def skelton(self):
layers = QtGui.QInputDialog.getInteger(self, '# of layers',"Enter your # of Layers \nincluding input but not output")
layers = layers[0]
size_layers = []
size_layers.extend([0]*layers)
for i in range(len(size_layers)):
size_layers[i] = QtGui.QInputDialog.getInteger(self, 'Size of layer',"Enter the size of Layer each layer")[0]
size_layers.append(1)
size_layers.insert(0,500)
self.n = nt.neuralnet(*size_layers)
self.d = ds.dataset(size_layers[0],1)
self.d.generateDataSet()
self.n.loadTrainingData(self.d.getTrainingDataset())
def __init__(self, parent=None):
super(ExampleApp, self).__init__(parent)
self.setupUi(self)
# palette = QtGui.QPalette()
# palette.setBrush(palette.Background,QtGui.QBrush(QtGui.QPixmap("waves.jpg")))
# self.setPalette(palette)
self.lineEdit.setDragEnabled(True)
self.lineEdit.setAcceptDrops(True)
self.progressBar =self.progressBar
self.Teach.clicked.connect(self.generations)
self.Open_file.clicked.connect(self.file_open)
self.Quit.clicked.connect(self.close_app)
self.n = nt.neuralnet(500,300,100,1)
self.d = ds.dataset(500,1)
self.d.generateDataSet()
self.n.loadTrainingData(self.d.getTrainingDataset())
self.setWindowIcon(QtGui.QIcon('wolf.png'))
self.setWindowTitle("OCR GUI by Bryan Moore")
self.construct.clicked.connect(self.skelton)
from ocrn import dataset as ds
from ocrn import feature as ft
from ocrn import neuralnet as nt
import numpy as np
print "Ocrn: Optical Character Recognition using Neural Network\nLatest version available at http://github.com/swvist\n"
n = nt.neuralnet(100,80,1)
print "Neural Network Initialized"
d = ds.dataset(100,1)
print "Training Data Set Initialized"
if d.generateDataSet():
print "Training Data Set Generated"
if n.loadTrainingData(d.getTrainingDataset()):
print "Training Data Set loaded"
while(True):
x = raw_input("q: quit \t t: teach \t e: test \nWhat?\t:\t")
if x == "q":
break
elif x == "t":
t = int(raw_input("How many times?\t:\t"))
#n.teach(t)
n.teachUntilConvergence(max=t)
elif x == "e":
e = raw_input("Enter input file\t:\t")
x = n.activate(ft.feature.getImageFeatureVector(e))
print "\nThere is a high probability that the image is '"+str(chr(x))+"'\n"
from ocrn import dataset as ds
from ocrn import feature as ft
from ocrn import neuralnet as nt
import numpy as np
print "Ocrn: Optical Character Recognition using Neural Network\nLatest version available at http://github.com/swvist\n"
n = nt.neuralnet(100,80,1)
print "Neural Network Initialized"
d = ds.dataset(100,1)
print "Training Data Set Initialized"
if d.generateDataSet():
print "Training Data Set Generated"
if n.loadTrainingData(d.getTrainingDataset()):
print "Training Data Set loaded"
while(True):
x = raw_input("q: quit \t t: teach \t e: test \nWhat?\t:\t")
if x == "q":
break
elif x == "t":
t = int(raw_input("How many times?\t:\t"))
n.teach(t)
elif x == "e":
e = raw_input("Enter input file\t:\t")
x = n.activate(ft.feature.getImageFeatureVector(e))
print "\nThere is a high probability that the image is '"+str(unichr(x))+"'\n"
else:
from ocrn import dataset as ds
from ocrn import feature as ft
from ocrn import neuralnet as nt
import numpy as np
print "\n \nOCR Prototype: Neural Networks w/ training data and test data \n \n"
n = nt.neuralnet(500,100,1)
print "Neural Network Initialized"
d = ds.dataset(500,1)
print "Training Data Set Initialized"
if d.generateDataSet():
print "Training Data Set Generated"
if n.loadTrainingData(d.getTrainingDataset()):
print "Training Data Set loaded"
while(True):
x = raw_input("q: quit \t t: teach \t e: test \nWhat?\t:\t")
if x == "q":
break
elif x == "t":
t = int(raw_input("How many times do you want to train your data?\t:\t"))
n.teach(t)
elif x == "e":
e = raw_input("Enter input file, make sure it is the absolute form and NOT in the string form\t:\t")
x = n.activate(ft.feature.getImageFeatureVector(e))
print "\nThe highest probability letter from that the image is '"+str(unichr(x))+"'\n"
else:
def __init__(self): """ Create a __new__ Grinder object. """ self.neural_network = nn.neuralnet(100,80,1) self.data_set = ds.dataset(100,1)
def reset(self): self.neural_network = nn.neuralnet(100,80,1) self.data_set = ds.dataset(100,1)