Deel
Deel; A High level deep neural network description language.
You can create your own deep neural network application in a second.
Goal
Describe deep neural network, training and using in simple syntax.
Dependency
Chainer 1.7.1 or higher
Python 2.7.8 or highter
(Optional) OpenCv 2.4.12 or higher
Install and test
$ git clone https://github.com/uei/deel.git
$ cd deel
$ python setup.py install
$ cd deel/data
$ ./getCaltech101.sh
$ cd ../misc
$ ./getPretrainedModels.sh
$ cd ..
$ python test.py###Examples
####CNN classifier
deel = Deel()
CNN = GoogLeNet()
CNN.Input("deel.png")
CNN.classify()
ShowLabels()####CNN trainer
nin = NetworkInNetwork()
InputBatch(train="data/train.txt",
val="data/test.txt")
def workout(x,t):
nin.classify(x)
return nin.backprop(t)
BatchTrain(workout)####CNN classifier with OpenCV camera (you need OpenCV2)
import cv2
from deel import *
from deel.network import *
from deel.commands import *
deel = Deel()
CNN = GoogLeNet()
cam = cv2.VideoCapture(0)
while True:
ret, img = cam.read()
CNN.Input(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
CNN.classify()
labels = GetLabels()
if labels[0][1] == 'Band':
print 'BAND'
cv2.imwrite('band.png',img)
cv2.imshow('cam', img)
if cv2.waitKey(10) > 0:
break
cam.release()
cv2.destroyAllWindows()####CNN-DQN with Unity (using with https://github.com/wbap/ml-agent-for-unity)
from deel import *
from deel.network import *
from deel.commands import *
from deel.agentServer import *
deel = Deel()
CNN = AlexNet()
QNET = DQN()
def trainer(x):
CNN.feature(x)
return QNET.actionAndLearn()
StartAgent(trainer)####ResNet Inferrence
from deel import *
from deel.network import *
from deel.network.googlenet import *
from deel.network.resnet152 import *
from deel.commands import *
import time
deel = Deel()
CNN = ResNet152()
CNN.Input("test.jpg")
CNN.classify()
ShowLabels()####ResNet Finetuning
from deel import *
from deel.network import *
from deel.commands import *
from deel.network.resnet152 import *
#from deel.network.googlenet import *
import chainer.functions as F
import time
deel = Deel(gpu=-1)
CNN = ResNet152()
InputBatch(train="data/train.txt",
val="data/test.txt")
def workout(x,t):
CNN.batch_feature(x,t)
return CNN.backprop(t)
def checkout():
CNN.save('model_google_cpu.hdf5')
BatchTrain(workout,checkout)
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