Deep_Learning_Codebasics.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Deep Learning Codebasics.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "gzf1bQaZ-0ad"
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import numpy as np\n",
        "import pandas as pd\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XF-2yKtC-6AT"
      },
      "source": [
        "(X_train, y_train),(X_test,y_test)=keras.datasets.mnist.load_data()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "aLESssK__lYI",
        "outputId": "4213530b-d392-4cdc-d86c-ae3984f4f48c"
      },
      "source": [
        "plt.matshow(X_train[2])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f14f51b5bd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6X_y8w97_s77",
        "outputId": "2983c62b-ba04-410d-8c19-7ff4c7e25647"
      },
      "source": [
        "y_train[2]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uNCsGoBl_-RB"
      },
      "source": [
        "X_train=X_train/255\n",
        "X_test=X_test/255"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yP-0SADJDY_F",
        "outputId": "a4ead93a-0645-4641-bec7-3c4933a878c8"
      },
      "source": [
        "X_train.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p2Kc1oQIAHNI"
      },
      "source": [
        "X_train_falttened=X_train.reshape(len(X_train), 28*28)\n",
        "X_test_falttened=X_test.reshape(len(X_test), 28*28)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gxi4MQbEAWRZ",
        "outputId": "c877d568-a974-4ec2-8252-9697496c3b01"
      },
      "source": [
        "X_train_falttened[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "       0.        , 0.        , 0.        , 0.97647059, 0.99215686,\n",
              "       0.97647059, 0.25098039, 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
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              "       0.        , 0.        , 0.        , 0.18039216, 0.50980392,\n",
              "       0.71764706, 0.99215686, 0.99215686, 0.81176471, 0.00784314,\n",
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              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.15294118,\n",
              "       0.58039216, 0.89803922, 0.99215686, 0.99215686, 0.99215686,\n",
              "       0.98039216, 0.71372549, 0.        , 0.        , 0.        ,\n",
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              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.09411765, 0.44705882, 0.86666667, 0.99215686, 0.99215686,\n",
              "       0.99215686, 0.99215686, 0.78823529, 0.30588235, 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.09019608, 0.25882353, 0.83529412, 0.99215686,\n",
              "       0.99215686, 0.99215686, 0.99215686, 0.77647059, 0.31764706,\n",
              "       0.00784314, 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.07058824, 0.67058824, 0.85882353,\n",
              "       0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.76470588,\n",
              "       0.31372549, 0.03529412, 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.21568627, 0.6745098 ,\n",
              "       0.88627451, 0.99215686, 0.99215686, 0.99215686, 0.99215686,\n",
              "       0.95686275, 0.52156863, 0.04313725, 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.53333333, 0.99215686, 0.99215686, 0.99215686,\n",
              "       0.83137255, 0.52941176, 0.51764706, 0.0627451 , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
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              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
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              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bJIR6hbUAhCH",
        "outputId": "5019975a-d1a4-4c1d-d160-e70b20952ae1"
      },
      "source": [
        "model=keras.Sequential([\n",
        "    keras.layers.Dense(10, input_shape=(784,), activation='sigmoid')\n",
        "])\n",
        "\n",
        "model.compile(\n",
        "    optimizer='adam',\n",
        "    loss='sparse_categorical_crossentropy',\n",
        "    metrics=['accuracy']\n",
        ")\n",
        "\n",
        "model.fit(X_train_falttened,y_train,epochs=5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 3s 1ms/step - loss: 0.7187 - accuracy: 0.8157\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 2s 1ms/step - loss: 0.3086 - accuracy: 0.9141\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 2s 1ms/step - loss: 0.2813 - accuracy: 0.9220\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 2s 1ms/step - loss: 0.2779 - accuracy: 0.9238\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 2s 1ms/step - loss: 0.2713 - accuracy: 0.9257\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f14f413eed0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_z1oIh7KCgjA",
        "outputId": "90b3430f-a69c-4a67-aeca-fc497fcd9cc5"
      },
      "source": [
        "model.evaluate(X_test_falttened,y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "313/313 [==============================] - 0s 953us/step - loss: 0.2665 - accuracy: 0.9259\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.26646506786346436, 0.9258999824523926]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "NrpeljVoEAn8",
        "outputId": "217f4bf7-6e74-4f1c-fc4d-efb864487cad"
      },
      "source": [
        "plt.matshow(X_test[1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f14f2fe9690>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 103
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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yf73cm9T5/s6NiNmjFWoNgZ87uD0QEf2NNdAC/bWnl/vr5d6kevvjcgBIjhAAkms6BFY3fPxW6K89vdxfL/cm1dhfo/cEADSv6ZEAgIYRAkByhACQHCEAJEcIAMn9H3Z2+kt0r5nJAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rBlMQEi1Dxhs",
        "outputId": "99c65d41-1f0a-445e-ee9e-9fdaeff48f8e"
      },
      "source": [
        "y_predicted=model.predict(X_test_falttened)\n",
        "y_predicted[1]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([4.3326843e-01, 4.8804283e-03, 9.9967289e-01, 2.9356396e-01,\n",
              "       2.6118652e-10, 7.6401794e-01, 8.3615392e-01, 1.1173115e-12,\n",
              "       1.2495929e-01, 3.5962693e-09], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2ilSs4VzD6ty",
        "outputId": "9ac432f2-b540-48e4-a063-2ad9a026c9c8"
      },
      "source": [
        "np.argmax(y_predicted[1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DJc8cwSYEmZ3",
        "outputId": "846063f8-4a1e-4200-f40e-9edcbb4064f9"
      },
      "source": [
        "y_predicted_labels=[np.argmax(i) for i in y_predicted]\n",
        "y_predicted_labels[:5]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[7, 2, 1, 0, 4]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jw4hQkuQFpBE",
        "outputId": "ab93b0d1-ea4a-48d1-c921-51a06b10d333"
      },
      "source": [
        "y_test[:5]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([7, 2, 1, 0, 4], dtype=uint8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z5OZ1IIvFVGE",
        "outputId": "51786e3c-cc4a-45ac-9c1f-68fd3eefc88e"
      },
      "source": [
        "cm=tf.math.confusion_matrix(labels=y_test,predictions=y_predicted_labels)\n",
        "cm"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(10, 10), dtype=int32, numpy=\n",
              "array([[ 964,    0,    2,    2,    0,    4,    5,    2,    1,    0],\n",
              "       [   0, 1113,    3,    2,    0,    1,    4,    2,   10,    0],\n",
              "       [   4,    7,  943,   11,    6,    2,   11,    9,   33,    6],\n",
              "       [   4,    0,   27,  913,    0,   23,    3,   11,   23,    6],\n",
              "       [   1,    1,   10,    1,  892,    0,   10,    4,   10,   53],\n",
              "       [  11,    3,    7,   30,    7,  769,   17,    6,   34,    8],\n",
              "       [  11,    3,   11,    1,    7,    6,  914,    2,    3,    0],\n",
              "       [   1,    5,   26,    4,    3,    0,    0,  951,    2,   36],\n",
              "       [   8,    9,    9,   17,    9,   22,   10,   11,  870,    9],\n",
              "       [  10,    7,    2,   11,   16,    5,    0,   21,    7,  930]],\n",
              "      dtype=int32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "id": "E3GqOjMQFe52",
        "outputId": "d88ef76f-dd46-4d77-fa90-f8daf752c502"
      },
      "source": [
        "import seaborn as sns\n",
        "plt.figure(figsize=(10,7))\n",
        "sns.heatmap(cm,annot=True,fmt='d')\n",
        "plt.xlabel(\"Predicted\")\n",
        "plt.ylabel(\"Truth\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(69.0, 0.5, 'Truth')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 109
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JXOZPhXDGtUV",
        "outputId": "173e49b0-72a5-49a2-ece3-b611effc2ed7"
      },
      "source": [
        "model=keras.Sequential([\n",
        "    keras.layers.Dense(100, input_shape=(784,), activation='relu'),\n",
        "    keras.layers.Dense(10, activation='sigmoid')\n",
        "])\n",
        "\n",
        "model.compile(\n",
        "    optimizer='adam',\n",
        "    loss='sparse_categorical_crossentropy',\n",
        "    metrics=['accuracy']\n",
        ")\n",
        "\n",
        "model.fit(X_train_falttened,y_train,epochs=5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.4559 - accuracy: 0.8713\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.1276 - accuracy: 0.9628\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.0912 - accuracy: 0.9733\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.0688 - accuracy: 0.9800\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.0521 - accuracy: 0.9845\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f14f259e550>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uyx4BgKcG9P0",
        "outputId": "e8832270-6eb2-4581-f364-98bd5be5748d"
      },
      "source": [
        "model.evaluate(X_test_falttened,y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "313/313 [==============================] - 0s 1ms/step - loss: 0.0807 - accuracy: 0.9741\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.080692820250988, 0.9740999937057495]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "id": "3jqxyslzHF25",
        "outputId": "def424ac-e1d1-424f-e3f4-69edd98cb18a"
      },
      "source": [
        "y_predicted=model.predict(X_test_falttened)\n",
        "y_predicted_labels=[np.argmax(i) for i in y_predicted]\n",
        "cm=tf.math.confusion_matrix(labels=y_test,predictions=y_predicted_labels)\n",
        "\n",
        "import seaborn as sns\n",
        "plt.figure(figsize=(10,7))\n",
        "sns.heatmap(cm,annot=True,fmt='d')\n",
        "plt.xlabel(\"Predicted\")\n",
        "plt.ylabel(\"Truth\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(69.0, 0.5, 'Truth')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 112
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WGNckkT9Hf3o",
        "outputId": "2a4cadc0-853c-4d20-9f25-49b4ddc47236"
      },
      "source": [
        "model=keras.Sequential([\n",
        "    keras.layers.Flatten(input_shape=(28,28)),\n",
        "    keras.layers.Dense(100, activation='relu'),\n",
        "    keras.layers.Dense(10, activation='sigmoid')\n",
        "])\n",
        "\n",
        "model.compile(\n",
        "    optimizer='adam',\n",
        "    loss='sparse_categorical_crossentropy',\n",
        "    metrics=['accuracy']\n",
        ")\n",
        "\n",
        "model.fit(X_train,y_train,epochs=5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.4484 - accuracy: 0.8732\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.1354 - accuracy: 0.9605\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.0904 - accuracy: 0.9726\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.0676 - accuracy: 0.9801\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.0524 - accuracy: 0.9842\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f14f0ae84d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H2Wuyc5XIAdF",
        "outputId": "dae1bd4e-fe78-4dd7-dcbf-1701433cbb84"
      },
      "source": [
        "model.evaluate(X_test,y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "313/313 [==============================] - 0s 1ms/step - loss: 0.0795 - accuracy: 0.9758\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.07945682853460312, 0.9757999777793884]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gn4Ma5ceIR0_"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}