SADE.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e53f33d3-8a7d-402d-bfc0-566a905faa6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "import pickle\n",
    "#from torchsummary import summary\n",
    "from collections import OrderedDict\n",
    "from torch.utils.tensorboard import SummaryWriter\n",
    "import datetime\n",
    "import time\n",
    "import copy\n",
    "import torchvision.datasets as datasets\n",
    "mnist_trainset = datasets.MNIST(root='./data', train=True, download=True, transform=None)\n",
    "mnist_testset = datasets.MNIST(root='./data', train=False, download=True, transform=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b7cb97ff-7acb-44ef-b9d6-979b457b79da",
   "metadata": {},
   "outputs": [],
   "source": [
    "class DNN(torch.nn.Module):\n",
    "    def __init__(self, layers):\n",
    "        super(DNN, self).__init__()\n",
    "        \n",
    "        # parameters\n",
    "        self.depth = len(layers)-1\n",
    "        self.activation = torch.nn.ReLU\n",
    "        \n",
    "        layer_list = list()\n",
    "        for i in range(self.depth-1): \n",
    "            layer_list.append(\n",
    "                ('layer_%d' % i, torch.nn.Linear(layers[i], layers[i+1]))\n",
    "            )\n",
    "            layer_list.append(('activation_%d' % i, self.activation()))\n",
    "            \n",
    "        layer_list.append(\n",
    "            ('layer_%d' % (self.depth - 1), torch.nn.Linear(layers[-2], layers[-1]))\n",
    "        )\n",
    "        \n",
    "        layerDict = OrderedDict(layer_list)\n",
    "        \n",
    "        # deploy layers\n",
    "        self.layers = torch.nn.Sequential(layerDict)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        out = self.layers(x)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "99960ef9-e149-4b11-8df1-c52f218fab7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "class DE_MLP():\n",
    "    def __init__(self, outdim=1,maxdepth=70,mindepth=5,minneuron=4,maxneuron=10,bsize=10,epoch=100,initSize=20,maxiter=10,stopcount=3,\\\n",
    "                 trainingset=None,validationset=None,trainingTarget=None,validateTarget=None,crossover=1):\n",
    "        self.best=[]\n",
    "        self.mean=[]\n",
    "        self.outdim=outdim\n",
    "        self.maxdepth=maxdepth\n",
    "        self.mindepth=mindepth\n",
    "        self.minneuron = minneuron\n",
    "        self.maxneuron = maxneuron\n",
    "        self.bsize = bsize\n",
    "        self.epoch = epoch\n",
    "        self.stopcount = stopcount\n",
    "        self.pplSize = initSize\n",
    "        self.maxiter = maxiter\n",
    "        self.training = trainingset.reshape((trainingset.shape[0],-1))\n",
    "        self.validationSet = validationset.reshape((validationset.shape[0],-1))\n",
    "        self.target=trainingTarget\n",
    "        self.validationTarget = validateTarget\n",
    "        self.MLPlayerlist = []\n",
    "        self.depthlist=np.random.choice(range(self.mindepth,self.maxdepth),self.pplSize,replace=True)\n",
    "        self.crossover=crossover\n",
    "        self.tb = SummaryWriter('./Statistic/'+datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n",
    "        self.device = torch.device('cpu')\n",
    "\n",
    "        for i in range(self.pplSize):\n",
    "            depth = self.depthlist[i]\n",
    "            tmp=[]\n",
    "            tmp.append(self.training.shape[1])\n",
    "            for j in range(depth):\n",
    "                tmp.append(np.random.choice(range(self.minneuron,self.maxneuron),1,replace=False)[0])\n",
    "            tmp.append(self.outdim)\n",
    "            tmp=np.array(tmp)\n",
    "            self.MLPlayerlist.append(tmp)\n",
    "\n",
    "    def fit(self,config,id_,p=None):\n",
    "        dnn = DNN(config)\n",
    "        dnn.layers.to(self.device)\n",
    "        best = float('inf')\n",
    "        stop=0\n",
    "        opt = torch.optim.Adam(dnn.layers.parameters(), lr=0.001)\n",
    "        loss = torch.nn.BCEWithLogitsLoss()\n",
    "        batch = self.training.shape[0]//self.bsize\n",
    "        vbatch = self.validationSet.shape[0]//self.bsize\n",
    "        idxs = [x for x in range(self.training.shape[0])]\n",
    "        vidxs = [x for x in range(self.validationSet.shape[0])]\n",
    "        for e in range(self.epoch):\n",
    "            start=time.time()\n",
    "            np.random.shuffle(idxs)\n",
    "            dnn.layers.train()\n",
    "            batchloss=0\n",
    "            for i in range(batch):\n",
    "                idx=idxs[i*self.bsize:i*self.bsize+self.bsize]\n",
    "                opt.zero_grad()\n",
    "                data = torch.tensor(self.training[idx]).float().to(self.device)\n",
    "                y = torch.tensor(self.target[idx]).float().to(self.device)\n",
    "                yhat = dnn(data)\n",
    "                l = loss(yhat,y)\n",
    "                batchloss+=l.item()\n",
    "                l.backward()\n",
    "                opt.step()\n",
    "\n",
    "            dnn.layers.eval()\n",
    "            np.random.shuffle(vidxs)\n",
    "            vloss=0\n",
    "            for i in range(vbatch):\n",
    "                vidx=vidxs[i*self.bsize:i*self.bsize+self.bsize]\n",
    "                vdata = torch.tensor(self.validationSet[vidx]).float().to(self.device)\n",
    "                vy = torch.tensor(self.validationTarget[vidx]).float().to(self.device)\n",
    "                vyhat = dnn(vdata)\n",
    "                vl = loss(vyhat,vy)\n",
    "                vloss += vl.item()\n",
    "            vloss = vloss/vbatch\n",
    "            if(vloss<best):\n",
    "                best=vloss\n",
    "            else:\n",
    "                stop+=1\n",
    "            end=time.time()\n",
    "            if(p is not None):\n",
    "                print(f'Process: {p:3d}, ConfigID: {id_:3d}, Epoch: {e:3d}, Training Loss: {(batchloss/batch):10.8f}, Validation Loss: {(vloss):10.8f},\\\n",
    "                    Best: {best:10.8f}, StopCount/Limit: {stop:3d}/{self.stopcount:3d}, Time:{(end-start):10.8f}')\n",
    "            else:\n",
    "                print(f'ConfigID: {id_:3d}, Epoch: {e:3d}, Training Loss: {(batchloss/batch):10.8f}, Validation Loss: {(vloss):10.8f},\\\n",
    "                    Best: {best:10.8f}, StopCount/Limit: {stop:3d}/{self.stopcount:3d}, Time:{(end-start):10.8f}')\n",
    "            if(stop>=self.stopcount):\n",
    "                return best,config,id_\n",
    "    \n",
    "    def mutation_rand_1_z(self,x1,xs,beta,debug=False):\n",
    "        indim = x1[0]\n",
    "        x1 = x1[1:-1] # remove in/out dim\n",
    "        xs[0] = xs[0][1:-1]\n",
    "        xs[1] = xs[1][1:-1]\n",
    "        if(debug):\n",
    "            print(f'M1 : x1 len {x1.shape[0]} xs0 len {xs[0].shape[0]} xs1 len {xs[1].shape[0]}')\n",
    "            print(f'M1 : x1 {x1} \\nM1 : xs0 {xs[0]} \\nM1 : xs1 len {xs[1]}')\n",
    "        #\n",
    "        # A. Mutating the # of layers\n",
    "        minlen = np.min([x1.shape[0],xs[0].shape[0],xs[1].shape[0]])\n",
    "        if(debug): print(f'M1 : minlen {minlen}')\n",
    "        newminlen = minlen\n",
    "        targetlen=int(np.floor((x1.shape[0]) + beta * (xs[0].shape[0] - xs[1].shape[0])))\n",
    "        if(targetlen==0): targetlen=x1.shape[0]\n",
    "        elif(targetlen<0): targetlen=abs(targetlen)\n",
    "        if(targetlen<self.mindepth): targetlen=self.mindepth\n",
    "        if(targetlen>self.maxdepth): targetlen=self.maxdepth\n",
    "        if(targetlen < minlen): newminlen=targetlen\n",
    "        if(debug): print(f'M1 : New Min Len :{newminlen}, Length Mutation :{targetlen}')\n",
    "        #    \n",
    "        # B. Mutating the # of neurons\n",
    "        # As lengths of x1, xs[0], xs[1] and new length can possibly be different, \n",
    "        # 1) do the mutation for # of neurons for new minlen, \n",
    "        # 2) apply the same rule to remaining if needed\n",
    "        xa = x1[:newminlen] + beta * (xs[0][:newminlen] - xs[1][:newminlen])\n",
    "        if(targetlen>minlen):\n",
    "            xaa = np.zeros((targetlen-minlen))\n",
    "            a,b,c=None,None,None\n",
    "            for i in range(targetlen-newminlen):\n",
    "                if(x1.shape[0]<=newminlen+i): a=np.random.choice(range(self.minneuron,self.maxneuron),1,replace=False)[0]\n",
    "                elif(x1.shape[0]>newminlen+i): a=x1[newminlen+i]\n",
    "                if(xs[0].shape[0]<=newminlen+i): b=np.random.choice(range(self.minneuron,self.maxneuron),1,replace=False)[0]\n",
    "                elif(xs[0].shape[0]>newminlen+i): b=xs[0][newminlen+i]\n",
    "                if(xs[1].shape[0]<=newminlen+i): c=np.random.choice(range(self.minneuron,self.maxneuron),1,replace=False)[0]\n",
    "                elif(xs[1].shape[0]>newminlen+i): c=xs[1][newminlen+i]\n",
    "                xaa[i]=a + beta * (b - c)\n",
    "            xa = np.concatenate((xa, xaa), axis=None)\n",
    "        for i in range(xa.shape[0]):\n",
    "            if(xa[i]>self.maxneuron): xa[i]=self.maxneuron\n",
    "            elif(xa[i]<self.minneuron): xa[i]=self.minneuron\n",
    "            xa[i] = np.floor(xa[i])\n",
    "        xa = np.concatenate((np.array(indim,dtype=int),np.array(xa,dtype=int),np.array(self.outdim,dtype=int)), axis=None,dtype=int)\n",
    "        return xa\n",
    "    \n",
    "    def mutation_current2best_2_z(self,x,xp,xs,beta,debug=False):\n",
    "        indim = x[0]\n",
    "        x = x[1:-1]\n",
    "        xp = xp[1:-1]\n",
    "        xs[0] = xs[0][1:-1]\n",
    "        xs[1] = xs[1][1:-1]\n",
    "        if(debug):\n",
    "            print(f'M1 : x len {x.shape[0]} xp len {xp.shape[0]} xs0 len {xs[0].shape[0]} xs1 len {xs[1].shape[0]}')\n",
    "            print(f'M1 : x {x} \\nM1 : xp {xp} \\nM1 : xs0 {xs[0]} \\nM1 : xs1 len {xs[1]}')\n",
    "        #\n",
    "        # A. Mutating the # of layers\n",
    "        minlen = np.min([x.shape[0],xp.shape[0],xs[0].shape[0],xs[1].shape[0]])\n",
    "        if(debug): print(f'M1 : minlen {minlen}')\n",
    "        newminlen = minlen\n",
    "        targetlen=int(np.floor( (x.shape[0]) + beta * (xp.shape[0] - x.shape[0]) + beta * (xs[0].shape[0] - xs[1].shape[0]) ))\n",
    "        if(targetlen==0): targetlen=xp.shape[0]\n",
    "        elif(targetlen<0): targetlen=abs(targetlen)\n",
    "        if(targetlen<self.mindepth): targetlen=self.mindepth\n",
    "        if(targetlen>self.maxdepth): targetlen=self.maxdepth\n",
    "        if(targetlen < minlen): newminlen=targetlen\n",
    "        if(debug): print(f'M1 : New Min Len :{newminlen}, Length Mutation :{targetlen}')\n",
    "        #    \n",
    "        # B. Mutating the # of neurons\n",
    "        # As lengths of x, xp, xs[0], xs[1] and new length can possibly be different, \n",
    "        # 1) do the mutation for # of neurons for new minlen, \n",
    "        # 2) apply the same rule to remaining if needed\n",
    "        xa = x[:newminlen] + beta * (xp[:newminlen] - x[:newminlen]) + beta * (xs[0][:newminlen] - xs[1][:newminlen])\n",
    "        if(targetlen>minlen):\n",
    "            xaa = np.zeros((targetlen-minlen))\n",
    "            a,b,c,d=None,None,None,None\n",
    "            for i in range(targetlen-newminlen):\n",
    "                if(x.shape[0]<=newminlen+i): a=np.random.choice(range(self.minneuron,self.maxneuron),1,replace=False)[0]\n",
    "                elif(x.shape[0]>newminlen+i): a=x[newminlen+i]\n",
    "                if(xp.shape[0]<=newminlen+i): b=np.random.choice(range(self.minneuron,self.maxneuron),1,replace=False)[0]\n",
    "                elif(xp.shape[0]>newminlen+i): b=xp[newminlen+i]\n",
    "                if(xs[0].shape[0]<=newminlen+i): c=np.random.choice(range(self.minneuron,self.maxneuron),1,replace=False)[0]\n",
    "                elif(xs[0].shape[0]>newminlen+i): c=xs[0][newminlen+i]\n",
    "                if(xs[1].shape[0]<=newminlen+i): d=np.random.choice(range(self.minneuron,self.maxneuron),1,replace=False)[0]\n",
    "                elif(xs[1].shape[0]>newminlen+i): d=xs[1][newminlen+i]\n",
    "                xaa[i]=a + beta * (b - a) + beta * (c - d)\n",
    "            xa = np.concatenate((xa, xaa), axis=None)\n",
    "        for i in range(xa.shape[0]):\n",
    "            if(xa[i]>self.maxneuron): xa[i]=self.maxneuron\n",
    "            elif(xa[i]<self.minneuron): xa[i]=self.minneuron\n",
    "            xa[i] = np.floor(xa[i])\n",
    "        xa = np.concatenate((np.array(indim,dtype=int),np.array(xa,dtype=int),np.array(self.outdim,dtype=int)), axis=None,dtype=int)\n",
    "        return xa\n",
    "    \n",
    "    def crossoverUnif(self,parent,unit,cr):\n",
    "        r = np.random.uniform(0,1,1)[0]\n",
    "        p = parent[1:-1]\n",
    "        u = unit[1:-1]\n",
    "        child = [parent[0]]\n",
    "        if(p.shape[0] > u.shape[0]): u = np.resize(u,p.shape[0])\n",
    "        elif(u.shape[0] > p.shape[0]): p = np.resize(p,u.shape[0])\n",
    "        jr = np.random.choice(range(u.shape[0]),size=1)[0]\n",
    "        for j in range(u.shape[0]):\n",
    "            if r <= cr or j == jr: child.append(u[j])\n",
    "            else: child.append(p[j])\n",
    "        child.append(parent[-1])\n",
    "        return np.array(child)\n",
    "\n",
    "    def run(self):\n",
    "        current_gen=self.MLPlayerlist\n",
    "        scores = np.zeros((self.pplSize))\n",
    "        #initial Run\n",
    "        print('Initial Run Start')\n",
    "        for i in range(len(current_gen)):\n",
    "            b,_,_ = self.fit(current_gen[i],i)\n",
    "            scores[i]=b\n",
    "        print('Initial Run End')\n",
    "        currentbest = np.min(scores)\n",
    "        currentmean = np.mean(scores)\n",
    "        currentbestidx = np.argmin(scores)\n",
    "        print(f'Init Run Best: {currentbest}, Mean: {currentmean}, ID:{currentbestidx}, config: {current_gen[currentbestidx]}')\n",
    "        # initial factors\n",
    "        p1,beta,crm = 0.5,0,0.5\n",
    "        crs = []\n",
    "        progress = []\n",
    "        #Generation Run\n",
    "        for i in range(self.maxiter):\n",
    "            structureStatistic=np.zeros((self.pplSize,5))\n",
    "            updatecount=0\n",
    "            start=time.time()\n",
    "            ns1,nf1,ns2,nf2 = 0,0,0,0\n",
    "            print(f'Gen {i} Run Start')\n",
    "            for j in range(self.pplSize):\n",
    "                parent = current_gen[j]\n",
    "                # factors\n",
    "                while beta <= 0 or beta > 2: beta = np.random.normal(loc=0.5,scale=0.3,size=1)[0]\n",
    "                if i%5==0: cr = np.random.normal(loc=crm,scale=0.1,size=1)[0]\n",
    "                r = np.random.uniform(low=0,high=1,size=1)[0]\n",
    "                # strategy selection + mutation\n",
    "                if r <= p1: \n",
    "                    idx0,idx1,idxt = np.random.choice(range(0,self.pplSize),3,replace=False)\n",
    "                    target = current_gen[idxt]\n",
    "                    diff = [current_gen[idx0],current_gen[idx1]]\n",
    "                    unitvector = self.mutation_rand_1_z(target,diff,beta)\n",
    "                else:\n",
    "                    idxt = np.argmax(scores)\n",
    "                    idx0,idx1 = np.random.choice(np.delete(np.arange(self.pplSize),idxt),2,replace=False)\n",
    "                    target = current_gen[idxt]\n",
    "                    diff = [current_gen[idx0],current_gen[idx1]]\n",
    "                    unitvector = self.mutation_current2best_2_z(parent,target,diff,beta)\n",
    "                # crossover\n",
    "                child = self.crossoverUnif(parent,unitvector,cr)\n",
    "                print(f'Next Gen: {child}')\n",
    "                structureStatistic[j,0]= child.shape[0]\n",
    "                structureStatistic[j,1]= np.mean(child)\n",
    "                structureStatistic[j,2]= np.median(child)\n",
    "                structureStatistic[j,3]= np.quantile(child,0.25)\n",
    "                structureStatistic[j,4]= np.quantile(child,0.75)\n",
    "                # selection\n",
    "                s,_,_ = self.fit(child,j)\n",
    "                if(s < scores[j]):\n",
    "                    updatecount+=1\n",
    "                    scores[j]=s\n",
    "                    current_gen[j]=child\n",
    "                    crs.append(cr)\n",
    "                    if r <= p1: ns1 += 1\n",
    "                    else: ns2 += 1\n",
    "                else:\n",
    "                    if r <= p1: nf1 += 1\n",
    "                    else: nf2 += 1\n",
    "            if i%5==4: \n",
    "                crm = np.mean(crs)\n",
    "                crs = []\n",
    "            if(ns2*(ns1+nf1)+ns1*(ns2+nf2) == 0): p1 = 0\n",
    "            else: p1 = (ns1*(ns2+nf2))/(ns2*(ns1+nf1)+ns1*(ns2+nf2))\n",
    "            print(f'Gen {i} Run End')\n",
    "            end=time.time()\n",
    "            currentbest = np.min(scores)\n",
    "            currentmean = np.mean(scores)\n",
    "            currentmedian = np.median(scores)\n",
    "            currentq25 = np.quantile(scores,0.25)\n",
    "            currentq75 = np.quantile(scores,0.75)\n",
    "            currentbestidx = np.argmin(scores)\n",
    "            genMeanLen = np.mean(structureStatistic[:,0])\n",
    "            genMedianLen = np.median(structureStatistic[:,0])\n",
    "            genq25Len = np.quantile(structureStatistic[:,0],0.25)\n",
    "            genq75Len = np.quantile(structureStatistic[:,0],0.75)\n",
    "            genMeanNode=np.median(structureStatistic[:,1])\n",
    "            genMedianNode=np.median(structureStatistic[:,2])\n",
    "            genq25Node = np.median(structureStatistic[:,3])\n",
    "            genq75Node = np.median(structureStatistic[:,4])\n",
    "            print(f'Run {i:3d} Best: {currentbest}, Mean: {currentmean}, ID:{currentbestidx}, config: {current_gen[currentbestidx]}, updatecount: {updatecount:3d}, Generation RunTime: {(end-start):10.8f}')\n",
    "            self.tb.add_scalars(\"Scores Statistic (Generation)\", {'best':currentbest,'mean':currentmean,'median':currentmedian,'q25':currentq25,'q75':currentq75}, i)\n",
    "            self.tb.add_scalars(\"Structure Statistic (Generation) #HiddenLayer\", {'mean':genMeanLen,'median':genMedianLen,'q25':genq25Len,'q75':genq75Len}, i)\n",
    "            self.tb.add_scalars(\"Structure Statistic (Generation) #Node\", {'mean':genMeanNode,'median':genMedianNode,'q25':genq25Node,'q75':genq75Node}, i)\n",
    "            self.tb.add_scalar('Update Count',updatecount,i)\n",
    "            self.tb.add_scalar('RunTime',(end-start),i)\n",
    "            \n",
    "        print(f'Run Completed : Best Score(loss): {np.min(scores)} , Config: {current_gen[np.argmin(scores)]}')\n",
    "        return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a332b483-a48e-46f8-bc94-b4f36144eb2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "trainingt = torch.nn.functional.one_hot(mnist_trainset.targets,num_classes=10)\n",
    "validationt = torch.nn.functional.one_hot(mnist_testset.targets,num_classes=10)\n",
    "# (self, outdim=1,maxdepth=70,mindepth=5,minneuron=4,maxneuron=10,bsize=10,epoch=100,initSize=20,maxiter=10,stopcount=3,\n",
    "#   trainingset=None,validationset=None,trainingTarget=None,validateTarget=None)\n",
    "d = DE_MLP(outdim=10,maxdepth=10,initSize=3,trainingset=mnist_trainset.data, validationset=mnist_testset.data, trainingTarget=trainingt,validateTarget=validationt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7b02b92e-097f-4204-9639-c8a195e64ce3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial Run Start\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/hk/kbn1q_sj4jz4lh0m7x05vff80000gn/T/ipykernel_8683/2761892728.py:55: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  data = torch.tensor(self.training[idx]).float().to(self.device)\n",
      "/var/folders/hk/kbn1q_sj4jz4lh0m7x05vff80000gn/T/ipykernel_8683/2761892728.py:56: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  y = torch.tensor(self.target[idx]).float().to(self.device)\n",
      "/var/folders/hk/kbn1q_sj4jz4lh0m7x05vff80000gn/T/ipykernel_8683/2761892728.py:68: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  vdata = torch.tensor(self.validationSet[vidx]).float().to(self.device)\n",
      "/var/folders/hk/kbn1q_sj4jz4lh0m7x05vff80000gn/T/ipykernel_8683/2761892728.py:69: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  vy = torch.tensor(self.validationTarget[vidx]).float().to(self.device)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ConfigID:   0, Epoch:   0, Training Loss: 0.17834260, Validation Loss: 0.12730922,                    Best: 0.12730922, StopCount/Limit:   0/  3, Time:13.70070195\n",
      "ConfigID:   0, Epoch:   1, Training Loss: 0.11331318, Validation Loss: 0.10673362,                    Best: 0.10673362, StopCount/Limit:   0/  3, Time:11.33942890\n",
      "ConfigID:   0, Epoch:   2, Training Loss: 0.09406550, Validation Loss: 0.08833804,                    Best: 0.08833804, StopCount/Limit:   0/  3, Time:10.49488521\n",
      "ConfigID:   0, Epoch:   3, Training Loss: 0.08546831, Validation Loss: 0.08142302,                    Best: 0.08142302, StopCount/Limit:   0/  3, Time:9.31559181\n",
      "ConfigID:   0, Epoch:   4, Training Loss: 0.08118595, Validation Loss: 0.08888370,                    Best: 0.08142302, StopCount/Limit:   1/  3, Time:12.13620281\n",
      "ConfigID:   0, Epoch:   5, Training Loss: 0.07803679, Validation Loss: 0.07782919,                    Best: 0.07782919, StopCount/Limit:   1/  3, Time:12.18234611\n",
      "ConfigID:   0, Epoch:   6, Training Loss: 0.07506507, Validation Loss: 0.07450866,                    Best: 0.07450866, StopCount/Limit:   1/  3, Time:16.65064192\n",
      "ConfigID:   0, Epoch:   7, Training Loss: 0.07321765, Validation Loss: 0.07065107,                    Best: 0.07065107, StopCount/Limit:   1/  3, Time:15.70127606\n",
      "ConfigID:   0, Epoch:   8, Training Loss: 0.07212559, Validation Loss: 0.06977473,                    Best: 0.06977473, StopCount/Limit:   1/  3, Time:10.00128722\n",
      "ConfigID:   0, Epoch:   9, Training Loss: 0.07112409, Validation Loss: 0.06971683,                    Best: 0.06971683, StopCount/Limit:   1/  3, Time:9.85400605\n",
      "ConfigID:   0, Epoch:  10, Training Loss: 0.07027692, Validation Loss: 0.07410945,                    Best: 0.06971683, StopCount/Limit:   2/  3, Time:8.19505382\n",
      "ConfigID:   0, Epoch:  11, Training Loss: 0.06948175, Validation Loss: 0.07616844,                    Best: 0.06971683, StopCount/Limit:   3/  3, Time:11.76899385\n",
      "ConfigID:   1, Epoch:   0, Training Loss: 0.16637258, Validation Loss: 0.12860552,                    Best: 0.12860552, StopCount/Limit:   0/  3, Time:11.96194577\n",
      "ConfigID:   1, Epoch:   1, Training Loss: 0.11391135, Validation Loss: 0.11302414,                    Best: 0.11302414, StopCount/Limit:   0/  3, Time:9.08038688\n",
      "ConfigID:   1, Epoch:   2, Training Loss: 0.10335298, Validation Loss: 0.10320356,                    Best: 0.10320356, StopCount/Limit:   0/  3, Time:12.02762103\n",
      "ConfigID:   1, Epoch:   3, Training Loss: 0.09769692, Validation Loss: 0.09750959,                    Best: 0.09750959, StopCount/Limit:   0/  3, Time:13.03338289\n",
      "ConfigID:   1, Epoch:   4, Training Loss: 0.09424945, Validation Loss: 0.09779375,                    Best: 0.09750959, StopCount/Limit:   1/  3, Time:10.61794710\n",
      "ConfigID:   1, Epoch:   5, Training Loss: 0.09273604, Validation Loss: 0.09394865,                    Best: 0.09394865, StopCount/Limit:   1/  3, Time:11.16950417\n",
      "ConfigID:   1, Epoch:   6, Training Loss: 0.09046286, Validation Loss: 0.09975151,                    Best: 0.09394865, StopCount/Limit:   2/  3, Time:10.32020712\n",
      "ConfigID:   1, Epoch:   7, Training Loss: 0.08847235, Validation Loss: 0.09666134,                    Best: 0.09394865, StopCount/Limit:   3/  3, Time:12.27093482\n",
      "ConfigID:   2, Epoch:   0, Training Loss: 0.28734837, Validation Loss: 0.24356144,                    Best: 0.24356144, StopCount/Limit:   0/  3, Time:9.85364890\n",
      "ConfigID:   2, Epoch:   1, Training Loss: 0.24078511, Validation Loss: 0.23439431,                    Best: 0.23439431, StopCount/Limit:   0/  3, Time:12.39267302\n",
      "ConfigID:   2, Epoch:   2, Training Loss: 0.23751050, Validation Loss: 0.23579253,                    Best: 0.23439431, StopCount/Limit:   1/  3, Time:10.64497471\n",
      "ConfigID:   2, Epoch:   3, Training Loss: 0.23505260, Validation Loss: 0.23344896,                    Best: 0.23344896, StopCount/Limit:   1/  3, Time:11.37726593\n",
      "ConfigID:   2, Epoch:   4, Training Loss: 0.23471692, Validation Loss: 0.23428236,                    Best: 0.23344896, StopCount/Limit:   2/  3, Time:12.61684108\n",
      "ConfigID:   2, Epoch:   5, Training Loss: 0.23352279, Validation Loss: 0.23051945,                    Best: 0.23051945, StopCount/Limit:   2/  3, Time:11.37690496\n",
      "ConfigID:   2, Epoch:   6, Training Loss: 0.23804564, Validation Loss: 0.23587791,                    Best: 0.23051945, StopCount/Limit:   3/  3, Time:11.97625303\n",
      "Initial Run End\n",
      "Init Run Best: 0.06971682619361673, Mean: 0.13139497518341522, ID:0, config: [784   7   6   4   9   5   9  10]\n",
      "Gen 0 Run Start\n",
      "Next Gen: [784   7   6   4   9   5   9  10]\n",
      "ConfigID:   0, Epoch:   0, Training Loss: 0.18107896, Validation Loss: 0.12616832,                    Best: 0.12616832, StopCount/Limit:   0/  3, Time:10.73979688\n",
      "ConfigID:   0, Epoch:   1, Training Loss: 0.12077368, Validation Loss: 0.11677406,                    Best: 0.11677406, StopCount/Limit:   0/  3, Time:10.49029112\n",
      "ConfigID:   0, Epoch:   2, Training Loss: 0.10822635, Validation Loss: 0.10855973,                    Best: 0.10855973, StopCount/Limit:   0/  3, Time:12.67357492\n",
      "ConfigID:   0, Epoch:   3, Training Loss: 0.10083214, Validation Loss: 0.09604658,                    Best: 0.09604658, StopCount/Limit:   0/  3, Time:9.78716207\n",
      "ConfigID:   0, Epoch:   4, Training Loss: 0.09727189, Validation Loss: 0.09344073,                    Best: 0.09344073, StopCount/Limit:   0/  3, Time:9.53872514\n",
      "ConfigID:   0, Epoch:   5, Training Loss: 0.09571851, Validation Loss: 0.09460378,                    Best: 0.09344073, StopCount/Limit:   1/  3, Time:9.23264503\n",
      "ConfigID:   0, Epoch:   6, Training Loss: 0.09350085, Validation Loss: 0.09721595,                    Best: 0.09344073, StopCount/Limit:   2/  3, Time:9.09166718\n",
      "ConfigID:   0, Epoch:   7, Training Loss: 0.09327521, Validation Loss: 0.09881317,                    Best: 0.09344073, StopCount/Limit:   3/  3, Time:9.02420664\n",
      "Next Gen: [784   9   4   4   6   8   9  10]\n",
      "ConfigID:   1, Epoch:   0, Training Loss: 0.21398535, Validation Loss: 0.14491928,                    Best: 0.14491928, StopCount/Limit:   0/  3, Time:9.64317799\n",
      "ConfigID:   1, Epoch:   1, Training Loss: 0.13133302, Validation Loss: 0.12474779,                    Best: 0.12474779, StopCount/Limit:   0/  3, Time:10.71951008\n",
      "ConfigID:   1, Epoch:   2, Training Loss: 0.11503230, Validation Loss: 0.11404968,                    Best: 0.11404968, StopCount/Limit:   0/  3, Time:9.03894496\n",
      "ConfigID:   1, Epoch:   3, Training Loss: 0.10895498, Validation Loss: 0.11280127,                    Best: 0.11280127, StopCount/Limit:   0/  3, Time:9.08226323\n",
      "ConfigID:   1, Epoch:   4, Training Loss: 0.10622853, Validation Loss: 0.10225091,                    Best: 0.10225091, StopCount/Limit:   0/  3, Time:9.00488400\n",
      "ConfigID:   1, Epoch:   5, Training Loss: 0.10294303, Validation Loss: 0.10056832,                    Best: 0.10056832, StopCount/Limit:   0/  3, Time:9.98933387\n",
      "ConfigID:   1, Epoch:   6, Training Loss: 0.09967653, Validation Loss: 0.09687144,                    Best: 0.09687144, StopCount/Limit:   0/  3, Time:9.67349601\n",
      "ConfigID:   1, Epoch:   7, Training Loss: 0.09690627, Validation Loss: 0.09474217,                    Best: 0.09474217, StopCount/Limit:   0/  3, Time:9.24992704\n",
      "ConfigID:   1, Epoch:   8, Training Loss: 0.09555120, Validation Loss: 0.09030873,                    Best: 0.09030873, StopCount/Limit:   0/  3, Time:9.32736802\n",
      "ConfigID:   1, Epoch:   9, Training Loss: 0.09342520, Validation Loss: 0.09212584,                    Best: 0.09030873, StopCount/Limit:   1/  3, Time:9.15272331\n",
      "ConfigID:   1, Epoch:  10, Training Loss: 0.09168703, Validation Loss: 0.08937973,                    Best: 0.08937973, StopCount/Limit:   1/  3, Time:9.37372518\n",
      "ConfigID:   1, Epoch:  11, Training Loss: 0.09079051, Validation Loss: 0.09161672,                    Best: 0.08937973, StopCount/Limit:   2/  3, Time:8.86033201\n",
      "ConfigID:   1, Epoch:  12, Training Loss: 0.08968527, Validation Loss: 0.09429663,                    Best: 0.08937973, StopCount/Limit:   3/  3, Time:8.86487293\n",
      "Next Gen: [784   4   4   8   5   9  10]\n",
      "ConfigID:   2, Epoch:   0, Training Loss: 0.31356714, Validation Loss: 0.20687701,                    Best: 0.20687701, StopCount/Limit:   0/  3, Time:7.51789689\n",
      "ConfigID:   2, Epoch:   1, Training Loss: 0.17792030, Validation Loss: 0.15869617,                    Best: 0.15869617, StopCount/Limit:   0/  3, Time:7.77328110\n",
      "ConfigID:   2, Epoch:   2, Training Loss: 0.15241343, Validation Loss: 0.14687184,                    Best: 0.14687184, StopCount/Limit:   0/  3, Time:7.71056318\n",
      "ConfigID:   2, Epoch:   3, Training Loss: 0.14526383, Validation Loss: 0.14075852,                    Best: 0.14075852, StopCount/Limit:   0/  3, Time:7.45126867\n",
      "ConfigID:   2, Epoch:   4, Training Loss: 0.13892598, Validation Loss: 0.13803964,                    Best: 0.13803964, StopCount/Limit:   0/  3, Time:7.49538279\n",
      "ConfigID:   2, Epoch:   5, Training Loss: 0.13542154, Validation Loss: 0.13991228,                    Best: 0.13803964, StopCount/Limit:   1/  3, Time:7.58429289\n",
      "ConfigID:   2, Epoch:   6, Training Loss: 0.13314451, Validation Loss: 0.13620776,                    Best: 0.13620776, StopCount/Limit:   1/  3, Time:7.52102709\n",
      "ConfigID:   2, Epoch:   7, Training Loss: 0.13188607, Validation Loss: 0.13198787,                    Best: 0.13198787, StopCount/Limit:   1/  3, Time:7.82168913\n",
      "ConfigID:   2, Epoch:   8, Training Loss: 0.13118478, Validation Loss: 0.13274233,                    Best: 0.13198787, StopCount/Limit:   2/  3, Time:7.18743801\n",
      "ConfigID:   2, Epoch:   9, Training Loss: 0.12974622, Validation Loss: 0.13038022,                    Best: 0.13038022, StopCount/Limit:   2/  3, Time:7.55023313\n",
      "ConfigID:   2, Epoch:  10, Training Loss: 0.12902895, Validation Loss: 0.13103378,                    Best: 0.13038022, StopCount/Limit:   3/  3, Time:7.45751810\n",
      "Gen 0 Run End\n",
      "Run   0 Best: 0.06971682619361673, Mean: 0.09649225934893668, ID:0, config: [784   7   6   4   9   5   9  10], updatecount:   2, Generation RunTime: 285.66303611\n",
      "Gen 1 Run Start\n",
      "Next Gen: [784   8   6   4   9   5   9  10]\n",
      "ConfigID:   0, Epoch:   0, Training Loss: 0.21276478, Validation Loss: 0.13264693,                    Best: 0.13264693, StopCount/Limit:   0/  3, Time:20.25096083\n",
      "ConfigID:   0, Epoch:   1, Training Loss: 0.12239037, Validation Loss: 0.11015854,                    Best: 0.11015854, StopCount/Limit:   0/  3, Time:12.84999800\n",
      "ConfigID:   0, Epoch:   2, Training Loss: 0.10541781, Validation Loss: 0.09650517,                    Best: 0.09650517, StopCount/Limit:   0/  3, Time:8.45052791\n",
      "ConfigID:   0, Epoch:   3, Training Loss: 0.09701722, Validation Loss: 0.09927446,                    Best: 0.09650517, StopCount/Limit:   1/  3, Time:18.73753595\n",
      "ConfigID:   0, Epoch:   4, Training Loss: 0.09043779, Validation Loss: 0.08826123,                    Best: 0.08826123, StopCount/Limit:   1/  3, Time:9.87853217\n",
      "ConfigID:   0, Epoch:   5, Training Loss: 0.08685946, Validation Loss: 0.09083913,                    Best: 0.08826123, StopCount/Limit:   2/  3, Time:10.85088396\n",
      "ConfigID:   0, Epoch:   6, Training Loss: 0.08370737, Validation Loss: 0.08132482,                    Best: 0.08132482, StopCount/Limit:   2/  3, Time:9.14236093\n",
      "ConfigID:   0, Epoch:   7, Training Loss: 0.08027475, Validation Loss: 0.08165502,                    Best: 0.08132482, StopCount/Limit:   3/  3, Time:9.16570711\n",
      "Next Gen: [784   9   4   4   6   8   9  10]\n",
      "ConfigID:   1, Epoch:   0, Training Loss: 0.24302056, Validation Loss: 0.19443872,                    Best: 0.19443872, StopCount/Limit:   0/  3, Time:9.15390491\n",
      "ConfigID:   1, Epoch:   1, Training Loss: 0.17923451, Validation Loss: 0.16415384,                    Best: 0.16415384, StopCount/Limit:   0/  3, Time:9.48502612\n",
      "ConfigID:   1, Epoch:   2, Training Loss: 0.16248999, Validation Loss: 0.15567300,                    Best: 0.15567300, StopCount/Limit:   0/  3, Time:9.67028809\n",
      "ConfigID:   1, Epoch:   3, Training Loss: 0.15377512, Validation Loss: 0.13361382,                    Best: 0.13361382, StopCount/Limit:   0/  3, Time:9.26374102\n",
      "ConfigID:   1, Epoch:   4, Training Loss: 0.12515063, Validation Loss: 0.12622131,                    Best: 0.12622131, StopCount/Limit:   0/  3, Time:10.40752697\n",
      "ConfigID:   1, Epoch:   5, Training Loss: 0.11383092, Validation Loss: 0.10965147,                    Best: 0.10965147, StopCount/Limit:   0/  3, Time:12.17137289\n",
      "ConfigID:   1, Epoch:   6, Training Loss: 0.10978395, Validation Loss: 0.10314972,                    Best: 0.10314972, StopCount/Limit:   0/  3, Time:11.25892496\n",
      "ConfigID:   1, Epoch:   7, Training Loss: 0.10580293, Validation Loss: 0.10395207,                    Best: 0.10314972, StopCount/Limit:   1/  3, Time:9.97369027\n",
      "ConfigID:   1, Epoch:   8, Training Loss: 0.10196787, Validation Loss: 0.09765343,                    Best: 0.09765343, StopCount/Limit:   1/  3, Time:9.33810186\n",
      "ConfigID:   1, Epoch:   9, Training Loss: 0.09599160, Validation Loss: 0.09397160,                    Best: 0.09397160, StopCount/Limit:   1/  3, Time:9.29154897\n",
      "ConfigID:   1, Epoch:  10, Training Loss: 0.09723798, Validation Loss: 0.09388458,                    Best: 0.09388458, StopCount/Limit:   1/  3, Time:9.25555396\n",
      "ConfigID:   1, Epoch:  11, Training Loss: 0.09227917, Validation Loss: 0.09430445,                    Best: 0.09388458, StopCount/Limit:   2/  3, Time:12.29176807\n",
      "ConfigID:   1, Epoch:  12, Training Loss: 0.09039568, Validation Loss: 0.09359753,                    Best: 0.09359753, StopCount/Limit:   2/  3, Time:12.88856292\n",
      "ConfigID:   1, Epoch:  13, Training Loss: 0.08909338, Validation Loss: 0.09249553,                    Best: 0.09249553, StopCount/Limit:   2/  3, Time:11.71707892\n",
      "ConfigID:   1, Epoch:  14, Training Loss: 0.08799053, Validation Loss: 0.09161021,                    Best: 0.09161021, StopCount/Limit:   2/  3, Time:9.62275577\n",
      "ConfigID:   1, Epoch:  15, Training Loss: 0.08861802, Validation Loss: 0.09028221,                    Best: 0.09028221, StopCount/Limit:   2/  3, Time:9.58425927\n",
      "ConfigID:   1, Epoch:  16, Training Loss: 0.08707853, Validation Loss: 0.09560436,                    Best: 0.09028221, StopCount/Limit:   3/  3, Time:12.69641280\n",
      "Next Gen: [784   7   4   8   5   9  10]\n",
      "ConfigID:   2, Epoch:   0, Training Loss: 0.28913973, Validation Loss: 0.25738500,                    Best: 0.25738500, StopCount/Limit:   0/  3, Time:10.44998693\n",
      "ConfigID:   2, Epoch:   1, Training Loss: 0.24534593, Validation Loss: 0.24164265,                    Best: 0.24164265, StopCount/Limit:   0/  3, Time:10.22598124\n",
      "ConfigID:   2, Epoch:   2, Training Loss: 0.24120922, Validation Loss: 0.24060658,                    Best: 0.24060658, StopCount/Limit:   0/  3, Time:12.02056122\n",
      "ConfigID:   2, Epoch:   3, Training Loss: 0.23964610, Validation Loss: 0.23748320,                    Best: 0.23748320, StopCount/Limit:   0/  3, Time:7.60920024\n",
      "ConfigID:   2, Epoch:   4, Training Loss: 0.23839019, Validation Loss: 0.23383215,                    Best: 0.23383215, StopCount/Limit:   0/  3, Time:7.40562987\n",
      "ConfigID:   2, Epoch:   5, Training Loss: 0.23580224, Validation Loss: 0.24029437,                    Best: 0.23383215, StopCount/Limit:   1/  3, Time:7.48759127\n",
      "ConfigID:   2, Epoch:   6, Training Loss: 0.23655992, Validation Loss: 0.23164716,                    Best: 0.23164716, StopCount/Limit:   1/  3, Time:7.34302211\n",
      "ConfigID:   2, Epoch:   7, Training Loss: 0.23314295, Validation Loss: 0.22831576,                    Best: 0.22831576, StopCount/Limit:   1/  3, Time:8.55144310\n",
      "ConfigID:   2, Epoch:   8, Training Loss: 0.23447571, Validation Loss: 0.23006817,                    Best: 0.22831576, StopCount/Limit:   2/  3, Time:12.27249002\n",
      "ConfigID:   2, Epoch:   9, Training Loss: 0.23275252, Validation Loss: 0.23592987,                    Best: 0.22831576, StopCount/Limit:   3/  3, Time:10.40321875\n",
      "Gen 1 Run End\n",
      "Run   1 Best: 0.06971682619361673, Mean: 0.09649225934893668, ID:0, config: [784   7   6   4   9   5   9  10], updatecount:   0, Generation RunTime: 371.20537114\n",
      "Gen 2 Run Start\n",
      "Next Gen: [784   5   6   5   8   5   5  10]\n",
      "ConfigID:   0, Epoch:   0, Training Loss: 0.28526480, Validation Loss: 0.25762350,                    Best: 0.25762350, StopCount/Limit:   0/  3, Time:11.80671477\n",
      "ConfigID:   0, Epoch:   1, Training Loss: 0.25396411, Validation Loss: 0.25173958,                    Best: 0.25173958, StopCount/Limit:   0/  3, Time:10.68429995\n",
      "ConfigID:   0, Epoch:   2, Training Loss: 0.24807930, Validation Loss: 0.24732451,                    Best: 0.24732451, StopCount/Limit:   0/  3, Time:11.83166313\n",
      "ConfigID:   0, Epoch:   3, Training Loss: 0.24695659, Validation Loss: 0.24547231,                    Best: 0.24547231, StopCount/Limit:   0/  3, Time:10.12117195\n",
      "ConfigID:   0, Epoch:   4, Training Loss: 0.24498395, Validation Loss: 0.24407963,                    Best: 0.24407963, StopCount/Limit:   0/  3, Time:12.02561092\n",
      "ConfigID:   0, Epoch:   5, Training Loss: 0.24440134, Validation Loss: 0.24606482,                    Best: 0.24407963, StopCount/Limit:   1/  3, Time:12.61277008\n",
      "ConfigID:   0, Epoch:   6, Training Loss: 0.24514320, Validation Loss: 0.24400419,                    Best: 0.24400419, StopCount/Limit:   1/  3, Time:11.86724901\n",
      "ConfigID:   0, Epoch:   7, Training Loss: 0.24486881, Validation Loss: 0.24437028,                    Best: 0.24400419, StopCount/Limit:   2/  3, Time:8.85635400\n",
      "ConfigID:   0, Epoch:   8, Training Loss: 0.24388186, Validation Loss: 0.24215755,                    Best: 0.24215755, StopCount/Limit:   2/  3, Time:7.94579196\n",
      "ConfigID:   0, Epoch:   9, Training Loss: 0.24460601, Validation Loss: 0.24434729,                    Best: 0.24215755, StopCount/Limit:   3/  3, Time:8.07043219\n",
      "Next Gen: [784   6   4   5   6   7   6  10]\n",
      "ConfigID:   1, Epoch:   0, Training Loss: 0.24965172, Validation Loss: 0.19994467,                    Best: 0.19994467, StopCount/Limit:   0/  3, Time:9.77359724\n",
      "ConfigID:   1, Epoch:   1, Training Loss: 0.19053294, Validation Loss: 0.18014035,                    Best: 0.18014035, StopCount/Limit:   0/  3, Time:8.71342397\n",
      "ConfigID:   1, Epoch:   2, Training Loss: 0.17263384, Validation Loss: 0.16995618,                    Best: 0.16995618, StopCount/Limit:   0/  3, Time:7.94895482\n",
      "ConfigID:   1, Epoch:   3, Training Loss: 0.16475778, Validation Loss: 0.16093431,                    Best: 0.16093431, StopCount/Limit:   0/  3, Time:7.89549589\n",
      "ConfigID:   1, Epoch:   4, Training Loss: 0.14742275, Validation Loss: 0.13765937,                    Best: 0.13765937, StopCount/Limit:   0/  3, Time:12.48011017\n",
      "ConfigID:   1, Epoch:   5, Training Loss: 0.13661668, Validation Loss: 0.13844533,                    Best: 0.13765937, StopCount/Limit:   1/  3, Time:9.54987979\n",
      "ConfigID:   1, Epoch:   6, Training Loss: 0.13306055, Validation Loss: 0.13167646,                    Best: 0.13167646, StopCount/Limit:   1/  3, Time:9.33522105\n",
      "ConfigID:   1, Epoch:   7, Training Loss: 0.12967758, Validation Loss: 0.12903250,                    Best: 0.12903250, StopCount/Limit:   1/  3, Time:9.03112197\n",
      "ConfigID:   1, Epoch:   8, Training Loss: 0.12793377, Validation Loss: 0.13231027,                    Best: 0.12903250, StopCount/Limit:   2/  3, Time:8.25596094\n",
      "ConfigID:   1, Epoch:   9, Training Loss: 0.12859622, Validation Loss: 0.13080579,                    Best: 0.12903250, StopCount/Limit:   3/  3, Time:7.73187399\n",
      "Next Gen: [784   4   4   8   4  10  10]\n",
      "ConfigID:   2, Epoch:   0, Training Loss: 0.19834633, Validation Loss: 0.14012874,                    Best: 0.14012874, StopCount/Limit:   0/  3, Time:6.80926394\n",
      "ConfigID:   2, Epoch:   1, Training Loss: 0.13277031, Validation Loss: 0.12181920,                    Best: 0.12181920, StopCount/Limit:   0/  3, Time:6.93541694\n",
      "ConfigID:   2, Epoch:   2, Training Loss: 0.12037663, Validation Loss: 0.11851899,                    Best: 0.11851899, StopCount/Limit:   0/  3, Time:6.72189093\n",
      "ConfigID:   2, Epoch:   3, Training Loss: 0.11473951, Validation Loss: 0.11608153,                    Best: 0.11608153, StopCount/Limit:   0/  3, Time:6.86447477\n",
      "ConfigID:   2, Epoch:   4, Training Loss: 0.11120950, Validation Loss: 0.12139249,                    Best: 0.11608153, StopCount/Limit:   1/  3, Time:8.46719885\n",
      "ConfigID:   2, Epoch:   5, Training Loss: 0.10931562, Validation Loss: 0.11488749,                    Best: 0.11488749, StopCount/Limit:   1/  3, Time:8.58386517\n",
      "ConfigID:   2, Epoch:   6, Training Loss: 0.10706917, Validation Loss: 0.10569521,                    Best: 0.10569521, StopCount/Limit:   1/  3, Time:7.90610790\n",
      "ConfigID:   2, Epoch:   7, Training Loss: 0.10540790, Validation Loss: 0.10895667,                    Best: 0.10569521, StopCount/Limit:   2/  3, Time:7.39477110\n",
      "ConfigID:   2, Epoch:   8, Training Loss: 0.10490429, Validation Loss: 0.10340106,                    Best: 0.10340106, StopCount/Limit:   2/  3, Time:7.04440498\n",
      "ConfigID:   2, Epoch:   9, Training Loss: 0.10369645, Validation Loss: 0.10243239,                    Best: 0.10243239, StopCount/Limit:   2/  3, Time:6.82075429\n",
      "ConfigID:   2, Epoch:  10, Training Loss: 0.10270622, Validation Loss: 0.10291393,                    Best: 0.10243239, StopCount/Limit:   3/  3, Time:6.73429179\n",
      "Gen 2 Run End\n",
      "Run   2 Best: 0.06971682619361673, Mean: 0.08717631580948364, ID:0, config: [784   7   6   4   9   5   9  10], updatecount:   1, Generation RunTime: 276.84735203\n",
      "Gen 3 Run Start\n",
      "Next Gen: [784   5   6   5   8   5   5  10]\n",
      "ConfigID:   0, Epoch:   0, Training Loss: 0.33599276, Validation Loss: 0.32535651,                    Best: 0.32535651, StopCount/Limit:   0/  3, Time:8.84152389\n",
      "ConfigID:   0, Epoch:   1, Training Loss: 0.32517561, Validation Loss: 0.32499664,                    Best: 0.32499664, StopCount/Limit:   0/  3, Time:7.67130399\n",
      "ConfigID:   0, Epoch:   2, Training Loss: 0.32515332, Validation Loss: 0.32506499,                    Best: 0.32499664, StopCount/Limit:   1/  3, Time:7.64453363\n",
      "ConfigID:   0, Epoch:   3, Training Loss: 0.32513232, Validation Loss: 0.32530491,                    Best: 0.32499664, StopCount/Limit:   2/  3, Time:7.48035312\n",
      "ConfigID:   0, Epoch:   4, Training Loss: 0.32511666, Validation Loss: 0.32502703,                    Best: 0.32499664, StopCount/Limit:   3/  3, Time:7.45043397\n",
      "Next Gen: [784   9   4   5   6   8   9  10]\n",
      "ConfigID:   1, Epoch:   0, Training Loss: 0.16008163, Validation Loss: 0.11771945,                    Best: 0.11771945, StopCount/Limit:   0/  3, Time:7.93684769\n",
      "ConfigID:   1, Epoch:   1, Training Loss: 0.10364124, Validation Loss: 0.10007847,                    Best: 0.10007847, StopCount/Limit:   0/  3, Time:10.64019704\n",
      "ConfigID:   1, Epoch:   2, Training Loss: 0.09021733, Validation Loss: 0.09067055,                    Best: 0.09067055, StopCount/Limit:   0/  3, Time:11.10473990\n",
      "ConfigID:   1, Epoch:   3, Training Loss: 0.08408021, Validation Loss: 0.07740552,                    Best: 0.07740552, StopCount/Limit:   0/  3, Time:12.03079081\n",
      "ConfigID:   1, Epoch:   4, Training Loss: 0.08104502, Validation Loss: 0.08791546,                    Best: 0.07740552, StopCount/Limit:   1/  3, Time:10.21966386\n",
      "ConfigID:   1, Epoch:   5, Training Loss: 0.07935728, Validation Loss: 0.07921371,                    Best: 0.07740552, StopCount/Limit:   2/  3, Time:8.45964789\n",
      "ConfigID:   1, Epoch:   6, Training Loss: 0.07823273, Validation Loss: 0.07737941,                    Best: 0.07737941, StopCount/Limit:   2/  3, Time:9.32350492\n",
      "ConfigID:   1, Epoch:   7, Training Loss: 0.07736324, Validation Loss: 0.07614372,                    Best: 0.07614372, StopCount/Limit:   2/  3, Time:12.41467786\n",
      "ConfigID:   1, Epoch:   8, Training Loss: 0.07677009, Validation Loss: 0.08042638,                    Best: 0.07614372, StopCount/Limit:   3/  3, Time:7.83805585\n",
      "Next Gen: [784   4   4   8   4  10  10]\n",
      "ConfigID:   2, Epoch:   0, Training Loss: 0.27620982, Validation Loss: 0.21724063,                    Best: 0.21724063, StopCount/Limit:   0/  3, Time:8.28944206\n",
      "ConfigID:   2, Epoch:   1, Training Loss: 0.19443148, Validation Loss: 0.17695358,                    Best: 0.17695358, StopCount/Limit:   0/  3, Time:8.43356085\n",
      "ConfigID:   2, Epoch:   2, Training Loss: 0.17864549, Validation Loss: 0.17884132,                    Best: 0.17695358, StopCount/Limit:   1/  3, Time:8.15122604\n",
      "ConfigID:   2, Epoch:   3, Training Loss: 0.17408142, Validation Loss: 0.17438211,                    Best: 0.17438211, StopCount/Limit:   1/  3, Time:9.09757733\n",
      "ConfigID:   2, Epoch:   4, Training Loss: 0.17165002, Validation Loss: 0.16905465,                    Best: 0.16905465, StopCount/Limit:   1/  3, Time:6.85871601\n",
      "ConfigID:   2, Epoch:   5, Training Loss: 0.17041918, Validation Loss: 0.16483686,                    Best: 0.16483686, StopCount/Limit:   1/  3, Time:7.53943992\n",
      "ConfigID:   2, Epoch:   6, Training Loss: 0.16983243, Validation Loss: 0.16499788,                    Best: 0.16483686, StopCount/Limit:   2/  3, Time:8.34011412\n",
      "ConfigID:   2, Epoch:   7, Training Loss: 0.16507559, Validation Loss: 0.16181085,                    Best: 0.16181085, StopCount/Limit:   2/  3, Time:7.88078904\n",
      "ConfigID:   2, Epoch:   8, Training Loss: 0.16486755, Validation Loss: 0.16291645,                    Best: 0.16181085, StopCount/Limit:   3/  3, Time:12.05382514\n",
      "Gen 3 Run End\n",
      "Run   3 Best: 0.06971682619361673, Mean: 0.08276430990876786, ID:0, config: [784   7   6   4   9   5   9  10], updatecount:   1, Generation RunTime: 205.72730017\n",
      "Gen 4 Run Start\n",
      "Next Gen: [784   7   6   4   6   5   9  10]\n",
      "ConfigID:   0, Epoch:   0, Training Loss: 0.25710456, Validation Loss: 0.19779739,                    Best: 0.19779739, StopCount/Limit:   0/  3, Time:11.97578096\n",
      "ConfigID:   0, Epoch:   1, Training Loss: 0.17329346, Validation Loss: 0.15911543,                    Best: 0.15911543, StopCount/Limit:   0/  3, Time:11.91429591\n",
      "ConfigID:   0, Epoch:   2, Training Loss: 0.15619965, Validation Loss: 0.14981497,                    Best: 0.14981497, StopCount/Limit:   0/  3, Time:9.97339511\n",
      "ConfigID:   0, Epoch:   3, Training Loss: 0.14799807, Validation Loss: 0.14628391,                    Best: 0.14628391, StopCount/Limit:   0/  3, Time:11.87408376\n",
      "ConfigID:   0, Epoch:   4, Training Loss: 0.14515489, Validation Loss: 0.14563692,                    Best: 0.14563692, StopCount/Limit:   0/  3, Time:12.22641110\n",
      "ConfigID:   0, Epoch:   5, Training Loss: 0.14285900, Validation Loss: 0.14341789,                    Best: 0.14341789, StopCount/Limit:   0/  3, Time:11.38008976\n",
      "ConfigID:   0, Epoch:   6, Training Loss: 0.14155179, Validation Loss: 0.15577816,                    Best: 0.14341789, StopCount/Limit:   1/  3, Time:11.72067595\n",
      "ConfigID:   0, Epoch:   7, Training Loss: 0.14159600, Validation Loss: 0.14139078,                    Best: 0.14139078, StopCount/Limit:   1/  3, Time:27.33244181\n",
      "ConfigID:   0, Epoch:   8, Training Loss: 0.14050567, Validation Loss: 0.14276411,                    Best: 0.14139078, StopCount/Limit:   2/  3, Time:9.89049911\n",
      "ConfigID:   0, Epoch:   9, Training Loss: 0.14071107, Validation Loss: 0.14292091,                    Best: 0.14139078, StopCount/Limit:   3/  3, Time:8.40748096\n",
      "Next Gen: [784   9   4   6   6   8   9  10]\n",
      "ConfigID:   1, Epoch:   0, Training Loss: 0.31099165, Validation Loss: 0.24679243,                    Best: 0.24679243, StopCount/Limit:   0/  3, Time:9.29158330\n",
      "ConfigID:   1, Epoch:   1, Training Loss: 0.24201855, Validation Loss: 0.23333445,                    Best: 0.23333445, StopCount/Limit:   0/  3, Time:9.24783421\n",
      "ConfigID:   1, Epoch:   2, Training Loss: 0.22943044, Validation Loss: 0.22580295,                    Best: 0.22580295, StopCount/Limit:   0/  3, Time:7.85584807\n",
      "ConfigID:   1, Epoch:   3, Training Loss: 0.22138537, Validation Loss: 0.22419937,                    Best: 0.22419937, StopCount/Limit:   0/  3, Time:8.02108097\n",
      "ConfigID:   1, Epoch:   4, Training Loss: 0.21636779, Validation Loss: 0.21350736,                    Best: 0.21350736, StopCount/Limit:   0/  3, Time:8.46179509\n",
      "ConfigID:   1, Epoch:   5, Training Loss: 0.21352575, Validation Loss: 0.20793572,                    Best: 0.20793572, StopCount/Limit:   0/  3, Time:7.71879101\n",
      "ConfigID:   1, Epoch:   6, Training Loss: 0.21447185, Validation Loss: 0.20927484,                    Best: 0.20793572, StopCount/Limit:   1/  3, Time:8.96786308\n",
      "ConfigID:   1, Epoch:   7, Training Loss: 0.21420909, Validation Loss: 0.20850639,                    Best: 0.20793572, StopCount/Limit:   2/  3, Time:8.35951710\n",
      "ConfigID:   1, Epoch:   8, Training Loss: 0.21093623, Validation Loss: 0.21274471,                    Best: 0.20793572, StopCount/Limit:   3/  3, Time:8.05818701\n",
      "Next Gen: [784   4   4   8   4  10  10]\n",
      "ConfigID:   2, Epoch:   0, Training Loss: 0.28464529, Validation Loss: 0.25765433,                    Best: 0.25765433, StopCount/Limit:   0/  3, Time:7.02753687\n",
      "ConfigID:   2, Epoch:   1, Training Loss: 0.25794846, Validation Loss: 0.25435355,                    Best: 0.25435355, StopCount/Limit:   0/  3, Time:8.21154618\n",
      "ConfigID:   2, Epoch:   2, Training Loss: 0.25494488, Validation Loss: 0.24936838,                    Best: 0.24936838, StopCount/Limit:   0/  3, Time:7.58835101\n",
      "ConfigID:   2, Epoch:   3, Training Loss: 0.25398231, Validation Loss: 0.25002133,                    Best: 0.24936838, StopCount/Limit:   1/  3, Time:14.95931506\n",
      "ConfigID:   2, Epoch:   4, Training Loss: 0.25331793, Validation Loss: 0.24800569,                    Best: 0.24800569, StopCount/Limit:   1/  3, Time:10.68101478\n",
      "ConfigID:   2, Epoch:   5, Training Loss: 0.25442927, Validation Loss: 0.27060119,                    Best: 0.24800569, StopCount/Limit:   2/  3, Time:11.83294725\n",
      "ConfigID:   2, Epoch:   6, Training Loss: 0.25446484, Validation Loss: 0.24871220,                    Best: 0.24800569, StopCount/Limit:   3/  3, Time:9.03141713\n",
      "Gen 4 Run End\n",
      "Run   4 Best: 0.06971682619361673, Mean: 0.08276430990876786, ID:0, config: [784   7   6   4   9   5   9  10], updatecount:   0, Generation RunTime: 272.04530001\n",
      "Gen 5 Run Start\n",
      "Next Gen: [784   5   6   5   8   5   5  10]\n",
      "ConfigID:   0, Epoch:   0, Training Loss: 0.31664007, Validation Loss: 0.23583686,                    Best: 0.23583686, StopCount/Limit:   0/  3, Time:9.40362096\n",
      "ConfigID:   0, Epoch:   1, Training Loss: 0.21737815, Validation Loss: 0.20643874,                    Best: 0.20643874, StopCount/Limit:   0/  3, Time:10.27020788\n",
      "ConfigID:   0, Epoch:   2, Training Loss: 0.20315289, Validation Loss: 0.20155787,                    Best: 0.20155787, StopCount/Limit:   0/  3, Time:11.55651212\n",
      "ConfigID:   0, Epoch:   3, Training Loss: 0.19827543, Validation Loss: 0.19532925,                    Best: 0.19532925, StopCount/Limit:   0/  3, Time:8.79590893\n",
      "ConfigID:   0, Epoch:   4, Training Loss: 0.18483178, Validation Loss: 0.17554065,                    Best: 0.17554065, StopCount/Limit:   0/  3, Time:9.81583691\n",
      "ConfigID:   0, Epoch:   5, Training Loss: 0.17257461, Validation Loss: 0.17058189,                    Best: 0.17058189, StopCount/Limit:   0/  3, Time:9.93225813\n",
      "ConfigID:   0, Epoch:   6, Training Loss: 0.16042883, Validation Loss: 0.15831537,                    Best: 0.15831537, StopCount/Limit:   0/  3, Time:8.84098816\n",
      "ConfigID:   0, Epoch:   7, Training Loss: 0.15310648, Validation Loss: 0.15431136,                    Best: 0.15431136, StopCount/Limit:   0/  3, Time:10.10201478\n",
      "ConfigID:   0, Epoch:   8, Training Loss: 0.15144686, Validation Loss: 0.15686953,                    Best: 0.15431136, StopCount/Limit:   1/  3, Time:8.66549087\n",
      "ConfigID:   0, Epoch:   9, Training Loss: 0.14952875, Validation Loss: 0.15469481,                    Best: 0.15431136, StopCount/Limit:   2/  3, Time:8.30348325\n",
      "ConfigID:   0, Epoch:  10, Training Loss: 0.14874820, Validation Loss: 0.15092599,                    Best: 0.15092599, StopCount/Limit:   2/  3, Time:8.64024210\n",
      "ConfigID:   0, Epoch:  11, Training Loss: 0.14756050, Validation Loss: 0.15277497,                    Best: 0.15092599, StopCount/Limit:   3/  3, Time:7.83772802\n",
      "Next Gen: [784   9   4   5   6   9   9  10]\n",
      "ConfigID:   1, Epoch:   0, Training Loss: 0.19582442, Validation Loss: 0.13394198,                    Best: 0.13394198, StopCount/Limit:   0/  3, Time:8.35881400\n",
      "ConfigID:   1, Epoch:   1, Training Loss: 0.11878881, Validation Loss: 0.10012367,                    Best: 0.10012367, StopCount/Limit:   0/  3, Time:9.41490722\n",
      "ConfigID:   1, Epoch:   2, Training Loss: 0.09190913, Validation Loss: 0.07774428,                    Best: 0.07774428, StopCount/Limit:   0/  3, Time:8.30715084\n",
      "ConfigID:   1, Epoch:   3, Training Loss: 0.07900739, Validation Loss: 0.07125720,                    Best: 0.07125720, StopCount/Limit:   0/  3, Time:9.52960300\n",
      "ConfigID:   1, Epoch:   4, Training Loss: 0.06998689, Validation Loss: 0.07102290,                    Best: 0.07102290, StopCount/Limit:   0/  3, Time:9.16467881\n",
      "ConfigID:   1, Epoch:   5, Training Loss: 0.06475239, Validation Loss: 0.06654971,                    Best: 0.06654971, StopCount/Limit:   0/  3, Time:10.43953800\n",
      "ConfigID:   1, Epoch:   6, Training Loss: 0.06246929, Validation Loss: 0.06552855,                    Best: 0.06552855, StopCount/Limit:   0/  3, Time:7.13813806\n",
      "ConfigID:   1, Epoch:   7, Training Loss: 0.06100555, Validation Loss: 0.07881052,                    Best: 0.06552855, StopCount/Limit:   1/  3, Time:10.10352087\n",
      "ConfigID:   1, Epoch:   8, Training Loss: 0.05953579, Validation Loss: 0.06111658,                    Best: 0.06111658, StopCount/Limit:   1/  3, Time:8.11625218\n",
      "ConfigID:   1, Epoch:   9, Training Loss: 0.05739038, Validation Loss: 0.06147833,                    Best: 0.06111658, StopCount/Limit:   2/  3, Time:9.19833589\n",
      "ConfigID:   1, Epoch:  10, Training Loss: 0.05591637, Validation Loss: 0.05692493,                    Best: 0.05692493, StopCount/Limit:   2/  3, Time:7.14693785\n",
      "ConfigID:   1, Epoch:  11, Training Loss: 0.05488806, Validation Loss: 0.05814500,                    Best: 0.05692493, StopCount/Limit:   3/  3, Time:7.43654704\n",
      "Next Gen: [784   4   4   8   4  10  10]\n",
      "ConfigID:   2, Epoch:   0, Training Loss: 0.30709131, Validation Loss: 0.24607888,                    Best: 0.24607888, StopCount/Limit:   0/  3, Time:5.85801291\n",
      "ConfigID:   2, Epoch:   1, Training Loss: 0.24203957, Validation Loss: 0.23439259,                    Best: 0.23439259, StopCount/Limit:   0/  3, Time:9.36330271\n",
      "ConfigID:   2, Epoch:   2, Training Loss: 0.22203161, Validation Loss: 0.21886262,                    Best: 0.21886262, StopCount/Limit:   0/  3, Time:11.86192799\n",
      "ConfigID:   2, Epoch:   3, Training Loss: 0.21054472, Validation Loss: 0.20188321,                    Best: 0.20188321, StopCount/Limit:   0/  3, Time:9.69475293\n",
      "ConfigID:   2, Epoch:   4, Training Loss: 0.19894013, Validation Loss: 0.19459131,                    Best: 0.19459131, StopCount/Limit:   0/  3, Time:8.40450406\n",
      "ConfigID:   2, Epoch:   5, Training Loss: 0.19345879, Validation Loss: 0.18505963,                    Best: 0.18505963, StopCount/Limit:   0/  3, Time:8.54423332\n",
      "ConfigID:   2, Epoch:   6, Training Loss: 0.17466934, Validation Loss: 0.16765222,                    Best: 0.16765222, StopCount/Limit:   0/  3, Time:9.23808479\n",
      "ConfigID:   2, Epoch:   7, Training Loss: 0.16615304, Validation Loss: 0.16295527,                    Best: 0.16295527, StopCount/Limit:   0/  3, Time:6.58698392\n",
      "ConfigID:   2, Epoch:   8, Training Loss: 0.16299445, Validation Loss: 0.16292076,                    Best: 0.16292076, StopCount/Limit:   0/  3, Time:6.30948710\n",
      "ConfigID:   2, Epoch:   9, Training Loss: 0.16101142, Validation Loss: 0.16129413,                    Best: 0.16129413, StopCount/Limit:   0/  3, Time:6.30752087\n",
      "ConfigID:   2, Epoch:  10, Training Loss: 0.15953397, Validation Loss: 0.15934501,                    Best: 0.15934501, StopCount/Limit:   0/  3, Time:8.79622674\n",
      "ConfigID:   2, Epoch:  11, Training Loss: 0.15823680, Validation Loss: 0.15769994,                    Best: 0.15769994, StopCount/Limit:   0/  3, Time:7.94143796\n",
      "ConfigID:   2, Epoch:  12, Training Loss: 0.15719116, Validation Loss: 0.15778594,                    Best: 0.15769994, StopCount/Limit:   1/  3, Time:7.92080998\n",
      "ConfigID:   2, Epoch:  13, Training Loss: 0.15639939, Validation Loss: 0.15869737,                    Best: 0.15769994, StopCount/Limit:   2/  3, Time:7.73328686\n",
      "ConfigID:   2, Epoch:  14, Training Loss: 0.15485666, Validation Loss: 0.15343492,                    Best: 0.15343492, StopCount/Limit:   2/  3, Time:6.32046604\n",
      "ConfigID:   2, Epoch:  15, Training Loss: 0.15296258, Validation Loss: 0.15443593,                    Best: 0.15343492, StopCount/Limit:   3/  3, Time:6.01401901\n",
      "Gen 5 Run End\n",
      "Run   5 Best: 0.05692493488476612, Mean: 0.07635804981548185, ID:1, config: [784   9   4   5   6   9   9  10], updatecount:   1, Generation RunTime: 343.44533515\n",
      "Gen 6 Run Start\n",
      "Next Gen: [784   6   4   5   6   8   6  10]\n",
      "ConfigID:   0, Epoch:   0, Training Loss: 0.23574536, Validation Loss: 0.21023742,                    Best: 0.21023742, StopCount/Limit:   0/  3, Time:7.38625312\n",
      "ConfigID:   0, Epoch:   1, Training Loss: 0.20216543, Validation Loss: 0.20543041,                    Best: 0.20543041, StopCount/Limit:   0/  3, Time:7.33109283\n",
      "ConfigID:   0, Epoch:   2, Training Loss: 0.19011215, Validation Loss: 0.18712205,                    Best: 0.18712205, StopCount/Limit:   0/  3, Time:8.38085198\n",
      "ConfigID:   0, Epoch:   3, Training Loss: 0.17886994, Validation Loss: 0.18154495,                    Best: 0.18154495, StopCount/Limit:   0/  3, Time:9.74398375\n",
      "ConfigID:   0, Epoch:   4, Training Loss: 0.17038223, Validation Loss: 0.17596827,                    Best: 0.17596827, StopCount/Limit:   0/  3, Time:9.32366109\n",
      "ConfigID:   0, Epoch:   5, Training Loss: 0.15269670, Validation Loss: 0.15165872,                    Best: 0.15165872, StopCount/Limit:   0/  3, Time:8.32811403\n",
      "ConfigID:   0, Epoch:   6, Training Loss: 0.13964249, Validation Loss: 0.14332452,                    Best: 0.14332452, StopCount/Limit:   0/  3, Time:9.13279200\n",
      "ConfigID:   0, Epoch:   7, Training Loss: 0.13394721, Validation Loss: 0.13646371,                    Best: 0.13646371, StopCount/Limit:   0/  3, Time:9.63506985\n",
      "ConfigID:   0, Epoch:   8, Training Loss: 0.13059273, Validation Loss: 0.13370313,                    Best: 0.13370313, StopCount/Limit:   0/  3, Time:8.80216098\n",
      "ConfigID:   0, Epoch:   9, Training Loss: 0.12759903, Validation Loss: 0.13473091,                    Best: 0.13370313, StopCount/Limit:   1/  3, Time:8.94777226\n",
      "ConfigID:   0, Epoch:  10, Training Loss: 0.12522300, Validation Loss: 0.13311487,                    Best: 0.13311487, StopCount/Limit:   1/  3, Time:7.97448897\n",
      "ConfigID:   0, Epoch:  11, Training Loss: 0.12493401, Validation Loss: 0.13218318,                    Best: 0.13218318, StopCount/Limit:   1/  3, Time:6.88204479\n",
      "ConfigID:   0, Epoch:  12, Training Loss: 0.12392645, Validation Loss: 0.13179070,                    Best: 0.13179070, StopCount/Limit:   1/  3, Time:10.94875526\n",
      "ConfigID:   0, Epoch:  13, Training Loss: 0.12105439, Validation Loss: 0.12608973,                    Best: 0.12608973, StopCount/Limit:   1/  3, Time:8.13354135\n",
      "ConfigID:   0, Epoch:  14, Training Loss: 0.11982810, Validation Loss: 0.12460450,                    Best: 0.12460450, StopCount/Limit:   1/  3, Time:8.09005904\n",
      "ConfigID:   0, Epoch:  15, Training Loss: 0.11745679, Validation Loss: 0.13450986,                    Best: 0.12460450, StopCount/Limit:   2/  3, Time:8.51010203\n",
      "ConfigID:   0, Epoch:  16, Training Loss: 0.11563697, Validation Loss: 0.12005073,                    Best: 0.12005073, StopCount/Limit:   2/  3, Time:8.57589817\n",
      "ConfigID:   0, Epoch:  17, Training Loss: 0.11210657, Validation Loss: 0.11910060,                    Best: 0.11910060, StopCount/Limit:   2/  3, Time:8.23402190\n",
      "ConfigID:   0, Epoch:  18, Training Loss: 0.11154327, Validation Loss: 0.11379290,                    Best: 0.11379290, StopCount/Limit:   2/  3, Time:7.84853721\n",
      "ConfigID:   0, Epoch:  19, Training Loss: 0.10938532, Validation Loss: 0.11584917,                    Best: 0.11379290, StopCount/Limit:   3/  3, Time:7.81640792\n",
      "Next Gen: [784   9   4   5   6  10   9  10]\n",
      "ConfigID:   1, Epoch:   0, Training Loss: 0.22227702, Validation Loss: 0.18865624,                    Best: 0.18865624, StopCount/Limit:   0/  3, Time:6.77719927\n",
      "ConfigID:   1, Epoch:   1, Training Loss: 0.16691449, Validation Loss: 0.13745105,                    Best: 0.13745105, StopCount/Limit:   0/  3, Time:6.73769903\n",
      "ConfigID:   1, Epoch:   2, Training Loss: 0.12200913, Validation Loss: 0.10703497,                    Best: 0.10703497, StopCount/Limit:   0/  3, Time:6.99402881\n",
      "ConfigID:   1, Epoch:   3, Training Loss: 0.10451572, Validation Loss: 0.09705913,                    Best: 0.09705913, StopCount/Limit:   0/  3, Time:6.56359291\n",
      "ConfigID:   1, Epoch:   4, Training Loss: 0.09766694, Validation Loss: 0.09412209,                    Best: 0.09412209, StopCount/Limit:   0/  3, Time:6.81505084\n",
      "ConfigID:   1, Epoch:   5, Training Loss: 0.09357988, Validation Loss: 0.09488716,                    Best: 0.09412209, StopCount/Limit:   1/  3, Time:8.14207506\n",
      "ConfigID:   1, Epoch:   6, Training Loss: 0.08974703, Validation Loss: 0.09779775,                    Best: 0.09412209, StopCount/Limit:   2/  3, Time:8.56945205\n",
      "ConfigID:   1, Epoch:   7, Training Loss: 0.08720532, Validation Loss: 0.08946467,                    Best: 0.08946467, StopCount/Limit:   2/  3, Time:7.92839503\n",
      "ConfigID:   1, Epoch:   8, Training Loss: 0.08497132, Validation Loss: 0.09599026,                    Best: 0.08946467, StopCount/Limit:   3/  3, Time:7.45146179\n",
      "Next Gen: [784   4   4   7   4  10  10]\n",
      "ConfigID:   2, Epoch:   0, Training Loss: 0.18777440, Validation Loss: 0.13756299,                    Best: 0.13756299, StopCount/Limit:   0/  3, Time:6.29913092\n",
      "ConfigID:   2, Epoch:   1, Training Loss: 0.12861669, Validation Loss: 0.11780064,                    Best: 0.11780064, StopCount/Limit:   0/  3, Time:6.38297105\n",
      "ConfigID:   2, Epoch:   2, Training Loss: 0.11719730, Validation Loss: 0.11743287,                    Best: 0.11743287, StopCount/Limit:   0/  3, Time:8.98022294\n",
      "ConfigID:   2, Epoch:   3, Training Loss: 0.11417676, Validation Loss: 0.11485838,                    Best: 0.11485838, StopCount/Limit:   0/  3, Time:9.79675817\n",
      "ConfigID:   2, Epoch:   4, Training Loss: 0.11229527, Validation Loss: 0.10878707,                    Best: 0.10878707, StopCount/Limit:   0/  3, Time:6.61901593\n",
      "ConfigID:   2, Epoch:   5, Training Loss: 0.11070942, Validation Loss: 0.10792671,                    Best: 0.10792671, StopCount/Limit:   0/  3, Time:5.89270234\n",
      "ConfigID:   2, Epoch:   6, Training Loss: 0.10961875, Validation Loss: 0.11163435,                    Best: 0.10792671, StopCount/Limit:   1/  3, Time:7.12811279\n",
      "ConfigID:   2, Epoch:   7, Training Loss: 0.10877149, Validation Loss: 0.10861354,                    Best: 0.10792671, StopCount/Limit:   2/  3, Time:7.52254486\n",
      "ConfigID:   2, Epoch:   8, Training Loss: 0.10807593, Validation Loss: 0.10645467,                    Best: 0.10645467, StopCount/Limit:   2/  3, Time:7.45347309\n",
      "ConfigID:   2, Epoch:   9, Training Loss: 0.10809646, Validation Loss: 0.10765856,                    Best: 0.10645467, StopCount/Limit:   3/  3, Time:7.44870925\n",
      "Gen 6 Run End\n",
      "Run   6 Best: 0.05692493488476612, Mean: 0.07635804981548185, ID:1, config: [784   9   4   5   6   9   9  10], updatecount:   0, Generation RunTime: 309.56264305\n",
      "Gen 7 Run Start\n",
      "Next Gen: [784   7   6   5   9   5   9  10]\n",
      "ConfigID:   0, Epoch:   0, Training Loss: 0.18320710, Validation Loss: 0.14477508,                    Best: 0.14477508, StopCount/Limit:   0/  3, Time:8.90642214\n",
      "ConfigID:   0, Epoch:   1, Training Loss: 0.12690602, Validation Loss: 0.11233812,                    Best: 0.11233812, StopCount/Limit:   0/  3, Time:9.79589105\n",
      "ConfigID:   0, Epoch:   2, Training Loss: 0.11177141, Validation Loss: 0.10198476,                    Best: 0.10198476, StopCount/Limit:   0/  3, Time:10.50275207\n",
      "ConfigID:   0, Epoch:   3, Training Loss: 0.10373840, Validation Loss: 0.09953067,                    Best: 0.09953067, StopCount/Limit:   0/  3, Time:8.15526390\n",
      "ConfigID:   0, Epoch:   4, Training Loss: 0.09787811, Validation Loss: 0.08931788,                    Best: 0.08931788, StopCount/Limit:   0/  3, Time:9.51092505\n",
      "ConfigID:   0, Epoch:   5, Training Loss: 0.09290954, Validation Loss: 0.08961923,                    Best: 0.08931788, StopCount/Limit:   1/  3, Time:9.23302722\n",
      "ConfigID:   0, Epoch:   6, Training Loss: 0.08982651, Validation Loss: 0.08629388,                    Best: 0.08629388, StopCount/Limit:   1/  3, Time:9.09902310\n",
      "ConfigID:   0, Epoch:   7, Training Loss: 0.08767504, Validation Loss: 0.08579817,                    Best: 0.08579817, StopCount/Limit:   1/  3, Time:8.27385926\n",
      "ConfigID:   0, Epoch:   8, Training Loss: 0.08619216, Validation Loss: 0.08807597,                    Best: 0.08579817, StopCount/Limit:   2/  3, Time:8.08234978\n",
      "ConfigID:   0, Epoch:   9, Training Loss: 0.08477916, Validation Loss: 0.08615270,                    Best: 0.08579817, StopCount/Limit:   3/  3, Time:8.05007410\n",
      "Next Gen: [784   9   4   5   6   7   9  10]\n",
      "ConfigID:   1, Epoch:   0, Training Loss: 0.19662692, Validation Loss: 0.12176679,                    Best: 0.12176679, StopCount/Limit:   0/  3, Time:10.92426705\n",
      "ConfigID:   1, Epoch:   1, Training Loss: 0.11742105, Validation Loss: 0.10943858,                    Best: 0.10943858, StopCount/Limit:   0/  3, Time:9.81293797\n",
      "ConfigID:   1, Epoch:   2, Training Loss: 0.10241534, Validation Loss: 0.10764694,                    Best: 0.10764694, StopCount/Limit:   0/  3, Time:10.05145693\n",
      "ConfigID:   1, Epoch:   3, Training Loss: 0.09393783, Validation Loss: 0.09005195,                    Best: 0.09005195, StopCount/Limit:   0/  3, Time:9.21832585\n",
      "ConfigID:   1, Epoch:   4, Training Loss: 0.08987213, Validation Loss: 0.09067241,                    Best: 0.09005195, StopCount/Limit:   1/  3, Time:9.11162901\n",
      "ConfigID:   1, Epoch:   5, Training Loss: 0.08726139, Validation Loss: 0.08603562,                    Best: 0.08603562, StopCount/Limit:   1/  3, Time:8.13095808\n",
      "ConfigID:   1, Epoch:   6, Training Loss: 0.08531307, Validation Loss: 0.08935665,                    Best: 0.08603562, StopCount/Limit:   2/  3, Time:7.59012771\n",
      "ConfigID:   1, Epoch:   7, Training Loss: 0.08225422, Validation Loss: 0.08460943,                    Best: 0.08460943, StopCount/Limit:   2/  3, Time:7.39198399\n",
      "ConfigID:   1, Epoch:   8, Training Loss: 0.07882029, Validation Loss: 0.08207741,                    Best: 0.08207741, StopCount/Limit:   2/  3, Time:7.63165808\n",
      "ConfigID:   1, Epoch:   9, Training Loss: 0.07665351, Validation Loss: 0.07927281,                    Best: 0.07927281, StopCount/Limit:   2/  3, Time:7.68104386\n",
      "ConfigID:   1, Epoch:  10, Training Loss: 0.07568202, Validation Loss: 0.07961816,                    Best: 0.07927281, StopCount/Limit:   3/  3, Time:7.18982816\n",
      "Next Gen: [784   4   4   8   5  10  10]\n",
      "ConfigID:   2, Epoch:   0, Training Loss: 0.30506060, Validation Loss: 0.27585641,                    Best: 0.27585641, StopCount/Limit:   0/  3, Time:8.29813695\n",
      "ConfigID:   2, Epoch:   1, Training Loss: 0.25466045, Validation Loss: 0.24056349,                    Best: 0.24056349, StopCount/Limit:   0/  3, Time:7.82825112\n",
      "ConfigID:   2, Epoch:   2, Training Loss: 0.23700395, Validation Loss: 0.23560256,                    Best: 0.23560256, StopCount/Limit:   0/  3, Time:8.87263727\n",
      "ConfigID:   2, Epoch:   3, Training Loss: 0.23398253, Validation Loss: 0.23423425,                    Best: 0.23423425, StopCount/Limit:   0/  3, Time:7.19259882\n",
      "ConfigID:   2, Epoch:   4, Training Loss: 0.23273328, Validation Loss: 0.23137774,                    Best: 0.23137774, StopCount/Limit:   0/  3, Time:9.08491898\n",
      "ConfigID:   2, Epoch:   5, Training Loss: 0.23196212, Validation Loss: 0.23126889,                    Best: 0.23126889, StopCount/Limit:   0/  3, Time:7.47811985\n",
      "ConfigID:   2, Epoch:   6, Training Loss: 0.23134891, Validation Loss: 0.23298081,                    Best: 0.23126889, StopCount/Limit:   1/  3, Time:6.60767221\n",
      "ConfigID:   2, Epoch:   7, Training Loss: 0.23063571, Validation Loss: 0.23194504,                    Best: 0.23126889, StopCount/Limit:   2/  3, Time:6.62765312\n",
      "ConfigID:   2, Epoch:   8, Training Loss: 0.23018013, Validation Loss: 0.22982087,                    Best: 0.22982087, StopCount/Limit:   2/  3, Time:6.34047079\n",
      "ConfigID:   2, Epoch:   9, Training Loss: 0.22987982, Validation Loss: 0.23003409,                    Best: 0.22982087, StopCount/Limit:   3/  3, Time:6.28474498\n",
      "Gen 7 Run End\n",
      "Run   7 Best: 0.05692493488476612, Mean: 0.07635804981548185, ID:1, config: [784   9   4   5   6   9   9  10], updatecount:   0, Generation RunTime: 258.98856997\n",
      "Gen 8 Run Start\n",
      "Next Gen: [784   5   6   5   8   5   5  10]\n",
      "ConfigID:   0, Epoch:   0, Training Loss: 0.22364504, Validation Loss: 0.17529580,                    Best: 0.17529580, StopCount/Limit:   0/  3, Time:8.28255701\n",
      "ConfigID:   0, Epoch:   1, Training Loss: 0.17103390, Validation Loss: 0.17400193,                    Best: 0.17400193, StopCount/Limit:   0/  3, Time:7.02437878\n",
      "ConfigID:   0, Epoch:   2, Training Loss: 0.16389611, Validation Loss: 0.16330516,                    Best: 0.16330516, StopCount/Limit:   0/  3, Time:7.49147964\n",
      "ConfigID:   0, Epoch:   3, Training Loss: 0.16046082, Validation Loss: 0.15754967,                    Best: 0.15754967, StopCount/Limit:   0/  3, Time:8.18132305\n",
      "ConfigID:   0, Epoch:   4, Training Loss: 0.15786662, Validation Loss: 0.15548948,                    Best: 0.15548948, StopCount/Limit:   0/  3, Time:6.95900106\n",
      "ConfigID:   0, Epoch:   5, Training Loss: 0.15598159, Validation Loss: 0.16044542,                    Best: 0.15548948, StopCount/Limit:   1/  3, Time:6.90679193\n",
      "ConfigID:   0, Epoch:   6, Training Loss: 0.15507715, Validation Loss: 0.15576453,                    Best: 0.15548948, StopCount/Limit:   2/  3, Time:6.71044183\n",
      "ConfigID:   0, Epoch:   7, Training Loss: 0.15361817, Validation Loss: 0.15554483,                    Best: 0.15548948, StopCount/Limit:   3/  3, Time:7.06221914\n",
      "Next Gen: [784   6   4   5   6   9   9  10]\n",
      "ConfigID:   1, Epoch:   0, Training Loss: 0.22366751, Validation Loss: 0.14772771,                    Best: 0.14772771, StopCount/Limit:   0/  3, Time:7.02632880\n",
      "ConfigID:   1, Epoch:   1, Training Loss: 0.12784306, Validation Loss: 0.11285997,                    Best: 0.11285997, StopCount/Limit:   0/  3, Time:6.82111001\n",
      "ConfigID:   1, Epoch:   2, Training Loss: 0.10952550, Validation Loss: 0.10344005,                    Best: 0.10344005, StopCount/Limit:   0/  3, Time:7.53783131\n",
      "ConfigID:   1, Epoch:   3, Training Loss: 0.10202257, Validation Loss: 0.10016253,                    Best: 0.10016253, StopCount/Limit:   0/  3, Time:7.17663884\n",
      "ConfigID:   1, Epoch:   4, Training Loss: 0.09533872, Validation Loss: 0.09116710,                    Best: 0.09116710, StopCount/Limit:   0/  3, Time:7.50848103\n",
      "ConfigID:   1, Epoch:   5, Training Loss: 0.09108170, Validation Loss: 0.08842764,                    Best: 0.08842764, StopCount/Limit:   0/  3, Time:7.51974082\n",
      "ConfigID:   1, Epoch:   6, Training Loss: 0.08953135, Validation Loss: 0.09215107,                    Best: 0.08842764, StopCount/Limit:   1/  3, Time:7.83694792\n",
      "ConfigID:   1, Epoch:   7, Training Loss: 0.08803624, Validation Loss: 0.08854973,                    Best: 0.08842764, StopCount/Limit:   2/  3, Time:9.68357992\n",
      "ConfigID:   1, Epoch:   8, Training Loss: 0.08678331, Validation Loss: 0.08555031,                    Best: 0.08555031, StopCount/Limit:   2/  3, Time:11.41702080\n",
      "ConfigID:   1, Epoch:   9, Training Loss: 0.08526306, Validation Loss: 0.08623707,                    Best: 0.08555031, StopCount/Limit:   3/  3, Time:8.06043506\n",
      "Next Gen: [784   4   4   7   4  10  10]\n",
      "ConfigID:   2, Epoch:   0, Training Loss: 0.20689249, Validation Loss: 0.15525577,                    Best: 0.15525577, StopCount/Limit:   0/  3, Time:7.14115977\n",
      "ConfigID:   2, Epoch:   1, Training Loss: 0.14457560, Validation Loss: 0.13393383,                    Best: 0.13393383, StopCount/Limit:   0/  3, Time:6.74487710\n",
      "ConfigID:   2, Epoch:   2, Training Loss: 0.12628770, Validation Loss: 0.12132131,                    Best: 0.12132131, StopCount/Limit:   0/  3, Time:6.43366289\n",
      "ConfigID:   2, Epoch:   3, Training Loss: 0.11712259, Validation Loss: 0.11194436,                    Best: 0.11194436, StopCount/Limit:   0/  3, Time:6.48584700\n",
      "ConfigID:   2, Epoch:   4, Training Loss: 0.11033760, Validation Loss: 0.10632298,                    Best: 0.10632298, StopCount/Limit:   0/  3, Time:6.72115588\n",
      "ConfigID:   2, Epoch:   5, Training Loss: 0.10779029, Validation Loss: 0.10590785,                    Best: 0.10590785, StopCount/Limit:   0/  3, Time:6.44011402\n",
      "ConfigID:   2, Epoch:   6, Training Loss: 0.10564220, Validation Loss: 0.10501523,                    Best: 0.10501523, StopCount/Limit:   0/  3, Time:6.57469010\n",
      "ConfigID:   2, Epoch:   7, Training Loss: 0.10408850, Validation Loss: 0.10297566,                    Best: 0.10297566, StopCount/Limit:   0/  3, Time:6.50728393\n",
      "ConfigID:   2, Epoch:   8, Training Loss: 0.10213923, Validation Loss: 0.10212815,                    Best: 0.10212815, StopCount/Limit:   0/  3, Time:6.40913892\n",
      "ConfigID:   2, Epoch:   9, Training Loss: 0.10178071, Validation Loss: 0.10056542,                    Best: 0.10056542, StopCount/Limit:   0/  3, Time:6.57519388\n",
      "ConfigID:   2, Epoch:  10, Training Loss: 0.10104048, Validation Loss: 0.10222471,                    Best: 0.10056542, StopCount/Limit:   1/  3, Time:6.62962198\n",
      "ConfigID:   2, Epoch:  11, Training Loss: 0.10023909, Validation Loss: 0.09971318,                    Best: 0.09971318, StopCount/Limit:   1/  3, Time:6.54349184\n",
      "ConfigID:   2, Epoch:  12, Training Loss: 0.09971248, Validation Loss: 0.09985027,                    Best: 0.09971318, StopCount/Limit:   2/  3, Time:7.23042297\n",
      "ConfigID:   2, Epoch:  13, Training Loss: 0.09869726, Validation Loss: 0.09930553,                    Best: 0.09930553, StopCount/Limit:   2/  3, Time:6.79603767\n",
      "ConfigID:   2, Epoch:  14, Training Loss: 0.09837512, Validation Loss: 0.10562046,                    Best: 0.09930553, StopCount/Limit:   3/  3, Time:10.87156010\n",
      "Gen 8 Run End\n",
      "Run   8 Best: 0.05692493488476612, Mean: 0.0753157628334205, ID:1, config: [784   9   4   5   6   9   9  10], updatecount:   1, Generation RunTime: 243.34367990\n",
      "Gen 9 Run Start\n",
      "Next Gen: [784   7   6   4   8   5   9  10]\n",
      "ConfigID:   0, Epoch:   0, Training Loss: 0.31000078, Validation Loss: 0.26518997,                    Best: 0.26518997, StopCount/Limit:   0/  3, Time:21.74352574\n",
      "ConfigID:   0, Epoch:   1, Training Loss: 0.25897796, Validation Loss: 0.25666255,                    Best: 0.25666255, StopCount/Limit:   0/  3, Time:8.21214199\n",
      "ConfigID:   0, Epoch:   2, Training Loss: 0.25454051, Validation Loss: 0.25199485,                    Best: 0.25199485, StopCount/Limit:   0/  3, Time:7.90895414\n",
      "ConfigID:   0, Epoch:   3, Training Loss: 0.25259260, Validation Loss: 0.25140452,                    Best: 0.25140452, StopCount/Limit:   0/  3, Time:7.46565032\n",
      "ConfigID:   0, Epoch:   4, Training Loss: 0.24335230, Validation Loss: 0.23340607,                    Best: 0.23340607, StopCount/Limit:   0/  3, Time:9.08591890\n",
      "ConfigID:   0, Epoch:   5, Training Loss: 0.22945641, Validation Loss: 0.22100350,                    Best: 0.22100350, StopCount/Limit:   0/  3, Time:9.59025383\n",
      "ConfigID:   0, Epoch:   6, Training Loss: 0.21862752, Validation Loss: 0.21270940,                    Best: 0.21270940, StopCount/Limit:   0/  3, Time:8.42245388\n",
      "ConfigID:   0, Epoch:   7, Training Loss: 0.21184817, Validation Loss: 0.20613163,                    Best: 0.20613163, StopCount/Limit:   0/  3, Time:7.96308088\n",
      "ConfigID:   0, Epoch:   8, Training Loss: 0.19579074, Validation Loss: 0.18603728,                    Best: 0.18603728, StopCount/Limit:   0/  3, Time:7.63429594\n",
      "ConfigID:   0, Epoch:   9, Training Loss: 0.18596080, Validation Loss: 0.17916805,                    Best: 0.17916805, StopCount/Limit:   0/  3, Time:7.42435575\n",
      "ConfigID:   0, Epoch:  10, Training Loss: 0.17864914, Validation Loss: 0.17335936,                    Best: 0.17335936, StopCount/Limit:   0/  3, Time:7.18974328\n",
      "ConfigID:   0, Epoch:  11, Training Loss: 0.17329039, Validation Loss: 0.16751750,                    Best: 0.16751750, StopCount/Limit:   0/  3, Time:7.22593617\n",
      "ConfigID:   0, Epoch:  12, Training Loss: 0.16954867, Validation Loss: 0.16568977,                    Best: 0.16568977, StopCount/Limit:   0/  3, Time:7.15153098\n",
      "ConfigID:   0, Epoch:  13, Training Loss: 0.16750125, Validation Loss: 0.16695209,                    Best: 0.16568977, StopCount/Limit:   1/  3, Time:7.55266404\n",
      "ConfigID:   0, Epoch:  14, Training Loss: 0.16530126, Validation Loss: 0.16093201,                    Best: 0.16093201, StopCount/Limit:   1/  3, Time:7.81997323\n",
      "ConfigID:   0, Epoch:  15, Training Loss: 0.16396940, Validation Loss: 0.16208033,                    Best: 0.16093201, StopCount/Limit:   2/  3, Time:8.55543685\n",
      "ConfigID:   0, Epoch:  16, Training Loss: 0.16356405, Validation Loss: 0.16437415,                    Best: 0.16093201, StopCount/Limit:   3/  3, Time:9.06188297\n",
      "Next Gen: [784   9   4   5   6   9   9  10]\n",
      "ConfigID:   1, Epoch:   0, Training Loss: 0.23011966, Validation Loss: 0.16070203,                    Best: 0.16070203, StopCount/Limit:   0/  3, Time:9.41809797\n",
      "ConfigID:   1, Epoch:   1, Training Loss: 0.14293113, Validation Loss: 0.12690577,                    Best: 0.12690577, StopCount/Limit:   0/  3, Time:9.60890388\n",
      "ConfigID:   1, Epoch:   2, Training Loss: 0.12161345, Validation Loss: 0.11953048,                    Best: 0.11953048, StopCount/Limit:   0/  3, Time:8.11250305\n",
      "ConfigID:   1, Epoch:   3, Training Loss: 0.11501397, Validation Loss: 0.11301623,                    Best: 0.11301623, StopCount/Limit:   0/  3, Time:8.54564404\n",
      "ConfigID:   1, Epoch:   4, Training Loss: 0.11183923, Validation Loss: 0.11137423,                    Best: 0.11137423, StopCount/Limit:   0/  3, Time:9.01355696\n",
      "ConfigID:   1, Epoch:   5, Training Loss: 0.11001807, Validation Loss: 0.11916921,                    Best: 0.11137423, StopCount/Limit:   1/  3, Time:9.40746593\n",
      "ConfigID:   1, Epoch:   6, Training Loss: 0.10865990, Validation Loss: 0.11389290,                    Best: 0.11137423, StopCount/Limit:   2/  3, Time:10.91290689\n",
      "ConfigID:   1, Epoch:   7, Training Loss: 0.10816864, Validation Loss: 0.11252480,                    Best: 0.11137423, StopCount/Limit:   3/  3, Time:9.17982006\n",
      "Next Gen: [784   4   4   7   4  10  10]\n",
      "ConfigID:   2, Epoch:   0, Training Loss: 0.33699054, Validation Loss: 0.32502781,                    Best: 0.32502781, StopCount/Limit:   0/  3, Time:7.75305796\n",
      "ConfigID:   2, Epoch:   1, Training Loss: 0.32509131, Validation Loss: 0.32505043,                    Best: 0.32502781, StopCount/Limit:   1/  3, Time:8.73067594\n",
      "ConfigID:   2, Epoch:   2, Training Loss: 0.32509572, Validation Loss: 0.32507058,                    Best: 0.32502781, StopCount/Limit:   2/  3, Time:6.82854605\n",
      "ConfigID:   2, Epoch:   3, Training Loss: 0.32509900, Validation Loss: 0.32508223,                    Best: 0.32502781, StopCount/Limit:   3/  3, Time:9.12018013\n",
      "Gen 9 Run End\n",
      "Run   9 Best: 0.05692493488476612, Mean: 0.0753157628334205, ID:1, config: [784   9   4   5   6   9   9  10], updatecount:   0, Generation RunTime: 256.68089175\n",
      "Run Completed : Best Score(loss): 0.05692493488476612 , Config: [784   9   4   5   6   9   9  10]\n"
     ]
    }
   ],
   "source": [
    "d.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd4ade25-6f16-4dc8-93f5-db46fe5c0ff4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}