognition_digit_image("../../hand-writing-number/1.png", 1)
recognition_digit_image("../../hand-writing-number/2.png", 2)
recognition_digit_image("../../hand-writing-number/3.png", 3)
recognition_digit_image("../../hand-writing-number/4.png", 4)
recognition_digit_image("../../hand-writing-number/5.png", 5)
recognition_digit_image("../../hand-writing-number/6.png", 6)
recognition_digit_image("../../hand-writing-number/7.png", 7)
recognition_digit_image("../../hand-writing-number/8.png", 8)
recognition_digit_image("../../hand-writing-number/9.png", 9)
# judging '9' is success.
