{
  "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": 26,
      "metadata": {
        "id": "2F6ZW8s2TK12"
      },
      "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 = keras.utils.to_categorical(y_train, 10)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_img = Input((28, 28))\n",
        "x = Flatten()(input_img)\n",
        "x = Dense(256, activation='relu')(x)\n",
        "x = Dense(128, activation='relu')(x)\n",
        "x = Dense(64, activation='relu')(x)\n",
        "Classif = Dense(10, activation='softmax')(x)"
      ],
      "metadata": {
        "id": "llfdgGwoTcO1"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = keras.Model(input_img, Classif)"
      ],
      "metadata": {
        "id": "2yrM66AMUa1O"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='adam', loss='categorical_crossentropy')"
      ],
      "metadata": {
        "id": "FWuTEvwxVEKU"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(x_train, y_train, epochs=5, batch_size=30, shuffle=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j_PDLrF8VENz",
        "outputId": "bf561cc5-36e0-47b6-a5d1-a1d7e1aa7e07"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "2000/2000 [==============================] - 16s 7ms/step - loss: 0.2133\n",
            "Epoch 2/5\n",
            "2000/2000 [==============================] - 12s 6ms/step - loss: 0.0902\n",
            "Epoch 3/5\n",
            "2000/2000 [==============================] - 12s 6ms/step - loss: 0.0650\n",
            "Epoch 4/5\n",
            "2000/2000 [==============================] - 12s 6ms/step - loss: 0.0508\n",
            "Epoch 5/5\n",
            "2000/2000 [==============================] - 13s 6ms/step - loss: 0.0389\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fef21e73a00>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict(x_train[:1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BQIQVSATWJAa",
        "outputId": "81e468a9-528c-4467-84a7-e50b8167ee36"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 98ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[2.7693006e-11, 1.0839771e-06, 2.3587077e-08, 8.1381639e-03,\n",
              "        2.0295085e-12, 9.9185455e-01, 9.6902228e-09, 1.0457582e-08,\n",
              "        1.3851497e-06, 4.7160506e-06]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train[:1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f5Cvv8UYeUeH",
        "outputId": "7b387d52-9ef1-4864-b3b3-f5317e9efbf8"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 0., 0., 0., 0., 1., 0., 0., 0., 0.]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    }
  ]
}