3. conv_layer_further_removed.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "conv_layer_further_removed.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/pseudope/Parrot_5th_Level1/blob/main/conv_layer_further_removed.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qWz40uYohBIN",
        "outputId": "506a5bb0-026b-4b6f-c5b1-b1cccdbd1d51"
      },
      "source": [
        "#!/usr/bin/env python3\n",
        "!pip install einops\n",
        "#!pip install tensorflow --user\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from tensorflow.data import Dataset\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from tensorflow.keras.optimizers import Adam, RMSprop\n",
        "from tensorflow.keras.layers import Input, Dense, Conv2D, Dropout, Flatten, Activation, MaxPooling2D, BatchNormalization\n",
        "\n",
        "from einops import rearrange\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting einops\n",
            "  Downloading einops-0.3.2-py3-none-any.whl (25 kB)\n",
            "Installing collected packages: einops\n",
            "Successfully installed einops-0.3.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S3WNN5P4hBIO",
        "outputId": "e47de6f3-1629-41bc-f6f8-e4ff4c3c67d0"
      },
      "source": [
        "(X_train, y_train), (X_test, y_test) = tf.keras.datasets.mnist.load_data()\n",
        "\n",
        "print(\"X_train shape: {}\".format(X_train.shape))\n",
        "print(\"X_test shape: {}\".format(X_test.shape))"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 0s 0us/step\n",
            "11501568/11490434 [==============================] - 0s 0us/step\n",
            "X_train shape: (60000, 28, 28)\n",
            "X_test shape: (10000, 28, 28)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 657
        },
        "id": "KB832lechBIP",
        "outputId": "9bf43a16-cb4b-45d1-eaae-626871c9bde8"
      },
      "source": [
        "fig = plt.figure(figsize=(9, 9))\n",
        "\n",
        "for i in range(9):\n",
        "    plt.subplot(3, 3, 1 + i)\n",
        "    plt.tight_layout()\n",
        "    plt.imshow(X_train[i], cmap=\"gray\", interpolation=\"none\")\n",
        "    target = y_train[i]\n",
        "    plt.title(\"Ground Truth: {}\".format(int(target)))\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 648x648 with 9 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nq1waxKDhBIP"
      },
      "source": [
        "target_size = 10   # 0 ~ 9, 총 9개\n",
        "\n",
        "### 주의: 흑백 데이터여도 channel 차원을 살려주어야 정상적으로 작동합니다\n",
        "X_train = rearrange(X_train, \"b h w -> b h w 1\")\n",
        "X_test = rearrange(X_test, \"b h w -> b h w 1\")\n",
        "\n",
        "X_train = X_train.astype(\"float32\") / (2 ** 8 - 1)   # 255로 scaling\n",
        "X_test = X_test.astype(\"float32\") / (2 ** 8 - 1)     # 255로 scaling\n",
        "\n",
        "y_train = to_categorical(y_train, target_size)       # 타겟을 정수로 변환\n",
        "y_test = to_categorical(y_test, target_size)         # 타겟을 정수로 변환"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7xA3xa64hBIP"
      },
      "source": [
        "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SyT8hqophBIQ",
        "outputId": "e98d1efc-8162-478d-9c73-8a2b3990cd3c"
      },
      "source": [
        "print(X_train.shape)\n",
        "print(X_val.shape)\n",
        "print(X_test.shape)\n",
        "print(y_train.shape)\n",
        "print(y_val.shape)\n",
        "print(y_test.shape)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(48000, 28, 28, 1)\n",
            "(12000, 28, 28, 1)\n",
            "(10000, 28, 28, 1)\n",
            "(48000, 10)\n",
            "(12000, 10)\n",
            "(10000, 10)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "haTxYAXahBIQ",
        "outputId": "622156cb-4097-432c-bbb1-75b09cc6c6dc"
      },
      "source": [
        "inputs = Input(shape=(28, 28, 1))\n",
        "\n",
        "X = Conv2D(filters=512, kernel_size=(3, 3), padding=\"Same\")(inputs)\n",
        "X = BatchNormalization()(X)\n",
        "X = Activation(\"relu\")(X)\n",
        "X = Conv2D(filters=128, kernel_size=(3, 3), padding=\"Same\", activation=\"relu\")(X)\n",
        "X = Dropout(0.5)(X)\n",
        "X = MaxPooling2D(pool_size=(2, 2), strides=(2, 2))(X)\n",
        "\n",
        "#X = Conv2D(filters=256, kernel_size=(5, 5), padding=\"Same\")(X)\n",
        "#X = BatchNormalization()(X)\n",
        "#X = Activation(\"relu\")(X)\n",
        "#X = Conv2D(filters=128, kernel_size=(5, 5), padding=\"Same\", activation=\"relu\")(X)\n",
        "#X = Dropout(0.5)(X)\n",
        "#X = MaxPooling2D(pool_size=(2, 2), strides=(2, 2))(X)\n",
        "\n",
        "#X = Conv2D(filters=128, kernel_size=(3, 3), padding=\"Same\")(X)\n",
        "#X = BatchNormalization()(X)\n",
        "#X = Activation(\"relu\")(X)\n",
        "#X = Conv2D(filters=64, kernel_size=(1, 1), padding=\"Same\", activation=\"relu\")(X)\n",
        "#X = Dropout(0.3)(X)\n",
        "#X = MaxPooling2D(pool_size=(2, 2), strides=(2, 2))(X)\n",
        "\n",
        "X = Flatten()(X)\n",
        "X = Dense(1024, activation=\"relu\")(X)\n",
        "X = BatchNormalization()(X)\n",
        "X = Dropout(0.2)(X)\n",
        "X = Dense(512, activation=\"relu\")(X)\n",
        "X = BatchNormalization()(X)\n",
        "X = Dropout(0.2)(X)\n",
        "X = Dense(256, activation=\"relu\")(X)\n",
        "X = BatchNormalization()(X)\n",
        "X = Dropout(0.2)(X)\n",
        "outputs = Dense(10, activation=\"softmax\")(X)\n",
        "\n",
        "model = Model(inputs=inputs, outputs=outputs)\n",
        "model.summary()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_2 (InputLayer)         [(None, 28, 28, 1)]       0         \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 28, 28, 512)       5120      \n",
            "_________________________________________________________________\n",
            "batch_normalization_5 (Batch (None, 28, 28, 512)       2048      \n",
            "_________________________________________________________________\n",
            "activation_2 (Activation)    (None, 28, 28, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 28, 28, 128)       589952    \n",
            "_________________________________________________________________\n",
            "dropout_5 (Dropout)          (None, 28, 28, 128)       0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 14, 14, 128)       0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 25088)             0         \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 1024)              25691136  \n",
            "_________________________________________________________________\n",
            "batch_normalization_6 (Batch (None, 1024)              4096      \n",
            "_________________________________________________________________\n",
            "dropout_6 (Dropout)          (None, 1024)              0         \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 512)               524800    \n",
            "_________________________________________________________________\n",
            "batch_normalization_7 (Batch (None, 512)               2048      \n",
            "_________________________________________________________________\n",
            "dropout_7 (Dropout)          (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_6 (Dense)              (None, 256)               131328    \n",
            "_________________________________________________________________\n",
            "batch_normalization_8 (Batch (None, 256)               1024      \n",
            "_________________________________________________________________\n",
            "dropout_8 (Dropout)          (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dense_7 (Dense)              (None, 10)                2570      \n",
            "=================================================================\n",
            "Total params: 26,954,122\n",
            "Trainable params: 26,949,514\n",
            "Non-trainable params: 4,608\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MqkQ72d1hBIS",
        "outputId": "7596755a-2853-4945-ffba-cd54bbd03bce"
      },
      "source": [
        "def scheduler(epoch, lr):\n",
        "  if epoch < 8:\n",
        "    return lr\n",
        "  else:\n",
        "    return lr * tf.math.exp(-0.1)\n",
        "\n",
        "callback = [\n",
        "            tf.keras.callbacks.LearningRateScheduler(scheduler),\n",
        "            tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=3)\n",
        "]\n",
        "\n",
        "model.compile(optimizer='adam', loss=\"categorical_crossentropy\", metrics=[\"accuracy\"])\n",
        "history = model.fit(X_train, y_train, batch_size=256, epochs=15, callbacks = callback, validation_data=(X_val, y_val), verbose=1)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/15\n",
            "188/188 [==============================] - 61s 297ms/step - loss: 0.1758 - accuracy: 0.9457 - val_loss: 19.8509 - val_accuracy: 0.1098\n",
            "Epoch 2/15\n",
            "188/188 [==============================] - 51s 271ms/step - loss: 0.0571 - accuracy: 0.9823 - val_loss: 14.2100 - val_accuracy: 0.0932\n",
            "Epoch 3/15\n",
            "188/188 [==============================] - 51s 272ms/step - loss: 0.0397 - accuracy: 0.9876 - val_loss: 3.2278 - val_accuracy: 0.4287\n",
            "Epoch 4/15\n",
            "188/188 [==============================] - 51s 271ms/step - loss: 0.0351 - accuracy: 0.9886 - val_loss: 0.3259 - val_accuracy: 0.8922\n",
            "Epoch 5/15\n",
            "188/188 [==============================] - 51s 271ms/step - loss: 0.0271 - accuracy: 0.9909 - val_loss: 0.1371 - val_accuracy: 0.9557\n",
            "Epoch 6/15\n",
            "188/188 [==============================] - 51s 271ms/step - loss: 0.0232 - accuracy: 0.9926 - val_loss: 0.0836 - val_accuracy: 0.9747\n",
            "Epoch 7/15\n",
            "188/188 [==============================] - 51s 271ms/step - loss: 0.0206 - accuracy: 0.9929 - val_loss: 0.0776 - val_accuracy: 0.9770\n",
            "Epoch 8/15\n",
            "188/188 [==============================] - 51s 271ms/step - loss: 0.0160 - accuracy: 0.9949 - val_loss: 0.0615 - val_accuracy: 0.9814\n",
            "Epoch 9/15\n",
            "188/188 [==============================] - 51s 271ms/step - loss: 0.0145 - accuracy: 0.9953 - val_loss: 0.0653 - val_accuracy: 0.9786\n",
            "Epoch 10/15\n",
            "188/188 [==============================] - 51s 271ms/step - loss: 0.0132 - accuracy: 0.9957 - val_loss: 0.0644 - val_accuracy: 0.9801\n",
            "Epoch 11/15\n",
            "188/188 [==============================] - 51s 271ms/step - loss: 0.0087 - accuracy: 0.9970 - val_loss: 0.0565 - val_accuracy: 0.9815\n",
            "Epoch 12/15\n",
            "188/188 [==============================] - 51s 271ms/step - loss: 0.0065 - accuracy: 0.9980 - val_loss: 0.0495 - val_accuracy: 0.9854\n",
            "Epoch 13/15\n",
            "188/188 [==============================] - 51s 270ms/step - loss: 0.0044 - accuracy: 0.9986 - val_loss: 0.0374 - val_accuracy: 0.9893\n",
            "Epoch 14/15\n",
            "188/188 [==============================] - 51s 271ms/step - loss: 0.0043 - accuracy: 0.9985 - val_loss: 0.0536 - val_accuracy: 0.9837\n",
            "Epoch 15/15\n",
            "188/188 [==============================] - 51s 270ms/step - loss: 0.0033 - accuracy: 0.9991 - val_loss: 0.0587 - val_accuracy: 0.9832\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yPRsfabXhBIS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7c58f012-d73e-4049-ff5c-465e126bfe35"
      },
      "source": [
        "model.evaluate(X_test, y_test, batch_size=256)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "40/40 [==============================] - 4s 91ms/step - loss: 0.0641 - accuracy: 0.9813\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.0641421526670456, 0.9812999963760376]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mZ49DBA3hBIT"
      },
      "source": [
        "def acc_loss_plot(hist):\n",
        "    \n",
        "    fit, loss_ax = plt.subplots()\n",
        "    acc_ax = loss_ax.twinx()\n",
        "    \n",
        "    loss_ax.plot(hist.history[\"loss\"], \"y\", label=\"train_loss\")\n",
        "    loss_ax.plot(hist.history[\"val_loss\"], \"r\", label=\"val_loss\")\n",
        "    loss_ax.set_xlabel(\"Epoch\")\n",
        "    loss_ax.set_ylabel(\"Loss\")\n",
        "    loss_ax.legend(loc = \"upper left\")\n",
        "    \n",
        "    acc_ax.plot(hist.history[\"accuracy\"], \"b\", label=\"train_acc\")\n",
        "    acc_ax.plot(hist.history[\"val_accuracy\"], \"g\", label=\"val_acc\")\n",
        "    acc_ax.set_ylabel(\"Accuracy\")\n",
        "    acc_ax.legend(loc = \"upper right\")\n",
        "    \n",
        "    plt.show()"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qVs8gpOZhBIT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "2b9c729d-6a2f-476d-8744-a594dbb2eca8"
      },
      "source": [
        "acc_loss_plot(history)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kFD0TsyM9t24"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}