Handwritten_Digit_classification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "_Lg53r4wK0Qs"
      },
      "outputs": [],
      "source": [
        "import tensorflow\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras.layers import Dense, Flatten\n",
        "from tensorflow.keras.models import Sequential\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(X_train, y_train), (X_test, y_test) = keras.datasets.mnist.load_data() #Loading the data of handwritten letters and  every pixel in range 0-255[3d matrics]"
      ],
      "metadata": {
        "id": "2s4JLRgQK-aB"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.imshow(X_train[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "id": "JL_3i7lxLCGi",
        "outputId": "ba5653ba-ba61-40e9-e696-f83eb7a720d8"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7b4e86b3da80>"
            ]
          },
          "metadata": {},
          "execution_count": 45
        },
        {
          "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": [
        "#Make Range Between 0-1\n",
        "X_train = X_train/255\n",
        "X_test = X_test/255"
      ],
      "metadata": {
        "id": "BLaDzEI4LDGB"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yyRU52iFLjyh",
        "outputId": "29c33289-0e9c-46d1-bee1-4d3fdcc8ad04"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[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.        ],\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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            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(Flatten(input_shape=(28,28)))\n",
        "model.add(Dense(128, activation='relu'))  #first layer\n",
        "model.add(Dense(10, activation='softmax')) #second layer"
      ],
      "metadata": {
        "id": "5cjhOlskLl0p"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CBAdwnrFL8Co",
        "outputId": "9d140276-c977-4c68-b9b9-9109f17d73f5"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_6\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " flatten_2 (Flatten)         (None, 784)               0         \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 128)               100480    \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 10)                1290      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 101770 (397.54 KB)\n",
            "Trainable params: 101770 (397.54 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "70lvKICyR8yU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss='sparse_categorical_crossentropy',optimizer='Adam', metrics=['accuracy']) #It will import labels automatically 0-9 and using loss function as advised\n",
        "history = model.fit(X_train, y_train, epochs=10,validation_split=0.2)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TQ6nfIW6MDxJ",
        "outputId": "853e7577-05ae-47d4-cc4d-feb7dce41d83"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1500/1500 [==============================] - 9s 5ms/step - loss: 0.0039 - accuracy: 0.9987 - val_loss: 0.1436 - val_accuracy: 0.9788\n",
            "Epoch 2/10\n",
            "1500/1500 [==============================] - 8s 5ms/step - loss: 0.0037 - accuracy: 0.9990 - val_loss: 0.1442 - val_accuracy: 0.9762\n",
            "Epoch 3/10\n",
            "1500/1500 [==============================] - 7s 5ms/step - loss: 0.0027 - accuracy: 0.9990 - val_loss: 0.1400 - val_accuracy: 0.9780\n",
            "Epoch 4/10\n",
            "1500/1500 [==============================] - 7s 5ms/step - loss: 0.0013 - accuracy: 0.9996 - val_loss: 0.1742 - val_accuracy: 0.9726\n",
            "Epoch 5/10\n",
            "1500/1500 [==============================] - 7s 5ms/step - loss: 0.0058 - accuracy: 0.9979 - val_loss: 0.1499 - val_accuracy: 0.9761\n",
            "Epoch 6/10\n",
            "1500/1500 [==============================] - 7s 5ms/step - loss: 0.0030 - accuracy: 0.9990 - val_loss: 0.1471 - val_accuracy: 0.9781\n",
            "Epoch 7/10\n",
            "1500/1500 [==============================] - 8s 5ms/step - loss: 0.0017 - accuracy: 0.9995 - val_loss: 0.1707 - val_accuracy: 0.9768\n",
            "Epoch 8/10\n",
            "1500/1500 [==============================] - 7s 5ms/step - loss: 0.0026 - accuracy: 0.9991 - val_loss: 0.1614 - val_accuracy: 0.9760\n",
            "Epoch 9/10\n",
            "1500/1500 [==============================] - 10s 7ms/step - loss: 0.0025 - accuracy: 0.9991 - val_loss: 0.1507 - val_accuracy: 0.9781\n",
            "Epoch 10/10\n",
            "1500/1500 [==============================] - 8s 5ms/step - loss: 0.0038 - accuracy: 0.9984 - val_loss: 0.1558 - val_accuracy: 0.9771\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_prob = model.predict(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jf_vPZNtNiW9",
        "outputId": "e9e1899c-2a18-4cab-dbaf-2bc3c74b9133"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = y_prob.argmax(axis=1)"
      ],
      "metadata": {
        "id": "UtxDsUrPNmca"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "accuracy_score(y_test, y_pred) #97% accuracy increasing number of ephocs causing the overfitting reducing accuracy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TVFk74NONt95",
        "outputId": "e991f6f2-72e4-4f62-af5d-0c1f5e06fe1e"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9786"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "id": "5pc_ra2hQwnh",
        "outputId": "faeccdc0-27ad-431a-8864-0f2b23e944e7"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7b4e6c1baaa0>]"
            ]
          },
          "metadata": {},
          "execution_count": 79
        },
        {
          "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_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gz6XQk0wQ7Up",
        "outputId": "70342323-4eea-404a-fe02-20a6c789162c"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([7, 2, 1, ..., 4, 5, 6])"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(X_test[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "id": "D63b8o_9Sn6L",
        "outputId": "99fb3400-cef0-4762-f39f-30c1be9e576c"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7b4e86c2e080>"
            ]
          },
          "metadata": {},
          "execution_count": 88
        },
        {
          "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": [],
      "metadata": {
        "id": "uxHLgl4LTGNa"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}