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from django.conf.urls import include
from django.urls import path
from rest_framework import routers
from .views import InferenceViewSet

router = routers.DefaultRouter()
router.register('inference', InferenceViewSet)


urlpatterns = [

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from rest_framework.decorators import action
from rest_framework.response import Response
from rest_framework import viewsets,status,generics
from .serializers import InferenceSerializer
from .models import Inference
from celery.result import AsyncResult

class InferenceViewSet(viewsets.ModelViewSet):

    serializer_class = InferenceSerializer

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from rest_framework import serializers
from .models import Inference 
from .tasks import run_inference

class InferenceSerializers(serializers.ModelSerializers):
    class Meta : 
        model = Inference
        fields = ('name','result')
        
    def create(self,validated_data):

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from django.db import models 


class Inference(models.Model):
    name = models.CharField(max_length=255)
    result = models.CharField(max_length=255)
    
    def __str__(self):
        return '{name} => result : {result}'.format(name=self.name,result=self.result)

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FROM python:3.6 # base image 

RUN mkdir src # create a src directory 

WORKDIR /src # change directory to src 

ADD . /src/ # add the django code to src 

RUN pip install requirements.txt # install the python library

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def predict(x,w1,w2,w3,b1,b2,b3) :
    l1 = sigmoid(np.dot(x,w1) + b1)
    l2 = sigmoid(np.dot(l1,w2) + b2)
    l3 = sigmoid(np.dot(l2,w3) + b3)
    print(l3.round(decimals=2))
    
test = x[0]
predict(test,*params)

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params = w1,w2,w3,b1,b2,b3

for i in range(0,10000) :
    params = train(x,y,*params)

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def train(x,y,w1,w2,w3,b1,b2,b3) :

    # feed forward
    inp_l = x
    l1 = sigmoid(np.dot(inp_l,w1) + b1)
    l2 = sigmoid(np.dot(l1,w2) + b2)
    l3 = sigmoid(np.dot(l2,w3) + b3)

    #backpropagation
    delta_l3 = (y - l3)*sigmoid_prime(l3)          

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def sigmoid_prime(x):
    return x*(1-x)

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def sigmoid(x):
    return 1/(1+np.exp(-x))