material/deep_learning/cnn_fashion_mnist.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "cnn_fashion_mnist.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c503DSvxvOU5"
      },
      "source": [
        "# 이전 DNN 코드"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G8M2QsSUxGZn"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import optimizers\n",
        "from tensorflow.keras.layers import Dense, Flatten, Input\n",
        "\n",
        "import time"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "98Ly9UIFxIjO"
      },
      "source": [
        "(raw_train_x, raw_train_y), (raw_test_x, raw_test_y) = tf.keras.datasets.fashion_mnist.load_data()\n",
        "\n",
        "train_x = raw_train_x/255\n",
        "test_x = raw_test_x/255\n",
        "\n",
        "train_y = raw_train_y\n",
        "test_y = raw_test_y"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gZ-hK0S5xIl7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f3a41118-04fb-4587-96be-6bb25f1d2f95"
      },
      "source": [
        "\n",
        "model = keras.Sequential()\n",
        "model.add(Input((28,28)))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(10, activation='relu'))\n",
        "model.add(Dense(10, activation='relu'))\n",
        "model.add(Dense(10, activation='softmax'))\n",
        "\n",
        "model.compile(optimizer=\"adam\", loss=\"sparse_categorical_crossentropy\", metrics=[\"accuracy\"])\n",
        "model.summary()\n",
        "\n",
        "\n",
        "model.fit(train_x, train_y, epochs=5, verbose=1, batch_size=128)\n",
        "\n",
        "\n",
        "loss, acc = model.evaluate(test_x, test_y)\n",
        "print(\"loss=\",loss)\n",
        "print(\"acc=\",acc)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Please add `keras.layers.InputLayer` instead of `keras.Input` to Sequential model. `keras.Input` is intended to be used by Functional model.\n",
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "flatten_3 (Flatten)          (None, 784)               0         \n",
            "_________________________________________________________________\n",
            "dense_9 (Dense)              (None, 10)                7850      \n",
            "_________________________________________________________________\n",
            "dense_10 (Dense)             (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_11 (Dense)             (None, 10)                110       \n",
            "=================================================================\n",
            "Total params: 8,070\n",
            "Trainable params: 8,070\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/5\n",
            "469/469 [==============================] - 2s 2ms/step - loss: 0.8797 - accuracy: 0.7228\n",
            "Epoch 2/5\n",
            "469/469 [==============================] - 1s 2ms/step - loss: 0.5115 - accuracy: 0.8221\n",
            "Epoch 3/5\n",
            "469/469 [==============================] - 1s 2ms/step - loss: 0.4663 - accuracy: 0.8394\n",
            "Epoch 4/5\n",
            "469/469 [==============================] - 1s 2ms/step - loss: 0.4428 - accuracy: 0.8459\n",
            "Epoch 5/5\n",
            "469/469 [==============================] - 1s 2ms/step - loss: 0.4293 - accuracy: 0.8512\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.4597 - accuracy: 0.8409\n",
            "loss= 0.4597318470478058\n",
            "acc= 0.8409000039100647\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cHcZ3XNF2qkV"
      },
      "source": [
        "# 기존 CNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vP9m1uS2fxj1"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import optimizers\n",
        "from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D, Input, Reshape\n",
        "\n",
        "import time"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h-Hlstlof5l6"
      },
      "source": [
        "(raw_train_x, raw_train_y), (raw_test_x, raw_test_y) = tf.keras.datasets.mnist.load_data()\n",
        "\n",
        "train_x = raw_train_x/255\n",
        "test_x = raw_test_x/255\n",
        "\n",
        "train_y = raw_train_y\n",
        "test_y = raw_test_y"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nRkSjvdSkgfc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2c0c36da-8d99-45ad-e79b-b006463d03e4"
      },
      "source": [
        "model = keras.Sequential()\n",
        "model.add(Input((28,28)))\n",
        "model.add(Reshape((28,28,1)))\n",
        "model.add(Conv2D(32, (3, 3)))\n",
        "model.add(MaxPooling2D((2, 2)))\n",
        "model.add(Conv2D(64, (3, 3)))\n",
        "model.add(MaxPooling2D((2, 2)))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(10, activation='relu'))\n",
        "model.add(Dense(10, activation='relu'))\n",
        "model.add(Dense(10, activation='softmax'))\n",
        "\n",
        "\n",
        "model.compile(optimizer=\"adam\", loss=\"sparse_categorical_crossentropy\", metrics=[\"accuracy\"])\n",
        "model.summary()\n",
        "\n",
        "\n",
        "model.fit(train_x, train_y, epochs=5, verbose=1, batch_size=128)\n",
        "\n",
        "\n",
        "loss, acc = model.evaluate(test_x, test_y)\n",
        "print(\"loss=\",loss)\n",
        "print(\"acc=\",acc)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Please add `keras.layers.InputLayer` instead of `keras.Input` to Sequential model. `keras.Input` is intended to be used by Functional model.\n",
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "reshape_2 (Reshape)          (None, 28, 28, 1)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_9 (Conv2D)            (None, 26, 26, 32)        320       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_4 (MaxPooling2 (None, 13, 13, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_10 (Conv2D)           (None, 11, 11, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_5 (MaxPooling2 (None, 5, 5, 64)          0         \n",
            "_________________________________________________________________\n",
            "flatten_4 (Flatten)          (None, 1600)              0         \n",
            "_________________________________________________________________\n",
            "dense_12 (Dense)             (None, 10)                16010     \n",
            "_________________________________________________________________\n",
            "dense_13 (Dense)             (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_14 (Dense)             (None, 10)                110       \n",
            "=================================================================\n",
            "Total params: 35,046\n",
            "Trainable params: 35,046\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/5\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.4250 - accuracy: 0.8729\n",
            "Epoch 2/5\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.1358 - accuracy: 0.9589\n",
            "Epoch 3/5\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0999 - accuracy: 0.9702\n",
            "Epoch 4/5\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0820 - accuracy: 0.9757\n",
            "Epoch 5/5\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0696 - accuracy: 0.9790\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.0775 - accuracy: 0.9763\n",
            "loss= 0.0774669349193573\n",
            "acc= 0.9763000011444092\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sHiloN1vToNE"
      },
      "source": [
        "# 다른 CNN 모델\n",
        "\n",
        "copy from https://dacon.io/competitions/open/235594/codeshare/2297?page=1&dtype=recent"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zoKCVTnCVEYn"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import optimizers\n",
        "from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D, Input, Reshape\n",
        "from tensorflow.keras.layers import Dropout, BatchNormalization\n",
        "from tensorflow.keras.optimizers import Adam, RMSprop"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KmSoutPETqCF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aff9df06-db19-4311-9fb1-86e198681a91"
      },
      "source": [
        "model = keras.Sequential()\n",
        "model.add(Input((28,28)))\n",
        "model.add(Reshape((28,28,1)))\n",
        "model.add(Conv2D(64,(3,3),activation='relu',padding='same'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dropout(0.3))\n",
        "model.add(Conv2D(32,(3,3),activation='relu',padding='same'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Conv2D(32,(5,5),activation='relu',padding='same')) \n",
        "model.add(BatchNormalization())\n",
        "model.add(Conv2D(32,(5,5),activation='relu',padding='same'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Conv2D(32,(5,5),activation='relu',padding='same'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D((3,3)))\n",
        "model.add(Dropout(0.3))\n",
        "model.add(Conv2D(64,(3,3),activation='relu',padding='same'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Conv2D(64,(5,5),activation='relu',padding='same')) \n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D((3,3)))\n",
        "model.add(Dropout(0.3))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(128,activation='relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dropout(0.3))\n",
        "model.add(Dense(64,activation='relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dropout(0.3))\n",
        "model.add(Dense(10,activation='softmax'))\n",
        "\n",
        "model.summary()\n",
        "\n",
        "model.compile(loss='sparse_categorical_crossentropy', optimizer = RMSprop(learning_rate=0.003),metrics=['acc'])\n",
        "\n",
        "model.fit(train_x, train_y, epochs=5, verbose=1, batch_size=128)\n",
        "\n",
        "loss, acc = model.evaluate(test_x, test_y)\n",
        "print(\"loss=\",loss)\n",
        "print(\"acc=\",acc)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Please add `keras.layers.InputLayer` instead of `keras.Input` to Sequential model. `keras.Input` is intended to be used by Functional model.\n",
            "Model: \"sequential_5\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "reshape_3 (Reshape)          (None, 28, 28, 1)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_11 (Conv2D)           (None, 28, 28, 64)        640       \n",
            "_________________________________________________________________\n",
            "batch_normalization_9 (Batch (None, 28, 28, 64)        256       \n",
            "_________________________________________________________________\n",
            "dropout_5 (Dropout)          (None, 28, 28, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_12 (Conv2D)           (None, 28, 28, 32)        18464     \n",
            "_________________________________________________________________\n",
            "batch_normalization_10 (Batc (None, 28, 28, 32)        128       \n",
            "_________________________________________________________________\n",
            "conv2d_13 (Conv2D)           (None, 28, 28, 32)        25632     \n",
            "_________________________________________________________________\n",
            "batch_normalization_11 (Batc (None, 28, 28, 32)        128       \n",
            "_________________________________________________________________\n",
            "conv2d_14 (Conv2D)           (None, 28, 28, 32)        25632     \n",
            "_________________________________________________________________\n",
            "batch_normalization_12 (Batc (None, 28, 28, 32)        128       \n",
            "_________________________________________________________________\n",
            "conv2d_15 (Conv2D)           (None, 28, 28, 32)        25632     \n",
            "_________________________________________________________________\n",
            "batch_normalization_13 (Batc (None, 28, 28, 32)        128       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_6 (MaxPooling2 (None, 9, 9, 32)          0         \n",
            "_________________________________________________________________\n",
            "dropout_6 (Dropout)          (None, 9, 9, 32)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_16 (Conv2D)           (None, 9, 9, 64)          18496     \n",
            "_________________________________________________________________\n",
            "batch_normalization_14 (Batc (None, 9, 9, 64)          256       \n",
            "_________________________________________________________________\n",
            "conv2d_17 (Conv2D)           (None, 9, 9, 64)          102464    \n",
            "_________________________________________________________________\n",
            "batch_normalization_15 (Batc (None, 9, 9, 64)          256       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_7 (MaxPooling2 (None, 3, 3, 64)          0         \n",
            "_________________________________________________________________\n",
            "dropout_7 (Dropout)          (None, 3, 3, 64)          0         \n",
            "_________________________________________________________________\n",
            "flatten_5 (Flatten)          (None, 576)               0         \n",
            "_________________________________________________________________\n",
            "dense_15 (Dense)             (None, 128)               73856     \n",
            "_________________________________________________________________\n",
            "batch_normalization_16 (Batc (None, 128)               512       \n",
            "_________________________________________________________________\n",
            "dropout_8 (Dropout)          (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_16 (Dense)             (None, 64)                8256      \n",
            "_________________________________________________________________\n",
            "batch_normalization_17 (Batc (None, 64)                256       \n",
            "_________________________________________________________________\n",
            "dropout_9 (Dropout)          (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_17 (Dense)             (None, 10)                650       \n",
            "=================================================================\n",
            "Total params: 301,770\n",
            "Trainable params: 300,746\n",
            "Non-trainable params: 1,024\n",
            "_________________________________________________________________\n",
            "Epoch 1/5\n",
            "469/469 [==============================] - 14s 25ms/step - loss: 0.1938 - acc: 0.9422\n",
            "Epoch 2/5\n",
            "469/469 [==============================] - 12s 25ms/step - loss: 0.0569 - acc: 0.9830\n",
            "Epoch 3/5\n",
            "469/469 [==============================] - 12s 25ms/step - loss: 0.0447 - acc: 0.9875\n",
            "Epoch 4/5\n",
            "469/469 [==============================] - 11s 24ms/step - loss: 0.0391 - acc: 0.9892\n",
            "Epoch 5/5\n",
            "469/469 [==============================] - 11s 24ms/step - loss: 0.0335 - acc: 0.9910\n",
            "313/313 [==============================] - 2s 4ms/step - loss: 0.0179 - acc: 0.9943\n",
            "loss= 0.017904674634337425\n",
            "acc= 0.9943000078201294\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}