import random
import pandas as pd
import initializeMNIST
"""This area are the hyperparamaters"""
datainputs, answers = initializeMNIST.datasetup()
layer1size = 80
layer1connectionnumbers = sparse1 = 15
layer2size = 40
layer2connectionnumbers = sparse2 = 8
layer3size = 30
layer3connectionnumbers = sparse3 = 5
layer4size = 15
layer4connectionnumbers = sparse4 = 3
layer5size = 1
layer5connectionnumbers = dense1 = 15
"""This area sets up the network structure"""
layer1 = []
inputs = len(datainputs[0])
for x in range(layer1size):
	layer1.append(initializeMNIST.neuron(inputs,sparse1))
layer2 = []
inputs = len(layer1)
for x in range(layer2size):
	layer2.append(initializeMNIST.neuron(inputs,sparse2))
layer3 = []
inputs = len(layer2)
for x in range(layer3size):
	layer3.append(initializeMNIST.neuron(inputs,sparse3))
layer4 = []
abcd = []
inputs = len(layer3)
for x in range(layer4size):
	layer4.append(initializeMNIST.neuron(inputs,sparse4))
	abcd.append(-20)
	if x%2 ==0:
		abcd[x] = abcd[x]* -1
	layer5 = []
inputs = len(layer4)
for x in range(layer5size):
	layer5.append(initializeMNIST.neuron(inputs,dense1))
"""This Area does the feedforward calculations"""
megabatcherror= 1
megabatcherror2= 1
counter = 1
for epochs in range(4000):
	batchcounter = 0
	megabatcherror = megabatcherror / counter
	#print(megabatcherror)
	#print(megabatcherror2)
	megabatcherror=0
	megabatcherror2= 0
	for imagenumber in range(len(datainputs)):
		imagenumber = 1
		for x in range(3000):
			counter = 1
			batchcounter = 1
			expectedanswer = answers[imagenumber]
			#print(datainputs[imagenumber],"DATADATDATDATDATDATDATDADTADTADTATDATDTADTADATDATDTADATDDATDADTATDATDTADTADTATDATDTADTATDATDTADTA")
			inputs1 = initializeMNIST.feedforward(layer1,datainputs[imagenumber],expectedanswer,base=True)
			#print(inputs1,"1111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111")
			inputs2 = initializeMNIST.feedforward(layer2,inputs1,expectedanswer)
			#print(inputs2,"2222222222222222222222222222222222222222222222222222222222222222222222222222222222222222222222222222222222222")
			inputs3 = initializeMNIST.feedforward(layer3,inputs2,expectedanswer)
			#print(inputs3,"3333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333")
			inputs4 = initializeMNIST.feedforward(layer4,inputs3,expectedanswer)
			#print(inputs4,"444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444")
			inputs5 = initializeMNIST.feedforward(layer5,abcd,expectedanswer,final=True)
			#print(inputs4,"55555555555555555555555555555555555555555555555555555555555555555555555555555555555555555555555555555555555555")
			#print(layer1[0].weights)
			#print(layer3[layer4[0].targets[0]].outputs,layer3[layer4[0].targets[1]].outputs,layer3[layer4[0].targets[2]].outputs)
			#print(" ")
			print(layer5[0].outputs,expectedanswer)
			#print(layer2[0].backproperror)
			megabatcherror += abs(expectedanswer - layer5[0].outputs)
			megabatcherror2 += (expectedanswer - layer5[0].outputs)
			for i in range(len(layer5)):
				layer5[i].backprop(inputs4,expectedanswer,layer4,batchcounter,counter)
			for i in range(len(layer4)):
				layer4[i].backprop(inputs3,layer4[i].backproperror,layer3,batchcounter,counter)
				print(layer4[i].backproperror)
			for i in range(len(layer3)):
				layer3[i].backprop(inputs2,layer3[i].backproperror,layer2,batchcounter,counter)
			for i in range(len(layer2)):
				layer2[i].backprop(inputs1,layer2[i].backproperror,layer1,batchcounter,counter)
			for i in range(len(layer1)):
				layer1[i].backpropfloor(datainputs[imagenumber],layer1[i].backproperror,batchcounter,counter)
			#print(layer5[0].weights)
			print(" ")
			print(inputs4)
		batchcounter+=1