7-3.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pqxnslml3tu6"
      },
      "source": [
        "# 신경망 모델 훈련"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fN7zF6Zy3tvE"
      },
      "source": [
        "<table align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/rickiepark/hg-mldl/blob/master/7-3.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />구글 코랩에서 실행하기</a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "M54mUYlhwnex"
      },
      "outputs": [],
      "source": [
        "# 실행마다 동일한 결과를 얻기 위해 케라스에 랜덤 시드를 사용하고 텐서플로 연산을 결정적으로 만듭니다.\n",
        "import tensorflow as tf\n",
        "\n",
        "tf.keras.utils.set_random_seed(42)\n",
        "tf.config.experimental.enable_op_determinism()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XGP-X65EmJBg"
      },
      "source": [
        "## 손실 곡선"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hZsGl9udlqZk",
        "outputId": "03aebc93-253e-4f21-b8c1-b1510d68c038"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
            "29515/29515 [==============================] - 0s 1us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
            "26421880/26421880 [==============================] - 2s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
            "5148/5148 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
            "4422102/4422102 [==============================] - 1s 0us/step\n"
          ]
        }
      ],
      "source": [
        "from tensorflow import keras\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "(train_input, train_target), (test_input, test_target) = \\\n",
        "    keras.datasets.fashion_mnist.load_data()\n",
        "\n",
        "train_scaled = train_input / 255.0\n",
        "\n",
        "train_scaled, val_scaled, train_target, val_target = train_test_split(\n",
        "    train_scaled, train_target, test_size=0.2, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "iogH7o0Ll6uL"
      },
      "outputs": [],
      "source": [
        "def model_fn(a_layer=None):\n",
        "    model = keras.Sequential()\n",
        "    model.add(keras.layers.Flatten(input_shape=(28, 28)))\n",
        "    model.add(keras.layers.Dense(100, activation='relu'))\n",
        "    if a_layer:\n",
        "        model.add(a_layer)\n",
        "    model.add(keras.layers.Dense(10, activation='softmax'))\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Eh6hM4DNdzu",
        "outputId": "3a49d79d-0174-4d91-f9ff-65fc858aaec0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " flatten (Flatten)           (None, 784)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 100)               78500     \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 10)                1010      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 79510 (310.59 KB)\n",
            "Trainable params: 79510 (310.59 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model = model_fn()\n",
        "\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "P-UK21N_mCM0"
      },
      "outputs": [],
      "source": [
        "model.compile(loss='sparse_categorical_crossentropy', metrics='accuracy')\n",
        "\n",
        "history = model.fit(train_scaled, train_target, epochs=5, verbose=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1ix_EA2LpaR0",
        "outputId": "dd6b6bbe-f34b-4081-b23a-d561ef27fb17"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dict_keys(['loss', 'accuracy'])\n"
          ]
        }
      ],
      "source": [
        "print(history.history.keys())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "uz_TCdfPmG6e",
        "outputId": "85241052-c0bb-4e29-c024-885cab73359e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.plot(history.history['loss'])\n",
        "plt.xlabel('epoch')\n",
        "plt.ylabel('loss')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "id": "CpmK9lXQcBe9",
        "outputId": "6e7b4130-2c06-410a-e839-56da975a2a94"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.plot(history.history['accuracy'])\n",
        "plt.xlabel('epoch')\n",
        "plt.ylabel('accuracy')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "5cJlWITXqJWr"
      },
      "outputs": [],
      "source": [
        "model = model_fn()\n",
        "model.compile(loss='sparse_categorical_crossentropy', metrics='accuracy')\n",
        "\n",
        "history = model.fit(train_scaled, train_target, epochs=20, verbose=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "id": "YT87Fjo2qKPC",
        "outputId": "fe59e8a1-d806-4a93-82ca-a85c43d2a3c8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.xlabel('epoch')\n",
        "plt.ylabel('loss')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bGqf6ceRr3zO"
      },
      "source": [
        "## 검증 손실"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "4_iHvMxwu2D2"
      },
      "outputs": [],
      "source": [
        "model = model_fn()\n",
        "model.compile(loss='sparse_categorical_crossentropy', metrics='accuracy')\n",
        "\n",
        "history = model.fit(train_scaled, train_target, epochs=20, verbose=0,\n",
        "                    validation_data=(val_scaled, val_target))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nJ5RGEmLu5KI",
        "outputId": "21a50d4a-56f0-447c-c74b-8b85396ac74f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n"
          ]
        }
      ],
      "source": [
        "print(history.history.keys())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        },
        "id": "zcpm7CpXu5vC",
        "outputId": "a3e18542-93f8-4efb-ee30-f178fe8f3119"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.xlabel('epoch')\n",
        "plt.ylabel('loss')\n",
        "plt.legend(['train', 'val'])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "qC8gDwo3qcJv"
      },
      "outputs": [],
      "source": [
        "model = model_fn()\n",
        "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy',\n",
        "              metrics='accuracy')\n",
        "\n",
        "history = model.fit(train_scaled, train_target, epochs=20, verbose=0,\n",
        "                    validation_data=(val_scaled, val_target))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "k8wWnyFzsLKb",
        "outputId": "e4e317dd-d607-4c65-e1ff-8c40395721a9"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.xlabel('epoch')\n",
        "plt.ylabel('loss')\n",
        "plt.legend(['train', 'val'])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F-dFY8lrYXm3"
      },
      "source": [
        "## 드롭아웃"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AppFtFKgsk--",
        "outputId": "1986a0e5-ec64-415f-d691-2f85d863438b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " flatten_4 (Flatten)         (None, 784)               0         \n",
            "                                                                 \n",
            " dense_8 (Dense)             (None, 100)               78500     \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 100)               0         \n",
            "                                                                 \n",
            " dense_9 (Dense)             (None, 10)                1010      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 79510 (310.59 KB)\n",
            "Trainable params: 79510 (310.59 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model = model_fn(keras.layers.Dropout(0.3))\n",
        "\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "_TSe7oM9v1lW"
      },
      "outputs": [],
      "source": [
        "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy',\n",
        "              metrics='accuracy')\n",
        "\n",
        "history = model.fit(train_scaled, train_target, epochs=20, verbose=0,\n",
        "                    validation_data=(val_scaled, val_target))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "Rj_syB_iv30l",
        "outputId": "44bd26ac-e647-4f6b-f2f6-5c567da8f3c0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.xlabel('epoch')\n",
        "plt.ylabel('loss')\n",
        "plt.legend(['train', 'val'])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hQi91PCQorHr"
      },
      "source": [
        "## 모델 저장과 복원"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "npsYdX3rv6Oa"
      },
      "outputs": [],
      "source": [
        "model = model_fn(keras.layers.Dropout(0.3))\n",
        "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy',\n",
        "              metrics='accuracy')\n",
        "\n",
        "history = model.fit(train_scaled, train_target, epochs=10, verbose=0,\n",
        "                    validation_data=(val_scaled, val_target))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "hWVYzt0Y2FPm"
      },
      "outputs": [],
      "source": [
        "model.save_weights('model-weights.h5')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "NissHzcq3xbN",
        "outputId": "d50e1450-21d4-4c7a-9383-bd6cc746dba9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3079: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n"
          ]
        }
      ],
      "source": [
        "model.save('model-whole.h5')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2vljkGGu3AUl",
        "outputId": "16356027-5c4f-43d8-89d6-efa4606af725",
        "scrolled": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-rw-r--r-- 1 root root 333320 Nov 11 04:10 model-weights.h5\n",
            "-rw-r--r-- 1 root root 981176 Nov 11 04:10 model-whole.h5\n"
          ]
        }
      ],
      "source": [
        "!ls -al *.h5"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "7-t6gC5Z3GCM"
      },
      "outputs": [],
      "source": [
        "model = model_fn(keras.layers.Dropout(0.3))\n",
        "\n",
        "model.load_weights('model-weights.h5')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pHV9tBnzi8St",
        "outputId": "17c8b6db-79b8-42c5-fc77-3fa02d201004"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "375/375 [==============================] - 1s 2ms/step\n",
            "0.8775\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "val_labels = np.argmax(model.predict(val_scaled), axis=-1)\n",
        "print(np.mean(val_labels == val_target))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sRM3Vpki4QyH",
        "outputId": "a972c42d-35d1-4cc1-9c58-bdcbff331089"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3388 - accuracy: 0.8775\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.3387581706047058, 0.8774999976158142]"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "model = keras.models.load_model('model-whole.h5')\n",
        "\n",
        "model.evaluate(val_scaled, val_target)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4NTCF3YD3EyA"
      },
      "source": [
        "## 콜백"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L2lKN_934VB4",
        "outputId": "4d1de57d-e539-4d38-939f-84a8a217e40b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7acba3ec6e00>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "model = model_fn(keras.layers.Dropout(0.3))\n",
        "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy',\n",
        "              metrics='accuracy')\n",
        "\n",
        "checkpoint_cb = keras.callbacks.ModelCheckpoint('best-model.h5',\n",
        "                                                save_best_only=True)\n",
        "\n",
        "model.fit(train_scaled, train_target, epochs=20, verbose=0,\n",
        "          validation_data=(val_scaled, val_target),\n",
        "          callbacks=[checkpoint_cb])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qYISeH6U5oh9",
        "outputId": "340cf8d3-b488-45f2-9dd0-e1366ac82a04"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3177 - accuracy: 0.8865\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.317716509103775, 0.8865000009536743]"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "model = keras.models.load_model('best-model.h5')\n",
        "\n",
        "model.evaluate(val_scaled, val_target)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "HLLlkR0s5Nd8"
      },
      "outputs": [],
      "source": [
        "model = model_fn(keras.layers.Dropout(0.3))\n",
        "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy',\n",
        "              metrics='accuracy')\n",
        "\n",
        "checkpoint_cb = keras.callbacks.ModelCheckpoint('best-model.h5',\n",
        "                                                save_best_only=True)\n",
        "early_stopping_cb = keras.callbacks.EarlyStopping(patience=2,\n",
        "                                                  restore_best_weights=True)\n",
        "\n",
        "history = model.fit(train_scaled, train_target, epochs=20, verbose=0,\n",
        "                    validation_data=(val_scaled, val_target),\n",
        "                    callbacks=[checkpoint_cb, early_stopping_cb])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b6TazMcDxqXD",
        "outputId": "57cc7b8d-a46e-4c60-dc7e-643a54b5e08f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10\n"
          ]
        }
      ],
      "source": [
        "print(early_stopping_cb.stopped_epoch)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "QrUNYGPB6Kq7",
        "outputId": "94bfbc85-ec83-4159-dc34-8a3ba4b5c6b4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.xlabel('epoch')\n",
        "plt.ylabel('loss')\n",
        "plt.legend(['train', 'val'])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H-7y1qlg5yqO",
        "outputId": "505183b6-c098-4134-8177-c8f73f62b6ad"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3230 - accuracy: 0.8803\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.32295477390289307, 0.8803333044052124]"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "model.evaluate(val_scaled, val_target)"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
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