딥러닝_최장호_202192006_2022_10_05.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b4a3b79e",
   "metadata": {},
   "source": [
    "#### (과제 1) Fashion MNIST dataset\n",
    "- AutoEncoder architecture를 다음과 같이 수정하여 test dataset의 처음 10개 data에 대해 AutoEncoding한 결과를 출력하시오<br>\n",
    "(1) Dataset: Fashion MNIST<br>\n",
    "(2) Encoder: 784 $\\rightarrow$ 500 $\\rightarrow$ 300  $\\rightarrow$ 200  $\\rightarrow$  100  $\\rightarrow$  50  $\\rightarrow$  30<br>\n",
    "(3) Decoder: 30  $\\rightarrow$  50  $\\rightarrow$  100  $\\rightarrow$  200  $\\rightarrow$  300  $\\rightarrow$  500  $\\rightarrow$  784<br>\n",
    "(4) Activation function: SELU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "379f656a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "from tensorflow.keras import Model\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Flatten, Reshape, Layer, Input\n",
    "from tensorflow.keras.optimizers import SGD\n",
    "from tensorflow.keras.datasets import fashion_mnist\n",
    "from tensorflow.keras.metrics import binary_accuracy\n",
    "from tensorflow.keras.utils import plot_model\n",
    "from tensorflow.keras import backend as K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "756ac982",
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras\n",
    "from keras import layers\n",
    "\n",
    "from keras.datasets import fashion_mnist\n",
    "import numpy as np\n",
    "(x_train, _), (x_test, _) = fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "287603e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "(X_train_full, y_train_full), (X_test, y_test) = keras.datasets.fashion_mnist.load_data()\n",
    "X_train_full = X_train_full.astype(np.float32) / 255\n",
    "X_test = X_test.astype(np.float32) / 255\n",
    "X_train, X_valid = X_train_full[:-5000], X_train_full[-5000:]\n",
    "y_train, y_valid = y_train_full[:-5000], y_train_full[-5000:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "660b14b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def rounded_accuracy(y_true, y_pred):\n",
    "    return keras.metrics.binary_accuracy(tf.round(y_true), tf.round(y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e33ae56f",
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.random.set_seed(42)\n",
    "np.random.seed(42)\n",
    "#인코더\n",
    "stacked_encoder = keras.models.Sequential([\n",
    "    keras.layers.Flatten(input_shape=[28, 28]), #input 28x28 벡터로 \n",
    "    keras.layers.Dense(784, activation=\"selu\"),# selu acriv, 100개 노드\n",
    "    keras.layers.Dense(500, activation=\"selu\"),\n",
    "    keras.layers.Dense(300, activation=\"selu\"),\n",
    "    keras.layers.Dense(200, activation=\"selu\"),\n",
    "    keras.layers.Dense(100, activation=\"selu\"),\n",
    "    keras.layers.Dense(50, activation=\"selu\"),\n",
    "    keras.layers.Dense(30, activation=\"selu\"),\n",
    "])\n",
    "#디코더네트워크\n",
    "stacked_decoder = keras.models.Sequential([\n",
    "    keras.layers.Dense(30, activation=\"selu\", input_shape=[30]),\n",
    "    keras.layers.Dense(50, activation=\"selu\"),\n",
    "    keras.layers.Dense(100, activation=\"selu\"),\n",
    "    keras.layers.Dense(200, activation=\"selu\"),\n",
    "    keras.layers.Dense(300, activation=\"selu\"),\n",
    "    keras.layers.Dense(500, activation=\"selu\"),\n",
    "    keras.layers.Dense(784, activation=\"selu\"),\n",
    "    keras.layers.Dense(28 * 28, activation=\"sigmoid\"),\n",
    "    keras.layers.Reshape([28, 28])\n",
    "])\n",
    "\n",
    "stacked_ae = keras.models.Sequential([stacked_encoder, stacked_decoder])\n",
    "stacked_ae.compile(loss=\"binary_crossentropy\",\n",
    "                   optimizer=keras.optimizers.SGD(learning_rate=1.5), metrics=[rounded_accuracy])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ffba1de4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "1719/1719 [==============================] - 18s 10ms/step - loss: 5.5549 - rounded_accuracy: 0.6273 - val_loss: 5.7512 - val_rounded_accuracy: 0.6241\n",
      "Epoch 2/20\n",
      "1719/1719 [==============================] - 19s 11ms/step - loss: 5.7425 - rounded_accuracy: 0.6248 - val_loss: 5.7512 - val_rounded_accuracy: 0.6241\n",
      "Epoch 3/20\n",
      "1719/1719 [==============================] - 20s 12ms/step - loss: 5.7425 - rounded_accuracy: 0.6248 - val_loss: 5.7512 - val_rounded_accuracy: 0.6241\n",
      "Epoch 4/20\n",
      "1719/1719 [==============================] - 20s 12ms/step - loss: 5.7425 - rounded_accuracy: 0.6248 - val_loss: 5.7512 - val_rounded_accuracy: 0.6241\n",
      "Epoch 5/20\n",
      "1719/1719 [==============================] - 20s 12ms/step - loss: 5.7426 - rounded_accuracy: 0.6248 - val_loss: 5.7512 - val_rounded_accuracy: 0.6241\n",
      "Epoch 6/20\n",
      "1719/1719 [==============================] - 21s 12ms/step - loss: 5.7425 - rounded_accuracy: 0.6248 - val_loss: 5.7512 - val_rounded_accuracy: 0.6241\n",
      "Epoch 7/20\n",
      "1719/1719 [==============================] - 24s 14ms/step - loss: 5.7425 - rounded_accuracy: 0.6248 - val_loss: 5.7512 - val_rounded_accuracy: 0.6241\n",
      "Epoch 8/20\n",
      "1719/1719 [==============================] - 28s 16ms/step - loss: 5.7425 - rounded_accuracy: 0.6248 - val_loss: 5.7512 - val_rounded_accuracy: 0.6241\n",
      "Epoch 9/20\n",
      "1719/1719 [==============================] - 29s 17ms/step - loss: 5.7425 - rounded_accuracy: 0.6248 - val_loss: 5.7512 - val_rounded_accuracy: 0.6241\n",
      "Epoch 10/20\n",
      "1719/1719 [==============================] - 23s 13ms/step - loss: 5.7425 - rounded_accuracy: 0.6248 - val_loss: 5.7512 - val_rounded_accuracy: 0.6241\n",
      "Epoch 11/20\n",
      "1719/1719 [==============================] - 21s 12ms/step - loss: 5.7425 - rounded_accuracy: 0.6248 - val_loss: 5.7512 - val_rounded_accuracy: 0.6241\n",
      "Epoch 12/20\n",
      "1719/1719 [==============================] - 22s 13ms/step - loss: 5.7425 - rounded_accuracy: 0.6248 - val_loss: 5.7512 - val_rounded_accuracy: 0.6241\n",
      "Epoch 13/20\n",
      "1719/1719 [==============================] - 24s 14ms/step - loss: 5.7425 - rounded_accuracy: 0.6248 - val_loss: 5.7512 - val_rounded_accuracy: 0.6241\n",
      "Epoch 14/20\n",
      "1719/1719 [==============================] - 28s 16ms/step - loss: 5.7425 - rounded_accuracy: 0.6248 - val_loss: 5.7512 - val_rounded_accuracy: 0.6241\n",
      "Epoch 15/20\n",
      "1719/1719 [==============================] - 34s 20ms/step - loss: 5.7425 - rounded_accuracy: 0.6248 - val_loss: 5.7512 - val_rounded_accuracy: 0.6241\n",
      "Epoch 16/20\n",
      "1719/1719 [==============================] - 26s 15ms/step - loss: 5.7425 - rounded_accuracy: 0.6248 - val_loss: 5.7512 - val_rounded_accuracy: 0.6241\n",
      "Epoch 17/20\n",
      "1719/1719 [==============================] - 22s 13ms/step - loss: 5.7425 - rounded_accuracy: 0.6248 - val_loss: 5.7512 - val_rounded_accuracy: 0.6241\n",
      "Epoch 18/20\n",
      "1719/1719 [==============================] - 24s 14ms/step - loss: 5.7425 - rounded_accuracy: 0.6248 - val_loss: 5.7512 - val_rounded_accuracy: 0.6241\n",
      "Epoch 19/20\n",
      "1719/1719 [==============================] - 26s 15ms/step - loss: 5.7425 - rounded_accuracy: 0.6248 - val_loss: 5.7512 - val_rounded_accuracy: 0.6241\n",
      "Epoch 20/20\n",
      "1719/1719 [==============================] - 31s 18ms/step - loss: 5.7425 - rounded_accuracy: 0.6248 - val_loss: 5.7512 - val_rounded_accuracy: 0.6241\n"
     ]
    }
   ],
   "source": [
    "history = stacked_ae.fit(X_train, X_train, epochs=20,\n",
    "                         validation_data=(X_valid, X_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "806c0d76",
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_reconstructions(model, images=X_valid, n_images=10):\n",
    "    reconstructions = model.predict(images[:n_images])\n",
    "    fig = plt.figure(figsize=(n_images * 1.5, 3))\n",
    "    for image_index in range(n_images):\n",
    "        plt.subplot(2, n_images, 1 + image_index)\n",
    "        plot_image(images[image_index])\n",
    "        plt.subplot(2, n_images, 1 + n_images + image_index)\n",
    "        plot_image(reconstructions[image_index])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6bdd7434",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_image(image):\n",
    "    plt.imshow(image, cmap=\"binary\")\n",
    "    plt.axis(\"off\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e8039b11",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 18ms/step\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x216 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_reconstructions(stacked_ae)\n",
    "# save_fig(\"reconstruction_plot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ebfb82e",
   "metadata": {},
   "source": [
    "#### (과제 2)  MNIST dataset\n",
    "- AutoEncoder architecture를 다음과 같이 수정하여 test dataset의 처음 10개 data에 대해 AutoEncoding한 결과를 출력하시오<br>\n",
    "(1) Dataset: MNIST<br>\n",
    "(2) Encoder: 784 $\\rightarrow$ 500 $\\rightarrow$  100  $\\rightarrow$  50  $\\rightarrow$  30<br>\n",
    "(3) Decoder: 30  $\\rightarrow$  50  $\\rightarrow$  100  $\\rightarrow$  500  $\\rightarrow$  784<br>\n",
    "(4) Activation function: SELU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7d618f2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow\n",
    "import keras\n",
    "keras.__version__\n",
    "\n",
    "from keras import models\n",
    "from keras import layers\n",
    "\n",
    "from keras.datasets import mnist\n",
    "(train_images, train_labels), (test_images, test_labels) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "af67beed",
   "metadata": {},
   "outputs": [],
   "source": [
    "(X_train_full, y_train_full), (X_test, y_test) = keras.datasets.mnist.load_data()\n",
    "\n",
    "X_train_full = X_train_full.astype(np.float32) / 255\n",
    "X_test = X_test.astype(np.float32) / 255\n",
    "X_train, X_valid = X_train_full[:-5000], X_train_full[-5000:]\n",
    "y_train, y_valid = y_train_full[:-5000], y_train_full[-5000:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "97f6a67c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def rounded_accuracy(y_true, y_pred):\n",
    "    return keras.metrics.binary_accuracy(tf.round(y_true), tf.round(y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ba1f19a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.random.set_seed(42)\n",
    "np.random.seed(42)\n",
    "#인코더\n",
    "stacked_encoder = keras.models.Sequential([\n",
    "    keras.layers.Flatten(input_shape=[28, 28]), #input 28x28 벡터로 \n",
    "    keras.layers.Dense(784, activation=\"selu\"),# selu acriv, 100개 노드\n",
    "    keras.layers.Dense(500, activation=\"selu\"),\n",
    "    keras.layers.Dense(300, activation=\"selu\"),\n",
    "    keras.layers.Dense(200, activation=\"selu\"),\n",
    "    keras.layers.Dense(100, activation=\"selu\"),\n",
    "    keras.layers.Dense(50, activation=\"selu\"),\n",
    "    keras.layers.Dense(30, activation=\"selu\"),\n",
    "])\n",
    "#디코더네트워크\n",
    "stacked_decoder = keras.models.Sequential([\n",
    "    keras.layers.Dense(30, activation=\"selu\", input_shape=[30]),\n",
    "    keras.layers.Dense(50, activation=\"selu\"),\n",
    "    keras.layers.Dense(100, activation=\"selu\"),\n",
    "    keras.layers.Dense(200, activation=\"selu\"),\n",
    "    keras.layers.Dense(300, activation=\"selu\"),\n",
    "    keras.layers.Dense(500, activation=\"selu\"),\n",
    "    keras.layers.Dense(784, activation=\"selu\"),\n",
    "    keras.layers.Dense(28 * 28, activation=\"sigmoid\"),\n",
    "    keras.layers.Reshape([28, 28])\n",
    "])\n",
    "\n",
    "stacked_ae = keras.models.Sequential([stacked_encoder, stacked_decoder])\n",
    "stacked_ae.compile(loss=\"binary_crossentropy\",\n",
    "                   optimizer=keras.optimizers.SGD(learning_rate=1.5), metrics=[rounded_accuracy])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1f5fe7e2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "1719/1719 [==============================] - 18s 10ms/step - loss: 0.1604 - rounded_accuracy: 0.9311 - val_loss: 0.1248 - val_rounded_accuracy: 0.9522\n",
      "Epoch 2/20\n",
      "1719/1719 [==============================] - 19s 11ms/step - loss: 1.3566 - rounded_accuracy: 0.8930 - val_loss: 2.3910 - val_rounded_accuracy: 0.8435\n",
      "Epoch 3/20\n",
      "1719/1719 [==============================] - 19s 11ms/step - loss: 2.3955 - rounded_accuracy: 0.8431 - val_loss: 2.3910 - val_rounded_accuracy: 0.8435\n",
      "Epoch 4/20\n",
      "1719/1719 [==============================] - 20s 11ms/step - loss: 2.3955 - rounded_accuracy: 0.8431 - val_loss: 2.3910 - val_rounded_accuracy: 0.8435\n",
      "Epoch 5/20\n",
      "1719/1719 [==============================] - 21s 12ms/step - loss: 2.3955 - rounded_accuracy: 0.8431 - val_loss: 2.3910 - val_rounded_accuracy: 0.8435\n",
      "Epoch 6/20\n",
      "1719/1719 [==============================] - 21s 12ms/step - loss: 2.3955 - rounded_accuracy: 0.8431 - val_loss: 2.3910 - val_rounded_accuracy: 0.8435\n",
      "Epoch 7/20\n",
      "1719/1719 [==============================] - 20s 12ms/step - loss: 2.3955 - rounded_accuracy: 0.8431 - val_loss: 2.3910 - val_rounded_accuracy: 0.8435\n",
      "Epoch 8/20\n",
      "1719/1719 [==============================] - 21s 12ms/step - loss: 2.3955 - rounded_accuracy: 0.8431 - val_loss: 2.3910 - val_rounded_accuracy: 0.8435\n",
      "Epoch 9/20\n",
      "1719/1719 [==============================] - 24s 14ms/step - loss: 2.3955 - rounded_accuracy: 0.8431 - val_loss: 2.3910 - val_rounded_accuracy: 0.8435\n",
      "Epoch 10/20\n",
      "1719/1719 [==============================] - 27s 16ms/step - loss: 2.3955 - rounded_accuracy: 0.8431 - val_loss: 2.3910 - val_rounded_accuracy: 0.8435\n",
      "Epoch 11/20\n",
      "1719/1719 [==============================] - 26s 15ms/step - loss: 2.3955 - rounded_accuracy: 0.8431 - val_loss: 2.3910 - val_rounded_accuracy: 0.8435\n",
      "Epoch 12/20\n",
      "1719/1719 [==============================] - 22s 13ms/step - loss: 2.3955 - rounded_accuracy: 0.8431 - val_loss: 2.3910 - val_rounded_accuracy: 0.8435\n",
      "Epoch 13/20\n",
      "1719/1719 [==============================] - 20s 12ms/step - loss: 2.3955 - rounded_accuracy: 0.8431 - val_loss: 2.3910 - val_rounded_accuracy: 0.8435\n",
      "Epoch 14/20\n",
      "1719/1719 [==============================] - 20s 12ms/step - loss: 2.3955 - rounded_accuracy: 0.8431 - val_loss: 2.3910 - val_rounded_accuracy: 0.8435\n",
      "Epoch 15/20\n",
      "1719/1719 [==============================] - 21s 12ms/step - loss: 2.3955 - rounded_accuracy: 0.8431 - val_loss: 2.3910 - val_rounded_accuracy: 0.8435\n",
      "Epoch 16/20\n",
      "1719/1719 [==============================] - 23s 14ms/step - loss: 2.3955 - rounded_accuracy: 0.8431 - val_loss: 2.3910 - val_rounded_accuracy: 0.8435\n",
      "Epoch 17/20\n",
      "1719/1719 [==============================] - 26s 15ms/step - loss: 2.3955 - rounded_accuracy: 0.8431 - val_loss: 2.3910 - val_rounded_accuracy: 0.8435\n",
      "Epoch 18/20\n",
      "1719/1719 [==============================] - 30s 17ms/step - loss: 2.3955 - rounded_accuracy: 0.8431 - val_loss: 2.3910 - val_rounded_accuracy: 0.8435\n",
      "Epoch 19/20\n",
      "1719/1719 [==============================] - 24s 14ms/step - loss: 2.3955 - rounded_accuracy: 0.8431 - val_loss: 2.3910 - val_rounded_accuracy: 0.8435\n",
      "Epoch 20/20\n",
      "1719/1719 [==============================] - 21s 12ms/step - loss: 2.3955 - rounded_accuracy: 0.8431 - val_loss: 2.3910 - val_rounded_accuracy: 0.8435\n"
     ]
    }
   ],
   "source": [
    "history = stacked_ae.fit(X_train, X_train, epochs=20,\n",
    "                         validation_data=(X_valid, X_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1d40e1fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_reconstructions(model, images=X_valid, n_images=10):\n",
    "    reconstructions = model.predict(images[:n_images])\n",
    "    fig = plt.figure(figsize=(n_images * 1.5, 3))\n",
    "    for image_index in range(n_images):\n",
    "        plt.subplot(2, n_images, 1 + image_index)\n",
    "        plot_image(images[image_index])\n",
    "        plt.subplot(2, n_images, 1 + n_images + image_index)\n",
    "        plot_image(reconstructions[image_index])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b105697a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_image(image):\n",
    "    plt.imshow(image, cmap=\"binary\")\n",
    "    plt.axis(\"off\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "acca83bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 141ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_reconstructions(stacked_ae)\n",
    "# save_fig(\"reconstruction_plot\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}