7-2.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iNFOeMfl3tIu"
      },
      "source": [
        "# 심층 신경망"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zKfwb8gS3tI2"
      },
      "source": [
        "<table align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/rickiepark/hg-mldl/blob/master/7-2.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": "ZT8SDtZVv41l"
      },
      "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": "dPE5XsFhcfVD"
      },
      "source": [
        "## 2개의 층"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4sNOMcdaFVKa",
        "outputId": "4af696d2-94c6-4523-a4cb-552792574b48"
      },
      "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 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
            "26421880/26421880 [==============================] - 1s 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",
        "\n",
        "(train_input, train_target), (test_input, test_target) = keras.datasets.fashion_mnist.load_data()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "aJJiRMa6FkWx"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "train_scaled = train_input / 255.0\n",
        "train_scaled = train_scaled.reshape(-1, 28*28)\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": 4,
      "metadata": {
        "id": "MCZWQiEwF6MD"
      },
      "outputs": [],
      "source": [
        "dense1 = keras.layers.Dense(100, activation='sigmoid', input_shape=(784,))\n",
        "dense2 = keras.layers.Dense(10, activation='softmax')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Agy5gCVhcrm-"
      },
      "source": [
        "## 심층 신경망 만들기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "xmWL7kOoGB4P"
      },
      "outputs": [],
      "source": [
        "model = keras.Sequential([dense1, dense2])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "em0xDl8qa12J",
        "outputId": "9cd9a669-2595-4783-869c-433ceac72cdd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \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.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qAi41rBTdk7k"
      },
      "source": [
        "## 층을 추가하는 다른 방법"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "0XeV6V4ha3I8"
      },
      "outputs": [],
      "source": [
        "model = keras.Sequential([\n",
        "    keras.layers.Dense(100, activation='sigmoid', input_shape=(784,), name='hidden'),\n",
        "    keras.layers.Dense(10, activation='softmax', name='output')\n",
        "], name='패션 MNIST 모델')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bwXDLSOWbm3L",
        "outputId": "336b4ac7-3f82-40fb-e225-fa115072f7b9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"패션 MNIST 모델\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " hidden (Dense)              (None, 100)               78500     \n",
            "                                                                 \n",
            " output (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.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "yZSAxgZCbax7"
      },
      "outputs": [],
      "source": [
        "model = keras.Sequential()\n",
        "model.add(keras.layers.Dense(100, activation='sigmoid', input_shape=(784,)))\n",
        "model.add(keras.layers.Dense(10, activation='softmax'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bW2coaNQboe5",
        "outputId": "843207b4-596c-4500-b5c0-6dcd7a7bb786"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_2 (Dense)             (None, 100)               78500     \n",
            "                                                                 \n",
            " dense_3 (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.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kkYrPJembpYk",
        "outputId": "b8849ec0-5274-4dc8-a653-c80b1fb6772b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "1500/1500 [==============================] - 12s 3ms/step - loss: 0.5710 - accuracy: 0.8064\n",
            "Epoch 2/5\n",
            "1500/1500 [==============================] - 4s 3ms/step - loss: 0.4132 - accuracy: 0.8509\n",
            "Epoch 3/5\n",
            "1500/1500 [==============================] - 5s 3ms/step - loss: 0.3776 - accuracy: 0.8646\n",
            "Epoch 4/5\n",
            "1500/1500 [==============================] - 5s 3ms/step - loss: 0.3530 - accuracy: 0.8732\n",
            "Epoch 5/5\n",
            "1500/1500 [==============================] - 5s 3ms/step - loss: 0.3344 - accuracy: 0.8782\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x793a140c0a00>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "model.compile(loss='sparse_categorical_crossentropy', metrics='accuracy')\n",
        "\n",
        "model.fit(train_scaled, train_target, epochs=5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_dfXJDhwcyAK"
      },
      "source": [
        "## 렐루 활성화 함수"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "dG7yF8g6b062"
      },
      "outputs": [],
      "source": [
        "model = keras.Sequential()\n",
        "model.add(keras.layers.Flatten(input_shape=(28, 28)))\n",
        "model.add(keras.layers.Dense(100, activation='relu'))\n",
        "model.add(keras.layers.Dense(10, activation='softmax'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zHogWhu6g90a",
        "outputId": "4d4b7714-d834-499b-a928-2c9bd198134f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " flatten (Flatten)           (None, 784)               0         \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 100)               78500     \n",
            "                                                                 \n",
            " dense_5 (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.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "JfPe_ruQdhqA"
      },
      "outputs": [],
      "source": [
        "(train_input, train_target), (test_input, test_target) = 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": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9PGejuuhdvvk",
        "outputId": "9d6860e5-ea5a-4240-eb45-9a6783c52321"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "1500/1500 [==============================] - 5s 3ms/step - loss: 0.5290 - accuracy: 0.8113\n",
            "Epoch 2/5\n",
            "1500/1500 [==============================] - 5s 4ms/step - loss: 0.3920 - accuracy: 0.8576\n",
            "Epoch 3/5\n",
            "1500/1500 [==============================] - 4s 3ms/step - loss: 0.3525 - accuracy: 0.8726\n",
            "Epoch 4/5\n",
            "1500/1500 [==============================] - 5s 3ms/step - loss: 0.3301 - accuracy: 0.8821\n",
            "Epoch 5/5\n",
            "1500/1500 [==============================] - 5s 3ms/step - loss: 0.3141 - accuracy: 0.8867\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x793a0a1f9660>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "model.compile(loss='sparse_categorical_crossentropy', metrics='accuracy')\n",
        "\n",
        "model.fit(train_scaled, train_target, epochs=5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lVYLpnjeep4y",
        "outputId": "1f3d6a5f-e210-442e-97d0-e5c393561466"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3683 - accuracy: 0.8726\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.3683287501335144, 0.8725833296775818]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "model.evaluate(val_scaled, val_target)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3YtLsmGAoavz"
      },
      "source": [
        "## 옵티마이저"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "NxVj04Haocwa"
      },
      "outputs": [],
      "source": [
        "model.compile(optimizer='sgd', loss='sparse_categorical_crossentropy', metrics='accuracy')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "1426O4G8Hpfi"
      },
      "outputs": [],
      "source": [
        "sgd = keras.optimizers.SGD()\n",
        "model.compile(optimizer=sgd, loss='sparse_categorical_crossentropy', metrics='accuracy')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "Sh-HDiULlp18"
      },
      "outputs": [],
      "source": [
        "sgd = keras.optimizers.SGD(learning_rate=0.1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "uF1XolBXsl3a"
      },
      "outputs": [],
      "source": [
        "sgd = keras.optimizers.SGD(momentum=0.9, nesterov=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "Hy2MENbL170j"
      },
      "outputs": [],
      "source": [
        "adagrad = keras.optimizers.Adagrad()\n",
        "model.compile(optimizer=adagrad, loss='sparse_categorical_crossentropy', metrics='accuracy')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "KkpbSMXWtakr"
      },
      "outputs": [],
      "source": [
        "rmsprop = keras.optimizers.RMSprop()\n",
        "model.compile(optimizer=rmsprop, loss='sparse_categorical_crossentropy', metrics='accuracy')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "Gdu0hQIAz4JW"
      },
      "outputs": [],
      "source": [
        "model = keras.Sequential()\n",
        "model.add(keras.layers.Flatten(input_shape=(28, 28)))\n",
        "model.add(keras.layers.Dense(100, activation='relu'))\n",
        "model.add(keras.layers.Dense(10, activation='softmax'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2tcxIfILoi1t",
        "outputId": "f808142b-a65b-4e7a-a58d-4463fff4b8c7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "1500/1500 [==============================] - 7s 4ms/step - loss: 0.5266 - accuracy: 0.8154\n",
            "Epoch 2/5\n",
            "1500/1500 [==============================] - 5s 3ms/step - loss: 0.3957 - accuracy: 0.8588\n",
            "Epoch 3/5\n",
            "1500/1500 [==============================] - 4s 3ms/step - loss: 0.3564 - accuracy: 0.8705\n",
            "Epoch 4/5\n",
            "1500/1500 [==============================] - 5s 4ms/step - loss: 0.3280 - accuracy: 0.8796\n",
            "Epoch 5/5\n",
            "1500/1500 [==============================] - 4s 3ms/step - loss: 0.3085 - accuracy: 0.8851\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x793a0a05ebf0>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics='accuracy')\n",
        "\n",
        "model.fit(train_scaled, train_target, epochs=5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8gxAWehsv9Gi",
        "outputId": "4d93e991-ae20-45fb-93e0-35f160829dd8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3485 - accuracy: 0.8763\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.3484817445278168, 0.8762500286102295]"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "model.evaluate(val_scaled, val_target)"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "7-2 심층 신경망.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "default:Python",
      "language": "python",
      "name": "conda-env-default-py"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.10"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}