Multi_Class_Classification_of_MNIST_by_ANN.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Sankytanky100/ML-DL-Google-Collab-Projects/blob/main/Multi_Class_Classification_of_MNIST_by_ANN.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": "pELLUNVzJWB6"
      },
      "outputs": [],
      "source": [
        "import tensorflow\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Dense,Flatten"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(X_train,y_train),(X_test,y_test) = keras.datasets.mnist.load_data()"
      ],
      "metadata": {
        "id": "KRTH93VyMYF7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bbae4b64-9e72-4083-b69c-a1f30647bc1b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11490434/11490434 [==============================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m0hHUKoNMpsZ",
        "outputId": "2426e550-8988-40ed-cf78-f0685528857f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 28, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "skzwnglnMzl6",
        "outputId": "79535313-10e0-4961-bc1b-ab27df1a8c73"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5, 0, 4, ..., 5, 6, 8], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.imshow(X_train[2])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "jr2MuKsKRH5G",
        "outputId": "982a99cb-2d83-4348-e4d8-55dd80887df7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7bd9a6a4ee00>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "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": [
        "X_train = X_train/255\n",
        "X_test = X_test/255"
      ],
      "metadata": {
        "id": "dJmO2WLlRRT1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "i1WA9FioRdtB",
        "outputId": "1e7342e3-1f8f-4537-ac66-3c14e8d6b820"
      },
      "execution_count": null,
      "outputs": [
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            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add(Flatten(input_shape=(28,28)))\n",
        "model.add(Dense(128,activation='relu'))\n",
        "model.add(Dense(32,activation='relu'))\n",
        "model.add(Dense(10,activation='softmax'))"
      ],
      "metadata": {
        "id": "jr78UU88Rzm6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ECogPfG5SmNI",
        "outputId": "536253c0-ee74-41cf-9c98-19e8f4b6e00e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " flatten (Flatten)           (None, 784)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 128)               100480    \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 32)                4128      \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 10)                330       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 104938 (409.91 KB)\n",
            "Trainable params: 104938 (409.91 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss='sparse_categorical_crossentropy',optimizer='Adam',metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "0yC51WgjSsLV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(X_train,y_train,epochs=25,validation_split=0.2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L-fkwmIvWT8M",
        "outputId": "214d37ab-24b7-4082-e4f4-7b4b977fcc34"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/25\n",
            "1500/1500 [==============================] - 15s 6ms/step - loss: 0.2791 - accuracy: 0.9189 - val_loss: 0.1443 - val_accuracy: 0.9579\n",
            "Epoch 2/25\n",
            "1500/1500 [==============================] - 5s 4ms/step - loss: 0.1199 - accuracy: 0.9648 - val_loss: 0.1123 - val_accuracy: 0.9655\n",
            "Epoch 3/25\n",
            "1500/1500 [==============================] - 5s 3ms/step - loss: 0.0851 - accuracy: 0.9734 - val_loss: 0.0997 - val_accuracy: 0.9709\n",
            "Epoch 4/25\n",
            "1500/1500 [==============================] - 6s 4ms/step - loss: 0.0630 - accuracy: 0.9801 - val_loss: 0.0965 - val_accuracy: 0.9714\n",
            "Epoch 5/25\n",
            "1500/1500 [==============================] - 5s 3ms/step - loss: 0.0487 - accuracy: 0.9847 - val_loss: 0.0937 - val_accuracy: 0.9743\n",
            "Epoch 6/25\n",
            "1500/1500 [==============================] - 5s 4ms/step - loss: 0.0414 - accuracy: 0.9866 - val_loss: 0.1030 - val_accuracy: 0.9722\n",
            "Epoch 7/25\n",
            "1500/1500 [==============================] - 5s 3ms/step - loss: 0.0325 - accuracy: 0.9898 - val_loss: 0.1080 - val_accuracy: 0.9701\n",
            "Epoch 8/25\n",
            "1500/1500 [==============================] - 5s 3ms/step - loss: 0.0282 - accuracy: 0.9905 - val_loss: 0.1122 - val_accuracy: 0.9726\n",
            "Epoch 9/25\n",
            "1500/1500 [==============================] - 6s 4ms/step - loss: 0.0237 - accuracy: 0.9926 - val_loss: 0.1113 - val_accuracy: 0.9726\n",
            "Epoch 10/25\n",
            "1500/1500 [==============================] - 5s 3ms/step - loss: 0.0167 - accuracy: 0.9949 - val_loss: 0.1101 - val_accuracy: 0.9743\n",
            "Epoch 11/25\n",
            "1500/1500 [==============================] - 6s 4ms/step - loss: 0.0193 - accuracy: 0.9943 - val_loss: 0.1173 - val_accuracy: 0.9739\n",
            "Epoch 12/25\n",
            "1500/1500 [==============================] - 5s 3ms/step - loss: 0.0150 - accuracy: 0.9950 - val_loss: 0.1335 - val_accuracy: 0.9722\n",
            "Epoch 13/25\n",
            "1500/1500 [==============================] - 6s 4ms/step - loss: 0.0154 - accuracy: 0.9944 - val_loss: 0.1293 - val_accuracy: 0.9732\n",
            "Epoch 14/25\n",
            "1500/1500 [==============================] - 6s 4ms/step - loss: 0.0139 - accuracy: 0.9953 - val_loss: 0.1379 - val_accuracy: 0.9728\n",
            "Epoch 15/25\n",
            "1500/1500 [==============================] - 5s 4ms/step - loss: 0.0124 - accuracy: 0.9957 - val_loss: 0.1289 - val_accuracy: 0.9757\n",
            "Epoch 16/25\n",
            "1500/1500 [==============================] - 5s 3ms/step - loss: 0.0122 - accuracy: 0.9958 - val_loss: 0.1396 - val_accuracy: 0.9741\n",
            "Epoch 17/25\n",
            "1500/1500 [==============================] - 5s 3ms/step - loss: 0.0096 - accuracy: 0.9968 - val_loss: 0.1670 - val_accuracy: 0.9703\n",
            "Epoch 18/25\n",
            "1500/1500 [==============================] - 6s 4ms/step - loss: 0.0138 - accuracy: 0.9955 - val_loss: 0.1515 - val_accuracy: 0.9752\n",
            "Epoch 19/25\n",
            "1500/1500 [==============================] - 5s 3ms/step - loss: 0.0080 - accuracy: 0.9975 - val_loss: 0.1549 - val_accuracy: 0.9747\n",
            "Epoch 20/25\n",
            "1500/1500 [==============================] - 6s 4ms/step - loss: 0.0095 - accuracy: 0.9967 - val_loss: 0.1777 - val_accuracy: 0.9715\n",
            "Epoch 21/25\n",
            "1500/1500 [==============================] - 5s 3ms/step - loss: 0.0109 - accuracy: 0.9961 - val_loss: 0.1555 - val_accuracy: 0.9746\n",
            "Epoch 22/25\n",
            "1500/1500 [==============================] - 5s 4ms/step - loss: 0.0064 - accuracy: 0.9980 - val_loss: 0.1810 - val_accuracy: 0.9707\n",
            "Epoch 23/25\n",
            "1500/1500 [==============================] - 5s 3ms/step - loss: 0.0099 - accuracy: 0.9966 - val_loss: 0.1568 - val_accuracy: 0.9762\n",
            "Epoch 24/25\n",
            "1500/1500 [==============================] - 5s 3ms/step - loss: 0.0106 - accuracy: 0.9962 - val_loss: 0.1678 - val_accuracy: 0.9741\n",
            "Epoch 25/25\n",
            "1500/1500 [==============================] - 5s 4ms/step - loss: 0.0064 - accuracy: 0.9979 - val_loss: 0.1911 - val_accuracy: 0.9735\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_prob = model.predict(X_test)"
      ],
      "metadata": {
        "id": "CazuknkLW_LY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "241c6e5f-3f39-4585-b36f-be110d790f85"
      },
      "execution_count": null,
      "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": "5nELQpFrXQIi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "accuracy_score(y_test,y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jcyA9XVHXY6o",
        "outputId": "ce18569b-909f-40b3-bc30-6f0ef3972ccd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9767"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "NT2HxBnDXv_V",
        "outputId": "e996227c-ae57-47e3-ff81-be0d9dc2b9c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7bd9001eeaa0>]"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "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": [
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "Ln1DIjDOY8E7",
        "outputId": "9e9236f3-3bf6-4c53-baac-78b90560e33e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7bd90029c1c0>]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "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": [
        "plt.imshow(X_test[1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "F0eTtwmSZGQn",
        "outputId": "35c8658e-5125-4f10-cd11-3ae63d6f3c78"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7bd9000d27d0>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "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": [
        "model.predict(X_test[1].reshape(1,28,28)).argmax(axis=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mBKWjIxealMY",
        "outputId": "6353574d-d88b-4bfb-8543-95f5f8ab4e1e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 39ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2])"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "onfLjtuZatoe"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}