{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "**15장 – RNN과 CNN을 사용해 시퀀스 처리하기**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_이 노트북은 15장에 있는 모든 샘플 코드와 연습문제 해답을 가지고 있습니다._" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", " \n", "
\n", " 구글 코랩에서 실행하기\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 설정" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "먼저 몇 개의 모듈을 임포트합니다. 맷플롯립 그래프를 인라인으로 출력하도록 만들고 그림을 저장하는 함수를 준비합니다. 또한 파이썬 버전이 3.5 이상인지 확인합니다(파이썬 2.x에서도 동작하지만 곧 지원이 중단되므로 파이썬 3을 사용하는 것이 좋습니다). 사이킷런 버전이 0.20 이상인지와 텐서플로 버전이 2.0 이상인지 확인합니다." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "No GPU was detected. LSTMs and CNNs can be very slow without a GPU.\n" ] } ], "source": [ "# 파이썬 ≥3.5 필수\n", "import sys\n", "assert sys.version_info >= (3, 5)\n", "\n", "# 코랩에서 실행되는 노트북인가요?\n", "IS_COLAB = \"google.colab\" in sys.modules\n", "\n", "# 사이킷런 ≥0.20 필수\n", "import sklearn\n", "assert sklearn.__version__ >= \"0.20\"\n", "\n", "# 텐서플로 ≥2.0 필수\n", "import tensorflow as tf\n", "from tensorflow import keras\n", "assert tf.__version__ >= \"2.0\"\n", "\n", "if not tf.config.list_physical_devices('GPU'):\n", " print(\"감지된 GPU가 없습니다. GPU가 없으면 LSTM과 CNN이 매우 느릴 수 있습니다.\")\n", " if IS_COLAB:\n", " print(\"런타임 > 런타임 유형 변경 메뉴를 선택하고 하드웨어 가속기로 GPU를 고르세요.\")\n", "\n", "# 공통 모듈 임포트\n", "import numpy as np\n", "import os\n", "from pathlib import Path\n", "\n", "# 노트북 실행 결과를 동일하게 유지하기 위해\n", "np.random.seed(42)\n", "tf.random.set_seed(42)\n", "\n", "# 깔끔한 그래프 출력을 위해\n", "%matplotlib inline\n", "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "mpl.rc('axes', labelsize=14)\n", "mpl.rc('xtick', labelsize=12)\n", "mpl.rc('ytick', labelsize=12)\n", "\n", "# 그림을 저장할 위치\n", "PROJECT_ROOT_DIR = \".\"\n", "CHAPTER_ID = \"rnn\"\n", "IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\n", "os.makedirs(IMAGES_PATH, exist_ok=True)\n", "\n", "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n", " path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n", " print(\"그림 저장\", fig_id)\n", " if tight_layout:\n", " plt.tight_layout()\n", " plt.savefig(path, format=fig_extension, dpi=resolution)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 기본적인 RNN" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 데이터셋 생성" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def generate_time_series(batch_size, n_steps):\n", " freq1, freq2, offsets1, offsets2 = np.random.rand(4, batch_size, 1)\n", " time = np.linspace(0, 1, n_steps)\n", " series = 0.5 * np.sin((time - offsets1) * (freq1 * 10 + 10)) # 웨이브 1\n", " series += 0.2 * np.sin((time - offsets2) * (freq2 * 20 + 20)) # + 웨이브 2\n", " series += 0.1 * (np.random.rand(batch_size, n_steps) - 0.5) # + 잡음\n", " return series[..., np.newaxis].astype(np.float32)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "np.random.seed(42)\n", "\n", "n_steps = 50\n", "series = generate_time_series(10000, n_steps + 1)\n", "X_train, y_train = series[:7000, :n_steps], series[:7000, -1]\n", "X_valid, y_valid = series[7000:9000, :n_steps], series[7000:9000, -1]\n", "X_test, y_test = series[9000:, :n_steps], series[9000:, -1]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((7000, 50, 1), (7000, 1))" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train.shape, y_train.shape" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Saving figure time_series_plot\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "def plot_series(series, y=None, y_pred=None, x_label=\"$t$\", y_label=\"$x(t)$\", legend=True):\n", " plt.plot(series, \".-\")\n", " if y is not None:\n", " plt.plot(n_steps, y, \"bo\", label=\"Target\")\n", " if y_pred is not None:\n", " plt.plot(n_steps, y_pred, \"rx\", markersize=10, label=\"Prediction\")\n", " plt.grid(True)\n", " if x_label:\n", " plt.xlabel(x_label, fontsize=16)\n", " if y_label:\n", " plt.ylabel(y_label, fontsize=16, rotation=0)\n", " plt.hlines(0, 0, 100, linewidth=1)\n", " plt.axis([0, n_steps + 1, -1, 1])\n", " if legend and (y or y_pred):\n", " plt.legend(fontsize=14, loc=\"upper left\")\n", "\n", "fig, axes = plt.subplots(nrows=1, ncols=3, sharey=True, figsize=(12, 4))\n", "for col in range(3):\n", " plt.sca(axes[col])\n", " plot_series(X_valid[col, :, 0], y_valid[col, 0],\n", " y_label=(\"$x(t)$\" if col==0 else None),\n", " legend=(col == 0))\n", "save_fig(\"time_series_plot\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**노트**: 이 노트북에서 파란 점은 타깃을 나타내고 빨강 X 표시는 예측을 나타냅니다. 처음에 책에서 파란 X 표시를 타깃에 사용하고 빨강 점을 예측에 사용했다가 나중에 바꾸었습니다. 혼동을 드려 죄송합니다." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 기준 성능 계산하기" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "단순한 예측 (마지막 관측값을 사용해 예측합니다):" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.020211367" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_pred = X_valid[:, -1]\n", "np.mean(keras.losses.mean_squared_error(y_valid, y_pred))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_series(X_valid[0, :, 0], y_valid[0, 0], y_pred[0, 0])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "선형 예측:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/20\n", "219/219 [==============================] - 1s 3ms/step - loss: 0.1398 - val_loss: 0.0545\n", "Epoch 2/20\n", "219/219 [==============================] - 0s 690us/step - loss: 0.0443 - val_loss: 0.0266\n", "Epoch 3/20\n", "219/219 [==============================] - 0s 631us/step - loss: 0.0237 - val_loss: 0.0157\n", "Epoch 4/20\n", "219/219 [==============================] - 0s 738us/step - loss: 0.0142 - val_loss: 0.0116\n", "Epoch 5/20\n", "219/219 [==============================] - 0s 740us/step - loss: 0.0110 - val_loss: 0.0098\n", "Epoch 6/20\n", "219/219 [==============================] - 0s 615us/step - loss: 0.0093 - val_loss: 0.0087\n", "Epoch 7/20\n", "219/219 [==============================] - 0s 590us/step - loss: 0.0083 - val_loss: 0.0079\n", "Epoch 8/20\n", "219/219 [==============================] - 0s 581us/step - loss: 0.0074 - val_loss: 0.0071\n", "Epoch 9/20\n", "219/219 [==============================] - 0s 562us/step - loss: 0.0064 - val_loss: 0.0066\n", "Epoch 10/20\n", "219/219 [==============================] - 0s 570us/step - loss: 0.0063 - val_loss: 0.0062\n", "Epoch 11/20\n", "219/219 [==============================] - 0s 576us/step - loss: 0.0059 - val_loss: 0.0057\n", "Epoch 12/20\n", "219/219 [==============================] - 0s 645us/step - loss: 0.0054 - val_loss: 0.0055\n", "Epoch 13/20\n", "219/219 [==============================] - 0s 578us/step - loss: 0.0052 - val_loss: 0.0052\n", "Epoch 14/20\n", "219/219 [==============================] - 0s 596us/step - loss: 0.0050 - val_loss: 0.0049\n", "Epoch 15/20\n", "219/219 [==============================] - 0s 707us/step - loss: 0.0048 - val_loss: 0.0048\n", "Epoch 16/20\n", "219/219 [==============================] - 0s 635us/step - loss: 0.0046 - val_loss: 0.0048\n", "Epoch 17/20\n", "219/219 [==============================] - 0s 604us/step - loss: 0.0046 - val_loss: 0.0045\n", "Epoch 18/20\n", "219/219 [==============================] - 0s 647us/step - loss: 0.0043 - val_loss: 0.0044\n", "Epoch 19/20\n", "219/219 [==============================] - 0s 659us/step - loss: 0.0042 - val_loss: 0.0043\n", "Epoch 20/20\n", "219/219 [==============================] - 0s 769us/step - loss: 0.0043 - val_loss: 0.0042\n" ] } ], "source": [ "np.random.seed(42)\n", "tf.random.set_seed(42)\n", "\n", "model = keras.models.Sequential([\n", " keras.layers.Flatten(input_shape=[50, 1]),\n", " keras.layers.Dense(1)\n", "])\n", "\n", "model.compile(loss=\"mse\", optimizer=\"adam\")\n", "history = model.fit(X_train, y_train, epochs=20,\n", " validation_data=(X_valid, y_valid))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "63/63 [==============================] - 0s 414us/step - loss: 0.0042\n" ] }, { "data": { "text/plain": [ "0.004168087150901556" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.evaluate(X_valid, y_valid)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "def plot_learning_curves(loss, val_loss):\n", " plt.plot(np.arange(len(loss)) + 0.5, loss, \"b.-\", label=\"Training loss\")\n", " plt.plot(np.arange(len(val_loss)) + 1, val_loss, \"r.-\", label=\"Validation loss\")\n", " plt.gca().xaxis.set_major_locator(mpl.ticker.MaxNLocator(integer=True))\n", " plt.axis([1, 20, 0, 0.05])\n", " plt.legend(fontsize=14)\n", " plt.xlabel(\"Epochs\")\n", " plt.ylabel(\"Loss\")\n", " plt.grid(True)\n", "\n", "plot_learning_curves(history.history[\"loss\"], history.history[\"val_loss\"])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "y_pred = model.predict(X_valid)\n", "plot_series(X_valid[0, :, 0], y_valid[0, 0], y_pred[0, 0])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 간단한 RNN 사용하기" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/20\n", "219/219 [==============================] - 2s 5ms/step - loss: 0.1554 - val_loss: 0.0489\n", "Epoch 2/20\n", "219/219 [==============================] - 1s 4ms/step - loss: 0.0409 - val_loss: 0.0296\n", "Epoch 3/20\n", "219/219 [==============================] - 1s 4ms/step - loss: 0.0277 - val_loss: 0.0218\n", "Epoch 4/20\n", "219/219 [==============================] - 1s 4ms/step - loss: 0.0208 - val_loss: 0.0177\n", "Epoch 5/20\n", "219/219 [==============================] - 1s 4ms/step - loss: 0.0174 - val_loss: 0.0151\n", "Epoch 6/20\n", "219/219 [==============================] - 1s 4ms/step - loss: 0.0146 - val_loss: 0.0134\n", "Epoch 7/20\n", "219/219 [==============================] - 1s 4ms/step - loss: 0.0138 - val_loss: 0.0123\n", "Epoch 8/20\n", "219/219 [==============================] - 1s 4ms/step - loss: 0.0128 - val_loss: 0.0116\n", "Epoch 9/20\n", "219/219 [==============================] - 1s 4ms/step - loss: 0.0118 - val_loss: 0.0112\n", "Epoch 10/20\n", "219/219 [==============================] - 1s 4ms/step - loss: 0.0117 - val_loss: 0.0110\n", "Epoch 11/20\n", "219/219 [==============================] - 1s 4ms/step - loss: 0.0112 - val_loss: 0.0109\n", "Epoch 12/20\n", "219/219 [==============================] - 1s 4ms/step - loss: 0.0115 - val_loss: 0.0109\n", "Epoch 13/20\n", "219/219 [==============================] - 1s 4ms/step - loss: 0.0114 - val_loss: 0.0109\n", "Epoch 14/20\n", "219/219 [==============================] - 1s 4ms/step - loss: 0.0114 - val_loss: 0.0109\n", "Epoch 15/20\n", "219/219 [==============================] - 1s 4ms/step - loss: 0.0113 - val_loss: 0.0109\n", "Epoch 16/20\n", "219/219 [==============================] - 1s 4ms/step - loss: 0.0114 - val_loss: 0.0109\n", "Epoch 17/20\n", "219/219 [==============================] - 1s 4ms/step - loss: 0.0114 - val_loss: 0.0109\n", "Epoch 18/20\n", "219/219 [==============================] - 1s 4ms/step - loss: 0.0115 - val_loss: 0.0109\n", "Epoch 19/20\n", "219/219 [==============================] - 1s 5ms/step - loss: 0.0115 - val_loss: 0.0109\n", "Epoch 20/20\n", "219/219 [==============================] - 1s 4ms/step - loss: 0.0116 - val_loss: 0.0109\n" ] } ], "source": [ "np.random.seed(42)\n", "tf.random.set_seed(42)\n", "\n", "model = keras.models.Sequential([\n", " keras.layers.SimpleRNN(1, input_shape=[None, 1])\n", "])\n", "\n", "optimizer = keras.optimizers.Adam(learning_rate=0.005)\n", "model.compile(loss=\"mse\", optimizer=optimizer)\n", "history = model.fit(X_train, y_train, epochs=20,\n", " validation_data=(X_valid, y_valid))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "63/63 [==============================] - 0s 2ms/step - loss: 0.0109\n" ] }, { "data": { "text/plain": [ "0.010881561785936356" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.evaluate(X_valid, y_valid)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_learning_curves(history.history[\"loss\"], history.history[\"val_loss\"])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "y_pred = model.predict(X_valid)\n", "plot_series(X_valid[0, :, 0], y_valid[0, 0], y_pred[0, 0])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 심층 RNN" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/20\n", "219/219 [==============================] - 5s 17ms/step - loss: 0.1324 - val_loss: 0.0090\n", "Epoch 2/20\n", "219/219 [==============================] - 3s 15ms/step - loss: 0.0078 - val_loss: 0.0065\n", "Epoch 3/20\n", "219/219 [==============================] - 3s 15ms/step - loss: 0.0057 - val_loss: 0.0045\n", "Epoch 4/20\n", "219/219 [==============================] - 3s 15ms/step - loss: 0.0045 - val_loss: 0.0040\n", "Epoch 5/20\n", "219/219 [==============================] - 3s 15ms/step - loss: 0.0044 - val_loss: 0.0040\n", "Epoch 6/20\n", "219/219 [==============================] - 3s 15ms/step - loss: 0.0038 - val_loss: 0.0036\n", "Epoch 7/20\n", "219/219 [==============================] - 3s 15ms/step - loss: 0.0036 - val_loss: 0.0040\n", "Epoch 8/20\n", "219/219 [==============================] - 3s 15ms/step - loss: 0.0038 - val_loss: 0.0033\n", "Epoch 9/20\n", "219/219 [==============================] - 3s 15ms/step - loss: 0.0037 - val_loss: 0.0032\n", "Epoch 10/20\n", "219/219 [==============================] - 3s 15ms/step - loss: 0.0035 - val_loss: 0.0031\n", "Epoch 11/20\n", "219/219 [==============================] - 3s 15ms/step - loss: 0.0034 - val_loss: 0.0030\n", "Epoch 12/20\n", "219/219 [==============================] - 3s 15ms/step - loss: 0.0033 - val_loss: 0.0031\n", "Epoch 13/20\n", "219/219 [==============================] - 3s 15ms/step - loss: 0.0034 - val_loss: 0.0031\n", "Epoch 14/20\n", "219/219 [==============================] - 3s 15ms/step - loss: 0.0034 - val_loss: 0.0032\n", "Epoch 15/20\n", "219/219 [==============================] - 3s 15ms/step - loss: 0.0034 - val_loss: 0.0033\n", "Epoch 16/20\n", "219/219 [==============================] - 3s 15ms/step - loss: 0.0037 - val_loss: 0.0030\n", "Epoch 17/20\n", "219/219 [==============================] - 3s 14ms/step - loss: 0.0034 - val_loss: 0.0029\n", "Epoch 18/20\n", "219/219 [==============================] - 3s 14ms/step - loss: 0.0031 - val_loss: 0.0030\n", "Epoch 19/20\n", "219/219 [==============================] - 3s 14ms/step - loss: 0.0032 - val_loss: 0.0029\n", "Epoch 20/20\n", "219/219 [==============================] - 3s 14ms/step - loss: 0.0033 - val_loss: 0.0029\n" ] } ], "source": [ "np.random.seed(42)\n", "tf.random.set_seed(42)\n", "\n", "model = keras.models.Sequential([\n", " keras.layers.SimpleRNN(20, return_sequences=True, input_shape=[None, 1]),\n", " keras.layers.SimpleRNN(20, return_sequences=True),\n", " keras.layers.SimpleRNN(1)\n", "])\n", "\n", "model.compile(loss=\"mse\", optimizer=\"adam\")\n", "history = model.fit(X_train, y_train, epochs=20,\n", " validation_data=(X_valid, y_valid))" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "63/63 [==============================] - 0s 3ms/step - loss: 0.0029\n" ] }, { "data": { "text/plain": [ "0.002910564187914133" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.evaluate(X_valid, y_valid)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_learning_curves(history.history[\"loss\"], history.history[\"val_loss\"])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "y_pred = model.predict(X_valid)\n", "plot_series(X_valid[0, :, 0], y_valid[0, 0], y_pred[0, 0])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "두 번째 `SimpleRNN` 층은 마지막 출력만 반환합니다:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/20\n", "219/219 [==============================] - 3s 12ms/step - loss: 0.0566 - val_loss: 0.0052\n", "Epoch 2/20\n", "219/219 [==============================] - 2s 11ms/step - loss: 0.0048 - val_loss: 0.0036\n", "Epoch 3/20\n", "219/219 [==============================] - 2s 11ms/step - loss: 0.0036 - val_loss: 0.0031\n", "Epoch 4/20\n", "219/219 [==============================] - 2s 11ms/step - loss: 0.0033 - val_loss: 0.0033\n", "Epoch 5/20\n", "219/219 [==============================] - 2s 11ms/step - loss: 0.0033 - val_loss: 0.0034\n", "Epoch 6/20\n", "219/219 [==============================] - 3s 11ms/step - loss: 0.0031 - val_loss: 0.0029\n", "Epoch 7/20\n", "219/219 [==============================] - 2s 11ms/step - loss: 0.0030 - val_loss: 0.0034\n", "Epoch 8/20\n", "219/219 [==============================] - 3s 12ms/step - loss: 0.0033 - val_loss: 0.0028\n", "Epoch 9/20\n", "219/219 [==============================] - 3s 12ms/step - loss: 0.0031 - val_loss: 0.0028\n", "Epoch 10/20\n", "219/219 [==============================] - 3s 12ms/step - loss: 0.0029 - val_loss: 0.0029\n", "Epoch 11/20\n", "219/219 [==============================] - 3s 12ms/step - loss: 0.0029 - val_loss: 0.0027\n", "Epoch 12/20\n", "219/219 [==============================] - 3s 12ms/step - loss: 0.0029 - val_loss: 0.0031\n", "Epoch 13/20\n", "219/219 [==============================] - 3s 12ms/step - loss: 0.0029 - val_loss: 0.0031\n", "Epoch 14/20\n", "219/219 [==============================] - 2s 11ms/step - loss: 0.0031 - val_loss: 0.0030\n", "Epoch 15/20\n", "219/219 [==============================] - 2s 11ms/step - loss: 0.0030 - val_loss: 0.0030\n", "Epoch 16/20\n", "219/219 [==============================] - 2s 11ms/step - loss: 0.0030 - val_loss: 0.0027\n", "Epoch 17/20\n", "219/219 [==============================] - 2s 11ms/step - loss: 0.0030 - val_loss: 0.0028\n", "Epoch 18/20\n", "219/219 [==============================] - 2s 11ms/step - loss: 0.0029 - val_loss: 0.0027\n", "Epoch 19/20\n", "219/219 [==============================] - 2s 11ms/step - loss: 0.0029 - val_loss: 0.0028\n", "Epoch 20/20\n", "219/219 [==============================] - 2s 11ms/step - loss: 0.0030 - val_loss: 0.0026\n" ] } ], "source": [ "np.random.seed(42)\n", "tf.random.set_seed(42)\n", "\n", "model = keras.models.Sequential([\n", " keras.layers.SimpleRNN(20, return_sequences=True, input_shape=[None, 1]),\n", " keras.layers.SimpleRNN(20),\n", " keras.layers.Dense(1)\n", "])\n", "\n", "model.compile(loss=\"mse\", optimizer=\"adam\")\n", "history = model.fit(X_train, y_train, epochs=20,\n", " validation_data=(X_valid, y_valid))" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "63/63 [==============================] - 0s 3ms/step - loss: 0.0026\n" ] }, { "data": { "text/plain": [ "0.002623623702675104" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.evaluate(X_valid, y_valid)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_learning_curves(history.history[\"loss\"], history.history[\"val_loss\"])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "y_pred = model.predict(X_valid)\n", "plot_series(X_valid[0, :, 0], y_valid[0, 0], y_pred[0, 0])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 여러 타임 스텝 앞을 예측하기" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "np.random.seed(43) # 42는 훈련 세트에 있는 첫 번째 시리즈를 반환하기 때문에 다른 값으로 지정합니다\n", "\n", "series = generate_time_series(1, n_steps + 10)\n", "X_new, Y_new = series[:, :n_steps], series[:, n_steps:]\n", "X = X_new\n", "for step_ahead in range(10):\n", " y_pred_one = model.predict(X[:, step_ahead:])[:, np.newaxis, :]\n", " X = np.concatenate([X, y_pred_one], axis=1)\n", "\n", "Y_pred = X[:, n_steps:]" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1, 10, 1)" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Y_pred.shape" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Saving figure forecast_ahead_plot\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "def plot_multiple_forecasts(X, Y, Y_pred):\n", " n_steps = X.shape[1]\n", " ahead = Y.shape[1]\n", " plot_series(X[0, :, 0])\n", " plt.plot(np.arange(n_steps, n_steps + ahead), Y[0, :, 0], \"bo-\", label=\"Actual\")\n", " plt.plot(np.arange(n_steps, n_steps + ahead), Y_pred[0, :, 0], \"rx-\", label=\"Forecast\", markersize=10)\n", " plt.axis([0, n_steps + ahead, -1, 1])\n", " plt.legend(fontsize=14)\n", "\n", "plot_multiple_forecasts(X_new, Y_new, Y_pred)\n", "save_fig(\"forecast_ahead_plot\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "이 모델을 사용해 다음 10개의 값을 예측해 보겠습니다. 먼저 아홉 개의 타임 스텝을 더 가진 시퀀스를 다시 생성해야 합니다." ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "np.random.seed(42)\n", "\n", "n_steps = 50\n", "series = generate_time_series(10000, n_steps + 10)\n", "X_train, Y_train = series[:7000, :n_steps], series[:7000, -10:, 0]\n", "X_valid, Y_valid = series[7000:9000, :n_steps], series[7000:9000, -10:, 0]\n", "X_test, Y_test = series[9000:, :n_steps], series[9000:, -10:, 0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "이제 하나씩 다음 10개의 값을 예측합니다:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "X = X_valid\n", "for step_ahead in range(10):\n", " y_pred_one = model.predict(X)[:, np.newaxis, :]\n", " X = np.concatenate([X, y_pred_one], axis=1)\n", "\n", "Y_pred = X[:, n_steps:, 0]" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(2000, 10)" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Y_pred.shape" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.027510857" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.mean(keras.metrics.mean_squared_error(Y_valid, Y_pred))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "이 성능을 단순한 예측이나 간단한 선형 모델과 비교해 보죠:" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.25697407" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Y_naive_pred = np.tile(X_valid[:, -1], 10) # 마지막 타임 스텝 값을 선택해 10번 반복합니다\n", "np.mean(keras.metrics.mean_squared_error(Y_valid, Y_naive_pred))" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/20\n", "219/219 [==============================] - 0s 1ms/step - loss: 0.2186 - val_loss: 0.0606\n", "Epoch 2/20\n", "219/219 [==============================] - 0s 743us/step - loss: 0.0535 - val_loss: 0.0425\n", "Epoch 3/20\n", "219/219 [==============================] - 0s 727us/step - loss: 0.0406 - val_loss: 0.0353\n", "Epoch 4/20\n", "219/219 [==============================] - 0s 731us/step - loss: 0.0343 - val_loss: 0.0311\n", "Epoch 5/20\n", "219/219 [==============================] - 0s 743us/step - loss: 0.0300 - val_loss: 0.0283\n", "Epoch 6/20\n", "219/219 [==============================] - 0s 721us/step - loss: 0.0278 - val_loss: 0.0264\n", "Epoch 7/20\n", "219/219 [==============================] - 0s 722us/step - loss: 0.0262 - val_loss: 0.0249\n", "Epoch 8/20\n", "219/219 [==============================] - 0s 731us/step - loss: 0.0246 - val_loss: 0.0237\n", "Epoch 9/20\n", "219/219 [==============================] - 0s 725us/step - loss: 0.0236 - val_loss: 0.0229\n", "Epoch 10/20\n", "219/219 [==============================] - 0s 735us/step - loss: 0.0228 - val_loss: 0.0222\n", "Epoch 11/20\n", "219/219 [==============================] - 0s 743us/step - loss: 0.0220 - val_loss: 0.0216\n", "Epoch 12/20\n", "219/219 [==============================] - 0s 733us/step - loss: 0.0214 - val_loss: 0.0212\n", "Epoch 13/20\n", "219/219 [==============================] - 0s 714us/step - loss: 0.0212 - val_loss: 0.0208\n", "Epoch 14/20\n", "219/219 [==============================] - 0s 739us/step - loss: 0.0207 - val_loss: 0.0207\n", "Epoch 15/20\n", "219/219 [==============================] - 0s 712us/step - loss: 0.0207 - val_loss: 0.0202\n", "Epoch 16/20\n", "219/219 [==============================] - 0s 723us/step - loss: 0.0199 - val_loss: 0.0199\n", "Epoch 17/20\n", "219/219 [==============================] - 0s 738us/step - loss: 0.0197 - val_loss: 0.0195\n", "Epoch 18/20\n", "219/219 [==============================] - 0s 715us/step - loss: 0.0190 - val_loss: 0.0192\n", "Epoch 19/20\n", "219/219 [==============================] - 0s 719us/step - loss: 0.0189 - val_loss: 0.0189\n", "Epoch 20/20\n", "219/219 [==============================] - 0s 726us/step - loss: 0.0188 - val_loss: 0.0187\n" ] } ], "source": [ "np.random.seed(42)\n", "tf.random.set_seed(42)\n", "\n", "model = keras.models.Sequential([\n", " keras.layers.Flatten(input_shape=[50, 1]),\n", " keras.layers.Dense(10)\n", "])\n", "\n", "model.compile(loss=\"mse\", optimizer=\"adam\")\n", "history = model.fit(X_train, Y_train, epochs=20,\n", " validation_data=(X_valid, Y_valid))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "이제 동시에 다음 10개의 값을 모두 예측하는 RNN을 만들어 보겠습니다:" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/20\n", "219/219 [==============================] - 3s 12ms/step - loss: 0.1216 - val_loss: 0.0317\n", "Epoch 2/20\n", "219/219 [==============================] - 2s 11ms/step - loss: 0.0294 - val_loss: 0.0200\n", "Epoch 3/20\n", "219/219 [==============================] - 3s 11ms/step - loss: 0.0198 - val_loss: 0.0160\n", "Epoch 4/20\n", "219/219 [==============================] - 2s 11ms/step - loss: 0.0162 - val_loss: 0.0144\n", "Epoch 5/20\n", "219/219 [==============================] - 2s 11ms/step - loss: 0.0144 - val_loss: 0.0118\n", "Epoch 6/20\n", "219/219 [==============================] - 2s 11ms/step - loss: 0.0127 - val_loss: 0.0112\n", "Epoch 7/20\n", "219/219 [==============================] - 2s 11ms/step - loss: 0.0119 - val_loss: 0.0110\n", "Epoch 8/20\n", "219/219 [==============================] - 2s 11ms/step - loss: 0.0114 - val_loss: 0.0103\n", "Epoch 9/20\n", "219/219 [==============================] - 3s 12ms/step - loss: 0.0110 - val_loss: 0.0112\n", "Epoch 10/20\n", "219/219 [==============================] - 2s 11ms/step - loss: 0.0118 - val_loss: 0.0100\n", "Epoch 11/20\n", "219/219 [==============================] - 2s 11ms/step - loss: 0.0109 - val_loss: 0.0103\n", "Epoch 12/20\n", "219/219 [==============================] - 2s 11ms/step - loss: 0.0104 - val_loss: 0.0096\n", "Epoch 13/20\n", "219/219 [==============================] - 2s 11ms/step - loss: 0.0103 - val_loss: 0.0100\n", "Epoch 14/20\n", "219/219 [==============================] - 2s 11ms/step - loss: 0.0101 - val_loss: 0.0103\n", "Epoch 15/20\n", "219/219 [==============================] - 2s 11ms/step - loss: 0.0095 - val_loss: 0.0107\n", "Epoch 16/20\n", "219/219 [==============================] - 2s 11ms/step - loss: 0.0095 - val_loss: 0.0089\n", "Epoch 17/20\n", "219/219 [==============================] - 2s 11ms/step - loss: 0.0092 - val_loss: 0.0111\n", "Epoch 18/20\n", "219/219 [==============================] - 2s 11ms/step - loss: 0.0098 - val_loss: 0.0094\n", "Epoch 19/20\n", "219/219 [==============================] - 2s 11ms/step - loss: 0.0090 - val_loss: 0.0083\n", "Epoch 20/20\n", "219/219 [==============================] - 2s 11ms/step - loss: 0.0092 - val_loss: 0.0085\n" ] } ], "source": [ "np.random.seed(42)\n", "tf.random.set_seed(42)\n", "\n", "model = keras.models.Sequential([\n", " keras.layers.SimpleRNN(20, return_sequences=True, input_shape=[None, 1]),\n", " keras.layers.SimpleRNN(20),\n", " keras.layers.Dense(10)\n", "])\n", "\n", "model.compile(loss=\"mse\", optimizer=\"adam\")\n", "history = model.fit(X_train, Y_train, epochs=20,\n", " validation_data=(X_valid, Y_valid))" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "np.random.seed(43)\n", "\n", "series = generate_time_series(1, 50 + 10)\n", "X_new, Y_new = series[:, :50, :], series[:, -10:, :]\n", "Y_pred = model.predict(X_new)[..., np.newaxis]" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_multiple_forecasts(X_new, Y_new, Y_pred)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "이제 타임 스텝마다 다음 10 스텝을 예측하는 RNN을 만들어 보겠습니다. 즉 타임 스텝 0에서 49까지를 기반으로 타임 스텝 50에서 59를 예측하는 것이 아니라, 타임 스텝 0에서 타임 스텝 1에서 10까지 예측하고 그다음 타임 스텝 1에서 타임 스텝 2에서 11까지 예측합니다. 마지막 타임 스텝에서는 타임 스텝 50에서 59까지 예측합니다. 이 모델은 인과 모델입니다. 어떤 타임 스텝에서 예측을 만들 때 과거 타임 스텝만 볼 수 있습니다." ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "np.random.seed(42)\n", "\n", "n_steps = 50\n", "series = generate_time_series(10000, n_steps + 10)\n", "X_train = series[:7000, :n_steps]\n", "X_valid = series[7000:9000, :n_steps]\n", "X_test = series[9000:, :n_steps]\n", "Y = np.empty((10000, n_steps, 10))\n", "for step_ahead in range(1, 10 + 1):\n", " Y[..., step_ahead - 1] = series[..., step_ahead:step_ahead + n_steps, 0]\n", "Y_train = Y[:7000]\n", "Y_valid = Y[7000:9000]\n", "Y_test = Y[9000:]" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((7000, 50, 1), (7000, 50, 10))" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train.shape, Y_train.shape" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/20\n", "219/219 [==============================] - 4s 12ms/step - loss: 0.0705 - last_time_step_mse: 0.0621 - val_loss: 0.0429 - val_last_time_step_mse: 0.0324\n", "Epoch 2/20\n", "219/219 [==============================] - 3s 12ms/step - loss: 0.0413 - last_time_step_mse: 0.0301 - val_loss: 0.0366 - val_last_time_step_mse: 0.0264\n", "Epoch 3/20\n", "219/219 [==============================] - 3s 11ms/step - loss: 0.0333 - last_time_step_mse: 0.0223 - val_loss: 0.0343 - val_last_time_step_mse: 0.0244\n", "Epoch 4/20\n", "219/219 [==============================] - 2s 11ms/step - loss: 0.0306 - last_time_step_mse: 0.0200 - val_loss: 0.0284 - val_last_time_step_mse: 0.0164\n", "Epoch 5/20\n", "219/219 [==============================] - 2s 11ms/step - loss: 0.0281 - last_time_step_mse: 0.0167 - val_loss: 0.0282 - val_last_time_step_mse: 0.0196\n", "Epoch 6/20\n", "219/219 [==============================] - 3s 11ms/step - loss: 0.0259 - last_time_step_mse: 0.0137 - val_loss: 0.0215 - val_last_time_step_mse: 0.0081\n", "Epoch 7/20\n", "219/219 [==============================] - 2s 11ms/step - loss: 0.0234 - last_time_step_mse: 0.0105 - val_loss: 0.0220 - val_last_time_step_mse: 0.0089\n", "Epoch 8/20\n", "219/219 [==============================] - 2s 11ms/step - loss: 0.0216 - last_time_step_mse: 0.0085 - val_loss: 0.0217 - val_last_time_step_mse: 0.0091\n", "Epoch 9/20\n", "219/219 [==============================] - 3s 12ms/step - loss: 0.0214 - last_time_step_mse: 0.0089 - val_loss: 0.0202 - val_last_time_step_mse: 0.0074\n", "Epoch 10/20\n", "219/219 [==============================] - 3s 12ms/step - loss: 0.0210 - last_time_step_mse: 0.0085 - val_loss: 0.0211 - val_last_time_step_mse: 0.0086\n", "Epoch 11/20\n", "219/219 [==============================] - 3s 11ms/step - loss: 0.0203 - last_time_step_mse: 0.0078 - val_loss: 0.0201 - val_last_time_step_mse: 0.0078\n", "Epoch 12/20\n", "219/219 [==============================] - 2s 11ms/step - loss: 0.0203 - last_time_step_mse: 0.0079 - val_loss: 0.0194 - val_last_time_step_mse: 0.0073\n", "Epoch 13/20\n", "219/219 [==============================] - 2s 11ms/step - loss: 0.0198 - last_time_step_mse: 0.0074 - val_loss: 0.0209 - val_last_time_step_mse: 0.0085\n", "Epoch 14/20\n", "219/219 [==============================] - 3s 12ms/step - loss: 0.0197 - last_time_step_mse: 0.0073 - val_loss: 0.0189 - val_last_time_step_mse: 0.0067\n", "Epoch 15/20\n", "219/219 [==============================] - 3s 12ms/step - loss: 0.0192 - last_time_step_mse: 0.0072 - val_loss: 0.0182 - val_last_time_step_mse: 0.0066\n", "Epoch 16/20\n", "219/219 [==============================] - 2s 11ms/step - loss: 0.0187 - last_time_step_mse: 0.0066 - val_loss: 0.0196 - val_last_time_step_mse: 0.0095\n", "Epoch 17/20\n", "219/219 [==============================] - 2s 11ms/step - loss: 0.0187 - last_time_step_mse: 0.0067 - val_loss: 0.0185 - val_last_time_step_mse: 0.0072\n", "Epoch 18/20\n", "219/219 [==============================] - 2s 11ms/step - loss: 0.0186 - last_time_step_mse: 0.0067 - val_loss: 0.0179 - val_last_time_step_mse: 0.0064\n", "Epoch 19/20\n", "219/219 [==============================] - 3s 11ms/step - loss: 0.0185 - last_time_step_mse: 0.0068 - val_loss: 0.0172 - val_last_time_step_mse: 0.0058\n", "Epoch 20/20\n", "219/219 [==============================] - 2s 11ms/step - loss: 0.0181 - last_time_step_mse: 0.0066 - val_loss: 0.0205 - val_last_time_step_mse: 0.0096\n" ] } ], "source": [ "np.random.seed(42)\n", "tf.random.set_seed(42)\n", "\n", "model = keras.models.Sequential([\n", " keras.layers.SimpleRNN(20, return_sequences=True, input_shape=[None, 1]),\n", " keras.layers.SimpleRNN(20, return_sequences=True),\n", " keras.layers.TimeDistributed(keras.layers.Dense(10))\n", "])\n", "\n", "def last_time_step_mse(Y_true, Y_pred):\n", " return keras.metrics.mean_squared_error(Y_true[:, -1], Y_pred[:, -1])\n", "\n", "model.compile(loss=\"mse\", optimizer=keras.optimizers.Adam(learning_rate=0.01), metrics=[last_time_step_mse])\n", "history = model.fit(X_train, Y_train, epochs=20,\n", " validation_data=(X_valid, Y_valid))" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "np.random.seed(43)\n", "\n", "series = generate_time_series(1, 50 + 10)\n", "X_new, Y_new = series[:, :50, :], series[:, 50:, :]\n", "Y_pred = model.predict(X_new)[:, -1][..., np.newaxis]" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_multiple_forecasts(X_new, Y_new, Y_pred)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 배치 정규화를 사용한 심층 RNN" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/20\n", "219/219 [==============================] - 4s 13ms/step - loss: 0.4750 - last_time_step_mse: 0.5027 - val_loss: 0.0877 - val_last_time_step_mse: 0.0832\n", "Epoch 2/20\n", "219/219 [==============================] - 3s 12ms/step - loss: 0.0561 - last_time_step_mse: 0.0468 - val_loss: 0.0549 - val_last_time_step_mse: 0.0462\n", "Epoch 3/20\n", "219/219 [==============================] - 3s 12ms/step - loss: 0.0486 - last_time_step_mse: 0.0394 - val_loss: 0.0451 - val_last_time_step_mse: 0.0358\n", "Epoch 4/20\n", "219/219 [==============================] - 3s 12ms/step - loss: 0.0443 - last_time_step_mse: 0.0344 - val_loss: 0.0418 - val_last_time_step_mse: 0.0314\n", "Epoch 5/20\n", "219/219 [==============================] - 3s 12ms/step - loss: 0.0414 - last_time_step_mse: 0.0315 - val_loss: 0.0391 - val_last_time_step_mse: 0.0287\n", "Epoch 6/20\n", "219/219 [==============================] - 3s 12ms/step - loss: 0.0391 - last_time_step_mse: 0.0281 - val_loss: 0.0379 - val_last_time_step_mse: 0.0273\n", "Epoch 7/20\n", "219/219 [==============================] - 3s 12ms/step - loss: 0.0370 - last_time_step_mse: 0.0257 - val_loss: 0.0367 - val_last_time_step_mse: 0.0248\n", "Epoch 8/20\n", "219/219 [==============================] - 3s 12ms/step - loss: 0.0352 - last_time_step_mse: 0.0236 - val_loss: 0.0363 - val_last_time_step_mse: 0.0249\n", "Epoch 9/20\n", "219/219 [==============================] - 3s 12ms/step - loss: 0.0340 - last_time_step_mse: 0.0224 - val_loss: 0.0332 - val_last_time_step_mse: 0.0208\n", "Epoch 10/20\n", "219/219 [==============================] - 3s 12ms/step - loss: 0.0332 - last_time_step_mse: 0.0213 - val_loss: 0.0335 - val_last_time_step_mse: 0.0214\n", "Epoch 11/20\n", "219/219 [==============================] - 3s 12ms/step - loss: 0.0325 - last_time_step_mse: 0.0214 - val_loss: 0.0323 - val_last_time_step_mse: 0.0203\n", "Epoch 12/20\n", "219/219 [==============================] - 3s 12ms/step - loss: 0.0316 - last_time_step_mse: 0.0196 - val_loss: 0.0333 - val_last_time_step_mse: 0.0210\n", "Epoch 13/20\n", "219/219 [==============================] - 3s 12ms/step - loss: 0.0312 - last_time_step_mse: 0.0192 - val_loss: 0.0310 - val_last_time_step_mse: 0.0187\n", "Epoch 14/20\n", "219/219 [==============================] - 3s 12ms/step - loss: 0.0308 - last_time_step_mse: 0.0187 - val_loss: 0.0310 - val_last_time_step_mse: 0.0189\n", "Epoch 15/20\n", "219/219 [==============================] - 3s 12ms/step - loss: 0.0302 - last_time_step_mse: 0.0183 - val_loss: 0.0298 - val_last_time_step_mse: 0.0178\n", "Epoch 16/20\n", "219/219 [==============================] - 3s 12ms/step - loss: 0.0298 - last_time_step_mse: 0.0177 - val_loss: 0.0293 - val_last_time_step_mse: 0.0174\n", "Epoch 17/20\n", "219/219 [==============================] - 3s 12ms/step - loss: 0.0294 - last_time_step_mse: 0.0173 - val_loss: 0.0315 - val_last_time_step_mse: 0.0200\n", "Epoch 18/20\n", "219/219 [==============================] - 3s 12ms/step - loss: 0.0289 - last_time_step_mse: 0.0167 - val_loss: 0.0295 - val_last_time_step_mse: 0.0174\n", "Epoch 19/20\n", "219/219 [==============================] - 3s 12ms/step - loss: 0.0287 - last_time_step_mse: 0.0168 - val_loss: 0.0290 - val_last_time_step_mse: 0.0163\n", "Epoch 20/20\n", "219/219 [==============================] - 3s 12ms/step - loss: 0.0281 - last_time_step_mse: 0.0161 - val_loss: 0.0288 - val_last_time_step_mse: 0.0164\n" ] } ], "source": [ "np.random.seed(42)\n", "tf.random.set_seed(42)\n", "\n", "model = keras.models.Sequential([\n", " keras.layers.SimpleRNN(20, return_sequences=True, input_shape=[None, 1]),\n", " keras.layers.BatchNormalization(),\n", " keras.layers.SimpleRNN(20, return_sequences=True),\n", " keras.layers.BatchNormalization(),\n", " keras.layers.TimeDistributed(keras.layers.Dense(10))\n", "])\n", "\n", "model.compile(loss=\"mse\", optimizer=\"adam\", metrics=[last_time_step_mse])\n", "history = model.fit(X_train, Y_train, epochs=20,\n", " validation_data=(X_valid, Y_valid))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 층 정규화를 사용한 심층 RNN" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "from tensorflow.keras.layers import LayerNormalization" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "class LNSimpleRNNCell(keras.layers.Layer):\n", " def __init__(self, units, activation=\"tanh\", **kwargs):\n", " super().__init__(**kwargs)\n", " self.state_size = units\n", " self.output_size = units\n", " self.simple_rnn_cell = keras.layers.SimpleRNNCell(units,\n", " activation=None)\n", " self.layer_norm = LayerNormalization()\n", " self.activation = keras.activations.get(activation)\n", " def get_initial_state(self, inputs=None, batch_size=None, dtype=None):\n", " if inputs is not None:\n", " batch_size = tf.shape(inputs)[0]\n", " dtype = inputs.dtype\n", " return [tf.zeros([batch_size, self.state_size], dtype=dtype)]\n", " def call(self, inputs, states):\n", " outputs, new_states = self.simple_rnn_cell(inputs, states)\n", " norm_outputs = self.activation(self.layer_norm(outputs))\n", " return norm_outputs, [norm_outputs]" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/20\n", "219/219 [==============================] - 7s 26ms/step - loss: 0.2860 - last_time_step_mse: 0.2822 - val_loss: 0.0734 - val_last_time_step_mse: 0.0624\n", "Epoch 2/20\n", "219/219 [==============================] - 5s 25ms/step - loss: 0.0679 - last_time_step_mse: 0.0546 - val_loss: 0.0566 - val_last_time_step_mse: 0.0423\n", "Epoch 3/20\n", "219/219 [==============================] - 5s 25ms/step - loss: 0.0553 - last_time_step_mse: 0.0406 - val_loss: 0.0509 - val_last_time_step_mse: 0.0342\n", "Epoch 4/20\n", "219/219 [==============================] - 5s 25ms/step - loss: 0.0485 - last_time_step_mse: 0.0328 - val_loss: 0.0442 - val_last_time_step_mse: 0.0286\n", "Epoch 5/20\n", "219/219 [==============================] - 5s 24ms/step - loss: 0.0435 - last_time_step_mse: 0.0281 - val_loss: 0.0418 - val_last_time_step_mse: 0.0258\n", "Epoch 6/20\n", "219/219 [==============================] - 5s 24ms/step - loss: 0.0404 - last_time_step_mse: 0.0249 - val_loss: 0.0382 - val_last_time_step_mse: 0.0229\n", "Epoch 7/20\n", "219/219 [==============================] - 5s 24ms/step - loss: 0.0374 - last_time_step_mse: 0.0228 - val_loss: 0.0351 - val_last_time_step_mse: 0.0206\n", "Epoch 8/20\n", "219/219 [==============================] - 5s 25ms/step - loss: 0.0352 - last_time_step_mse: 0.0208 - val_loss: 0.0337 - val_last_time_step_mse: 0.0185\n", "Epoch 9/20\n", "219/219 [==============================] - 6s 26ms/step - loss: 0.0331 - last_time_step_mse: 0.0190 - val_loss: 0.0319 - val_last_time_step_mse: 0.0184\n", "Epoch 10/20\n", "219/219 [==============================] - 5s 25ms/step - loss: 0.0322 - last_time_step_mse: 0.0185 - val_loss: 0.0311 - val_last_time_step_mse: 0.0172\n", "Epoch 11/20\n", "219/219 [==============================] - 5s 25ms/step - loss: 0.0308 - last_time_step_mse: 0.0174 - val_loss: 0.0301 - val_last_time_step_mse: 0.0170\n", "Epoch 12/20\n", "219/219 [==============================] - 5s 25ms/step - loss: 0.0300 - last_time_step_mse: 0.0166 - val_loss: 0.0291 - val_last_time_step_mse: 0.0159\n", "Epoch 13/20\n", "219/219 [==============================] - 5s 25ms/step - loss: 0.0293 - last_time_step_mse: 0.0158 - val_loss: 0.0283 - val_last_time_step_mse: 0.0148\n", "Epoch 14/20\n", "219/219 [==============================] - 5s 25ms/step - loss: 0.0286 - last_time_step_mse: 0.0154 - val_loss: 0.0277 - val_last_time_step_mse: 0.0149\n", "Epoch 15/20\n", "219/219 [==============================] - 5s 24ms/step - loss: 0.0278 - last_time_step_mse: 0.0147 - val_loss: 0.0273 - val_last_time_step_mse: 0.0145\n", "Epoch 16/20\n", "219/219 [==============================] - 5s 23ms/step - loss: 0.0275 - last_time_step_mse: 0.0142 - val_loss: 0.0272 - val_last_time_step_mse: 0.0149\n", "Epoch 17/20\n", "219/219 [==============================] - 5s 23ms/step - loss: 0.0267 - last_time_step_mse: 0.0139 - val_loss: 0.0259 - val_last_time_step_mse: 0.0128\n", "Epoch 18/20\n", "219/219 [==============================] - 5s 23ms/step - loss: 0.0264 - last_time_step_mse: 0.0135 - val_loss: 0.0258 - val_last_time_step_mse: 0.0130\n", "Epoch 19/20\n", "219/219 [==============================] - 5s 24ms/step - loss: 0.0258 - last_time_step_mse: 0.0132 - val_loss: 0.0257 - val_last_time_step_mse: 0.0131\n", "Epoch 20/20\n", "219/219 [==============================] - 5s 23ms/step - loss: 0.0252 - last_time_step_mse: 0.0124 - val_loss: 0.0250 - val_last_time_step_mse: 0.0121\n" ] } ], "source": [ "np.random.seed(42)\n", "tf.random.set_seed(42)\n", "\n", "model = keras.models.Sequential([\n", " keras.layers.RNN(LNSimpleRNNCell(20), return_sequences=True,\n", " input_shape=[None, 1]),\n", " keras.layers.RNN(LNSimpleRNNCell(20), return_sequences=True),\n", " keras.layers.TimeDistributed(keras.layers.Dense(10))\n", "])\n", "\n", "model.compile(loss=\"mse\", optimizer=\"adam\", metrics=[last_time_step_mse])\n", "history = model.fit(X_train, Y_train, epochs=20,\n", " validation_data=(X_valid, Y_valid))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 사용자 정의 RNN 클래스 만들기" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "class MyRNN(keras.layers.Layer):\n", " def __init__(self, cell, return_sequences=False, **kwargs):\n", " super().__init__(**kwargs)\n", " self.cell = cell\n", " self.return_sequences = return_sequences\n", " self.get_initial_state = getattr(\n", " self.cell, \"get_initial_state\", self.fallback_initial_state)\n", " def fallback_initial_state(self, inputs):\n", " batch_size = tf.shape(inputs)[0]\n", " return [tf.zeros([batch_size, self.cell.state_size], dtype=inputs.dtype)]\n", " @tf.function\n", " def call(self, inputs):\n", " states = self.get_initial_state(inputs)\n", " shape = tf.shape(inputs)\n", " batch_size = shape[0]\n", " n_steps = shape[1]\n", " sequences = tf.TensorArray(\n", " inputs.dtype, size=(n_steps if self.return_sequences else 0))\n", " outputs = tf.zeros(shape=[batch_size, self.cell.output_size], dtype=inputs.dtype)\n", " for step in tf.range(n_steps):\n", " outputs, states = self.cell(inputs[:, step], states)\n", " if self.return_sequences:\n", " sequences = sequences.write(step, outputs)\n", " if self.return_sequences:\n", " return tf.transpose(sequences.stack(), [1, 0, 2])\n", " else:\n", " return outputs" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/20\n", "219/219 [==============================] - 7s 27ms/step - loss: 0.2860 - last_time_step_mse: 0.2822 - val_loss: 0.0734 - val_last_time_step_mse: 0.0624\n", "Epoch 2/20\n", "219/219 [==============================] - 6s 26ms/step - loss: 0.0679 - last_time_step_mse: 0.0546 - val_loss: 0.0566 - val_last_time_step_mse: 0.0423\n", "Epoch 3/20\n", "219/219 [==============================] - 6s 26ms/step - loss: 0.0553 - last_time_step_mse: 0.0406 - val_loss: 0.0509 - val_last_time_step_mse: 0.0342\n", "Epoch 4/20\n", "219/219 [==============================] - 6s 26ms/step - loss: 0.0485 - last_time_step_mse: 0.0328 - val_loss: 0.0442 - val_last_time_step_mse: 0.0286\n", "Epoch 5/20\n", "219/219 [==============================] - 6s 25ms/step - loss: 0.0435 - last_time_step_mse: 0.0281 - val_loss: 0.0418 - val_last_time_step_mse: 0.0258\n", "Epoch 6/20\n", "219/219 [==============================] - 6s 26ms/step - loss: 0.0404 - last_time_step_mse: 0.0249 - val_loss: 0.0382 - val_last_time_step_mse: 0.0229\n", "Epoch 7/20\n", "219/219 [==============================] - 6s 26ms/step - loss: 0.0374 - last_time_step_mse: 0.0228 - val_loss: 0.0351 - val_last_time_step_mse: 0.0206\n", "Epoch 8/20\n", "219/219 [==============================] - 6s 25ms/step - loss: 0.0352 - last_time_step_mse: 0.0208 - val_loss: 0.0337 - val_last_time_step_mse: 0.0185\n", "Epoch 9/20\n", "219/219 [==============================] - 6s 25ms/step - loss: 0.0331 - last_time_step_mse: 0.0190 - val_loss: 0.0319 - val_last_time_step_mse: 0.0184\n", "Epoch 10/20\n", "219/219 [==============================] - 6s 25ms/step - loss: 0.0322 - last_time_step_mse: 0.0185 - val_loss: 0.0311 - val_last_time_step_mse: 0.0172\n", "Epoch 11/20\n", "219/219 [==============================] - 6s 26ms/step - loss: 0.0308 - last_time_step_mse: 0.0174 - val_loss: 0.0301 - val_last_time_step_mse: 0.0170\n", "Epoch 12/20\n", "219/219 [==============================] - 6s 26ms/step - loss: 0.0300 - last_time_step_mse: 0.0166 - val_loss: 0.0291 - val_last_time_step_mse: 0.0159\n", "Epoch 13/20\n", "219/219 [==============================] - 6s 27ms/step - loss: 0.0293 - last_time_step_mse: 0.0158 - val_loss: 0.0283 - val_last_time_step_mse: 0.0148\n", "Epoch 14/20\n", "219/219 [==============================] - 6s 27ms/step - loss: 0.0286 - last_time_step_mse: 0.0154 - val_loss: 0.0277 - val_last_time_step_mse: 0.0149\n", "Epoch 15/20\n", "219/219 [==============================] - 6s 26ms/step - loss: 0.0278 - last_time_step_mse: 0.0147 - val_loss: 0.0273 - val_last_time_step_mse: 0.0145\n", "Epoch 16/20\n", "219/219 [==============================] - 6s 26ms/step - loss: 0.0275 - last_time_step_mse: 0.0142 - val_loss: 0.0272 - val_last_time_step_mse: 0.0149\n", "Epoch 17/20\n", "219/219 [==============================] - 6s 26ms/step - loss: 0.0267 - last_time_step_mse: 0.0139 - val_loss: 0.0259 - val_last_time_step_mse: 0.0128\n", "Epoch 18/20\n", "219/219 [==============================] - 6s 26ms/step - loss: 0.0264 - last_time_step_mse: 0.0135 - val_loss: 0.0258 - val_last_time_step_mse: 0.0130\n", "Epoch 19/20\n", "219/219 [==============================] - 6s 27ms/step - loss: 0.0258 - last_time_step_mse: 0.0132 - val_loss: 0.0257 - val_last_time_step_mse: 0.0131\n", "Epoch 20/20\n", "219/219 [==============================] - 6s 27ms/step - loss: 0.0252 - last_time_step_mse: 0.0124 - val_loss: 0.0250 - val_last_time_step_mse: 0.0121\n" ] } ], "source": [ "np.random.seed(42)\n", "tf.random.set_seed(42)\n", "\n", "model = keras.models.Sequential([\n", " MyRNN(LNSimpleRNNCell(20), return_sequences=True,\n", " input_shape=[None, 1]),\n", " MyRNN(LNSimpleRNNCell(20), return_sequences=True),\n", " keras.layers.TimeDistributed(keras.layers.Dense(10))\n", "])\n", "\n", "model.compile(loss=\"mse\", optimizer=\"adam\", metrics=[last_time_step_mse])\n", "history = model.fit(X_train, Y_train, epochs=20,\n", " validation_data=(X_valid, Y_valid))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# LSTM" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/20\n", "219/219 [==============================] - 8s 23ms/step - loss: 0.0979 - last_time_step_mse: 0.0877 - val_loss: 0.0554 - val_last_time_step_mse: 0.0364\n", "Epoch 2/20\n", "219/219 [==============================] - 4s 20ms/step - loss: 0.0515 - last_time_step_mse: 0.0326 - val_loss: 0.0427 - val_last_time_step_mse: 0.0222\n", "Epoch 3/20\n", "219/219 [==============================] - 4s 20ms/step - loss: 0.0407 - last_time_step_mse: 0.0196 - val_loss: 0.0367 - val_last_time_step_mse: 0.0157\n", "Epoch 4/20\n", "219/219 [==============================] - 4s 20ms/step - loss: 0.0356 - last_time_step_mse: 0.0156 - val_loss: 0.0334 - val_last_time_step_mse: 0.0132\n", "Epoch 5/20\n", "219/219 [==============================] - 4s 20ms/step - loss: 0.0330 - last_time_step_mse: 0.0138 - val_loss: 0.0314 - val_last_time_step_mse: 0.0121\n", "Epoch 6/20\n", "219/219 [==============================] - 4s 20ms/step - loss: 0.0313 - last_time_step_mse: 0.0124 - val_loss: 0.0298 - val_last_time_step_mse: 0.0112\n", "Epoch 7/20\n", "219/219 [==============================] - 5s 21ms/step - loss: 0.0297 - last_time_step_mse: 0.0118 - val_loss: 0.0291 - val_last_time_step_mse: 0.0120\n", "Epoch 8/20\n", "219/219 [==============================] - 4s 21ms/step - loss: 0.0289 - last_time_step_mse: 0.0109 - val_loss: 0.0278 - val_last_time_step_mse: 0.0099\n", "Epoch 9/20\n", "219/219 [==============================] - 4s 20ms/step - loss: 0.0282 - last_time_step_mse: 0.0110 - val_loss: 0.0278 - val_last_time_step_mse: 0.0113\n", "Epoch 10/20\n", "219/219 [==============================] - 4s 20ms/step - loss: 0.0276 - last_time_step_mse: 0.0107 - val_loss: 0.0268 - val_last_time_step_mse: 0.0101\n", "Epoch 11/20\n", "219/219 [==============================] - 4s 20ms/step - loss: 0.0270 - last_time_step_mse: 0.0104 - val_loss: 0.0263 - val_last_time_step_mse: 0.0096\n", "Epoch 12/20\n", "219/219 [==============================] - 4s 20ms/step - loss: 0.0265 - last_time_step_mse: 0.0100 - val_loss: 0.0263 - val_last_time_step_mse: 0.0105\n", "Epoch 13/20\n", "219/219 [==============================] - 4s 20ms/step - loss: 0.0260 - last_time_step_mse: 0.0098 - val_loss: 0.0257 - val_last_time_step_mse: 0.0100\n", "Epoch 14/20\n", "219/219 [==============================] - 4s 20ms/step - loss: 0.0258 - last_time_step_mse: 0.0097 - val_loss: 0.0252 - val_last_time_step_mse: 0.0091\n", "Epoch 15/20\n", "219/219 [==============================] - 4s 21ms/step - loss: 0.0255 - last_time_step_mse: 0.0100 - val_loss: 0.0251 - val_last_time_step_mse: 0.0092\n", "Epoch 16/20\n", "219/219 [==============================] - 4s 20ms/step - loss: 0.0252 - last_time_step_mse: 0.0094 - val_loss: 0.0248 - val_last_time_step_mse: 0.0089\n", "Epoch 17/20\n", "219/219 [==============================] - 4s 20ms/step - loss: 0.0248 - last_time_step_mse: 0.0093 - val_loss: 0.0248 - val_last_time_step_mse: 0.0098\n", "Epoch 18/20\n", "219/219 [==============================] - 4s 20ms/step - loss: 0.0247 - last_time_step_mse: 0.0092 - val_loss: 0.0246 - val_last_time_step_mse: 0.0091\n", "Epoch 19/20\n", "219/219 [==============================] - 4s 21ms/step - loss: 0.0243 - last_time_step_mse: 0.0092 - val_loss: 0.0238 - val_last_time_step_mse: 0.0085\n", "Epoch 20/20\n", "219/219 [==============================] - 4s 20ms/step - loss: 0.0239 - last_time_step_mse: 0.0088 - val_loss: 0.0238 - val_last_time_step_mse: 0.0086\n" ] } ], "source": [ "np.random.seed(42)\n", "tf.random.set_seed(42)\n", "\n", "model = keras.models.Sequential([\n", " keras.layers.LSTM(20, return_sequences=True, input_shape=[None, 1]),\n", " keras.layers.LSTM(20, return_sequences=True),\n", " keras.layers.TimeDistributed(keras.layers.Dense(10))\n", "])\n", "\n", "model.compile(loss=\"mse\", optimizer=\"adam\", metrics=[last_time_step_mse])\n", "history = model.fit(X_train, Y_train, epochs=20,\n", " validation_data=(X_valid, Y_valid))" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "63/63 [==============================] - 0s 4ms/step - loss: 0.0238 - last_time_step_mse: 0.0086\n" ] }, { "data": { "text/plain": [ "[0.023788681253790855, 0.00856079813092947]" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.evaluate(X_valid, Y_valid)" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_learning_curves(history.history[\"loss\"], history.history[\"val_loss\"])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [], "source": [ "np.random.seed(43)\n", "\n", "series = generate_time_series(1, 50 + 10)\n", "X_new, Y_new = series[:, :50, :], series[:, 50:, :]\n", "Y_pred = model.predict(X_new)[:, -1][..., np.newaxis]" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_multiple_forecasts(X_new, Y_new, Y_pred)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# GRU" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/20\n", "219/219 [==============================] - 8s 26ms/step - loss: 0.0995 - last_time_step_mse: 0.0940 - val_loss: 0.0538 - val_last_time_step_mse: 0.0450\n", "Epoch 2/20\n", "219/219 [==============================] - 5s 24ms/step - loss: 0.0495 - last_time_step_mse: 0.0383 - val_loss: 0.0441 - val_last_time_step_mse: 0.0326\n", "Epoch 3/20\n", "219/219 [==============================] - 5s 24ms/step - loss: 0.0432 - last_time_step_mse: 0.0321 - val_loss: 0.0390 - val_last_time_step_mse: 0.0275\n", "Epoch 4/20\n", "219/219 [==============================] - 5s 24ms/step - loss: 0.0379 - last_time_step_mse: 0.0261 - val_loss: 0.0339 - val_last_time_step_mse: 0.0202\n", "Epoch 5/20\n", "219/219 [==============================] - 5s 23ms/step - loss: 0.0333 - last_time_step_mse: 0.0192 - val_loss: 0.0312 - val_last_time_step_mse: 0.0164\n", "Epoch 6/20\n", "219/219 [==============================] - 5s 23ms/step - loss: 0.0310 - last_time_step_mse: 0.0158 - val_loss: 0.0294 - val_last_time_step_mse: 0.0143\n", "Epoch 7/20\n", "219/219 [==============================] - 5s 23ms/step - loss: 0.0295 - last_time_step_mse: 0.0146 - val_loss: 0.0300 - val_last_time_step_mse: 0.0162\n", "Epoch 8/20\n", "219/219 [==============================] - 5s 24ms/step - loss: 0.0287 - last_time_step_mse: 0.0136 - val_loss: 0.0278 - val_last_time_step_mse: 0.0130\n", "Epoch 9/20\n", "219/219 [==============================] - 5s 23ms/step - loss: 0.0277 - last_time_step_mse: 0.0133 - val_loss: 0.0273 - val_last_time_step_mse: 0.0127\n", "Epoch 10/20\n", "219/219 [==============================] - 5s 24ms/step - loss: 0.0273 - last_time_step_mse: 0.0128 - val_loss: 0.0264 - val_last_time_step_mse: 0.0121\n", "Epoch 11/20\n", "219/219 [==============================] - 5s 24ms/step - loss: 0.0265 - last_time_step_mse: 0.0122 - val_loss: 0.0268 - val_last_time_step_mse: 0.0135\n", "Epoch 12/20\n", "219/219 [==============================] - 5s 23ms/step - loss: 0.0264 - last_time_step_mse: 0.0122 - val_loss: 0.0261 - val_last_time_step_mse: 0.0123\n", "Epoch 13/20\n", "219/219 [==============================] - 5s 23ms/step - loss: 0.0259 - last_time_step_mse: 0.0117 - val_loss: 0.0254 - val_last_time_step_mse: 0.0116\n", "Epoch 14/20\n", "219/219 [==============================] - 5s 23ms/step - loss: 0.0257 - last_time_step_mse: 0.0116 - val_loss: 0.0254 - val_last_time_step_mse: 0.0116\n", "Epoch 15/20\n", "219/219 [==============================] - 5s 24ms/step - loss: 0.0254 - last_time_step_mse: 0.0118 - val_loss: 0.0250 - val_last_time_step_mse: 0.0112\n", "Epoch 16/20\n", "219/219 [==============================] - 5s 24ms/step - loss: 0.0252 - last_time_step_mse: 0.0114 - val_loss: 0.0250 - val_last_time_step_mse: 0.0114\n", "Epoch 17/20\n", "219/219 [==============================] - 5s 24ms/step - loss: 0.0248 - last_time_step_mse: 0.0113 - val_loss: 0.0249 - val_last_time_step_mse: 0.0118\n", "Epoch 18/20\n", "219/219 [==============================] - 5s 24ms/step - loss: 0.0246 - last_time_step_mse: 0.0109 - val_loss: 0.0244 - val_last_time_step_mse: 0.0108\n", "Epoch 19/20\n", "219/219 [==============================] - 5s 24ms/step - loss: 0.0243 - last_time_step_mse: 0.0107 - val_loss: 0.0240 - val_last_time_step_mse: 0.0105\n", "Epoch 20/20\n", "219/219 [==============================] - 5s 24ms/step - loss: 0.0239 - last_time_step_mse: 0.0105 - val_loss: 0.0238 - val_last_time_step_mse: 0.0103\n" ] } ], "source": [ "np.random.seed(42)\n", "tf.random.set_seed(42)\n", "\n", "model = keras.models.Sequential([\n", " keras.layers.GRU(20, return_sequences=True, input_shape=[None, 1]),\n", " keras.layers.GRU(20, return_sequences=True),\n", " keras.layers.TimeDistributed(keras.layers.Dense(10))\n", "])\n", "\n", "model.compile(loss=\"mse\", optimizer=\"adam\", metrics=[last_time_step_mse])\n", "history = model.fit(X_train, Y_train, epochs=20,\n", " validation_data=(X_valid, Y_valid))" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "63/63 [==============================] - 0s 4ms/step - loss: 0.0238 - last_time_step_mse: 0.0103\n" ] }, { "data": { "text/plain": [ "[0.023785505443811417, 0.010262809693813324]" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.evaluate(X_valid, Y_valid)" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_learning_curves(history.history[\"loss\"], history.history[\"val_loss\"])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:5 out of the last 508 calls to .predict_function at 0x7febe272c290> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" ] } ], "source": [ "np.random.seed(43)\n", "\n", "series = generate_time_series(1, 50 + 10)\n", "X_new, Y_new = series[:, :50, :], series[:, 50:, :]\n", "Y_pred = model.predict(X_new)[:, -1][..., np.newaxis]" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_multiple_forecasts(X_new, Y_new, Y_pred)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1D 합성곱 층을 사용해 시퀀스 처리하기" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```\n", "1D conv layer with kernel size 4, stride 2, VALID padding:\n", "\n", " |-----2-----| |-----5---...------| |-----23----|\n", " |-----1-----| |-----4-----| ... |-----22----|\n", " |-----0----| |-----3-----| |---...|-----21----|\n", "X: 0 1 2 3 4 5 6 7 8 9 10 11 12 ... 42 43 44 45 46 47 48 49\n", "Y: 1 2 3 4 5 6 7 8 9 10 11 12 13 ... 43 44 45 46 47 48 49 50\n", " /10 11 12 13 14 15 16 17 18 19 20 21 22 ... 52 53 54 55 56 57 58 59\n", "\n", "Output:\n", "\n", "X: 0/3 2/5 4/7 6/9 8/11 10/13 .../43 42/45 44/47 46/49\n", "Y: 4/13 6/15 8/17 10/19 12/21 14/23 .../53 46/55 48/57 50/59\n", "```" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/20\n", "219/219 [==============================] - 6s 16ms/step - loss: 0.0908 - last_time_step_mse: 0.0845 - val_loss: 0.0477 - val_last_time_step_mse: 0.0396\n", "Epoch 2/20\n", "219/219 [==============================] - 3s 14ms/step - loss: 0.0437 - last_time_step_mse: 0.0357 - val_loss: 0.0367 - val_last_time_step_mse: 0.0285\n", "Epoch 3/20\n", "219/219 [==============================] - 3s 14ms/step - loss: 0.0356 - last_time_step_mse: 0.0282 - val_loss: 0.0307 - val_last_time_step_mse: 0.0218\n", "Epoch 4/20\n", "219/219 [==============================] - 3s 13ms/step - loss: 0.0293 - last_time_step_mse: 0.0201 - val_loss: 0.0259 - val_last_time_step_mse: 0.0152\n", "Epoch 5/20\n", "219/219 [==============================] - 3s 13ms/step - loss: 0.0256 - last_time_step_mse: 0.0152 - val_loss: 0.0246 - val_last_time_step_mse: 0.0141\n", "Epoch 6/20\n", "219/219 [==============================] - 3s 13ms/step - loss: 0.0239 - last_time_step_mse: 0.0129 - val_loss: 0.0227 - val_last_time_step_mse: 0.0115\n", "Epoch 7/20\n", "219/219 [==============================] - 3s 13ms/step - loss: 0.0228 - last_time_step_mse: 0.0116 - val_loss: 0.0225 - val_last_time_step_mse: 0.0116\n", "Epoch 8/20\n", "219/219 [==============================] - 3s 13ms/step - loss: 0.0222 - last_time_step_mse: 0.0111 - val_loss: 0.0216 - val_last_time_step_mse: 0.0105\n", "Epoch 9/20\n", "219/219 [==============================] - 3s 13ms/step - loss: 0.0215 - last_time_step_mse: 0.0109 - val_loss: 0.0217 - val_last_time_step_mse: 0.0109\n", "Epoch 10/20\n", "219/219 [==============================] - 3s 13ms/step - loss: 0.0216 - last_time_step_mse: 0.0107 - val_loss: 0.0210 - val_last_time_step_mse: 0.0102\n", "Epoch 11/20\n", "219/219 [==============================] - 3s 13ms/step - loss: 0.0210 - last_time_step_mse: 0.0103 - val_loss: 0.0208 - val_last_time_step_mse: 0.0100\n", "Epoch 12/20\n", "219/219 [==============================] - 3s 13ms/step - loss: 0.0209 - last_time_step_mse: 0.0102 - val_loss: 0.0208 - val_last_time_step_mse: 0.0102\n", "Epoch 13/20\n", "219/219 [==============================] - 3s 13ms/step - loss: 0.0206 - last_time_step_mse: 0.0098 - val_loss: 0.0206 - val_last_time_step_mse: 0.0101\n", "Epoch 14/20\n", "219/219 [==============================] - 3s 13ms/step - loss: 0.0205 - last_time_step_mse: 0.0100 - val_loss: 0.0204 - val_last_time_step_mse: 0.0099\n", "Epoch 15/20\n", "219/219 [==============================] - 3s 13ms/step - loss: 0.0202 - last_time_step_mse: 0.0099 - val_loss: 0.0199 - val_last_time_step_mse: 0.0093\n", "Epoch 16/20\n", "219/219 [==============================] - 3s 13ms/step - loss: 0.0202 - last_time_step_mse: 0.0097 - val_loss: 0.0201 - val_last_time_step_mse: 0.0095\n", "Epoch 17/20\n", "219/219 [==============================] - 3s 13ms/step - loss: 0.0197 - last_time_step_mse: 0.0094 - val_loss: 0.0197 - val_last_time_step_mse: 0.0091\n", "Epoch 18/20\n", "219/219 [==============================] - 3s 13ms/step - loss: 0.0195 - last_time_step_mse: 0.0090 - val_loss: 0.0192 - val_last_time_step_mse: 0.0086\n", "Epoch 19/20\n", "219/219 [==============================] - 3s 13ms/step - loss: 0.0190 - last_time_step_mse: 0.0088 - val_loss: 0.0188 - val_last_time_step_mse: 0.0084\n", "Epoch 20/20\n", "219/219 [==============================] - 3s 13ms/step - loss: 0.0186 - last_time_step_mse: 0.0084 - val_loss: 0.0184 - val_last_time_step_mse: 0.0080\n" ] } ], "source": [ "np.random.seed(42)\n", "tf.random.set_seed(42)\n", "\n", "model = keras.models.Sequential([\n", " keras.layers.Conv1D(filters=20, kernel_size=4, strides=2, padding=\"valid\",\n", " input_shape=[None, 1]),\n", " keras.layers.GRU(20, return_sequences=True),\n", " keras.layers.GRU(20, return_sequences=True),\n", " keras.layers.TimeDistributed(keras.layers.Dense(10))\n", "])\n", "\n", "model.compile(loss=\"mse\", optimizer=\"adam\", metrics=[last_time_step_mse])\n", "history = model.fit(X_train, Y_train[:, 3::2], epochs=20,\n", " validation_data=(X_valid, Y_valid[:, 3::2]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## WaveNet" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```\n", "C2 /\\ /\\ /\\ /\\ /\\ /\\ /\\ /\\ /\\ /\\ /\\ /\\ /\\.../\\ /\\ /\\ /\\ /\\ /\\\n", " \\ / \\ / \\ / \\ / \\ / \\ / \\ / \\ / \\ / \\\n", " / \\ / \\ / \\ / \\\n", "C1 /\\ /\\ /\\ /\\ /\\ /\\ /\\ /\\ /\\ /\\ /\\ /\\ /.../\\ /\\ /\\ /\\ /\\ /\\ /\\\n", "X: 0 1 2 3 4 5 6 7 8 9 10 11 12 ... 43 44 45 46 47 48 49\n", "Y: 1 2 3 4 5 6 7 8 9 10 11 12 13 ... 44 45 46 47 48 49 50\n", " /10 11 12 13 14 15 16 17 18 19 20 21 22 ... 53 54 55 56 57 58 59\n", "```" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/20\n", "219/219 [==============================] - 2s 7ms/step - loss: 0.0981 - last_time_step_mse: 0.0891 - val_loss: 0.0365 - val_last_time_step_mse: 0.0231\n", "Epoch 2/20\n", "219/219 [==============================] - 1s 7ms/step - loss: 0.0340 - last_time_step_mse: 0.0212 - val_loss: 0.0294 - val_last_time_step_mse: 0.0166\n", "Epoch 3/20\n", "219/219 [==============================] - 1s 7ms/step - loss: 0.0291 - last_time_step_mse: 0.0163 - val_loss: 0.0269 - val_last_time_step_mse: 0.0145\n", "Epoch 4/20\n", "219/219 [==============================] - 1s 6ms/step - loss: 0.0265 - last_time_step_mse: 0.0141 - val_loss: 0.0254 - val_last_time_step_mse: 0.0130\n", "Epoch 5/20\n", "219/219 [==============================] - 1s 6ms/step - loss: 0.0251 - last_time_step_mse: 0.0129 - val_loss: 0.0244 - val_last_time_step_mse: 0.0122\n", "Epoch 6/20\n", "219/219 [==============================] - 2s 7ms/step - loss: 0.0242 - last_time_step_mse: 0.0121 - val_loss: 0.0233 - val_last_time_step_mse: 0.0108\n", "Epoch 7/20\n", "219/219 [==============================] - 1s 6ms/step - loss: 0.0234 - last_time_step_mse: 0.0112 - val_loss: 0.0230 - val_last_time_step_mse: 0.0109\n", "Epoch 8/20\n", "219/219 [==============================] - 1s 7ms/step - loss: 0.0228 - last_time_step_mse: 0.0105 - val_loss: 0.0228 - val_last_time_step_mse: 0.0105\n", "Epoch 9/20\n", "219/219 [==============================] - 1s 6ms/step - loss: 0.0222 - last_time_step_mse: 0.0105 - val_loss: 0.0225 - val_last_time_step_mse: 0.0107\n", "Epoch 10/20\n", "219/219 [==============================] - 2s 7ms/step - loss: 0.0221 - last_time_step_mse: 0.0102 - val_loss: 0.0214 - val_last_time_step_mse: 0.0092\n", "Epoch 11/20\n", "219/219 [==============================] - 1s 7ms/step - loss: 0.0214 - last_time_step_mse: 0.0095 - val_loss: 0.0211 - val_last_time_step_mse: 0.0091\n", "Epoch 12/20\n", "219/219 [==============================] - 1s 7ms/step - loss: 0.0212 - last_time_step_mse: 0.0092 - val_loss: 0.0214 - val_last_time_step_mse: 0.0099\n", "Epoch 13/20\n", "219/219 [==============================] - 1s 7ms/step - loss: 0.0209 - last_time_step_mse: 0.0090 - val_loss: 0.0204 - val_last_time_step_mse: 0.0084\n", "Epoch 14/20\n", "219/219 [==============================] - 1s 6ms/step - loss: 0.0207 - last_time_step_mse: 0.0088 - val_loss: 0.0202 - val_last_time_step_mse: 0.0084\n", "Epoch 15/20\n", "219/219 [==============================] - 2s 7ms/step - loss: 0.0202 - last_time_step_mse: 0.0085 - val_loss: 0.0198 - val_last_time_step_mse: 0.0079\n", "Epoch 16/20\n", "219/219 [==============================] - 1s 7ms/step - loss: 0.0205 - last_time_step_mse: 0.0086 - val_loss: 0.0197 - val_last_time_step_mse: 0.0080\n", "Epoch 17/20\n", "219/219 [==============================] - 1s 6ms/step - loss: 0.0196 - last_time_step_mse: 0.0078 - val_loss: 0.0194 - val_last_time_step_mse: 0.0077\n", "Epoch 18/20\n", "219/219 [==============================] - 1s 7ms/step - loss: 0.0194 - last_time_step_mse: 0.0074 - val_loss: 0.0192 - val_last_time_step_mse: 0.0076\n", "Epoch 19/20\n", "219/219 [==============================] - 2s 7ms/step - loss: 0.0193 - last_time_step_mse: 0.0077 - val_loss: 0.0188 - val_last_time_step_mse: 0.0072\n", "Epoch 20/20\n", "219/219 [==============================] - 2s 7ms/step - loss: 0.0190 - last_time_step_mse: 0.0073 - val_loss: 0.0188 - val_last_time_step_mse: 0.0072\n" ] } ], "source": [ "np.random.seed(42)\n", "tf.random.set_seed(42)\n", "\n", "model = keras.models.Sequential()\n", "model.add(keras.layers.InputLayer(input_shape=[None, 1]))\n", "for rate in (1, 2, 4, 8) * 2:\n", " model.add(keras.layers.Conv1D(filters=20, kernel_size=2, padding=\"causal\",\n", " activation=\"relu\", dilation_rate=rate))\n", "model.add(keras.layers.Conv1D(filters=10, kernel_size=1))\n", "model.compile(loss=\"mse\", optimizer=\"adam\", metrics=[last_time_step_mse])\n", "history = model.fit(X_train, Y_train, epochs=20,\n", " validation_data=(X_valid, Y_valid))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "다음은 논문에 정의된 원본 WaveNet입니다: ReLU 대신에 GatedActivationUnit과 스킵 연결을 사용합니다. 또한 점점 더 시퀀스가 짧아지는 것을 피하기 위해 왼쪽에 0으로 패딩합니다:" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [], "source": [ "class GatedActivationUnit(keras.layers.Layer):\n", " def __init__(self, activation=\"tanh\", **kwargs):\n", " super().__init__(**kwargs)\n", " self.activation = keras.activations.get(activation)\n", " def call(self, inputs):\n", " n_filters = inputs.shape[-1] // 2\n", " linear_output = self.activation(inputs[..., :n_filters])\n", " gate = keras.activations.sigmoid(inputs[..., n_filters:])\n", " return self.activation(linear_output) * gate" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [], "source": [ "def wavenet_residual_block(inputs, n_filters, dilation_rate):\n", " z = keras.layers.Conv1D(2 * n_filters, kernel_size=2, padding=\"causal\",\n", " dilation_rate=dilation_rate)(inputs)\n", " z = GatedActivationUnit()(z)\n", " z = keras.layers.Conv1D(n_filters, kernel_size=1)(z)\n", " return keras.layers.Add()([z, inputs]), z" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [], "source": [ "keras.backend.clear_session()\n", "np.random.seed(42)\n", "tf.random.set_seed(42)\n", "\n", "n_layers_per_block = 3 # 10 in the paper\n", "n_blocks = 1 # 3 in the paper\n", "n_filters = 32 # 128 in the paper\n", "n_outputs = 10 # 256 in the paper\n", "\n", "inputs = keras.layers.Input(shape=[None, 1])\n", "z = keras.layers.Conv1D(n_filters, kernel_size=2, padding=\"causal\")(inputs)\n", "skip_to_last = []\n", "for dilation_rate in [2**i for i in range(n_layers_per_block)] * n_blocks:\n", " z, skip = wavenet_residual_block(z, n_filters, dilation_rate)\n", " skip_to_last.append(skip)\n", "z = keras.activations.relu(keras.layers.Add()(skip_to_last))\n", "z = keras.layers.Conv1D(n_filters, kernel_size=1, activation=\"relu\")(z)\n", "Y_proba = keras.layers.Conv1D(n_outputs, kernel_size=1, activation=\"softmax\")(z)\n", "\n", "model = keras.models.Model(inputs=[inputs], outputs=[Y_proba])" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/2\n", "219/219 [==============================] - 3s 9ms/step - loss: 0.1387 - last_time_step_mse: 0.1347 - val_loss: 0.1229 - val_last_time_step_mse: 0.1199\n", "Epoch 2/2\n", "219/219 [==============================] - 2s 8ms/step - loss: 0.1222 - last_time_step_mse: 0.1161 - val_loss: 0.1217 - val_last_time_step_mse: 0.1189\n" ] } ], "source": [ "model.compile(loss=\"mse\", optimizer=\"adam\", metrics=[last_time_step_mse])\n", "history = model.fit(X_train, Y_train, epochs=2,\n", " validation_data=(X_valid, Y_valid))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "이 장에서 RNN의 기초 사항을 살펴 보았고 RNN을 사용해 시퀀스(소위 시계열)을 처리했습니다. CNN을 포함하여 시퀀스를 처리하는 다른 방법도 알아 보았습니다. 다음 장에서는 RNN을 자연어 처리에 적용해 보겠습니다. 그리고 RNN에 대해 더 자세히 배워 보겠습니다(양방향 RNN, 상태가 있는 RNN과 상태가 없는 RNN, 인코더-디코더, 어텐션을 사용한 인코더-디코더). 또한 어텐션만 사용하는 구조인 트랜스포머도 살펴 보겠습니다." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 연습문제 해답" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. to 8." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "부록 A 참조." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 9. SketchRNN 데이터셋 다루기" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_연습문제: 텐서플로 데이터셋에서 제공하는 SketchRNN 데이터셋으로 분류 모델을 훈련해보세요._" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "이 데이터셋은 아직 TFDS에서 제공하지 않습니다. 아직 [풀 리퀘스트](https://github.com/tensorflow/datasets/pull/361)가 진행 중입니다. 다행히 이 데이터는 TFRecord로 제공되므로 다운로드해보죠(3,450,000 훈련 스케치와 345,000 테스트 스케치가 포함된 이 데이터셋은 1GB 정도되기 때문에 다운로드 시간이 조금 걸립니다):" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [], "source": [ "DOWNLOAD_ROOT = \"http://download.tensorflow.org/data/\"\n", "FILENAME = \"quickdraw_tutorial_dataset_v1.tar.gz\"\n", "filepath = keras.utils.get_file(FILENAME,\n", " DOWNLOAD_ROOT + FILENAME,\n", " cache_subdir=\"datasets/quickdraw\",\n", " extract=True)" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [], "source": [ "quickdraw_dir = Path(filepath).parent\n", "train_files = sorted([str(path) for path in quickdraw_dir.glob(\"training.tfrecord-*\")])\n", "eval_files = sorted([str(path) for path in quickdraw_dir.glob(\"eval.tfrecord-*\")])" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['/Users/ageron/.keras/datasets/quickdraw/training.tfrecord-00000-of-00010',\n", " '/Users/ageron/.keras/datasets/quickdraw/training.tfrecord-00001-of-00010',\n", " '/Users/ageron/.keras/datasets/quickdraw/training.tfrecord-00002-of-00010',\n", " '/Users/ageron/.keras/datasets/quickdraw/training.tfrecord-00003-of-00010',\n", " '/Users/ageron/.keras/datasets/quickdraw/training.tfrecord-00004-of-00010',\n", " '/Users/ageron/.keras/datasets/quickdraw/training.tfrecord-00005-of-00010',\n", " '/Users/ageron/.keras/datasets/quickdraw/training.tfrecord-00006-of-00010',\n", " '/Users/ageron/.keras/datasets/quickdraw/training.tfrecord-00007-of-00010',\n", " '/Users/ageron/.keras/datasets/quickdraw/training.tfrecord-00008-of-00010',\n", " '/Users/ageron/.keras/datasets/quickdraw/training.tfrecord-00009-of-00010']" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_files" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['/Users/ageron/.keras/datasets/quickdraw/eval.tfrecord-00000-of-00010',\n", " '/Users/ageron/.keras/datasets/quickdraw/eval.tfrecord-00001-of-00010',\n", " '/Users/ageron/.keras/datasets/quickdraw/eval.tfrecord-00002-of-00010',\n", " '/Users/ageron/.keras/datasets/quickdraw/eval.tfrecord-00003-of-00010',\n", " '/Users/ageron/.keras/datasets/quickdraw/eval.tfrecord-00004-of-00010',\n", " '/Users/ageron/.keras/datasets/quickdraw/eval.tfrecord-00005-of-00010',\n", " '/Users/ageron/.keras/datasets/quickdraw/eval.tfrecord-00006-of-00010',\n", " '/Users/ageron/.keras/datasets/quickdraw/eval.tfrecord-00007-of-00010',\n", " '/Users/ageron/.keras/datasets/quickdraw/eval.tfrecord-00008-of-00010',\n", " '/Users/ageron/.keras/datasets/quickdraw/eval.tfrecord-00009-of-00010']" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "eval_files" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [], "source": [ "with open(quickdraw_dir / \"eval.tfrecord.classes\") as test_classes_file:\n", " test_classes = test_classes_file.readlines()\n", " \n", "with open(quickdraw_dir / \"training.tfrecord.classes\") as train_classes_file:\n", " train_classes = train_classes_file.readlines()" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [], "source": [ "assert train_classes == test_classes\n", "class_names = [name.strip().lower() for name in train_classes]" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['aircraft carrier',\n", " 'airplane',\n", " 'alarm clock',\n", " 'ambulance',\n", " 'angel',\n", " 'animal migration',\n", " 'ant',\n", " 'anvil',\n", " 'apple',\n", " 'arm',\n", " 'asparagus',\n", " 'axe',\n", " 'backpack',\n", " 'banana',\n", " 'bandage',\n", " 'barn',\n", " 'baseball',\n", " 'baseball bat',\n", " 'basket',\n", " 'basketball',\n", " 'bat',\n", " 'bathtub',\n", " 'beach',\n", " 'bear',\n", " 'beard',\n", " 'bed',\n", " 'bee',\n", " 'belt',\n", " 'bench',\n", " 'bicycle',\n", " 'binoculars',\n", " 'bird',\n", " 'birthday cake',\n", " 'blackberry',\n", " 'blueberry',\n", " 'book',\n", " 'boomerang',\n", " 'bottlecap',\n", " 'bowtie',\n", " 'bracelet',\n", " 'brain',\n", " 'bread',\n", " 'bridge',\n", " 'broccoli',\n", " 'broom',\n", " 'bucket',\n", " 'bulldozer',\n", " 'bus',\n", " 'bush',\n", " 'butterfly',\n", " 'cactus',\n", " 'cake',\n", " 'calculator',\n", " 'calendar',\n", " 'camel',\n", " 'camera',\n", " 'camouflage',\n", " 'campfire',\n", " 'candle',\n", " 'cannon',\n", " 'canoe',\n", " 'car',\n", " 'carrot',\n", " 'castle',\n", " 'cat',\n", " 'ceiling fan',\n", " 'cell phone',\n", " 'cello',\n", " 'chair',\n", " 'chandelier',\n", " 'church',\n", " 'circle',\n", " 'clarinet',\n", " 'clock',\n", " 'cloud',\n", " 'coffee cup',\n", " 'compass',\n", " 'computer',\n", " 'cookie',\n", " 'cooler',\n", " 'couch',\n", " 'cow',\n", " 'crab',\n", " 'crayon',\n", " 'crocodile',\n", " 'crown',\n", " 'cruise ship',\n", " 'cup',\n", " 'diamond',\n", " 'dishwasher',\n", " 'diving board',\n", " 'dog',\n", " 'dolphin',\n", " 'donut',\n", " 'door',\n", " 'dragon',\n", " 'dresser',\n", " 'drill',\n", " 'drums',\n", " 'duck',\n", " 'dumbbell',\n", " 'ear',\n", " 'elbow',\n", " 'elephant',\n", " 'envelope',\n", " 'eraser',\n", " 'eye',\n", " 'eyeglasses',\n", " 'face',\n", " 'fan',\n", " 'feather',\n", " 'fence',\n", " 'finger',\n", " 'fire hydrant',\n", " 'fireplace',\n", " 'firetruck',\n", " 'fish',\n", " 'flamingo',\n", " 'flashlight',\n", " 'flip flops',\n", " 'floor lamp',\n", " 'flower',\n", " 'flying saucer',\n", " 'foot',\n", " 'fork',\n", " 'frog',\n", " 'frying pan',\n", " 'garden',\n", " 'garden hose',\n", " 'giraffe',\n", " 'goatee',\n", " 'golf club',\n", " 'grapes',\n", " 'grass',\n", " 'guitar',\n", " 'hamburger',\n", " 'hammer',\n", " 'hand',\n", " 'harp',\n", " 'hat',\n", " 'headphones',\n", " 'hedgehog',\n", " 'helicopter',\n", " 'helmet',\n", " 'hexagon',\n", " 'hockey puck',\n", " 'hockey stick',\n", " 'horse',\n", " 'hospital',\n", " 'hot air balloon',\n", " 'hot dog',\n", " 'hot tub',\n", " 'hourglass',\n", " 'house',\n", " 'house plant',\n", " 'hurricane',\n", " 'ice cream',\n", " 'jacket',\n", " 'jail',\n", " 'kangaroo',\n", " 'key',\n", " 'keyboard',\n", " 'knee',\n", " 'knife',\n", " 'ladder',\n", " 'lantern',\n", " 'laptop',\n", " 'leaf',\n", " 'leg',\n", " 'light bulb',\n", " 'lighter',\n", " 'lighthouse',\n", " 'lightning',\n", " 'line',\n", " 'lion',\n", " 'lipstick',\n", " 'lobster',\n", " 'lollipop',\n", " 'mailbox',\n", " 'map',\n", " 'marker',\n", " 'matches',\n", " 'megaphone',\n", " 'mermaid',\n", " 'microphone',\n", " 'microwave',\n", " 'monkey',\n", " 'moon',\n", " 'mosquito',\n", " 'motorbike',\n", " 'mountain',\n", " 'mouse',\n", " 'moustache',\n", " 'mouth',\n", " 'mug',\n", " 'mushroom',\n", " 'nail',\n", " 'necklace',\n", " 'nose',\n", " 'ocean',\n", " 'octagon',\n", " 'octopus',\n", " 'onion',\n", " 'oven',\n", " 'owl',\n", " 'paint can',\n", " 'paintbrush',\n", " 'palm tree',\n", " 'panda',\n", " 'pants',\n", " 'paper clip',\n", " 'parachute',\n", " 'parrot',\n", " 'passport',\n", " 'peanut',\n", " 'pear',\n", " 'peas',\n", " 'pencil',\n", " 'penguin',\n", " 'piano',\n", " 'pickup truck',\n", " 'picture frame',\n", " 'pig',\n", " 'pillow',\n", " 'pineapple',\n", " 'pizza',\n", " 'pliers',\n", " 'police car',\n", " 'pond',\n", " 'pool',\n", " 'popsicle',\n", " 'postcard',\n", " 'potato',\n", " 'power outlet',\n", " 'purse',\n", " 'rabbit',\n", " 'raccoon',\n", " 'radio',\n", " 'rain',\n", " 'rainbow',\n", " 'rake',\n", " 'remote control',\n", " 'rhinoceros',\n", " 'rifle',\n", " 'river',\n", " 'roller coaster',\n", " 'rollerskates',\n", " 'sailboat',\n", " 'sandwich',\n", " 'saw',\n", " 'saxophone',\n", " 'school bus',\n", " 'scissors',\n", " 'scorpion',\n", " 'screwdriver',\n", " 'sea turtle',\n", " 'see saw',\n", " 'shark',\n", " 'sheep',\n", " 'shoe',\n", " 'shorts',\n", " 'shovel',\n", " 'sink',\n", " 'skateboard',\n", " 'skull',\n", " 'skyscraper',\n", " 'sleeping bag',\n", " 'smiley face',\n", " 'snail',\n", " 'snake',\n", " 'snorkel',\n", " 'snowflake',\n", " 'snowman',\n", " 'soccer ball',\n", " 'sock',\n", " 'speedboat',\n", " 'spider',\n", " 'spoon',\n", " 'spreadsheet',\n", " 'square',\n", " 'squiggle',\n", " 'squirrel',\n", " 'stairs',\n", " 'star',\n", " 'steak',\n", " 'stereo',\n", " 'stethoscope',\n", " 'stitches',\n", " 'stop sign',\n", " 'stove',\n", " 'strawberry',\n", " 'streetlight',\n", " 'string bean',\n", " 'submarine',\n", " 'suitcase',\n", " 'sun',\n", " 'swan',\n", " 'sweater',\n", " 'swing set',\n", " 'sword',\n", " 'syringe',\n", " 't-shirt',\n", " 'table',\n", " 'teapot',\n", " 'teddy-bear',\n", " 'telephone',\n", " 'television',\n", " 'tennis racquet',\n", " 'tent',\n", " 'the eiffel tower',\n", " 'the great wall of china',\n", " 'the mona lisa',\n", " 'tiger',\n", " 'toaster',\n", " 'toe',\n", " 'toilet',\n", " 'tooth',\n", " 'toothbrush',\n", " 'toothpaste',\n", " 'tornado',\n", " 'tractor',\n", " 'traffic light',\n", " 'train',\n", " 'tree',\n", " 'triangle',\n", " 'trombone',\n", " 'truck',\n", " 'trumpet',\n", " 'umbrella',\n", " 'underwear',\n", " 'van',\n", " 'vase',\n", " 'violin',\n", " 'washing machine',\n", " 'watermelon',\n", " 'waterslide',\n", " 'whale',\n", " 'wheel',\n", " 'windmill',\n", " 'wine bottle',\n", " 'wine glass',\n", " 'wristwatch',\n", " 'yoga',\n", " 'zebra',\n", " 'zigzag']" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted(class_names)" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [], "source": [ "def parse(data_batch):\n", " feature_descriptions = {\n", " \"ink\": tf.io.VarLenFeature(dtype=tf.float32),\n", " \"shape\": tf.io.FixedLenFeature([2], dtype=tf.int64),\n", " \"class_index\": tf.io.FixedLenFeature([1], dtype=tf.int64)\n", " }\n", " examples = tf.io.parse_example(data_batch, feature_descriptions)\n", " flat_sketches = tf.sparse.to_dense(examples[\"ink\"])\n", " sketches = tf.reshape(flat_sketches, shape=[tf.size(data_batch), -1, 3])\n", " lengths = examples[\"shape\"][:, 0]\n", " labels = examples[\"class_index\"][:, 0]\n", " return sketches, lengths, labels" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [], "source": [ "def quickdraw_dataset(filepaths, batch_size=32, shuffle_buffer_size=None,\n", " n_parse_threads=5, n_read_threads=5, cache=False):\n", " dataset = tf.data.TFRecordDataset(filepaths,\n", " num_parallel_reads=n_read_threads)\n", " if cache:\n", " dataset = dataset.cache()\n", " if shuffle_buffer_size:\n", " dataset = dataset.shuffle(shuffle_buffer_size)\n", " dataset = dataset.batch(batch_size)\n", " dataset = dataset.map(parse, num_parallel_calls=n_parse_threads)\n", " return dataset.prefetch(1)" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [], "source": [ "train_set = quickdraw_dataset(train_files, shuffle_buffer_size=10000)\n", "valid_set = quickdraw_dataset(eval_files[:5])\n", "test_set = quickdraw_dataset(eval_files[5:])" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sketches = tf.Tensor(\n", "[[[-0.07058823 0.04255319 0. ]\n", " [-0.01568627 0.0425532 0. ]\n", " [-0.09803921 0.03191489 0. ]\n", " ...\n", " [ 0. 0. 0. ]\n", " [ 0. 0. 0. ]\n", " [ 0. 0. 0. ]]\n", "\n", " [[ 0.07058824 0.27741933 0. ]\n", " [-0.02745098 0.06451613 0. ]\n", " [-0.02352941 0. 0. ]\n", " ...\n", " [ 0. 0. 0. ]\n", " [ 0. 0. 0. ]\n", " [ 0. 0. 0. ]]\n", "\n", " [[-0.17857143 0.06666667 0. ]\n", " [-0.26020408 0.15294117 0. ]\n", " [-0.01020408 0.01568627 0. ]\n", " ...\n", " [ 0. 0. 0. ]\n", " [ 0. 0. 0. ]\n", " [ 0. 0. 0. ]]\n", "\n", " ...\n", "\n", " [[ 0.03056769 -0.01176471 0. ]\n", " [ 0.29694325 0. 0. ]\n", " [ 0.38864627 0.04705882 0. ]\n", " ...\n", " [ 0. 0. 0. ]\n", " [ 0. 0. 0. ]\n", " [ 0. 0. 0. ]]\n", "\n", " [[ 0.34901962 0.02985072 0. ]\n", " [ 0.10588235 0.07462686 0. ]\n", " [ 0.01176471 -0.35820895 0. ]\n", " ...\n", " [ 0. 0. 0. ]\n", " [ 0. 0. 0. ]\n", " [ 0. 0. 0. ]]\n", "\n", " [[ 0.01176471 0. 0. ]\n", " [ 0.00392157 0.03448276 0. ]\n", " [ 0.00784314 0.21551724 0. ]\n", " ...\n", " [ 0. 0. 0. ]\n", " [ 0. 0. 0. ]\n", " [ 0. 0. 0. ]]], shape=(32, 195, 3), dtype=float32)\n", "lengths = tf.Tensor(\n", "[ 44 30 18 44 20 21 26 44 17 43 47 44 34 39 50 28 24 29\n", " 37 17 195 64 78 49 45 33 28 19 17 56 12 30], shape=(32,), dtype=int64)\n", "labels = tf.Tensor(\n", "[ 70 247 266 10 149 170 268 252 53 121 11 5 116 209 199 50 244 32\n", " 327 140 22 58 8 151 204 167 39 275 143 333 152 71], shape=(32,), dtype=int64)\n" ] } ], "source": [ "for sketches, lengths, labels in train_set.take(1):\n", " print(\"sketches =\", sketches)\n", " print(\"lengths =\", lengths)\n", " print(\"labels =\", labels)" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "def draw_sketch(sketch, label=None):\n", " origin = np.array([[0., 0., 0.]])\n", " sketch = np.r_[origin, sketch]\n", " stroke_end_indices = np.argwhere(sketch[:, -1]==1.)[:, 0]\n", " coordinates = np.cumsum(sketch[:, :2], axis=0)\n", " strokes = np.split(coordinates, stroke_end_indices + 1)\n", " title = class_names[label.numpy()] if label is not None else \"Try to guess\"\n", " plt.title(title)\n", " plt.plot(coordinates[:, 0], -coordinates[:, 1], \"y:\")\n", " for stroke in strokes:\n", " plt.plot(stroke[:, 0], -stroke[:, 1], \".-\")\n", " plt.axis(\"off\")\n", "\n", "def draw_sketches(sketches, lengths, labels):\n", " n_sketches = len(sketches)\n", " n_cols = 4\n", " n_rows = (n_sketches - 1) // n_cols + 1\n", " plt.figure(figsize=(n_cols * 3, n_rows * 3.5))\n", " for index, sketch, length, label in zip(range(n_sketches), sketches, lengths, labels):\n", " plt.subplot(n_rows, n_cols, index + 1)\n", " draw_sketch(sketch[:length], label)\n", " plt.show()\n", "\n", "for sketches, lengths, labels in train_set.take(1):\n", " draw_sketches(sketches, lengths, labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "대부분의 스케치는 100개 포인트 이하로 구성되어 있습니다:" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "lengths = np.concatenate([lengths for _, lengths, _ in train_set.take(1000)])\n", "plt.hist(lengths, bins=150, density=True)\n", "plt.axis([0, 200, 0, 0.03])\n", "plt.xlabel(\"length\")\n", "plt.ylabel(\"density\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [], "source": [ "def crop_long_sketches(dataset, max_length=100):\n", " return dataset.map(lambda inks, lengths, labels: (inks[:, :max_length], labels))\n", "\n", "cropped_train_set = crop_long_sketches(train_set)\n", "cropped_valid_set = crop_long_sketches(valid_set)\n", "cropped_test_set = crop_long_sketches(test_set)" ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/2\n", "107813/107813 [==============================] - 2182s 20ms/step - loss: 3.8473 - accuracy: 0.2086 - sparse_top_k_categorical_accuracy: 0.4242 - val_loss: 2.6672 - val_accuracy: 0.3872 - val_sparse_top_k_categorical_accuracy: 0.6771\n", "Epoch 2/2\n", "107813/107813 [==============================] - 2049s 19ms/step - loss: 2.3393 - accuracy: 0.4502 - sparse_top_k_categorical_accuracy: 0.7367 - val_loss: 2.1072 - val_accuracy: 0.4968 - val_sparse_top_k_categorical_accuracy: 0.7759\n" ] } ], "source": [ "model = keras.models.Sequential([\n", " keras.layers.Conv1D(32, kernel_size=5, strides=2, activation=\"relu\"),\n", " keras.layers.BatchNormalization(),\n", " keras.layers.Conv1D(64, kernel_size=5, strides=2, activation=\"relu\"),\n", " keras.layers.BatchNormalization(),\n", " keras.layers.Conv1D(128, kernel_size=3, strides=2, activation=\"relu\"),\n", " keras.layers.BatchNormalization(),\n", " keras.layers.LSTM(128, return_sequences=True),\n", " keras.layers.LSTM(128),\n", " keras.layers.Dense(len(class_names), activation=\"softmax\")\n", "])\n", "optimizer = keras.optimizers.SGD(learning_rate=1e-2, clipnorm=1.)\n", "model.compile(loss=\"sparse_categorical_crossentropy\",\n", " optimizer=optimizer,\n", " metrics=[\"accuracy\", \"sparse_top_k_categorical_accuracy\"])\n", "history = model.fit(cropped_train_set, epochs=2,\n", " validation_data=cropped_valid_set)" ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [], "source": [ "y_test = np.concatenate([labels for _, _, labels in test_set])\n", "y_probas = model.predict(test_set)" ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.6899671" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.mean(keras.metrics.sparse_top_k_categorical_accuracy(y_test, y_probas))" ] }, { "cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Top-5 predictions:\n", " 1. firetruck 46.565%\n", " 2. police car 30.455%\n", " 3. ambulance 3.810%\n", " 4. car 3.695%\n", " 5. cannon 3.371%\n", "Answer: firetruck\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Top-5 predictions:\n", " 1. mouth 23.162%\n", " 2. pond 14.151%\n", " 3. pool 12.582%\n", " 4. beard 11.375%\n", " 5. goatee 9.808%\n", "Answer: mouth\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Top-5 predictions:\n", " 1. jail 71.532%\n", " 2. fence 6.519%\n", " 3. swing set 5.708%\n", " 4. grass 3.302%\n", " 5. rain 3.023%\n", "Answer: jail\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Top-5 predictions:\n", " 1. baseball 79.233%\n", " 2. watermelon 7.687%\n", " 3. basketball 5.259%\n", " 4. clock 1.659%\n", " 5. compass 1.101%\n", "Answer: baseball\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Top-5 predictions:\n", " 1. basketball 51.888%\n", " 2. baseball 17.328%\n", " 3. onion 12.688%\n", " 4. watermelon 9.989%\n", " 5. brain 2.216%\n", "Answer: baseball\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Top-5 predictions:\n", " 1. lantern 7.235%\n", " 2. toothpaste 6.845%\n", " 3. drill 6.254%\n", " 4. lighthouse 4.624%\n", " 5. crayon 3.566%\n", "Answer: brain\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Top-5 predictions:\n", " 1. animal migration 8.771%\n", " 2. blackberry 7.932%\n", " 3. blueberry 6.413%\n", " 4. peas 5.549%\n", " 5. bracelet 3.623%\n", "Answer: helicopter\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Top-5 predictions:\n", " 1. vase 42.793%\n", " 2. wine glass 13.744%\n", " 3. shovel 8.136%\n", " 4. house plant 5.144%\n", " 5. sailboat 4.850%\n", "Answer: vase\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Top-5 predictions:\n", " 1. anvil 25.870%\n", " 2. drill 9.670%\n", " 3. nail 7.246%\n", " 4. screwdriver 5.611%\n", " 5. knee 4.355%\n", "Answer: anvil\n" ] }, { "data": { "image/png": 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2bGIs8zn3DL3EkvhDAMNWFJnjyibH6jLeRxpX1L0kWKcVFrTI/8mOSGUAwOAfCTQGRuiLgbZt8VwtW4uLk4qala2RSum7U/pUI4AXWvPMH8LZziNV4+I/9KUd9jWBIsmhBQAqIz77j1mXhJ/vy/5cUfcGQw/Ygt+VHNoeAEIBq7nTb+8+bGJ8wLLzL52j7T+swbMjgCMc4iMEk8Jgk0Bvd2bbX7fnWAsqlnqe6K8VzEXztOFFK5RPtDgRgY6AEXqjr/t0Rb2BRP6WXeiLiq8IVJg+9NcVhaZRdHa0X7dbBeu0wool6kjJoUMtiU+UbSpKn/o57nE+bM23vvfGxN/zpkVW9qddAABDL3CI32TC8OWlyYuGTYz/oz+6dUW9ASyeqw2vXOL5pfi8JfEJ8vSOJ/qj72CdRmXLlJ1VUxxiys6Jkk17pEtfxCyJ328pMFtli/5W0Kp8NJDxJI13eSuLm5RnCLSNQ3y0uLrjlf7q2xX1emJfEyiXHFra9fviisSRlePjL/RJZ+ndHwz+mEC6LfgYS+ZTPEkhAQCDv27Nt6IAZhW0Kg8Nlii/ZbO9w/SQ9J03JlTB9HsYoZf6s383oGk9aJrlPbDYUV8DAAY3E2iLyvG9nLddE4Y+msFPIhUkDwCQHAqFNTvY6befLmhVbiGjY0lBn3TeCww9pxTKUwDUZWXm1eWTYv0qaMAVdY9JXOc/pjipPg0A4Sz765YCa4eMb7cydFpWmjwn0CGNz4a0I4EEkKpIi1TqhHP0CyKDbs67i2CdVlFJ6jOCaXsAx5ZPitUPhB2u+9EDVt6SNTu3XZ4EAOEs+7bsi8PTMtn+0jla5bAGzxgAUwAMN2VmycLjAnQ0UgOPifXcp9ffmDMDgYTHWeyLigATjpeu6hiwlGjuSL02DF0kVOfl3KR8UPrIUdkXhzM3+hj6HjHNuaY0qXaVhVuYUJ2zl5eaD1WPja/szY7qfsXQ/QroedkS2ctLzdqyybEBzfHnjtRrYNlsb3HpcuUTApUBQMRn7511SbjX+wdbbvfppsK3FjYrI2WbtmZwdGWuvSipOpeWnBXb4N0eA0WwTisub1BeUSyxLYCTYIT6ZRZobbiiXg3Nd/j2LWxR3u76fVFlYr+qcfF3etNm503Zu/vD0p8Z/GcC6XGPs1JLiOkA7ocRCvXa6IHA0A80ZedJ2SI9kuVMyb44PHugTQJc9+O3GPpxhVCe7HakvGpcfNkGtqUCOD7is//uj0plDE4S6PGVOdbjId1+aqPO62HoRwN4SrZIALCyI9LnA21SF66o0wTrNCU7LJ4vgHIwAFgS2x0Be/O8aZH1FvTSOdrenoS4sQDy1gQq0uKiqanIfN4XFRP9F4UbcgHkZvwJ+o+lc7SJpUKdJRwIAnUdHoX1yM3Rl7iiBgBDzy2X1WcUi/YDAAZ/3liSPGZ9YjiCdZpS3qAcrVhifDnUIwFQUuV3PUk6Q3LopeKp/bt83mcY+vhyqLOSCpPqUAKAhNTszIKBNex/bPI+degfWfvqHfJ9ADYHAIf4PcF0BIxQR48aSIVUTrFkvkJLCBlAI4PvXTos+WzFhPgHfWh6vxKs07wljcq9WkKcCuDlFYXm2KJmpRKDcHZmkxZ18x0+I69NvlpyUl+hHX67tS3P2rZ6bLx5rTcaOjWUJc/JaZdOz4pKuwKQIz57Uaffvr+kSZ0BI2T2g/n9h6EXRnz2Z1lRqdSS+E7ZpvMG48aHLjZNUaeSrNwA4IJuR+sTqnOC5/LONcZPNN7lLS1pUv+I1CLJtpbEjmTjNgLdDSP07dq6rK6p/2XOOVg7etCMauvE0HcF8BSDSxpLzHtLp8TOHmiT1sUmJ+pF87Thhc3yi76YVNl1LKk4r6qm+D2M0Op3fBj67hGffb0WF4ekR/WFMc15oKnYvL96bHydIZ0pQfNrAFSAEgAO3hiEvWy299aSRmWqYGpEKk/2RlHnfNMStaHvbgt+gRj5SBWkLw4FrC9W5tp7VI+NJ7pfunSOlivZ9I/CZmUv2aZtGRxry7MX2RJfWHR29Ln16ba6pv4ygK9DeqZAwGreofDT5tZY0dyGcFU9gO+DtaMHz4tkakf8dQAu6cy2O2yJd8w5P7L2l+ZBtPq5yYg6Vus/zxsXtQxuimtse+NiMwBzAEzpXkclekP2Lr6YNJbBZxBIT6hOiycpDPRikWTryx8/N+l4bwMYAFnZSuhny1G2its+AIBEZrg68GO8MVo2L2IGXtOk6Af/vfaP/R/UD2DxXK2qvEH9p+TQKAbPXlSVvHCtiXcMvaoj27rTH5ZGE4gBxDHAcSpDX9SGrrbr1oKckDzSFvxuUmX2xsW+ndn2c/6wdBSMEKcXScaEs+xbsyNSMVKJvh9vzTOf7vQ7j/VmkaS6pp4AvA9gM4JzB0O8HKwd/d60OadJT/946jYA9q70//inhK3t1RQt8wJEAFDkXZ5ojhU/xBDvbZ375dfb5n2+8NaJD/TpC6gzI7BdUuWP1SR5iDGVjI67V3fd8ru9+0s2XV7YLBcRaLdVTlsAroIRur4vbV0bQ1vUhl4G4DEA+7Tmme/ntck/EejUzmz7kdZ86xTJxt7emLg2v1XejkDFlsQtrfnWh3lt0ljlyszk6TjqpmtqvmwZcT2AscHa0f9c27XVNfUBAHtsn//JpLAZ2GNRx5Z+AAUAoIq4k3S0NwC8X54V9KyIlUZNx/NcxnxzQz8WwL8dYmtZWfKKYRPjd3WdCtZpJNk4vqRR2VuxxBEAdgAAS+JvmPC6bGEKACKQjUEQUThkRb10jjalpFG9UbaJbMFTw9n2lXqHvBWDZzgCn0gOTWLw7wGQqfAbqiluAPBiJku6bXHZ0wcoUuI1rxRNrEwU+oO1o+113/U/0qP85nuXvj61OVq684+h4TqAXQFIaVcmhl6+dAbrNMkXFS8VNSsHAfgYqRfCJcE6jYpWyPv7YtLvHeKTBFM1g5lAbznETy2pSL5TtdjzYUxzPvMkaMdOv32J3iHLcH3qPiCVwPAsBt8e19i2ZD7UGxNXyTYdZAuOmgp7tISQADQ6xHVLKpLPVI2LZzypSnoK7w0ACuCYgDggE6NqdU39dIBndLkpAG4I1o6u2aDGDN3P4H8RaMzKHOuH3HZ5FwC7MfgPSZXP9iSFjJQ78fqKQjMY15xbK8fHv+p2/7kAbotpzpXems5re/loGWNIVbwN1mm6KfPDAO4E8GJIt3bJDouXZZsOAgDJIZ8lc1NTkXkNgEpxdcflfSHoNKPwSxiCoPTvmeAVgOIAbABQRfyss2aPO2R9G1k8VxvF4IUEGsPgF4nRZsrcAuBNAk2xZP6uqci8HUAxjNBhRWdHJ3UX9NI52rEMvgnAfG9cXJehZ8sIQ2ekNvTKqNf+wheTAg74CQDVAjSi6zSDbybQ7HUtkmSK9Nz0OwAoE27Cb9vGKF1tbbdZudNmKR6zsvYK1o7uUQKdxHX+P3qS4lc57BxisyNgL9dDUg2B5sMIrXnvpaF7Y5rTItnwWDIP810abuzdE2WWoSFqQz8IwCMOsbcz22kIdIqtu6LHGMxJhY/wXNHZ7xtAt7/yIcunRFY2x0qP6avFlrF3nn3o60uO/CdAXgBHBGtHrzbeJFinFapJuqRsuXpR9+OmzE8qFt2H1PtErEedGvodAM5uLE5OKzkrdlsvHyHjbNSiDtZp5I2Jp4pWyEcT6EcA3wA4epXLHABX9vcUU3VNvQdA3CtHrvtm5olX9HFfmxGcV2VhVh4w7MWL7p06+xYgtRG2pFE5UkuIIwCM6X6PJfGpS4cln1nf5O+dN2Wf4Q9L/wTwdxihi9Z1/UCw8YaeGnpWFdS5BDom6rWjikl+xRJHM3g+gKMAID1aD1RYZCUAxKysPnd3grWjf55y9/gxn6zYe+Eni/f/2wvTD64+TP5wu0qoh6YT3fxCS775UkGrcqQ8vcOpXs9+Fs/VdiyNK3UJ1Wn0JEWfflB7w0Yp6sVztQMroN5OoG0Z/KUWF9s4AkVRr32GJWP3QKeEuGp/qCWljwHcNxBTTCOK3xn1cdO+2EL/bwgY3bedGXrZ3cB+K7MeDwcckS/BmRZmrcXMibxmS9yc3yofTCCPKTvjCs6NPr6BfYgKqH9ngrW8NDmucvyvwwoGExudqOPX+48vs9THmcBgXkGgHcD8bkN58sKqxZ5FAO60Bb+kXR4+fCDtVIS5NwAMz/uib4oVGXp1S755jS8qDvNBKgJAWUmnpTFXvHlJ8yWF71o7bnuLWZt/dOjLgyyZWxSL9lKu7Pxmne2uAQZfQKBDiTGpcny8T7OW9paNR9SGLgBcrkFcA4AcYgJQwuC/EuiaqnFxK3mt/0nVFKrk0ICHR76/fFQTwNYP7cMzlrR82WzvgbJFl3QtTxe0Koj47Jgt+K+SQ4+oV3R+XQ5gzzuOLPlT60vfHxn5aufvpeImtWzJXlXj4os2tN8l92qnlpN6oyU5LyuWuDdTz9NXbBQvisE6rTx3pfSB3iGXdR2zBbcuLzWvHTYxdjMALJ2jTRrW4JndrltP5ZwfGbPm1vqH6pr6BwHsFawdvcWGtpFenj6hdLm6u2zTaADbA4Al8ZeyTfdZEj8pT+/4YZV7dihokRdkR6T8x+39v73EnLzN5jnfPrh9/qd/unXiA+v/xzb0LFvw17bEJcvKzO2qx8Z/3NDn6S8G/0ht6NtWQX26K/9GmvmSQ2OHTYy1pK+RyqFOsSTuaM+xp+QMjKW/Il9bsR+DerYlrBvBOo2Km5T9vHFxVCWpJwumynTasTdtwRctHZZ8q2pcapvYb/54hr57JanzmZDfVGTWXrT4rOll2Yte/qF921OTtuqrrqk/Plg7en2FfbPkUIVwcPDGIGhgkIt6xZ2+ywshX5auQQiHmJsLrceLVygnrZKmdjyBdpVtnFI9Nj4oCgYlbE9ptf5DtEcXp+KX92XwCaWqclbX8jQxXmsqMl9JeJy/V46Pfy0BqFpDE+03Z12RA3m6YGqKeZyRxVOj7wcBTJtz2iGfieQzwY6tjgPwdHVN/fsAXu/JvPmy2d4byqBOZPD1ZHS83qNnGQQMTvfD0KWIz56TFZXGdjv6vUN8iri641c+6qJ52ublDep/AXwu27THYKjxXV1T7wUQBXh6sPaomau9yNAVAKNW5ljX+zul7WSbvAAS4Sz750iW80LxCuUaGKF1x1Qbutzht/8v0CkdnVScz1RTHAoj9Ks9lunAqLkAxqYCoWABdGdZ9qLg8Nwvcz9vGfFGS6xkWVXgh85dCj9ouXXiAwkYeoUt+PuY17FaCqzC6rHxni3MDAIGnaitvwbyZJseBPDL7EVntv2ePywdvrql21it/1EtTn9cOiz5x4oJ8Q2brsow1TX12wD4L4DTg7Wj7//lhKFrKwrNc9QknZETkssB5KaXpxvSy9PPrXV5elUMPQ/AIwAOadett9pz7MPXJL707ptrU4FQDIBWdxkAQILZ+ag6U9mWFnmmeCZE3g3v+bLlqE3l2Yu8lf6fcj9u2ueZpONp1tW2YaFkrg7QoNp3Oajcj6VztOMLZOVB2SZP+lC4Xbeuyzk/svrVQEPf2QvxBwbfNVgEDQB7ly44/P3lo7Bd/qfhxXP/UCRbdGlJo1IhQEcWNSvZlsSwJH5MtukBwfRSzvmR9R4Fl9yrHV+kKvepSVIIND7n/Mi8dbxLLEgHQikAmQCO26VwYWuhr3GHN5ce1pKwvfrm+rc753tX7H5i56fDRtjfb30Djl/0M5fnW46yPYD9G8IV+Q3hKgJwDACEknkA2AGQqK6pHzT7LgfPSG3opzB4HoE0ALAFfy05dByM0PeruzxdJuJzxaRSAm29poLuA8GUm6c+pDXrJ/9Vu3dBtpPYh0CqQxwSTI8mFWf+sjLz1V7VJjf0PzD4flNhtS3POq3krNjDPbmtJzvaO/6efai/U7wU0u0fckLy1t3duWlzTpMcFmXP/nSynONpfag9kb9X+pQF4Kpg7egB2+3SnQEXdbBO8+S3yg/7w9JxXcda88z3O/3OYWurrto0y1dbvEK5dEWhObvo7OiU/rF2zdjXBAolh461BZ/CjjhITqVKXwbipxrKze8BzOptfcJgnSYVrZBn+WLSJAAL23Xr1JzzI9ctgjgAAB4LSURBVD9l5gkAGLofwH8cYm1JRXKftc1tV9fUGwCuBtgGKIlBtEN+YN0PQy8q1pR3vHGxJQAwuIVAZ+T/Jfpc/trvyy6C/CdTdn6K+pxz+8fY1dpRxuAx4WzHyHZEPgCSHPr5abF788OJQ+yHlOsrYHQ4wzLTV6AgS/7QF5O2NmXnMcUSf845P5LRGi+m5MxXbLGZYDqrB4s16W9Guh5A5raVZYAB2yTgzAjsCeDjLkF3+O32lgJrXxihtacfMPSRDJ5PoHLFEqdXj433bzYkQ9+s5XbfA9Eb/MsANBDoDk+CqC3PehXAbgC2mMknOz968kMZ2xpm6FsDeD8rIrZoKjIfaig3T8p00aKOm7KeVmzxO07NjtycTnmwRrbK+WokAGyT+4UxmAQNDNBI3XiX954iKBPTv9oMvrotz/rbOgWaEvRrADQGO+kt+X3OstnegxWTLihokUu6LU9HHeKrBdNj6hWd33T/Zolc+UhudeCHjATON9zjnV4ilCuFg04CHVo8NZr5+WJDP9EP6eh0SWdCqnjSKKwli6lHjm/hlSP84qU167Xvsj/oX1GnUhHcWgJ1IgAkVMdSkzSKjI53qnvWwigACoHA4K4tUhkfJbotT4+QbTq6DOp2AGBJ/Jls42JT5qeyLgmvtvRxdU19FpCtft22yyO9MiK11/LSMigzoj4nEfU5BxWeE814DuimWb6ziiDfSqDPAGyDlKDXGa77ZcuI75De6T7Y6DdRL5qn7VqiKo96kil3w5T5pcYSc3LVuHhwPZpZQKmXEi09omwOQx+ZidDSrx5VxBY/avtpCXFUhVBPkRwa1rU8bUl8UUN58o2qcfGPgG414FZP16JfcENtCdZp+eWycp9iid8DeKQ9x55aPimW8dmd0D+yDi4Iy7MSHm7VEnQggG3R8yxLefjFrx5c9I+oDX2/Mll5VbGE6hDbgukcxaLZVePWM0mMEXoPhn4wgIsAHM/gCQDGkaHfbgt+ZElF8vP1mipLLU/v5xD/cUtFm5IuumkJB682FidfSap8Q+X4+H9lrHl5elX2K3/lqLcbDsFuRe8mNyiO2tCrizXlP7JFui14uuTQteWTYpl3swx9ax3yw7bgFU3F5uFV4+LtSH3r9WiAyNeadhTEfVNDspf0qaiDdRpVLFHPlUB/Vywhxz1OR3OhObZiQvz/NrjRlLA/YvBxBBIMFgCmSQ5Nq1iiwpzp/1KxxHMJ1fmkscT8ompc/OvUfb/kensLgG9ljlXrl6Thsk1ewZRIqs5PId2cX9Ss/JWMjpUlG2heKJFbAQAlWctWO7++9mfTRwF4TIuTlC6sufol9l6yaJ62awWp9YKJJYf2qxoXX29bGZSfpzUPypTFfSbqxHV+b7Zf/Cg5VJo+NEdLiPMqJsR7FuSzdhZQKnuoQqnVsZNCAavSVHhsXptMAM73JIVStdgDZ0ZgsWD6isGHApC7NuTqIcnsCNhLckLyZQCe818UDvszYNgXLSPiAOLP/XxCj3Z2A6kPvxanR4qh/IFA3xHo2PJJse8yYM5vMfTcIk15wxHITsrOPtplnev/4QPQFi+KtMWL+qZ8dS/pG1EbepUH4v88raLUkhit+da1xVOjV2au/V/ckFFI+3966syd6f615gLzGEfg9KIVsg3gYALJQGp3OYHuF0yTN2R5ugdUA1jU4xBPQ/dUQZ1FoD+GAlajJyH21i7r7JtqXYbuA/CsFifP8lLzvLLJsQ3KeVJdUy+wKfnUi+dqV5QL9UrJIQ3AQlPhPxdPjWZ+1Em9yKze/zNC8ULgUaR+uk8FyumR/a4epwNYT4p9DQdLwupRxtK0G3CvYNrNIb5+Za49vXpspE+myIJ1mqdYU97W4rQLgU4smxzb4FiZQyqfKXll8TFi69wv8/t8/+UGkDlRGzo5xBdXsDoTAGKac683LqZ6azoH3u8yQu9RKjfIKPRxrrf2RJ6vOvDjOneQmzMD+5aS8gYTAMYJ4uqOJ6r7yihDp/xs+RVvXOy6Msf6R+55kV4Ff9ksVwBAgXfFmkP9BpCMiHrxXK2gXKj3SA6NcYhbl5Ul/z5sYnxQBLf8wtpG9gxRXVPvB7yeb1fu0F5dUz9yjStthj5WAd1tC7QsK0tO7dWLc8+o9Yel/Tr89n2550Uu7G1jry/5vQkA7y47aFBuwO31Mnnrbb7d8trkZsmhMQx+WTBtP+gE3U8Q7D+m/3kYgFfTUXG/EKzTvC23+/4DYB6AtySHtu9rQbfc7nsAwCUAZgU6pTMz1Gxe+r+D0qfecFEb+kgY+j/z2uQFvqjA4orEd4uqkkfCCA2K7VQDQbFv2cXpfwr8b6k5haEXVi1SXyhoVXZuzTMXAjgCRqi1L+2J3pB9bkGrcmooYH0H4C+Z2hW0e/E7+6b/OygTjG6Y+2HoIx3wO5RKtM3EGF85Pj4vw7ZtdITNQFfeaAvdlpob7vEeXwblHwQqsSSekP+X6Nw+N8bQR/sg3WxJvHBlrn2ofkHmXkAdFqUAUORbPiAlPNbFhvrUo9KCBlIpZYszZ9LGSXVNvQbow5DaXvUZ0oH4kb9lTy6JK3dbMsKKhf3l6R0f9rUtS+dok8pTGaz+I9t06Nri0jeET1aMXAwAz/18Qt/MpfeSDRX1Akqlp+1R8MumQLGv4ZCmaLkXwP3B2tHzYegSDFyfBakmoTrfNxWbx1eOj3/Z54YY+vYlknpHXGO2JeeY7IvDfbGUnQsgFqwdndHw10yxYT5RaibhYABXYYDrewwWKvw/1wiysXfpgoWL5mlVHX57KYAaAPd4kmKHfhJ0JYAXJRutrfnmgdkXh5f1RTdb5HxzgE/uHDwl8lZhw6f0+mGKbGPi69ZdcnM8rcGHV95T4LSr8wGUrCg05xWdHZ3cH/0vmqdtXaoo7ykmSQT63bCJ8YyHqXZh2aruU/pmkSgTDKrd5Bsr1TX1OUD28HFK/UvMvFAwxRKqc0jR2dFX+8UAQ88uVZTnJZvymorNcSVnxfpM0ACwqHOLRgB9k/gyA7iizgBbB7466bDId+JCfvoIU+GgatLvPJd3LumXzlMbL55QTVHVmW2PKzkrVtcPveYB2KBAqP5gwHeTb/QYevbHUvXSEXZQD2U7n67MNw/uSb3yTBCs06TcldJXeoe8DYDxMEL9Mq06/IpHEx4p3hhK5p882PYnAkOsOld/s3iudkBMcxp2sRbps/nwJXpYjOgvQcPQqWyZco/eIW+zotCc31+Crq6pHxm3fWoomVeB1ayaDgZcUW8ohn7IsKXqM5Ip/Geal+L6xBm39HMev8tUU4yzJL4r6nOO6cd+j0ylLPvVBt1BhSvq9SRYp1HnTdk3AXhRMC2ZiilPvuXsBImslv6yYfnd3vsAXAvgftmmc3pTO30D6Pom6irZvKAf++4RrqjXB0P35rfK7/nD0oVJxXljdOLaaa9G9hsDMGyW7u6Pr2I2AmNKGpU/hQJWc2NxckImy073hN2K3jsacECwZ2IQZWXqjjv70VMMfRiAJ/1hafeWfHN+ONs57qtvN7ukWwbRdebKyIANBxDoIQYv7AjYR1VM6P9iQos7Nysu8Da3fXT1OKO/++4prqh7wNI52pRiWb1ZtmAR6LiCc6NPFwBATX1rOnhJoDdfxf/bFLzGDQxL7tVOKhPq/cLBjwQ6qmJCvE8j/FZHahtXyTAAD/Z33+uD636sC0OfVN6g3mHJLK0oMo+BEXp6wQKiibMmXQHw3UgNDA6AaRvyVRyrzb4awLsMvhbAq6tN92Xom5c3qHdbMmNJRfL0vg5ZXRMBtX1HAAEAfVXPPSO4ol4DwTotK3Gd/xEAswn0SqffriqeGutK+fU73dM+E+lpgPTP+mcrMvTbvHHJALBquq9fsK8JFAN4STA5TBhROT7e51F+a2Knwg/PB4CRpa+vNjvVYMEV9eow9OLCZvkHT1KcaEl8G4DRX28fy1qwgI5MX/Emp4KVkgBAcID1cD2CdZpiXxNwAHRlbE0gleP5Vy5MsE6rSHicHxk8DMDvtcs6+3T5e11827ZDgUeKJYp8y98dSDvWhbuiuCqGvjuAJxlc0Fhi3lM6JTYNABYsoOeRSsu1xahRbAPAVpc/uZ8szDc0Kdr26YwzC3vYfgWAxd2OFALYCqv61IauOcSvEGOfZWXm9PJJsWsz9IQbTHVN/VcAgsHa0YNvC3k33JG6G8tme+90iN8D4MQ1PvDb4fGlCxZQbvr0OQBGdgkaAL6/bszbguwrVyYKC6pr6vdYV/utt/nOQ1rQtuDmYFVChhFqSeUxCV3fJehgnaYw+AHBtC+APw8GQR9zk1EEYDtsBJGZ7uwHABi6DKC2DOrUzmw7ZEu83392jeYCuAFAK4B5o0bxamsIRszAnQAuE7AvBHDyGtqniM9ekB9Vfpc+MlO6qmN69WouDdZp5O8Un6Xrrp9PRsf9q7ms3yn0NY0DgN8NezE8GHN9dGeTH6kXzdM2twW/DuDCcJb97Ke7Rq7NOT+yZNQo/hzA8FGjeK0xFcHa0R1VgR9eA/FJZ88+c/ffXJDKiuRkRaXfAcDS8uS5MELT19Re1SJ1Rn6bsm1zgfkuGR239O7pMsenK/YsAph1deVjA23LutikRe3MCOxQ3KR8TYx9GTzpoz0iKxwJ4xYsIBUARo3iHu3B2z7/PzcxC35n2UHjux9fcq92OIBfsrDagguHTYzdsaZ27GsC5xFoOoC5kSxnvw17qr6hLV60LUBf3T7p3w0Dbcu62HRFbejHE2OhcCD/tHnir2R0zEEqRfAuo0bxehUcmjW57m2G+L/2RP5JqaTrAAz9jIqlnhcAIKk4zQAU6aqONcaHLJvtvVk4uNmS+CUAU/o5nmOtTJtzmiTI3leQtXCgbekJm5xPHazTJF9UvFoE5QAmfPrxiEh1QuNvtwQwahS396LpmwH8Ybuc/1wTv/7kfTWIvQAgFLD+rV8Q+fNa7zT0Q0uhnBPJclpaCqyTq8fGrV7YkXFMRz3cYcm/f/nLAjh2oM1ZJ5uWqA394HJZuV+xRElbrvVt3kp5n4TGYtQozkR64Xe3y/o6/Fz8bxd0+wL8nX5B5K213RS+MXtUNqQnCfR1dkQ6IPvicG8+WH3Cm0sPqwKApKP+c4BN6RGbjqgNfT8AL8sWkUPM2WExGUYoPipDzf/kPWVPYVN21++LKxI7rmsH+dI52iHFCeUlS+IW2aYjYIQGnaABIGwGdgHQvnD5AW8PtC09YVPyqfcHwAQCMWzVFPtkquEVd/qeEEzvA8C3zjBzm3jdx7/7/omj1xqKaujl5Q3qPGJKLCtLngojNGg3svrk8OEeKf5ZsHb0oE2L0J1NSdQLkF6OTueoXtDrFg3dC0N/q6hZOR4Aol779uOdKx9MwDMC4JlYw3anxXO1Kga/SKAc2aZ9K8fHX+m1LX3EuXNOL41ZvqqdCj4aNC+u62LTEXWGE/AsnqvtByAKYD8AWFaaPNd3afgvDqSmdIy1AOABcFD3+5wZgQOLm5RvGdiOwcfBCH3SGzv6mud+OmF7hsDKRN696aSgl62rcOhA48Z+bAiGfiSA7pV5q2CEFgNdRe35dYBUAOSVIi37lL/2t7lT774xLYY3AcgM5nbdfoeA23NC8oswQn1TEmNdGLoAkLO0PLmrJ0FFhS1KHEBhS755lJok5evQDlsmWdlyZ89/4wErqRHIQeobb9Bm5nJFvR4E6zSpcrF6g2C6EADCWXakLc+qrBwf/1We5rTLMUqQRTmelUZbvFAB8Mwb6nnfVYkVFwAQ6eqyv9xjC17aEbBZi4sHvXHxii34U+mqjvWPm06V0ctbXpLcmRhVJU1qCEBhW651qHCQnxOSlwMojHuc3SSbNMUiBYC0uqZsgCX8z8hf9vikIgqvghEalHnIXVH3EOuvgbyk6jT4YpKWPjQjWJWYsa5Fkr/M+ZP+6uLRF0TMwAW70bfaw56ZQoHtEGCGffZ4LSE6ZJt2jmnOaGLsqSXELzNScY9jKSa9IDn0flx1JBbY3RsXHwBob9et3zFh89x2+WcAhQnV2UE4pCsWCazBrbQktmWbfgLQHM6ycyyZozkh+SUAzc0FZoEt8YqSJvWtlTlWQA9JoxOO52wvJX0fO1vhZXs3+0L5cUchm5AKkXVH6o0aQ98VwC++ry34GOmqjmfXp4nqmvrSqsAPj+d3OvvsLz6PtMN/67/sw68K1o7+dU46Q88DsFu7bp0E4BA9JFkE2nLV9hgMS4ajWPQdgOaIz85KeFjKWyk/A6C5LdfyxzUnWrZcfQ1Ac2ue1Zr/l8jaV0oNvTqhOjNli06THEILB94+Kzlt5Ic8XAIodpV837Rx8gsF6OO6Ob3FFfU6WH63997SRvWXmI7FFYnDK8fHX9rQ9o7823WHfdO289UA9sn1tCwfUfzuzHunzp611psM3WDw9HQxVJtAtZ3ZtuG/KJyRlccl92onFbQoU7xxsT+DnZBuf9Ppty+vmBCvr66pnw7gGgBnBmtH/ysT/fU1rqjXhKF7HOJ7BdOf0kcWAjg8Ey901TX1pHvaxhL4nvZEvoRUabxLgrWjF63BlpEAXsX/8oH3/qvf0AmpmZlLABxmC3aEg38Q6FYYoaVdl40w6rZsjRd9v1Phh7OeudA4u1d99hOuqFdDwz3eEWXLlCcJVAEAzQXme0zYr+jsaEYXH/4y50/5L/x8/AVJx3M+wREjit9dWORr/MOsyXW/DXzqwY7zHmHocnOBeZkvKi7Oikp+AE1Rr/Pv5kLzjqpx8d98qKbNOY1eXnS05ZHizSsThWMGY56PVXFFvQqhf2SdonfIv6QAYPCJZHT0aQxxdU19xWaB7+b/3LH1ToCzDBCXAngwkyt4wTott7xBGatY4mwAm8c0x0yqznV6h1wLI7TGigDVNfUnA/xgOs1YDIM0gU13XFF3kZqvrUEqnVcXu8AIfdZfJux/7R1HLOncbCaAEUW+ZY07F35UM+es2b3zYw093xY8zZb4CtUUAik36oZlpclnyybH1uiTV9fU037lL897p+Gg0xlS15SfBeCqYO3oQTmV14UragCL5mlVuSvltwOd0jAAsCT+qqE8eXzVuHi/F+qprqkX5dmLLuhMBm7oSOYKAPcBuCxYO3q9Sl0snqvtk9MuXxnolA4A4Iv47G/ac+w7ypepd60rkWUqiTxmAzixNGtxx/JIhYpUbXcT7ki9EWDoOznEbwmmQPrIbQAughEa0PLT5845vXT+jydOY4jzBdnO7sXvvKXJsRPuO+e2jrXeaOg7AbiEwacygZjwoOTQ9TBCPao5M+muiZNeXXzUdJvlEpnMq0dv/tgNT/946p5I+/ODXdDAJi7qlbdkXZ7bLv/ibiwvSc4tnRKbMJA2rUp1Tf3mm+nfvvRzaJstAF4E0EUAngjWjv7fH87QqTXPPFW26Ba9Qy4AEDZlvn9ZWbKualz8gx72IykicYXN8gy/0hEJJXMPCdaOHtSZmNbEpifq1CzCwVHNPsoXl/ZKH3UYvC8ZHYP2j7j7jLm/b4mVXA9gp99nvb7yHPHMc9tYjXcnNGdrX0yaAmCPpOLYUZ/zQE5IPg9GqMfJ30+59aLt3lt+4B0ADszxtL68f/nL42+f9O/+Ke/RB2xaojb0kQx+jUBdS92Ie5wmLSF2gxHqk/JsmaS6pl6a5H/03guS9WcqsCCIu2IxfgBwY2e2/W//ReHY+rQ5+saZU4IdW8yKWVlJh6WzAPzzV98CGyGbjqgNvTDucd7XEmLzrkOWxN8tHZbcs3psfGAi5HqKoRcAGNXht87yxsT+iiUUAGAG5jt78UP5O87/oWP4CR9cNbHHG4ZTFXpxA4C/5GtNLXuVvnX6rMl1L/TRE/QrQ17UwTqtonKxOlkwTQOQ3e2UCeCAQRnDYOi5AA4IZ9ljhYPDuoKoHOJE1GvHsqJSFoGEwzDPV88MP915WAFS1bIuAFC/rpF2yt3jj/i4aeTDzbFSHcCtAC4N1o7u91zXfcXQFbWh++IeZ7pk06WKRQSgA0DAEmybKr/gjYtrB42gDV0HsH9Mc05yBP/BFxUagYjBiU6/E1FMqvPGxRMAPoIRMlddXdxh+oPHhk29FsDwzQLfrdgs57sz5k296zejbnVNPQEYR3Du8Clhz/C8L89/4vxrb+3XZ+0Hhpyog3VaVl6bfEWgUxoLoCScZf9kSxwNdEjbEehLh/g4cXXHzwNqpKH7AeyXVJyjkiqfkRUR3nSwUiKc7cSI8Vh2RPo3gA9ghHo0glbX1Cvb5n12S7Bjy7Nils8BaBYAI1g7ug0Aps4+s/LDxv3/3Rwr+R2A17bN+2zi85dc/lPfPeTAMXREncqH96ek4tyumiKbwe8QaEZMcy71xsXBUa/9gS8mHQwjFO5v05LX+n2qKfZl8EGRLOfsrIjISovYjGQ5YVviF/UO+R4A761tybonjL5xZvlXrbteAWCyKuLJzfTv3v525U4/eqXw6QnH6/PJ4ZlhUzd+E/I6hNjoRR2s0yQ1STeULleOJtDWtuCvG0vMB4qblPtlm/4Pqdjkf7fn2Gf2V9ajxHV+zZMUIwEcGM6yz8qKiDxK7Vm0o167LeHhD3Pb5VsAvAsjFFlHcxvEdlc+vEtAbX+jMVqRXlRiHp77xfUvXHrZFX3R32Bi4837kQqdPKJCqDdKDm2fVJwVqkl/kBx6EsBEJv6RgTiBjs05P/JsTh+a0nyHTytsUfYAcGDEZ0/wmmIYUjufHNmixrY86638NuUGAG/7Lg13+vrQli6+nnnyf6pr5temdrWTAMj+78qd+v1baiDYKEW9dI52doFHOV9LiC0kh35uzTOv7fQ7M6rHxk0Y+qSyZcodCQ/bjSXJEysmxJ/PdP9L7tW0iqWeXQEcGNOcM/KS8tbpU6yYtKQ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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Top-5 predictions:\n", " 1. hurricane 34.674%\n", " 2. tornado 16.056%\n", " 3. blackberry 7.664%\n", " 4. squiggle 5.489%\n", " 5. zigzag 4.906%\n", "Answer: pillow\n" ] } ], "source": [ "n_new = 10\n", "Y_probas = model.predict(sketches)\n", "top_k = tf.nn.top_k(Y_probas, k=5)\n", "for index in range(n_new):\n", " plt.figure(figsize=(3, 3.5))\n", " draw_sketch(sketches[index])\n", " plt.show()\n", " print(\"Top-5 predictions:\".format(index + 1))\n", " for k in range(5):\n", " class_name = class_names[top_k.indices[index, k]]\n", " proba = 100 * top_k.values[index, k]\n", " print(\" {}. {} {:.3f}%\".format(k + 1, class_name, proba))\n", " print(\"Answer: {}\".format(class_names[labels[index].numpy()]))" ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:From /Users/ageron/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1786: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "If using Keras pass *_constraint arguments to layers.\n", "INFO:tensorflow:Assets written to: my_sketchrnn/assets\n" ] } ], "source": [ "model.save(\"my_sketchrnn\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 10. 바흐 합창곡\n", "\n", "_연습문제: [바흐 합창곡](https://homl.info/bach) 데이터셋을 다운로드하여 압축을 풉니다. 이 데이터셋은 요한 제바스티안 바흐가 작곡한 382개의 합창곡으로 구성되어 있습니다. 각 곡은 100에서 640까지 타임 스텝 길이입니다. 각 타임 스텝은 4개의 정수를 담고 있습니다. 각 정수는 피아노 음표의 인덱스에 해당합니다(연주되는 음표가 없다는 것을 의미하는 0은 제외). 코랄의 타임 스텝 시퀀스가 주어지면 다음 타임 스텝(4개의 음표)을 예측할 수 있는 순환 모델, 합성곱 모델 또는 두 가지를 합친 모델을 훈련하세요. 그 다음 이 모델을 사용해 한 번에 하나의 음표씩 바흐와 같은 음악을 생성하세요. 코랄의 시작 부분을 모델에 주입하고 다음 타임 스텝을 예측합니다. 이 타임 스텝을 입력 시퀀스에 추가하여 모델이 다음 음표를 예측하게 만드는 식입니다. 또 바흐를 위한 [구글 두들](https://www.google.com/doodles/celebrating-johann-sebastian-bach)에 사용한 구글의 [Coconet 모델](https://homl.info/coconet)을 확인해보세요._" ] }, { "cell_type": "code", "execution_count": 82, "metadata": {}, "outputs": [], "source": [ "DOWNLOAD_ROOT = \"https://github.com/ageron/handson-ml2/raw/master/datasets/jsb_chorales/\"\n", "FILENAME = \"jsb_chorales.tgz\"\n", "filepath = keras.utils.get_file(FILENAME,\n", " DOWNLOAD_ROOT + FILENAME,\n", " cache_subdir=\"datasets/jsb_chorales\",\n", " extract=True)" ] }, { "cell_type": "code", "execution_count": 83, "metadata": {}, "outputs": [], "source": [ "jsb_chorales_dir = Path(filepath).parent\n", "train_files = sorted(jsb_chorales_dir.glob(\"train/chorale_*.csv\"))\n", "valid_files = sorted(jsb_chorales_dir.glob(\"valid/chorale_*.csv\"))\n", "test_files = sorted(jsb_chorales_dir.glob(\"test/chorale_*.csv\"))" ] }, { "cell_type": "code", "execution_count": 84, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "\n", "def load_chorales(filepaths):\n", " return [pd.read_csv(filepath).values.tolist() for filepath in filepaths]\n", "\n", "train_chorales = load_chorales(train_files)\n", "valid_chorales = load_chorales(valid_files)\n", "test_chorales = load_chorales(test_files)" ] }, { "cell_type": "code", "execution_count": 85, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[[74, 70, 65, 58],\n", " [74, 70, 65, 58],\n", " [74, 70, 65, 58],\n", " [74, 70, 65, 58],\n", " [75, 70, 58, 55],\n", " [75, 70, 58, 55],\n", " [75, 70, 60, 55],\n", " [75, 70, 60, 55],\n", " [77, 69, 62, 50],\n", " [77, 69, 62, 50],\n", " [77, 69, 62, 50],\n", " [77, 69, 62, 50],\n", " [77, 70, 62, 55],\n", " [77, 70, 62, 55],\n", " [77, 69, 62, 55],\n", " [77, 69, 62, 55],\n", " [75, 67, 63, 48],\n", " [75, 67, 63, 48],\n", " [75, 69, 63, 48],\n", " [75, 69, 63, 48],\n", " [74, 70, 65, 46],\n", " [74, 70, 65, 46],\n", " [74, 70, 65, 46],\n", " [74, 70, 65, 46],\n", " [72, 69, 65, 53],\n", 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70, 63, 51],\n", " [79, 70, 63, 51],\n", " [79, 70, 63, 51],\n", " [79, 70, 63, 51],\n", " [77, 70, 63, 58],\n", " [77, 70, 63, 58],\n", " [77, 70, 60, 58],\n", " [77, 70, 60, 58],\n", " [77, 70, 62, 46],\n", " [77, 70, 62, 46],\n", " [77, 68, 62, 46],\n", " [75, 68, 62, 46],\n", " [75, 67, 58, 51],\n", " [75, 67, 58, 51],\n", " [75, 67, 58, 51],\n", " [75, 67, 58, 51],\n", " [75, 67, 58, 51],\n", " [75, 67, 58, 51],\n", " [75, 67, 58, 51],\n", " [75, 67, 58, 51],\n", " [74, 67, 58, 55],\n", " [74, 67, 58, 55],\n", " [74, 67, 58, 55],\n", " [74, 67, 58, 55],\n", " [75, 67, 58, 53],\n", " [75, 67, 58, 53],\n", " [75, 67, 58, 51],\n", " [75, 67, 58, 51],\n", " [77, 65, 58, 50],\n", " [77, 65, 58, 50],\n", " [77, 65, 56, 50],\n", " [77, 65, 56, 50],\n", " [70, 63, 55, 51],\n", " [70, 63, 55, 51],\n", " [70, 63, 55, 51],\n", " [70, 63, 55, 51],\n", " [75, 65, 60, 45],\n", " [75, 65, 60, 45],\n", " [75, 65, 60, 45],\n", " [75, 65, 60, 45],\n", " [74, 65, 58, 46],\n", " [74, 65, 58, 46],\n", " [74, 65, 58, 46],\n", " [74, 65, 58, 46],\n", " [72, 65, 57, 53],\n", " [72, 65, 57, 53],\n", " [72, 65, 57, 53],\n", " [72, 65, 57, 53],\n", " [72, 65, 57, 53],\n", " [72, 65, 57, 53],\n", " [72, 65, 57, 53],\n", " [72, 65, 57, 53],\n", " [74, 65, 58, 58],\n", " [74, 65, 58, 58],\n", " [74, 65, 58, 58],\n", " [74, 65, 58, 58],\n", " [75, 67, 58, 57],\n", " [75, 67, 58, 57],\n", " [75, 67, 58, 55],\n", " [75, 67, 58, 55],\n", " [77, 65, 60, 57],\n", " [77, 65, 60, 57],\n", " [77, 65, 60, 53],\n", " [77, 65, 60, 53],\n", " [74, 65, 58, 58],\n", " [74, 65, 58, 58],\n", " [74, 65, 58, 58],\n", " [74, 65, 58, 58],\n", " [72, 67, 58, 51],\n", " [72, 67, 58, 51],\n", " [72, 67, 58, 51],\n", " [72, 67, 58, 51],\n", " [72, 65, 57, 53],\n", " [72, 65, 57, 53],\n", " [72, 65, 57, 53],\n", " [72, 65, 57, 53],\n", " [70, 65, 62, 46],\n", " [70, 65, 62, 46],\n", " [70, 65, 62, 46],\n", " [70, 65, 62, 46],\n", " [70, 65, 62, 46],\n", " [70, 65, 62, 46],\n", " [70, 65, 62, 46],\n", " [70, 65, 62, 46]]" ] }, "execution_count": 85, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_chorales[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "음표의 범위는 36(C1 = 옥타브 1의 C)에서 81(A5 = 옥타브 5의 A)까지이고 무음을 위해 0을 추가합니다:" ] }, { "cell_type": "code", "execution_count": 86, "metadata": {}, "outputs": [], "source": [ "notes = set()\n", "for chorales in (train_chorales, valid_chorales, test_chorales):\n", " for chorale in chorales:\n", " for chord in chorale:\n", " notes |= set(chord)\n", "\n", "n_notes = len(notes)\n", "min_note = min(notes - {0})\n", "max_note = max(notes)\n", "\n", "assert min_note == 36\n", "assert max_note == 81" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "이 코랄을 듣기 위한 몇 개의 함수를 만들어 보죠(자세한 내용을 이해할 필요는 없습니다. 사실 MIDI 플레이어처럼 더 간단한 방법이 있지만 그냥 하나의 합성기(synthesizer)를 만들어 보고 싶었습니다):" ] }, { "cell_type": "code", "execution_count": 87, "metadata": {}, "outputs": [], "source": [ "from IPython.display import Audio\n", "\n", "def notes_to_frequencies(notes):\n", " # 한 옥타브 올라갈 때 주파수는 두배가 됩니다; 옥타브마다 12개의 반음이 있습니다;\n", " # 옥타브 4의 A는 440Hz이고 음표 번호는 69입니다.\n", " return 2 ** ((np.array(notes) - 69) / 12) * 440\n", "\n", "def frequencies_to_samples(frequencies, tempo, sample_rate):\n", " note_duration = 60 / tempo # tempo는 분당 박자 수로 측정합니다\n", " # 매 박자마다 딸깍거리는 소리를 줄이기 위해 주파수를 반올림하여 각 음의 끝에서 샘플을 0에 가깝게 만듭니다.\n", " frequencies = np.round(note_duration * frequencies) / note_duration\n", " n_samples = int(note_duration * sample_rate)\n", " time = np.linspace(0, note_duration, n_samples)\n", " sine_waves = np.sin(2 * np.pi * frequencies.reshape(-1, 1) * time)\n", " # (음표 0 = 무음을 포함해) 9Hz 이하인 주파수는 모두 삭제합니다\n", " sine_waves *= (frequencies > 9.).reshape(-1, 1)\n", " return sine_waves.reshape(-1)\n", "\n", "def chords_to_samples(chords, tempo, sample_rate):\n", " freqs = notes_to_frequencies(chords)\n", " freqs = np.r_[freqs, freqs[-1:]] # 마지막 음표를 조금 더 길게합니다\n", " merged = np.mean([frequencies_to_samples(melody, tempo, sample_rate)\n", " for melody in freqs.T], axis=0)\n", " n_fade_out_samples = sample_rate * 60 // tempo # 마지막 음을 희미하게 합니다\n", " fade_out = np.linspace(1., 0., n_fade_out_samples)**2\n", " merged[-n_fade_out_samples:] *= fade_out\n", " return merged\n", "\n", "def play_chords(chords, tempo=160, amplitude=0.1, sample_rate=44100, filepath=None):\n", " samples = amplitude * chords_to_samples(chords, tempo, sample_rate)\n", " if filepath:\n", " from scipy.io import wavfile\n", " samples = (2**15 * samples).astype(np.int16)\n", " wavfile.write(filepath, sample_rate, samples)\n", " return display(Audio(filepath))\n", " else:\n", " return display(Audio(samples, rate=sample_rate))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "이제 몇 개의 코랄을 들어 보죠:" ] }, { "cell_type": "code", "execution_count": 88, "metadata": {}, "outputs": [], "source": [ "for index in range(3):\n", " play_chords(train_chorales[index])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "멋지네요! :)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "새로운 코랄을 생성하기 위해서는 이전의 화음이 주어졌을 때 다음 화음을 예측할 수 있는 모델을 훈련해야 합니다. 한 번에 4개의 음표를 예측하는 식으로 다음 화음을 예측한다면 잘 어울리지 않는 음표를 얻게 됩니다(믿으세요. 제가 해 보았습니다). 한 번에 하나의 음표를 예측하는 것이 간단하고 더 낫습니다. 따라서 모든 코랄을 전처리하여 각 화음을 아르페지오로 바꾸어야 합니다(즉, 동시에 연주되는 음표가 아니라 음표의 시퀀스). 그다음 이전의 모든 음표가 주어졌을 때 다음 음표를 예측하는 모델을 훈련할 수 있습니다. 시퀀스-투-시퀀스 방식을 사용하겠습니다. 신경망에 한 윈도를 주입하고 한 타임 스텝 미래로 이동한 윈도를 예측합니다.\n", "\n", "또한 0에서 46까지 범위를 갖도록 값을 이동시키겠습니다. 여기에서 0은 무음을 나타내고 1에서 46까지는 36(C1)에서 81(A5)까지를 나타냅니다.\n", "\n", "128 음표(즉, 32개 화음)의 윈도에서 모델을 훈련하겠습니다.\n", "\n", "이 데이터셋은 메모리에 올라갈 수 있기 때문에 파이썬 코드를 사용해 RAM에서 코랄을 전처리할 수 있지만 여기에서는 tf.data를 사용해 전처리하는 방법을 시연하겠습니다(다음 장에서 tf.data를 사용해 윈도를 만드는 과정을 자세히 설명하겠습니다)." ] }, { "cell_type": "code", "execution_count": 89, "metadata": {}, "outputs": [], "source": [ "def create_target(batch):\n", " X = batch[:, :-1]\n", " Y = batch[:, 1:] # 각 스텝에서 아르페지오에 있는 다음 음표를 예측합니다\n", " return X, Y\n", "\n", "def preprocess(window):\n", " window = tf.where(window == 0, window, window - min_note + 1) # 값 이동\n", " return tf.reshape(window, [-1]) # 아르페지오로 변환\n", "\n", "def bach_dataset(chorales, batch_size=32, shuffle_buffer_size=None,\n", " window_size=32, window_shift=16, cache=True):\n", " def batch_window(window):\n", " return window.batch(window_size + 1)\n", "\n", " def to_windows(chorale):\n", " dataset = tf.data.Dataset.from_tensor_slices(chorale)\n", " dataset = dataset.window(window_size + 1, window_shift, drop_remainder=True)\n", " return dataset.flat_map(batch_window)\n", "\n", " chorales = tf.ragged.constant(chorales, ragged_rank=1)\n", " dataset = tf.data.Dataset.from_tensor_slices(chorales)\n", " dataset = dataset.flat_map(to_windows).map(preprocess)\n", " if cache:\n", " dataset = dataset.cache()\n", " if shuffle_buffer_size:\n", " dataset = dataset.shuffle(shuffle_buffer_size)\n", " dataset = dataset.batch(batch_size)\n", " dataset = dataset.map(create_target)\n", " return dataset.prefetch(1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "훈련 세트, 검증 세트, 테스트 세트를 만듭니다:" ] }, { "cell_type": "code", "execution_count": 90, "metadata": {}, "outputs": [], "source": [ "train_set = bach_dataset(train_chorales, shuffle_buffer_size=1000)\n", "valid_set = bach_dataset(valid_chorales)\n", "test_set = bach_dataset(test_chorales)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "이제 모델을 만듭니다:\n", "\n", "* 음표를 실수 값으로 모델에 직접 주입할 수 있지만 좋은 결과를 얻지 못할 것입니다. 음표 간의 관계는 단순하지 않습니다. 예를 들어 C3을 C4로 바꾼다면 두 음표 사이에 반음이 12개(즉 한 옥타브) 떨어져 있음에도 멜로디는 여전히 괜찮게 들립니다. 반대로 C3을 C\\#3으로 바꾼다면 바로 다음 음표임에도 화음이 매우 좋지 않습니다. 따라서 `Embedding` 층을 사용해 음표를 작은 벡터 표현으로 바꾸겠습니다(임베딩에 대해서는 16장을 참고하세요). 5-차원 임베딩을 사용하므로 첫 번째 층의 출력은 `[batch_size, window_size, 5]` 크기가 됩니다.\n", "* 그 다음 이 데이터를 4개의 `Conv1D` 층을 쌓고 dilation 비율을 두 배씩 늘린 작은 WeveNet 신경망에 주입합니다. 빠른 수렴을 위해 이 층 다음에 `BatchNormalization` 층을 배치합니다.\n", "* 그다음 하나의 `LSTM` 층이 장기 패턴을 감지합니다.\n", "* 마지막으로 `Dense` 층이 최종 음표 확률을 생성합니다. 타임 스텝과 (무음을 포함해) 가능한 음표 마다 배치에 있는 각 코랄에 대해 하나의 확률을 예측합니다. 따라서 출력 크기는 `[batch_size, window_size, 47]`가 됩니다." ] }, { "cell_type": "code", "execution_count": 91, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"sequential\"\n", "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "embedding (Embedding) (None, None, 5) 235 \n", "_________________________________________________________________\n", "conv1d (Conv1D) (None, None, 32) 352 \n", "_________________________________________________________________\n", "batch_normalization (BatchNo (None, None, 32) 128 \n", "_________________________________________________________________\n", "conv1d_1 (Conv1D) (None, None, 48) 3120 \n", "_________________________________________________________________\n", "batch_normalization_1 (Batch (None, None, 48) 192 \n", "_________________________________________________________________\n", "conv1d_2 (Conv1D) (None, None, 64) 6208 \n", "_________________________________________________________________\n", "batch_normalization_2 (Batch (None, None, 64) 256 \n", "_________________________________________________________________\n", "conv1d_3 (Conv1D) (None, None, 96) 12384 \n", "_________________________________________________________________\n", "batch_normalization_3 (Batch (None, None, 96) 384 \n", "_________________________________________________________________\n", "lstm (LSTM) (None, None, 256) 361472 \n", "_________________________________________________________________\n", "dense (Dense) (None, None, 47) 12079 \n", "=================================================================\n", "Total params: 396,810\n", "Trainable params: 396,330\n", "Non-trainable params: 480\n", "_________________________________________________________________\n" ] } ], "source": [ "n_embedding_dims = 5\n", "\n", "model = keras.models.Sequential([\n", " keras.layers.Embedding(input_dim=n_notes, output_dim=n_embedding_dims,\n", " input_shape=[None]),\n", " keras.layers.Conv1D(32, kernel_size=2, padding=\"causal\", activation=\"relu\"),\n", " keras.layers.BatchNormalization(),\n", " keras.layers.Conv1D(48, kernel_size=2, padding=\"causal\", activation=\"relu\", dilation_rate=2),\n", " keras.layers.BatchNormalization(),\n", " keras.layers.Conv1D(64, kernel_size=2, padding=\"causal\", activation=\"relu\", dilation_rate=4),\n", " keras.layers.BatchNormalization(),\n", " keras.layers.Conv1D(96, kernel_size=2, padding=\"causal\", activation=\"relu\", dilation_rate=8),\n", " keras.layers.BatchNormalization(),\n", " keras.layers.LSTM(256, return_sequences=True),\n", " keras.layers.Dense(n_notes, activation=\"softmax\")\n", "])\n", "\n", "model.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "이제 모델을 컴파일하고 훈련할 준비가 되었습니다!" ] }, { "cell_type": "code", "execution_count": 92, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/20\n", "98/98 [==============================] - 17s 171ms/step - loss: 1.8198 - accuracy: 0.5358 - val_loss: 3.7675 - val_accuracy: 0.0428\n", "Epoch 2/20\n", "98/98 [==============================] - 15s 152ms/step - loss: 0.8885 - accuracy: 0.7641 - val_loss: 4.1054 - val_accuracy: 0.0470\n", "Epoch 3/20\n", "98/98 [==============================] - 16s 165ms/step - loss: 0.7471 - accuracy: 0.7930 - val_loss: 3.8600 - val_accuracy: 0.0368\n", "Epoch 4/20\n", "98/98 [==============================] - 16s 165ms/step - loss: 0.6749 - accuracy: 0.8083 - val_loss: 3.0490 - val_accuracy: 0.2196\n", "Epoch 5/20\n", "98/98 [==============================] - 15s 157ms/step - loss: 0.6221 - accuracy: 0.8188 - val_loss: 1.7138 - val_accuracy: 0.5153\n", "Epoch 6/20\n", "98/98 [==============================] - 16s 163ms/step - loss: 0.5833 - accuracy: 0.8283 - val_loss: 1.9068 - val_accuracy: 0.4570\n", "Epoch 7/20\n", "98/98 [==============================] - 16s 165ms/step - loss: 0.5484 - accuracy: 0.8362 - val_loss: 0.7930 - val_accuracy: 0.7678\n", "Epoch 8/20\n", "98/98 [==============================] - 16s 159ms/step - loss: 0.5163 - accuracy: 0.8447 - val_loss: 0.6577 - val_accuracy: 0.8091\n", "Epoch 9/20\n", "98/98 [==============================] - 15s 158ms/step - loss: 0.4877 - accuracy: 0.8519 - val_loss: 0.6239 - val_accuracy: 0.8180\n", "Epoch 10/20\n", "98/98 [==============================] - 17s 171ms/step - loss: 0.4607 - accuracy: 0.8595 - val_loss: 0.6330 - val_accuracy: 0.8151\n", "Epoch 11/20\n", "98/98 [==============================] - 15s 156ms/step - loss: 0.4369 - accuracy: 0.8657 - val_loss: 0.6248 - val_accuracy: 0.8179\n", "Epoch 12/20\n", "98/98 [==============================] - 16s 167ms/step - loss: 0.4125 - accuracy: 0.8726 - val_loss: 0.6046 - val_accuracy: 0.8248\n", "Epoch 13/20\n", "98/98 [==============================] - 16s 162ms/step - loss: 0.3924 - accuracy: 0.8784 - val_loss: 0.6618 - val_accuracy: 0.8096\n", "Epoch 14/20\n", "98/98 [==============================] - 16s 159ms/step - loss: 0.3713 - accuracy: 0.8847 - val_loss: 0.6919 - val_accuracy: 0.8067\n", "Epoch 15/20\n", "98/98 [==============================] - 17s 176ms/step - loss: 0.3562 - accuracy: 0.8889 - val_loss: 0.6123 - val_accuracy: 0.8236\n", "Epoch 16/20\n", "98/98 [==============================] - 16s 165ms/step - loss: 0.3328 - accuracy: 0.8969 - val_loss: 0.6547 - val_accuracy: 0.8133\n", "Epoch 17/20\n", "98/98 [==============================] - 15s 156ms/step - loss: 0.3182 - accuracy: 0.9011 - val_loss: 0.6322 - val_accuracy: 0.8202\n", "Epoch 18/20\n", "98/98 [==============================] - 16s 167ms/step - loss: 0.3007 - accuracy: 0.9069 - val_loss: 0.6929 - val_accuracy: 0.8037\n", "Epoch 19/20\n", "98/98 [==============================] - 16s 168ms/step - loss: 0.2869 - accuracy: 0.9103 - val_loss: 0.6446 - val_accuracy: 0.8220\n", "Epoch 20/20\n", "98/98 [==============================] - 17s 173ms/step - loss: 0.2703 - accuracy: 0.9158 - val_loss: 0.6439 - val_accuracy: 0.8189\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 92, "metadata": {}, "output_type": "execute_result" } ], "source": [ "optimizer = keras.optimizers.Nadam(learning_rate=1e-3)\n", "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=optimizer,\n", " metrics=[\"accuracy\"])\n", "model.fit(train_set, epochs=20, validation_data=valid_set)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "여기서는 하이퍼파라미터 탐색을 많이 수행하지 않았습니다. 자유롭게 이 모델을 사용해 하이퍼파라미터를 탐색하고 최적화해 보세요. 예를 들어 `LSTM` 층을 제거하고 `Conv1D` 층으로 바꿀 수 있습니다. 층의 개수, 학습률, 옵티마이저 등을 실험해 볼 수 있습니다." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "검증 세트에 대한 모델의 성능이 만족스럽다면 모델을 저장하고 테스트 세트에서 마지막으로 평가합니다:" ] }, { "cell_type": "code", "execution_count": 93, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 34/Unknown - 2s 66ms/step - loss: 0.6557 - accuracy: 0.8164" ] }, { "data": { "text/plain": [ "[0.6556663916391485, 0.8164004]" ] }, "execution_count": 93, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.save(\"my_bach_model.h5\")\n", "model.evaluate(test_set)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**노트:** 이 예제에서는 테스트 세트가 진짜로 필요하지 않습니다. 모델이 생성한 음악을 듣는 것이 최종 평가가 되기 때문입니다. 따라서 필요하다면 테스트 세트를 훈련 세트에 넣고 모델을 다시 훈련하여 조금 더 나은 모델을 얻을 수 있습니다." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "이제 새로운 코럴을 생성하는 함수를 만들어 보죠. 몇 개의 시드 화음을 주고 이를 (모델이 기대하는 포맷인) 아르페지오로 변환합니다. 그다음 모델을 사용해 다음 음표을 예측합니다. 마지막에 4개씩 음표를 모아서 다시 화음을 만들고 최종 코럴을 반환합니다." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**경고**: `model.predict_classes(X)`는 deprecated 되었습니다. 대신 `np.argmax(model.predict(X), axis=-1)`을 사용하세요." ] }, { "cell_type": "code", "execution_count": 94, "metadata": {}, "outputs": [], "source": [ "def generate_chorale(model, seed_chords, length):\n", " arpegio = preprocess(tf.constant(seed_chords, dtype=tf.int64))\n", " arpegio = tf.reshape(arpegio, [1, -1])\n", " for chord in range(length):\n", " for note in range(4):\n", " #next_note = model.predict_classes(arpegio)[:1, -1:]\n", " next_note = np.argmax(model.predict(arpegio), axis=-1)[:1, -1:]\n", " arpegio = tf.concat([arpegio, next_note], axis=1)\n", " arpegio = tf.where(arpegio == 0, arpegio, arpegio + min_note - 1)\n", " return tf.reshape(arpegio, shape=[-1, 4])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "이 함수를 테스트하려면 시드 화음이 필요합니다. 테스트 코럴 중 하나에 있는 처음 8개의 화음을 사용해 보죠(실제로 이는 4번 반복되는 2개의 화음입니다):" ] }, { "cell_type": "code", "execution_count": 95, "metadata": {}, "outputs": [], "source": [ "seed_chords = test_chorales[2][:8]\n", "play_chords(seed_chords, amplitude=0.2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "첫 번째 코럴을 생성할 준비를 마쳤습니다! 56개의 화음을 생성하여 총 64개의 화음, 즉 4 소절(소절마다 4개의 화음인 4/4박으로 가정합니다)을 만들어 보겠습니다:" ] }, { "cell_type": "code", "execution_count": 96, "metadata": {}, "outputs": [], "source": [ "new_chorale = generate_chorale(model, seed_chords, 56)\n", "play_chords(new_chorale)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "이 방식에는 한가지 단점이 있습니다: 너무 보수적인 경우가 많습니다. 실제로 이 모델은 모험을 하지 않아 항상 가장 높은 확률의 음표를 선택합니다. 이전 음표를 반복하면 충분히 듣기 좋고 가장 덜 위험하기 때문에 이 알고리즘은 마지막 음표를 오래 지속시키는 경향이 있습니다. 상당히 지루합니다. 또한 이 모델을 여러 번 실행하면 항상 같은 멜로디를 생성할 것입니다.\n", "\n", "조금 더 신나게 만들어 보죠! 항상 가장 높은 점수의 음표를 선택하는 대신, 예측된 확률을 기반으로 랜덤하게 다음 음표를 선택하겠습니다. 예를 들어, 모델이 75% 확률로 C3를 예측하고 25% 확률로 G3를 예측했다면 이 확률대로 랜덤하게 두 음표 중 하나를 선택하겠습니다. 또한 `temperature` 매개변수를 추가하여 시스템의 온도(즉 대담성)를 제어하겠습니다. 높은 온도는 예측 확률을 비슷하게 만들어 가능성이 높은 음표의 확률을 줄이고 가능성이 낮은 음표의 확률을 높입니다." ] }, { "cell_type": "code", "execution_count": 97, "metadata": {}, "outputs": [], "source": [ "def generate_chorale_v2(model, seed_chords, length, temperature=1):\n", " arpegio = preprocess(tf.constant(seed_chords, dtype=tf.int64))\n", " arpegio = tf.reshape(arpegio, [1, -1])\n", " for chord in range(length):\n", " for note in range(4):\n", " next_note_probas = model.predict(arpegio)[0, -1:]\n", " rescaled_logits = tf.math.log(next_note_probas) / temperature\n", " next_note = tf.random.categorical(rescaled_logits, num_samples=1)\n", " arpegio = tf.concat([arpegio, next_note], axis=1)\n", " arpegio = tf.where(arpegio == 0, arpegio, arpegio + min_note - 1)\n", " return tf.reshape(arpegio, shape=[-1, 4])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "이 함수로 3개의 코랄을 생성해 보겠습니다. 하나는 차갑게, 하나는 중간으로, 하나는 뜨겁게 만듭니다(시드, 길이, 온도를 사용해 자유롭게 실험해 보세요). 다음 코드는 각 코랄을 별개의 파일에 저장합니다. 마음에 드는 음악을 만날 때까지 이 셀을 반복해서 실행할 수 있습니다!\n", "\n", "**가장 아름다운 코럴을 트위터 @aureliengeron로 공유해 주시면 정말 감사하겠습니다! :))**" ] }, { "cell_type": "code", "execution_count": 98, "metadata": { "scrolled": true }, "outputs": [], "source": [ "new_chorale_v2_cold = generate_chorale_v2(model, seed_chords, 56, temperature=0.8)\n", "play_chords(new_chorale_v2_cold, filepath=\"bach_cold.wav\")" ] }, { "cell_type": "code", "execution_count": 99, "metadata": {}, "outputs": [], "source": [ "new_chorale_v2_medium = generate_chorale_v2(model, seed_chords, 56, temperature=1.0)\n", "play_chords(new_chorale_v2_medium, filepath=\"bach_medium.wav\")" ] }, { "cell_type": "code", "execution_count": 100, "metadata": {}, "outputs": [], "source": [ "new_chorale_v2_hot = generate_chorale_v2(model, seed_chords, 56, temperature=1.5)\n", "play_chords(new_chorale_v2_hot, filepath=\"bach_hot.wav\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "마지막으로 재미있는 실험을 해 볼 수 있습니다: 친구에게 마음에 드는 코럴 몇 개와 진짜 코럴을 보내고 어떤 것이 진짜인지 물어 보세요!" ] }, { "cell_type": "code", "execution_count": 101, "metadata": {}, "outputs": [], "source": [ "play_chords(test_chorales[2][:64], filepath=\"bach_test_4.wav\")" ] } ], "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": "3.7.3" }, "nav_menu": {}, "toc": { "navigate_menu": true, "number_sections": true, "sideBar": true, "threshold": 6, "toc_cell": false, "toc_section_display": "block", "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 4 }