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neural_network.py
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import cv2
import numpy as np
import csv
from pybrain.datasets.supervised import SupervisedDataSet
from pybrain.tools.shortcuts import buildNetwork
from pybrain.supervised.trainers import BackpropTrainer, RPropMinusTrainer
from pybrain.tools.customxml import NetworkWriter
from pybrain.tools.customxml import NetworkReader
class Brain:
def __init__(self):
classes = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 12, 13, 14, 15, 17, 25])
self.samples = []
self.labels = []
# load the images from the GTSRB directory
for i in range(len(classes)):
prefix = "GTSRB/" + format(classes[i], '05d') + '/'
file = open(prefix + 'GT-' + format(classes[i], '05d') + '.csv')
reader = csv.reader(file, delimiter=';')
next(reader, None)
for row in reader:
image = cv2.imread(prefix + row[0])
self.samples.append(image)
self.labels.append(i)
# resize the images to 10x10 pixels
self.samples = [cv2.resize(s, (10, 10)) for s in self.samples]
# normalize the pixel intensity values
self.samples = np.array(self.samples).astype(np.float32) / 255
self.samples = [s.flatten() for s in self.samples]
np.random.seed(0)
np.random.shuffle(self.samples)
np.random.seed(0)
np.random.shuffle(self.labels)
self.totalEpochs = 0
def test_train(self, epochs=1):
print("Training...")
# split the array in a way that the net will be
# trained with 70% of the images and
# tested with the rest
split = int(len(self.samples) * 0.7)
train_samples = self.samples[0:split]
train_labels = self.labels[0:split]
test_samples = self.samples[split:]
test_labels = self.labels[split:]
# build the net with 300 input values representing
# each pixel of the 10x10 image (100 values)
# and its Red,Green,Blue values (3 values)
net = buildNetwork(300, 300, 1)
ds = SupervisedDataSet(300, 1)
for i in range(len(train_samples)):
ds.addSample(tuple(np.array(train_samples[i], dtype='float64')), (train_labels[i],))
trainer = BackpropTrainer(net, ds, verbose=True)
trainer.trainEpochs(epochs)
self.totalEpochs = epochs
error = 0
counter = 0
for i in range(0, 100):
output = net.activate(tuple(np.array(test_samples[i], dtype='float64')))
if round(output[0]) != test_labels[i]:
counter += 1
print(counter, " : output : ", output[0], " real answer : ", test_labels[i])
error += 1
else:
counter += 1
print(counter, " : output : ", output[0], " real answer : ", test_labels[i])
print("Trained with " + str(epochs) + " epochs; Total: " + str(self.totalEpochs) + ";")
return error
def train_clean(self, epochs=1):
print("Training...")
self.totalEpochs = epochs
train_samples = self.samples
train_labels = self.labels
self.net_shared = buildNetwork(300, 300, 1)
self.ds_shared = SupervisedDataSet(300, 1)
for i in range(len(train_samples)):
self.ds_shared.addSample(tuple(np.array(train_samples[i], dtype='float64')), (train_labels[i],))
self.trainer_shared = BackpropTrainer(self.net_shared, self.ds_shared, verbose=True)
self.trainer_shared.trainEpochs(epochs)
print("Trained with " + str(epochs) + " epochs; Total: " + str(self.totalEpochs) + ";")
def train_more(self, epochs=1):
print("Training...")
self.totalEpochs += epochs
self.trainer_shared.trainEpochs(epochs)
print("Trained with " + str(epochs) + " epochs more; Total: " + str(self.totalEpochs) + ";")
def test_image(self, filename):
image = cv2.imread(filename)
images = [image]
images = [cv2.resize(s, (10, 10)) for s in images]
images = np.array(images).astype(np.float32) / 255
images = [s.flatten() for s in images]
output = self.net_shared.activate(tuple(np.array(images[0], dtype='float64')))
print("Output: ", output[0])
return output[0]
def import_network(self, filename):
train_samples = self.samples
train_labels = self.labels
np.random.seed(0)
np.random.shuffle(train_samples)
np.random.seed(0)
np.random.shuffle(train_labels)
self.net_shared = NetworkReader.readFrom(filename)
self.ds_shared = SupervisedDataSet(300, 1)
for i in range(len(train_samples)):
self.ds_shared.addSample(tuple(np.array(train_samples[i], dtype='float64')), (train_labels[i],))
self.trainer_shared = BackpropTrainer(self.net_shared, self.ds_shared, verbose=True)
def export_network(self, filename):
NetworkWriter.writeToFile(self.net_shared, filename)
def test_allsamples(self):
error = 0
for i in range(len(self.samples)):
output = self.net_shared.activate(tuple(np.array(self.samples[i], dtype='float64')))
if round(output[0]) != self.labels[i]:
error += 1
return error/len(self.samples)