Deel; A High level deep neural network description language.
You can create your own deep neural network application in a second.
Describe deep neural network, training and using in simple syntax.
Chainer 1.7.1
Python 2.7.1
$ git clone https://github.com/uei/deel.git
$ pip install -r requirements.txt
$ 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(img)
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)####CNN-LSTM trainer (done, not test)
InputBatch(train="data/train_lstm.tsv")
CNN = GoogLeNet()
RNN = LSTM()
def trainer(x,t):
CNN.classify(x)
RNN.learn(t)
return RNN.backprop()
BatchTrain(trainer)