week2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyM1qhVCyC/LxEGd84htm0XH",
      "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/dayoungcho/CAU_DL4AI/blob/main/week2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## MNIST data"
      ],
      "metadata": {
        "id": "l4OdCNNMRKVB"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "id": "AfjrgX5_NJm-"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.datasets import mnist\n",
        "(train_images, train_labels), (test_images, test_labels) = mnist.load_data()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(train_images.shape)\n",
        "print(test_images.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iy90_vTHNyKV",
        "outputId": "31c970f2-43c0-4afe-a631-bca353db3fbf"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(60000, 28, 28)\n",
            "(10000, 28, 28)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(train_labels)\n",
        "print(test_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yYJPVYO4Pt1n",
        "outputId": "b5bdea93-0074-490e-a2bd-f82ace5f724d"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[5 0 4 ... 5 6 8]\n",
            "[7 2 1 ... 4 5 6]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(train_labels))\n",
        "print(len(test_labels))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jDQwyRRJON0r",
        "outputId": "b053726b-c089-4621-ac44-ec826724ea5e"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "60000\n",
            "10000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(train_images.ndim)   # number of dimension\n",
        "print(train_images.dtype)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PHf1ljDoP2UC",
        "outputId": "c2897540-2515-4473-bac5-b4b0a174a242"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3\n",
            "uint8\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# visualizing image\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import random\n",
        "\n",
        "index = random.randint(0,60000)\n",
        "plt.imshow(train_images[index], cmap = plt.cm.binary)\n",
        "plt.show"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 600
        },
        "id": "juOKQnmfP-kH",
        "outputId": "b1f4c5ac-a377-437a-936e-ff9ed703f193"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show(close=None, block=None)>"
            ],
            "text/html": [
              "<div style=\"max-width:800px; border: 1px solid var(--colab-border-color);\"><style>\n",
              "      pre.function-repr-contents {\n",
              "        overflow-x: auto;\n",
              "        padding: 8px 12px;\n",
              "        max-height: 500px;\n",
              "      }\n",
              "\n",
              "      pre.function-repr-contents.function-repr-contents-collapsed {\n",
              "        cursor: pointer;\n",
              "        max-height: 100px;\n",
              "      }\n",
              "    </style>\n",
              "    <pre style=\"white-space: initial; background:\n",
              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>matplotlib.pyplot.show</b><br/>def show(*args, **kwargs)</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/usr/local/lib/python3.10/dist-packages/matplotlib/pyplot.py</a>Display all open figures.\n",
              "\n",
              "Parameters\n",
              "----------\n",
              "block : bool, optional\n",
              "    Whether to wait for all figures to be closed before returning.\n",
              "\n",
              "    If `True` block and run the GUI main loop until all figure windows\n",
              "    are closed.\n",
              "\n",
              "    If `False` ensure that all figure windows are displayed and return\n",
              "    immediately.  In this case, you are responsible for ensuring\n",
              "    that the event loop is running to have responsive figures.\n",
              "\n",
              "    Defaults to True in non-interactive mode and to False in interactive\n",
              "    mode (see `.pyplot.isinteractive`).\n",
              "\n",
              "See Also\n",
              "--------\n",
              "ion : Enable interactive mode, which shows / updates the figure after\n",
              "      every plotting command, so that calling ``show()`` is not necessary.\n",
              "ioff : Disable interactive mode.\n",
              "savefig : Save the figure to an image file instead of showing it on screen.\n",
              "\n",
              "Notes\n",
              "-----\n",
              "**Saving figures to file and showing a window at the same time**\n",
              "\n",
              "If you want an image file as well as a user interface window, use\n",
              "`.pyplot.savefig` before `.pyplot.show`. At the end of (a blocking)\n",
              "``show()`` the figure is closed and thus unregistered from pyplot. Calling\n",
              "`.pyplot.savefig` afterwards would save a new and thus empty figure. This\n",
              "limitation of command order does not apply if the show is non-blocking or\n",
              "if you keep a reference to the figure and use `.Figure.savefig`.\n",
              "\n",
              "**Auto-show in jupyter notebooks**\n",
              "\n",
              "The jupyter backends (activated via ``%matplotlib inline``,\n",
              "``%matplotlib notebook``, or ``%matplotlib widget``), call ``show()`` at\n",
              "the end of every cell by default. Thus, you usually don&#x27;t have to call it\n",
              "explicitly there.</pre>\n",
              "      <script>\n",
              "      if (google.colab.kernel.accessAllowed && google.colab.files && google.colab.files.view) {\n",
              "        for (const element of document.querySelectorAll('.filepath')) {\n",
              "          element.style.display = 'block'\n",
              "          element.onclick = (event) => {\n",
              "            event.preventDefault();\n",
              "            event.stopPropagation();\n",
              "            google.colab.files.view(element.textContent, 401);\n",
              "          };\n",
              "        }\n",
              "      }\n",
              "      for (const element of document.querySelectorAll('.function-repr-contents')) {\n",
              "        element.onclick = (event) => {\n",
              "          event.preventDefault();\n",
              "          event.stopPropagation();\n",
              "          element.classList.toggle('function-repr-contents-collapsed');\n",
              "        };\n",
              "      }\n",
              "      </script>\n",
              "      </div>"
            ]
          },
          "metadata": {},
          "execution_count": 58
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Modeling"
      ],
      "metadata": {
        "id": "V39UcpkKRODB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# preparing image data -> flattening, normalizing\n",
        "\n",
        "train_images = train_images.reshape((60000, 28 * 28))\n",
        "train_images = train_images.astype(\"float32\") / 255\n",
        "test_images = test_images.reshape((10000, 28 * 28))\n",
        "test_images = test_images.astype(\"float32\") / 255"
      ],
      "metadata": {
        "id": "4R4tDIOyQVIi"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(train_images.shape)\n",
        "print(test_images.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aHkoHwC4RZjh",
        "outputId": "b7c27f77-4363-44e2-8cf9-60b84e339942"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(60000, 784)\n",
            "(10000, 784)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "model = keras.Sequential([\n",
        "    layers.Dense(512, activation=\"relu\"),\n",
        "    layers.Dense(10, activation=\"softmax\")\n",
        "])"
      ],
      "metadata": {
        "id": "s7pOUYJMReY6"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer=\"rmsprop\",\n",
        "              loss=\"sparse_categorical_crossentropy\",\n",
        "              metrics=[\"accuracy\"])"
      ],
      "metadata": {
        "id": "IcZG7oIDRjya"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(train_images, train_labels, epochs=5, batch_size=128)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HjqLAyiaRl0n",
        "outputId": "2c3ea33a-35bb-45c6-ad40-50d4263d4a9e"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "469/469 [==============================] - 3s 4ms/step - loss: 0.2346 - accuracy: 0.9279\n",
            "Epoch 2/5\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0834 - accuracy: 0.9744\n",
            "Epoch 3/5\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0543 - accuracy: 0.9833\n",
            "Epoch 4/5\n",
            "469/469 [==============================] - 1s 3ms/step - loss: 0.0392 - accuracy: 0.9873\n",
            "Epoch 5/5\n",
            "469/469 [==============================] - 1s 3ms/step - loss: 0.0279 - accuracy: 0.9907\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7d2348177bb0>"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.utils import plot_model\n",
        "plot_model(model, show_shapes=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "id": "8BgmqezARp28",
        "outputId": "6ddbb143-bc0a-42b4-a2c7-4757da6daf43"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# checking predicted values\n",
        "\n",
        "test_digits = test_images[0:10]\n",
        "predictions = model.predict(test_digits)\n",
        "predictions[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sL8k4mW8R8It",
        "outputId": "26bfe301-f97e-4038-8094-369763eb581f"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 74ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3.1378533e-12, 9.3686603e-10, 4.3073896e-09, 7.6037843e-08,\n",
              "       4.0686752e-13, 1.5062916e-11, 5.0321529e-16, 9.9999988e-01,\n",
              "       3.8240643e-12, 4.4900382e-08], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions[0].argmax()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-B3F8uF-SCOk",
        "outputId": "613dbe59-eb00-4a70-93f3-0ef62324f00e"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions[0][7]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_l2IwgcCSCwK",
        "outputId": "8b1a35f4-1fbf-4f27-9058-59debcb29a66"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9999999"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_labels[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dzszSA4HSFA9",
        "outputId": "3386271c-394d-4d08-c7af-be91efe25d02"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# evaluation\n",
        "\n",
        "test_loss, test_acc = model.evaluate(test_images, test_labels)\n",
        "print(f\"test_acc: {test_acc}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eBeh1U64SKgh",
        "outputId": "ee167d65-22cb-4118-b022-912a9768160d"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 2s 5ms/step - loss: 0.0829 - accuracy: 0.9784\n",
            "test_acc: 0.9783999919891357\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: make model with 5 layers and evaluate it\n",
        "\n",
        "model = keras.Sequential([\n",
        "    layers.Dense(512, activation=\"relu\"),\n",
        "    layers.Dense(512, activation=\"relu\"),\n",
        "    layers.Dense(512, activation=\"relu\"),\n",
        "    layers.Dense(512, activation=\"relu\"),\n",
        "    layers.Dense(512, activation=\"relu\"),\n",
        "    layers.Dense(10, activation=\"softmax\")\n",
        "])\n"
      ],
      "metadata": {
        "id": "pQjpCp-mS3MO"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer=\"rmsprop\",\n",
        "              loss=\"sparse_categorical_crossentropy\",\n",
        "              metrics=[\"accuracy\"])\n",
        "model.fit(train_images, train_labels, epochs=5, batch_size=128)\n",
        "test_loss, test_acc = model.evaluate(test_images, test_labels)\n",
        "print(f\"test_acc: {test_acc}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n7vBNG7MU3P6",
        "outputId": "68abe467-7400-459b-f0a2-7d9acbcbc569"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "469/469 [==============================] - 4s 5ms/step - loss: 0.3079 - accuracy: 0.9038\n",
            "Epoch 2/5\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.1056 - accuracy: 0.9696\n",
            "Epoch 3/5\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0725 - accuracy: 0.9788\n",
            "Epoch 4/5\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0508 - accuracy: 0.9848\n",
            "Epoch 5/5\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0389 - accuracy: 0.9885\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.0607 - accuracy: 0.9834\n",
            "test_acc: 0.9833999872207642\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8C6SY_5KVUU0"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}