データセットの確かめ.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/atsu8864/Graduation-Research/blob/main/%E3%83%87%E3%83%BC%E3%82%BF%E3%82%BB%E3%83%83%E3%83%88%E3%81%AE%E7%A2%BA%E3%81%8B%E3%82%81.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BkKsWDmgV2Hj"
      },
      "source": [
        "import tensorflow as tf"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "usvGvb_HWDfL",
        "outputId": "46d5b432-816e-45fa-b5db-94e223a3a3cc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "mnist = tf.keras.datasets.mnist\n",
        "\n",
        "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
        "x_train, x_test = x_train / 255.0, x_test / 255.0"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZMsvM5O5dRPl"
      },
      "source": [
        "model = tf.keras.models.Sequential([\n",
        "                                    tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
        "                                    tf.keras.layers.Dense(128, activation='relu'),\n",
        "                                    tf.keras.layers.Dropout(0.2),\n",
        "                                    tf.keras.layers.Dense(10)\n",
        "])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_8yS0aMDd0r6"
      },
      "source": [
        "predictions = model(x_train[:1]).numpy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AOHvpCFjd8Vq",
        "outputId": "d0354561-13d9-48ea-fbc3-9fa1ba169b3b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "predictions"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      dtype=float32)"
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          "execution_count": 7
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "tf.nn.softmax(predictions).numpy()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.12441458, 0.07263412, 0.03675248, 0.13891892, 0.05449054,\n",
              "        0.10441507, 0.11240379, 0.07052626, 0.11743043, 0.16801375]],\n",
              "      dtype=float32)"
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          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Q7AV3-vemwV"
      },
      "source": [
        "loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LI8mnju1nzYF",
        "outputId": "10046c56-f99e-47c7-a759-07e39abc9696",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "loss_fn(y_train[:1], predictions).numpy()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.2593813"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g5QtTsAEoA1s"
      },
      "source": [
        "model.compile(optimizer='adam', loss=loss_fn, metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3wrxgMlbpDB0",
        "outputId": "66416009-5aaa-46f1-e4f9-eac5fa81abfa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "model.fit(x_train, y_train, epochs=5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.0651 - accuracy: 0.9794\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.0589 - accuracy: 0.9815\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.0528 - accuracy: 0.9833\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.0478 - accuracy: 0.9844\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.0442 - accuracy: 0.9854\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f0e005876d8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
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        "id": "GXnGyae_q-D_",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "model.evaluate(x_test, y_test, verbose=2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "313/313 - 0s - loss: 0.0724 - accuracy: 0.9786\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.0723738819360733, 0.978600025177002]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lONBG70vrLc-"
      },
      "source": [
        "probability_model = tf.keras.Sequential([\n",
        "                                         model,\n",
        "                                         tf.keras.layers.Softmax()\n",
        "])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8aWwdCYtrtHL",
        "outputId": "afdcdc21-d813-4f0a-a9d5-f4cd8f21d413",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        }
      },
      "source": [
        "\n",
        "probability_model(x_test[:5])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(5, 10), dtype=float32, numpy=\n",
              "array([[1.8164333e-10, 2.4005242e-10, 6.3364268e-08, 5.4533425e-06,\n",
              "        2.7893215e-12, 2.2475093e-10, 1.7565657e-14, 9.9999416e-01,\n",
              "        4.6853130e-09, 1.8037137e-07],\n",
              "       [4.1107114e-11, 6.6178786e-06, 9.9999309e-01, 2.9361632e-07,\n",
              "        1.5177470e-20, 4.1447540e-11, 6.5817776e-15, 4.6426318e-18,\n",
              "        6.2869800e-11, 8.4376914e-18],\n",
              "       [1.8773881e-07, 9.9996114e-01, 5.7412194e-07, 1.0370734e-08,\n",
              "        1.7612435e-06, 7.5837736e-09, 3.1419422e-08, 3.4059132e-05,\n",
              "        2.2638637e-06, 2.4932845e-10],\n",
              "       [9.9966133e-01, 1.8961481e-08, 1.8283802e-04, 8.0993906e-10,\n",
              "        7.2875474e-07, 2.0574575e-05, 7.1856884e-06, 2.9541798e-06,\n",
              "        7.6173791e-08, 1.2424550e-04],\n",
              "       [4.5988889e-07, 8.2426438e-13, 1.9111756e-08, 3.4585076e-12,\n",
              "        9.9973148e-01, 2.9041250e-11, 2.0258368e-07, 1.2602176e-06,\n",
              "        1.4257844e-10, 2.6653122e-04]], dtype=float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BTNoSAp_x5Sk"
      },
      "source": [
        "neural network"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6x21NzIar-f5",
        "outputId": "d725f6f9-f63e-4be5-eaf9-61ef661dc8d4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from tensorflow import keras\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "print(tf.__version__)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.3.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A0lJzJWNvBzr"
      },
      "source": [
        "fashion_mnist = keras.datasets.fashion_mnist\n",
        "\n",
        "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yr8VSs-hvkrF"
      },
      "source": [
        "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
        "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qb3MKKyjwFjb",
        "outputId": "fc0bb572-dde8-4c68-f896-a66ae25cc798",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "train_images.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c-SpcFzSwIW5",
        "outputId": "1c8e2d7d-2ddc-406b-eb9b-4298806631cc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(train_labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "60000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rLHohX3jwLyb",
        "outputId": "a82f9ec0-3af6-4cc2-cfcc-c09b70030e16",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "train_labels"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([9, 0, 0, ..., 3, 0, 5], dtype=uint8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QLdH1e-XwNiZ",
        "outputId": "00cbc69a-895f-476e-88e5-be09ebec131b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "test_images.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 28, 28)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9too6GHMwPvJ",
        "outputId": "8716f8d3-cbb6-4741-f4f7-cd95e2b51d39",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(test_labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wMgvM2F_yqIh",
        "outputId": "4e13d518-959c-4e62-9eaf-66fbf28998f5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "plt.figure()\n",
        "plt.imshow(train_images[0])\n",
        "plt.colorbar()\n",
        "plt.grid(False)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JB6ZwK130KPP"
      },
      "source": [
        "train_images = train_images / 255.0\n",
        "test_images = test_images / 255.0"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ILmjhLpe0wRW",
        "outputId": "2a6041d8-bd90-40ad-bf38-3def6535a172",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        }
      },
      "source": [
        "plt.figure(figsize=(10, 10))\n",
        "for i in range(25):\n",
        "  plt.subplot(5, 5, i+1)\n",
        "  plt.grid(False)\n",
        "  plt.xticks([])\n",
        "  plt.yticks([])\n",
        "  plt.imshow(train_images[i], cmap=plt.cm.binary)\n",
        "  plt.xlabel(class_names[train_labels[i]])\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 25 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KWr-wR6I1TQ3",
        "outputId": "d96e2bb5-aebb-4619-9541-a7f15d796ec2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 714
        }
      },
      "source": [
        "help(plt.xticks)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Help on function xticks in module matplotlib.pyplot:\n",
            "\n",
            "xticks(ticks=None, labels=None, **kwargs)\n",
            "    Get or set the current tick locations and labels of the x-axis.\n",
            "    \n",
            "    Pass no arguments to return the current values without modifying them.\n",
            "    \n",
            "    Parameters\n",
            "    ----------\n",
            "    ticks : array-like, optional\n",
            "        The list of xtick locations.  Passing an empty list removes all xticks.\n",
            "    labels : array-like, optional\n",
            "        The labels to place at the given *ticks* locations.  This argument can\n",
            "        only be passed if *ticks* is passed as well.\n",
            "    **kwargs\n",
            "        `.Text` properties can be used to control the appearance of the labels.\n",
            "    \n",
            "    Returns\n",
            "    -------\n",
            "    locs\n",
            "        The list of xtick locations.\n",
            "    labels\n",
            "        The list of xlabel `.Text` objects.\n",
            "    \n",
            "    Notes\n",
            "    -----\n",
            "    Calling this function with no arguments (e.g. ``xticks()``) is the pyplot\n",
            "    equivalent of calling `~.Axes.get_xticks` and `~.Axes.get_xticklabels` on\n",
            "    the current axes.\n",
            "    Calling this function with arguments is the pyplot equivalent of calling\n",
            "    `~.Axes.set_xticks` and `~.Axes.set_xticklabels` on the current axes.\n",
            "    \n",
            "    Examples\n",
            "    --------\n",
            "    >>> locs, labels = xticks()  # Get the current locations and labels.\n",
            "    >>> xticks(np.arange(0, 1, step=0.2))  # Set label locations.\n",
            "    >>> xticks(np.arange(3), ['Tom', 'Dick', 'Sue'])  # Set text labels.\n",
            "    >>> xticks([0, 1, 2], ['January', 'February', 'March'],\n",
            "    ...        rotation=20)  # Set text labels and properties.\n",
            "    >>> xticks([])  # Disable xticks.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "72o7IJ6c1l31"
      },
      "source": [
        "model = keras.Sequential([\n",
        "                          keras.layers.Flatten(input_shape=(28, 28)),\n",
        "                          keras.layers.Dense(128, activation='relu'),\n",
        "                          keras.layers.Dense(10, activation='softmax')\n",
        "])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1bf88d6j1odV"
      },
      "source": [
        "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3j2qPkIA2z42",
        "outputId": "a0c8f8d7-1f19-4c0e-90ae-33a39e02a110",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "model.fit(train_images, train_labels, epochs=5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.4998 - accuracy: 0.8248\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3776 - accuracy: 0.8645\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3369 - accuracy: 0.8776\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3132 - accuracy: 0.8851\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2948 - accuracy: 0.8906\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f0dffcd2b70>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qKqTP7RV3NFq",
        "outputId": "f4144a8c-dbf3-4eb1-bfee-101c6d04fc3c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)\n",
        "print('\\nTest accuracy:', test_acc)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "313/313 - 0s - loss: 0.3777 - accuracy: 0.8622\n",
            "\n",
            "Test accuracy: 0.8622000217437744\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vkfQWGhQ4Emy"
      },
      "source": [
        "predictions = model.predict(test_images)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XcUtJeCu4RdK",
        "outputId": "a2833603-2427-453d-d803-c56d70983a91",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "predictions[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([7.04312561e-06, 4.74652362e-09, 4.82990643e-08, 1.66942566e-08,\n",
              "       1.16879896e-07, 9.42949962e-04, 6.42198472e-07, 1.72023922e-02,\n",
              "       3.72027966e-06, 9.81843054e-01], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w1Uej4dH4U7X",
        "outputId": "718b67e9-4035-4338-8d02-58afb3d980a2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "np.argmax(predictions[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3JEE0neu5tKW",
        "outputId": "5f68c78f-3fdd-4a58-c243-4d78f9487356",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "test_labels[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2RWHRAbo5xA-"
      },
      "source": [
        "def plot_image(i, predictions_array, true_label, img):\n",
        "    predictions_array, true_label, img = predictions_array[i], true_label[i], img[i]\n",
        "    plt.grid(False)\n",
        "    plt.xticks([])\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "\n",
        "    plt.imshow(img, cmap=plt.cm.binary)\n",
        "\n",
        "    predicted_label = np.argmax(predictions_array)\n",
        "    if predicted_label == true_label:\n",
        "       color = 'blue'  \n",
        "    else:\n",
        "       color = 'red'\n",
        "\n",
        "    plt.xlabel(\"{} {:2.0f}% ({})\".format(class_names[predicted_label],100*np.max(predictions_array),\n",
        "                                        class_names[true_label]),color=color)\n",
        "def plot_value_array(i, predictions_array, true_label):\n",
        "    predictions_array, true_label = predictions_array[i], true_label[i]\n",
        "    plt.grid(False)\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    thisplot = plt.bar(range(10), predictions_array, color=\"#777777\")\n",
        "    plt.ylim([0, 1])\n",
        "    predicted_label = np.argmax(predictions_array)\n",
        "\n",
        "    thisplot[predicted_label].set_color('red')\n",
        "    thisplot[true_label].set_color('blue')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7aRxXemm7n7i",
        "outputId": "c9fe5d29-ddd6-4dae-a117-c492d134d5db",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        }
      },
      "source": [
        "i = 0\n",
        "plt.figure(figsize=(6, 3))\n",
        "plt.subplot(1, 2, 1)\n",
        "plot_image(i, predictions, test_labels, test_images)\n",
        "plt.subplot(1, 2, 2)\n",
        "plot_value_array(i, predictions, test_labels)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hH0EYPP9_scI",
        "outputId": "6c545e0a-90a1-47bc-f6f0-c1a160871832",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        }
      },
      "source": [
        "i = 12\n",
        "plt.figure(figsize=(6, 3))\n",
        "plt.subplot(1, 2, 1)\n",
        "plot_image(i, predictions, test_labels, test_images)\n",
        "plt.subplot(1, 2, 2)\n",
        "plot_value_array(i, predictions, test_labels)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kQgxgSSrk/wEOeLX7d0ZfcgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rSD8scXLANX9",
        "outputId": "169e2d30-896c-48d8-bd8b-957323d5832b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        }
      },
      "source": [
        "num_rows = 5\n",
        "num_cols = 3\n",
        "num_images = num_rows*num_cols\n",
        "plt.figure(figsize=(2*2*num_cols, 2*num_rows))\n",
        "for i in range(num_images):\n",
        "    plt.subplot(num_rows, 2*num_cols, 2*i+1)\n",
        "    plot_image(i, predictions, test_labels, test_images)\n",
        "    plt.subplot(num_rows, 2*num_cols, 2*i+2)\n",
        "    plot_value_array(i, predictions, test_labels)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x720 with 30 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1-a45_jGAzk4",
        "outputId": "1c691de0-add2-4e3e-ecad-9c4006b42163",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "img = test_images[0]\n",
        "print(img.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(28, 28)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HkuX4hptBXQl",
        "outputId": "5a0bb658-4226-425f-e63e-c2d2e7515771",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "img = (np.expand_dims(img, 0))\n",
        "print(img.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1, 28, 28)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_u6rCzqGBdJq",
        "outputId": "91de0c7f-22a0-47a0-cb5b-f568c6d0f41b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "predictions_single = model.predict(img)\n",
        "print(predictions_single)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[7.0431388e-06 4.7465236e-09 4.8299064e-08 1.6694194e-08 1.1687956e-07\n",
            "  9.4295223e-04 6.4219790e-07 1.7202383e-02 3.7202760e-06 9.8184305e-01]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b-fBjy9UBjDq",
        "outputId": "43229d2b-7f31-4a38-e2ce-7a0d1737466f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "source": [
        "plot_value_array(0, predictions_single, test_labels) \n",
        "_ = plt.xticks(range(10), class_names, rotation = 45)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GO4wSPdfBsmq",
        "outputId": "db2020dd-652b-4c63-a0ae-10c1a28f97d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "np.argmax(predictions_single[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "USlr1hF3Cvoq"
      },
      "source": [
        "import functools\n",
        "\n",
        "import numpy as np\n",
        "import tensorflow as tf"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9WYCXf98KMl2",
        "outputId": "53b69aed-c6db-4349-a029-4646054d4402",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "TRAIN_DATA_URL = \"https://storage.googleapis.com/tf-datasets/titanic/train.csv\"\n",
        "TEST_DATA_URL = \"https://storage.googleapis.com/tf-datasets/titanic/eval.csv\"\n",
        "\n",
        "train_file_path = tf.keras.utils.get_file(\"train.csv\", TRAIN_DATA_URL)\n",
        "test_file_path = tf.keras.utils.get_file(\"eval.csv\", TEST_DATA_URL)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tf-datasets/titanic/train.csv\n",
            "32768/30874 [===============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tf-datasets/titanic/eval.csv\n",
            "16384/13049 [=====================================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vDxGh4h1Kz3y"
      },
      "source": [
        "from matplotlib import pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kZz_UKLiLCMJ"
      },
      "source": [
        "np.set_printoptions(precision=3, suppress=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9WyCPt9HLVxt",
        "outputId": "c2f661e7-ac79-4e33-f24b-72b72df46a97",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "!head {train_file_path}"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "survived,sex,age,n_siblings_spouses,parch,fare,class,deck,embark_town,alone\n",
            "0,male,22.0,1,0,7.25,Third,unknown,Southampton,n\n",
            "1,female,38.0,1,0,71.2833,First,C,Cherbourg,n\n",
            "1,female,26.0,0,0,7.925,Third,unknown,Southampton,y\n",
            "1,female,35.0,1,0,53.1,First,C,Southampton,n\n",
            "0,male,28.0,0,0,8.4583,Third,unknown,Queenstown,y\n",
            "0,male,2.0,3,1,21.075,Third,unknown,Southampton,n\n",
            "1,female,27.0,0,2,11.1333,Third,unknown,Southampton,n\n",
            "1,female,14.0,1,0,30.0708,Second,unknown,Cherbourg,n\n",
            "1,female,4.0,1,1,16.7,Third,G,Southampton,n\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3CSMLW3SL3cr"
      },
      "source": [
        "LABELS = [0, 1]\n",
        "LABEL_COLUMN = 'survived'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qXvS4Iu3MJTF"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "\n",
        "import tensorflow_datasets as tfds"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QrNPRiW-Vz7L",
        "outputId": "0e236fe5-e36a-4494-f92e-ac1f2d1fc547",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "tfds.list_builders()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['abstract_reasoning',\n",
              " 'accentdb',\n",
              " 'aeslc',\n",
              " 'aflw2k3d',\n",
              " 'ag_news_subset',\n",
              " 'ai2_arc',\n",
              " 'ai2_arc_with_ir',\n",
              " 'amazon_us_reviews',\n",
              " 'anli',\n",
              " 'arc',\n",
              " 'bair_robot_pushing_small',\n",
              " 'bccd',\n",
              " 'beans',\n",
              " 'big_patent',\n",
              " 'bigearthnet',\n",
              " 'billsum',\n",
              " 'binarized_mnist',\n",
              " 'binary_alpha_digits',\n",
              " 'blimp',\n",
              " 'bool_q',\n",
              " 'c4',\n",
              " 'caltech101',\n",
              " 'caltech_birds2010',\n",
              " 'caltech_birds2011',\n",
              " 'cars196',\n",
              " 'cassava',\n",
              " 'cats_vs_dogs',\n",
              " 'celeb_a',\n",
              " 'celeb_a_hq',\n",
              " 'cfq',\n",
              " 'chexpert',\n",
              " 'cifar10',\n",
              " 'cifar100',\n",
              " 'cifar10_1',\n",
              " 'cifar10_corrupted',\n",
              " 'citrus_leaves',\n",
              " 'cityscapes',\n",
              " 'civil_comments',\n",
              " 'clevr',\n",
              " 'clic',\n",
              " 'clinc_oos',\n",
              " 'cmaterdb',\n",
              " 'cnn_dailymail',\n",
              " 'coco',\n",
              " 'coco_captions',\n",
              " 'coil100',\n",
              " 'colorectal_histology',\n",
              " 'colorectal_histology_large',\n",
              " 'common_voice',\n",
              " 'coqa',\n",
              " 'cos_e',\n",
              " 'cosmos_qa',\n",
              " 'covid19sum',\n",
              " 'crema_d',\n",
              " 'curated_breast_imaging_ddsm',\n",
              " 'cycle_gan',\n",
              " 'deep_weeds',\n",
              " 'definite_pronoun_resolution',\n",
              " 'dementiabank',\n",
              " 'diabetic_retinopathy_detection',\n",
              " 'div2k',\n",
              " 'dmlab',\n",
              " 'downsampled_imagenet',\n",
              " 'dsprites',\n",
              " 'dtd',\n",
              " 'duke_ultrasound',\n",
              " 'emnist',\n",
              " 'eraser_multi_rc',\n",
              " 'esnli',\n",
              " 'eurosat',\n",
              " 'fashion_mnist',\n",
              " 'flic',\n",
              " 'flores',\n",
              " 'food101',\n",
              " 'forest_fires',\n",
              " 'fuss',\n",
              " 'gap',\n",
              " 'geirhos_conflict_stimuli',\n",
              " 'genomics_ood',\n",
              " 'german_credit_numeric',\n",
              " 'gigaword',\n",
              " 'glue',\n",
              " 'goemotions',\n",
              " 'gpt3',\n",
              " 'groove',\n",
              " 'gtzan',\n",
              " 'gtzan_music_speech',\n",
              " 'hellaswag',\n",
              " 'higgs',\n",
              " 'horses_or_humans',\n",
              " 'i_naturalist2017',\n",
              " 'imagenet2012',\n",
              " 'imagenet2012_corrupted',\n",
              " 'imagenet2012_real',\n",
              " 'imagenet2012_subset',\n",
              " 'imagenet_a',\n",
              " 'imagenet_r',\n",
              " 'imagenet_resized',\n",
              " 'imagenet_v2',\n",
              " 'imagenette',\n",
              " 'imagewang',\n",
              " 'imdb_reviews',\n",
              " 'irc_disentanglement',\n",
              " 'iris',\n",
              " 'kitti',\n",
              " 'kmnist',\n",
              " 'lfw',\n",
              " 'librispeech',\n",
              " 'librispeech_lm',\n",
              " 'libritts',\n",
              " 'ljspeech',\n",
              " 'lm1b',\n",
              " 'lost_and_found',\n",
              " 'lsun',\n",
              " 'malaria',\n",
              " 'math_dataset',\n",
              " 'mctaco',\n",
              " 'mnist',\n",
              " 'mnist_corrupted',\n",
              " 'movie_lens',\n",
              " 'movie_rationales',\n",
              " 'movielens',\n",
              " 'moving_mnist',\n",
              " 'multi_news',\n",
              " 'multi_nli',\n",
              " 'multi_nli_mismatch',\n",
              " 'natural_questions',\n",
              " 'natural_questions_open',\n",
              " 'newsroom',\n",
              " 'nsynth',\n",
              " 'nyu_depth_v2',\n",
              " 'omniglot',\n",
              " 'open_images_challenge2019_detection',\n",
              " 'open_images_v4',\n",
              " 'openbookqa',\n",
              " 'opinion_abstracts',\n",
              " 'opinosis',\n",
              " 'opus',\n",
              " 'oxford_flowers102',\n",
              " 'oxford_iiit_pet',\n",
              " 'para_crawl',\n",
              " 'patch_camelyon',\n",
              " 'paws_wiki',\n",
              " 'paws_x_wiki',\n",
              " 'pet_finder',\n",
              " 'pg19',\n",
              " 'places365_small',\n",
              " 'plant_leaves',\n",
              " 'plant_village',\n",
              " 'plantae_k',\n",
              " 'qa4mre',\n",
              " 'qasc',\n",
              " 'quickdraw_bitmap',\n",
              " 'radon',\n",
              " 'reddit',\n",
              " 'reddit_disentanglement',\n",
              " 'reddit_tifu',\n",
              " 'resisc45',\n",
              " 'robonet',\n",
              " 'rock_paper_scissors',\n",
              " 'rock_you',\n",
              " 'salient_span_wikipedia',\n",
              " 'samsum',\n",
              " 'savee',\n",
              " 'scan',\n",
              " 'scene_parse150',\n",
              " 'scicite',\n",
              " 'scientific_papers',\n",
              " 'sentiment140',\n",
              " 'shapes3d',\n",
              " 'smallnorb',\n",
              " 'snli',\n",
              " 'so2sat',\n",
              " 'speech_commands',\n",
              " 'spoken_digit',\n",
              " 'squad',\n",
              " 'stanford_dogs',\n",
              " 'stanford_online_products',\n",
              " 'starcraft_video',\n",
              " 'stl10',\n",
              " 'sun397',\n",
              " 'super_glue',\n",
              " 'svhn_cropped',\n",
              " 'ted_hrlr_translate',\n",
              " 'ted_multi_translate',\n",
              " 'tedlium',\n",
              " 'tf_flowers',\n",
              " 'the300w_lp',\n",
              " 'tiny_shakespeare',\n",
              " 'titanic',\n",
              " 'trec',\n",
              " 'trivia_qa',\n",
              " 'tydi_qa',\n",
              " 'uc_merced',\n",
              " 'ucf101',\n",
              " 'vctk',\n",
              " 'vgg_face2',\n",
              " 'visual_domain_decathlon',\n",
              " 'voc',\n",
              " 'voxceleb',\n",
              " 'voxforge',\n",
              " 'waymo_open_dataset',\n",
              " 'web_questions',\n",
              " 'wider_face',\n",
              " 'wiki40b',\n",
              " 'wikihow',\n",
              " 'wikipedia',\n",
              " 'wikipedia_toxicity_subtypes',\n",
              " 'wine_quality',\n",
              " 'winogrande',\n",
              " 'wmt14_translate',\n",
              " 'wmt15_translate',\n",
              " 'wmt16_translate',\n",
              " 'wmt17_translate',\n",
              " 'wmt18_translate',\n",
              " 'wmt19_translate',\n",
              " 'wmt_t2t_translate',\n",
              " 'wmt_translate',\n",
              " 'wordnet',\n",
              " 'xnli',\n",
              " 'xquad',\n",
              " 'xsum',\n",
              " 'yelp_polarity_reviews',\n",
              " 'yes_no']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ISX7yGgEV2KS",
        "outputId": "a508976e-7522-4d0d-f60c-2baf512d2dd4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "ds = tfds.load('mnist', split='train', shuffle_files=True)\n",
        "assert isinstance(ds, tf.data.Dataset)\n",
        "print(ds)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<_OptionsDataset shapes: {image: (28, 28, 1), label: ()}, types: {image: tf.uint8, label: tf.int64}>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bnyVcG8RWTFD"
      },
      "source": [
        "?tfds.load"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "naxalC28WkLO",
        "outputId": "f0b81120-e581-464c-cfb8-4b3574da5a4b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "builder = tfds.builder('mnist')\n",
        "builder.download_and_prepare()\n",
        "ds = builder.as_dataset(split='train', shuffle_files=True)\n",
        "print(ds)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<_OptionsDataset shapes: {image: (28, 28, 1), label: ()}, types: {image: tf.uint8, label: tf.int64}>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qdsYfphlXA4M",
        "outputId": "f19e9ab2-eff3-442c-ea08-2a283f62e2b3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "ds = ds.take(1)\n",
        "\n",
        "for example in ds:\n",
        "    print(list(example.keys()))\n",
        "    image = example[\"image\"]\n",
        "    label = example[\"label\"]\n",
        "    print(image.shape, label)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['image', 'label']\n",
            "(28, 28, 1) tf.Tensor(4, shape=(), dtype=int64)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LYW28FeaXjWe",
        "outputId": "c19b0e00-a3b0-4d6c-d962-4ccb38b8b02e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "ds = tfds.load('mnist', split='train', as_supervised=True)\n",
        "ds = ds.take(1)\n",
        "\n",
        "for image, label in ds:\n",
        "    print(image.shape, label)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(28, 28, 1) tf.Tensor(4, shape=(), dtype=int64)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PkMb-GwFYC8Q",
        "outputId": "f47e2ef2-035e-40e1-eabc-76e1fb07a4ae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "for image, label in tfds.as_numpy(ds):\n",
        "    print(type(image), type(label), label)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'numpy.ndarray'> <class 'numpy.int64'> 4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Ja0HaBAYO7l",
        "outputId": "203f66b6-1c1d-4016-c548-dfb4fadd3f74",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "image, label = tfds.as_numpy(tfds.load(\n",
        "    'mnist',\n",
        "    split='test',\n",
        "    batch_size=-1,\n",
        "    as_supervised=True,\n",
        "))\n",
        "print(type(image), image.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'numpy.ndarray'> (10000, 28, 28, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7e9Xq6yUYqhA",
        "outputId": "dcd863f5-6bbc-411a-a387-2716b9da6a21",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 184
        }
      },
      "source": [
        "ds, info = tfds.load('mnist', split='train', with_info=True)\n",
        "tfds.as_dataframe(ds.take(4), info)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "                <tr>\n",
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              "                        <td id=\"T_e35b58f8_17e8_11eb_b8a9_0242ac1c0002row1_col1\" class=\"data row1 col1\" >1</td>\n",
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              "            <tr>\n",
              "                        <th id=\"T_e35b58f8_17e8_11eb_b8a9_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
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              "                        <th id=\"T_e35b58f8_17e8_11eb_b8a9_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "                        <td id=\"T_e35b58f8_17e8_11eb_b8a9_0242ac1c0002row3_col0\" class=\"data row3 col0\" ><img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAAA40lEQVR4nGNgGJpg//8OdCEWKM2obvgv/++6/zcZnJQebPuNqkjvLxI4IIgiJ3/v79/3b//9/fvv7993f/9NQzE2TZ6hc8J3JwYGBgaGK7cYeJE12n79+1cCxlH9+/cVB5LOlxy/pryHSUYyMO78gSR5W/vzU7gxfAz/cfq36fvfDw445Fr+/P3bg12KMebb37+7WbDKKSz89/fvNRmscjpb//79u0YBq5z0yb9//2bhcEvXv78fs1ixyzX//PsuHYc+gbvwAMcEmX//3kFzJxOc9YCRoesJLp3sx9+p4JIjGQAAnrpmBs0pxioAAAAASUVORK5CYII=\" alt=\"Img\" /></td>\n",
              "                        <td id=\"T_e35b58f8_17e8_11eb_b8a9_0242ac1c0002row3_col1\" class=\"data row3 col1\" >7</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
            ],
            "text/plain": [
              "                                               image  label\n",
              "0  [[[0], [0], [0], [0], [0], [0], [0], [0], [0],...      4\n",
              "1  [[[0], [0], [0], [0], [0], [0], [0], [0], [0],...      1\n",
              "2  [[[0], [0], [0], [0], [0], [0], [0], [0], [0],...      0\n",
              "3  [[[0], [0], [0], [0], [0], [0], [0], [0], [0],...      7"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "REFMIt6hZL4a",
        "outputId": "39a6ae4e-7d37-4ec8-8d31-bd951086c271",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 534
        }
      },
      "source": [
        "ds, info = tfds.load('mnist', split='train', with_info=True)\n",
        "fig = tfds.show_examples(ds, info)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x648 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SST5A4Mlb1Np"
      },
      "source": [
        "builder = tfds.builder('mnist')\n",
        "info = builder.info"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i57nx_03cI1k",
        "outputId": "6380ab3b-b959-4805-b6bf-87f129cc5824",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        }
      },
      "source": [
        "print(info)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tfds.core.DatasetInfo(\n",
            "    name='mnist',\n",
            "    version=3.0.1,\n",
            "    description='The MNIST database of handwritten digits.',\n",
            "    homepage='http://yann.lecun.com/exdb/mnist/',\n",
            "    features=FeaturesDict({\n",
            "        'image': Image(shape=(28, 28, 1), dtype=tf.uint8),\n",
            "        'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=10),\n",
            "    }),\n",
            "    total_num_examples=70000,\n",
            "    splits={\n",
            "        'test': 10000,\n",
            "        'train': 60000,\n",
            "    },\n",
            "    supervised_keys=('image', 'label'),\n",
            "    citation=\"\"\"@article{lecun2010mnist,\n",
            "      title={MNIST handwritten digit database},\n",
            "      author={LeCun, Yann and Cortes, Corinna and Burges, CJ},\n",
            "      journal={ATT Labs [Online]. Available: http://yann.lecun.com/exdb/mnist},\n",
            "      volume={2},\n",
            "      year={2010}\n",
            "    }\"\"\",\n",
            "    redistribution_info=,\n",
            ")\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jRxpNorTcKKM",
        "outputId": "b7af7389-f0b0-4465-978d-7590acc84602",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "info.features"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "FeaturesDict({\n",
              "    'image': Image(shape=(28, 28, 1), dtype=tf.uint8),\n",
              "    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=10),\n",
              "})"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dP91EFLMcOQB",
        "outputId": "7fad4761-369b-4f4e-d800-c31ce2ce9d47",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "print(info.features[\"label\"].num_classes)\n",
        "print(info.features[\"label\"].names)\n",
        "print(info.features[\"label\"].int2str(7))  # Human readable version (8 -> 'cat')\n",
        "print(info.features[\"label\"].str2int('7'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10\n",
            "['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']\n",
            "7\n",
            "7\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R8tHHitTcR4m",
        "outputId": "6b001b19-735c-4451-d69c-4558d479ddee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "print(info.features.shape)\n",
        "print(info.features.dtype)\n",
        "print(info.features['image'].shape)\n",
        "print(info.features['image'].dtype)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'image': (28, 28, 1), 'label': ()}\n",
            "{'image': tf.uint8, 'label': tf.int64}\n",
            "(28, 28, 1)\n",
            "<dtype: 'uint8'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZuA4QJQbcci2",
        "outputId": "2abef7fb-de64-45c9-d896-ab40afb2e319",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(info.splits)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'test': <tfds.core.SplitInfo num_examples=10000>, 'train': <tfds.core.SplitInfo num_examples=60000>}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-2-ZGkwNcei5",
        "outputId": "5d8df2e3-5f6f-4c25-a425-c0bfb91ccf09",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(list(info.splits.keys()))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['test', 'train']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HJUuUHGwcg-D"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}