MNIST_classifier_pytorch.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "MNIST_classifier_pytorch.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/shivamswarnkar/Image-Classifier/blob/master/MNIST_classifier_pytorch.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6MUn9W0EKfYH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch\n",
        "from torch import nn\n",
        "import torch.nn.functional as F\n",
        "from torch import optim\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y3b-ij6zw0Hd",
        "colab_type": "text"
      },
      "source": [
        "# Downloading Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FvC8d3zwey8T",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# download data and load it\n",
        "from torchvision import datasets, transforms\n",
        "\n",
        "# a transform to normalize the data\n",
        "transform = transforms.Compose([transforms.ToTensor(), \n",
        "                                transforms.Normalize((0.5,), (0.5, )),\n",
        "                               ])\n",
        "\n",
        "# Download and load the training data\n",
        "trainset = datasets.MNIST('~/.pytorch/MNIST_data/', download=True, train=True, transform=transform)\n",
        "trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VCTqLjVbw4CR",
        "colab_type": "text"
      },
      "source": [
        "# Util functions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jvjGl1odq617",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# util function to display image next to predicted probabilities\n",
        "def view_classify(img, ps, actual_digit=None):\n",
        "  fig, (ax1, ax2) = plt.subplots(1, 2)\n",
        "  fig.suptitle('Digit vs Predicted Probabilities')\n",
        "  ax1.imshow(img.view(1,28,28).squeeze(), cmap=\"Greys_r\")\n",
        "  ax1.title.set_text(f'Actual Digit={actual_digit}')\n",
        "  ax2.barh(range(10), ps.numpy().squeeze())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-RB9RnNynqq1",
        "colab_type": "text"
      },
      "source": [
        "# Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EBt7mIJ2ntqw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "outputId": "dbf8bb0e-412b-4d4b-b528-d186a7e1908f"
      },
      "source": [
        "# define NN\n",
        "model = nn.Sequential(nn.Linear(784, 256),\n",
        "                     nn.ReLU(),\n",
        "                     nn.Linear(256, 128),\n",
        "                     nn.ReLU(),\n",
        "                     nn.Linear(128,64),\n",
        "                     nn.ReLU(),\n",
        "                     nn.Linear(64,10),\n",
        "                     nn.LogSoftmax(dim=1))\n",
        "\n",
        "# define criterion (for loss)\n",
        "criterion = nn.NLLLoss()\n",
        "\n",
        "# define optimizer (using Stoch Gradient desc. for this)\n",
        "optimizer = optim.SGD(model.parameters(), lr=0.01)\n",
        "\n",
        "# hypter params\n",
        "epochs = 25\n",
        "\n",
        "\n",
        "# train\n",
        "for epoch in range(epochs):\n",
        "  running_loss = 0\n",
        "  \n",
        "  # go through each batch\n",
        "  for images, labels in trainloader:\n",
        "    # flatten the image\n",
        "    images = images.view(images.shape[0], -1)\n",
        "    \n",
        "    # training pass\n",
        "    optimizer.zero_grad()\n",
        "    loss = criterion(model(images), labels)\n",
        "    \n",
        "    \n",
        "    # backprop\n",
        "    loss.backward()\n",
        "    \n",
        "    # update weight\n",
        "    optimizer.step()\n",
        "    \n",
        "    # add total loss\n",
        "    running_loss += loss.item()\n",
        "  \n",
        "  else:\n",
        "    print(f'Training loss: {running_loss/len(trainloader)}')"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training loss: 1.4035405133769456\n",
            "Training loss: 0.4334237770612306\n",
            "Training loss: 0.34427017808310006\n",
            "Training loss: 0.30041993357765395\n",
            "Training loss: 0.2667970632645748\n",
            "Training loss: 0.23591750099667227\n",
            "Training loss: 0.20950685413693315\n",
            "Training loss: 0.18771494509600628\n",
            "Training loss: 0.16865022907823896\n",
            "Training loss: 0.15299064933316414\n",
            "Training loss: 0.14001078420340507\n",
            "Training loss: 0.1289814652513657\n",
            "Training loss: 0.11881382047716997\n",
            "Training loss: 0.11044942059798409\n",
            "Training loss: 0.10182370058000723\n",
            "Training loss: 0.09491732804331063\n",
            "Training loss: 0.08824124893765332\n",
            "Training loss: 0.08305915703238455\n",
            "Training loss: 0.0773864933160513\n",
            "Training loss: 0.072711419866188\n",
            "Training loss: 0.06828166207652102\n",
            "Training loss: 0.0640430528122479\n",
            "Training loss: 0.060499298213514435\n",
            "Training loss: 0.05706844845218763\n",
            "Training loss: 0.05357971683398747\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mZ44Tx3tqSc1",
        "colab_type": "text"
      },
      "source": [
        "# Testing How it did"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BWoH0yRp5IyJ",
        "colab_type": "text"
      },
      "source": [
        "### Plot Digit vs Predicted Probabilities"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QAU9kBLB2HFZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "8f6e30c1-6344-42fc-c73e-95937e4b6765"
      },
      "source": [
        "to_display = 5\n",
        "with torch.no_grad():\n",
        "  for images, labels in trainloader:\n",
        "    # flatten image\n",
        "    ps = torch.exp(model(images.view(images.shape[0], -1)))\n",
        "    _, predicted_labels = ps.max(dim=1)\n",
        "    for i in range(len(predicted_labels)):\n",
        "      to_display -=1\n",
        "      view_classify(images[i].view(1,28,28), ps[i], actual_digit=labels[i])\n",
        "      if to_display < 0:\n",
        "        break\n",
        "    break"
      ],
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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O6GZmmXBCNzPLhBO6mVkmnNDNzDLhhG5mlgkndDOzTDihm5llwgndzCwTTuhmZpnwV/8z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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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mBaXs2clm1hmSXgWeaXYcZYYAK5odRBnHVJ3ymEZHxNCOrsTDBZvVxzMRMa7Z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\nTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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uAMo4nu71t3igATH5oqiZWUH4CN3MrCCc0M26IelASfMlLZB0ds78oZKuS/Pv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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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TI6I5Ipr79BtU7zTNSlT1xaKIuJdkEiizPDkYeCkiXgaQdAfJHaxuavNVZg3muVzMSv0J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\ngg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fb4upXvG4+8XMrECc1M3MCsRJ3aw+ZjY6gDKOp2O9Laa6xOMLpWZmBeKWuplZ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\nyP8C+OX/N/PEWQkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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mbsI0dcJkgHHjxhVyfMHWPB4oNavUF7oJraBafqZu1tsVrZvQ+hYXdbMcReom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\nzcwK5P8DbXlAhQgbKCgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ScMuKLuz5Onf",
        "colab_type": "text"
      },
      "source": [
        "### Calculate Accuracy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ms_YC0v5v-_d",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "19fc48a6-eca2-4dd6-d4dd-e80866b8a695"
      },
      "source": [
        "wrong_classified = 0\n",
        "with torch.no_grad():\n",
        "  for images, labels in trainloader:\n",
        "    # flatten image\n",
        "    _, predicted_labels = torch.exp(model(images.view(images.shape[0], -1))).max(dim=1)\n",
        "    wrong_classified += len((labels-predicted_labels).nonzero())"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model Accuracy:0.9875066631130064\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3POf03oj0OJH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "6c102627-5d73-45d1-e77d-4efd2ff2fcbb"
      },
      "source": [
        "print(f'Model Accuracy:{round(100-(wrong_classified/(64*len(trainloader)))*100, 3)}%')\n",
        "print(f'Total Wrong classified: {wrong_classified}')"
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model Accuracy:98.751%\n",
            "Total Wrong classified: 750\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9SZmDiCr5SMx",
        "colab_type": "text"
      },
      "source": [
        "### Plot Wrongly Classified Examples"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wTVAKkQe0ljW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "0d93a658-7b21-44a4-8fb8-2a5240880495"
      },
      "source": [
        "to_display = 5\n",
        "with torch.no_grad():\n",
        "  for images, labels in trainloader:\n",
        "    # flatten image\n",
        "    ps = torch.exp(model(images.view(images.shape[0], -1)))\n",
        "    _, predicted_labels = ps.max(dim=1)\n",
        "    for i in range(len(predicted_labels)):\n",
        "      if(predicted_labels[i] != labels[i] and to_display > 0):\n",
        "        to_display -=1\n",
        "        view_classify(images[i].view(1,28,28), ps[i], actual_digit=labels[i])\n",
        "      if to_display < 0:\n",
        "        break"
      ],
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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P/pvVehbYS9Lm6Wv29id7F2rWpznQzapExP3AbOBBsqmUNyKd2DfryzyXSwGGD6/9StCj\njjqqpm3KlPzZFF577bWattNPPz237+23397F6gwgIqaRzVhp1m/4CN3MrCQc6GZmJeFANzMrCQe6\nmVlJ+KRoATbbbLOatm233bam7Yorrsh9/D777FPTdsstNR9kBGDUqFE1bS+++GInFZpZf+QjdDOz\nknCgm5mVhAPdzKwkHOhmZiXhQDczKwlf5VKApUtrvgeEadPq/5T5sGHDatpWrVqV2zebW8rMBgIf\noZuZlYQD3cysJBzoZmYl4UA3MysJnxTth3bccceatogooBIz60t6FOiSFgGvAOuAtRHR0oiizMys\n6xpxhP6JiFjZgPWYmVkPeAzdzKwkehroAdwiaa6kyXkdJE2W1CqptYfbMjOzDvR0yGWfiFgqaRvg\nVklPRMTdlR0iYgbpG9Ml+cydmVkv6VGgR8TS9O9ySb8D9gTu7vhR1lPTp08vugQz64O6PeQi6d2S\nhrbdBj4DPNaowszMrGt6coQ+CvhdmvxpY+DqiPjPhlRlZmZd1u1Aj4ingQ82sBYzM+sBX7ZoZlYS\n/uh/HzZ06NDc9o985CM1bcuWLcvtu2bNmobWZGZ9lwPdrAEeXbqa8VNueuf+oumHFFiNDVQecjEz\nKwkHuplZSTjQzcxKwoFulkPSlpJmS3pC0nxJexddk1lnfFK0D9tss81y24cMGVLTNmvWrNy+b7zx\nRkNrGkAuAv4zIj4naRNg86ILMuuMA92siqRhwL7ACQAR8RbwVpE1mdXDQy5mtXYEVgCXS3pI0i/S\nfEVmfZoD3azWxsCHgEsiYg/gVWBKdafKuf7Xvba62TWa1XCgm9VaAiyJiPvT/dlkAb+BiJgRES0R\n0TJo82FNLdAsj8fQ+7Djjz++7r5TptQcQFo3RcTzkhZL2iUingT2Bx4vui6zzjjQzfJ9BZiVrnB5\nGjix4HrMOuVAN8sREQ8DLUXXYdYVHkM3MysJB7qZWUk40M3MSqLTMXRJM4FDgeURsVtqGw5cB4wH\nFgFHRsTLvVdm+Q0aNKimrb2rXNatW1fTtnq1r4Mu0gfGDKPVc6Bbweo5Qr8COLCqbQpwW0TsDNxG\nzocuzMysuToN9Ii4G3ipqnkicGW6fSVweIPrMjOzLuruZYujIqLtSyyfB0a111HSZGByN7djZmZ1\n6vF16BERkqKD5TOAGQAd9TMzs57pbqC/IGl0RCyTNBpY3siiBqJdd921pm233XbL7XvZZZf1djlm\n1g9197LFOcCkdHsScGNjyjEzs+7qNNAlXQPcC+wiaYmkk4HpwKclPQV8Kt03M7MCdTrkEhFHt7No\n/wbXYmZmPeBPipqZlYQD3cysJDx9bh9xwAEH1LStX78+t++1117b2+WYWT/kI3Qzs5JwoJuZlYQD\n3cysJBzoZmYl4ZOiBRg6dGhN20knnVTT9uabb+Y+/o477mh4TWbW//kI3cysJBzoZmYl4UA3MysJ\nB7qZWUn4pGgB9tprr5q2vPnQZ8+e3YxyzKwkfIRuZlYSDnQzs5JwoJuZlYQD3cysJBzoZmYl0elV\nLpJmAocCyyNit9R2HnAqsCJ1mxoRN/dWkWVz+OGH17Tlfcz/nHPOaUY5ZlYS9RyhXwEcmNP+k4jY\nPf04zM3MCtZpoEfE3cBLTajFrE+RNEjSQ5L+UHQtZvXoyRj6GZIekTRT0lbtdZI0WVKrpNYebMus\nCGcB84suwqxe3Q30S4CdgN2BZcCP2+sYETMioiUiWrq5LbOmk7Q9cAjwi6JrMatXtz76HxEvtN2W\ndBngt6RdcMQRR9S0rVmzpqZt4cKFzSjH8v0U+BZQO3l9ImkyMBlg3LhxTSrLrH3dOkKXNLri7hHA\nY40px6x4ktqu6prbUb/Kd58jR45sUnVm7avnssVrgP2AEZKWANOA/STtDgSwCPhSL9Zo1mwfBQ6T\ndDCwKbCFpKsi4tiC6zLrUKeBHhFH5zT/shdqMesTIuIc4BwASfsB33CYW3/gT4qamZWE50M360BE\n3AncWXAZZnVxoPeis88+O7d99OjRNW2XXHJJb5djZiXnIRczs5JwoJuZlYQD3cysJBzoZmYloYho\n3sak5m2sD1i+fHlu+4gRI2ra1q5dW9P21FNP5T7+/e9/f88Ka8dOO+1U0zZ9+vTcvp///Od7pYae\niggVsd2WlpZobfX8c9Y7JM2tZz4sH6GbmZWEA93MrCQc6GZmJeFANzMrCQe6mVlJ+KP/DXLiiSfW\ntOVdzQL5V7S8+uqrNW3ve9/7ch+/cuXKmrYLL7wwt2/ePN3bbLNNbt+JEyfWtC1btiy3r5n1PT5C\nNzMrCQe6mVlJONDNzErCgW5mVhL1fKfoWOBXwCiy7xCdEREXSRoOXAeMJ/te0SMj4uXeK7Vv22KL\nLeruu2TJkpq2vI/zn3nmmbmPP/fcc2vazj///Ny+b7/9dk1b3klZgJkzZ9a0TZs2LbevmfU99Ryh\nrwW+HhETgL2A0yVNAKYAt0XEzsBt6b6ZmRWk00CPiGUR8WC6/QowHxgDTASuTN2uBA7vrSLNzKxz\nXboOXdJ4YA/gfmBURLRdpPw82ZBM3mMmA5O7X6KZmdWj7pOikoYAvwG+GhF/rVwW2Ry8uVPjRsSM\niGipZ+pHMzPrvroCXdK7yMJ8VkT8NjW/IGl0Wj4ayJ/828zMmqKeq1wE/BKYHxGVny+fA0wCpqd/\nb+yVCvuJBQsW1LStWbMmt+/FF19c0/b666/XtF1wwQW5j89rP+aYY3L73nfffTVtCxcuzO1rZv1b\nPWPoHwWOAx6V9HBqm0oW5NdLOhl4Bjiyd0o0M7N6dBroEXEP0N7Xeu3f2HLMzKy7/ElRM7OS8PS5\nZg3w6NLVjJ9yU8PXu2j6IQ1fp5WXA71Bbrqp9j9zV6YD6KlZs2Y1bVtm1jd5yMXMrCQc6GZmJeFA\nNzMrCQe6WRVJYyXdIelxSfMknVV0TWb18ElRs1ptU0Y/KGkoMFfSrRHxeNGFmXXER+hmVTqYMtqs\nT3Ogm3Wgasposz7NgW7Wjo6mjE7LJ0tqldS67rXVzS/QrIoD3SxHO1NGb6Byrv9Bmw9rboFmORzo\nZlU6mDLarE9zoJvVapsy+pOSHk4/BxddlFlnfNmiWZVOpow267N8hG5mVhIOdDOzknCgm5mVRD1f\nEj0W+BUwCghgRkRcJOk84FRgReo6NSJu7q1CzfqyD4wZRqu/jMIKVs9J0dx5LdKyn0TEj3qvPDMz\nq1c9XxK9DFiWbr8iyfNamJn1QV0aQ8+Z1+IMSY9ImilpqwbXZmZmXaCIqK9jNq/FXcAPIuK3kkYB\nK8nG1b8PjI6Ik3IeNxmYnO7+fUOqNmtHRBRy/fjg0TvH6Ek/LWLTVhIdfSG4pLkR0dLZOuo6Qs+b\n1yIiXoiIdRGxHrgM2DPvsZXzXdSzLTMz655OA729eS0kja7odgTwWOPLMzOzetVzlUvbvBaPSno4\ntU0Fjpa0O9mQyyLgS71SoZmZ1aWeq1zam9fC15ybmfUh/qSomVlJONDNzErCgW5mVhIOdDOzknCg\nm5mVhAPdzKwkHOhmZiXhQDczK4lmf0n0SuCZdHtEul823q/i7FB0AWZFamqgR8TIttuSWss4YZf3\ny8yK4iEXM7OSaPaQi1kp+TtFrS8o8gh9RoHb7k3eLzMrRGGBHhGlDAjvl5kVxWPoZmYl4UA3MyuJ\npge6pAMlPSlpgaQpzd5+I0maKWm5pMcq2oZLulXSU+nfrYqssTskjZV0h6THJc2TdFZq7/f7ZlZm\nTQ10SYOAi4GDgAlkX2M3oZk1NNgVwIFVbVOA2yJiZ+C2dL+/WQt8PSImAHsBp6ffUxn2zay0mn2E\nviewICKejoi3gGuBiU2uoWEi4m7gparmicCV6faVwOFNLaoBImJZRDyYbr8CzAfGUIJ9MyuzZgf6\nGGBxxf0lqa1MRkXEsnT7eWBUkcX0lKTxwB7A/ZRs3zpSpqFBGzh8UrQXRUQAUXQd3SVpCPAb4KsR\n8dfKZf193zpSwqFBGyCaHehLgbEV97dPbWXygqTRAOnf5QXX0y2S3kUW5rMi4repuRT7VodSDQ3a\nwNHsQH8A2FnSjpI2Ab4AzGlyDb1tDjAp3Z4E3FhgLd0iScAvgfkRcWHFon6/b3UaCEODVkLNnm1x\nraQzgD8Bg4CZETGvmTU0kqRrgP2AEZKWANOA6cD1kk4mmyr4yOIq7LaPAscBj0p6OLVNpRz71jCS\nJgOTAcaNG1dwNWYFTM4VETcDNzd7u70hIo5uZ9H+TS2kwSLiHkDtLO7X+1anuoYG03QIMwBaWlpK\neT7B+hefFDWrNRCGBq2EPH2uWZWyDQ3awOFAN8tRpqFBGzg85GJmVhIOdDOzknCgm5mVhAPdzKwk\nHOhmZiXhQDczKwkHuplZSTjQzcxKwoFuZlYSDnQzs5JwoJuZlYQD3cysJBzoZmYloey7fs2sJyS9\nAjxZdB3ACGBl0UXgOvL0pJYdImJkZ508fa5ZYzwZES1FFyGp1XX0vTqgObV4yMXMrCQc6GZmJeFA\nN2uMGUUXkLiODfWVOqAJtfikqJlZSfgI3cysJBzoZnWSdKCkJyUtkDQlZ/lgSdel5fdLGl9gLV+T\n9LikRyTdJmmHIuqo6PdPkkJSr1zlUU8dko5Mz8k8SVcXUYekcZLukPRQ+t0c3NACIsI//vFPJz/A\nIGAh8HfAJsCfgQlVff4ZuDTd/gJwXYG1fALYPN3+cm/UUk8dqd9Q4G7gPqCloOdjZ+AhYKt0f5uC\n6pgBfDndngAsamQNPkI3q8+ewIKIeDoi3gKuBSZW9ZkIXJluzwb2l6QiaomIOyLitXT3PmD7IupI\nvg9cALzRCzXUW8epwMUR8TJARCwvqI4Atki3hwHPNbIAB7pZfcYAiyvuL0ltuX0iYi2wGti6oFoq\nnQz8sYg6JH0IGBsRN/XC9uuuA3gv8F5J/yXpPkkHFlTHecCxkpYANwNfaWQB/qSoWYlJOhZoAT5e\nwLY3Ai4ETmj2tnNsTDbssh/Zu5W7JX0gIlY1uY6jgSsi4seS9gZ+LWm3iFjfiJX7CN2sPkuBsRX3\nt09tuX0kbUz2lvrFgmpB0qeAbwOHRcSbBdQxFNgNuFPSImAvYE4vnBit5/lYAsyJiLcj4i/A/yML\n+GbXcTJwPUBE3AtsSjbHS0M40M3q8wCws6QdJW1CdtJzTlWfOcCkdPtzwO2Rzn41uxZJewA/Jwvz\n3hgv7rSOiFgdESMiYnxEjCcbyz8sIlqbWUdyA9nROZJGkA3BPF1AHc8C+6c6diUL9BWNKsCBblaH\nNCZ+BvAnYD5wfUTMk/Q9SYelbr8Etpa0APga0O5lfE2o5X8DQ4D/kPSwpOpgaVYdva7OOv4EvCjp\nceAO4JsR0dB3T3XW8XXgVEl/Bq4BTmjki74/KWpmVhI+QjczKwkHuplZSTjQzcxKwoFuZlYSDnQz\ns5JwoJuZlYQD3cysJBzoZmYl8f8BlcjYevuwZmcAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
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vAa81ue2TgUsiYi1ARKxsYtsBbJ3ubwMsbVDbNXFCN6vOWGBRyePFqSy3TkS8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\nYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9N6v0JHCgpIHpZ/YOJ9sLNevRHOhmZSLiHmA+cB/ZpZS3Ix3YN+vJfC0XsxwRcQ7ZFSvN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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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32wwRcVCVVbu0NJEGi4h7AFVZPaD3rUY1DROmqROmAXR0dBTy+oINPL5QalZp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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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CNzMrKCd4M7OCcoI3MysoJ3gzs4JS9t3JZtYdkl4GnmhC08OBlf2o3Wa23cw+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\ngjczK6j/Ac2yhji6FUvPAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "brlwO13U48n7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Ro9mANw5chD",
        "colab_type": "text"
      },
      "source": [
        "### Todo - Split Train/Test data, Try to increase accuracy. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WXOl20vg5iV4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}