CIFAR10_CNN.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/singhsourav0/Deep-Learning-Odyssey/blob/main/CIFAR10_CNN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<!DOCTYPE html>\n",
        "<html lang=\"en\">\n",
        "<head>\n",
        "<meta charset=\"UTF-8\">\n",
        "<meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\">\n",
        "<title>Image Classification Using Convolutional Neural Networks</title>\n",
        "<style>\n",
        "  body {\n",
        "    font-family: Arial, sans-serif;\n",
        "  }\n",
        "  .container {\n",
        "    max-width: 800px;\n",
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        "    padding: 20px;\n",
        "    background-color: #f5f5dc; /* clay color */\n",
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        "  h1 {\n",
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        "</head>\n",
        "<body>\n",
        "\n",
        "<div class=\"container\">\n",
        "  <h1>Image Classification Using Convolutional Neural Networks</h1>\n",
        "  <p>In this Notebook, we will demonstrate how to perform image classification using the CIFAR-10 dataset in TensorFlow. We will begin by training a model using a simple Artificial Neural Network (ANN) and then compare its performance with a Convolutional Neural Network (CNN).</p>\n",
        "  <p>First, we will train a model with an ANN to establish a baseline accuracy. Then, we will implement a CNN and observe how its performance improves over the ANN model. This tutorial aims to provide insights into why CNNs are preferred over ANNs for image classification tasks.</p>\n",
        "  <h2>Instructions:</h2>\n",
        "  <ol>\n",
        "    <li>Load the CIFAR-10 dataset.</li>\n",
        "    <li>Preprocess the data.</li>\n",
        "    <li>Construct a simple ANN model and train it on the dataset.</li>\n",
        "    <li>Evaluate the performance of the ANN model.</li>\n",
        "    <li>Build a CNN model and train it on the same dataset.</li>\n",
        "    <li>Compare the accuracy of the CNN model with the ANN model.</li>\n",
        "  </ol>\n",
        "  <p>By following these steps, you will gain a better understanding of the advantages of using CNNs for image classification tasks.</p>\n",
        "</div>\n",
        "\n",
        "</body>\n",
        "</html>\n"
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      "execution_count": 5,
      "metadata": {
        "id": "c03j4HM7Lslb"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras import datasets, layers, models\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "o6MZaX7dWjY7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b7bc7e33-b553-4023-e9cf-5a5bb887b0e8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
            "170498071/170498071 [==============================] - 3s 0us/step\n"
          ]
        }
      ],
      "source": [
        "(x_train, y_train),(x_test,y_test)= datasets.cifar10.load_data()"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fQvzYrWi1qlG"
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aqfHPaIerWrA",
        "outputId": "2d466ea1-2a94-4c92-d954-502ab7afe2fd"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(50000, 32, 32, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_test.shape"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "wPRVBEpFr5g7",
        "outputId": "bc90e530-a73d-4e2b-9d26-9be51e5fa9cf"
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      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 32, 32, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(x_train[0])"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "KIm38LF3r7vF",
        "outputId": "7ed2d095-aefc-49f9-ce90-e0398fc1afd1"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x799707fa4700>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ixEkXVhhvduP",
        "outputId": "43bb4447-86c4-4818-e8a8-15c99b9ab9ae"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[6],\n",
              "       [9],\n",
              "       [9],\n",
              "       [4],\n",
              "       [1]], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train = y_train.reshape(-1,)\n",
        "y_train[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TsOlBdIMsDVh",
        "outputId": "ed7d946a-0e2b-4092-c5ff-6259950c545b"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([6, 9, 9, 4, 1], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "y_test = y_test.reshape(-1,)"
      ],
      "metadata": {
        "id": "FpfJ4Vocviet"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "classes = [\"airplane\",\"automobile\",\"bird\",\"cat\",\"deer\",\"dog\",\"frog\",\"horse\",\"ship\",\"truck\"]"
      ],
      "metadata": {
        "id": "14YlX_A1vm-t"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_sample(X, y, index):\n",
        "    plt.figure(figsize = (15,2))\n",
        "    plt.imshow(X[index])\n",
        "    plt.xlabel(classes[y[index]])"
      ],
      "metadata": {
        "id": "sjKyEe_7vqHQ"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_sample(x_train, y_train, 0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "PqR9tkXevs6d",
        "outputId": "df3302e7-9a66-458a-e578-0c69f859592c"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x200 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_sample(x_train, y_train, 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "J5f8gt9Bv15b",
        "outputId": "86f12974-bf1b-4fe7-998f-7ebb581d0982"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x200 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hHapojQ2v82s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "normalizing the data into 0 to 1 and the image is in rgb . so the range of number of element in matrices is from 0 to 255 .so we will divide all no from 255\n"
      ],
      "metadata": {
        "id": "vU3n3vNOwEGv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = x_train / 255.0\n",
        "X_test = x_test / 255.0"
      ],
      "metadata": {
        "id": "cksnl1o0wZnX"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "first we build ANN model to check our model accuracy just for knowledge check"
      ],
      "metadata": {
        "id": "Y606PH6HwiC8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ann = models.Sequential([\n",
        "        layers.Flatten(input_shape=(32,32,3)),\n",
        "        layers.Dense(3000, activation='relu'),\n",
        "        layers.Dense(1000, activation='relu'),\n",
        "        layers.Dense(10, activation='softmax')\n",
        "    ])\n",
        "\n",
        "ann.compile(optimizer='SGD',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "ann.fit(X_train, y_train, epochs=5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pf4bi9ViwaIl",
        "outputId": "14b42fa9-df7f-418f-b51b-b1ab8a3a3395"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "1563/1563 [==============================] - 157s 100ms/step - loss: 1.8103 - accuracy: 0.3546\n",
            "Epoch 2/5\n",
            "1563/1563 [==============================] - 140s 90ms/step - loss: 1.6229 - accuracy: 0.4277\n",
            "Epoch 3/5\n",
            "1563/1563 [==============================] - 155s 99ms/step - loss: 1.5419 - accuracy: 0.4570\n",
            "Epoch 4/5\n",
            "1563/1563 [==============================] - 157s 101ms/step - loss: 1.4802 - accuracy: 0.4776\n",
            "Epoch 5/5\n",
            "1563/1563 [==============================] - 152s 98ms/step - loss: 1.4339 - accuracy: 0.4932\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7996ab16bf10>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix , classification_report\n",
        "import numpy as np\n",
        "y_pred = ann.predict(X_test)\n",
        "y_pred_classes = [np.argmax(element) for element in y_pred]\n",
        "\n",
        "print(\"Classification Report: \\n\", classification_report(y_test, y_pred_classes))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zuY_uxG-wxsl",
        "outputId": "6cc18bc6-18fa-4940-a348-98c9a21e570d"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 8s 26ms/step\n",
            "Classification Report: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.73      0.35      0.47      1000\n",
            "           1       0.63      0.57      0.60      1000\n",
            "           2       0.39      0.31      0.35      1000\n",
            "           3       0.35      0.16      0.22      1000\n",
            "           4       0.52      0.27      0.36      1000\n",
            "           5       0.24      0.74      0.36      1000\n",
            "           6       0.58      0.40      0.48      1000\n",
            "           7       0.64      0.41      0.50      1000\n",
            "           8       0.59      0.65      0.62      1000\n",
            "           9       0.51      0.66      0.58      1000\n",
            "\n",
            "    accuracy                           0.45     10000\n",
            "   macro avg       0.52      0.45      0.45     10000\n",
            "weighted avg       0.52      0.45      0.45     10000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now let us build a convolutional neural network to train our images"
      ],
      "metadata": {
        "id": "WE8S8CNBxEhY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cnn = models.Sequential([\n",
        "    layers.Conv2D(filters=32, kernel_size=(3, 3), activation='relu', input_shape=(32, 32, 3)),\n",
        "    layers.MaxPooling2D((2, 2)),\n",
        "\n",
        "    layers.Conv2D(filters=64, kernel_size=(3, 3), activation='relu'),\n",
        "    layers.MaxPooling2D((2, 2)),\n",
        "\n",
        "    layers.Flatten(),\n",
        "    layers.Dense(64, activation='relu'),\n",
        "    layers.Dense(10, activation='softmax')\n",
        "])"
      ],
      "metadata": {
        "id": "0q8buFYqxFxP"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cnn.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "YN0grAIsxPAC"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cnn.fit(X_train, y_train, epochs=10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "skImW6ftyVbn",
        "outputId": "e4f1dddc-8749-46e6-db9c-8bbb40a0b871"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1563/1563 [==============================] - 64s 40ms/step - loss: 1.4633 - accuracy: 0.4762\n",
            "Epoch 2/10\n",
            "1563/1563 [==============================] - 58s 37ms/step - loss: 1.1184 - accuracy: 0.6050\n",
            "Epoch 3/10\n",
            "1563/1563 [==============================] - 65s 41ms/step - loss: 0.9939 - accuracy: 0.6516\n",
            "Epoch 4/10\n",
            "1563/1563 [==============================] - 57s 36ms/step - loss: 0.9139 - accuracy: 0.6812\n",
            "Epoch 5/10\n",
            "1563/1563 [==============================] - 56s 36ms/step - loss: 0.8530 - accuracy: 0.7048\n",
            "Epoch 6/10\n",
            "1563/1563 [==============================] - 55s 35ms/step - loss: 0.7975 - accuracy: 0.7228\n",
            "Epoch 7/10\n",
            "1563/1563 [==============================] - 55s 35ms/step - loss: 0.7470 - accuracy: 0.7417\n",
            "Epoch 8/10\n",
            "1563/1563 [==============================] - 64s 41ms/step - loss: 0.7065 - accuracy: 0.7551\n",
            "Epoch 9/10\n",
            "1563/1563 [==============================] - 56s 36ms/step - loss: 0.6668 - accuracy: 0.7680\n",
            "Epoch 10/10\n",
            "1563/1563 [==============================] - 57s 37ms/step - loss: 0.6312 - accuracy: 0.7797\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7996955938b0>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cnn.evaluate(X_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2C0u4mn8ybKr",
        "outputId": "986bace3-ac43-43e4-cce3-392102e5d476"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 3s 11ms/step - loss: 0.9243 - accuracy: 0.7001\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.9242792725563049, 0.7001000046730042]"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = cnn.predict(X_test)\n",
        "y_pred[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wc2b7v8Eybqk",
        "outputId": "fca1ac68-e033-4bbb-ce4f-a3ff6dd56da4"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 4s 13ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[2.1109325e-03, 3.5674970e-05, 5.9412206e-03, 7.7377468e-01,\n",
              "        1.6946128e-03, 2.4995588e-02, 1.8682975e-01, 8.0899626e-05,\n",
              "        4.5201136e-03, 1.6603792e-05],\n",
              "       [6.1851827e-04, 3.1935781e-02, 4.3519476e-06, 6.3978058e-07,\n",
              "        4.7274941e-07, 2.8981679e-08, 8.6588855e-08, 1.6249241e-09,\n",
              "        9.6550214e-01, 1.9380397e-03],\n",
              "       [1.6163221e-01, 5.2683871e-02, 2.6337747e-03, 4.2595170e-03,\n",
              "        1.8743634e-03, 2.1438130e-04, 2.9398821e-04, 2.7976141e-04,\n",
              "        7.6040989e-01, 1.5718209e-02],\n",
              "       [9.5396316e-01, 7.4439049e-03, 9.2174616e-03, 4.2026045e-04,\n",
              "        5.5416586e-04, 4.1522762e-06, 8.5797737e-06, 3.6508944e-05,\n",
              "        2.7880350e-02, 4.7134230e-04],\n",
              "       [4.3608402e-06, 3.0715776e-06, 5.9406646e-02, 3.4905761e-01,\n",
              "        2.0792387e-01, 2.8578013e-02, 3.5491559e-01, 3.1196341e-05,\n",
              "        7.9397032e-05, 1.0983546e-07]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_classes = [np.argmax(element) for element in y_pred]\n",
        "y_classes[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IE5pJKkbyd7a",
        "outputId": "8723dc2e-2290-42ef-b1a4-275c963cbe56"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[3, 8, 8, 0, 6]"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9802yJARygJ1",
        "outputId": "baa4fcce-9989-4c30-eafc-3815f10c47d5"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3, 8, 8, 0, 6], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_sample(X_test, y_test,3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "18aNzV1KyiIe",
        "outputId": "60fbfd56-a631-4969-de98-2037575e40a0"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x200 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classes[y_classes[3]]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "ph3jX2Lyyktr",
        "outputId": "911b0f62-57b8-4ca7-d51e-ab2eaf41a4a2"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'airplane'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classes[y_classes[3]]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "WZTs6PEXypbg",
        "outputId": "f68c8a8d-1ea6-4e39-bc4f-25cace913122"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'airplane'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        " <p style=\"color: #7e4a27;\">Singhsourav0 </p>"
      ],
      "metadata": {
        "id": "vk-AyFmq2XBs"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vRkQoIivyp1-"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOdDcKXziDbloWw4TxrDdXT",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}