csutortok_17/Mnist_számok_felismerése_csütörtök.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPx4VRwaS6bNzqEM3e+SJHV",
      "include_colab_link": true
    },
    "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/Indian1999/csutortok_17/blob/main/Mnist_sz%C3%A1mok_felismer%C3%A9se_cs%C3%BCt%C3%B6rt%C3%B6k.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "F-vUiiNHVEMi"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from keras.datasets import mnist\n",
        "from keras.utils import to_categorical\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout, Activation, Conv2D, MaxPool2D, Flatten"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
        "\n",
        "#print(x_train[0])\n",
        "print(x_train.shape)\n",
        "unique, counts = np.unique(y_train, return_counts = True)\n",
        "print(unique)\n",
        "print(counts)\n",
        "unique, counts = np.unique(y_test, return_counts = True)\n",
        "print(unique)\n",
        "print(counts)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N55qf14vXg6M",
        "outputId": "0b9df36a-95d6-487c-9e29-cb723d870631"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "\u001b[1m11490434/11490434\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n",
            "(60000, 28, 28)\n",
            "[0 1 2 3 4 5 6 7 8 9]\n",
            "[5923 6742 5958 6131 5842 5421 5918 6265 5851 5949]\n",
            "[0 1 2 3 4 5 6 7 8 9]\n",
            "[ 980 1135 1032 1010  982  892  958 1028  974 1009]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "indeces = np.random.randint(0, len(x_train), size = 25)\n",
        "images = x_train[indeces]\n",
        "\n",
        "plt.figure(figsize = (5,5))\n",
        "for i in range(len(images)):\n",
        "    plt.subplot(5,5, i + 1) #(sorok száma, oszlopok száma, hanyadik  suplotra dolgozzunk?)\n",
        "    plt.imshow(images[i], cmap=\"gray\")\n",
        "    plt.axis(\"off\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "collapsed": true,
        "id": "gcLifea3ZVj0",
        "outputId": "3729489f-b32f-4746-9f73-b3f82bcc99f5"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 25 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Az adatok előkészítése"
      ],
      "metadata": {
        "id": "L7RyszLVda2w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_train = to_categorical(y_train)\n",
        "y_test = to_categorical(y_test)\n",
        "print(y_train[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a-Qb19cAdZ4m",
        "outputId": "fdc1d2ab-e0bc-41d3-9ee5-6ead791f37ef"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = x_train.reshape(-1, 28*28)\n",
        "x_train = x_train.astype(\"float32\") / 255\n",
        "x_test = x_test.reshape(-1, 28*28)\n",
        "x_test = x_test.astype(\"float32\") / 255"
      ],
      "metadata": {
        "collapsed": true,
        "id": "nTM6hCj0eXjt"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(Dense(256, input_dim=28*28))\n",
        "model.add(Activation(\"relu\")) # A 256 Neuron réteg a ReLU aktivációs függvényt használja\n",
        "model.add(Dense(256))\n",
        "model.add(Activation(\"relu\"))\n",
        "model.add(Dense(10))\n",
        "model.add(Activation(\"softmax\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "mCEnXRoyRzsk",
        "outputId": "81cbcb09-daa8-4e87-a209-156b1c9dd6d2"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "collapsed": true,
        "id": "X-XXuTxRUa4R",
        "outputId": "0d39511c-eb49-4f67-9a09-3c92e29945fa"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_1\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ dense_4 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)                 │         \u001b[38;5;34m200,960\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_3 (\u001b[38;5;33mActivation\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_5 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)                 │          \u001b[38;5;34m65,792\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_4 (\u001b[38;5;33mActivation\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_6 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)                  │           \u001b[38;5;34m2,570\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_5 (\u001b[38;5;33mActivation\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)                  │               \u001b[38;5;34m0\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ dense_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)                 │         <span style=\"color: #00af00; text-decoration-color: #00af00\">200,960</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)                 │          <span style=\"color: #00af00; text-decoration-color: #00af00\">65,792</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)                  │           <span style=\"color: #00af00; text-decoration-color: #00af00\">2,570</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)                  │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m269,322\u001b[0m (1.03 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">269,322</span> (1.03 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m269,322\u001b[0m (1.03 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">269,322</span> (1.03 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss = \"categorical_crossentropy\", optimizer=\"adam\", metrics = [\"accuracy\"])"
      ],
      "metadata": {
        "id": "AT-cKQujV2IH"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(x_train, y_train, epochs = 20, batch_size = 128)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "pZC3Jd0LXihz",
        "outputId": "41208276-960c-4a5d-8bbd-17f865a81aed"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 11ms/step - accuracy: 0.8607 - loss: 0.4868\n",
            "Epoch 2/20\n",
            "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 8ms/step - accuracy: 0.9692 - loss: 0.1016\n",
            "Epoch 3/20\n",
            "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 9ms/step - accuracy: 0.9810 - loss: 0.0630\n",
            "Epoch 4/20\n",
            "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9856 - loss: 0.0436\n",
            "Epoch 5/20\n",
            "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 9ms/step - accuracy: 0.9897 - loss: 0.0326\n",
            "Epoch 6/20\n",
            "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 16ms/step - accuracy: 0.9919 - loss: 0.0245\n",
            "Epoch 7/20\n",
            "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 9ms/step - accuracy: 0.9934 - loss: 0.0196\n",
            "Epoch 8/20\n",
            "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 9ms/step - accuracy: 0.9951 - loss: 0.0169\n",
            "Epoch 9/20\n",
            "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9950 - loss: 0.0146\n",
            "Epoch 10/20\n",
            "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 8ms/step - accuracy: 0.9963 - loss: 0.0108\n",
            "Epoch 11/20\n",
            "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 9ms/step - accuracy: 0.9968 - loss: 0.0102\n",
            "Epoch 12/20\n",
            "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 9ms/step - accuracy: 0.9958 - loss: 0.0138\n",
            "Epoch 13/20\n",
            "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 8ms/step - accuracy: 0.9969 - loss: 0.0089\n",
            "Epoch 14/20\n",
            "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 11ms/step - accuracy: 0.9969 - loss: 0.0091\n",
            "Epoch 15/20\n",
            "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 8ms/step - accuracy: 0.9985 - loss: 0.0055\n",
            "Epoch 16/20\n",
            "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 8ms/step - accuracy: 0.9962 - loss: 0.0108\n",
            "Epoch 17/20\n",
            "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 11ms/step - accuracy: 0.9984 - loss: 0.0048\n",
            "Epoch 18/20\n",
            "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 8ms/step - accuracy: 0.9956 - loss: 0.0121\n",
            "Epoch 19/20\n",
            "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 9ms/step - accuracy: 0.9968 - loss: 0.0089\n",
            "Epoch 20/20\n",
            "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.9970 - loss: 0.0090\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.history.History at 0x7c019514e850>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "indeces = np.random.randint(0, len(x_test), size = 25)\n",
        "images = x_test[indeces]\n",
        "predictions = model.predict(images)\n",
        "plt.figure(figsize = (5,5))\n",
        "for i in range(25):\n",
        "    plt.subplot(5,5, i+1)\n",
        "    plt.imshow(images[i].reshape((28,28)), cmap=\"gray\")\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    plt.xlabel(np.argmax(predictions[i]))\n",
        "    plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        },
        "collapsed": true,
        "id": "MpsZ8fi-Y2DH",
        "outputId": "c2af50b3-252e-4cc0-ad43-67e259d28ad6"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 25 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loss, acc = model.evaluate(x_test, y_test)\n",
        "print(f\"Loss: {round(loss,2)}, Accuracy: {round(acc,2)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ibxT_Krzcels",
        "outputId": "7c20cfa4-9528-4ffc-c7b8-71a9c57030c4"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9805 - loss: 0.0973\n",
            "Loss: 0.08, Accuracy: 0.98\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_conv = Sequential()\n",
        "model_conv.add(Conv2D(32, (3,3), input_shape = (28,28,1)))\n",
        "model_conv.add(Activation(\"relu\"))\n",
        "model_conv.add(MaxPool2D((2,2)))\n",
        "model_conv.add(Flatten())\n",
        "model_conv.add(Dense(100))\n",
        "model_conv.add(Activation(\"relu\"))\n",
        "model_conv.add(Dense(10))\n",
        "model_conv.add(Activation(\"softmax\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TMRLB5YgiHfQ",
        "outputId": "d29753c7-f8f9-4a0b-e64e-5ba4717a3182"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/keras/src/layers/convolutional/base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_conv.compile(loss = \"categorical_crossentropy\", optimizer=\"adam\", metrics = [\"accuracy\"])"
      ],
      "metadata": {
        "id": "MCUhWlLPjlk1"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_conv.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 385
        },
        "id": "ziemdo26jvGZ",
        "outputId": "27422749-640d-4e1f-c06e-d6f8905c5f65"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_2\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_2\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │             \u001b[38;5;34m320\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_6 (\u001b[38;5;33mActivation\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5408\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_7 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)                 │         \u001b[38;5;34m540,900\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_7 (\u001b[38;5;33mActivation\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_8 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)                  │           \u001b[38;5;34m1,010\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_8 (\u001b[38;5;33mActivation\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)                  │               \u001b[38;5;34m0\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5408</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)                 │         <span style=\"color: #00af00; text-decoration-color: #00af00\">540,900</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)                  │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,010</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)                  │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m542,230\u001b[0m (2.07 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">542,230</span> (2.07 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m542,230\u001b[0m (2.07 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">542,230</span> (2.07 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}