Digit_Prediction_Tenserflow.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Digit_Prediction_Tenserflow.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PPFkAs94bPcj"
      },
      "source": [
        "# **Importing libraries and datasets**\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cPJ1J87w0Nym"
      },
      "source": [
        "#Import libraries\n",
        "import tensorflow as tf"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZN3Eo0z_2DMP"
      },
      "source": [
        "#Import dataset\n",
        "mnist=tf.keras.datasets.mnist"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OEViKe1_bVi-"
      },
      "source": [
        "# **Dividing train set and test set**\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "elKyHVpg2TCz",
        "outputId": "7867bb95-9d59-4c3c-b307-c279af72b76e"
      },
      "source": [
        "#Divide the data set into training set and testing set\n",
        "(x_train,y_train) , (x_test,y_test) = mnist.load_data()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MyRmRZZmbcbF"
      },
      "source": [
        "# **Visualize the train set**\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Veh_vQsO24dT",
        "outputId": "000e55b7-c291-42b6-b10b-261218fa8221"
      },
      "source": [
        "#How data looks\n",
        "\n",
        "#Shape of the tensor(Image of 28X28 pixels. Total 60,000 images)\n",
        "print(x_train.shape)\n",
        "\n",
        "print(x_train[1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "flcOM3-zblZ0"
      },
      "source": [
        "# **importing libraries for better visualisation**\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HrcngRRS3Dlk"
      },
      "source": [
        "#Visualisation\n",
        "import matplotlib.pyplot as plt\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "f91DR4ub3sQt",
        "outputId": "282193f6-e052-482b-f007-1c3294d5601c"
      },
      "source": [
        "plt.imshow(x_train[0])\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "YI9WSZj54Kdq",
        "outputId": "f063db17-22b4-49bd-ea66-2233b0f2c17d"
      },
      "source": [
        "plt.imshow(x_train[0],cmap='gray', vmin=0, vmax=255)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "w0LRWL4Y4Pnf",
        "outputId": "a7c23a4f-604d-45e8-f292-84fc59ca15c0"
      },
      "source": [
        "plt.imshow(x_train[1],cmap=plt.cm.binary)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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/or8vaSi7zSv7vUv01ZH3jZ/LAkFwgA4IgrADQRB2IAjCDgRB2IEgCDsQBGEHgvh/HY9V64R+SmQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EUPgpnep4_0Q",
        "outputId": "b6cad6d0-1e8a-4594-81ab-815a0da06eed"
      },
      "source": [
        "print(y_train[0])\n",
        "print(y_train[1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "5\n",
            "0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N_PNXs-KbD02"
      },
      "source": [
        "# **Normalising inputs for better efficiency**\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "83zRhQJW5X5-"
      },
      "source": [
        "#Normalise the training and testing set\n",
        "\n",
        "x_train=tf.keras.utils.normalize(x_train,axis=1)\n",
        "x_test=tf.keras.utils.normalize(x_test,axis=1)\n",
        "\n",
        "#This is done to reduce the data size which\n",
        "#was earlier ranged between 0-255 now ranges\n",
        "#between 0 to 1 and hence training the model\n",
        "#much more efficiently and fast.\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "UywzJ7mY6R4a",
        "outputId": "82d8d11e-0f46-4ca4-edfd-b42f1241dab9"
      },
      "source": [
        "plt.imshow(x_train[1],cmap=plt.cm.binary)\n",
        "plt.show()\n",
        "print(x_train[1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
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            " [0.         0.         0.         0.         0.         0.\n",
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            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
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            "  0.         0.         0.         0.        ]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IC-MN5BIagrK"
      },
      "source": [
        "# **Creating Model**\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nMhDup6_L6OA"
      },
      "source": [
        "#Creating Model\n",
        "model=tf.keras.models.Sequential()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x0t4Lfm1ase5"
      },
      "source": [
        "# **Adding input/output layers**\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ArLLMLzAMLUu"
      },
      "source": [
        "#Flattening every image from 28X28 into single dimension i,e 1X784\n",
        "model.add(tf.keras.layers.Flatten())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DSMYOMmSMH4V"
      },
      "source": [
        "#Adding few layers\n",
        "model.add(tf.keras.layers.Dense(128,activation='relu'))\n",
        "model.add(tf.keras.layers.Dense(128,activation='relu'))\n",
        "model.add(tf.keras.layers.Dense(128,activation='relu'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-4cQ5FAUN_0Z"
      },
      "source": [
        "#Adding output layer\n",
        "model.add(tf.keras.layers.Dense(10,activation='softmax'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L-SZdXTQaz3O"
      },
      "source": [
        "# **Configuring the model for training**\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k1M8SbBTPFuj"
      },
      "source": [
        "#Adding Compiler\n",
        "model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PT9k946_Z05Q"
      },
      "source": [
        "# **Training the model on train set**\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gnMm6xZaQ3zO",
        "outputId": "c197b59e-a3c1-4b75-b8ad-d8425d34f35e"
      },
      "source": [
        "#Training the model on training dataset 3 times\n",
        "model.fit(x_train,y_train,epochs=3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/3\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2524 - accuracy: 0.9232\n",
            "Epoch 2/3\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1049 - accuracy: 0.9674\n",
            "Epoch 3/3\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0729 - accuracy: 0.9768\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f2fad44e850>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UGEUblvxaAWL"
      },
      "source": [
        "# **Loss and accuracy of model on test set**\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qgkdrcw4RmJH",
        "outputId": "1052b9fd-18ab-426b-a894-68f93a8b5dbd"
      },
      "source": [
        "#Finding loss and accuracy of the model on testing data set\n",
        "val_loss,val_acc=model.evaluate(x_test,y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "313/313 [==============================] - 1s 2ms/step - loss: 0.0961 - accuracy: 0.9712\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BUWe1oD-YgwL"
      },
      "source": [
        "# **Predict the test set results**\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jGy4RXkNTZwB"
      },
      "source": [
        "#Generating output predictions for input samples of testing data set\n",
        "predictions=model.predict(x_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2MGMpX79XDJ1"
      },
      "source": [
        "#import numpy library\n",
        "import numpy as np\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yPSyTcl7Zgco"
      },
      "source": [
        "# **Visualize the test set results**\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wHqqPVl2XO7G",
        "outputId": "04a29e7d-9cc0-4ee6-9710-4ecc049d13b2"
      },
      "source": [
        "#Visualize the test set results\n",
        "print(np.argmax(predictions[7]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "9\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "gId-tjZRXoER",
        "outputId": "b8ed67b7-3ea4-4f8a-aa95-bd21c22a2814"
      },
      "source": [
        "#Visualize the test set results\n",
        "plt.imshow(x_test[7],cmap=plt.cm.binary)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}