AttributError : 'NeuralNetwork' object has no attribute 'initialize'? - python-2.7

Question

I am learning python programming and machine learning for my academic project and i found interest in number plate recognition.
By executing below code, i am getting error, which is mentioned below after the code
values=
['0','1','2','3','4','5','6','7','8','9','A','B','C','D','E','F','G','H','J','K','L','M','N','P','R','S','T','U','V','W','X','Z']
keys=range(32)
data_map=dict((keys, values))
def get_ann(data_map):
feature_mat=[]
label_mat=[]
for keys in data_map:
path_train="/home/sagar/Project data set/ANPR/ann/%s"%data_map[keys]
filenames=get_imlist(path_train)
perfeature_mat=[]
perlabel_mat=[]
for image in filenames[0]:
raw_image=cv2.imread(image)
raw_image=cv2.cvtColor(raw_image, cv2.COLOR_BGR2GRAY)
#resize the image into 5 cols(width) and 10 rows(height)
raw_image=cv2.resize(raw_image,(5,10), interpolation=cv2.INTER_AREA)
#Do a hard thresholding.
_,th2=cv2.threshold(raw_image, 70, 255, cv2.THRESH_BINARY)
#generate features
horz_hist=np.sum(th2==255, axis=0)
vert_hist=np.sum(th2==255, axis=1)
sample=th2.flatten()
#concatenate these features together
feature=np.concatenate([horz_hist, vert_hist, sample])
# append these features together along with their respective labels
perfeature_mat.append(feature)
perlabel_mat.append(keys)
feature_mat.append(perfeature_mat)
label_mat.append(perlabel_mat)
# These are the final product.
bigfeature_mat=np.vstack(feature_mat)
biglabel_mat=np.hstack(label_mat)
# As usual. We need to convert them into double type for Shogun.
bigfeature_mat=np.array(bigfeature_mat, dtype='double')
biglabel_mat=np.array(biglabel_mat, dtype='double')
#shogun works in a way in which columns are samples and rows are features.
#Hence we need to transpose the observation matrix
obs_matrix=bigfeature_mat.T
#convert the observation matrix and the labels into Shogun RealFeatures and MulticlassLabels structures resp. .
sg_features=RealFeatures(obs_matrix)
sg_labels=MulticlassLabels(biglabel_mat)
#initialize a simple ANN in Shogun with one hidden layer.
layers=DynamicObjectArray()
layers.append_element(NeuralInputLayer(65))
layers.append_element(NeuralLogisticLayer(65))
layers.append_element(NeuralSoftmaxLayer(32))
net=NeuralNetwork(layers)
net.quick_connect()
net.initialize()
net.io.set_loglevel(MSG_INFO)
net.l1_coefficient=3e-4
net.epsilon = 1e-6
net.max_num_epochs = 600
net.set_labels(sg_labels)
net.train(sg_features)
return net
The errors:
AttributeError Traceback (most recent call last)
<ipython-input-28-30225c91fe73> in <module>()
----> 1 net=get_ann(data_map)
<ipython-input-27-809f097ce563> in get_ann(data_map)
59 net=NeuralNetwork(layers)
60 net.quick_connect()
---> 61 net.initialize()
62
63 net.io.set_loglevel(MSG_INFO)
AttributeError: 'NeuralNetwork' object has no attribute 'initialize'
Platform used: Ubuntu 14.04, Python 2.7, opencv-2.4.9, iPython notebook and shogun toolbox.
Can any one please help me in resolving this error? Thanks in advance.
The other code samples are as follows, which have been executed before the above code.
from modshogun import *
def get_vstacked_data(path):
filenames=np.array(get_imlist(path))
#read the image
#convert the image into grayscale.
#change its data-type to double.
#flatten it
vmat=[]
for i in range(filenames[0].shape[0]):
temp=cv2.imread(filenames[0][i])
temp=cv2.cvtColor(temp, cv2.COLOR_BGR2GRAY)
temp=cv2.equalizeHist(temp)
temp=np.array(temp, dtype='double')
temp=temp.flatten()
vmat.append(temp)
vmat=np.vstack(vmat)
return vmat
def get_svm():
#set path for positive training images
path_train='/home/sagar/resized/'
pos_trainmat=get_vstacked_data(path_train)
#set path for negative training images
path_train='/home/sagar/rezize/'
neg_trainmat=get_vstacked_data(path_train)
#form the observation matrix
obs_matrix=np.vstack([pos_trainmat, neg_trainmat])
#shogun works in a way in which columns are samples and rows are features.
#Hence we need to transpose the observation matrix
obs_matrix=obs_matrix.T
#get the labels. Positive training images are marked with +1 and negative with -1
labels=np.ones(obs_matrix.shape[1])
labels[pos_trainmat.shape[0]:obs_matrix.shape[1]]*=-1
#convert the observation matrix and the labels into Shogun RealFeatures and BinaryLabels structures resp. .
sg_features=RealFeatures(obs_matrix)
sg_labels=BinaryLabels(labels)
#Initialise a basic LibSVM in Shogun.
width=2
#kernel=GaussianKernel(sg_features, sg_features, width)
kernel=LinearKernel(sg_features, sg_features)
C=1.0
svm=LibSVM(C, kernel, sg_labels)
_=svm.train()
_=svm.apply(sg_features)
return svm
ocr classification
def validate_ann(cnt):
rect=cv2.minAreaRect(cnt)
box=cv2.cv.BoxPoints(rect)
box=np.int0(box)
output=False
width=rect[1][0]
height=rect[1][1]
if ((width!=0) & (height!=0)):
if (((height/width>1.12) & (height>width)) | ((width/height>1.12) & (width>height))):
if((height*width<1700) & (height*width>100)):
if((max(width, height)<64) & (max(width, height)>35)):
output=True
return output

Answer 1

Probably there are some method deprecations issues with Shogun.
Try to replace:
net.initialize()
With:
net.initialize_neural_network()