{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "Ndo4ERqnwQOU" }, "source": [ "##### Copyright 2020 The TensorFlow Authors." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "cellView": "form", "execution": { "iopub.execute_input": "2022-12-14T23:23:58.723035Z", "iopub.status.busy": "2022-12-14T23:23:58.722320Z", "iopub.status.idle": "2022-12-14T23:23:58.726792Z", "shell.execute_reply": "2022-12-14T23:23:58.726127Z" }, "id": "MTKwbguKwT4R" }, "outputs": [], "source": [ "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# https://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License." ] }, { "cell_type": "markdown", "metadata": { "id": "xfNT-mlFwxVM" }, "source": [ "# Autoencoder 소개" ] }, { "cell_type": "markdown", "metadata": { "id": "0TD5ZrvEMbhZ" }, "source": [ "\n", " \n", " \n", " \n", " \n", "
TensorFlow.org에서 보기 Google Colab에서 실행하기 GitHub에서 소그 보기노트북 다운론드하기
" ] }, { "cell_type": "markdown", "metadata": { "id": "ITZuApL56Mny" }, "source": [ "이 튜토리얼에서는 3가지 예(기본 사항, 이미지 노이즈 제거 및 이상 감지)를 통해 autoencoder를 소개합니다.\n", "\n", "autoencoder는 입력을 출력에 복사하도록 훈련된 특수한 유형의 신경망입니다. 예를 들어, 손으로 쓴 숫자의 이미지가 주어지면 autoencoder는 먼저 이미지를 더 낮은 차원의 잠재 표현으로 인코딩한 다음 잠재 표현을 다시 이미지로 디코딩합니다. autoencoder는 재구성 오류를 최소화하면서 데이터를 압축하는 방법을 학습합니다.\n", "\n", "autoencoder에 대해 자세히 알아보려면 Ian Goodfellow, Yoshua Bengio 및 Aaron Courville의 [딥 러닝](https://www.deeplearningbook.org/)에서 14장을 읽어보세요." ] }, { "cell_type": "markdown", "metadata": { "id": "e1_Y75QXJS6h" }, "source": [ "## TensorFlow 및 기타 라이브러리 가져오기" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T23:23:58.730585Z", "iopub.status.busy": "2022-12-14T23:23:58.730094Z", "iopub.status.idle": "2022-12-14T23:24:01.596074Z", "shell.execute_reply": "2022-12-14T23:24:01.595317Z" }, "id": "YfIk2es3hJEd" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2022-12-14 23:24:00.482364: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n", "2022-12-14 23:24:00.482499: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n", "2022-12-14 23:24:00.482510: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n" ] } ], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "import tensorflow as tf\n", "\n", "from sklearn.metrics import accuracy_score, precision_score, recall_score\n", "from sklearn.model_selection import train_test_split\n", "from tensorflow.keras import layers, losses\n", "from tensorflow.keras.datasets import fashion_mnist\n", "from tensorflow.keras.models import Model" ] }, { "cell_type": "markdown", "metadata": { "id": "iYn4MdZnKCey" }, "source": [ "## 데이터세트 로드하기\n", "\n", "시작하려면 Fashon MNIST 데이터세트를 사용하여 기본 autoencoder를 훈련합니다. 이 데이터세트의 각 이미지는 28x28 픽셀입니다. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T23:24:01.601205Z", "iopub.status.busy": "2022-12-14T23:24:01.600430Z", "iopub.status.idle": "2022-12-14T23:24:02.130818Z", "shell.execute_reply": "2022-12-14T23:24:02.129859Z" }, "id": "YZm503-I_tji" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(60000, 28, 28)\n", "(10000, 28, 28)\n" ] } ], "source": [ "(x_train, _), (x_test, _) = fashion_mnist.load_data()\n", "\n", "x_train = x_train.astype('float32') / 255.\n", "x_test = x_test.astype('float32') / 255.\n", "\n", "print (x_train.shape)\n", "print (x_test.shape)" ] }, { "cell_type": "markdown", "metadata": { "id": "VEdCXSwCoKok" }, "source": [ "## 첫 번째 예: 기본 autoencoder\n", "\n", "![기본 오토 인코더 결과](images/intro_autoencoder_result.png)\n", "\n", "두 개의 Dense 레이어로 autoencoder를 정의합니다. 이미지를 64차원 잠재 벡터로 압축하는 `encoder`와 잠재 공간에서 원본 이미지를 재구성하는 `decoder`입니다.\n", "\n", "모델을 정의하려면 [Keras Model Subclassing API](https://www.tensorflow.org/guide/keras/custom_layers_and_models)를 사용하세요.\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T23:24:02.135291Z", "iopub.status.busy": "2022-12-14T23:24:02.134616Z", "iopub.status.idle": "2022-12-14T23:24:05.859351Z", "shell.execute_reply": "2022-12-14T23:24:05.858435Z" }, "id": "0MUxidpyChjX" }, "outputs": [], "source": [ "latent_dim = 64 \n", "\n", "class Autoencoder(Model):\n", " def __init__(self, latent_dim):\n", " super(Autoencoder, self).__init__()\n", " self.latent_dim = latent_dim \n", " self.encoder = tf.keras.Sequential([\n", " layers.Flatten(),\n", " layers.Dense(latent_dim, activation='relu'),\n", " ])\n", " self.decoder = tf.keras.Sequential([\n", " layers.Dense(784, activation='sigmoid'),\n", " layers.Reshape((28, 28))\n", " ])\n", "\n", " def call(self, x):\n", " encoded = self.encoder(x)\n", " decoded = self.decoder(encoded)\n", " return decoded\n", " \n", "autoencoder = Autoencoder(latent_dim) " ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T23:24:05.864355Z", "iopub.status.busy": "2022-12-14T23:24:05.863495Z", "iopub.status.idle": "2022-12-14T23:24:05.883530Z", "shell.execute_reply": "2022-12-14T23:24:05.882775Z" }, "id": "9I1JlqEIDCI4" }, "outputs": [], "source": [ "autoencoder.compile(optimizer='adam', loss=losses.MeanSquaredError())" ] }, { "cell_type": "markdown", "metadata": { "id": "7oJSeMTroABs" }, "source": [ "`x_train`을 입력과 대상으로 사용하여 모델을 훈련합니다. `encoder`는 데이터세트를 784차원에서 잠재 공간으로 압축하는 방법을 배우고, `decoder`는 원본 이미지를 재구성하는 방법을 배웁니다. ." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T23:24:05.888165Z", "iopub.status.busy": "2022-12-14T23:24:05.887559Z", "iopub.status.idle": "2022-12-14T23:24:49.736464Z", "shell.execute_reply": "2022-12-14T23:24:49.735683Z" }, "id": "h1RI9OfHDBsK" }, "outputs": [ { "name": "stdout", 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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "1859/1875 [============================>.] - ETA: 0s - loss: 0.0100" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0100 - val_loss: 0.0097\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 4/10\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 1/1875 [..............................] - ETA: 6s - loss: 0.0093" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 28/1875 [..............................] - ETA: 3s - loss: 0.0099" ] }, { "name": 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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0094 - val_loss: 0.0094\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 5/10\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 1/1875 [..............................] - ETA: 6s - loss: 0.0098" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 27/1875 [..............................] - ETA: 3s - loss: 0.0094" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 53/1875 [..............................] - ETA: 3s - loss: 0.0093" ] }, { "name": 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0s - loss: 0.0089" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0089 - val_loss: 0.0089\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 8/10\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 1/1875 [..............................] - ETA: 6s - loss: 0.0086" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 26/1875 [..............................] - ETA: 3s - loss: 0.0090" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 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2ms/step - loss: 0.0088 - val_loss: 0.0089\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 9/10\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 1/1875 [..............................] - ETA: 5s - loss: 0.0072" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 27/1875 [..............................] - ETA: 3s - loss: 0.0086" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 53/1875 [..............................] - ETA: 3s - loss: 0.0086" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "1863/1875 [============================>.] - ETA: 0s - loss: 0.0087" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0087 - val_loss: 0.0089\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "autoencoder.fit(x_train, x_train,\n", " epochs=10,\n", " shuffle=True,\n", " validation_data=(x_test, x_test))" ] }, { "cell_type": "markdown", "metadata": { "id": "wAM1QBhtoC-n" }, "source": [ "모델이 훈련되었으므로 테스트 세트에서 이미지를 인코딩 및 디코딩하여 테스트해 보겠습니다." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T23:24:49.740916Z", "iopub.status.busy": "2022-12-14T23:24:49.740165Z", "iopub.status.idle": "2022-12-14T23:24:49.828101Z", "shell.execute_reply": "2022-12-14T23:24:49.827295Z" }, "id": "Pbr5WCj7FQUi" }, "outputs": [], "source": [ "encoded_imgs = autoencoder.encoder(x_test).numpy()\n", "decoded_imgs = autoencoder.decoder(encoded_imgs).numpy()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T23:24:49.832584Z", "iopub.status.busy": "2022-12-14T23:24:49.831817Z", "iopub.status.idle": "2022-12-14T23:24:50.555570Z", "shell.execute_reply": "2022-12-14T23:24:50.554770Z" }, "id": "s4LlDOS6FUA1" }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "n = 10\n", "plt.figure(figsize=(20, 4))\n", "for i in range(n):\n", " # display original\n", " ax = plt.subplot(2, n, i + 1)\n", " plt.imshow(x_test[i])\n", " plt.title(\"original\")\n", " plt.gray()\n", " ax.get_xaxis().set_visible(False)\n", " ax.get_yaxis().set_visible(False)\n", "\n", " # display reconstruction\n", " ax = plt.subplot(2, n, i + 1 + n)\n", " plt.imshow(decoded_imgs[i])\n", " plt.title(\"reconstructed\")\n", " plt.gray()\n", " ax.get_xaxis().set_visible(False)\n", " ax.get_yaxis().set_visible(False)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "r4gv6G8PoRQE" }, "source": [ "## 두 번째 예: 이미지 노이즈 제거\n", "\n", "![이미지 노이즈 제거 결과](images/image_denoise_fmnist_results.png)\n", "\n", "autoencoder는 이미지에서 노이즈를 제거하도록 훈련될 수도 있습니다. 다음 섹션에서는 각 이미지에 임의의 노이즈를 적용하여 Fashion MNIST 데이터세트의 노이즈 버전을 생성합니다. 그런 다음 노이즈가 있는 이미지를 입력으로 사용하고 원본 이미지를 대상으로 사용하여 autoencoder를 훈련합니다.\n", "\n", "이전에 수정한 내용을 생략하기 위해 데이터세트를 다시 가져오겠습니다." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T23:24:50.559949Z", "iopub.status.busy": "2022-12-14T23:24:50.559345Z", "iopub.status.idle": "2022-12-14T23:24:50.942542Z", "shell.execute_reply": "2022-12-14T23:24:50.941678Z" }, "id": "gDYHJA2PCQ3m" }, "outputs": [], "source": [ "(x_train, _), (x_test, _) = fashion_mnist.load_data()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T23:24:50.946946Z", "iopub.status.busy": "2022-12-14T23:24:50.946201Z", "iopub.status.idle": "2022-12-14T23:24:51.040906Z", "shell.execute_reply": "2022-12-14T23:24:51.040138Z" }, "id": "uJZ-TcaqDBr5" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(60000, 28, 28, 1)\n" ] } ], "source": [ "x_train = x_train.astype('float32') / 255.\n", "x_test = x_test.astype('float32') / 255.\n", "\n", "x_train = x_train[..., tf.newaxis]\n", "x_test = x_test[..., tf.newaxis]\n", "\n", "print(x_train.shape)" ] }, { "cell_type": "markdown", "metadata": { "id": "aPZl_6P65_8R" }, "source": [ "이미지에 임의의 노이즈를 추가합니다." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T23:24:51.044785Z", "iopub.status.busy": "2022-12-14T23:24:51.044491Z", "iopub.status.idle": "2022-12-14T23:24:51.287573Z", "shell.execute_reply": "2022-12-14T23:24:51.286818Z" }, "id": "axSMyxC354fc" }, "outputs": [], "source": [ "noise_factor = 0.2\n", "x_train_noisy = x_train + noise_factor * tf.random.normal(shape=x_train.shape) \n", "x_test_noisy = x_test + noise_factor * tf.random.normal(shape=x_test.shape) \n", "\n", "x_train_noisy = tf.clip_by_value(x_train_noisy, clip_value_min=0., clip_value_max=1.)\n", "x_test_noisy = tf.clip_by_value(x_test_noisy, clip_value_min=0., clip_value_max=1.)" ] }, { "cell_type": "markdown", "metadata": { "id": "wRxHe4XXltNd" }, "source": [ "노이즈가 있는 이미지를 플롯합니다.\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T23:24:51.292133Z", "iopub.status.busy": "2022-12-14T23:24:51.291563Z", "iopub.status.idle": "2022-12-14T23:24:52.064140Z", "shell.execute_reply": "2022-12-14T23:24:52.063346Z" }, "id": "thKUmbVVCQpt" }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "n = 10\n", "plt.figure(figsize=(20, 2))\n", "for i in range(n):\n", " ax = plt.subplot(1, n, i + 1)\n", " plt.title(\"original + noise\")\n", " plt.imshow(tf.squeeze(x_test_noisy[i]))\n", " plt.gray()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "Sy9SY8jGl5aP" }, "source": [ "### 컨볼루셔널 autoencoder 정의하기" ] }, { "cell_type": "markdown", "metadata": { "id": "vT_BhZngWMwp" }, "source": [ "이 예제에서는 encoder에 Conv2D 레이어를 사용하고 decoder에 Conv2DTranspose 레이어를 사용하여 컨볼루셔널 autoencoder를 훈련합니다." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T23:24:52.067878Z", "iopub.status.busy": "2022-12-14T23:24:52.067589Z", "iopub.status.idle": "2022-12-14T23:24:52.116811Z", "shell.execute_reply": "2022-12-14T23:24:52.116061Z" }, "id": "R5KjoIlYCQko" }, "outputs": [], "source": [ "class Denoise(Model):\n", " def __init__(self):\n", " super(Denoise, self).__init__()\n", " self.encoder = tf.keras.Sequential([\n", " layers.Input(shape=(28, 28, 1)),\n", " layers.Conv2D(16, (3, 3), activation='relu', padding='same', strides=2),\n", " layers.Conv2D(8, (3, 3), activation='relu', padding='same', strides=2)])\n", "\n", " self.decoder = tf.keras.Sequential([\n", " layers.Conv2DTranspose(8, kernel_size=3, strides=2, activation='relu', padding='same'),\n", " layers.Conv2DTranspose(16, kernel_size=3, strides=2, activation='relu', padding='same'),\n", " layers.Conv2D(1, kernel_size=(3, 3), activation='sigmoid', padding='same')])\n", "\n", " def call(self, x):\n", " encoded = self.encoder(x)\n", " decoded = self.decoder(encoded)\n", " return decoded\n", "\n", "autoencoder = Denoise()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T23:24:52.120841Z", "iopub.status.busy": "2022-12-14T23:24:52.120309Z", "iopub.status.idle": "2022-12-14T23:24:52.129177Z", "shell.execute_reply": "2022-12-14T23:24:52.128563Z" }, "id": "QYKbiDFYCQfj" }, "outputs": [], "source": [ "autoencoder.compile(optimizer='adam', loss=losses.MeanSquaredError())" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T23:24:52.132703Z", "iopub.status.busy": "2022-12-14T23:24:52.132404Z", "iopub.status.idle": "2022-12-14T23:26:16.469331Z", "shell.execute_reply": "2022-12-14T23:26:16.468579Z" }, "id": "IssFr1BNCQX3" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/10\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 1/1875 [..............................] - ETA: 1:37:21 - loss: 0.1734" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 13/1875 [..............................] - ETA: 7s - loss: 0.1656 " ] }, { "name": "stdout", 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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "1874/1875 [============================>.] - ETA: 0s - loss: 0.0159" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "1875/1875 [==============================] - 11s 4ms/step - loss: 0.0159 - val_loss: 0.0090\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 2/10\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 1/1875 [..............................] - ETA: 9s - loss: 0.0093" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 15/1875 [..............................] - ETA: 7s - loss: 0.0088" ] }, { "name": 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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "1864/1875 [============================>.] - ETA: 0s - loss: 0.0084" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0084 - val_loss: 0.0080\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 3/10\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 1/1875 [..............................] - ETA: 9s - loss: 0.0077" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 14/1875 [..............................] - ETA: 7s - loss: 0.0079" ] }, { "name": 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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0074 - val_loss: 0.0073\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 5/10\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 1/1875 [..............................] - ETA: 10s - loss: 0.0076" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 14/1875 [..............................] - ETA: 7s - loss: 0.0072 " ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 27/1875 [..............................] - ETA: 7s - loss: 0.0072" ] }, { "name": 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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0068 - val_loss: 0.0067\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "autoencoder.fit(x_train_noisy, x_train,\n", " epochs=10,\n", " shuffle=True,\n", " validation_data=(x_test_noisy, x_test))" ] }, { "cell_type": "markdown", "metadata": { "id": "G85xUVBGTAKp" }, "source": [ "encoder의 요약을 살펴보겠습니다. 이미지가 28x28에서 7x7로 어떻게 다운샘플링되는지 확인하세요." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T23:26:16.473530Z", "iopub.status.busy": "2022-12-14T23:26:16.472852Z", "iopub.status.idle": "2022-12-14T23:26:16.484267Z", "shell.execute_reply": "2022-12-14T23:26:16.483556Z" }, "id": "oEpxlX6sTEQz" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"sequential_2\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " Layer (type) Output Shape Param # \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "=================================================================\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " conv2d (Conv2D) (None, 14, 14, 16) 160 \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " conv2d_1 (Conv2D) (None, 7, 7, 8) 1160 \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "=================================================================\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Total params: 1,320\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Trainable params: 1,320\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Non-trainable params: 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n" ] } ], "source": [ "autoencoder.encoder.summary()" ] }, { "cell_type": "markdown", "metadata": { "id": "DDZBfMx1UtXx" }, "source": [ "decoder는 이미지를 7x7에서 28x28로 다시 업샘플링합니다." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T23:26:16.490946Z", "iopub.status.busy": "2022-12-14T23:26:16.490294Z", "iopub.status.idle": "2022-12-14T23:26:16.503072Z", "shell.execute_reply": "2022-12-14T23:26:16.502401Z" }, "id": "pbeQtYMaUpro" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"sequential_3\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " Layer (type) Output Shape Param # \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "=================================================================\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " conv2d_transpose (Conv2DTra (None, 14, 14, 8) 584 \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " nspose) \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " conv2d_transpose_1 (Conv2DT (None, 28, 28, 16) 1168 \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " ranspose) \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " conv2d_2 (Conv2D) (None, 28, 28, 1) 145 \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "=================================================================\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Total params: 1,897\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Trainable params: 1,897\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Non-trainable params: 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n" ] } ], "source": [ "autoencoder.decoder.summary()" ] }, { "cell_type": "markdown", "metadata": { "id": "A7-VAuEy_N6M" }, "source": [ "autoencoder에서 생성된 노이즈가 있는 이미지와 노이즈가 제거 된 이미지를 모두 플롯합니다." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T23:26:16.510967Z", "iopub.status.busy": "2022-12-14T23:26:16.510287Z", "iopub.status.idle": "2022-12-14T23:26:16.832882Z", "shell.execute_reply": "2022-12-14T23:26:16.832055Z" }, "id": "t5IyPi1fCQQz" }, "outputs": [], "source": [ "encoded_imgs = autoencoder.encoder(x_test_noisy).numpy()\n", "decoded_imgs = autoencoder.decoder(encoded_imgs).numpy()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T23:26:16.837244Z", "iopub.status.busy": "2022-12-14T23:26:16.836565Z", "iopub.status.idle": "2022-12-14T23:26:17.609792Z", "shell.execute_reply": "2022-12-14T23:26:17.608961Z" }, "id": "sfxr9NdBCP_x" }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "n = 10\n", "plt.figure(figsize=(20, 4))\n", "for i in range(n):\n", "\n", " # display original + noise\n", " ax = plt.subplot(2, n, i + 1)\n", " plt.title(\"original + noise\")\n", " plt.imshow(tf.squeeze(x_test_noisy[i]))\n", " plt.gray()\n", " ax.get_xaxis().set_visible(False)\n", " ax.get_yaxis().set_visible(False)\n", "\n", " # display reconstruction\n", " bx = plt.subplot(2, n, i + n + 1)\n", " plt.title(\"reconstructed\")\n", " plt.imshow(tf.squeeze(decoded_imgs[i]))\n", " plt.gray()\n", " bx.get_xaxis().set_visible(False)\n", " bx.get_yaxis().set_visible(False)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "ErGrTnWHoUYl" }, "source": [ "## 세 번째 예: 이상 감지\n", "\n", "## 개요\n", "\n", "이 예에서는 [ECG5000 데이터세트](http://www.timeseriesclassification.com/description.php?Dataset=ECG5000)에서 이상을 감지하도록 autoencoder를 훈련합니다. 이 데이터세트에는 각각 140개의 데이터 포인트가 있는 5,000개의 [심전도](https://en.wikipedia.org/wiki/Electrocardiography)가 포함되어 있습니다. 단순화 된 버전의 데이터세트를 사용하고, 각 예제는 `0`(비정상 리듬에 해당) 또는 `1`(정상 리듬에 해당)으로 레이블이 지정됩니다. 여러분은 비정상 리듬을 식별하는 데 관심이 있습니다.\n", "\n", "참고: 레이블이 지정된 데이터세트를 사용하므로 지도 학습 문제라고 표현할 수 있습니다. 이 예의 목표는 사용 가능한 레이블이 없는 더 큰 데이터세트에 적용할 수 있는 이상 감지 개념을 설명하는 것입니다(예: 정상 리듬이 수천 개이고 비정상 리듬이 적은 경우).\n", "\n", "autoencoder를 사용하여 이상을 어떻게 감지하겠습니까? autoencoder는 재구성 오류를 최소화하도록 훈련되었습니다. autoencoder는 정상 리듬으로만 훈련한 다음 이 autoencoder를 사용하여 모든 데이터를 재구성합니다. 여기서 가설은 비정상 리듬의 경우에 재구성 오류가 더 클 것이라는 것입니다. 그런 다음 재구성 오류가 고정 임계값을 초과하는 경우, 리듬을 이상으로 분류합니다." ] }, { "cell_type": "markdown", "metadata": { "id": "i5estNaur_Mh" }, "source": [ "### ECG 데이터 로드하기" ] }, { "cell_type": "markdown", "metadata": { "id": "y35nsXLPsDNX" }, "source": [ "사용할 데이터세트는 [timeseriesclassification.com](http://www.timeseriesclassification.com/description.php?Dataset=ECG5000)의 데이터세트를 기반으로 합니다.\n" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T23:26:17.613950Z", "iopub.status.busy": "2022-12-14T23:26:17.613658Z", "iopub.status.idle": "2022-12-14T23:26:17.803288Z", "shell.execute_reply": "2022-12-14T23:26:17.802546Z" }, "id": "KmKRDJWgsFYa" }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " 0 1 2 3 4 5 6 \\\n", "0 -0.112522 -2.827204 -3.773897 -4.349751 -4.376041 -3.474986 -2.181408 \n", "1 -1.100878 -3.996840 -4.285843 -4.506579 -4.022377 -3.234368 -1.566126 \n", "2 -0.567088 -2.593450 -3.874230 -4.584095 -4.187449 -3.151462 -1.742940 \n", "3 0.490473 -1.914407 -3.616364 -4.318823 -4.268016 -3.881110 -2.993280 \n", "4 0.800232 -0.874252 -2.384761 -3.973292 -4.338224 -3.802422 -2.534510 \n", "\n", " 7 8 9 ... 131 132 133 134 \\\n", "0 -1.818286 -1.250522 -0.477492 ... 0.792168 0.933541 0.796958 0.578621 \n", "1 -0.992258 -0.754680 0.042321 ... 0.538356 0.656881 0.787490 0.724046 \n", "2 -1.490659 -1.183580 -0.394229 ... 0.886073 0.531452 0.311377 -0.021919 \n", "3 -1.671131 -1.333884 -0.965629 ... 0.350816 0.499111 0.600345 0.842069 \n", "4 -1.783423 -1.594450 -0.753199 ... 1.148884 0.958434 1.059025 1.371682 \n", "\n", " 135 136 137 138 139 140 \n", "0 0.257740 0.228077 0.123431 0.925286 0.193137 1.0 \n", "1 0.555784 0.476333 0.773820 1.119621 -1.436250 1.0 \n", "2 -0.713683 -0.532197 0.321097 0.904227 -0.421797 1.0 \n", "3 0.952074 0.990133 1.086798 1.403011 -0.383564 1.0 \n", "4 1.277392 0.960304 0.971020 1.614392 1.421456 1.0 \n", "\n", "[5 rows x 141 columns]" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Download the dataset\n", "dataframe = pd.read_csv('http://storage.googleapis.com/download.tensorflow.org/data/ecg.csv', header=None)\n", "raw_data = dataframe.values\n", "dataframe.head()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T23:26:17.806848Z", "iopub.status.busy": "2022-12-14T23:26:17.806575Z", "iopub.status.idle": "2022-12-14T23:26:17.815344Z", "shell.execute_reply": "2022-12-14T23:26:17.814604Z" }, "id": "UmuCPVYKsKKx" }, "outputs": [], "source": [ "# The last element contains the labels\n", "labels = raw_data[:, -1]\n", "\n", "# The other data points are the electrocadriogram data\n", "data = raw_data[:, 0:-1]\n", "\n", "train_data, test_data, train_labels, test_labels = train_test_split(\n", " data, labels, test_size=0.2, random_state=21\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "byK2vP7hsMbz" }, "source": [ "데이터를 `[0,1]`로 정규화합니다.\n" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T23:26:17.819435Z", "iopub.status.busy": "2022-12-14T23:26:17.818798Z", "iopub.status.idle": "2022-12-14T23:26:17.851042Z", "shell.execute_reply": "2022-12-14T23:26:17.850213Z" }, "id": "tgMZVWRKsPx6" }, "outputs": [], "source": [ "min_val = tf.reduce_min(train_data)\n", "max_val = tf.reduce_max(train_data)\n", "\n", "train_data = (train_data - min_val) / (max_val - min_val)\n", "test_data = (test_data - min_val) / (max_val - min_val)\n", "\n", "train_data = tf.cast(train_data, tf.float32)\n", "test_data = tf.cast(test_data, tf.float32)" ] }, { "cell_type": "markdown", "metadata": { "id": "BdSYr2IPsTiz" }, "source": [ "이 데이터세트에서 `1`로 레이블이 지정된 정상 리듬만 사용하여 autoencoder를 훈련합니다. 정상 리듬과 비정상 리듬을 분리합니다." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T23:26:17.855858Z", "iopub.status.busy": "2022-12-14T23:26:17.855162Z", "iopub.status.idle": "2022-12-14T23:26:17.892278Z", "shell.execute_reply": "2022-12-14T23:26:17.891477Z" }, "id": "VvK4NRe8sVhE" }, "outputs": [], "source": [ "train_labels = train_labels.astype(bool)\n", "test_labels = test_labels.astype(bool)\n", "\n", "normal_train_data = train_data[train_labels]\n", "normal_test_data = test_data[test_labels]\n", "\n", "anomalous_train_data = train_data[~train_labels]\n", "anomalous_test_data = test_data[~test_labels]" ] }, { "cell_type": "markdown", "metadata": { "id": "wVcTBDo-CqFS" }, "source": [ "정상적인 ECG를 플롯합니다. " ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T23:26:17.897116Z", "iopub.status.busy": "2022-12-14T23:26:17.896508Z", "iopub.status.idle": "2022-12-14T23:26:18.048010Z", "shell.execute_reply": "2022-12-14T23:26:18.047280Z" }, "id": "ZTlMIrpmseYe" }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.grid()\n", "plt.plot(np.arange(140), normal_train_data[0])\n", "plt.title(\"A Normal ECG\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "QpI9by2ZA0NN" }, "source": [ "비정상적인 ECG를 플롯합니다." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T23:26:18.051768Z", "iopub.status.busy": "2022-12-14T23:26:18.051385Z", "iopub.status.idle": "2022-12-14T23:26:18.190706Z", "shell.execute_reply": "2022-12-14T23:26:18.189680Z" }, "id": "zrpXREF2siBr" }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.grid()\n", "plt.plot(np.arange(140), anomalous_train_data[0])\n", "plt.title(\"An Anomalous ECG\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "0DS6QKZJslZz" }, "source": [ "### 모델 빌드하기" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T23:26:18.194462Z", "iopub.status.busy": "2022-12-14T23:26:18.194160Z", "iopub.status.idle": "2022-12-14T23:26:18.212418Z", "shell.execute_reply": "2022-12-14T23:26:18.211584Z" }, "id": "bf6owZQDsp9y" }, "outputs": [], "source": [ "class AnomalyDetector(Model):\n", " def __init__(self):\n", " super(AnomalyDetector, self).__init__()\n", " self.encoder = tf.keras.Sequential([\n", " layers.Dense(32, activation=\"relu\"),\n", " layers.Dense(16, activation=\"relu\"),\n", " layers.Dense(8, activation=\"relu\")])\n", " \n", " self.decoder = tf.keras.Sequential([\n", " layers.Dense(16, activation=\"relu\"),\n", " layers.Dense(32, activation=\"relu\"),\n", " layers.Dense(140, activation=\"sigmoid\")])\n", " \n", " def call(self, x):\n", " encoded = self.encoder(x)\n", " decoded = self.decoder(encoded)\n", " return decoded\n", "\n", "autoencoder = AnomalyDetector()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T23:26:18.215791Z", "iopub.status.busy": "2022-12-14T23:26:18.215515Z", "iopub.status.idle": "2022-12-14T23:26:18.224778Z", "shell.execute_reply": "2022-12-14T23:26:18.224088Z" }, "id": "gwRpBBbg463S" }, "outputs": [], "source": [ "autoencoder.compile(optimizer='adam', loss='mae')" ] }, { "cell_type": "markdown", "metadata": { "id": "zuTy60STBEy4" }, "source": [ "autoencoder는 일반 ECG만 사용하여 훈련되지만, 전체 테스트세트를 사용하여 평가됩니다." ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T23:26:18.228508Z", "iopub.status.busy": "2022-12-14T23:26:18.227960Z", "iopub.status.idle": "2022-12-14T23:26:21.640635Z", "shell.execute_reply": "2022-12-14T23:26:21.639854Z" }, "id": "V6NFSs-jsty2" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/20\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "1/5 [=====>........................] - ETA: 8s - loss: 0.0591" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "5/5 [==============================] - 2s 43ms/step - loss: 0.0578 - val_loss: 0.0533\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 2/20\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "1/5 [=====>........................] - ETA: 0s - loss: 0.0568" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "5/5 [==============================] - 0s 11ms/step - loss: 0.0558 - val_loss: 0.0518\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 3/20\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "1/5 [=====>........................] - ETA: 0s - loss: 0.0542" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "5/5 [==============================] - 0s 11ms/step - loss: 0.0534 - val_loss: 0.0504\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 4/20\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "1/5 [=====>........................] - ETA: 0s - loss: 0.0516" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "5/5 [==============================] - 0s 11ms/step - loss: 0.0504 - val_loss: 0.0487\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 5/20\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "1/5 [=====>........................] - ETA: 0s - loss: 0.0481" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "5/5 [==============================] - 0s 11ms/step - loss: 0.0470 - val_loss: 0.0470\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 6/20\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "1/5 [=====>........................] - ETA: 0s - loss: 0.0448" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "5/5 [==============================] - 0s 11ms/step - loss: 0.0437 - val_loss: 0.0451\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 7/20\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "1/5 [=====>........................] - ETA: 0s - loss: 0.0416" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "5/5 [==============================] - 0s 11ms/step - loss: 0.0403 - val_loss: 0.0432\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 8/20\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "1/5 [=====>........................] - ETA: 0s - loss: 0.0385" ] }, { "name": "stdout", "output_type": "stream", "text": [ 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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "5/5 [==============================] - 0s 11ms/step - loss: 0.0322 - val_loss: 0.0392\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 11/20\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "1/5 [=====>........................] - ETA: 0s - loss: 0.0311" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "5/5 [==============================] - 0s 12ms/step - loss: 0.0303 - val_loss: 0.0384\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 12/20\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "1/5 [=====>........................] - ETA: 0s - loss: 0.0293" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "5/5 [==============================] - 0s 11ms/step - loss: 0.0286 - val_loss: 0.0374\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 13/20\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "1/5 [=====>........................] - ETA: 0s - loss: 0.0277" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "5/5 [==============================] - 0s 11ms/step - loss: 0.0272 - val_loss: 0.0364\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 14/20\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "1/5 [=====>........................] - ETA: 0s - loss: 0.0256" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "5/5 [==============================] - 0s 11ms/step - loss: 0.0259 - val_loss: 0.0359\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 15/20\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "1/5 [=====>........................] - ETA: 0s - loss: 0.0257" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "5/5 [==============================] - 0s 11ms/step - loss: 0.0248 - val_loss: 0.0353\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 16/20\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "1/5 [=====>........................] - ETA: 0s - loss: 0.0247" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "5/5 [==============================] - 0s 11ms/step - loss: 0.0239 - val_loss: 0.0347\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 17/20\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "1/5 [=====>........................] - ETA: 0s - loss: 0.0239" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "5/5 [==============================] - 0s 11ms/step - loss: 0.0233 - val_loss: 0.0344\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 18/20\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "1/5 [=====>........................] - ETA: 0s - loss: 0.0229" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "5/5 [==============================] - 0s 12ms/step - loss: 0.0228 - val_loss: 0.0339\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 19/20\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "1/5 [=====>........................] - ETA: 0s - loss: 0.0222" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "5/5 [==============================] - 0s 12ms/step - loss: 0.0223 - val_loss: 0.0335\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 20/20\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "1/5 [=====>........................] - ETA: 0s - loss: 0.0221" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "5/5 [==============================] - 0s 11ms/step - loss: 0.0218 - val_loss: 0.0332\n" ] } ], "source": [ "history = autoencoder.fit(normal_train_data, normal_train_data, \n", " epochs=20, \n", " batch_size=512,\n", " validation_data=(test_data, test_data),\n", " shuffle=True)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T23:26:21.644416Z", "iopub.status.busy": "2022-12-14T23:26:21.643887Z", "iopub.status.idle": "2022-12-14T23:26:21.874358Z", "shell.execute_reply": "2022-12-14T23:26:21.873689Z" }, "id": "OEexphFwwTQS" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(history.history[\"loss\"], label=\"Training Loss\")\n", "plt.plot(history.history[\"val_loss\"], label=\"Validation Loss\")\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": { "id": "ceI5lKv1BT-A" }, "source": [ "재구성 오류가 정상 훈련 예제에서 하나의 표준 편차보다 큰 경우, ECG를 비정상으로 분류합니다. 먼저, 훈련 세트의 정상 ECG, autoencoder에 의해 인코딩 및 디코딩된 후의 재구성, 재구성 오류를 플롯해 보겠습니다." ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T23:26:21.878497Z", "iopub.status.busy": "2022-12-14T23:26:21.877834Z", "iopub.status.idle": "2022-12-14T23:26:22.092564Z", "shell.execute_reply": "2022-12-14T23:26:22.091790Z" }, "id": "hmsk4DuktxJ2" }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "encoded_data = autoencoder.encoder(normal_test_data).numpy()\n", "decoded_data = autoencoder.decoder(encoded_data).numpy()\n", "\n", "plt.plot(normal_test_data[0], 'b')\n", "plt.plot(decoded_data[0], 'r')\n", "plt.fill_between(np.arange(140), decoded_data[0], normal_test_data[0], color='lightcoral')\n", "plt.legend(labels=[\"Input\", \"Reconstruction\", \"Error\"])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "ocA_q9ufB_aF" }, "source": [ "이번에는 비정상적인 테스트 예제에서 비슷한 플롯을 만듭니다." ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T23:26:22.096639Z", "iopub.status.busy": "2022-12-14T23:26:22.096024Z", "iopub.status.idle": "2022-12-14T23:26:22.258907Z", "shell.execute_reply": "2022-12-14T23:26:22.258201Z" }, "id": "vNFTuPhLwTBn" }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "encoded_data = autoencoder.encoder(anomalous_test_data).numpy()\n", "decoded_data = autoencoder.decoder(encoded_data).numpy()\n", "\n", "plt.plot(anomalous_test_data[0], 'b')\n", "plt.plot(decoded_data[0], 'r')\n", "plt.fill_between(np.arange(140), decoded_data[0], anomalous_test_data[0], color='lightcoral')\n", "plt.legend(labels=[\"Input\", \"Reconstruction\", \"Error\"])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "ocimg3MBswdS" }, "source": [ "### 이상 감지하기" ] }, { "cell_type": "markdown", "metadata": { "id": "Xnh8wmkDsypN" }, "source": [ "재구성 손실이 고정 임계값보다 큰지 여부를 계산하여 이상을 감지합니다. 이 튜토리얼에서는 훈련 세트에서 정상 예제에 대한 평균 오차를 계산한 다음, 재구성 오류가 훈련 세트의 표준 편차보다 큰 경우 향후 예제를 비정상적인 것으로 분류합니다.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "TeuT8uTA5Y_w" }, "source": [ "훈련 세트에서 정상 ECG에 대한 재구성 오류를 플롯합니다." ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T23:26:22.263253Z", "iopub.status.busy": "2022-12-14T23:26:22.262705Z", "iopub.status.idle": "2022-12-14T23:26:22.726453Z", "shell.execute_reply": "2022-12-14T23:26:22.725447Z" }, "id": "N7FltOnHu4-l" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\r", " 1/74 [..............................] - ETA: 7s" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "37/74 [==============>...............] - ETA: 0s" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "74/74 [==============================] - ETA: 0s" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "74/74 [==============================] - 0s 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "reconstructions = autoencoder.predict(normal_train_data)\n", "train_loss = tf.keras.losses.mae(reconstructions, normal_train_data)\n", "\n", "plt.hist(train_loss[None,:], bins=50)\n", "plt.xlabel(\"Train loss\")\n", "plt.ylabel(\"No of examples\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "mh-3ChEF5hog" }, "source": [ "평균보다 표준 편차가 높은 임계값을 선택합니다." ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T23:26:22.730275Z", "iopub.status.busy": "2022-12-14T23:26:22.729768Z", "iopub.status.idle": "2022-12-14T23:26:22.735669Z", "shell.execute_reply": "2022-12-14T23:26:22.734788Z" }, "id": "82hkl0Chs3P_" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Threshold: 0.033277668\n" ] } ], "source": [ "threshold = np.mean(train_loss) + np.std(train_loss)\n", "print(\"Threshold: \", threshold)" ] }, { "cell_type": "markdown", "metadata": { "id": "uEGlA1Be50Nj" }, "source": [ "참고: 테스트 예제를 이상 항목으로 분류하는 임계값을 선택하는 데 사용할 수 있는 다른 전략이 있습니다. 올바른 접근 방식은 데이터세트에 따라 다릅니다. 이 튜토리얼의 끝에 있는 링크를 통해 더 많은 것을 배울 수 있습니다. " ] }, { "cell_type": "markdown", "metadata": { "id": "zpLSDAeb51D_" }, "source": [ "테스트 세트에서 비정상적인 예제에 대한 재구성 오류를 조사하면 대부분 임계값보다 더 큰 재구성 오류가 있음을 알 수 있습니다. 임계값을 변경하여 분류자의 [정밀도](https://developers.google.com/machine-learning/glossary#precision)와 [재현율](https://developers.google.com/machine-learning/glossary#recall)을 조정할 수 있습니다. " ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T23:26:22.739610Z", "iopub.status.busy": "2022-12-14T23:26:22.738940Z", "iopub.status.idle": "2022-12-14T23:26:23.009444Z", "shell.execute_reply": "2022-12-14T23:26:23.008493Z" }, "id": "sKVwjQK955Wy" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\r", " 1/14 [=>............................] - ETA: 0s" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "14/14 [==============================] - 0s 2ms/step\n" ] }, { "data": { "image/png": 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14gAAAC7H4yAzatQobd26VXfeeafq1KnDAl8AAOA3HgeZZcuW6Z133lHPnnziJgAA8C+P18jUrl1bTZs29UUtAAAAHvE4yEycOFETJkzQTz/95It6AAAA3ObxW0uzZ8/W4cOHFR0drcaNG1+02Hf//v1eKw4AAOByPA4yffv29UEZAAAAnvM4yEyYMMEXdQAAAHiMu18DAADL8nhG5sKFC5oxY4ZWr16tY8eOqbS01GX/6dOnvVYcAADA5Xg8IzNp0iRNnz5dAwYMkMPhUHp6ulJTUxUUFKSJEyf6oEQAAIDyeRxkVqxYoYULF2rMmDEKDg7WQw89pL/97W8aP368du3a5YsaAQAAyuVxkCkoKFDbtm0lSTVr1pTD4ZAk3XfffdqwYYN3qwMAALgMj4NMw4YNlZ+fL0lq2rSpNm/eLEnau3evwsLCvFsdAADAZXgcZPr166fMzExJ0siRI/Xcc8+pefPmGjRokB577DGvFwgAAHApHl+1NGXKFOfXAwYMUHx8vD755BM1b95cvXr18mpxAAAAl+NxkPm9Ll26qEuXLpIkY4xsNluliwIAAHCHx28tDRkyRGfPnr1o/MiRI7rtttu8UhQAAIA7PA4y//d//6d27dopKyvLObZs2TK1b99edevW9WpxAAAAl+PxW0t79uzRn/70J91xxx0aM2aMDh06pA8++EDTp0/XsGHDfFEjAABAuTwOMiEhIXrppZdUvXp1/eUvf1FwcLC2b9+u5ORkX9QHAABwSR6/tXT+/HmNGTNGU6dOVUZGhpKTk5Wamqp//OMfvqgPAADgkjyekbnpppv0008/adu2berSpYuMMZo2bZpSU1P12GOPad68eb6oEwAA4CIez8jcdNNNysnJcV5ybbPZNHbsWGVlZemjjz7yeoEAAACX4vGMzKJFi8od79Chg7KzsytdEAAAgLs8npGRpNdff11du3ZVbGysjh49KkmaOXOmNm7c6NXiAAAALsfjIDN//nylp6fr3nvv1ZkzZ3ThwgVJUlRUlGbOnOnt+gAAAC7J47eW5syZo4ULF6pv374u91266aab9NRTT3m1OADuaTxug79LAAC/8HhGJi8vTx06dLhoPCwsrNxbFwAAAPiKx0GmSZMmysnJuWh848aNSkxM9Ohc8+fPV7t27RQZGanIyEglJyfrgw8+cO4/d+6c0tLSVKdOHdWsWVP9+/dXYWGhpyUDAICrlMdvLaWnpystLU3nzp2TMUZ79uzRm2++qcmTJ+tvf/ubR+dq2LChpkyZoubNm8sYo2XLlqlPnz769NNP1bp1a40ePVobNmzQmjVrZLfbNWLECKWmpmrnzp2elg0AAK5CNmOM8fRBK1as0MSJE3X48GFJUmxsrCZNmqShQ4dWuqDatWvrpZde0v3336969epp5cqVuv/++yVJX331lRITE5WVleX8HJvfKykpUUlJifP7oqIixcXFyeFwKDIystL1AYHoal0jc2RKT3+XAMBPioqKZLfbr/j3u0KXXw8cOFAHDx5UcXGxCgoKdOLEiUqHmAsXLuitt97S2bNnlZycrOzsbJ0/f14pKSnOYxISEtSoUSOXO2//3uTJk2W3251bXFxcpeoCAACBq0JB5lfVq1dX/fr1K1XAZ599ppo1ayosLEyPP/643n33XbVq1UoFBQUKDQ1VVFSUy/HR0dEqKCi45PkyMjLkcDic2/HjxytVHwAACFwer5HxtpYtWyonJ0cOh0Nvv/22Bg8erO3bt1f4fGFhYQoLC/NihQAAIFD5PciEhoaqWbNmkqSkpCTt3btXs2bN0oABA1RaWqozZ864zMoUFhYqJibGT9UCAIBAUqm3lnyhrKxMJSUlSkpKUkhIiDIzM537cnNzdezYMSUnJ/uxQgAAECjcCjK1a9fWd999J0l67LHH9OOPP3rlyTMyMvTRRx/pyJEj+uyzz5SRkaFt27Zp4MCBstvtGjp0qNLT07V161ZlZ2fr0UcfVXJy8iWvWAIAANcWt4JMaWmpioqKJEnLli3TuXPnvPLkp06d0qBBg9SyZUt169ZNe/fu1aZNm/SHP/xBkjRjxgzdd9996t+/v2677TbFxMRo7dq1XnluAABgfW59jswf/vAHFRYWKikpScuWLdOAAQMUERFR7rGLFy/2epGV4e516ICV8TkyAK427v79dmux7xtvvKEZM2bo8OHDstlscjgcXpuVAQAAqCi3gkx0dLTzTtdNmjTR66+/rjp16vi0MAAAgCvx+PLrvLw8X9QBAADgsQpdfr19+3b16tVLzZo1U7NmzdS7d2/t2LHD27UBAABclsdB5o033lBKSoqqV6+uUaNGadSoUYqIiFC3bt20cuVKX9QIAABQLo/vfp2YmKjhw4dr9OjRLuPTp0/XwoUL9eWXX3q1wMriqiVcC7hqCcDVxmd3v/7mm2/Uq1evi8Z79+7N+hkAAFClPA4ycXFxLrcN+NWHH36ouLg4rxQFAADgDo+vWhozZoxGjRqlnJwc3XzzzZKknTt3aunSpZo1a5bXCwQAALgUj4PME088oZiYGL388stavXq1pH+vm1m1apX69Onj9QIBAAAuxeMgI0n9+vVTv379vF0LAACARyr0OTIAAACBgCADAAAsiyADAAAsiyADAAAsq1JBxhgjDz8YGAAAwGsqFGSWL1+utm3bKiIiQhEREWrXrp1ef/11b9cGAABwWR5ffj19+nQ999xzGjFihLp27SpJ+vjjj/X444/ru+++u+geTAAAAL7icZCZM2eO5s+fr0GDBjnHevfurdatW2vixIkEGQAAUGU8fmspPz/feWuC37r55puVn5/vlaIAAADc4XGQadasmfPWBL+1atUqNW/e3CtFAQAAuMPjt5YmTZqkAQMG6KOPPnKukdm5c6cyMzPLDTgAAAC+4vGMTP/+/bV7927VrVtX69at07p161S3bl3t2bOH+y8BAIAqVaGbRiYlJemNN97wdi0AAAAe4ZN9AQCAZbk9IxMUFCSbzXbZY2w2m3755ZdKFwUAAOAOt4PMu+++e8l9WVlZmj17tsrKyrxSFAAAgDvcDjJ9+vS5aCw3N1fjxo3T+++/r4EDB+r555/3anEAAACXU6E1MidPntSwYcPUtm1b/fLLL8rJydGyZcsUHx/v7foAAAAuyaMg43A4NHbsWDVr1kyff/65MjMz9f7776tNmza+qg8AAOCS3H5radq0aZo6dapiYmL05ptvlvtWEwAAQFWyGWOMOwcGBQUpIiJCKSkpqlat2iWPW7t2rdeK84aioiLZ7XY5HA5FRkb6uxzAJxqP2+DvEnziyJSe/i4BgJ+4+/fb7RmZQYMGXfHyawAAgKrkdpBZunSpD8sAAADwHJ/sCwAALIsgAwAALIsgAwAALIsgAwAALMvtxb4AUNXcuazcnUu0vXUeAIGHGRkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZfg0ykydPVseOHVWrVi3Vr19fffv2VW5urssx586dU1pamurUqaOaNWuqf//+Kiws9FPFAAAgkPg1yGzfvl1paWnatWuXtmzZovPnz+vuu+/W2bNnnceMHj1a77//vtasWaPt27fr5MmTSk1N9WPVAAAgUPj1k303btzo8v3SpUtVv359ZWdn67bbbpPD4dCiRYu0cuVK3XXXXZKkJUuWKDExUbt27VKXLl0uOmdJSYlKSkqc3xcVFfm2CQAA4DcBdYsCh8MhSapdu7YkKTs7W+fPn1dKSorzmISEBDVq1EhZWVnlBpnJkydr0qRJVVMwAL9z5/YDAK5eAbPYt6ysTE8++aS6du2qNm3aSJIKCgoUGhqqqKgol2Ojo6NVUFBQ7nkyMjLkcDic2/Hjx31dOgAA8JOAmZFJS0vTgQMH9PHHH1fqPGFhYQoLC/NSVQAAIJAFxIzMiBEj9Pe//11bt25Vw4YNneMxMTEqLS3VmTNnXI4vLCxUTExMFVcJAAACjV+DjDFGI0aM0Lvvvqv//d//VZMmTVz2JyUlKSQkRJmZmc6x3NxcHTt2TMnJyVVdLgAACDB+fWspLS1NK1eu1HvvvadatWo5173Y7XZFRETIbrdr6NChSk9PV+3atRUZGamRI0cqOTm53IW+AADg2uLXIDN//nxJ0h133OEyvmTJEg0ZMkSSNGPGDAUFBal///4qKSlR9+7dNW/evCquFAAABCK/BhljzBWPCQ8P19y5czV37twqqAgAAFhJQCz2BQAAqAiCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsKxgfxcA4PIaj9vg7xIAIGAxIwMAACyLIAMAACyLIAMAACyLIAMAACyLIAMAACyLIAMAACyLIAMAACyLIAMAACyLIAMAACyLIAMAACyLWxQAgNy7FcSRKT2roBIAnmBGBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBZBBgAAWBa3KAD8yJ2PxQcAXBozMgAAwLL8GmQ++ugj9erVS7GxsbLZbFq3bp3LfmOMxo8frwYNGigiIkIpKSk6ePCgf4oFAAABx69B5uzZs2rfvr3mzp1b7v5p06Zp9uzZevXVV7V7927VqFFD3bt317lz56q4UgAAEIj8ukbmnnvu0T333FPuPmOMZs6cqWeffVZ9+vSRJC1fvlzR0dFat26dHnzwwaosFQAABKCAXSOTl5engoICpaSkOMfsdrs6d+6srKysSz6upKRERUVFLhsAALg6BWyQKSgokCRFR0e7jEdHRzv3lWfy5Mmy2+3OLS4uzqd1AgAA/wnYIFNRGRkZcjgczu348eP+LgkAAPhIwAaZmJgYSVJhYaHLeGFhoXNfecLCwhQZGemyAQCAq1PABpkmTZooJiZGmZmZzrGioiLt3r1bycnJfqwMAAAECr9etVRcXKxDhw45v8/Ly1NOTo5q166tRo0a6cknn9QLL7yg5s2bq0mTJnruuecUGxurvn37+q9oAAAQMPwaZPbt26c777zT+X16erokafDgwVq6dKmeeeYZnT17VsOHD9eZM2d0yy23aOPGjQoPD/dXyQAAIIDYjDHG30X4UlFRkex2uxwOB+tlEHC415K1HJnS098lANcMd/9+B+waGQAAgCshyAAAAMsiyAAAAMsiyAAAAMvy61VLwNWMhbxXH2+9piwaBryHGRkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZwf4uALCixuM2+LsEAICYkQEAABZGkAEAAJZFkAEAAJZFkAEAAJZFkAEAAJbFVUsAAK9w52q+I1N6VkEluJYwIwMAACyLIAMAACyLIAMAACyLIAMAACyLIAMAACyLq5aA3+E+SgDwb1a4Eo0ZGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFks9gUAi7LCQsxAx8/Q+piRAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlsVVS5XgrdXu7n4kvrdWzl/Lq/S5/QACQVX+d+it5wq03wlW/D1mxZqtgBkZAABgWZYIMnPnzlXjxo0VHh6uzp07a8+ePf4uCQAABICADzKrVq1Senq6JkyYoP3796t9+/bq3r27Tp065e/SAACAnwV8kJk+fbqGDRumRx99VK1atdKrr76q6tWra/Hixf4uDQAA+FlAL/YtLS1Vdna2MjIynGNBQUFKSUlRVlZWuY8pKSlRSUmJ83uHwyFJKioq8np9ZSU/XfEYd57XnfO4ey53eKtuK3L3Zw3Albd+lwXa7zEr1lyV/Fnzr+c1xlz+QBPAvv32WyPJfPLJJy7jTz/9tOnUqVO5j5kwYYKRxMbGxsbGxnYVbMePH79sVgjoGZmKyMjIUHp6uvP7srIynT59WnXq1JHNZvNjZd5RVFSkuLg4HT9+XJGRkf4ux+eutX4ler4Wer7W+pXomZ49Z4zRjz/+qNjY2MseF9BBpm7duqpWrZoKCwtdxgsLCxUTE1PuY8LCwhQWFuYyFhUV5asS/SYyMvKa+YchXXv9SvR8LbjW+pXo+VrhrZ7tdvsVjwnoxb6hoaFKSkpSZmamc6ysrEyZmZlKTk72Y2UAACAQBPSMjCSlp6dr8ODBuummm9SpUyfNnDlTZ8+e1aOPPurv0gAAgJ8FfJAZMGCA/vWvf2n8+PEqKCjQDTfcoI0bNyo6OtrfpflFWFiYJkyYcNHbZ1era61fiZ6vBddavxI9Xyv80bPNmCtd1wQAABCYAnqNDAAAwOUQZAAAgGURZAAAgGURZAAAgGURZPxs7ty5aty4scLDw9W5c2ft2bPnssevWbNGCQkJCg8PV9u2bfWPf/zDue/8+fMaO3as2rZtqxo1aig2NlaDBg3SyZMnfd2GR7zZsyRNnDhRCQkJqlGjhq677jqlpKRo9+7dvmzBY97u+bcef/xx2Ww2zZw508tVV5y3+x0yZIhsNpvL1qNHD1+24DFfvMZffvmlevfuLbvdrho1aqhjx446duyYr1rwmLd7/v1r/Ov20ksv+bINt3m73+LiYo0YMUINGzZURESE88bIgcTbPRcWFmrIkCGKjY1V9erV1aNHDx08eLByRXrnrkioiLfeesuEhoaaxYsXm88//9wMGzbMREVFmcLCwnKP37lzp6lWrZqZNm2a+eKLL8yzzz5rQkJCzGeffWaMMebMmTMmJSXFrFq1ynz11VcmKyvLdOrUySQlJVVlW5fl7Z6NMWbFihVmy5Yt5vDhw+bAgQNm6NChJjIy0pw6daqq2rosX/T8q7Vr15r27dub2NhYM2PGDB934h5f9Dt48GDTo0cPk5+f79xOnz5dVS1dkS96PnTokKldu7Z5+umnzf79+82hQ4fMe++9d8lzVjVf9Pzb1zc/P98sXrzY2Gw2c/jw4apq65J80e+wYcNM06ZNzdatW01eXp557bXXTLVq1cx7771XVW1dlrd7LisrM126dDG33nqr2bNnj/nqq6/M8OHDTaNGjUxxcXGF6yTI+FGnTp1MWlqa8/sLFy6Y2NhYM3ny5HKPf+CBB0zPnj1dxjp37mz+67/+65LPsWfPHiPJHD161DtFV1JV9OxwOIwk8+GHH3qn6EryVc8nTpww//Ef/2EOHDhg4uPjAybI+KLfwYMHmz59+vikXm/wRc8DBgwwDz/8sG8K9oKq+Lfcp08fc9ddd3mn4EryRb+tW7c2zz//vMsxN954o/nzn//sxcorzts95+bmGknmwIEDLuesV6+eWbhwYYXr5K0lPyktLVV2drZSUlKcY0FBQUpJSVFWVla5j8nKynI5XpK6d+9+yeMlyeFwyGazBcT9pqqi59LSUi1YsEB2u13t27f3XvEV5Kuey8rK9Mgjj+jpp59W69atfVN8BfjyNd62bZvq16+vli1b6oknntD333/v/QYqwBc9l5WVacOGDWrRooW6d++u+vXrq3Pnzlq3bp3P+vBEVfxbLiws1IYNGzR06FDvFV5Bvur35ptv1vr16/Xtt9/KGKOtW7fq66+/1t133+2bRjzgi55LSkokSeHh4S7nDAsL08cff1zhWgkyfvLdd9/pwoULF31CcXR0tAoKCsp9TEFBgUfHnzt3TmPHjtVDDz0UEDcs82XPf//731WzZk2Fh4drxowZ2rJli+rWrevdBirAVz1PnTpVwcHBGjVqlPeLrgRf9dujRw8tX75cmZmZmjp1qrZv36577rlHFy5c8H4THvJFz6dOnVJxcbGmTJmiHj16aPPmzerXr59SU1O1fft23zTigar4/bVs2TLVqlVLqamp3im6EnzV75w5c9SqVSs1bNhQoaGh6tGjh+bOnavbbrvN+014yBc9JyQkqFGjRsrIyNAPP/yg0tJSTZ06VSdOnFB+fn6Faw34WxSgYs6fP68HHnhAxhjNnz/f3+X43J133qmcnBx99913WrhwoR544AHt3r1b9evX93dpXpedna1Zs2Zp//79stls/i6nSjz44IPOr9u2bat27dqpadOm2rZtm7p16+bHynyjrKxMktSnTx+NHj1aknTDDTfok08+0auvvqrbb7/dn+VVicWLF2vgwIEu//d+tZkzZ4527dql9evXKz4+Xh999JHS0tIUGxt70czG1SAkJERr167V0KFDVbt2bVWrVk0pKSm65557ZCpxkwFmZPykbt26qlatmgoLC13GCwsLFRMTU+5jYmJi3Dr+1xBz9OhRbdmyJSBmYyTf9lyjRg01a9ZMXbp00aJFixQcHKxFixZ5t4EK8EXPO3bs0KlTp9SoUSMFBwcrODhYR48e1ZgxY9S4cWOf9OEuX77Gv3X99derbt26OnToUOWLriRf9Fy3bl0FBwerVatWLsckJiYGxFVLvn6dd+zYodzcXP3nf/6n94quBF/0+/PPP+tPf/qTpk+frl69eqldu3YaMWKEBgwYoL/+9a++acQDvnqNk5KSlJOTozNnzig/P18bN27U999/r+uvv77CtRJk/CQ0NFRJSUnKzMx0jpWVlSkzM1PJycnlPiY5OdnleEnasmWLy/G/hpiDBw/qww8/VJ06dXzTQAX4qufylJWVOd+P9Sdf9PzII4/on//8p3JycpxbbGysnn76aW3atMl3zbihql7jEydO6Pvvv1eDBg28U3gl+KLn0NBQdezYUbm5uS7HfP3114qPj/dyB57z9eu8aNEiJSUlBcQ6N8k3/Z4/f17nz59XUJDrn+Fq1ao5Z+T8ydevsd1uV7169XTw4EHt27dPffr0qXixFV4mjEp76623TFhYmFm6dKn54osvzPDhw01UVJQpKCgwxhjzyCOPmHHjxjmP37lzpwkODjZ//etfzZdffmkmTJjgcmlbaWmp6d27t2nYsKHJyclxuYyxpKTELz3+nrd7Li4uNhkZGSYrK8scOXLE7Nu3zzz66KMmLCzMZWW8P3m75/IE0lVL3u73xx9/NE899ZTJysoyeXl55sMPPzQ33nijad68uTl37pxfevw9X7zGa9euNSEhIWbBggXm4MGDZs6cOaZatWpmx44dVd5feXz137XD4TDVq1c38+fPr9J+rsQX/d5+++2mdevWZuvWreabb74xS5YsMeHh4WbevHlV3l95fNHz6tWrzdatW83hw4fNunXrTHx8vElNTa1UnQQZP5szZ45p1KiRCQ0NNZ06dTK7du1y7rv99tvN4MGDXY5fvXq1adGihQkNDTWtW7c2GzZscO7Ly8szksrdtm7dWkUdXZk3e/75559Nv379TGxsrAkNDTUNGjQwvXv3Nnv27KmqdtzizZ7LE0hBxhjv9vvTTz+Zu+++29SrV8+EhISY+Ph4M2zYMOcv00Dhi9d40aJFplmzZiY8PNy0b9/erFu3ztdteMQXPb/22msmIiLCnDlzxtfle8zb/ebn55shQ4aY2NhYEx4eblq2bGlefvllU1ZWVhXtuMXbPc+aNcs0bNjQhISEmEaNGplnn3220v+jbTOmEitsAAAA/Ig1MgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgCuGkeOHJHNZlNOTo6/SwFQRQgyALzGZrNddps4cWKlzr1u3Tqv1Qrg6hDs7wIAXD3y8/OdX69atUrjx493uYNzzZo1/VEWgKsYMzIAvCYmJsa52e122Ww2l7G33npLiYmJCg8PV0JCgubNm+d8bGlpqUaMGKEGDRooPDxc8fHxmjx5siSpcePGkqR+/frJZrM5v3fH9u3b1alTJ4WFhalBgwYaN26cfvnlF+f+t99+W23btlVERITq1KmjlJQUnT17VpK0bds2derUSTVq1FBUVJS6du2qo0ePVv4HBcBrmJEBUCVWrFih8ePH65VXXlGHDh306aefatiwYapRo4YGDx6s2bNna/369Vq9erUaNWqk48eP6/jx45KkvXv3qn79+lqyZIl69OihatWqufWc3377re69914NGTJEy5cv11dffaVhw4YpPDxcEydOVH5+vh566CFNmzZN/fr1048//qgdO3bIGKNffvlFffv21bBhw/Tmm2+qtLRUe/bskc1m8+WPCYCHCDIAqsSECRP08ssvKzU1VZLUpEkTffHFF3rttdc0ePBgHTt2TM2bN9ctt9wim82m+Ph452Pr1asnSYqKilJMTIzbzzlv3jzFxcXplVdekc1mU0JCgk6ePKmxY8dq/Pjxys/P1y+//KLU1FTn87Vt21aSdPr0aTkcDt13331q2rSpJCkxMdErPwsA3sNbSwB87uzZszp8+LCGDh2qmjVrOrcXXnhBhw8fliQNGTJEOTk5atmypUaNGqXNmzdX+nm//PJLJScnu8yidO3aVcXFxTpx4oTat2+vbt26qW3btvrjH/+ohQsX6ocffpAk1a5dW0OGDFH37t3Vq1cvzZo1y2UNEIDAQJAB4HPFxcWSpIULFyonJ8e5HThwQLt27ZIk3XjjjcrLy9Nf/vIX/fzzz3rggQd0//33+7SuatWqacuWLfrggw/UqlUrzZkzRy1btlReXp4kacmSJcrKytLNN9+sVatWqUWLFs56AQQGggwAn4uOjlZsbKy++eYbNWvWzGVr0qSJ87jIyEgNGDBACxcu1KpVq/TOO+/o9OnTkqSQkBBduHDBo+dNTExUVlaWjDHOsZ07d6pWrVpq2LChpH9f1t21a1dNmjRJn376qUJDQ/Xuu+86j+/QoYMyMjL0ySefqE2bNlq5cmVlfhQAvIw1MgCqxKRJkzRq1CjZ7Xb16NFDJSUl2rdvn3744Qelp6dr+vTpatCggTp06KCgoCCtWbNGMTExioqKkvTvK5cyMzPVtWtXhYWF6brrrrvic/73f/+3Zs6cqZEjR2rEiBHKzc3VhAkTlJ6erqCgIO3evVuZmZm6++67Vb9+fe3evVv/+te/lJiYqLy8PC1YsEC9e/dWbGyscnNzdfDgQQ0aNMjHPykAHjEA4ANLliwxdrvdZWzFihXmhhtuMKGhoea6664zt912m1m7dq0xxpgFCxaYG264wdSoUcNERkaabt26mf379zsfu379etOsWTMTHBxs4uPjy33OvLw8I8l8+umnzrFt27aZjh07mtDQUBMTE2PGjh1rzp8/b4wx5osvvjDdu3c39erVM2FhYaZFixZmzpw5xhhjCgoKTN++fU2DBg1MaGioiY+PN+PHjzcXLlzw3g8JQKXZjPnNnCsAAICFsEYGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABY1v8DvZVDJtYSrjwAAAAASUVORK5CYII=\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "reconstructions = autoencoder.predict(anomalous_test_data)\n", "test_loss = tf.keras.losses.mae(reconstructions, anomalous_test_data)\n", "\n", "plt.hist(test_loss[None, :], bins=50)\n", "plt.xlabel(\"Test loss\")\n", "plt.ylabel(\"No of examples\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "PFVk_XGE6AX2" }, "source": [ "재구성 오류가 임계값보다 큰 경우 ECG를 이상으로 분류합니다." ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T23:26:23.013742Z", "iopub.status.busy": "2022-12-14T23:26:23.013177Z", "iopub.status.idle": "2022-12-14T23:26:23.018814Z", "shell.execute_reply": "2022-12-14T23:26:23.018026Z" }, "id": "mkgJZfhh6CHr" }, "outputs": [], "source": [ "def predict(model, data, threshold):\n", " reconstructions = model(data)\n", " loss = tf.keras.losses.mae(reconstructions, data)\n", " return tf.math.less(loss, threshold)\n", "\n", "def print_stats(predictions, labels):\n", " print(\"Accuracy = {}\".format(accuracy_score(labels, predictions)))\n", " print(\"Precision = {}\".format(precision_score(labels, predictions)))\n", " print(\"Recall = {}\".format(recall_score(labels, predictions)))" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T23:26:23.022426Z", "iopub.status.busy": "2022-12-14T23:26:23.021819Z", "iopub.status.idle": "2022-12-14T23:26:23.038316Z", "shell.execute_reply": "2022-12-14T23:26:23.037570Z" }, "id": "sOcfXfXq6FBd" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy = 0.944\n", "Precision = 0.9941176470588236\n", "Recall = 0.9053571428571429\n" ] } ], "source": [ "preds = predict(autoencoder, test_data, threshold)\n", "print_stats(preds, test_labels)" ] }, { "cell_type": "markdown", "metadata": { "id": "HrJRef8Ln945" }, "source": [ "## 다음 단계\n", "\n", "autoencoder를 사용한 이상 탐지에 대해 자세히 알아보려면 Victor Dibia가 TensorFlow.js로 빌드한 훌륭한 [대화형 예제](https://anomagram.fastforwardlabs.com/#/)를 확인하세요. 실제 사용 사례의 경우, TensorFlow를 사용하여 [Airbus가 ISS 원격 측정 데이터에서 이상을 감지](https://blog.tensorflow.org/2020/04/how-airbus-detects-anomalies-iss-telemetry-data-tfx.html)하는 방법을 알아볼 수 있습니다. 기본 사항에 대해 자세히 알아보려면 François Chollet의 [블로그 게시물](https://blog.keras.io/building-autoencoders-in-keras.html)을 읽어보세요. 자세한 내용은 Ian Goodfellow, Yoshua Bengio, Aaron Courville의 [딥 러닝](https://www.deeplearningbook.org/)에서 14장을 확인하세요.\n" ] } ], "metadata": { "accelerator": "GPU", "colab": { "collapsed_sections": [], "name": "autoencoder.ipynb", "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "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": "3.9.16" } }, "nbformat": 4, "nbformat_minor": 0 }