Handwritten_Digit_Classification.ipynb

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      "authorship_tag": "ABX9TyNpqBG9vSHo5m4KnFhbDAtm",
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    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/MUKESH-SK/Machine-Learning-Deep-Learning-Projects/blob/main/Handwritten_Digit_Classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "m_lJxef6_-VF"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import datasets,layers,models\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import tensorflow_datasets as tfds"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(X_train,y_train),(X_test,y_test)=keras.datasets.mnist.load_data()"
      ],
      "metadata": {
        "id": "jn87PAT0FD8I"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UNxgjcve0ukv",
        "outputId": "0dea7397-ad04-4f3e-da0c-7536e120ad61"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5, 0, 4, ..., 5, 6, 8], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 172
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(X_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jLhPPLXYF0so",
        "outputId": "180a3546-c781-435f-d995-fe22d8605aff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "60000"
            ]
          },
          "metadata": {},
          "execution_count": 139
        }
      ]
    },
    {
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      "source": [
        "X_train"
      ],
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        "id": "HtpFidhEx4Sb",
        "outputId": "923d4161-bc49-43de-d2b5-e1290f587b7a"
      },
      "execution_count": null,
      "outputs": [
        {
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(X_test)"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "Dbqpnxe_GBKo",
        "outputId": "a74fff69-85a1-412a-e7d4-ee4a0dbd0e96"
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      "outputs": [
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              "10000"
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          "metadata": {},
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        "X_test.shape"
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        },
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        "outputId": "1322f1fb-93de-4e12-dc03-b4cd34d74a24"
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          "output_type": "execute_result",
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            "text/plain": [
              "(10000, 28, 28)"
            ]
          },
          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "classes = [0,1,2,3,4,5,6,7,8,9]"
      ],
      "metadata": {
        "id": "Gi6WlUbgWCcs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_sample(X,y,index):\n",
        "  plt.figure(figsize=(15,2))\n",
        "  plt.matshow(X[index])\n",
        "  plt.xlabel(classes[y[index]])"
      ],
      "metadata": {
        "id": "kIUrUTcbUF7_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.matshow(X_train[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "id": "zH8hTNBTGRUn",
        "outputId": "3c08b6e1-1bb8-4390-a5ca-08a1b5440a51"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fab3f8579d0>"
            ]
          },
          "metadata": {},
          "execution_count": 97
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c2U6xgTIHLRq",
        "outputId": "178bbcd9-b896-441f-85f0-8b96f076a7cf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = X_train/255\n",
        "y_train = y_train/255"
      ],
      "metadata": {
        "id": "ElpN3Z2qHS1C"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FrEXhq49HigT",
        "outputId": "c414848e-f5bb-413c-d186-640beff6fc69"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ann = tf.keras.Sequential([\n",
        "    layers.Flatten(),\n",
        "    layers.Dense(64,activation = 'relu'),\n",
        "    layers.Dense(32,activation = 'relu'),\n",
        "    layers.Dense(10,activation = 'softmax')\n",
        "])\n",
        "ann.compile(optimizer =tf.keras.optimizers.Adam(),\n",
        "               loss = tf.keras.losses.SparseCategoricalCrossentropy(),\n",
        "               metrics =['accuracy'])\n",
        "ann.fit(X_train,y_train,epochs =10, validation_data=(X_test, y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dzHcIYWfH5JB",
        "outputId": "28accf01-c51b-4a90-e32a-bc3e47af1c86"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 1.4277 - accuracy: 0.8087 - val_loss: 0.4608 - val_accuracy: 0.8771\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3449 - accuracy: 0.9103 - val_loss: 0.2861 - val_accuracy: 0.9233\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2402 - accuracy: 0.9352 - val_loss: 0.2461 - val_accuracy: 0.9353\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1946 - accuracy: 0.9468 - val_loss: 0.1894 - val_accuracy: 0.9487\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1611 - accuracy: 0.9548 - val_loss: 0.1686 - val_accuracy: 0.9521\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1440 - accuracy: 0.9593 - val_loss: 0.2184 - val_accuracy: 0.9436\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1327 - accuracy: 0.9629 - val_loss: 0.1636 - val_accuracy: 0.9551\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1197 - accuracy: 0.9665 - val_loss: 0.1459 - val_accuracy: 0.9619\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 7s 3ms/step - loss: 0.1120 - accuracy: 0.9679 - val_loss: 0.1546 - val_accuracy: 0.9589\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1059 - accuracy: 0.9697 - val_loss: 0.1592 - val_accuracy: 0.9612\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fab3c314ca0>"
            ]
          },
          "metadata": {},
          "execution_count": 156
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ann.evaluate(X_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hKjLOKK5Jj-S",
        "outputId": "4688b241-ed7d-4398-cc62-1754facf5d87"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step - loss: 0.1592 - accuracy: 0.9612\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.15915322303771973, 0.9611999988555908]"
            ]
          },
          "metadata": {},
          "execution_count": 157
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix ,classification_report\n",
        "y_pred = ann.predict(X_test)\n",
        "y_pred_classes = [np.argmax(element) for element in y_pred]\n",
        "print(\"Classification_report: \\n\",classification_report(y_test,y_pred_classes))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fT6LOiKiKL3z",
        "outputId": "d8870de1-de7f-473f-b862-e7e28492ad62"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step\n",
            "Classification_report: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.99      0.98       980\n",
            "           1       0.97      0.99      0.98      1135\n",
            "           2       0.96      0.97      0.96      1032\n",
            "           3       0.96      0.95      0.96      1010\n",
            "           4       0.93      0.98      0.95       982\n",
            "           5       0.96      0.96      0.96       892\n",
            "           6       0.98      0.96      0.97       958\n",
            "           7       0.97      0.94      0.96      1028\n",
            "           8       0.96      0.95      0.96       974\n",
            "           9       0.94      0.93      0.93      1009\n",
            "\n",
            "    accuracy                           0.96     10000\n",
            "   macro avg       0.96      0.96      0.96     10000\n",
            "weighted avg       0.96      0.96      0.96     10000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_classes[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sUYKs9rsJv2V",
        "outputId": "333ced16-77cf-4290-cd6e-4ec837b5bc32"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[7, 2, 1, 0, 4]"
            ]
          },
          "metadata": {},
          "execution_count": 159
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_sample(X_test,y_test,4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "sjfuKH37J8Es",
        "outputId": "9ad21104-c238-4e2d-a075-b2b482bcfc3f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x144 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7qFV1pdyJ-zt",
        "outputId": "6344301e-195d-47de-bbae-ff4acd5e219e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([7, 2, 1, 0, 4], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 163
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cnn = models.Sequential([\n",
        "\n",
        "    # cnn\n",
        "    layers.Conv2D(filters=64,kernel_size=(3,3),activation = 'relu',input_shape=(28,28,1)),\n",
        "    layers.MaxPooling2D((2,2)),\n",
        "\n",
        "    layers.Conv2D(filters=32,kernel_size=(3,3),activation = 'relu',input_shape=(28,28,1)),\n",
        "    layers.MaxPooling2D((2,2)),\n",
        "    # dense\n",
        "    layers.Flatten(input_shape=(28,28,1)),\n",
        "    layers.Dense(64,activation = 'relu'),\n",
        "    layers.Dense(32,activation = 'relu'),\n",
        "    layers.Dense(10,activation = 'softmax')\n",
        "])"
      ],
      "metadata": {
        "id": "7shYfvj4TuWx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cnn.compile(optimizer = 'adam',\n",
        "            loss = 'sparse_categorical_crossentropy',\n",
        "            metrics = ['accuracy'])"
      ],
      "metadata": {
        "id": "p82XIjs4afRN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cnn.fit(X_train,y_train,epochs=10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4mJaEPE_axCr",
        "outputId": "0dde1bb8-cab3-46a8-a993-4fd71ae85ada"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 84s 44ms/step - loss: 0.2437 - accuracy: 0.9360\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 81s 43ms/step - loss: 0.0719 - accuracy: 0.9782\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 80s 43ms/step - loss: 0.0552 - accuracy: 0.9832\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 84s 45ms/step - loss: 0.0446 - accuracy: 0.9858\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 83s 44ms/step - loss: 0.0377 - accuracy: 0.9878\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 79s 42ms/step - loss: 0.0329 - accuracy: 0.9902\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 78s 42ms/step - loss: 0.0271 - accuracy: 0.9917\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 103s 55ms/step - loss: 0.0250 - accuracy: 0.9924\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 113s 60ms/step - loss: 0.0228 - accuracy: 0.9927\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 82s 43ms/step - loss: 0.0196 - accuracy: 0.9938\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fab32eae7c0>"
            ]
          },
          "metadata": {},
          "execution_count": 166
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cnn.evaluate(X_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jHPoN6yia1PQ",
        "outputId": "4a2be8b9-a4c6-4e8c-c43f-c55563cd835b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 3s 10ms/step - loss: 0.0487 - accuracy: 0.9884\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.04873048886656761, 0.9883999824523926]"
            ]
          },
          "metadata": {},
          "execution_count": 168
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = cnn.predict(X_test)\n",
        "y_pred_classes = [np.argmax(element) for element in y_pred]\n",
        "print(\"Classification_report: \\n\",classification_report(y_test,y_pred_classes))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F3OoNyH15LA3",
        "outputId": "38f88aa0-2242-4b81-c688-5923da94a156"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 3s 10ms/step\n",
            "Classification_report: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.99      0.99       980\n",
            "           1       1.00      1.00      1.00      1135\n",
            "           2       0.99      0.99      0.99      1032\n",
            "           3       0.99      0.99      0.99      1010\n",
            "           4       0.98      1.00      0.99       982\n",
            "           5       0.99      0.99      0.99       892\n",
            "           6       0.99      0.99      0.99       958\n",
            "           7       0.98      0.99      0.98      1028\n",
            "           8       0.99      0.98      0.99       974\n",
            "           9       0.99      0.98      0.98      1009\n",
            "\n",
            "    accuracy                           0.99     10000\n",
            "   macro avg       0.99      0.99      0.99     10000\n",
            "weighted avg       0.99      0.99      0.99     10000\n",
            "\n"
          ]
        }
      ]
    }
  ]
}