MLP.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Copy of Exploration.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "tkebLvTBtECC",
        "colab_type": "code",
        "outputId": "91e56cc6-06de-418a-c60a-06645a260124",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "import numpy as np\n",
        "import math\n",
        "import torch\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pickle\n",
        "\n",
        "transform = transforms.Compose(\n",
        "  [transforms.ToTensor(),\n",
        "    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n",
        "\n",
        "trainset = torchvision.datasets.CIFAR10(root='./data', train=True,\n",
        "                                    download=True, transform=transform)\n",
        "trainloader = torch.utils.data.DataLoader(trainset,\n",
        "                                      shuffle=True, num_workers=2)\n",
        "\n",
        "testset = torchvision.datasets.CIFAR10(root='./data', train=False,\n",
        "                                    download=True, transform=transform)\n",
        "testloader = torch.utils.data.DataLoader(testset,\n",
        "                                      shuffle=True, num_workers=2)\n",
        "\n",
        "classes = ('plane', 'car', 'bird', 'cat',\n",
        "        'deer', 'dog', 'frog', 'horse', 'ship', 'truck')\n",
        "\n",
        "\n",
        "#getting the x(2d matrix of 50000*3072, input images), and y(1*3072, output label);\n",
        "tmpTrain = trainloader.dataset.data # shape of 50000*32*32*3 need to transform to 50000*3072\n",
        "x,y1,y2,y3 = tmpTrain.shape\n",
        "trainX = np.zeros([x, y1*y2*y3])\n",
        "tmpTrainY = trainloader.dataset.targets\n",
        "trainY = np.zeros([len(tmpTrainY), len(classes)])\n",
        "for i in range(len(tmpTrainY)):\n",
        "  trainY[i][tmpTrainY[i]] = 1\n",
        "tmpTest = testloader.dataset.data # shape of 10000*32*32*3 need to transform to 10000*3072\n",
        "xx, y4, y5, y6 = tmpTest.shape\n",
        "testY = testloader.dataset.targets\n",
        "testX = np.zeros([xx, y4*y5*y6])\n",
        "for i in range(x):\n",
        "  trainX[i] = tmpTrain[i].flatten()\n",
        "\n",
        "trainX = trainX / np.linalg.norm(trainX) \n",
        "for j in range(xx):\n",
        "  testX[j] = tmpTest[j].flatten()\n",
        "\n",
        "testX = testX / np.linalg.norm(testX)\n",
        "\n",
        "print(\"the shape of matrix trainX\" + str(trainX.shape))\n",
        "print(\"the size of trainY \" + str(trainY.shape))\n",
        "print(\"the shape of matrix testX\" + str(testX.shape))\n",
        "print(\"the size of testY \" + str(len(testY)))\n",
        "\n",
        "class MLP():\n",
        "  Ws = [] #weights\n",
        "  Zs = [] #hidden layers\n",
        "  AV = []#activation function\n",
        "\n",
        "  #use to graph the training and testing accuracy in function of epoch\n",
        "  xGraph = []\n",
        "  trainYGraph = []\n",
        "  testYGraph = []\n",
        "\n",
        "  #activation functions\n",
        "\n",
        "  def softmax(self,x):\n",
        "    N,D = x.shape\n",
        "    x_exp = np.exp(x - np.max(x, 1)[:, None])\n",
        "    return x_exp / np.sum(x_exp, axis=-1)[:, None]\n",
        "\n",
        "  def relu(self,x):\n",
        "    N, D = x.shape\n",
        "    ans = np.zeros([N,D])\n",
        "    for i in range(N):\n",
        "      for j in range(D):\n",
        "        if x[i][j] > 0:\n",
        "          ans[i][j] = x[i][j]\n",
        "        else:\n",
        "          ans[i][j] = 0\n",
        "    return ans\n",
        "\n",
        "  def sigmoid(self,x):\n",
        "    N, D = x.shape\n",
        "    ans = np.zeros([N,D])\n",
        "    for i in range(N):\n",
        "      for j in range(D):\n",
        "        try:\n",
        "          ans[i][j] = 1/(1+math.exp(-x[i][j]))\n",
        "        except OverflowError:\n",
        "          if(x[i][j])>0:\n",
        "            ans[i][j] = 1\n",
        "          else:\n",
        "            ans[i][j] = 0\n",
        "    return ans\n",
        "\n",
        "  def getYhead(self,X):\n",
        "    Y = []\n",
        "    N,D = X.shape\n",
        "    self.Zs.clear()\n",
        "    for layer in layerNumber:\n",
        "      self.Zs.append(np.zeros([N,layer]))\n",
        "    Yhead = np.zeros([N, 10])\n",
        "    tmp = X\n",
        "    for i in range(len(self.Ws)-1):\n",
        "      NN,DD = tmp.shape\n",
        "      tmp = self.relu(np.dot(tmp, self.Ws[i])) #Doing activation function for the Zs in layer\n",
        "      self.Zs[i] = tmp\n",
        "    Y = self.softmax(np.dot(tmp, self.Ws[len(self.Ws)-1])) #Doing softmax for final layer since it is multiclass classification\n",
        "    return Y\n",
        "\n",
        "  #Cost of one training result\n",
        "  def cost(self,Y1, Y2):\n",
        "    total = 0.0\n",
        "    for i in range(len(Y1)):\n",
        "      value = (Y1[i]-Y2[i])*(Y1[i]-Y2[i])\n",
        "      total = total + value\n",
        "    return total\n",
        "\n",
        "  #Average cost of all training result\n",
        "  def averageCost(self,Y1, Y2):\n",
        "    N,D = Y1.shape\n",
        "    total = 0.0\n",
        "    for i in range(N):\n",
        "      total = total + self.cost(Y1[i],Y2[i])\n",
        "    return total/N\n",
        "\n",
        "  def gradient(self,X, Y):\n",
        "    N,D = X.shape\n",
        "    Yh = self.getYhead(X)\n",
        "    print(\"Average cost so far \" + str(self.averageCost(Yh, Y)))\n",
        "    dY = (Yh - Y)\n",
        "    dZnext =dY\n",
        "    WsReversed = self.Ws.copy()\n",
        "    ZsReversed = self.Zs.copy()\n",
        "    WsReversed.reverse() #reverse them since we need to calculate the dw from last layer to the first layer\n",
        "    ZsReversed.reverse()\n",
        "    dWs =[]\n",
        "    w = WsReversed[0]\n",
        "    z = ZsReversed[0]\n",
        "    dW = np.dot(z.T, dZnext)\n",
        "    #Probably the problem come from here, I am trying to calculate the dWeight between last hidden layer and final layer\n",
        "    #now z is the last hidden layer, Yh is the final answer, in the video, derivative of w = al-1*(derivative of sigmoid)*dy\n",
        "    #al-1 is last hidden layer, Yh*(1-Yh) is the derivative of sigmoid, and dzNext is dy, however if Yh is all zero them dW\n",
        "    #would be zero and if will keeps like that, and I was like what???\n",
        "    dZnext = np.dot(dZnext, w.T)\n",
        "    dWs.append(dW)\n",
        "    for i in range(1, len(WsReversed)):\n",
        "      w = WsReversed[i]\n",
        "      zPrevious = ZsReversed[i-1]\n",
        "      if i >= len(ZsReversed):\n",
        "        z = X.copy()\n",
        "      else:\n",
        "        z = ZsReversed[i]\n",
        "      Zn, Zd = zPrevious.shape\n",
        "      tmp = np.zeros([Zn,Zd])\n",
        "      for i in range(Zn):\n",
        "        for j in range(Zd):\n",
        "          if zPrevious[i][j]>0:\n",
        "            tmp[i][j] = 1\n",
        "      dW = np.dot(z.T, dZnext*tmp)\n",
        "      dZnext = np.dot(dZnext*tmp, w.T)\n",
        "      dWs.append(dW)\n",
        "    dWs.reverse()\n",
        "    return dWs\n",
        "\n",
        "  def fit(self,layerNumber, trainX, trainY, testX, testY, lr, decay, eps, maxiterations, bsize, beta):\n",
        "    #fill Ws with initial random weight\n",
        "    #define weights for different layers, put them in a list called Ws, each weight has a shape of size(l-1)*size(l)\n",
        "    N,D = trainX.shape\n",
        "    start = D\n",
        "    N,DD =trainY.shape\n",
        "    end = layerNumber[0]\n",
        "    w = np.random.randn(start, end)*0.1\n",
        "    self.Ws.append(w)\n",
        "    for i in range(len(layerNumber)):\n",
        "      start = layerNumber[i]\n",
        "      if i+1 == len(layerNumber):\n",
        "        end = DD\n",
        "      else:\n",
        "        end = layerNumber[i+1]\n",
        "      w = np.random.randn(start, end)*0.1\n",
        "      #print(w)\n",
        "      self.Ws.append(w)\n",
        "    dW = np.inf*np.ones_like(self.Ws[len(self.Ws)-1])\n",
        "    dws = []\n",
        "    for w in self.Ws:\n",
        "      dw = np.zeros(w.shape)\n",
        "      dws.append(dw)\n",
        "    iter = 0\n",
        "    epoch = 0\n",
        "\n",
        "    while np.linalg.norm(dW) > eps and iter<maxiterations:\n",
        "      Num = N/bsize # we must run Num time iterations to get one epoch\n",
        "      for n in range(int(Num)):\n",
        "        start = n*bsize\n",
        "        end = start+bsize\n",
        "        g = self.gradient(trainX[start:end,:], trainY[start:end,:])\n",
        "        for i in range(len(self.Ws)):\n",
        "          dws[i] = (1-beta)*g[i]+beta*dws[i]\n",
        "          self.Ws[i] = self.Ws[i]-lr*dws[i]\n",
        "        dW = g[len(self.Ws)-1]\n",
        "        print(iter)\n",
        "        lr *= (1. / (1. + decay * iter))\n",
        "        iter = iter+1\n",
        "      epoch = epoch+1\n",
        "      trainingAccuracy = self.predict(trainX, tmpTrainY)\n",
        "      testingAccuracy = self.predict(testX, testY)\n",
        "      self.xGraph.append(epoch)\n",
        "      self.trainYGraph.append(trainingAccuracy)\n",
        "      self.testYGraph.append(testingAccuracy)\n",
        "      print(\"Epoch \" + str(epoch) + \": training accuracy \" + str(trainingAccuracy) + \", testing accuracy \" + str(testingAccuracy))\n",
        "    \n",
        "    self.xGraph = np.array(self.xGraph)\n",
        "    self.trainYGraph = np.array(self.trainYGraph)\n",
        "    self.testYGraph = np.array(self.testYGraph)\n",
        "\n",
        "  def getBiggestY(self, Y):\n",
        "    #print(Y) \n",
        "    N,D = Y.shape\n",
        "    result = np.zeros([N])\n",
        "    for i in range(N):\n",
        "      biggest = 0\n",
        "      biggestNum = 0\n",
        "      for j in range(D):\n",
        "        if Y[i][j]>biggest:\n",
        "          biggestNum = j\n",
        "          biggest = Y[i][j]\n",
        "        result[i] = biggestNum\n",
        "    print(result)\n",
        "    return result\n",
        "\n",
        "  def predict(self, testX, testY):\n",
        "    print(\"predicting ...\")\n",
        "    yHead = self.getYhead(testX)\n",
        "    yResult = self.getBiggestY(yHead)\n",
        "    totalRight = 0.0\n",
        "    for i in range(len(testY)):\n",
        "      if(yResult[i] == testY[i]):\n",
        "        totalRight = totalRight+1\n",
        "    return totalRight/len(testY)\n",
        "\n",
        "\n",
        "layerNumber = [800, 200, 50] #number of hidden layers and number of nodes in each layer\n",
        "theMLP = MLP()\n",
        "theMLP.fit(layerNumber, trainX, trainY, testX, testY, 0.1, 0.0000005, 1e-09, 5000, 1000, 0.99)\n",
        "print(theMLP.predict(testX, testY))\n",
        "\n"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "Average cost so far 0.6907663184141956\n",
            "2527\n",
            "Average cost so far 0.6980965798787884\n",
            "2528\n",
            "Average cost so far 0.6687890973656251\n",
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            "Average cost so far 0.689965852824206\n",
            "2530\n",
            "Average cost so far 0.6688647890094407\n",
            "2531\n",
            "Average cost so far 0.6894416129964821\n",
            "2532\n",
            "Average cost so far 0.7137392774325757\n",
            "2533\n",
            "Average cost so far 0.6983575128015573\n",
            "2534\n",
            "Average cost so far 0.6480250239920984\n",
            "2535\n",
            "Average cost so far 0.6693709160704253\n",
            "2536\n",
            "Average cost so far 0.6993301015546324\n",
            "2537\n",
            "Average cost so far 0.7239802961484313\n",
            "2538\n",
            "Average cost so far 0.6888597332115758\n",
            "2539\n",
            "Average cost so far 0.6972476164797119\n",
            "2540\n",
            "Average cost so far 0.6909880311260808\n",
            "2541\n",
            "Average cost so far 0.6811848008817992\n",
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            "Average cost so far 0.6776702422397223\n",
            "2543\n",
            "Average cost so far 0.7463489750573084\n",
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            "Average cost so far 0.7331618741312285\n",
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            "Average cost so far 0.7035697690117163\n",
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            "Average cost so far 0.7071956489604472\n",
            "2547\n",
            "Average cost so far 0.6782047205868257\n",
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            "Average cost so far 0.6802695146105713\n",
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            "Average cost so far 0.6837560094594124\n",
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            "Average cost so far 0.7013477062336042\n",
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            "Average cost so far 0.751171721005844\n",
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            "Average cost so far 0.6742802577331289\n",
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            "Average cost so far 0.6872003697659449\n",
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            "Average cost so far 0.7198330541565753\n",
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            "Average cost so far 0.7214203671049401\n",
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            "Average cost so far 0.7152447021707529\n",
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            "Average cost so far 0.6632676502286664\n",
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            "Average cost so far 0.7174562563880853\n",
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            "Average cost so far 0.6970019025386948\n",
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            "Average cost so far 0.712006587108031\n",
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            "Average cost so far 0.6993812323747455\n",
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            "Average cost so far 0.7170707860786776\n",
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            "Average cost so far 0.6804626674230773\n",
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            "Average cost so far 0.7371539301620293\n",
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            "Average cost so far 0.7093356347495522\n",
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            "Average cost so far 0.677284554407803\n",
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            "Average cost so far 0.6727946882572153\n",
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            "Average cost so far 0.6685224344331605\n",
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            "Average cost so far 0.7041807339267601\n",
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            "Average cost so far 0.6984823901849153\n",
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            "Average cost so far 0.6888534081418065\n",
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            "Average cost so far 0.7062198741288733\n",
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            "Average cost so far 0.6735158823241006\n",
            "2649\n",
            "Average cost so far 0.6983119160631999\n",
            "2650\n",
            "Average cost so far 0.692451683774111\n",
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            "Average cost so far 0.706490551334488\n",
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            "Average cost so far 0.6849605246670563\n",
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            "Average cost so far 0.7089835784085115\n",
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            "Average cost so far 0.6934504003556262\n",
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            "Average cost so far 0.7179465710573215\n",
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            "Average cost so far 0.7104409497406755\n",
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            "Average cost so far 0.7068552748857725\n",
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            "Average cost so far 0.7032724952125082\n",
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            "Average cost so far 0.7009954237613079\n",
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            "Average cost so far 0.7206750897026687\n",
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            "Average cost so far 0.718516647308556\n",
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            "Average cost so far 0.7089495504733766\n",
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            "Average cost so far 0.6830552795079191\n",
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            "Average cost so far 0.6855054217843053\n",
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            "Average cost so far 0.7358238396757923\n",
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            "Average cost so far 0.668084118698797\n",
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            "Average cost so far 0.6964772162515318\n",
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            "Average cost so far 0.6987195788397769\n",
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            "Average cost so far 0.6894848959591073\n",
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            "Average cost so far 0.6969418090317278\n",
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            "Average cost so far 0.7017742633544328\n",
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            "Average cost so far 0.6915138638919572\n",
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            "2680\n",
            "Average cost so far 0.6597462103044489\n",
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            "Average cost so far 0.6932084065138135\n",
            "2682\n",
            "Average cost so far 0.6757773135859344\n",
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            "Average cost so far 0.7078930642945707\n",
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            "Average cost so far 0.691815701199167\n",
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            "Average cost so far 0.712689788630073\n",
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            "Average cost so far 0.6680037274051227\n",
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            "Average cost so far 0.6969360618137409\n",
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            "Average cost so far 0.6796362308695469\n",
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            "Average cost so far 0.6626777786911907\n",
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            "2700\n",
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            "Average cost so far 0.6917561647576455\n",
            "2718\n",
            "Average cost so far 0.6867674922769258\n",
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            "Average cost so far 0.6805760322325334\n",
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            "Average cost so far 0.6751008541109778\n",
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            "Average cost so far 0.6937255312657752\n",
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            "Average cost so far 0.6940954619862267\n",
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            "Average cost so far 0.6791626437481912\n",
            "2730\n",
            "Average cost so far 0.7207407290820121\n",
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            "Average cost so far 0.6613809035553906\n",
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            "Average cost so far 0.6881935475671835\n",
            "2734\n",
            "Average cost so far 0.6762364720866756\n",
            "2735\n",
            "Average cost so far 0.6643297764410496\n",
            "2736\n",
            "Average cost so far 0.7153188732270379\n",
            "2737\n",
            "Average cost so far 0.6420617591082428\n",
            "2738\n",
            "Average cost so far 0.7402923490293181\n",
            "2739\n",
            "Average cost so far 0.7056103815696768\n",
            "2740\n",
            "Average cost so far 0.6878975494850517\n",
            "2741\n",
            "Average cost so far 0.697631392267663\n",
            "2742\n",
            "Average cost so far 0.687197585994021\n",
            "2743\n",
            "Average cost so far 0.685820920828461\n",
            "2744\n",
            "Average cost so far 0.6651898848804061\n",
            "2745\n",
            "Average cost so far 0.666711382233893\n",
            "2746\n",
            "Average cost so far 0.6809508463893469\n",
            "2747\n",
            "Average cost so far 0.6809756254258411\n",
            "2748\n",
            "Average cost so far 0.664222988282529\n",
            "2749\n",
            "predicting ...\n",
            "[6. 1. 9. ... 9. 9. 7.]\n",
            "predicting ...\n",
            "[3. 9. 8. ... 5. 6. 7.]\n",
            "Epoch 11: training accuracy 0.44906, testing accuracy 0.4401\n",
            "Average cost so far 0.6854175358831062\n",
            "2750\n",
            "Average cost so far 0.6527184712288787\n",
            "2751\n",
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            "Average cost so far 0.6667782296199941\n",
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            "2769\n",
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            "2770\n",
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            "2779\n",
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            "2780\n",
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            "2781\n",
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            "2787\n",
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            "2793\n",
            "Average cost so far 0.7331165777166972\n",
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            "2797\n",
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            "2800\n",
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            "2846\n",
            "Average cost so far 0.7111830613116787\n",
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            "Average cost so far 0.6932422519347072\n",
            "2849\n",
            "Average cost so far 0.6892824923234703\n",
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            "2851\n",
            "Average cost so far 0.7141241337258293\n",
            "2852\n",
            "Average cost so far 0.6960789819137887\n",
            "2853\n",
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            "2854\n",
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            "2865\n",
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            "2867\n",
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            "2870\n",
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            "2880\n",
            "Average cost so far 0.6871281700907051\n",
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            "Average cost so far 0.6840558270389306\n",
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            "Average cost so far 0.738616777463274\n",
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            "Average cost so far 0.7159272280445605\n",
            "2890\n",
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            "Average cost so far 0.6946984102230713\n",
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            "2898\n",
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            "2900\n",
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            "Average cost so far 0.7238578559107411\n",
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            "Average cost so far 0.660902744068504\n",
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            "Average cost so far 0.7059456365144574\n",
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            "Average cost so far 0.7124469731462253\n",
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            "Average cost so far 0.6819099443553336\n",
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            "Average cost so far 0.7410789343747595\n",
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            "Average cost so far 0.6924895867952372\n",
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            "Average cost so far 0.670514860841395\n",
            "2920\n",
            "Average cost so far 0.6721854222136429\n",
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            "Average cost so far 0.6837626373453278\n",
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            "Average cost so far 0.6737357988219845\n",
            "2923\n",
            "Average cost so far 0.6853417381444069\n",
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            "Average cost so far 0.6678289020487729\n",
            "2925\n",
            "Average cost so far 0.6883217783054608\n",
            "2926\n",
            "Average cost so far 0.672113069073507\n",
            "2927\n",
            "Average cost so far 0.6935881679891897\n",
            "2928\n",
            "Average cost so far 0.6758502310922238\n",
            "2929\n",
            "Average cost so far 0.7024117550777595\n",
            "2930\n",
            "Average cost so far 0.6424622853678883\n",
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            "Average cost so far 0.6839864381858493\n",
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            "Average cost so far 0.6810099476678669\n",
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            "Average cost so far 0.7152981146197593\n",
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            "Average cost so far 0.6845172975778864\n",
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            "Average cost so far 0.7040533928622742\n",
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            "Average cost so far 0.6798189002422684\n",
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            "Average cost so far 0.6960680250384786\n",
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            "Average cost so far 0.6569696641749176\n",
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            "Average cost so far 0.6911759222692221\n",
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            "Average cost so far 0.6885633613413469\n",
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            "Average cost so far 0.6991285633896127\n",
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            "Average cost so far 0.6619455651934895\n",
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            "Average cost so far 0.6929988160426815\n",
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            "Average cost so far 0.6463100840851098\n",
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            "Average cost so far 0.6701601896927543\n",
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            "Average cost so far 0.7103446764349326\n",
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            "Average cost so far 0.7121768981146235\n",
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            "Average cost so far 0.6963480350663565\n",
            "2990\n",
            "Average cost so far 0.6732681512679352\n",
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            "Average cost so far 0.682782687849223\n",
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            "Average cost so far 0.674130975518777\n",
            "2995\n",
            "Average cost so far 0.6668178952795978\n",
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            "Average cost so far 0.6757351264236915\n",
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            "Average cost so far 0.679361838996533\n",
            "2998\n",
            "Average cost so far 0.6645945555208654\n",
            "2999\n",
            "predicting ...\n",
            "[6. 1. 9. ... 9. 8. 7.]\n",
            "predicting ...\n",
            "[3. 8. 8. ... 5. 6. 7.]\n",
            "Epoch 12: training accuracy 0.45744, testing accuracy 0.4429\n",
            "Average cost so far 0.6889708214284781\n",
            "3000\n",
            "Average cost so far 0.6443948116554283\n",
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            "Average cost so far 0.6818488051634914\n",
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            "Average cost so far 0.6410238949905661\n",
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            "Average cost so far 0.7242244147208958\n",
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            "3091\n",
            "Average cost so far 0.6907711861565307\n",
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            "Average cost so far 0.6548546611400166\n",
            "3093\n",
            "Average cost so far 0.6705587255717768\n",
            "3094\n",
            "Average cost so far 0.6520283768952598\n",
            "3095\n",
            "Average cost so far 0.6974790941422855\n",
            "3096\n",
            "Average cost so far 0.6879437628502112\n",
            "3097\n",
            "Average cost so far 0.6820293030325933\n",
            "3098\n",
            "Average cost so far 0.6706327178653874\n",
            "3099\n",
            "Average cost so far 0.6742494199638281\n",
            "3100\n",
            "Average cost so far 0.7127829611095785\n",
            "3101\n",
            "Average cost so far 0.7008908385874199\n",
            "3102\n",
            "Average cost so far 0.6873997137717305\n",
            "3103\n",
            "Average cost so far 0.6287172511971298\n",
            "3104\n",
            "Average cost so far 0.6533784794505099\n",
            "3105\n",
            "Average cost so far 0.6976705180675986\n",
            "3106\n",
            "Average cost so far 0.664776885842213\n",
            "3107\n",
            "Average cost so far 0.6623066732680403\n",
            "3108\n",
            "Average cost so far 0.7103256219800679\n",
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            "Average cost so far 0.6427560738991421\n",
            "3110\n",
            "Average cost so far 0.6681161152337632\n",
            "3111\n",
            "Average cost so far 0.6592607370532648\n",
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            "Average cost so far 0.673024727441548\n",
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            "Average cost so far 0.6856399584400888\n",
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            "Average cost so far 0.6408366874168684\n",
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            "Average cost so far 0.6431575304823896\n",
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            "Average cost so far 0.6523861690555367\n",
            "3120\n",
            "Average cost so far 0.6583225096661139\n",
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            "Average cost so far 0.6272080652948356\n",
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            "Average cost so far 0.6674119518641162\n",
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            "Average cost so far 0.682542037100579\n",
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            "Average cost so far 0.6633377401753325\n",
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            "Average cost so far 0.6647742462667143\n",
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            "Average cost so far 0.6561449543991894\n",
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            "Average cost so far 0.6894679064257052\n",
            "3128\n",
            "Average cost so far 0.6414633330711339\n",
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            "Average cost so far 0.6062348619017406\n",
            "3130\n",
            "Average cost so far 0.6781207169939255\n",
            "3131\n",
            "Average cost so far 0.6835477859607731\n",
            "3132\n",
            "Average cost so far 0.6555096674071745\n",
            "3133\n",
            "Average cost so far 0.6870876830446836\n",
            "3134\n",
            "Average cost so far 0.7150087165753188\n",
            "3135\n",
            "Average cost so far 0.6876526467826148\n",
            "3136\n",
            "Average cost so far 0.7111897073438638\n",
            "3137\n",
            "Average cost so far 0.6681445154284593\n",
            "3138\n",
            "Average cost so far 0.7164565903347929\n",
            "3139\n",
            "Average cost so far 0.7070243148832721\n",
            "3140\n",
            "Average cost so far 0.6861592327056735\n",
            "3141\n",
            "Average cost so far 0.7099796736519767\n",
            "3142\n",
            "Average cost so far 0.696311079271553\n",
            "3143\n",
            "Average cost so far 0.6232726291282572\n",
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            "Average cost so far 0.6956962082640575\n",
            "3145\n",
            "Average cost so far 0.6658902892672337\n",
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            "Average cost so far 0.6450670930940247\n",
            "3147\n",
            "Average cost so far 0.6934462888244505\n",
            "3148\n",
            "Average cost so far 0.6563844207402029\n",
            "3149\n",
            "Average cost so far 0.6773798195108705\n",
            "3150\n",
            "Average cost so far 0.6921022085022364\n",
            "3151\n",
            "Average cost so far 0.7077861555455459\n",
            "3152\n",
            "Average cost so far 0.7090266020164994\n",
            "3153\n",
            "Average cost so far 0.6742084253652953\n",
            "3154\n",
            "Average cost so far 0.7051740595798515\n",
            "3155\n",
            "Average cost so far 0.6833442329375626\n",
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            "Average cost so far 0.6969658466595743\n",
            "3157\n",
            "Average cost so far 0.6823728931685897\n",
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            "Average cost so far 0.6606358613172252\n",
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            "Average cost so far 0.673741129542427\n",
            "3160\n",
            "Average cost so far 0.6747186195300914\n",
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            "Average cost so far 0.6880295112852062\n",
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            "Average cost so far 0.6912306176725791\n",
            "3163\n",
            "Average cost so far 0.6865752646827984\n",
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            "Average cost so far 0.6632962424890038\n",
            "3165\n",
            "Average cost so far 0.6697757052490975\n",
            "3166\n",
            "Average cost so far 0.7188639465408571\n",
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            "Average cost so far 0.7396414711330928\n",
            "3168\n",
            "Average cost so far 0.6985119613092676\n",
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            "Average cost so far 0.6783864700921225\n",
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            "Average cost so far 0.6751798003759184\n",
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            "Average cost so far 0.7058479450252538\n",
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            "Average cost so far 0.6649438290579286\n",
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            "Average cost so far 0.6679089526299937\n",
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            "Average cost so far 0.6786830072024361\n",
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            "Average cost so far 0.7045380847732523\n",
            "3180\n",
            "Average cost so far 0.6352899576809903\n",
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            "Average cost so far 0.6654819480172369\n",
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            "Average cost so far 0.6587104889192097\n",
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            "Average cost so far 0.6830124750029255\n",
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            "Average cost so far 0.689936708889581\n",
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            "Average cost so far 0.6720198870278244\n",
            "3187\n",
            "Average cost so far 0.6757040327188923\n",
            "3188\n",
            "Average cost so far 0.6407955431609065\n",
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            "Average cost so far 0.6926366368622777\n",
            "3190\n",
            "Average cost so far 0.6858587597451381\n",
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            "Average cost so far 0.6564175687663891\n",
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            "Average cost so far 0.698087627352652\n",
            "3193\n",
            "Average cost so far 0.6410431197878185\n",
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            "Average cost so far 0.6576485511480751\n",
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            "Average cost so far 0.6888072949981529\n",
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            "Average cost so far 0.6636028593221703\n",
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            "Average cost so far 0.676519162664415\n",
            "3200\n",
            "Average cost so far 0.692270735635494\n",
            "3201\n",
            "Average cost so far 0.6670870156964991\n",
            "3202\n",
            "Average cost so far 0.6818190308478678\n",
            "3203\n",
            "Average cost so far 0.7403837479845586\n",
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            "Average cost so far 0.7247373929142837\n",
            "3205\n",
            "Average cost so far 0.6814266452947974\n",
            "3206\n",
            "Average cost so far 0.6855483351274949\n",
            "3207\n",
            "Average cost so far 0.6337615292990665\n",
            "3208\n",
            "Average cost so far 0.6603086178010412\n",
            "3209\n",
            "Average cost so far 0.7008949041978273\n",
            "3210\n",
            "Average cost so far 0.6249961271630146\n",
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            "Average cost so far 0.6644113099097485\n",
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            "Average cost so far 0.7002272996308657\n",
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            "Average cost so far 0.6984282546550499\n",
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            "Average cost so far 0.6651803524027098\n",
            "3215\n",
            "Average cost so far 0.6790420603247665\n",
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            "Average cost so far 0.6899309651498021\n",
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            "Average cost so far 0.6843019153747495\n",
            "3218\n",
            "Average cost so far 0.6754056312440998\n",
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            "Average cost so far 0.6656708239589214\n",
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            "Average cost so far 0.6968630421199878\n",
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            "Average cost so far 0.6966224598511809\n",
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            "Average cost so far 0.6623122038359375\n",
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            "Average cost so far 0.6768243726309419\n",
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            "Average cost so far 0.6576991791005479\n",
            "3225\n",
            "Average cost so far 0.6740175292080446\n",
            "3226\n",
            "Average cost so far 0.6432990789356171\n",
            "3227\n",
            "Average cost so far 0.6801603608739428\n",
            "3228\n",
            "Average cost so far 0.6771577674083118\n",
            "3229\n",
            "Average cost so far 0.6623170748378229\n",
            "3230\n",
            "Average cost so far 0.7085892476539745\n",
            "3231\n",
            "Average cost so far 0.7173597283496855\n",
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            "Average cost so far 0.6361380450434908\n",
            "3233\n",
            "Average cost so far 0.669871525680549\n",
            "3234\n",
            "Average cost so far 0.6580267585777178\n",
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            "Average cost so far 0.6497659565494268\n",
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            "Average cost so far 0.6980141697625853\n",
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            "Average cost so far 0.6379006745301011\n",
            "3238\n",
            "Average cost so far 0.7155753546137915\n",
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            "Average cost so far 0.6873520091663132\n",
            "3240\n",
            "Average cost so far 0.6693899255421705\n",
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            "Average cost so far 0.6850764134239999\n",
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            "Average cost so far 0.6658713040747415\n",
            "3243\n",
            "Average cost so far 0.6656043839040359\n",
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            "Average cost so far 0.6450527779173246\n",
            "3245\n",
            "Average cost so far 0.6458523513740184\n",
            "3246\n",
            "Average cost so far 0.6731102264564764\n",
            "3247\n",
            "Average cost so far 0.6729920609872473\n",
            "3248\n",
            "Average cost so far 0.6385245685558795\n",
            "3249\n",
            "predicting ...\n",
            "[6. 1. 9. ... 9. 1. 9.]\n",
            "predicting ...\n",
            "[3. 9. 8. ... 5. 6. 7.]\n",
            "Epoch 13: training accuracy 0.461, testing accuracy 0.4515\n",
            "Average cost so far 0.6658511170102193\n",
            "3250\n",
            "Average cost so far 0.6238928956355744\n",
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            "Average cost so far 0.6738759137270003\n",
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            "Average cost so far 0.697038729689419\n",
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            "Average cost so far 0.6695866311158052\n",
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            "Average cost so far 0.6672442142049649\n",
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            "Average cost so far 0.7087105747378442\n",
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            "Average cost so far 0.6674821646279497\n",
            "3296\n",
            "Average cost so far 0.6646955663103639\n",
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            "Average cost so far 0.6477788558893487\n",
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            "3300\n",
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            "3321\n",
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            "3333\n",
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            "Average cost so far 0.6301250594128959\n",
            "3488\n",
            "Average cost so far 0.701537170652006\n",
            "3489\n",
            "Average cost so far 0.6819094495918603\n",
            "3490\n",
            "Average cost so far 0.6564781791644517\n",
            "3491\n",
            "Average cost so far 0.6694932125649038\n",
            "3492\n",
            "Average cost so far 0.6605821124270791\n",
            "3493\n",
            "Average cost so far 0.6604532531335308\n",
            "3494\n",
            "Average cost so far 0.637339122807631\n",
            "3495\n",
            "Average cost so far 0.6395020546609986\n",
            "3496\n",
            "Average cost so far 0.6581424700156213\n",
            "3497\n",
            "Average cost so far 0.6646446437730036\n",
            "3498\n",
            "Average cost so far 0.6336256780742052\n",
            "3499\n",
            "predicting ...\n",
            "[6. 1. 9. ... 9. 1. 7.]\n",
            "predicting ...\n",
            "[3. 9. 8. ... 5. 6. 7.]\n",
            "Epoch 14: training accuracy 0.47244, testing accuracy 0.4591\n",
            "Average cost so far 0.6660346826492372\n",
            "3500\n",
            "Average cost so far 0.6310936950499293\n",
            "3501\n",
            "Average cost so far 0.6551359400766132\n",
            "3502\n",
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            "Average cost so far 0.6541267750379051\n",
            "3518\n",
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            "3520\n",
            "Average cost so far 0.6507725058692221\n",
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            "3522\n",
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            "3533\n",
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            "Average cost so far 0.6396492834101998\n",
            "3543\n",
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            "3545\n",
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            "3546\n",
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            "3547\n",
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            "3550\n",
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            "Average cost so far 0.666604797698525\n",
            "3584\n",
            "Average cost so far 0.6535006500470488\n",
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            "3587\n",
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            "3588\n",
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            "3589\n",
            "Average cost so far 0.6370534484665905\n",
            "3590\n",
            "Average cost so far 0.6619238596921904\n",
            "3591\n",
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            "3592\n",
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            "3594\n",
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            "3595\n",
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            "3596\n",
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            "3597\n",
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            "3599\n",
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            "3600\n",
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            "3602\n",
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            "3609\n",
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            "3610\n",
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            "3641\n",
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            "3650\n",
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            "3652\n",
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            "Average cost so far 0.6682281124207401\n",
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            "Average cost so far 0.6909942851960297\n",
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            "Average cost so far 0.6463833311198318\n",
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            "Average cost so far 0.6571013976228169\n",
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            "Average cost so far 0.6396281187528393\n",
            "3745\n",
            "Average cost so far 0.639516655676356\n",
            "3746\n",
            "Average cost so far 0.6522327973329614\n",
            "3747\n",
            "Average cost so far 0.6647475822982198\n",
            "3748\n",
            "Average cost so far 0.625639944360032\n",
            "3749\n",
            "predicting ...\n",
            "[6. 1. 9. ... 9. 9. 7.]\n",
            "predicting ...\n",
            "[3. 8. 8. ... 5. 6. 7.]\n",
            "Epoch 15: training accuracy 0.4756, testing accuracy 0.4597\n",
            "Average cost so far 0.6706650455662594\n",
            "3750\n",
            "Average cost so far 0.6293249591931763\n",
            "3751\n",
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            "3764\n",
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            "3765\n",
            "Average cost so far 0.6770967686392075\n",
            "3766\n",
            "Average cost so far 0.6670565932663145\n",
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            "Average cost so far 0.691175575390769\n",
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            "3808\n",
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            "3853\n",
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            "Average cost so far 0.6637234923993762\n",
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            "3900\n",
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            "Average cost so far 0.7147588029258222\n",
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            "3979\n",
            "Average cost so far 0.6397208737071692\n",
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            "Average cost so far 0.6489141466486105\n",
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            "Average cost so far 0.6353615706797476\n",
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            "3986\n",
            "Average cost so far 0.6797099639401231\n",
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            "Average cost so far 0.6986029630109272\n",
            "3989\n",
            "Average cost so far 0.6751927134478107\n",
            "3990\n",
            "Average cost so far 0.6440332498950332\n",
            "3991\n",
            "Average cost so far 0.6627113211167993\n",
            "3992\n",
            "Average cost so far 0.6471269963448091\n",
            "3993\n",
            "Average cost so far 0.6463368098114249\n",
            "3994\n",
            "Average cost so far 0.6252323582373118\n",
            "3995\n",
            "Average cost so far 0.6334257912488824\n",
            "3996\n",
            "Average cost so far 0.649216258157301\n",
            "3997\n",
            "Average cost so far 0.6583377215755458\n",
            "3998\n",
            "Average cost so far 0.6157658604395152\n",
            "3999\n",
            "predicting ...\n",
            "[6. 1. 9. ... 9. 9. 7.]\n",
            "predicting ...\n",
            "[3. 9. 8. ... 5. 6. 7.]\n",
            "Epoch 16: training accuracy 0.47978, testing accuracy 0.4653\n",
            "Average cost so far 0.6615182775228158\n",
            "4000\n",
            "Average cost so far 0.6212296014612894\n",
            "4001\n",
            "Average cost so far 0.6454331265492389\n",
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            "4010\n",
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            "4011\n",
            "Average cost so far 0.6613331178059759\n",
            "4012\n",
            "Average cost so far 0.6860762789697883\n",
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            "4014\n",
            "Average cost so far 0.6778342496445862\n",
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            "4016\n",
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            "Average cost so far 0.6396526076541948\n",
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            "Average cost so far 0.6918200806166638\n",
            "4019\n",
            "Average cost so far 0.6912550727921769\n",
            "4020\n",
            "Average cost so far 0.6471477963937862\n",
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            "Average cost so far 0.6531886848987198\n",
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            "Average cost so far 0.6368429122388778\n",
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            "4025\n",
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            "Average cost so far 0.6511072352509664\n",
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            "Average cost so far 0.6478325777724488\n",
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            "4040\n",
            "Average cost so far 0.6487860315318329\n",
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            "Average cost so far 0.6275595088711754\n",
            "4043\n",
            "Average cost so far 0.6929840327406932\n",
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            "Average cost so far 0.6887253986776409\n",
            "4045\n",
            "Average cost so far 0.6545267648193206\n",
            "4046\n",
            "Average cost so far 0.6535731038748678\n",
            "4047\n",
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            "Average cost so far 0.6791505416568251\n",
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            "Average cost so far 0.667322894886926\n",
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            "Average cost so far 0.6810325194916561\n",
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            "Average cost so far 0.6946632754453357\n",
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            "Average cost so far 0.6634280219484567\n",
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            "Average cost so far 0.6637735059408579\n",
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            "Average cost so far 0.6889891840310703\n",
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            "Average cost so far 0.7163540804907526\n",
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            "Average cost so far 0.6318877198984763\n",
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            "Average cost so far 0.7195629789479999\n",
            "4079\n",
            "Average cost so far 0.6038643627930189\n",
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            "Average cost so far 0.6507763219651538\n",
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            "Average cost so far 0.6581301902535586\n",
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            "Average cost so far 0.6985118908234415\n",
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            "Average cost so far 0.6559825194318349\n",
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            "Average cost so far 0.6656381670495348\n",
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            "Average cost so far 0.6344357348246391\n",
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            "Average cost so far 0.6386415403423765\n",
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            "Average cost so far 0.6529998039408875\n",
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            "Average cost so far 0.6852971289352955\n",
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            "Average cost so far 0.6297995848434847\n",
            "4093\n",
            "Average cost so far 0.6445411218448609\n",
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            "Average cost so far 0.6383653533391912\n",
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            "Average cost so far 0.684933262653596\n",
            "4096\n",
            "Average cost so far 0.6664182515546596\n",
            "4097\n",
            "Average cost so far 0.6616748430155087\n",
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            "Average cost so far 0.6564397747020234\n",
            "4099\n",
            "Average cost so far 0.6550518408912258\n",
            "4100\n",
            "Average cost so far 0.6723282178670318\n",
            "4101\n",
            "Average cost so far 0.6734522425951809\n",
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            "Average cost so far 0.6678510148440746\n",
            "4103\n",
            "Average cost so far 0.6067059115744575\n",
            "4104\n",
            "Average cost so far 0.63031138537478\n",
            "4105\n",
            "Average cost so far 0.6703178906525277\n",
            "4106\n",
            "Average cost so far 0.6341151011005873\n",
            "4107\n",
            "Average cost so far 0.6342809958780465\n",
            "4108\n",
            "Average cost so far 0.6964367869625641\n",
            "4109\n",
            "Average cost so far 0.6229002362244492\n",
            "4110\n",
            "Average cost so far 0.6488839254673223\n",
            "4111\n",
            "Average cost so far 0.6330311906675918\n",
            "4112\n",
            "Average cost so far 0.6228171471065643\n",
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            "Average cost so far 0.6497676840952511\n",
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            "Average cost so far 0.657358746814729\n",
            "4115\n",
            "Average cost so far 0.6197791413442553\n",
            "4116\n",
            "Average cost so far 0.6464030873299729\n",
            "4117\n",
            "Average cost so far 0.6139824451890711\n",
            "4118\n",
            "Average cost so far 0.6214391510558269\n",
            "4119\n",
            "Average cost so far 0.6392397121685974\n",
            "4120\n",
            "Average cost so far 0.6330617347106874\n",
            "4121\n",
            "Average cost so far 0.6006326048706372\n",
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            "Average cost so far 0.6489939006258696\n",
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            "Average cost so far 0.6508580199748815\n",
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            "Average cost so far 0.6431573056358387\n",
            "4125\n",
            "Average cost so far 0.6477189178973609\n",
            "4126\n",
            "Average cost so far 0.6330512425819745\n",
            "4127\n",
            "Average cost so far 0.6598376447673334\n",
            "4128\n",
            "Average cost so far 0.6102568103024748\n",
            "4129\n",
            "Average cost so far 0.5867585168377402\n",
            "4130\n",
            "Average cost so far 0.6537713547262625\n",
            "4131\n",
            "Average cost so far 0.660039837567092\n",
            "4132\n",
            "Average cost so far 0.6273307515266847\n",
            "4133\n",
            "Average cost so far 0.6538426785530242\n",
            "4134\n",
            "Average cost so far 0.6817587610598285\n",
            "4135\n",
            "Average cost so far 0.6534465090748622\n",
            "4136\n",
            "Average cost so far 0.6918683371181811\n",
            "4137\n",
            "Average cost so far 0.6405696767074075\n",
            "4138\n",
            "Average cost so far 0.6860607045173688\n",
            "4139\n",
            "Average cost so far 0.6774866901435262\n",
            "4140\n",
            "Average cost so far 0.6594749191983135\n",
            "4141\n",
            "Average cost so far 0.6891631038544308\n",
            "4142\n",
            "Average cost so far 0.6782230580834568\n",
            "4143\n",
            "Average cost so far 0.6026330018422302\n",
            "4144\n",
            "Average cost so far 0.6943915592036112\n",
            "4145\n",
            "Average cost so far 0.6361104722350713\n",
            "4146\n",
            "Average cost so far 0.628151274853082\n",
            "4147\n",
            "Average cost so far 0.6586078097450166\n",
            "4148\n",
            "Average cost so far 0.6272516181227284\n",
            "4149\n",
            "Average cost so far 0.6671931862437145\n",
            "4150\n",
            "Average cost so far 0.6567103825930745\n",
            "4151\n",
            "Average cost so far 0.6559607026929412\n",
            "4152\n",
            "Average cost so far 0.6867761465264054\n",
            "4153\n",
            "Average cost so far 0.6445508998964595\n",
            "4154\n",
            "Average cost so far 0.6837241250240702\n",
            "4155\n",
            "Average cost so far 0.6456205616369144\n",
            "4156\n",
            "Average cost so far 0.6703939838114613\n",
            "4157\n",
            "Average cost so far 0.668750055814697\n",
            "4158\n",
            "Average cost so far 0.6473902810850825\n",
            "4159\n",
            "Average cost so far 0.6586861545775343\n",
            "4160\n",
            "Average cost so far 0.6539516717344269\n",
            "4161\n",
            "Average cost so far 0.6769755524606756\n",
            "4162\n",
            "Average cost so far 0.6731819089681557\n",
            "4163\n",
            "Average cost so far 0.6735236828847516\n",
            "4164\n",
            "Average cost so far 0.6462587682079548\n",
            "4165\n",
            "Average cost so far 0.6517227243660638\n",
            "4166\n",
            "Average cost so far 0.6889233195511547\n",
            "4167\n",
            "Average cost so far 0.7131359244348436\n",
            "4168\n",
            "Average cost so far 0.654077859173439\n",
            "4169\n",
            "Average cost so far 0.6515547141740313\n",
            "4170\n",
            "Average cost so far 0.6365959386583968\n",
            "4171\n",
            "Average cost so far 0.652401998687029\n",
            "4172\n",
            "Average cost so far 0.6534329432007192\n",
            "4173\n",
            "Average cost so far 0.6656002360807978\n",
            "4174\n",
            "Average cost so far 0.6280904107625482\n",
            "4175\n",
            "Average cost so far 0.6615048677538014\n",
            "4176\n",
            "Average cost so far 0.6465220913522218\n",
            "4177\n",
            "Average cost so far 0.6499752796420388\n",
            "4178\n",
            "Average cost so far 0.6442817133984723\n",
            "4179\n",
            "Average cost so far 0.6670557368582283\n",
            "4180\n",
            "Average cost so far 0.6025808894315101\n",
            "4181\n",
            "Average cost so far 0.6508133918493141\n",
            "4182\n",
            "Average cost so far 0.6399191067483321\n",
            "4183\n",
            "Average cost so far 0.6800723809156233\n",
            "4184\n",
            "Average cost so far 0.653721068595084\n",
            "4185\n",
            "Average cost so far 0.6710993944529878\n",
            "4186\n",
            "Average cost so far 0.6548198322174335\n",
            "4187\n",
            "Average cost so far 0.6705367474101422\n",
            "4188\n",
            "Average cost so far 0.625584794553331\n",
            "4189\n",
            "Average cost so far 0.6764479592940843\n",
            "4190\n",
            "Average cost so far 0.6640090171094982\n",
            "4191\n",
            "Average cost so far 0.6283475504488938\n",
            "4192\n",
            "Average cost so far 0.6746693086901263\n",
            "4193\n",
            "Average cost so far 0.6083838133710576\n",
            "4194\n",
            "Average cost so far 0.6376735907475483\n",
            "4195\n",
            "Average cost so far 0.65807226920767\n",
            "4196\n",
            "Average cost so far 0.642963499126711\n",
            "4197\n",
            "Average cost so far 0.7055930493823455\n",
            "4198\n",
            "Average cost so far 0.6699768890428851\n",
            "4199\n",
            "Average cost so far 0.6538975715601799\n",
            "4200\n",
            "Average cost so far 0.6640389241480132\n",
            "4201\n",
            "Average cost so far 0.6360530590910409\n",
            "4202\n",
            "Average cost so far 0.6535691434277062\n",
            "4203\n",
            "Average cost so far 0.7100895554763704\n",
            "4204\n",
            "Average cost so far 0.7133391858185696\n",
            "4205\n",
            "Average cost so far 0.6564423994983136\n",
            "4206\n",
            "Average cost so far 0.6629571370297016\n",
            "4207\n",
            "Average cost so far 0.6117990495080993\n",
            "4208\n",
            "Average cost so far 0.6387360194689752\n",
            "4209\n",
            "Average cost so far 0.6826592376496147\n",
            "4210\n",
            "Average cost so far 0.6058360269627151\n",
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            "Average cost so far 0.68153892362701\n",
            "4213\n",
            "Average cost so far 0.6783546652385327\n",
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            "Average cost so far 0.6460898343518547\n",
            "4215\n",
            "Average cost so far 0.6586929149700196\n",
            "4216\n",
            "Average cost so far 0.6583070496599873\n",
            "4217\n",
            "Average cost so far 0.6502693688316156\n",
            "4218\n",
            "Average cost so far 0.6600459047198511\n",
            "4219\n",
            "Average cost so far 0.6375167988293522\n",
            "4220\n",
            "Average cost so far 0.6773619266795609\n",
            "4221\n",
            "Average cost so far 0.6721290038112063\n",
            "4222\n",
            "Average cost so far 0.6260136276496272\n",
            "4223\n",
            "Average cost so far 0.6665361425038615\n",
            "4224\n",
            "Average cost so far 0.631163703275531\n",
            "4225\n",
            "Average cost so far 0.6552023162442873\n",
            "4226\n",
            "Average cost so far 0.5976337704273407\n",
            "4227\n",
            "Average cost so far 0.6654254295627304\n",
            "4228\n",
            "Average cost so far 0.6510730813830334\n",
            "4229\n",
            "Average cost so far 0.6335558969708353\n",
            "4230\n",
            "Average cost so far 0.6882610514555677\n",
            "4231\n",
            "Average cost so far 0.7034383202609094\n",
            "4232\n",
            "Average cost so far 0.6190192611643767\n",
            "4233\n",
            "Average cost so far 0.6466100060917906\n",
            "4234\n",
            "Average cost so far 0.6354251944060391\n",
            "4235\n",
            "Average cost so far 0.6405913994888972\n",
            "4236\n",
            "Average cost so far 0.6784798137323814\n",
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            "Average cost so far 0.627963762135776\n",
            "4238\n",
            "Average cost so far 0.6921983878187953\n",
            "4239\n",
            "Average cost so far 0.6746614295500265\n",
            "4240\n",
            "Average cost so far 0.643429270786632\n",
            "4241\n",
            "Average cost so far 0.6630659935708993\n",
            "4242\n",
            "Average cost so far 0.6494096087033071\n",
            "4243\n",
            "Average cost so far 0.644515687942709\n",
            "4244\n",
            "Average cost so far 0.6251050483763937\n",
            "4245\n",
            "Average cost so far 0.6324273646641136\n",
            "4246\n",
            "Average cost so far 0.6488693955385619\n",
            "4247\n",
            "Average cost so far 0.6550854444052362\n",
            "4248\n",
            "Average cost so far 0.6193781579558792\n",
            "4249\n",
            "predicting ...\n",
            "[6. 1. 9. ... 9. 9. 7.]\n",
            "predicting ...\n",
            "[3. 9. 8. ... 5. 6. 7.]\n",
            "Epoch 17: training accuracy 0.48092, testing accuracy 0.4682\n",
            "Average cost so far 0.6573624291698166\n",
            "4250\n",
            "Average cost so far 0.6227306738845547\n",
            "4251\n",
            "Average cost so far 0.6406515805371265\n",
            "4252\n",
            "Average cost so far 0.6674115768300551\n",
            "4253\n",
            "Average cost so far 0.6479758805727848\n",
            "4254\n",
            "Average cost so far 0.6291814944900836\n",
            "4255\n",
            "Average cost so far 0.6288217297036239\n",
            "4256\n",
            "Average cost so far 0.6445933030759599\n",
            "4257\n",
            "Average cost so far 0.6482603905696616\n",
            "4258\n",
            "Average cost so far 0.6295166203405996\n",
            "4259\n",
            "Average cost so far 0.662363748489485\n",
            "4260\n",
            "Average cost so far 0.6378536805085766\n",
            "4261\n",
            "Average cost so far 0.6590693513532782\n",
            "4262\n",
            "Average cost so far 0.6853276963760919\n",
            "4263\n",
            "Average cost so far 0.6330139572631898\n",
            "4264\n",
            "Average cost so far 0.6766556389288517\n",
            "4265\n",
            "Average cost so far 0.6704525984298509\n",
            "4266\n",
            "Average cost so far 0.65947880517776\n",
            "4267\n",
            "Average cost so far 0.6344540837620181\n",
            "4268\n",
            "Average cost so far 0.6861784449621156\n",
            "4269\n",
            "Average cost so far 0.6881540262183851\n",
            "4270\n",
            "Average cost so far 0.643455938585769\n",
            "4271\n",
            "Average cost so far 0.6524532965467212\n",
            "4272\n",
            "Average cost so far 0.6350737314995085\n",
            "4273\n",
            "Average cost so far 0.6560148661648932\n",
            "4274\n",
            "Average cost so far 0.7147044763392535\n",
            "4275\n",
            "Average cost so far 0.6410560554153335\n",
            "4276\n",
            "Average cost so far 0.6471597354206389\n",
            "4277\n",
            "Average cost so far 0.6430835739301134\n",
            "4278\n",
            "Average cost so far 0.6172128910073846\n",
            "4279\n",
            "Average cost so far 0.6580341738770923\n",
            "4280\n",
            "Average cost so far 0.6295340028628698\n",
            "4281\n",
            "Average cost so far 0.6647155189636708\n",
            "4282\n",
            "Average cost so far 0.6756277167805373\n",
            "4283\n",
            "Average cost so far 0.6434243162743908\n",
            "4284\n",
            "Average cost so far 0.6274497906913934\n",
            "4285\n",
            "Average cost so far 0.616708442400252\n",
            "4286\n",
            "Average cost so far 0.6518138284901959\n",
            "4287\n",
            "Average cost so far 0.661913748527505\n",
            "4288\n",
            "Average cost so far 0.6459752559764348\n",
            "4289\n",
            "Average cost so far 0.6433858110133519\n",
            "4290\n",
            "Average cost so far 0.6485913198316697\n",
            "4291\n",
            "Average cost so far 0.6242270594828295\n",
            "4292\n",
            "Average cost so far 0.6262899223709081\n",
            "4293\n",
            "Average cost so far 0.6932563793737414\n",
            "4294\n",
            "Average cost so far 0.6844059862619951\n",
            "4295\n",
            "Average cost so far 0.6505685702517112\n",
            "4296\n",
            "Average cost so far 0.6465426991963552\n",
            "4297\n",
            "Average cost so far 0.6371527820685629\n",
            "4298\n",
            "Average cost so far 0.6393537237788258\n",
            "4299\n",
            "Average cost so far 0.6371490476403354\n",
            "4300\n",
            "Average cost so far 0.6520046589981492\n",
            "4301\n",
            "Average cost so far 0.7037479912704395\n",
            "4302\n",
            "Average cost so far 0.6059842375527942\n",
            "4303\n",
            "Average cost so far 0.6393961303811562\n",
            "4304\n",
            "Average cost so far 0.67026474398874\n",
            "4305\n",
            "Average cost so far 0.6518534389732022\n",
            "4306\n",
            "Average cost so far 0.6765744710676403\n",
            "4307\n",
            "Average cost so far 0.6307692144051588\n",
            "4308\n",
            "Average cost so far 0.6625665441292361\n",
            "4309\n",
            "Average cost so far 0.6389556998920067\n",
            "4310\n",
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            "Average cost so far 0.6673860846071452\n",
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            "Average cost so far 0.6798930469254938\n",
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            "Average cost so far 0.6361283280710066\n",
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            "Average cost so far 0.6912986475205656\n",
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            "Average cost so far 0.6648631607624046\n",
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            "Average cost so far 0.6252285440560014\n",
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            "Average cost so far 0.6345170079890272\n",
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            "Average cost so far 0.6139657760435654\n",
            "4319\n",
            "Average cost so far 0.6615107703716463\n",
            "4320\n",
            "Average cost so far 0.6917741931633311\n",
            "4321\n",
            "Average cost so far 0.6613200923066386\n",
            "4322\n",
            "Average cost so far 0.6491932517644777\n",
            "4323\n",
            "Average cost so far 0.6644013927876675\n",
            "4324\n",
            "Average cost so far 0.7121641995703341\n",
            "4325\n",
            "Average cost so far 0.6318801120514125\n",
            "4326\n",
            "Average cost so far 0.6662856639710795\n",
            "4327\n",
            "Average cost so far 0.6721810742104115\n",
            "4328\n",
            "Average cost so far 0.7168071272322734\n",
            "4329\n",
            "Average cost so far 0.6053896295274717\n",
            "4330\n",
            "Average cost so far 0.6472128095767551\n",
            "4331\n",
            "Average cost so far 0.6522243492413936\n",
            "4332\n",
            "Average cost so far 0.695726480582683\n",
            "4333\n",
            "Average cost so far 0.6573912534180473\n",
            "4334\n",
            "Average cost so far 0.6476752621182515\n",
            "4335\n",
            "Average cost so far 0.6633003733311669\n",
            "4336\n",
            "Average cost so far 0.6217723832483079\n",
            "4337\n",
            "Average cost so far 0.7018386809563815\n",
            "4338\n",
            "Average cost so far 0.6311413178427313\n",
            "4339\n",
            "Average cost so far 0.6360248412103211\n",
            "4340\n",
            "Average cost so far 0.6491114090001598\n",
            "4341\n",
            "Average cost so far 0.6823229538814866\n",
            "4342\n",
            "Average cost so far 0.62402285448565\n",
            "4343\n",
            "Average cost so far 0.6420735200163656\n",
            "4344\n",
            "Average cost so far 0.6356281520758773\n",
            "4345\n",
            "Average cost so far 0.6811879736863145\n",
            "4346\n",
            "Average cost so far 0.6641655239330213\n",
            "4347\n",
            "Average cost so far 0.6634885708615754\n",
            "4348\n",
            "Average cost so far 0.6554749466163963\n",
            "4349\n",
            "Average cost so far 0.6550189364304173\n",
            "4350\n",
            "Average cost so far 0.669551587405777\n",
            "4351\n",
            "Average cost so far 0.6735902501542423\n",
            "4352\n",
            "Average cost so far 0.6656224164878992\n",
            "4353\n",
            "Average cost so far 0.6084324457459306\n",
            "4354\n",
            "Average cost so far 0.6290531095493754\n",
            "4355\n",
            "Average cost so far 0.6648148200747354\n",
            "4356\n",
            "Average cost so far 0.6304491401913307\n",
            "4357\n",
            "Average cost so far 0.6328852905760091\n",
            "4358\n",
            "Average cost so far 0.693180658806472\n",
            "4359\n",
            "Average cost so far 0.6203026623732003\n",
            "4360\n",
            "Average cost so far 0.6507398951570749\n",
            "4361\n",
            "Average cost so far 0.6314762436959445\n",
            "4362\n",
            "Average cost so far 0.6217910417998375\n",
            "4363\n",
            "Average cost so far 0.6481998514345335\n",
            "4364\n",
            "Average cost so far 0.656482459378883\n",
            "4365\n",
            "Average cost so far 0.6179101033751471\n",
            "4366\n",
            "Average cost so far 0.6460098549977943\n",
            "4367\n",
            "Average cost so far 0.6139724087659989\n",
            "4368\n",
            "Average cost so far 0.6188282363080351\n",
            "4369\n",
            "Average cost so far 0.6364556521406073\n",
            "4370\n",
            "Average cost so far 0.6311730468097579\n",
            "4371\n",
            "Average cost so far 0.5995098597320208\n",
            "4372\n",
            "Average cost so far 0.6473458974612468\n",
            "4373\n",
            "Average cost so far 0.6502946831892941\n",
            "4374\n",
            "Average cost so far 0.6442843616310964\n",
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            "Average cost so far 0.6493662230257696\n",
            "4376\n",
            "Average cost so far 0.6333332568661446\n",
            "4377\n",
            "Average cost so far 0.6563255493491944\n",
            "4378\n",
            "Average cost so far 0.6090547060404051\n",
            "4379\n",
            "Average cost so far 0.5875509208679965\n",
            "4380\n",
            "Average cost so far 0.6564399062194738\n",
            "4381\n",
            "Average cost so far 0.6567312359725017\n",
            "4382\n",
            "Average cost so far 0.6231304595716174\n",
            "4383\n",
            "Average cost so far 0.6508027869765853\n",
            "4384\n",
            "Average cost so far 0.6774128751027044\n",
            "4385\n",
            "Average cost so far 0.6531914280429202\n",
            "4386\n",
            "Average cost so far 0.6940769203918685\n",
            "4387\n",
            "Average cost so far 0.6371519118753857\n",
            "4388\n",
            "Average cost so far 0.6831057557486356\n",
            "4389\n",
            "Average cost so far 0.6762403256247426\n",
            "4390\n",
            "Average cost so far 0.6553280059888361\n",
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            "Average cost so far 0.6887136948065151\n",
            "4392\n",
            "Average cost so far 0.6752249620805089\n",
            "4393\n",
            "Average cost so far 0.6031397682183025\n",
            "4394\n",
            "Average cost so far 0.6897310577259428\n",
            "4395\n",
            "Average cost so far 0.6323927850740061\n",
            "4396\n",
            "Average cost so far 0.6247216030984307\n",
            "4397\n",
            "Average cost so far 0.6570264786320665\n",
            "4398\n",
            "Average cost so far 0.6257419457789851\n",
            "4399\n",
            "Average cost so far 0.6657735360960652\n",
            "4400\n",
            "Average cost so far 0.6557090695698706\n",
            "4401\n",
            "Average cost so far 0.6566389218231989\n",
            "4402\n",
            "Average cost so far 0.6853754601992597\n",
            "4403\n",
            "Average cost so far 0.641854763658621\n",
            "4404\n",
            "Average cost so far 0.682104815146051\n",
            "4405\n",
            "Average cost so far 0.6430046032664564\n",
            "4406\n",
            "Average cost so far 0.6664924673063285\n",
            "4407\n",
            "Average cost so far 0.6662958130085725\n",
            "4408\n",
            "Average cost so far 0.643441857084707\n",
            "4409\n",
            "Average cost so far 0.6570867201490986\n",
            "4410\n",
            "Average cost so far 0.6495559453603412\n",
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            "Average cost so far 0.6735754237614903\n",
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            "Average cost so far 0.6694828430997889\n",
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            "Average cost so far 0.671785761059485\n",
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            "Average cost so far 0.6432086843171859\n",
            "4415\n",
            "Average cost so far 0.6514164806738053\n",
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            "Average cost so far 0.6891251325228922\n",
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            "Average cost so far 0.709058788911458\n",
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            "Average cost so far 0.6522624605928792\n",
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            "Average cost so far 0.6494015019318873\n",
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            "Average cost so far 0.6367891519025017\n",
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            "Average cost so far 0.6503736305740472\n",
            "4422\n",
            "Average cost so far 0.651411647718049\n",
            "4423\n",
            "Average cost so far 0.6654066815897266\n",
            "4424\n",
            "Average cost so far 0.6251085404637055\n",
            "4425\n",
            "Average cost so far 0.6604701794034452\n",
            "4426\n",
            "Average cost so far 0.6442356349144223\n",
            "4427\n",
            "Average cost so far 0.6483224176454793\n",
            "4428\n",
            "Average cost so far 0.6438426714994133\n",
            "4429\n",
            "Average cost so far 0.6682722665475516\n",
            "4430\n",
            "Average cost so far 0.6000763371323817\n",
            "4431\n",
            "Average cost so far 0.6500981638482685\n",
            "4432\n",
            "Average cost so far 0.6361560114350304\n",
            "4433\n",
            "Average cost so far 0.6749694952787266\n",
            "4434\n",
            "Average cost so far 0.6512029265288735\n",
            "4435\n",
            "Average cost so far 0.6721216377964544\n",
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            "Average cost so far 0.6565109764453665\n",
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            "Average cost so far 0.668820834627856\n",
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            "Average cost so far 0.6206751504885095\n",
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            "Average cost so far 0.6740356640239493\n",
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            "Average cost so far 0.6607750313849438\n",
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            "Average cost so far 0.6416823494928298\n",
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            "Average cost so far 0.7044766023115533\n",
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            "Average cost so far 0.6708938363679557\n",
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            "Average cost so far 0.6503543368151948\n",
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            "Average cost so far 0.6601207801094824\n",
            "4451\n",
            "Average cost so far 0.6328014669862491\n",
            "4452\n",
            "Average cost so far 0.6507230452620031\n",
            "4453\n",
            "Average cost so far 0.7090695176203409\n",
            "4454\n",
            "Average cost so far 0.7171891367097507\n",
            "4455\n",
            "Average cost so far 0.656091910871869\n",
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            "Average cost so far 0.6631069975909454\n",
            "4457\n",
            "Average cost so far 0.6074327848246532\n",
            "4458\n",
            "Average cost so far 0.635945695149222\n",
            "4459\n",
            "Average cost so far 0.6816352042467667\n",
            "4460\n",
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            "Average cost so far 0.6703960462216222\n",
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            "Average cost so far 0.6278714189294649\n",
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            "Average cost so far 0.7025987325615661\n",
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            "Average cost so far 0.6754121937212837\n",
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            "Average cost so far 0.6416420248447481\n",
            "4491\n",
            "Average cost so far 0.6615748990571216\n",
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            "Average cost so far 0.6463403870986495\n",
            "4493\n",
            "Average cost so far 0.6429001168545239\n",
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            "Average cost so far 0.6224912097238802\n",
            "4495\n",
            "Average cost so far 0.631122490720196\n",
            "4496\n",
            "Average cost so far 0.6463096961382232\n",
            "4497\n",
            "Average cost so far 0.6532688610718321\n",
            "4498\n",
            "Average cost so far 0.6154794015466475\n",
            "4499\n",
            "predicting ...\n",
            "[6. 1. 9. ... 9. 1. 7.]\n",
            "predicting ...\n",
            "[3. 9. 8. ... 5. 6. 7.]\n",
            "Epoch 18: training accuracy 0.48262, testing accuracy 0.4685\n",
            "Average cost so far 0.6554523719587073\n",
            "4500\n",
            "Average cost so far 0.6195748413497107\n",
            "4501\n",
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            "Average cost so far 0.6845388698729881\n",
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            "4520\n",
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            "Average cost so far 0.6522252436909303\n",
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            "Average cost so far 0.6489743041561996\n",
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            "Average cost so far 0.6567845323708335\n",
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            "Average cost so far 0.6293195736712985\n",
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            "Average cost so far 0.6652633648968026\n",
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            "Average cost so far 0.6239391158578447\n",
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            "Average cost so far 0.6511226107448737\n",
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            "Average cost so far 0.6594568437717957\n",
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            "Average cost so far 0.6438681077518891\n",
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            "Average cost so far 0.6431109245717295\n",
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            "Average cost so far 0.6483368650361971\n",
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            "Average cost so far 0.6225176969775519\n",
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            "Average cost so far 0.624512317929624\n",
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            "Average cost so far 0.6914678571221912\n",
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            "Average cost so far 0.6844036709184137\n",
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            "Average cost so far 0.6494664031765117\n",
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            "Average cost so far 0.6435506467068339\n",
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            "Average cost so far 0.6368603873882256\n",
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            "Average cost so far 0.6405616948383109\n",
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            "Average cost so far 0.63794803993179\n",
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            "Average cost so far 0.6487904799147618\n",
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            "Average cost so far 0.7040544789067171\n",
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            "Average cost so far 0.6042590047788989\n",
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            "Average cost so far 0.63718410230124\n",
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            "Average cost so far 0.6520214890483464\n",
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            "Average cost so far 0.6727877950901648\n",
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            "Average cost so far 0.6278966652940973\n",
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            "Average cost so far 0.6596487763959269\n",
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            "Average cost so far 0.6396263684869631\n",
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            "Average cost so far 0.6589429938854626\n",
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            "Average cost so far 0.6658291972941819\n",
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            "4570\n",
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            "Average cost so far 0.7124314621341843\n",
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            "4577\n",
            "Average cost so far 0.6700174388513644\n",
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            "Average cost so far 0.7174197836010188\n",
            "4579\n",
            "Average cost so far 0.6049754861433386\n",
            "4580\n",
            "Average cost so far 0.6451367112154922\n",
            "4581\n",
            "Average cost so far 0.6519859484264139\n",
            "4582\n",
            "Average cost so far 0.6947400609271075\n",
            "4583\n",
            "Average cost so far 0.6556651973235637\n",
            "4584\n",
            "Average cost so far 0.6475144497440917\n",
            "4585\n",
            "Average cost so far 0.661959049497389\n",
            "4586\n",
            "Average cost so far 0.622683111414625\n",
            "4587\n",
            "Average cost so far 0.7019460127598554\n",
            "4588\n",
            "Average cost so far 0.6322610878573454\n",
            "4589\n",
            "Average cost so far 0.6351703640946368\n",
            "4590\n",
            "Average cost so far 0.64835607076771\n",
            "4591\n",
            "Average cost so far 0.6814457229732453\n",
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            "Average cost so far 0.6240311169311823\n",
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            "Average cost so far 0.6429570402158035\n",
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            "Average cost so far 0.6356952339307828\n",
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            "Average cost so far 0.6822073891151018\n",
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            "Average cost so far 0.6620981175494706\n",
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            "Average cost so far 0.6642633327245115\n",
            "4598\n",
            "Average cost so far 0.6547424611679828\n",
            "4599\n",
            "Average cost so far 0.654944449557853\n",
            "4600\n",
            "Average cost so far 0.6723848582991024\n",
            "4601\n",
            "Average cost so far 0.67127443100377\n",
            "4602\n",
            "Average cost so far 0.6651789402982804\n",
            "4603\n",
            "Average cost so far 0.607441095791535\n",
            "4604\n",
            "Average cost so far 0.6267490003497546\n",
            "4605\n",
            "Average cost so far 0.665815683767864\n",
            "4606\n",
            "Average cost so far 0.6280385555510294\n",
            "4607\n",
            "Average cost so far 0.6303476165234347\n",
            "4608\n",
            "Average cost so far 0.6909136485596186\n",
            "4609\n",
            "Average cost so far 0.620973509448434\n",
            "4610\n",
            "Average cost so far 0.6464994694172489\n",
            "4611\n",
            "Average cost so far 0.6322650290811649\n",
            "4612\n",
            "Average cost so far 0.6224571315197058\n",
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            "Average cost so far 0.6492238175593161\n",
            "4614\n",
            "Average cost so far 0.653570825663985\n",
            "4615\n",
            "Average cost so far 0.6160680600495682\n",
            "4616\n",
            "Average cost so far 0.6455571040530291\n",
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            "Average cost so far 0.6139373544561255\n",
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            "Average cost so far 0.6180021234215608\n",
            "4619\n",
            "Average cost so far 0.6357895808564451\n",
            "4620\n",
            "Average cost so far 0.6307718217579016\n",
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            "Average cost so far 0.6006062577954018\n",
            "4622\n",
            "Average cost so far 0.6445798466069611\n",
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            "Average cost so far 0.6492932871916479\n",
            "4624\n",
            "Average cost so far 0.6431102013106461\n",
            "4625\n",
            "Average cost so far 0.6495816476860969\n",
            "4626\n",
            "Average cost so far 0.6335460170343854\n",
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            "Average cost so far 0.6532094027185186\n",
            "4628\n",
            "Average cost so far 0.6078793829296021\n",
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            "Average cost so far 0.583987144841357\n",
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            "Average cost so far 0.6541790474235996\n",
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            "Average cost so far 0.6553361839743826\n",
            "4632\n",
            "Average cost so far 0.624042158118835\n",
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            "Average cost so far 0.6496364411489053\n",
            "4634\n",
            "Average cost so far 0.6747985001368025\n",
            "4635\n",
            "Average cost so far 0.6523871712236905\n",
            "4636\n",
            "Average cost so far 0.6915529684741293\n",
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            "Average cost so far 0.6339560735370242\n",
            "4638\n",
            "Average cost so far 0.6825143267192331\n",
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            "Average cost so far 0.675060195173363\n",
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            "Average cost so far 0.6541436062389194\n",
            "4641\n",
            "Average cost so far 0.6893546286772192\n",
            "4642\n",
            "Average cost so far 0.6726784088642479\n",
            "4643\n",
            "Average cost so far 0.6026071169470552\n",
            "4644\n",
            "Average cost so far 0.6864078157089669\n",
            "4645\n",
            "Average cost so far 0.6324213211794253\n",
            "4646\n",
            "Average cost so far 0.6233838926195034\n",
            "4647\n",
            "Average cost so far 0.6554631445829378\n",
            "4648\n",
            "Average cost so far 0.6248612431396908\n",
            "4649\n",
            "Average cost so far 0.6654388871873005\n",
            "4650\n",
            "Average cost so far 0.6574357728093061\n",
            "4651\n",
            "Average cost so far 0.6572479641056108\n",
            "4652\n",
            "Average cost so far 0.6860675286185185\n",
            "4653\n",
            "Average cost so far 0.6390952806169452\n",
            "4654\n",
            "Average cost so far 0.6816048338363465\n",
            "4655\n",
            "Average cost so far 0.6448467921535612\n",
            "4656\n",
            "Average cost so far 0.6668844999707262\n",
            "4657\n",
            "Average cost so far 0.6669318156646271\n",
            "4658\n",
            "Average cost so far 0.6421627765009486\n",
            "4659\n",
            "Average cost so far 0.6561567294071385\n",
            "4660\n",
            "Average cost so far 0.6487660269725812\n",
            "4661\n",
            "Average cost so far 0.6721140396324288\n",
            "4662\n",
            "Average cost so far 0.6718014112912207\n",
            "4663\n",
            "Average cost so far 0.6706480954108907\n",
            "4664\n",
            "Average cost so far 0.6431162482634634\n",
            "4665\n",
            "Average cost so far 0.650770682063862\n",
            "4666\n",
            "Average cost so far 0.6875836207143289\n",
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            "Average cost so far 0.7098616926108104\n",
            "4668\n",
            "Average cost so far 0.6524084337097982\n",
            "4669\n",
            "Average cost so far 0.6493945762745008\n",
            "4670\n",
            "Average cost so far 0.6345695936651612\n",
            "4671\n",
            "Average cost so far 0.6499956850258307\n",
            "4672\n",
            "Average cost so far 0.651944544021933\n",
            "4673\n",
            "Average cost so far 0.6661720495300927\n",
            "4674\n",
            "Average cost so far 0.625379656376992\n",
            "4675\n",
            "Average cost so far 0.6592124541931479\n",
            "4676\n",
            "Average cost so far 0.6437218430890306\n",
            "4677\n",
            "Average cost so far 0.6450638394892891\n",
            "4678\n",
            "Average cost so far 0.6444931072019929\n",
            "4679\n",
            "Average cost so far 0.6665606417704494\n",
            "4680\n",
            "Average cost so far 0.6001283236963975\n",
            "4681\n",
            "Average cost so far 0.64821963890301\n",
            "4682\n",
            "Average cost so far 0.6353191623948792\n",
            "4683\n",
            "Average cost so far 0.6756603667765829\n",
            "4684\n",
            "Average cost so far 0.6489266763814201\n",
            "4685\n",
            "Average cost so far 0.6703009094495688\n",
            "4686\n",
            "Average cost so far 0.6522754826015258\n",
            "4687\n",
            "Average cost so far 0.6677336377847339\n",
            "4688\n",
            "Average cost so far 0.6197292496776912\n",
            "4689\n",
            "Average cost so far 0.6734596579555127\n",
            "4690\n",
            "Average cost so far 0.658994595714777\n",
            "4691\n",
            "Average cost so far 0.6268145886575597\n",
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            "Average cost so far 0.6733041931753562\n",
            "4693\n",
            "Average cost so far 0.6125308561954748\n",
            "4694\n",
            "Average cost so far 0.6335917845393217\n",
            "4695\n",
            "Average cost so far 0.6572046527508085\n",
            "4696\n",
            "Average cost so far 0.6391222848631493\n",
            "4697\n",
            "Average cost so far 0.7033527212061117\n",
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            "Average cost so far 0.6708339635053031\n",
            "4699\n",
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            "4702\n",
            "Average cost so far 0.6489643332082881\n",
            "4703\n",
            "Average cost so far 0.7095695084471052\n",
            "4704\n",
            "Average cost so far 0.7149586269971172\n",
            "4705\n",
            "Average cost so far 0.6541753419436008\n",
            "4706\n",
            "Average cost so far 0.6619392909012407\n",
            "4707\n",
            "Average cost so far 0.6069275600257581\n",
            "4708\n",
            "Average cost so far 0.6355193933855042\n",
            "4709\n",
            "Average cost so far 0.6811834280246803\n",
            "4710\n",
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            "Average cost so far 0.6584484240793101\n",
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            "Average cost so far 0.6250756070302452\n",
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            "Average cost so far 0.6524649963846813\n",
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            "Average cost so far 0.6253379357818646\n",
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            "4732\n",
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            "4734\n",
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            "4736\n",
            "Average cost so far 0.6749346422070704\n",
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            "Average cost so far 0.6240233403841168\n",
            "4738\n",
            "Average cost so far 0.6850735403921704\n",
            "4739\n",
            "Average cost so far 0.6789603735755345\n",
            "4740\n",
            "Average cost so far 0.640442348088868\n",
            "4741\n",
            "Average cost so far 0.6606090394887864\n",
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            "Average cost so far 0.6461991321459105\n",
            "4743\n",
            "Average cost so far 0.6435216598001001\n",
            "4744\n",
            "Average cost so far 0.6229391218766078\n",
            "4745\n",
            "Average cost so far 0.6310858650648669\n",
            "4746\n",
            "Average cost so far 0.6452774529198\n",
            "4747\n",
            "Average cost so far 0.6538339638330217\n",
            "4748\n",
            "Average cost so far 0.6134107221611548\n",
            "4749\n",
            "predicting ...\n",
            "[6. 1. 9. ... 9. 1. 7.]\n",
            "predicting ...\n",
            "[3. 8. 8. ... 5. 6. 7.]\n",
            "Epoch 19: training accuracy 0.4825, testing accuracy 0.4645\n",
            "Average cost so far 0.657143307224529\n",
            "4750\n",
            "Average cost so far 0.6184570900210496\n",
            "4751\n",
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            "4755\n",
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            "4771\n",
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            "Average cost so far 0.6331183215087755\n",
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            "Average cost so far 0.7179382713094725\n",
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            "4776\n",
            "Average cost so far 0.6497203164769381\n",
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            "4778\n",
            "Average cost so far 0.6128981852195406\n",
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            "Average cost so far 0.6588625593942931\n",
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            "Average cost so far 0.6721444782299839\n",
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            "Average cost so far 0.607863493857192\n",
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            "Average cost so far 0.6257608119495994\n",
            "4855\n",
            "Average cost so far 0.6641788651538508\n",
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            "4857\n",
            "Average cost so far 0.630164160053867\n",
            "4858\n",
            "Average cost so far 0.6912079525773559\n",
            "4859\n",
            "Average cost so far 0.6178683360671026\n",
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            "Average cost so far 0.6491875794882356\n",
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            "Average cost so far 0.6745825075518376\n",
            "4885\n",
            "Average cost so far 0.651617546444971\n",
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            "Average cost so far 0.6904212876034133\n",
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            "Average cost so far 0.6341558519861682\n",
            "4888\n",
            "Average cost so far 0.6827104816614147\n",
            "4889\n",
            "Average cost so far 0.6739501026431851\n",
            "4890\n",
            "Average cost so far 0.6540672384744717\n",
            "4891\n",
            "Average cost so far 0.6885748992486311\n",
            "4892\n",
            "Average cost so far 0.6720435845563921\n",
            "4893\n",
            "Average cost so far 0.6028572078495045\n",
            "4894\n",
            "Average cost so far 0.6874621228908145\n",
            "4895\n",
            "Average cost so far 0.6315863320284949\n",
            "4896\n",
            "Average cost so far 0.6227162575796098\n",
            "4897\n",
            "Average cost so far 0.6555637005235617\n",
            "4898\n",
            "Average cost so far 0.625215196027795\n",
            "4899\n",
            "Average cost so far 0.6660537089398243\n",
            "4900\n",
            "Average cost so far 0.6557305790131713\n",
            "4901\n",
            "Average cost so far 0.6560363707583768\n",
            "4902\n",
            "Average cost so far 0.685232615842171\n",
            "4903\n",
            "Average cost so far 0.6394616604688409\n",
            "4904\n",
            "Average cost so far 0.6795658732509493\n",
            "4905\n",
            "Average cost so far 0.6437159077152238\n",
            "4906\n",
            "Average cost so far 0.6650536852713342\n",
            "4907\n",
            "Average cost so far 0.6659629570381099\n",
            "4908\n",
            "Average cost so far 0.6422538318522918\n",
            "4909\n",
            "Average cost so far 0.6559752679372984\n",
            "4910\n",
            "Average cost so far 0.6474305551954802\n",
            "4911\n",
            "Average cost so far 0.671975061638065\n",
            "4912\n",
            "Average cost so far 0.6702620274554683\n",
            "4913\n",
            "Average cost so far 0.6688825619193876\n",
            "4914\n",
            "Average cost so far 0.6423109287586682\n",
            "4915\n",
            "Average cost so far 0.6487436267884655\n",
            "4916\n",
            "Average cost so far 0.6868590119788758\n",
            "4917\n",
            "Average cost so far 0.7092338509822385\n",
            "4918\n",
            "Average cost so far 0.6530262754915904\n",
            "4919\n",
            "Average cost so far 0.64762933956873\n",
            "4920\n",
            "Average cost so far 0.6355046887477536\n",
            "4921\n",
            "Average cost so far 0.6495327991485794\n",
            "4922\n",
            "Average cost so far 0.6496996849332228\n",
            "4923\n",
            "Average cost so far 0.6649768923660436\n",
            "4924\n",
            "Average cost so far 0.6243092287018073\n",
            "4925\n",
            "Average cost so far 0.6590702608617829\n",
            "4926\n",
            "Average cost so far 0.6426475945131992\n",
            "4927\n",
            "Average cost so far 0.6461506085989377\n",
            "4928\n",
            "Average cost so far 0.64343940477091\n",
            "4929\n",
            "Average cost so far 0.665664648106996\n",
            "4930\n",
            "Average cost so far 0.5997830788837596\n",
            "4931\n",
            "Average cost so far 0.6479941254992214\n",
            "4932\n",
            "Average cost so far 0.6354692454555534\n",
            "4933\n",
            "Average cost so far 0.6750405315164463\n",
            "4934\n",
            "Average cost so far 0.6482351734839078\n",
            "4935\n",
            "Average cost so far 0.6697250378018951\n",
            "4936\n",
            "Average cost so far 0.6528697872434772\n",
            "4937\n",
            "Average cost so far 0.666671485684035\n",
            "4938\n",
            "Average cost so far 0.6197915892163409\n",
            "4939\n",
            "Average cost so far 0.6727249026853922\n",
            "4940\n",
            "Average cost so far 0.6582891096381961\n",
            "4941\n",
            "Average cost so far 0.6259580100125027\n",
            "4942\n",
            "Average cost so far 0.6725393803446298\n",
            "4943\n",
            "Average cost so far 0.6120410297634052\n",
            "4944\n",
            "Average cost so far 0.6335914305979262\n",
            "4945\n",
            "Average cost so far 0.657053649355249\n",
            "4946\n",
            "Average cost so far 0.6389326299061164\n",
            "4947\n",
            "Average cost so far 0.7027602382763355\n",
            "4948\n",
            "Average cost so far 0.6697285006928978\n",
            "4949\n",
            "Average cost so far 0.6481428164974963\n",
            "4950\n",
            "Average cost so far 0.6569278866717991\n",
            "4951\n",
            "Average cost so far 0.6313060458650362\n",
            "4952\n",
            "Average cost so far 0.6487448547200577\n",
            "4953\n",
            "Average cost so far 0.7091544997298932\n",
            "4954\n",
            "Average cost so far 0.7133467595824095\n",
            "4955\n",
            "Average cost so far 0.6536285705990436\n",
            "4956\n",
            "Average cost so far 0.6613571964751971\n",
            "4957\n",
            "Average cost so far 0.6060348793696372\n",
            "4958\n",
            "Average cost so far 0.634634498786782\n",
            "4959\n",
            "Average cost so far 0.6809161430123816\n",
            "4960\n",
            "Average cost so far 0.6053668493030048\n",
            "4961\n",
            "Average cost so far 0.6541618949606448\n",
            "4962\n",
            "Average cost so far 0.6794531916114246\n",
            "4963\n",
            "Average cost so far 0.6758093878167495\n",
            "4964\n",
            "Average cost so far 0.6437156019345416\n",
            "4965\n",
            "Average cost so far 0.6564865357452321\n",
            "4966\n",
            "Average cost so far 0.6557991397940824\n",
            "4967\n",
            "Average cost so far 0.6466496602742084\n",
            "4968\n",
            "Average cost so far 0.6579118598314095\n",
            "4969\n",
            "Average cost so far 0.6329499540502728\n",
            "4970\n",
            "Average cost so far 0.6755103778848239\n",
            "4971\n",
            "Average cost so far 0.668797085670881\n",
            "4972\n",
            "Average cost so far 0.623425737332542\n",
            "4973\n",
            "Average cost so far 0.6652385976206918\n",
            "4974\n",
            "Average cost so far 0.6279227465644097\n",
            "4975\n",
            "Average cost so far 0.6544462983312954\n",
            "4976\n",
            "Average cost so far 0.5935822547243136\n",
            "4977\n",
            "Average cost so far 0.6632183952377342\n",
            "4978\n",
            "Average cost so far 0.6517939266113782\n",
            "4979\n",
            "Average cost so far 0.6263967781733307\n",
            "4980\n",
            "Average cost so far 0.6852875298705863\n",
            "4981\n",
            "Average cost so far 0.7018262203472971\n",
            "4982\n",
            "Average cost so far 0.6185705034066893\n",
            "4983\n",
            "Average cost so far 0.6417014255968099\n",
            "4984\n",
            "Average cost so far 0.6329486375022377\n",
            "4985\n",
            "Average cost so far 0.6395547575130004\n",
            "4986\n",
            "Average cost so far 0.6748483771023228\n",
            "4987\n",
            "Average cost so far 0.6233467053711736\n",
            "4988\n",
            "Average cost so far 0.6863739043783871\n",
            "4989\n",
            "Average cost so far 0.6762085738527251\n",
            "4990\n",
            "Average cost so far 0.6397444911422852\n",
            "4991\n",
            "Average cost so far 0.6590195021949363\n",
            "4992\n",
            "Average cost so far 0.6452797734844583\n",
            "4993\n",
            "Average cost so far 0.6420083191268653\n",
            "4994\n",
            "Average cost so far 0.6210565633145552\n",
            "4995\n",
            "Average cost so far 0.6298334233956662\n",
            "4996\n",
            "Average cost so far 0.6447904441915159\n",
            "4997\n",
            "Average cost so far 0.652642015147737\n",
            "4998\n",
            "Average cost so far 0.6123701087099307\n",
            "4999\n",
            "predicting ...\n",
            "[6. 1. 9. ... 9. 1. 7.]\n",
            "predicting ...\n",
            "[3. 9. 8. ... 5. 6. 7.]\n",
            "Epoch 20: training accuracy 0.4834, testing accuracy 0.4667\n",
            "predicting ...\n",
            "[3. 9. 8. ... 5. 6. 7.]\n",
            "0.4667\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "f562916f-9920-405d-ee90-b497f6bbba7e",
        "id": "u39ppGYX9srs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 219
        }
      },
      "source": [
        "yHead = theMLP.getYhead(testX)\n",
        "yResult = theMLP.getBiggestY(yHead)\n",
        "totalCorrect = np.zeros(10)\n",
        "predictCorrect = np.zeros(10)\n",
        "for i in range(len(testY)):\n",
        "  totalCorrect[testY[i]] += 1\n",
        "  if yResult[i] == testY[i]:\n",
        "    predictCorrect[int(yResult[i])]+=1\n",
        "for i in range(10):\n",
        "  print(classes[i] + \" accuracy: \" + str(predictCorrect[i]/totalCorrect[i]))"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[3. 9. 8. ... 5. 6. 7.]\n",
            "plane accuracy: 0.534\n",
            "car accuracy: 0.59\n",
            "bird accuracy: 0.274\n",
            "cat accuracy: 0.248\n",
            "deer accuracy: 0.31\n",
            "dog accuracy: 0.385\n",
            "frog accuracy: 0.609\n",
            "horse accuracy: 0.563\n",
            "ship accuracy: 0.637\n",
            "truck accuracy: 0.517\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Og92oxuv4yC",
        "colab_type": "code",
        "outputId": "42c2ef7a-9074-40cc-b0d6-2439ed0109ef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "theMLP.xGraph = np.array(theMLP.xGraph)\n",
        "theMLP.trainYGraph = np.array(theMLP.trainYGraph)\n",
        "theMLP.testYGraph = np.array(theMLP.testYGraph)\n",
        "\n",
        "import matplotlib.pyplot as plt \n",
        "  \n",
        "# line 1 points \n",
        "x1 = theMLP.xGraph\n",
        "y1 = theMLP.trainYGraph\n",
        "# plotting the line 1 points  \n",
        "plt.plot(x1, y1, label = \"training Accuracy\") \n",
        "  \n",
        "# line 2 points \n",
        "x2 = theMLP.xGraph\n",
        "y2 = theMLP.testYGraph \n",
        "# plotting the line 2 points  \n",
        "plt.plot(x2, y2, label = \"testing accuracy\") \n",
        "  \n",
        "# naming the x axis \n",
        "plt.xlabel('# of training epochs') \n",
        "# naming the y axis \n",
        "plt.ylabel('accuracy') \n",
        "# giving a title to my graph \n",
        "plt.title('test and train performance of the above two models as a function of training epochs') \n",
        "  \n",
        "# show a legend on the plot \n",
        "plt.legend() \n",
        "  \n",
        "# function to show the plot \n",
        "plt.show() \n"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}