4_1_1.load_saved_models.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "\n",
    "print(tf.__version__)\n",
    "print(keras.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Enable Eager Mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if tf.__version__ < '2.0.0':\n",
    "    tf.enable_eager_execution()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 100\n",
    "n_class = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## MNIST Dataset #########################################################\n",
    "mnist = keras.datasets.mnist\n",
    "class_names = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']\n",
    "##########################################################################\n",
    "\n",
    "## Fashion MNIST Dataset #################################################\n",
    "#mnist = keras.datasets.fashion_mnist\n",
    "#class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']\n",
    "##########################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "(_, _), (test_images, test_labels) = mnist.load_data()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_test = test_images.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pixel값을 0~1사이 범위로 조정\n",
    "test_images = test_images.astype(np.float32) / 255.\n",
    "# CNN에 입력으로 넣기 위해 3차원->4차원으로 변경(channel에 1을 추가)\n",
    "test_images = np.expand_dims(test_images, axis=-1)\n",
    "# label을 onehot-encoding\n",
    "test_labels = to_categorical(test_labels, 10)    \n",
    "# Dataset 구성\n",
    "test_dataset = tf.data.Dataset.from_tensor_slices((test_images, test_labels)).batch(batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def avg_accuracy(model, dataset):\n",
    "    avg_acc = 0.\n",
    "    step = 0\n",
    "    for images, labels in dataset:        \n",
    "        predictions = model(images, training=False)\n",
    "        correct_prediction = tf.equal(tf.argmax(predictions, 1), tf.argmax(labels, 1))\n",
    "        acc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))     \n",
    "        avg_acc += acc\n",
    "        step += 1    \n",
    "    avg_acc = avg_acc / step\n",
    "    return avg_acc.numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_image(i, predictions_array, true_label, img):\n",
    "    predictions_array, true_label, img = predictions_array[i], true_label[i], img[i]\n",
    "    plt.grid(False)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "\n",
    "    plt.imshow(img[:,:,0],cmap=plt.cm.binary)\n",
    "\n",
    "    predicted_label = np.argmax(predictions_array)\n",
    "    if predicted_label == true_label:\n",
    "        color = 'blue'\n",
    "    else:\n",
    "        color = 'red'\n",
    "\n",
    "    plt.xlabel(\"{} {:2.0f}% ({})\".format(class_names[predicted_label],\n",
    "                                100*np.max(predictions_array),\n",
    "                                class_names[true_label]),\n",
    "                                color=color)\n",
    "\n",
    "def plot_value_array(i, predictions_array, true_label):\n",
    "    predictions_array, true_label = predictions_array[i], true_label[i]\n",
    "    plt.grid(False)\n",
    "    #plt.xticks([])\n",
    "    plt.xticks(range(n_class), class_names, rotation=90)\n",
    "    plt.yticks([])\n",
    "    thisplot = plt.bar(range(n_class), predictions_array, color=\"#777777\")\n",
    "    plt.ylim([0, 1]) \n",
    "    predicted_label = np.argmax(predictions_array)\n",
    " \n",
    "    thisplot[predicted_label].set_color('red')\n",
    "    thisplot[true_label].set_color('blue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading Saved Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Loading HDF5 Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cur_dir = os.getcwd()\n",
    "save_dir_name = 'saved_models'\n",
    "os.makedirs(save_dir_name, exist_ok=True)\n",
    "saved_model_path = os.path.join(cur_dir, save_dir_name, 'my_model.h5')\n",
    "saved_model_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_model1 = keras.models.load_model(saved_model_path)\n",
    "new_model1.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_accuracy(new_model1, test_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rnd_idx = np.random.randint(1, n_test//batch_size)\n",
    "img_cnt = 0\n",
    "for images, labels in test_dataset:\n",
    "    img_cnt += 1\n",
    "    if img_cnt != rnd_idx:\n",
    "        continue\n",
    "    predictions = new_model1(images, training=False)\n",
    "    num_rows = 5\n",
    "    num_cols = 3\n",
    "    num_images = num_rows*num_cols\n",
    "    labels = tf.argmax(labels, axis=-1)\n",
    "    plt.figure(figsize=(3*2*num_cols, 4*num_rows))\n",
    "    plt.subplots_adjust(hspace=1.0)\n",
    "    for i in range(num_images):\n",
    "        plt.subplot(num_rows, 2*num_cols, 2*i+1)\n",
    "        plot_image(i, predictions.numpy(), labels.numpy(), images.numpy())\n",
    "        plt.subplot(num_rows, 2*num_cols, 2*i+2)\n",
    "        plot_value_array(i, predictions.numpy(), labels.numpy())        \n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Loading saved_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "saved_model_path = os.path.join(cur_dir, save_dir_name, 'my_saved_model')\n",
    "saved_model_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_model2 = tf.keras.experimental.load_from_saved_model(saved_model_path)\n",
    "new_model2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_accuracy(new_model2, test_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rnd_idx = np.random.randint(1, n_test//batch_size)\n",
    "img_cnt = 0\n",
    "for images, labels in test_dataset:\n",
    "    img_cnt += 1\n",
    "    if img_cnt != rnd_idx:\n",
    "        continue\n",
    "    predictions = new_model2(images, training=False)\n",
    "    num_rows = 5\n",
    "    num_cols = 3\n",
    "    num_images = num_rows*num_cols\n",
    "    labels = tf.argmax(labels, axis=-1)\n",
    "    plt.figure(figsize=(3*2*num_cols, 4*num_rows))\n",
    "    plt.subplots_adjust(hspace=1.0)\n",
    "    for i in range(num_images):\n",
    "        plt.subplot(num_rows, 2*num_cols, 2*i+1)\n",
    "        plot_image(i, predictions.numpy(), labels.numpy(), images.numpy())\n",
    "        plt.subplot(num_rows, 2*num_cols, 2*i+2)\n",
    "        plot_value_array(i, predictions.numpy(), labels.numpy())        \n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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