{
  "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": null,
      "metadata": {
        "id": "kLutYXp-ecSf"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from tensorflow.keras.datasets import mnist\n",
        "from tensorflow import keras\n",
        "import keras.backend as K\n",
        "from tensorflow.keras.layers import Dense, Flatten, Reshape, Input, Lambda, BatchNormalization, Dropout\n",
        "\n",
        "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
        "\n",
        "x_train = x_train / 255\n",
        "x_test = x_test/ 255\n",
        "\n",
        "y_train = y_train % 2\n",
        "y_train = keras.utils.to_categorical(y_train, 10)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_img = Input((28, 28))\n",
        "x = Flatten()(input_img)\n",
        "x = Dense(128, activation = 'relu')(x)\n",
        "x = Dense(256, activation = 'relu')(x)\n",
        "x = Dense(64, activation = 'relu')(x)\n",
        "classif = Dense(10, activation = 'softmax')(x)"
      ],
      "metadata": {
        "id": "Ffd2RsvUedfQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = keras.Model(input_img, classif)"
      ],
      "metadata": {
        "id": "5aVLXHYNe5R_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer = 'adam', loss = 'categorical_crossentropy')"
      ],
      "metadata": {
        "id": "tG0HHttBVuxs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(x_train, y_train, epochs = 10, batch_size = 30, shuffle = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L6tEkyZdWIZy",
        "outputId": "2a98272e-fb00-440a-e4f6-10c92a477318"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "2000/2000 [==============================] - 12s 5ms/step - loss: 0.1153\n",
            "Epoch 2/10\n",
            "2000/2000 [==============================] - 11s 5ms/step - loss: 0.0524\n",
            "Epoch 3/10\n",
            "2000/2000 [==============================] - 9s 5ms/step - loss: 0.0384\n",
            "Epoch 4/10\n",
            "2000/2000 [==============================] - 11s 6ms/step - loss: 0.0308\n",
            "Epoch 5/10\n",
            "2000/2000 [==============================] - 11s 6ms/step - loss: 0.0250\n",
            "Epoch 6/10\n",
            "2000/2000 [==============================] - 11s 5ms/step - loss: 0.0199\n",
            "Epoch 7/10\n",
            "2000/2000 [==============================] - 10s 5ms/step - loss: 0.0168\n",
            "Epoch 8/10\n",
            "2000/2000 [==============================] - 11s 6ms/step - loss: 0.0142\n",
            "Epoch 9/10\n",
            "2000/2000 [==============================] - 11s 6ms/step - loss: 0.0131\n",
            "Epoch 10/10\n",
            "2000/2000 [==============================] - 9s 5ms/step - loss: 0.0110\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f7234824ee0>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict(x_train[:1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WGei66Vbdtzk",
        "outputId": "673fe3fb-8363-427e-c753-27e6471aaf51"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 100ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.21183645e-11, 1.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
              "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
              "        0.00000000e+00, 0.00000000e+00]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train[:1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YkhzAnVeePCm",
        "outputId": "c3c041c1-1ef4-441f-abb6-a771632a3617"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 1., 0., 0., 0., 0., 0., 0., 0., 0.]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    }
  ]
}