convolution_neural_network.ipynb

{
  "nbformat": 4,
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  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyPUyLhmimPUFMsuVQgQeEAB",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/miller00315/ia_studies/blob/main/convolution_neural_network.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install np_utils"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RJz2DD0QEOhI",
        "outputId": "aaef6286-bf9b-48db-e196-351c5c23b37d"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting np_utils\n",
            "  Downloading np_utils-0.6.0.tar.gz (61 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.0/62.0 kB\u001b[0m \u001b[31m1.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: numpy>=1.0 in /usr/local/lib/python3.10/dist-packages (from np_utils) (1.25.2)\n",
            "Building wheels for collected packages: np_utils\n",
            "  Building wheel for np_utils (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for np_utils: filename=np_utils-0.6.0-py3-none-any.whl size=56441 sha256=d08bc0c8fa6e86c956cccb4aacdd64f4b575233308b0d9f828e8a746b54ba5db\n",
            "  Stored in directory: /root/.cache/pip/wheels/b6/c7/50/2307607f44366dd021209f660045f8d51cb976514d30be7cc7\n",
            "Successfully built np_utils\n",
            "Installing collected packages: np_utils\n",
            "Successfully installed np_utils-0.6.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "nSK-UC4IA1zm"
      },
      "outputs": [],
      "source": [
        "from keras.datasets import cifar10\n",
        "from keras.utils import to_categorical\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from sklearn.metrics import confusion_matrix, accuracy_score"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(x_train, y_train), (X_test, y_test) = cifar10.load_data()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6RA45364FTnF",
        "outputId": "265d044c-f8ec-4af5-dc5a-ea0d535b73ea"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
            "170498071/170498071 [==============================] - 2s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "image = x_train[1]\n",
        "\n",
        "plt.imshow(image, interpolation='bicubic')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "Xh6LQN_XFhE7",
        "outputId": "842c7656-8845-4dea-86d2-3982f5488df0"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "Wr321LI9FUZl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rKpV5QDOF9IA",
        "outputId": "7e08ea4b-dcc1-46c3-94c6-369f27219bf5"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[6],\n",
              "       [9],\n",
              "       [9],\n",
              "       ...,\n",
              "       [9],\n",
              "       [1],\n",
              "       [1]], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iqMA1k4gGhPq",
        "outputId": "69ae0593-3c4d-4bea-c1ef-197a9a524b44"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(50000, 32, 32, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gfasz6sTH2Gf",
        "outputId": "28728e5f-f485-4027-bbfa-a982390e4959"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[[ 59,  62,  63],\n",
              "         [ 43,  46,  45],\n",
              "         [ 50,  48,  43],\n",
              "         ...,\n",
              "         [158, 132, 108],\n",
              "         [152, 125, 102],\n",
              "         [148, 124, 103]],\n",
              "\n",
              "        [[ 16,  20,  20],\n",
              "         [  0,   0,   0],\n",
              "         [ 18,   8,   0],\n",
              "         ...,\n",
              "         [123,  88,  55],\n",
              "         [119,  83,  50],\n",
              "         [122,  87,  57]],\n",
              "\n",
              "        [[ 25,  24,  21],\n",
              "         [ 16,   7,   0],\n",
              "         [ 49,  27,   8],\n",
              "         ...,\n",
              "         [118,  84,  50],\n",
              "         [120,  84,  50],\n",
              "         [109,  73,  42]],\n",
              "\n",
              "        ...,\n",
              "\n",
              "        [[208, 170,  96],\n",
              "         [201, 153,  34],\n",
              "         [198, 161,  26],\n",
              "         ...,\n",
              "         [160, 133,  70],\n",
              "         [ 56,  31,   7],\n",
              "         [ 53,  34,  20]],\n",
              "\n",
              "        [[180, 139,  96],\n",
              "         [173, 123,  42],\n",
              "         [186, 144,  30],\n",
              "         ...,\n",
              "         [184, 148,  94],\n",
              "         [ 97,  62,  34],\n",
              "         [ 83,  53,  34]],\n",
              "\n",
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              "         [234, 236, 247],\n",
              "         ...,\n",
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              "         [222, 223, 233]],\n",
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              "        [[222, 221, 229],\n",
              "         [239, 239, 249],\n",
              "         [233, 234, 246],\n",
              "         ...,\n",
              "         [223, 223, 236],\n",
              "         [227, 228, 238],\n",
              "         [210, 211, 220]],\n",
              "\n",
              "        [[213, 206, 211],\n",
              "         [234, 232, 239],\n",
              "         [231, 233, 244],\n",
              "         ...,\n",
              "         [220, 220, 232],\n",
              "         [220, 219, 232],\n",
              "         [202, 203, 215]],\n",
              "\n",
              "        ...,\n",
              "\n",
              "        [[150, 143, 135],\n",
              "         [140, 135, 127],\n",
              "         [132, 127, 120],\n",
              "         ...,\n",
              "         [224, 222, 218],\n",
              "         [230, 228, 225],\n",
              "         [241, 241, 238]],\n",
              "\n",
              "        [[137, 132, 126],\n",
              "         [130, 127, 120],\n",
              "         [125, 121, 115],\n",
              "         ...,\n",
              "         [181, 180, 178],\n",
              "         [202, 201, 198],\n",
              "         [212, 211, 207]],\n",
              "\n",
              "        [[122, 119, 114],\n",
              "         [118, 116, 110],\n",
              "         [120, 116, 111],\n",
              "         ...,\n",
              "         [179, 177, 173],\n",
              "         [164, 164, 162],\n",
              "         [163, 163, 161]]]], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = x_train.astype('float32')/ 255.0\n",
        "X_test = X_test.astype('float32')/255.0"
      ],
      "metadata": {
        "id": "OjHvUpa6HU6n"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vuulmMMwH947",
        "outputId": "dd9d8392-54d5-4e5a-b34b-5f62b87245e6"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "        [[0.7607843 , 0.8235294 , 0.9372549 ],\n",
              "         [0.7490196 , 0.8117647 , 0.9254902 ],\n",
              "         [0.74509805, 0.80784315, 0.92156863],\n",
              "         ...,\n",
              "         [0.6784314 , 0.7529412 , 0.8627451 ],\n",
              "         [0.67058825, 0.7490196 , 0.85490197],\n",
              "         [0.654902  , 0.74509805, 0.84705883]],\n",
              "\n",
              "        [[0.8156863 , 0.85882354, 0.95686275],\n",
              "         [0.8039216 , 0.84705883, 0.9411765 ],\n",
              "         [0.8       , 0.84313726, 0.9372549 ],\n",
              "         ...,\n",
              "         [0.6862745 , 0.7490196 , 0.8509804 ],\n",
              "         [0.6745098 , 0.74509805, 0.84705883],\n",
              "         [0.6627451 , 0.7490196 , 0.84313726]],\n",
              "\n",
              "        ...,\n",
              "\n",
              "        [[0.8117647 , 0.78039217, 0.70980394],\n",
              "         [0.79607844, 0.7647059 , 0.6862745 ],\n",
              "         [0.79607844, 0.76862746, 0.6784314 ],\n",
              "         ...,\n",
              "         [0.5294118 , 0.5176471 , 0.49803922],\n",
              "         [0.63529414, 0.61960787, 0.5882353 ],\n",
              "         [0.65882355, 0.6392157 , 0.5921569 ]],\n",
              "\n",
              "        [[0.7764706 , 0.74509805, 0.6666667 ],\n",
              "         [0.7411765 , 0.70980394, 0.62352943],\n",
              "         [0.7058824 , 0.6745098 , 0.5764706 ],\n",
              "         ...,\n",
              "         [0.69803923, 0.67058825, 0.627451  ],\n",
              "         [0.6862745 , 0.6627451 , 0.6117647 ],\n",
              "         [0.6862745 , 0.6627451 , 0.6039216 ]],\n",
              "\n",
              "        [[0.7764706 , 0.7411765 , 0.6784314 ],\n",
              "         [0.7411765 , 0.70980394, 0.63529414],\n",
              "         [0.69803923, 0.6666667 , 0.58431375],\n",
              "         ...,\n",
              "         [0.7647059 , 0.72156864, 0.6627451 ],\n",
              "         [0.76862746, 0.7411765 , 0.67058825],\n",
              "         [0.7647059 , 0.74509805, 0.67058825]]],\n",
              "\n",
              "\n",
              "       [[[0.8980392 , 0.8980392 , 0.9372549 ],\n",
              "         [0.9254902 , 0.92941177, 0.96862745],\n",
              "         [0.91764706, 0.9254902 , 0.96862745],\n",
              "         ...,\n",
              "         [0.8509804 , 0.85882354, 0.9137255 ],\n",
              "         [0.8666667 , 0.8745098 , 0.91764706],\n",
              "         [0.87058824, 0.8745098 , 0.9137255 ]],\n",
              "\n",
              "        [[0.87058824, 0.8666667 , 0.8980392 ],\n",
              "         [0.9372549 , 0.9372549 , 0.9764706 ],\n",
              "         [0.9137255 , 0.91764706, 0.9647059 ],\n",
              "         ...,\n",
              "         [0.8745098 , 0.8745098 , 0.9254902 ],\n",
              "         [0.8901961 , 0.89411765, 0.93333334],\n",
              "         [0.8235294 , 0.827451  , 0.8627451 ]],\n",
              "\n",
              "        [[0.8352941 , 0.80784315, 0.827451  ],\n",
              "         [0.91764706, 0.9098039 , 0.9372549 ],\n",
              "         [0.90588236, 0.9137255 , 0.95686275],\n",
              "         ...,\n",
              "         [0.8627451 , 0.8627451 , 0.9098039 ],\n",
              "         [0.8627451 , 0.85882354, 0.9098039 ],\n",
              "         [0.7921569 , 0.79607844, 0.84313726]],\n",
              "\n",
              "        ...,\n",
              "\n",
              "        [[0.5882353 , 0.56078434, 0.5294118 ],\n",
              "         [0.54901963, 0.5294118 , 0.49803922],\n",
              "         [0.5176471 , 0.49803922, 0.47058824],\n",
              "         ...,\n",
              "         [0.8784314 , 0.87058824, 0.85490197],\n",
              "         [0.9019608 , 0.89411765, 0.88235295],\n",
              "         [0.94509804, 0.94509804, 0.93333334]],\n",
              "\n",
              "        [[0.5372549 , 0.5176471 , 0.49411765],\n",
              "         [0.50980395, 0.49803922, 0.47058824],\n",
              "         [0.49019608, 0.4745098 , 0.4509804 ],\n",
              "         ...,\n",
              "         [0.70980394, 0.7058824 , 0.69803923],\n",
              "         [0.7921569 , 0.7882353 , 0.7764706 ],\n",
              "         [0.83137256, 0.827451  , 0.8117647 ]],\n",
              "\n",
              "        [[0.47843137, 0.46666667, 0.44705883],\n",
              "         [0.4627451 , 0.45490196, 0.43137255],\n",
              "         [0.47058824, 0.45490196, 0.43529412],\n",
              "         ...,\n",
              "         [0.7019608 , 0.69411767, 0.6784314 ],\n",
              "         [0.6431373 , 0.6431373 , 0.63529414],\n",
              "         [0.6392157 , 0.6392157 , 0.6313726 ]]]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train = to_categorical(y_train, 10)\n",
        "y_test = to_categorical(y_test, 10)"
      ],
      "metadata": {
        "id": "XIHGyBvhIF7u"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9zhFOheTIjID",
        "outputId": "19e4344c-64b7-4d3a-ae14-51c19baad316"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 0., ..., 0., 0., 1.],\n",
              "       [0., 0., 0., ..., 0., 0., 1.],\n",
              "       ...,\n",
              "       [0., 0., 0., ..., 0., 0., 1.],\n",
              "       [0., 1., 0., ..., 0., 0., 0.],\n",
              "       [0., 1., 0., ..., 0., 0., 0.]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "\n",
        "\n",
        "model.add(Conv2D(32, (3,3), padding='same', activation='relu', input_shape=(32, 32, 3)))\n",
        "\n",
        "model.add(Conv2D(32, (3,3), activation='relu'))\n",
        "\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "##############################################\n",
        "\n",
        "model.add(Conv2D(32, (3,3), padding='same', activation='relu'))\n",
        "\n",
        "model.add(Conv2D(32, (3,3), activation='relu'))\n",
        "\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "\n",
        "#######################################\n",
        "\n",
        "model.add(Flatten())\n",
        "\n",
        "model.add(Dense(512, activation='relu'))\n",
        "\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Dense(10, activation='softmax'))"
      ],
      "metadata": {
        "id": "Caju8i__Is_v"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "\n",
        "model.fit(x_train, y_train, batch_size=32, epochs=10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xKG95lcJK104",
        "outputId": "32b8797b-8d5d-42d2-d491-91c6e6086ce8"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1563/1563 [==============================] - 16s 6ms/step - loss: 1.5220 - accuracy: 0.4467\n",
            "Epoch 2/10\n",
            "1563/1563 [==============================] - 8s 5ms/step - loss: 1.1097 - accuracy: 0.6060\n",
            "Epoch 3/10\n",
            "1563/1563 [==============================] - 12s 8ms/step - loss: 0.9540 - accuracy: 0.6618\n",
            "Epoch 4/10\n",
            "1563/1563 [==============================] - 11s 7ms/step - loss: 0.8591 - accuracy: 0.6980\n",
            "Epoch 5/10\n",
            "1563/1563 [==============================] - 10s 7ms/step - loss: 0.7975 - accuracy: 0.7183\n",
            "Epoch 6/10\n",
            "1563/1563 [==============================] - 10s 6ms/step - loss: 0.7460 - accuracy: 0.7371\n",
            "Epoch 7/10\n",
            "1563/1563 [==============================] - 9s 6ms/step - loss: 0.7006 - accuracy: 0.7513\n",
            "Epoch 8/10\n",
            "1563/1563 [==============================] - 8s 5ms/step - loss: 0.6719 - accuracy: 0.7619\n",
            "Epoch 9/10\n",
            "1563/1563 [==============================] - 9s 6ms/step - loss: 0.6405 - accuracy: 0.7744\n",
            "Epoch 10/10\n",
            "1563/1563 [==============================] - 10s 7ms/step - loss: 0.6152 - accuracy: 0.7833\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7d79b18eaef0>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "previsoes = model.predict(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xs7SaK6fMGbV",
        "outputId": "fd8fe36b-5d89-4cfc-c087-79dbde3e366b"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "previsoes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "60wDVTzkMcsx",
        "outputId": "f5fe90b7-ecdd-4cc8-9ca1-c86e9653154b"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[2.28949636e-03, 3.21088266e-03, 4.74380655e-03, ...,\n",
              "        1.80551130e-02, 6.05147751e-03, 2.23096204e-03],\n",
              "       [2.75252815e-02, 7.45371589e-03, 3.37218875e-09, ...,\n",
              "        1.70020768e-11, 9.64639127e-01, 3.81921884e-04],\n",
              "       [2.72874892e-01, 1.24645345e-01, 3.22090276e-03, ...,\n",
              "        2.30512451e-04, 5.07769644e-01, 8.87919664e-02],\n",
              "       ...,\n",
              "       [5.13716877e-05, 5.50439040e-07, 6.26341347e-03, ...,\n",
              "        4.52487543e-02, 1.99390138e-06, 9.83360337e-07],\n",
              "       [2.48125643e-01, 6.58643663e-01, 1.71561539e-02, ...,\n",
              "        4.58371080e-03, 1.85989472e-03, 5.36999479e-03],\n",
              "       [1.07540234e-08, 9.03046665e-11, 6.91309197e-07, ...,\n",
              "        9.97502983e-01, 5.56913049e-09, 3.08920417e-10]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prediction_classes = np.argmax(previsoes, axis=1)"
      ],
      "metadata": {
        "id": "h9NSpLauMgBA"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prediction_classes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3XrdMRl1Mn0J",
        "outputId": "d77c4886-2dc2-43ec-bd38-36e892b21499"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3, 8, 8, ..., 5, 1, 7])"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test_classes = np.argmax(y_test, axis=1)"
      ],
      "metadata": {
        "id": "OTBKUV6_MvSV"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_test_classes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "38pKKfZHM4CO",
        "outputId": "ea357489-1f2b-4227-c860-af2187561d9c"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3, 8, 8, ..., 5, 1, 7])"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy = accuracy_score(y_test_classes, prediction_classes)"
      ],
      "metadata": {
        "id": "2Gu207ERM5bg"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YoQhgTjzNCOi",
        "outputId": "a0f3ed59-1029-42e8-a8c8-fdd4f4f32b6b"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7646"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "confusion = confusion_matrix(y_test_classes, prediction_classes)"
      ],
      "metadata": {
        "id": "9G1H1vkVNFtv"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "confusion"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6iCS2CbjNLNK",
        "outputId": "8944e1a2-e3a5-4e49-c5c9-4306f55be228"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[792,  11,  47,  19,  14,   8,   7,  17,  50,  35],\n",
              "       [ 15, 866,   7,   3,   2,   5,  10,   4,  15,  73],\n",
              "       [ 56,   1, 631,  59,  91,  64,  45,  33,  10,  10],\n",
              "       [ 17,   5,  49, 553,  71, 178,  58,  42,  14,  13],\n",
              "       [ 14,   2,  41,  55, 752,  18,  44,  63,   8,   3],\n",
              "       [  5,   2,  32, 160,  49, 657,  20,  65,   8,   2],\n",
              "       [  6,   3,  25,  50,  35,  22, 844,  10,   2,   3],\n",
              "       [  6,   2,  17,  25,  42,  53,   7, 840,   4,   4],\n",
              "       [ 72,  24,  10,   6,   3,   5,   5,   6, 846,  23],\n",
              "       [ 20,  49,   6,  11,   2,   5,   6,  15,  21, 865]])"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "v44hcvz_NRIk"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}