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Aurélien Géron - time series - processing_sequences_using_rnns_and_cnns.ipynb
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aur-lien-g-ron-time-series-processing_sequences_using_rnns_and_cnns.ipynb
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"colab": {
"name": "Aurélien Géron - time series - processing_sequences_using_rnns_and_cnns.ipynb",
"provenance": [],
"collapsed_sections": [
"kboffBHd1GMO",
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"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/ia35/c00b3388c6063d458cea4d0d16170b52/aur-lien-g-ron-time-series-processing_sequences_using_rnns_and_cnns.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "paJt34CY7D_b",
"colab_type": "text"
},
"source": [
"[![](http://bec552ebfe.url-de-test.ws/ml/buttonBackProp.png)](https://www.backprop.fr)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "IOCSupvf6BWB",
"colab_type": "text"
},
"source": [
"Ce notebook a été codé par Aurélien Géron (AG), auteur du livre \"Hands-On Machine Learning with Scikit-Learn and TensorFlow\". \n",
"\n",
"Ce livre est une référence, voire la référence dans le Machine Learning avec TensorFlow.\n",
"\n",
"[@aureliengeron](https://twitter.com/aureliengeron)\n",
"\n",
"La lecture de ce livre est fortement recommandée. \n",
"\n",
"Ce [notebook](https://github.com/ageron/handson-ml2/blob/master/15_processing_sequences_using_rnns_and_cnns.ipynb) accompage le chapitre 15 du livre essentiellement consacré aux Time Series.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "GSTdozTyJCHe",
"colab_type": "text"
},
"source": [
"**Chapter 15 – Processing Sequences Using RNNs and CNNs**"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4FJTrbf067qd",
"colab_type": "text"
},
"source": [
"[![](https://raw.githubusercontent.com/BackProp-fr/meetup/master/images/LogoBackPropTranspSmall.png)](https://www.backprop.fr)\n",
"Le logo BackProp est présenté chaque fois qu'une modification importante est apportée au code ou à chaque fois qu'un commentaire doit être signalé. \n",
"\n",
"Le texte en anglais est soit le texte d'origine soit un extrait de site qui apporte des explications."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_jM0DXStOBKE",
"colab_type": "text"
},
"source": [
"[![](https://raw.githubusercontent.com/BackProp-fr/meetup/master/images/LogoBackPropTranspSmall.png)](https://www.backprop.fr)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "KRKf4A4JODNv",
"colab_type": "text"
},
"source": [
"Des bouts de code ont été ajoutés afin de rendre le tutoriel plus simple car à moins d'être familier avec les Time Series et TensorFlow c'est parfois un peu ardu."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "RE6Nsd9VSj08",
"colab_type": "text"
},
"source": [
"Si vous souhaitez commencer avec un tutoriel plus simple, regardez celui de [Coursera](https://www.coursera.org/learn/tensorflow-sequences-time-series-and-prediction/home/welcome) consacré aux Time Series "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qmZnJ911RlVe",
"colab_type": "text"
},
"source": [
"autotime n'était pas dans le code d'AG"
]
},
{
"cell_type": "code",
"metadata": {
"id": "R3iNHqOlRi30",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 186
},
"outputId": "5f7ea81d-fba1-4557-afc3-c69538ce4154"
},
"source": [
"!pip install ipython-autotime\n",
"%load_ext autotime"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": [
"Collecting ipython-autotime\n",
" Downloading https://files.pythonhosted.org/packages/e6/f9/0626bbdb322e3a078d968e87e3b01341e7890544de891d0cb613641220e6/ipython-autotime-0.1.tar.bz2\n",
"Building wheels for collected packages: ipython-autotime\n",
" Building wheel for ipython-autotime (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for ipython-autotime: filename=ipython_autotime-0.1-cp36-none-any.whl size=1832 sha256=a921a0491859498c1e8d88108bea759dfc9f8c4241aad999aa8125e771ea4d44\n",
" Stored in directory: /root/.cache/pip/wheels/d2/df/81/2db1e54bc91002cec40334629bc39cfa86dff540b304ebcd6e\n",
"Successfully built ipython-autotime\n",
"Installing collected packages: ipython-autotime\n",
"Successfully installed ipython-autotime-0.1\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "t7Xt1g0WJCHi",
"colab_type": "text"
},
"source": [
"_This notebook contains all the sample code in chapter 15._"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HRizwyEWE_nu",
"colab_type": "text"
},
"source": [
"When dealing with time series (and other types of sequences such as sentences), the input features are generally represented as 3D arrays of shape [batch size, time steps, dimensionality], where dimensionality is 1 for univariate time series and more for multivariate time series\n",
"\n",
"Aurélien Géron : « Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "l2fQcXUL8027",
"colab_type": "text"
},
"source": [
"[![](https://raw.githubusercontent.com/BackProp-fr/meetup/master/images/LogoBackPropTranspSmall.png)](https://www.backprop.fr)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "79As5iqR82Tp",
"colab_type": "text"
},
"source": [
"Ce tutoriel est dense. Il permet d'étudier comment effectuer des prédictions sur des Time Series à l'aide de différentes méthodes (naïve, RNN, LSTM, GRU, Wavenet)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1XBsO4eYJCHn",
"colab_type": "text"
},
"source": [
"<table align=\"left\">\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/15_processing_sequences_using_rnns_and_cnns.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
" </td>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-v5JKUMLJCHq",
"colab_type": "text"
},
"source": [
"# Setup"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Z7zUZs3yJCHt",
"colab_type": "text"
},
"source": [
"First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead), as well as Scikit-Learn ≥0.20 and TensorFlow ≥2.0."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "iPGZIHeE7va2",
"colab_type": "text"
},
"source": [
"[![](https://raw.githubusercontent.com/BackProp-fr/meetup/master/images/LogoBackPropTranspSmall.png)](https://www.backprop.fr)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Osp2I-Gq7x2E",
"colab_type": "text"
},
"source": [
"Au moment du test de ce notebook sur Google Colab, Python est en version 3.6, Sklearn en 0.22, TensorFlow en 2.2\n",
"\n",
"TensorFlow est disponible en RC en 2.3 mais elle n'est pas utile pour cette exercice"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Wx8dP7vYMn6f",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"outputId": "895d2b4e-9854-4abf-bc9b-e2ec73cde3b8"
},
"source": [
"# Python ≥3.5 is required\n",
"import sys\n",
"assert sys.version_info >= (3, 5)"
],
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"text": [
"time: 1.56 ms\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "4zOY8graMsjt",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 71
},
"outputId": "a1408cd8-e3f1-48c4-c3f1-dd0bf6f529ea"
},
"source": [
"print(sys.version_info)"
],
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"text": [
"sys.version_info(major=3, minor=6, micro=9, releaselevel='final', serial=0)\n",
"time: 1.77 ms\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "465PmTcqM--i",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"outputId": "ae243825-fd59-439c-aa7d-392043a5e5af"
},
"source": [
"# Scikit-Learn ≥0.20 is required\n",
"import sklearn\n",
"assert sklearn.__version__ >= \"0.20\""
],
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"text": [
"time: 343 ms\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "-SCQ7Xll78K5",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 58
},
"outputId": "09941b3c-c06b-4f00-bfe0-3e8d4d40be9c"
},
"source": [
"sklearn.__version__"
],
"execution_count": 5,
"outputs": [
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.intrinsic": {
"type": "string"
},
"text/plain": [
"'0.22.2.post1'"
]
},
"metadata": {
"tags": []
},
"execution_count": 5
},
{
"output_type": "stream",
"text": [
"time: 10 ms\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "jqbqmRhJNKxh",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"outputId": "a2d2240b-1f9e-458a-cd43-9d5adc1f01a7"
},
"source": [
"try:\n",
" # %tensorflow_version only exists in Colab.\n",
" %tensorflow_version 2.x\n",
" IS_COLAB = True\n",
"except Exception:\n",
" IS_COLAB = False\n",
"\n",
"# TensorFlow ≥2.0 is required\n",
"import tensorflow as tf\n",
"from tensorflow import keras\n",
"assert tf.__version__ >= \"2.0\""
],
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"text": [
"time: 1.56 s\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Dh7DPM-ENVrJ",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 71
},
"outputId": "3b11d8a7-5889-470f-8367-e996e24adef4"
},
"source": [
"print(tf.__version__)"
],
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"text": [
"2.2.0\n",
"time: 2.94 ms\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5uERxafkQC-S",
"colab_type": "text"
},
"source": [
"[![](https://raw.githubusercontent.com/BackProp-fr/meetup/master/images/LogoBackPropTranspSmall.png)](https://www.backprop.fr)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xJmzAf_CQH2L",
"colab_type": "text"
},
"source": [
"Exécutez ce code avec un GPU sinon ça prendre beaucoup de temps"
]
},
{
"cell_type": "code",
"metadata": {
"id": "56V4LiVPNaox",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"outputId": "cf24035f-00f9-4d61-893b-2f055b256046"
},
"source": [
"if not tf.config.list_physical_devices('GPU'):\n",
" print(\"No GPU was detected. LSTMs and CNNs can be very slow without a GPU.\")\n",
" if IS_COLAB:\n",
" print(\"Go to Runtime > Change runtime and select a GPU hardware accelerator.\")"
],
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"text": [
"time: 1.44 s\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "h1v1axV6NpR0",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 104
},
"outputId": "fc8e2a4e-9816-4e0a-c461-1dd8830dca11"
},
"source": [
"tf.config.list_physical_devices()"
],
"execution_count": 9,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'),\n",
" PhysicalDevice(name='/physical_device:XLA_CPU:0', device_type='XLA_CPU'),\n",
" PhysicalDevice(name='/physical_device:XLA_GPU:0', device_type='XLA_GPU'),\n",
" PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]"
]
},
"metadata": {
"tags": []
},
"execution_count": 9
},
{
"output_type": "stream",
"text": [
"time: 12 ms\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8CK0AIwvDD4W",
"colab_type": "text"
},
"source": [
"[![](https://raw.githubusercontent.com/BackProp-fr/meetup/master/images/LogoBackPropTranspSmall.png)](https://www.backprop.fr)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HE1K15w5DFlq",
"colab_type": "text"
},
"source": [
"Il peut être intéressant de tester ce code avec un TPU, mais ça suppose d'en réécrire une partie."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "DQyTcM6gSCb6",
"colab_type": "text"
},
"source": [
"Il ne sert à rien de sauvegader les graphiques, mais le conde est maintenu"
]
},
{
"cell_type": "code",
"metadata": {
"id": "gSzw7WXYJCHv",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"outputId": "b38d54e9-ab63-4cb9-93b6-3fa47952e6a3"
},
"source": [
"# Common imports\n",
"import numpy as np\n",
"import os\n",
"from pathlib import Path\n",
"\n",
"# to make this notebook's output stable across runs\n",
"np.random.seed(42)\n",
"tf.random.set_seed(42)\n",
"\n",
"# To plot pretty figures\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",
"# Where to save the figures\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(\"Saving figure\", fig_id)\n",
" if tight_layout:\n",
" plt.tight_layout()\n",
" plt.savefig(path, format=fig_extension, dpi=resolution)"
],
"execution_count": 10,
"outputs": [
{
"output_type": "stream",
"text": [
"time: 13.3 ms\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7cTBT0UkJCH_",
"colab_type": "text"
},
"source": [
"# Basic RNNs"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "vH5WZhpKJCIC",
"colab_type": "text"
},
"source": [
"### Generate the Dataset"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xHhjAm3l-OKo",
"colab_type": "text"
},
"source": [
"[![](https://raw.githubusercontent.com/BackProp-fr/meetup/master/images/LogoBackPropTranspSmall.png)](https://www.backprop.fr)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ie2xVAQh93dh",
"colab_type": "text"
},
"source": [
"On crée un série, combinaison de plusieurs mouvements périodiques auxquels on ajoute du bruit."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "P-IqR82x-hEC",
"colab_type": "text"
},
"source": [
"np.newaxis permet d'ajouter un axe "
]
},
{
"cell_type": "code",
"metadata": {
"id": "ksLpABAoJCID",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"outputId": "047d333c-87bc-4404-9db0-965b02211a76"
},
"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)) # wave 1\n",
" series += 0.2 * np.sin((time - offsets2) * (freq2 * 20 + 20)) # + wave 2\n",
" series += 0.1 * (np.random.rand(batch_size, n_steps) - 0.5) # + noise\n",
" return series[..., np.newaxis].astype(np.float32)"
],
"execution_count": 13,
"outputs": [
{
"output_type": "stream",
"text": [
"time: 9.47 ms\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4SQ3Ammc_Bi4",
"colab_type": "text"
},
"source": [
"### <font color=\"orange\">np.newaxis expliqué</font>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "DpLvHGzk-57F",
"colab_type": "text"
},
"source": [
"[![](https://raw.githubusercontent.com/BackProp-fr/meetup/master/images/LogoBackPropTranspSmall.png)](https://www.backprop.fr)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "PUBcTStBKKbl",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"outputId": "cca23ed7-5658-4909-ffef-8430ecacc3f4"
},
"source": [
"x=np.array([1, 2, 3])"
],
"execution_count": 14,
"outputs": [
{
"output_type": "stream",
"text": [
"time: 1.02 ms\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "xHLNCj6JKUye",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"outputId": "dab9682f-0007-4079-d465-e918d1a05e94"
},
"source": [
"x, x.shape"
],
"execution_count": 15,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(array([1, 2, 3]), (3,))"
]
},
"metadata": {
"tags": []
},
"execution_count": 15
},
{
"output_type": "stream",
"text": [
"time: 11.2 ms\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "8SuyBl9HKWdM",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 87
},
"outputId": "d08f9db5-fd96-4470-8b03-0abd6694200b"
},
"source": [
"y=x[..., np.newaxis]\n",
"y, y.shape"
],
"execution_count": 16,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(array([[1],\n",
" [2],\n",
" [3]]), (3, 1))"
]
},
"metadata": {
"tags": []
},
"execution_count": 16
},
{
"output_type": "stream",
"text": [
"time: 3.24 ms\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "FwZeTuu4_LOn",
"colab_type": "text"
},
"source": [
"### <font color=\"orange\">generate_time_series expliqué</font>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "UKCBb6fC_b3K",
"colab_type": "text"
},
"source": [
"generate_time_series a 2 paramètres : batch_size et n_steps\n",
"\n",
"batch_size est le nombre de périodes et n_steps le nombre de valeurs par batch, pour rappel les données sont de type univariate"
]
},
{
"cell_type": "code",
"metadata": {
"id": "PgW_2ahUK3-9",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 71
},
"outputId": "19799c4c-33be-4d18-80f6-a6f0090aa896"
},
"source": [
"s = generate_time_series(1, 2)\n",
"s, s.shape"
],
"execution_count": 17,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(array([[[ 0.4586868 ],\n",
" [-0.28245834]]], dtype=float32), (1, 2, 1))"
]
},
"metadata": {
"tags": []
},
"execution_count": 17
},
{
"output_type": "stream",
"text": [
"time: 4.66 ms\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "1i6a4VkBLCOa",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 153
},
"outputId": "7654c63f-d7cb-4a5f-adda-2f9c416e0558"
},
"source": [
"s = generate_time_series(2, 3)\n",
"s, s.shape"
],
"execution_count": 18,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(array([[[-0.33964005],\n",
" [-0.311693 ],\n",
" [-0.5816709 ]],\n",
" \n",
" [[ 0.1780017 ],\n",
" [-0.38983342],\n",
" [ 0.4412147 ]]], dtype=float32), (2, 3, 1))"
]
},
"metadata": {
"tags": []
},
"execution_count": 18
},
{
"output_type": "stream",
"text": [
"time: 4.63 ms\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "9gz9gqzRQZ-O",
"colab_type": "text"
},
"source": [
"This function creates as many time series as requested (via the batch_size argument), each of length n_steps, and there is just one value per time step in each series (i.e., all series are univariate)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Ww4e43KAshrV",
"colab_type": "text"
},
"source": [
"Les labels seront les dernières valeurs de chacune des séries"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "teh0fJkQ1l1W",
"colab_type": "text"
},
"source": [
"[![](https://raw.githubusercontent.com/BackProp-fr/meetup/master/images/LogoBackPropTranspSmall.png)](https://www.backprop.fr)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qewKspyzABum",
"colab_type": "text"
},
"source": [
"AG crée des données de test (X_test) mais ne les utilise pas."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5JRuroxcVV44",
"colab_type": "text"
},
"source": [
"Normalement, après avoir testé sur les données de validation, il faudrait pooursuivre l'apprentissage avec ces données complètes et retester sur les données de test puis ajouter les données de test aux données d'apprentissage et de validation afin d'avoir un modèle exploitable pour les prévisions.\n",
"\n",
"On procède de cette façon car les dernières données sont les plus importantes dans les Time Series. C'est différent de ce qu'on fait en vision par exemple."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "zkofi2HNWD3Y",
"colab_type": "text"
},
"source": [
"Notez que y_train est la dernière valeur de la série"
]
},
{
"cell_type": "code",
"metadata": {
"id": "ARN_ln8qJCIJ",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"outputId": "82617d2f-86a0-41ed-d03d-da4daa397c29"
},
"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]"
],
"execution_count": 82,
"outputs": [
{
"output_type": "stream",
"text": [
"time: 50.3 ms\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gAN1_kn8AUFi",
"colab_type": "text"
},
"source": [
"[![](https://raw.githubusercontent.com/BackProp-fr/meetup/master/images/LogoBackPropTranspSmall.png)](https://www.backprop.fr)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "QIZ8JeUHAWBy",
"colab_type": "text"
},
"source": [
"Prenons pour exemple le dernier batch. Dans les données d'origine il a une longeur de 51."
]
},
{
"cell_type": "code",
"metadata": {
"id": "XPGDSQPltYQx",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 880
},
"outputId": "bb2910fa-6731-46ee-e5ca-c8f2c63e3fb5"
},
"source": [
"series[6999], len(series[6999])"
],
"execution_count": 21,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(array([[-0.6709723 ],\n",
" [-0.6589038 ],\n",
" [-0.64380425],\n",
" [-0.44133312],\n",
" [-0.2376148 ],\n",
" [ 0.0450904 ],\n",
" [ 0.24465525],\n",
" [ 0.48193365],\n",
" [ 0.6195285 ],\n",
" [ 0.69106025],\n",
" [ 0.61461014],\n",
" [ 0.48796204],\n",
" [ 0.36567944],\n",
" [ 0.11615019],\n",
" [-0.06356961],\n",
" [-0.2888692 ],\n",
" [-0.34959754],\n",
" [-0.46395704],\n",
" [-0.47311217],\n",
" [-0.4838065 ],\n",
" [-0.39081326],\n",
" [-0.29129842],\n",
" [-0.19490717],\n",
" [-0.07203644],\n",
" [ 0.0858146 ],\n",
" [ 0.17446214],\n",
" [ 0.17244498],\n",
" [ 0.21369067],\n",
" [ 0.3158718 ],\n",
" [ 0.25822714],\n",
" [ 0.3202146 ],\n",
" [ 0.34063017],\n",
" [ 0.3199259 ],\n",
" [ 0.23856544],\n",
" [ 0.11998892],\n",
" [ 0.06278632],\n",
" [-0.10352159],\n",
" [-0.2784541 ],\n",
" [-0.4111503 ],\n",
" [-0.49324057],\n",
" [-0.57831377],\n",
" [-0.4889276 ],\n",
" [-0.47431993],\n",
" [-0.3652512 ],\n",
" [-0.08726436],\n",
" [ 0.0908763 ],\n",
" [ 0.36150008],\n",
" [ 0.46026787],\n",
" [ 0.6671777 ],\n",
" [ 0.6631884 ],\n",
" [ 0.60062087]], dtype=float32), 51)"
]
},
"metadata": {
"tags": []
},
"execution_count": 21
},
{
"output_type": "stream",
"text": [
"time: 3.64 ms\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "zZfnXrmKAi4o",
"colab_type": "text"
},
"source": [
"X_train ne prend que les 50 premiers élements"
]
},
{
"cell_type": "code",
"metadata": {
"id": "D1emLTk8zr7J",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 864
},
"outputId": "b4a6d4bd-c054-4137-dcf5-70b6f6dfb5df"
},
"source": [
"X_train[6999], len(X_train[6999])"
],
"execution_count": 22,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(array([[-0.6709723 ],\n",
" [-0.6589038 ],\n",
" [-0.64380425],\n",
" [-0.44133312],\n",
" [-0.2376148 ],\n",
" [ 0.0450904 ],\n",
" [ 0.24465525],\n",
" [ 0.48193365],\n",
" [ 0.6195285 ],\n",
" [ 0.69106025],\n",
" [ 0.61461014],\n",
" [ 0.48796204],\n",
" [ 0.36567944],\n",
" [ 0.11615019],\n",
" [-0.06356961],\n",
" [-0.2888692 ],\n",
" [-0.34959754],\n",
" [-0.46395704],\n",
" [-0.47311217],\n",
" [-0.4838065 ],\n",
" [-0.39081326],\n",
" [-0.29129842],\n",
" [-0.19490717],\n",
" [-0.07203644],\n",
" [ 0.0858146 ],\n",
" [ 0.17446214],\n",
" [ 0.17244498],\n",
" [ 0.21369067],\n",
" [ 0.3158718 ],\n",
" [ 0.25822714],\n",
" [ 0.3202146 ],\n",
" [ 0.34063017],\n",
" [ 0.3199259 ],\n",
" [ 0.23856544],\n",
" [ 0.11998892],\n",
" [ 0.06278632],\n",
" [-0.10352159],\n",
" [-0.2784541 ],\n",
" [-0.4111503 ],\n",
" [-0.49324057],\n",
" [-0.57831377],\n",
" [-0.4889276 ],\n",
" [-0.47431993],\n",
" [-0.3652512 ],\n",
" [-0.08726436],\n",
" [ 0.0908763 ],\n",
" [ 0.36150008],\n",
" [ 0.46026787],\n",
" [ 0.6671777 ],\n",
" [ 0.6631884 ]], dtype=float32), 50)"
]
},
"metadata": {
"tags": []
},
"execution_count": 22
},
{
"output_type": "stream",
"text": [
"time: 7.19 ms\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "KLPkzntVAvkc",
"colab_type": "text"
},
"source": [
"et y_train, pour le batch en question, est le dernier élément de la série d'origine (51 élements)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "bDZw1AERw7h3",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"outputId": "785493bd-1979-4f68-9e3f-2213ab47efc5"
},
"source": [
"y_train[6999], series[6999][-1]"
],
"execution_count": 25,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(array([0.60062087], dtype=float32), array([0.60062087], dtype=float32))"
]
},
"metadata": {
"tags": []
},
"execution_count": 25
},
{
"output_type": "stream",
"text": [
"time: 7.02 ms\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7yMC2DgcQ_j_",
"colab_type": "text"
},
"source": [
"On a au départ 10000 batches de 51 series de type univariate"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Z_9AWUnHQ2Zl",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"outputId": "dc82260f-afff-46ed-d05b-e98ec0562e78"
},
"source": [
"series.shape"
],
"execution_count": 26,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(10000, 51, 1)"
]
},
"metadata": {
"tags": []
},
"execution_count": 26
},
{
"output_type": "stream",
"text": [
"time: 2.39 ms\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TUqTusOCA893",
"colab_type": "text"
},
"source": [
"mais uniquement 7000 pour X_train"
]
},
{
"cell_type": "code",
"metadata": {
"id": "aqn5znPEQ7wG",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"outputId": "f2cacaed-e07d-416d-d669-d9ca3105f330"
},
"source": [
"X_train.shape, y_train.shape"
],
"execution_count": 27,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"((7000, 50, 1), (7000, 1))"
]
},
"metadata": {
"tags": []
},
"execution_count": 27
},
{
"output_type": "stream",
"text": [
"time: 6.07 ms\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "_BIJw0iyJCIX",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 351
},
"outputId": "da383695-9d4c-4cc7-99b1-303c72cf0534"
},
"source": [
"def plot_series(series, y=None, y_pred=None, x_label=\"$t$\", y_label=\"$x(t)$\"):\n",
" plt.plot(series, \".-\")\n",
" if y is not None:\n",
" plt.plot(n_steps, y, \"bx\", markersize=10)\n",
" if y_pred is not None:\n",
" plt.plot(n_steps, y_pred, \"ro\")\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",
"\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",
"save_fig(\"time_series_plot\")\n",
"plt.show()"
],
"execution_count": 28,
"outputs": [
{
"output_type": "stream",
"text": [
"Saving figure time_series_plot\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x288 with 3 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
},
{
"output_type": "stream",
"text": [
"time: 1.56 s\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qeIzVFlSX9-O",
"colab_type": "text"
},
"source": [
"On affiche ci-dessus les 3 premières séries de 50 éléments. La croix dans le graphique indique le label, la valeur à prédire."
]
},
{
"cell_type": "code",
"metadata": {
"id": "q7NgYjoOXDbe",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 220
},
"outputId": "6fc01ce8-4e49-4ae6-a985-4ee2cad7c29c"
},
"source": [
"X_valid[0, :, 0]"
],
"execution_count": 33,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([-0.3761855 , -0.42404684, -0.3390344 , -0.271153 , -0.26532513,\n",
" -0.32099137, -0.46815926, -0.5769794 , -0.6041072 , -0.5708584 ,\n",
" -0.38275763, -0.12439152, 0.09827511, 0.22865155, 0.18679029,\n",
" 0.24767214, 0.16514015, 0.21874566, 0.25152925, 0.40883926,\n",
" 0.615185 , 0.73059636, 0.6266374 , 0.5755195 , 0.29736876,\n",
" 0.10385749, 0.03801422, 0.02386243, 0.02322038, 0.02215886,\n",
" 0.01596526, -0.14356734, -0.35600615, -0.49090433, -0.64607924,\n",
" -0.70009744, -0.62788457, -0.4255847 , -0.22938487, -0.14113732,\n",
" -0.14108056, -0.22598805, -0.26593843, -0.271487 , -0.07883997,\n",
" 0.1177286 , 0.3792994 , 0.5767727 , 0.59239095, 0.5643068 ],\n",
" dtype=float32)"
]
},
"metadata": {
"tags": []
},
"execution_count": 33
},
{
"output_type": "stream",
"text": [
"time: 6.41 ms\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "FU34D3-nXmQH",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"outputId": "ddae0d6c-88ab-49eb-a3e4-e01e85c14751"
},
"source": [
"y_valid[0, 0]"
],
"execution_count": 34,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0.4000832"
]
},
"metadata": {
"tags": []
},
"execution_count": 34
},
{
"output_type": "stream",
"text": [
"time: 2.42 ms\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "av-5zwD2JCIe",
"colab_type": "text"
},
"source": [
"### Computing Some Baselines"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "yShw4X-4E949",
"colab_type": "text"
},
"source": [
"### <font color=\"orange\">naive forecast</font>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "081ZNVF6JCIf",
"colab_type": "text"
},
"source": [
"Naive predictions (just predict the last observed value):"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gYxKyGdUDttq",
"colab_type": "text"
},
"source": [
"[![](https://raw.githubusercontent.com/BackProp-fr/meetup/master/images/LogoBackPropTranspSmall.png)](https://www.backprop.fr)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ddOaEyzTE2J4",
"colab_type": "text"
},
"source": [
"For [naïve](https://otexts.com/fpp2/simple-methods.html) forecasts, we simply set all forecasts to be the value of the last observation. "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "agCJ3L7KDvLx",
"colab_type": "text"
},
"source": [
"Lorsqu'on fait des prédictions sur les time series, il est courant de commencer par une approche naïve, qui permet d'avoir ainsi un benchmark"
]
},
{
"cell_type": "code",
"metadata": {
"id": "gedfN6ebHpXe",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"outputId": "6ef0f879-b7ea-48c0-b4c5-299719cf2b68"
},
"source": [
"X_valid.shape"
],
"execution_count": 45,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(2000, 50, 1)"
]
},
"metadata": {
"tags": []
},
"execution_count": 45
},
{
"output_type": "stream",
"text": [
"time: 6.73 ms\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "e8buEaqvIBpQ",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"outputId": "34895a5c-5821-4a2d-8f62-335226098917"
},
"source": [
"y_valid.shape"
],
"execution_count": 46,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(2000, 1)"
]
},
"metadata": {
"tags": []
},
"execution_count": 46
},
{
"output_type": "stream",
"text": [
"time: 2.68 ms\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "QfSwK5tEbOvO",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"outputId": "64c09b53-a5ce-431d-f97c-929fb9a51840"
},
"source": [
"y_pred = X_valid[:, -1]\n",
"np.mean(keras.losses.mean_squared_error(y_valid, y_pred))"
],
"execution_count": 47,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0.020211367"
]
},
"metadata": {
"tags": []
},
"execution_count": 47
},
{
"output_type": "stream",
"text": [
"time: 4.56 ms\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "SWzcx5-LbWdj",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"outputId": "0497a4cd-da92-45ff-a3eb-337c0e787cd5"
},
"source": [
"y_pred.shape"
],
"execution_count": 50,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(2000, 1)"
]
},
"metadata": {
"tags": []
},
"execution_count": 50
},
{
"output_type": "stream",
"text": [
"time: 3.01 ms\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "njDjQJeIbZ8k",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 330
},
"outputId": "739aa02b-1112-46d8-9760-98c2d7e2cfa2"
},
"source": [
"plot_series(X_valid[0, :, 0], y_valid[0, 0], y_pred[0, 0])\n",
"plt.show()"
],
"execution_count": 51,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
},
{
"output_type": "stream",
"text": [
"time: 224 ms\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "UKqmpI5UY-kV",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"outputId": "bdddb570-386b-486c-fd9c-87184304f8d6"
},
"source": [
"y_valid[0, 0], y_pred[0, 0]"
],
"execution_count": 41,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(0.4000832, 0.5643068)"
]
},
"metadata": {
"tags": []
},
"execution_count": 41
},
{
"output_type": "stream",
"text": [
"time: 2.94 ms\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "mO9c0bhXZWdw",
"colab_type": "text"
},
"source": [
"### <font color=\"orange\">linear forecast</font>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "rzcXC1rsJCIt",
"colab_type": "text"
},
"source": [
"Linear predictions:"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "WWgTfR_Jb3CH",
"colab_type": "text"
},
"source": [
"Parfois, au lieu d'une prédiction naïve, on choisit un benchmark avec une prédiction linéaire."
]
},
{
"cell_type": "code",
"metadata": {
"id": "8B7NggYmJCIu",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 715
},
"outputId": "05e09a47-1e01-440d-d4b2-d213ec9bcfc0"
},
"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))"
],
"execution_count": 52,
"outputs": [
{
"output_type": "stream",
"text": [
"Epoch 1/20\n",
"219/219 [==============================] - 1s 3ms/step - loss: 0.1001 - val_loss: 0.0545\n",
"Epoch 2/20\n",
"219/219 [==============================] - 1s 3ms/step - loss: 0.0379 - val_loss: 0.0266\n",
"Epoch 3/20\n",
"219/219 [==============================] - 1s 3ms/step - loss: 0.0202 - val_loss: 0.0157\n",
"Epoch 4/20\n",
"219/219 [==============================] - 1s 3ms/step - loss: 0.0131 - val_loss: 0.0116\n",
"Epoch 5/20\n",
"219/219 [==============================] - 1s 3ms/step - loss: 0.0103 - val_loss: 0.0098\n",
"Epoch 6/20\n",
"219/219 [==============================] - 1s 3ms/step - loss: 0.0089 - val_loss: 0.0087\n",
"Epoch 7/20\n",
"219/219 [==============================] - 1s 3ms/step - loss: 0.0080 - val_loss: 0.0079\n",
"Epoch 8/20\n",
"219/219 [==============================] - 1s 3ms/step - loss: 0.0073 - val_loss: 0.0071\n",
"Epoch 9/20\n",
"219/219 [==============================] - 1s 3ms/step - loss: 0.0066 - val_loss: 0.0066\n",
"Epoch 10/20\n",
"219/219 [==============================] - 1s 3ms/step - loss: 0.0061 - val_loss: 0.0062\n",
"Epoch 11/20\n",
"219/219 [==============================] - 1s 3ms/step - loss: 0.0057 - val_loss: 0.0057\n",
"Epoch 12/20\n",
"219/219 [==============================] - 1s 3ms/step - loss: 0.0054 - val_loss: 0.0055\n",
"Epoch 13/20\n",
"219/219 [==============================] - 1s 3ms/step - loss: 0.0052 - val_loss: 0.0052\n",
"Epoch 14/20\n",
"219/219 [==============================] - 1s 3ms/step - loss: 0.0049 - val_loss: 0.0049\n",
"Epoch 15/20\n",
"219/219 [==============================] - 1s 3ms/step - loss: 0.0048 - val_loss: 0.0048\n",
"Epoch 16/20\n",
"219/219 [==============================] - 1s 3ms/step - loss: 0.0046 - val_loss: 0.0048\n",
"Epoch 17/20\n",
"219/219 [==============================] - 1s 3ms/step - loss: 0.0045 - val_loss: 0.0045\n",
"Epoch 18/20\n",
"219/219 [==============================] - 1s 3ms/step - loss: 0.0044 - val_loss: 0.0044\n",
"Epoch 19/20\n",
"219/219 [==============================] - 1s 3ms/step - loss: 0.0043 - val_loss: 0.0043\n",
"Epoch 20/20\n",
"219/219 [==============================] - 1s 3ms/step - loss: 0.0042 - val_loss: 0.0042\n",
"time: 14.7 s\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3Ajop9BndMMX",
"colab_type": "text"
},
"source": [
"[![](https://raw.githubusercontent.com/BackProp-fr/meetup/master/images/LogoBackPropTranspSmall.png)](https://www.backprop.fr)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cXqwjxWSc6TM",
"colab_type": "text"
},
"source": [
"evaluate utilise ici loss=MSE, c'est à dire la même fonction que celle utilisée plus haut pour naïve"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3Aa0xfAAdX_R",
"colab_type": "text"
},
"source": [
"Retour de evaluate : \n",
"\n",
"Scalar test loss (if the [model](https://www.tensorflow.org/api_docs/python/tf/keras/Model#evaluate) has a single output and no metrics) or list of scalars (if the model has multiple outputs and/or metrics). The attribute model.metrics_names will give you the display labels for the scalar outputs."
]
},
{
"cell_type": "code",
"metadata": {
"id": "3pqz1EB4JCIz",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 71
},
"outputId": "7e122e32-01da-4362-e1ec-2372fdc7eb09"
},
"source": [
"model.evaluate(X_valid, y_valid)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"63/63 [==============================] - 0s 2ms/step - loss: 0.0042\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0.004168086219578981"
]
},
"metadata": {
"tags": []
},
"execution_count": 27
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "SIsdVtdGd1WI",
"colab_type": "text"
},
"source": [
"Le résultat est nettement meilleur que plus haut. \n",
"\n",
"Au lieu de 0.02... on obtient 0.004..."
]
},
{
"cell_type": "code",
"metadata": {
"id": "dfRyWWxTcogS",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"outputId": "2f4f37e4-c5da-4ba4-c27b-13a26528c084"
},
"source": [
"model.metrics_names "
],
"execution_count": 55,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['loss']"
]
},
"metadata": {
"tags": []
},
"execution_count": 55
},
{
"output_type": "stream",
"text": [
"time: 2.93 ms\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "N_epXeouJCI4",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 328
},
"outputId": "880b8b5b-fd50-4b12-927e-4560cb119a75"
},
"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()"
],
"execution_count": 56,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
},
{
"output_type": "stream",
"text": [
"time: 194 ms\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Ss1eOykXJCI8",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 330
},
"outputId": "3878a099-aa9e-4678-f516-126f3a1e1a78"
},
"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()"
],
"execution_count": 57,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
},
{
"output_type": "stream",
"text": [
"time: 387 ms\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "28YOSZa2elsX",
"colab_type": "text"
},
"source": [
"Le point rouge est la prédiction, la croix la vraie valeur"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4N-QpdtQJCJB",
"colab_type": "text"
},
"source": [
"### Using a Simple RNN - une seule couche"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "pXZSQxl_e0of",
"colab_type": "text"
},
"source": [
"[![](https://raw.githubusercontent.com/BackProp-fr/meetup/master/images/LogoBackPropTranspSmall.png)](https://www.backprop.fr)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "D6c5m3fJe4uC",
"colab_type": "text"
},
"source": [
"Au lieu d'un modèle linéaire, essayons un RNN, le plus simple possible, une seule couche, un seul neurone"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cxGKFhJEj8QP",
"colab_type": "text"
},
"source": [
"Pour une explication de RNN et LSTM, rien de mieux que le [blog](https://colah.github.io) de Chris Olah"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3-qYsH30jxn7",
"colab_type": "text"
},
"source": [
"[![](https://colah.github.io/posts/2015-08-Understanding-LSTMs/img/LSTM3-SimpleRNN.png)](https://colah.github.io/posts/2015-08-Understanding-LSTMs/)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "F7_mqGc_JCJC",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 715
},
"outputId": "00513ee1-739b-40da-df01-1fe630ee2c8e"
},
"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(lr=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))"
],
"execution_count": 58,
"outputs": [
{
"output_type": "stream",
"text": [
"Epoch 1/20\n",
"219/219 [==============================] - 10s 47ms/step - loss: 0.0967 - val_loss: 0.0489\n",
"Epoch 2/20\n",
"219/219 [==============================] - 10s 46ms/step - loss: 0.0369 - val_loss: 0.0296\n",
"Epoch 3/20\n",
"219/219 [==============================] - 10s 47ms/step - loss: 0.0253 - val_loss: 0.0218\n",
"Epoch 4/20\n",
"219/219 [==============================] - 10s 46ms/step - loss: 0.0198 - val_loss: 0.0177\n",
"Epoch 5/20\n",
"219/219 [==============================] - 10s 46ms/step - loss: 0.0166 - val_loss: 0.0151\n",
"Epoch 6/20\n",
"219/219 [==============================] - 10s 47ms/step - loss: 0.0146 - val_loss: 0.0134\n",
"Epoch 7/20\n",
"219/219 [==============================] - 10s 48ms/step - loss: 0.0132 - val_loss: 0.0123\n",
"Epoch 8/20\n",
"219/219 [==============================] - 11s 48ms/step - loss: 0.0124 - val_loss: 0.0116\n",
"Epoch 9/20\n",
"219/219 [==============================] - 11s 48ms/step - loss: 0.0118 - val_loss: 0.0112\n",
"Epoch 10/20\n",
"219/219 [==============================] - 10s 46ms/step - loss: 0.0116 - val_loss: 0.0110\n",
"Epoch 11/20\n",
"219/219 [==============================] - 10s 48ms/step - loss: 0.0114 - val_loss: 0.0109\n",
"Epoch 12/20\n",
"219/219 [==============================] - 10s 48ms/step - loss: 0.0114 - val_loss: 0.0109\n",
"Epoch 13/20\n",
"219/219 [==============================] - 11s 49ms/step - loss: 0.0114 - val_loss: 0.0109\n",
"Epoch 14/20\n",
"219/219 [==============================] - 11s 48ms/step - loss: 0.0114 - val_loss: 0.0109\n",
"Epoch 15/20\n",
"219/219 [==============================] - 11s 48ms/step - loss: 0.0114 - val_loss: 0.0109\n",
"Epoch 16/20\n",
"219/219 [==============================] - 11s 48ms/step - loss: 0.0114 - val_loss: 0.0109\n",
"Epoch 17/20\n",
"219/219 [==============================] - 10s 47ms/step - loss: 0.0114 - val_loss: 0.0109\n",
"Epoch 18/20\n",
"219/219 [==============================] - 10s 47ms/step - loss: 0.0114 - val_loss: 0.0109\n",
"Epoch 19/20\n",
"219/219 [==============================] - 11s 48ms/step - loss: 0.0114 - val_loss: 0.0109\n",
"Epoch 20/20\n",
"219/219 [==============================] - 10s 47ms/step - loss: 0.0114 - val_loss: 0.0109\n",
"time: 3min 29s\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "41e-zXTvgE2z",
"colab_type": "text"
},
"source": [
"[![](https://raw.githubusercontent.com/BackProp-fr/meetup/master/images/LogoBackPropTranspSmall.png)](https://www.backprop.fr)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "MQvPS_HngGdP",
"colab_type": "text"
},
"source": [
"Le temps d'exécution est beaucoup plus long que précédemment, plusieurs minutes ! pour un résultat moins bien qu'avec un modèle linéaire."
]
},
{
"cell_type": "code",
"metadata": {
"id": "HeAUQqQlUNqv",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 71
},
"outputId": "fb0f6208-ca89-4203-b30e-1b7961ed9904"
},
"source": [
"model.evaluate(X_valid, y_valid)"
],
"execution_count": 60,
"outputs": [
{
"output_type": "stream",
"text": [
"63/63 [==============================] - 0s 7ms/step - loss: 0.0109\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0.010881561785936356"
]
},
"metadata": {
"tags": []
},
"execution_count": 60
},
{
"output_type": "stream",
"text": [
"time: 514 ms\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "VvGKJAPrVKUq",
"colab_type": "text"
},
"source": [
"Note that for each neuron, a linear model has one parameter per input and per time step, plus a bias term (in the simple linear model we used, that’s a total of 51 parameters). In contrast, for each recurrent neuron in a simple RNN, there is just one parameter per input and per hidden state dimension (in a simple RNN, that’s just the number of recurrent neurons in the layer), plus a bias term. In this simple RNN, that’s a total of just three parameters. \n",
"\n",
"Aurélien Géron. « Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-h9CniJWlLKP",
"colab_type": "text"
},
"source": [
"L'explication du piètre résultat du RNN relativement à un modèle linéaire tient au faible nombre de paramètres (3) "
]
},
{
"cell_type": "code",
"metadata": {
"id": "DyZVwiLxU9rK",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 220
},
"outputId": "7c7c262c-20dc-44ae-f4f3-89162159310c"
},
"source": [
"model.summary()"
],
"execution_count": 61,
"outputs": [
{
"output_type": "stream",
"text": [
"Model: \"sequential_1\"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"simple_rnn (SimpleRNN) (None, 1) 3 \n",
"=================================================================\n",
"Total params: 3\n",
"Trainable params: 3\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n",
"time: 2.59 ms\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "y8nYjN6NJCJJ",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 312
},
"outputId": "d6a09198-0cf7-4e9b-9613-c5cc8f94c82c"
},
"source": [
"plot_learning_curves(history.history[\"loss\"], history.history[\"val_loss\"])\n",
"plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "aImMlDQCJCJM",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 313
},
"outputId": "9fd05526-60f8-49c1-b978-1b58c54e6090"
},
"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()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "mo0ASrtPJCJP",
"colab_type": "text"
},
"source": [
"## Deep RNNs"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TUI1_-jClhLF",
"colab_type": "text"
},
"source": [
"Ce modèle est différent du précédent en ce qu'il empile les RNN"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "GwmKijXOmRzM",
"colab_type": "text"
},
"source": [
"Il est import que return_sequences=True pour les 2 1ers RNN afin que toutes les sortes de la couche h soient calculées et transmises à la couche suivante. Si return_sequences n'est pas mis à True, alors (le comportement par défaut) seul le dernier output est transmis."
]
},
{
"cell_type": "code",
"metadata": {
"id": "7YqCagb8JCJQ",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 715
},
"outputId": "6ca4f572-7806-4eb7-bec8-32a0a66a35c2"
},
"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))"
],
"execution_count": 62,
"outputs": [
{
"output_type": "stream",
"text": [
"Epoch 1/20\n",
"219/219 [==============================] - 31s 140ms/step - loss: 0.0492 - val_loss: 0.0090\n",
"Epoch 2/20\n",
"219/219 [==============================] - 30s 138ms/step - loss: 0.0070 - val_loss: 0.0065\n",
"Epoch 3/20\n",
"219/219 [==============================] - 30s 137ms/step - loss: 0.0053 - val_loss: 0.0045\n",
"Epoch 4/20\n",
"219/219 [==============================] - 30s 139ms/step - loss: 0.0045 - val_loss: 0.0040\n",
"Epoch 5/20\n",
"219/219 [==============================] - 30s 136ms/step - loss: 0.0042 - val_loss: 0.0040\n",
"Epoch 6/20\n",
"219/219 [==============================] - 31s 140ms/step - loss: 0.0038 - val_loss: 0.0036\n",
"Epoch 7/20\n",
"219/219 [==============================] - 30s 137ms/step - loss: 0.0038 - val_loss: 0.0040\n",
"Epoch 8/20\n",
"219/219 [==============================] - 30s 137ms/step - loss: 0.0037 - val_loss: 0.0033\n",
"Epoch 9/20\n",
"219/219 [==============================] - 30s 136ms/step - loss: 0.0036 - val_loss: 0.0032\n",
"Epoch 10/20\n",
"219/219 [==============================] - 30s 135ms/step - loss: 0.0035 - val_loss: 0.0031\n",
"Epoch 11/20\n",
"219/219 [==============================] - 29s 135ms/step - loss: 0.0034 - val_loss: 0.0030\n",
"Epoch 12/20\n",
"219/219 [==============================] - 30s 138ms/step - loss: 0.0033 - val_loss: 0.0031\n",
"Epoch 13/20\n",
"219/219 [==============================] - 30s 138ms/step - loss: 0.0034 - val_loss: 0.0031\n",
"Epoch 14/20\n",
"219/219 [==============================] - 30s 135ms/step - loss: 0.0033 - val_loss: 0.0032\n",
"Epoch 15/20\n",
"219/219 [==============================] - 31s 140ms/step - loss: 0.0034 - val_loss: 0.0033\n",
"Epoch 16/20\n",
"219/219 [==============================] - 30s 137ms/step - loss: 0.0035 - val_loss: 0.0030\n",
"Epoch 17/20\n",
"219/219 [==============================] - 30s 135ms/step - loss: 0.0033 - val_loss: 0.0029\n",
"Epoch 18/20\n",
"219/219 [==============================] - 30s 136ms/step - loss: 0.0033 - val_loss: 0.0030\n",
"Epoch 19/20\n",
"219/219 [==============================] - 29s 134ms/step - loss: 0.0032 - val_loss: 0.0029\n",
"Epoch 20/20\n",
"219/219 [==============================] - 29s 134ms/step - loss: 0.0032 - val_loss: 0.0029\n",
"time: 10min 3s\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "goc5kdZkJCJT",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 71
},
"outputId": "0c645059-9388-4626-ebf3-e445a6bc0e6c"
},
"source": [
"model.evaluate(X_valid, y_valid)"
],
"execution_count": 63,
"outputs": [
{
"output_type": "stream",
"text": [
"63/63 [==============================] - 1s 13ms/step - loss: 0.0029\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0.002910558832809329"
]
},
"metadata": {
"tags": []
},
"execution_count": 63
},
{
"output_type": "stream",
"text": [
"time: 904 ms\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bmhmyC5Tn4p1",
"colab_type": "text"
},
"source": [
"[![](https://raw.githubusercontent.com/BackProp-fr/meetup/master/images/LogoBackPropTranspSmall.png)](https://www.backprop.fr)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "JuKNPH9gn6GO",
"colab_type": "text"
},
"source": [
"Cette fois le modèle linéaire et battu mais au prix d'un temps de calcul important (10 minutes avec un GPU) et d'une architecture plus complexe"
]
},
{
"cell_type": "code",
"metadata": {
"id": "iqsgrCkcJCJY",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 328
},
"outputId": "4f461f14-dc21-4f45-ca81-e853f6e27121"
},
"source": [
"plot_learning_curves(history.history[\"loss\"], history.history[\"val_loss\"])\n",
"plt.show()"
],
"execution_count": 64,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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+Wlnn6aefZubMmWzZsoWioiLKysoqvxiP5+DBg+zcuZPcoNfh8Xjo378/W7durSz75ptvuOeee1i1ahV79uzB7/fj9/v57rvvavxaDh06xI4dOzj77LOrlA8YMID58+dXKevRo0fl/datWwOwe/fuGu1n06ZNlJWVVdlPXFwcubm5lROvjRkzhqFDh9K5c2eGDRvGT3/6Uy688EI8Hg89e/bkvPPOo3v37gwbNozzzjuPUaNG0aJFixq/1nCI9DGY6UApkAlcBTwlIjkh6o0DLgV6Aj2A4cCNwRVEpCnwW2CdmwEbczLx9+8PixbBAw84ty4nlwpjx47l//7v/9i/fz+zZs0iIyODSy65BIC5c+cyfvx4xowZw3vvvceaNWu4+eabKS0tDWsMF198MXv27GHGjBmsWrWKf/3rX8THx4dtP9WH8Pd6vT9Yd6zjSrXdT58+fdi8eTMPP/wwfr+f6667jqFDh+L3+4mLi2PBggUsWLCAHj168Nxzz9GpUyc+//zzeu+/NiKWYEQkBRgJ3KOqBaq6HHgbuCZE9euAaaq6TVW3A9OAMdXqPAz8GaclZIypqdxcuPvuiCUXgFGjRpGUlMTs2bN5/vnnufbaayu/gJcvX07//v255ZZb6NOnDz/60Y/45ptvarzt9PR0WrVqVeUYg6ry8ccfVz7et28fX331Fb/97W8577zzOO2008jPz6e8vLyyTsUxl+onBwRLS0ujdevWrFixokr58uXL6datW41jPp6OHTuSkJBQZT8+n4+VK1dW2U/jxo0ZNWoUTz31FO+88w4ffPAB//nPfwAnEeXm5nLffffxySef0Lp1a+bOnRu2GGsikl1knYFyVd0YVPY5MChE3ZzAuuB6lS0dETkD6AfcDFxxrJ2KyDicFhGZmZksWbKkLrFXKigoqPc2wiWWYoHYiudkjCU9PZ38/Px6bcPn89V7G3U1atQo7rvvPg4cOMCVV15Jfn4+Pp+Pdu3aMWvWLN544w1OOeUU3njjDf75z3/SpEmTylhLSkrw+/1HffzLX/6SP/3pT2RnZ5OTk8Ozzz7Lzp07admyJfn5+cTHx9OsWTOefPJJmjZtys6dO/n9739PfHw8xcXF5Ofnk5GRQXJyMm+//TbNmzcnMTGR9PT0H+zr1ltv5aGHHqJt27b06tWLuXPnsmzZMpYtW0Z+fj4FBQWAcxyl+ntdVFR01Pe/rKyM8vJy8vPzSUpKYuzYsUycOJFGjRrRoUMHpk+fzq5du7j22mvJz8/niSeeIDMzkx49ehAfH8+sWbNIS0sjPT2dRYsWsWTJEoYMGULLli354osv2Lp1Kx07dgy5/+LiYnf+X+o613JtF2AgkFet7AZgSYi6PqBr0ONOgAICxAGrgTMD65YAv6hJDH379q3R/NTHcjLO9R4usRTPyRjLseZNr6lIz/Ue7NNPP1VAzzrrrCrxlJSU6PXXX69NmjTR9PR0vf766/UPf/iDtm/fvrLefffdpzk5OUd9XFZWpuPHj9f09HRNT0/XW265RX/5y1/qoEGDKussWrRIc3JyNDExUXNycvTdd9/VlJQUfeGFFypjefbZZzU7O1s9Hk/lc6vvy+fz6eTJk7Vt27bq9Xq1e/fuOm/evMr1mzZtUkA/+eSTKq8f0Ndff/2o7891112nF110UWUsxcXFevvtt2vLli01ISFB+/fvr8uWLaus/8wzz2jv3r01NTVVGzdurOecc46uWLFCVZ3PygUXXFD53FNPPVWnTJly1H0f67MFrNa6fu/X9Ym13hH0Bg5XK7sT+FuIugeBM4Ie9wXyA/dvBZ4PWmcJJkbEUjwnYywneoIJJZbiacixuJVgInmQfyMQLyKdgsp6Evog/brAulD1hgA/E5E8EckDzgKmicgTLsRsjDGmjiJ2DEZVC0XkTWCyiPwC6AVcgpMgqnsJuENE5uN0jd0J/CWwbgyQFFT3TeCvOKc/G2OMiRGRvtDyZuB5YDewD7hJVdeJyEDgH6pacYnrDOAU4MvA45mBMlT1QPAGRaQUOKSqByMQvzHGmBqKaIJR1f0417dUL18GpAY9VmBiYDneNgeHMURjjDFhYoNdGnMC0aqDWRhTb25+pizBGHOC8Hq9FBUVRTsMc5IpKysjPt6dzixLMMacIFq2bMn27ds5fPiwtWRMWPj9fnbt2kW6SwOe2mjKxpwg0tLSgCMj5dZFcXExSUlJx68YIbEUT0ONJSUlpXIk6XCzBGPMCSQtLa0y0dTFkiVL6N27dxgjqp9YisdiCT/rIjPGGOMKSzDGGGNcYQnGGGOMKyzBGGOMcYUlGGOMMa6wBGOMMcYVlmCMMca4whKMMcYYV1iCMcYY4wpLMMYYY1xhCcYYY4wrLMEYY4xxhSUYY4wxrrAEY4wxxhWWYIwxxrjCEowxxhhXWIIxxhjjCkswxhhjXGEJxhhjjCsswRhjjHGFJRhjjDGusARjjDHGFZZgjDHGuMISjDHGGFdYgjHGGOMKSzDGGGNcYQnGGGOMKyzBGGOMcUVEE4yIZIjIPBEpFJEtIjL6KPVERKaIyL7AMkVEJLCuuYisCJQfEJGVInJ2JF+HMcaY44uP8P6mA6VAJtALeEdEPlfVddXqjQMuBXoCCrwPbAKeBgqA64F/B9ZdAvxNRFqqanlEXoUxxpjjilgLRkRSgJHAPapaoKrLgbeBa0JUvw6YpqrbVHU7MA0YA6Cqxar6tar6AQF8QFMgIwIvwxhjTA2JqkZmRyK9gRWq2iiobAIwSFWHV6t7EBimqqsCj/sBi1W1cVCdL4CugBeYqao3HGW/43BaRGRmZvZ99dVX6/U6CgoKSE1Nrdc2wiWWYoHYisdiCS2WYoHYisdiCe3cc8/9VFX71enJqhqRBRgI5FUruwFYEqKuD+ga9LgTTneYVKuXBPwcuK4mMfTt21fra/HixfXeRrjEUiyqsRWPxRJaLMWiGlvxWCyhAau1jt/7kTwGUwCkVStLA/JrUDcNKAi82EqqWgy8IiIbRGSNqn4ezoCNMcbUXSTPItsIxItIp6CynkD1A/wEynrWoF4FL3BKvSM0xhgTNhFLMKpaCLwJTBaRlMCpxZcAL4eo/hJwh4i0EZHWwJ3ALAAROVNEBohIgogki8gknLPSVkXkhRhjjKmRSJ+mfDPwPLAb2AfcpKrrRGQg8A9VrTiqNQOnRfJl4PHMQBlAIvDnwPqyQJ2LVHXH8Xbu94frZRhjjDmeiCYYVd2Pc31L9fJlQGrQYwUmBpbqdf9J1e6zGisrq8uzjDHG1EWDGirGEowxxkROg0ow5XadvzHGREyDSjClpdGOwBhjGo56JxgR8YYjkEiwFowxxkROrRKMiNwmIiODHj8HFInI1yLSJezRhZkdgzHGmMipbQvmNmAPgIicA1wBjAbW4AxIGdMswRhjTOTU9jTlNjjD5gMMB15X1ddE5EtgWVgjc4ElGGOMiZzatmAOAS0D94cCiwL3y3AGnoxplmCMMSZyatuCWQA8KyKfAT8C/hEoz+FIyyZmlZWBzwdxcdGOxBhjTn61bcH8ClgBtABGBa7MB+gDvBLOwNyyd2+0IzDGmIahVi0YVT0E3Bqi/L6wReSyvDzIzIx2FMYYc/Kr7WnK3YJPRxaRoSIyW0TuFpETouNp585oR2CMMQ1DbbvIngd6A4hINvAWkIHTdfZgeENzR15etCMwxpiGobYJpivwWeD+KGCVqv4UuAZn6uKYZy0YY4yJjNommDigYkSvIcD8wP1vcCb9imkej7VgjDEmUmqbYNYCNwUmCBsCvBsobwPE/PlZXq+1YIwxJlJqm2AmATcAS4BXVLVixskRwMdhjMsVlmCMMSZyanua8lIRaQGkqer3QatmAIfDGpkLvF7rIjPGmEip9ZTJquoTkSIR6Q4o8I2qbg57ZC6wFowxxkROba+DiReRR4Dvgc+BL4HvReRPJ8K8MF4vFBZCQUG0IzHGmJNfbY/B/Am4Gvgl0BnoBNyEc5ryw+ENLfy8gRRorRhjjHFfbbvIRgPXq+r8oLJvRGQPMBOYELbIXFCRYPLyoFOn6MZijDEnu9q2YNJxrnmp7hugSf3DcZe1YIwxJnJqm2A+x5nVsrrbA+tiWnALxhhjjLtq20U2EZgvIucBHwXKzgRaAxeGMzA3xMfbmWTGGBMptWrBqOpSnIP7fwVSA8vrwPmEbtnEnMxMSzDGGBMJdbkOZgfwu+AyEekJjAxXUG5q1cq6yIwxJhJqewzmhJeVZS0YY4yJhAaXYKwFY4wxkdEgE8yePVBeHu1IjDHm5FajYzAi8vZxqqSFIZaIyMoCVdi9G1q3jnY0xhhz8qrpQf59NVi/qZ6xRESrVs7tzp2WYIwxxk01SjCq+l9uBxIpWVnOrR2HMcYYdzXIYzBgZ5IZY4zbIppgRCRDROaJSKGIbBGR0UepJyIyRUT2BZYpIiKBdZ1F5C0R2SMi+0XkPRHpUtMYMjOdW0swxhjjrki3YKYDpUAmcBXwlIjkhKg3DrgU6An0AIYDNwbWNQHeBroEtvMx8FZNA0hMhIwM6yIzxhi3RSzBiEgKztX+96hqgaoux0kU14Sofh0wTVW3qep2YBowBkBVP1bV51R1v6qWAY8CXUSkWU1jsYstjTHGfaKqkdmRSG9ghao2CiqbAAxS1eHV6h4EhqnqqsDjfsBiVW0cYruXAk+paquj7HccTouIzMzMvq+++ip33tmTkhIPTzzxr1q/joKCAlJTU2v9PDfEUiwQW/FYLKHFUiwQW/FYLKGde+65n6pqvzo9WVUjsgADgbxqZTcAS0LU9QFdgx53ApRAQgwqbwtsB35ekxj69u2rqqpXX63aoYPWyeLFi+v2RBfEUiyqsRWPxRJaLMWiGlvxWCyhAau1jt/7kTwGU8APL8hMA/JrUDcNKAi8WABEpAWwAHhSVV+pTSBZWc4xmAg13owxpkGKZILZCMSLSPBkxT2BdSHqrgusC1lPRJriJJe3VfW/axtIq1ZQXAwHD9b2mcYYY2oqYglGVQuBN4HJIpIiImcDlwAvh6j+EnCHiLQRkdbAncAsABFJA97DOZ7zm7rEUnGxpR3oN8YY90T6NOWbgWRgN/AKcJOqrhORgSJSEFRvBvA34EtgLfBOoAzgZ8CPgf8SkYKgpV1Ng6i42NJOVTbGGPfUesKx+lDV/TjXt1QvX4YzO2bFY8WZnnliiLovAi/WJw5rwRhjjPsa3FAxYC0YY4yJhAaZYNLTISnJWjDGGOOmBplgRI6cqmyMMcYdDTLBgNNNZi0YY4xxT4NNMNaCMcYYdzXYBGMtGGOMcVeDTTBZWbB/P5SURDsSY4w5OTXYBFNxqvKuXdGNwxhjTlYNNsHYxZbGGOOuBptg7GJLY4xxV4NPMNaCMcYYdzTYBNOypXPBpbVgjDHGHQ02wcTHQ4sW1oIxxhi3NNgEA3axpTHGuKlBJxi72NIYY9zToBNMVpYlGGOMcUuDTjCtWjkXWvr90Y7EGGNOPg0+wZSVOUPGGGOMCa8GnWAqrua3A/3GGBN+DTrB2MWWxhjjngadYKwFY4wx7mnQCcZaMMYY454GnWBSUyElxVowxhjjhgadYMAutjTGGLdYgrEEY4wxrmjwCcbGIzPGGHc0+ARjLRhjjHFHg08wWVlw6BAcPhztSIwx5uTS4BOMTZ1sjDHuaPAJxi62NMYYdzT4BGMXWxpjjDsafIKpaMFYgjHGmPBq8AmmRQuIi7MuMmOMCbeIJhgRyRCReSJSKCJbRGT0UeqJiEwRkX2BZYqISND6Z0TkaxHxi8iY+sTk8UBmprVgjDEm3CLdgpkOlAKZwFXAUyKSE6LeOOBSoCfQAxgO3Bi0/nPgZuCzcARlF1saY0z4RSzBiEgKMBK4R1ULVHU58DZwTYjq1wHTVHWbqm4HpgFjKlaq6nRVXQQUhyM2u9jSGGPCT1Q1MjsS6Q2sUNVGQWUTgEGqOrxa3YPAMFVdFXjcD1isqo2r1VsOzFTVWcfY7zicFhGZmZl9X3311R/UeeSRLqxalcFf/7ryuK+joKCA1NTU49aLhFiKBWIrHosltFiKBWIrHosltHPPPfdTVe1XpyerakQWYCCQV63sBmBJiLo+oGvQ406AEkiIQeXLgWqAcBwAABjnSURBVDE1jaFv374ayu9/r+rxqJaXh1xdxeLFi49fKUJiKRbV2IrHYgktlmJRja14LJbQgNVax+/9SB6DKQDSqpWlAfk1qJsGFARebNhlZYHfD3v3urF1Y4xpmCKZYDYC8SLSKaisJ7AuRN11gXXHq1c7eXmw8ofdYHaxpTHGhF/EEoyqFgJvApNFJEVEzgYuAV4OUf0l4A4RaSMirYE7gVkVK0UkQUSSAAG8IpIkIsd/Ldu3w5AhP0gylmCMMSb8In2a8s1AMrAbeAW4SVXXichAESkIqjcD+BvwJbAWeCdQVmEBUAScBTwTuH9OjSIoKoKnn65SZOORGWNM+MVHcmequh/n+pbq5cuA1KDHCkwMLKG2M7jOQYjASy9BQQE8/ji0bWvDxRhjjAsa1lAxbdrAkiXw0EMwfz6cdho89hjJ3nLS060FY4wx4dSwEkxWFpxzDtx9N6xbBwMGwK9/DWecwdAmn1gLxhhjwqhhJZhgp5zitGJeew3y8pi7pT9XrrgVDh6MdmTGGHNSaLgJBpzjMZdfDhs28H6nX/GzndOdbrPXXoMIjXBgjDEnq4adYCqkp/NCn79wVtzHFKS1gv/3/+CnP4Vvv412ZMYYc8KyBINzWcybb8IqXz9abVnFptsfg+XLISfHOSGgtDTaIRpjzAnHEgzOiWU+n3O/oDieX3x5O7v++RVcdBH87nfQuzcsWxbVGI0x5kRjCQYYPBgSE53Jxzwe+OADyD6zDVcl/pWvp/0dCguds88uvpgOzz0XcrgZY4wxVVmCAXJzYdEiePBBp2ds40a4+Wb4+9+h650XMaj5Or496yr0nXdoP3u2k2wWLIh22MYYE9MswQTk5jqXx+TmQqdO8NhjsG0bPPEE7CpI4dkPc/DjQQDKy2HECLj3Xti/P9qhG2NMTLIEcwyNG8OvfgXr18Oljw6m3JNIGXEUk8j69Fx44AFo3x5++1sb698YY6qxBFMDHg/0H59L4vJFrLtiHE9fsZjc4sV050sWJFyE/vGPaIcOMHEi7NoV7XCNMSYmWIKpjdxcDtx0BePn5rJtG9z0RHdua/Eq3XQdb/guxT91Gr52Hfl4wB2s/puNO2OMadgswdRRcPfZ4++dxqwhs+mqG3i59Ar6rPgz3Ud05OPc29i0bBt+f7SjNcaYyLMEU08eDwwb5pxxNuLOzlwvs+jC18zmanp/9BStzzmV5xJv5rJ+3/GrX8HMmbB6NRQXRztyY4xxV0TngznZjRwJTz4JW0pP5baEmTSZ/Hu6//2P/NeymYz5bCazvxjD5LK7ySKPn8gSNncYjOfsXHr1onJp1szZ1sqVzgWggwc7Z7YZY8yJxhJMGFVcT3MkMXSACU/D1t/BlCmMefZZxnieQxHwK+VbvFx/cB4TZl+AM/szZGdDu3awapUzuoDX65wyPXgwtGgBGRlOq8kYY2KdJZgwy80N0eLIzoYnnkDuvhuuuAL58EMAEvwlzN7/U15OTuZwRlv2JmezxZfNJ/9qx2nl2Wwlm62l2Uy6OZt80gAnuTRrBuelrKR/8Qcs6JrI911zadECmjd3klDFsmULfPEFDBlyjFZQLDWVYikWY0y9WYKJpDZtYOpU5xu/tBTi4uCmm5C4OFK2biVl61bab13IwOKdCFXPDChNTiM/PZt9KdkcLk+g+5b5eNRHed4U/vLxRL4s6sQmdVpBStXb9fcIc5pCRjOhcZrQuDE0ThPal2wk94MH8fjKUa+XPX+ZS8qoC0lpmoDIkX2H43s/5Db8fti3z5mretEimDTJuYg1IQFefx0uvpgqgRhjTiiWYCLth/1oP6gi5eV8+rcdrHtvK2e23krn5K0kbN1Ks8DCV1+BlgOQQCl3Hn7w+Pv9PrAchZSWkHnjpXAj7CODffFZHEjO4vuELDbsz+SAZvGMJ4uFZ2WR2C6TwsZZlDRujscbR1wctN+xkiafv8OL/RLJa9+f1JJ9pBfuoHHBToq/3cG6hTvJ8u9gt+xga+udZBTvIOlAHnG+sh8GU1ICI0ZQltqUwg45FHboRlHHHIpOyWFNWQ4fbcok9yyhf39nDLmkpCOL11uznGSNJWPcZwkmGkL2owWJj6fvz9rR92ftQq9fuRKGDEFLSpCEBHj5ZWfEZ6icKO1fnynXXgvlZUqCV5k1C3r30so6qlC08l8k/WosUlaG3xPH2qG/5lB5CnF78kjYn0dqfh4tD6xigO4khcPgB5YfCcOHh920JJ9UTuVbPPjRT6fhx0M8vh+EvZ+m7NDWfLW9FTs4l520Yget2UkrmrKfxxlPPGX4iGc6N5NScJictevIWfs6bXgGgNOBn5LB+qe6sYgc1gUtu2kJCIMSVjKY5TySksi6tNzK5FORjIqLnTP5/H6nEXnBBdC2rbM+IeHIbfD9UGXffOPMvN2/P5x5JjRq5CzJyc5tXFzNPg7hSnautTRjOZYaVLIfE9FjCeZEFGgFbXr+eU65/vqQ/zW9fwTPZB/5x+pdrYoAjbqdBt06wpIlxA0eTM8Q21m5EroNAW9JAdnePF59fBfdm+dBXh5xeXm02rWLVsuWoV/5nQ45ATl7AMUjRlLeohXlLVvz2c5WXParVhSUJ+H1Ovnw7F7OSQx+/5Hb7z7rQerqJRzsPZizc3Iry78oV+L37WLlzHVsW7CObuqklKu9c0ktO1AZ6+HkDA6mZdNy91pE/fjLJ/N+1q18l3Iahf5kCkuSKDiczJZdSXj8yRSTRJEvme+WJrE5MZlDZckcKk2ioMSLzy+cyUoGs4QlDOYjjv7N9MwzocsTEmBQwkoG+pcxvWkiX2fk/iAJFRY646ZWJLvLLoPWrZ1WWG2W7dud99Xnc7Zz6aXOMbnS0iNLx7yVtPt2EfNaJfJ5o9wq60pL4dAhyMtzfqOIOEm3SZMjSbUisfYoXEnPA0v4d+vBfNcmt8q6xERnGy++6PR2xsXB6NHOMcHSUqdxWlLi3G+/YyXtNi1kXssE1if1QYtL0OISpKSYkkMlHNhVQgIl/J1imqWUkOwpIUFLSNRivFpC5/L1/Kr0UeIox4eXe73/zUrPAPZrBnu1Gd9rE8o1rnIqDoBWrZz3pXHjI0tqqnO7f39HVq4Mva5xY2cQ3FWrnB8Tffs621Ot2W3F/X/9y9nGWWc5/7Ze75ElIcF5vyLZAnc7+Yo2oKmB+/Xrp6tXr67XNpYsWcLgwYPDE1A9RSqW434IAy0qf0kJnsREpwuwWsVw/aKtOHyVkACLFiq5HfOcZkTFsmCBc3ZDfXg8qNfrfBMCIPhaZKLJKfjj4vF74tl3IJ68ffH4iKOceDJaxJPePJ5y4inXeMqIJ77wIB23LUfUh1/i+CRrBDsS2lPkS6TQl8jh8kT25idyoDiREpzFH5+ILz6RYpIoIZFidco7+TbQ17+aT6Ufa+V0UEXUf2TBj6B48OPBT1KCn8aN/CTE+/HGK6f51zJhz2+I0zJ8Es8LHf7A7sY/IiHOR2JcOV6Pj907fezcWo4HH/H4aNvKR2azcvzlPrTMB+XlNC/czMV7X8SDDz9xvJt0CfvJIN5XSpyvlHh/CQmUVi6JOI+TpJREKSGRUhKklEb+AhrrocC76w4/QkF8E3aXZ7CPZuwnA3/TZmgT5/EeXwa7yzPYWdqMJoXbOaVoLUsZyAoGcJhGFJGMnxo2QwNq+qPkWAbErWSwfMCqpJ+wJjm3MvlUJKLSUqf1rOqc9NO1q5MAK1R8pXfPX0nf/CV82ngwaxvn/mAW+IICp7ddxPlhEOLfFlaupO1ZZ23fptq2Lq/FEkwtNcQEUyMrV/LtMVpUYdxN7ZLdG29A9+5Ov1hRUeXt+s+KWf9ZET1+VETndlXXUVzs7GTFiiM/53v3htNOc36Wl5ezf3c5q1aU4/GX4/WU07dnOekpvsr1lJc7Jy8Ej01X0W9W8RP+ZJCcDOnpVfoOC0oTWPefREo0gTJPAn3OTKRpZkLVPsYvv4SPPz7y/g4dCuefX9mP+e/vErn/j4kUlifii0/ij48mktMn8UhfZ2IirF3rTG9eVuZ88/7lL86JNPv2OaOc79vHzvX7WTpvH018+2km++neah9Jh/fDgQPHf22AP96LL7ERBb5k9hc7SaeIZBo1b0R6VjLlCY3wJSTj8ybjLTpI+8/+D/H7UE8c23tcSGmjJsT5SvGUl7J3ewn780rxBhJvi/RSmjQqxVNWiqe8BE95Kd7SQhJL8yv3fzghnbL4RpSLl3LxUiYJFJZ6KSjxUkoCZXjxJnvxpngplwTKPV584iWl/AB99r2PqB8VDx83/ykHEjOrJPNDh+Cgk+MRgX59nWvxKu3aBfPn08/nY7VqnX4HWBeZCY/cXL4rKeEUlzu5j3f4qqL7cPNxkl23QdDtWDuq3lx64okq28oAmgQlu/SaJLuFC49sQ7Wyz+iT5SWsWlrCWX1L6JNT4iS4ir6kkhKYM8dZ/H7nJ+vPfw6jRh2ZIc/jARHWf+Xh8y899OjtIae7U1a5fv16uO02tKwM8Xphxgzo0wfi452kF1g+/TyeFR/FkTsgjh/nVl1HfLyTFIYODWpG/vBnbyrgXwkrAu9N05q8N/ffX2U7nYBbLjjy/uaE2sYppzizAx7jF0croF3Q3ympokp5uZNk9u+HRx91+jkr3t+LLnLmfDp8GE9REZ6iIko3HWbVO0Uk+Q6T4imic7si0uO/h6IdcPCw88Nk/37wOyffiL+c7G+WONcUBBJvRpMEvtydSIk/gXxPOp1PTySjeuL94gunDy2QeFN6d4XTT3eSaGDZn1fK5hVlxPnLSPSU0v1HRaQlHXLWl5Y6t3v2gAb6BtVHbvES8Dau8t6UJsBeQAFRaL4Z2BFUIT+fKv2LdaGqDWbp27ev1tfixYvrvY1wiaVYVGMrnrDE8uGHqg895NzWYxvf/OIX9d6GJierxsU5t3XdVjhiqYinvu9LOOOpr8D76/N4jvn+Hvdl1+DvVNNtRCKW424nsI0+4Nc6fudG/Us/koslGHfFUjwnXSxh+lKPpfdFNYbiiaXkG2OxtIFtWsfvXOsiM+ZEcNy+QVMv4eriDcffKcZi2Q55dX26jWpljDHGFZZgjDHGuMISjDHGGFdYgjHGGOMKSzDGGGNcYQnGGGOMKyKaYEQkQ0TmiUihiGwRkdFHqSciMkVE9gWWKSJHhoATkV4i8qmIHA7c9gq1HWOMMdET6RbMdKAUyASuAp4SkZwQ9cYBlwI9gR7AcOBGABFJAN4CZgNNgReBtwLlxhhjYkTEEoyIpAAjgXtUtUBVlwNvA9eEqH4dME1Vt6nqdmAaMCawbjDOGGqPqWqJqv4ZZ0DWn7j8EowxxtRCJK/k7wyUq+rGoLLPgUEh6uYE1gXXywla94WqBg8D/UWg/N3qGxKRcTgtIoACEfm6buFXao4zRlwsiKVYILbisVhCi6VYILbisVhC61LXJ0YywaQCh6qVHQQaH6XuwWr1UgPHYaqvO9Z2UNVngKNMCVV7IrJaVfuFa3v1EUuxQGzFY7GEFkuxQGzFY7GEJiJ1nuMkksdgCoC0amVpQH4N6qYBBYFWS222Y4wxJkoimWA2AvEi0imorCewLkTddYF1oeqtA3oEn1WGcyJAqO0YY4yJkoglGFUtBN4EJotIioicDVwCvByi+kvAHSLSRkRaA3cCswLrlgA+4DYRSRSRWwLlH7gZf5CwdbeFQSzFArEVj8USWizFArEVj8USWp1jieiUySKSATwPDAX2Ab9R1f8VkYHAP1Q1NVBPgCnALwJPnQlMqjiwLyK9A2XdgA3AWFX9V8ReiDHGmOOKaIIxxhjTcNhQMcYYY1xhCcYYY4wrLMHUQOBkgucC46fli8gaEbkwBuLqJCLFIjI7BmK5UkQ2BMaZ+yZwXC0acXQQkfki8r2I5InIEyISkeu9ROQWEVktIiUiMqvauiEi8lVg/LzFItI+GrGIyJki8r6I7BeRPSLyuoi0ikYs1ercKyIqIue5Gcvx4hGRRiLypIjsFZGDIrI0irFcEfifyheR9SJyqcuxHPN7ri6fYUswNRMPbMUZdSAd+D3wmoh0iGJM4Izt9kmUY0BEhuKclPFfOBe8ngN8G6VwngR2A62AXjh/s5sjtO8dwIM4J7JUEpHmOGdQ3gNkAKuBudGIBWf8vmeADkB7nOvHXohSLACIyKnA5cBOl+OoSTzP4PyNTgvc/joasYhIG5zxFu/Auc7vLuB/RaSli7Ec9Xuurp/hSF7Jf8IKnGJ9f1DR30VkE9AX2ByNmETkSuAA8CHwo2jEEOQPwGRV/SjweHsUY+kIPKGqxUCeiLzLkWGGXKWqbwKISD+gbdCqy4B1qvp6YP39wF4R6aqqX0UyFlX9R3A9EXkC+KcbMRwvliDTgUk4Pw5cd7R4RKQrMAJoq6oVo458Go1YAvcPBP293hGRQuBUnB9QbsRyrO+5ZtThM2wtmDoQkUycsdWicnGniKQBk3F+3USViMQB/YAWIvIfEdkW6JZKjlJIjwFXBro62gAXEmKMugirMrZe4B/5GyKU+I7jHKJ4kbKIXA6UqOr8aMUQ5AxgC/CHQBfZlyIyMkqxrAY2iMgIEYkLdI+V4Iy7GBHVvufq9Bm2BFNLIuIF5gAvuvXrswYeAJ5T1W1R2n+wTMALjAIG4nRL9cZpXkfDUpwP/SFgG84/6v9FKZYKtRo/L1JEpAdwL073SzT23xh4CLg9GvsPoS3QHedv0xq4BXhRRE6LdCCq6sO54Px/cRLL/wI3Br7YXRfie65On2FLMLUgIh6ckQdKcT580YihF3Ae8Gg09h9CUeD2L6q6U1X3Av8D/DTSgQT+Pu/i9BWn4IxI2xTn+FA0xdz4eSLyI+AfwO2quixKYdwPvKyqm6O0/+qKgDLgQVUtVdV/AouBYZEOJHCyw59wpidJwDkuMlMiMLniUb7n6vQZtgRTQyIiwHM4v9hHqmpZlEIZjHOA9jsRyQMmACNF5LNoBKOq3+O0FIKv2I3W1bsZQDucYzAlqroP5wB2xJNdNVXG1hNnbqRTiV4Xa3tgIfCAqoYaqilShuAM+ZQX+Cxn4xxUnhSleEJ1P0Xrs9wLWKqqq1XVr6qfAKtwfly65hjfc3X6DFuCqbmncM4sGa6qRcer7KJncP6wvQLL08A7wPlRjOkF4FYRaSkiTXHOvPl7pIMItJ42ATeJSLyINMGZvC4i/daBfSYBcUCciCSJc4r0PKC7iIwMrL8XZ04j17pYjxZL4LjUBzhJ+Gm39l+TWHASTHeOfJZ34MxcOz1K8SwFvgPuDtQ5GzgXeC8KsXwCDKxosYgzPNZA3P8sH+17rm6fYVW15TgLzumcChTjNBUrlqtiILb7gdlRjsGLcwbQASAP+DOQFKVYeuEMiPo9zoRNrwGZEfxbaLXl/sC684CvcLphlgAdohELcF/gfvDnuCBa70u1epuB86L8d8oBVgKFwHrgZ1GM5RbgPzjdUN8Cd7ocyzG/5+ryGbaxyIwxxrjCusiMMca4whKMMcYYV1iCMcYY4wpLMMYYY1xhCcYYY4wrLMEYY4xxhSUYY04Q4syXMiracRhTU5ZgjKkBEZkV+IKvvnx0/Gcb0zDZfDDG1NxC4JpqZaXRCMSYE4G1YIypuRJVzau27IfK7qtbROSdwJSyW0Tk6uAni8jpIrJQRIrEmbJ4loikV6tzXWAekhIR2SUiL1aLIUOcaY4LReTbEPu4N7DvksAgki+58k4YUwOWYIwJnz8Ab+OMh/YM8FJgpsKK0Wffwxnb6QzgZ8BZBE2VKyI3AjNwBg/tgTMK9Npq+7gXeAtnZNu5wPMi0i7w/JE4o2vfDHQCLgY+duF1GlMjNhaZMTUgIrOAq3EGAgw2XVUniYgCM1X1hqDnLATyVPVqEbkBmIozHW9+YP1gnPlGOqnqf0RkG87Apb85SgwK/FFV7w48jseZWG2cqs4WkTtwRiPurtGbTsKYSnYMxpiaWwqMq1Z2IOj+ymrrVgIXBe6fhjO8efAETR8CfqCbiBwC2gCLjhND5XDtqlouInuAloGi13Fmh9wkIu/hTL72tqqWHGebxrjCusiMqbnDqvqfasveMGy3Nt0I1VsmSuD/WFW3Al1wWjGHgGnAp4HuOWMizhKMMeFzZojHGwL3NwCnB+ahr3AWzv/gBlXdDWzHmYSrzlS1WFXfUdVfAz/Gmd/k7Pps05i6si4yY2ouUUSyqpX5VHVP4P5lIvIJzmRMo3CSRf/Aujk4JwG8JCL3Ak1xDui/qar/CdT5b+BREdmFM0tpI2CIqk6rSXAiMgbnf3oVzskE/w+nxfPvWr5OY8LCEowxNXcesLNa2XagbeD+/cBInBk99wD/pc5c6qjqYRE5H3gM58yuYpyzwW6v2JCqPiUipcCdwBRgPzC/FvEdACbhnEzgxZmR8TJV3VSLbRgTNnYWmTFhEDjD63JV/Wu0YzEmVtgxGGOMMa6wBGOMMcYV1kVmjDHGFdaCMcYY4wpLMMYYY1xhCcYYY4wrLMEYY4xxhSUYY4wxrvj/LfV5tIs8PbYAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
},
{
"output_type": "stream",
"text": [
"time: 209 ms\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "TXOTzqGtJCJd",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 330
},
"outputId": "bec30d2a-74ee-4eb8-cf2f-b4c73d14a1eb"
},
"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()"
],
"execution_count": 65,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
},
{
"output_type": "stream",
"text": [
"time: 1.26 s\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ibvqN6uUoLn7",
"colab_type": "text"
},
"source": [
"Plutôt que d'avoir un SimpleRNN en dernière couche, AG recommande d'utiliser un Dense, ce qui paraît plus logique d'ailleurs. \n",
"\n",
"Le changement n'est pas fondamental mais on y gagne en simplicité et en flexibilité."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qdoGgiOcJCJg",
"colab_type": "text"
},
"source": [
"Make the second `SimpleRNN` layer return only the last output:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "zoFtjK2UJCJh",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 715
},
"outputId": "f76bab33-31b6-40ae-c287-dffc2f7d54a4"
},
"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))"
],
"execution_count": 93,
"outputs": [
{
"output_type": "stream",
"text": [
"Epoch 1/20\n",
"219/219 [==============================] - 21s 96ms/step - loss: 0.0232 - val_loss: 0.0052\n",
"Epoch 2/20\n",
"219/219 [==============================] - 21s 94ms/step - loss: 0.0043 - val_loss: 0.0036\n",
"Epoch 3/20\n",
"219/219 [==============================] - 20s 93ms/step - loss: 0.0035 - val_loss: 0.0031\n",
"Epoch 4/20\n",
"219/219 [==============================] - 20s 91ms/step - loss: 0.0033 - val_loss: 0.0033\n",
"Epoch 5/20\n",
"219/219 [==============================] - 20s 91ms/step - loss: 0.0033 - val_loss: 0.0034\n",
"Epoch 6/20\n",
"219/219 [==============================] - 20s 92ms/step - loss: 0.0031 - val_loss: 0.0029\n",
"Epoch 7/20\n",
"219/219 [==============================] - 20s 92ms/step - loss: 0.0031 - val_loss: 0.0034\n",
"Epoch 8/20\n",
"219/219 [==============================] - 20s 91ms/step - loss: 0.0032 - val_loss: 0.0028\n",
"Epoch 9/20\n",
"219/219 [==============================] - 20s 92ms/step - loss: 0.0031 - val_loss: 0.0028\n",
"Epoch 10/20\n",
"219/219 [==============================] - 20s 92ms/step - loss: 0.0030 - val_loss: 0.0029\n",
"Epoch 11/20\n",
"219/219 [==============================] - 21s 94ms/step - loss: 0.0029 - val_loss: 0.0027\n",
"Epoch 12/20\n",
"219/219 [==============================] - 21s 94ms/step - loss: 0.0030 - val_loss: 0.0031\n",
"Epoch 13/20\n",
"219/219 [==============================] - 21s 95ms/step - loss: 0.0030 - val_loss: 0.0031\n",
"Epoch 14/20\n",
"219/219 [==============================] - 21s 95ms/step - loss: 0.0030 - val_loss: 0.0030\n",
"Epoch 15/20\n",
"219/219 [==============================] - 21s 94ms/step - loss: 0.0030 - val_loss: 0.0030\n",
"Epoch 16/20\n",
"219/219 [==============================] - 21s 95ms/step - loss: 0.0030 - val_loss: 0.0027\n",
"Epoch 17/20\n",
"219/219 [==============================] - 20s 94ms/step - loss: 0.0029 - val_loss: 0.0028\n",
"Epoch 18/20\n",
"219/219 [==============================] - 21s 95ms/step - loss: 0.0030 - val_loss: 0.0027\n",
"Epoch 19/20\n",
"219/219 [==============================] - 20s 93ms/step - loss: 0.0029 - val_loss: 0.0028\n",
"Epoch 20/20\n",
"219/219 [==============================] - 21s 97ms/step - loss: 0.0029 - val_loss: 0.0026\n",
"time: 6min 52s\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "KKzDmUL1rDA2",
"colab_type": "text"
},
"source": [
"On gagne 4 minutes de temps de calcul alors qu'on garde la même performance sur les prévisions"
]
},
{
"cell_type": "code",
"metadata": {
"id": "WERMyKPQrHfJ",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 71
},
"outputId": "4b6dee9d-0170-4c87-b85f-0df860055f0e"
},
"source": [
"model.evaluate(X_valid, y_valid)"
],
"execution_count": 94,
"outputs": [
{
"output_type": "stream",
"text": [
"63/63 [==============================] - 1s 10ms/step - loss: 0.0026\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0.0026236246339976788"
]
},
"metadata": {
"tags": []
},
"execution_count": 94
},
{
"output_type": "stream",
"text": [
"time: 683 ms\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "zD1T_749JCJn",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 328
},
"outputId": "9dfc6927-e583-41b8-a43b-0107957ea32f"
},
"source": [
"plot_learning_curves(history.history[\"loss\"], history.history[\"val_loss\"])\n",
"plt.show()"
],
"execution_count": 95,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
},
{
"output_type": "stream",
"text": [
"time: 211 ms\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "x6_6UUc4JCJp",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 330
},
"outputId": "15c88dcd-4086-427f-deb3-ba57bb1c99ff"
},
"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()"
],
"execution_count": 71,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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5AdSa1UzPvLZ2H3vLa5mQ4d81nHzNnw933AHPPz+aO+7oerIBSzimD6trdLNyRylzxw/i3vMn+nXt9s6YMnQAMVHCWptxwPRAXlE53//nBgD+urIwYM3Cy5bBY4/BTTcV8thjX+zT6YywHRZtTKAt3nSIytpGvnnWWOZNCN4ItWbxMVFMGTbQZhwwPZJbUEqTx2kYbnI7zcL+/uHk22cjUsjXvta9ZjW7wjF91qLVexie3I/Tx/m3k7UrZoxMZuPeSprcnpDFYCLbKWOc+7iEwDQLtzVAoKPRax2xhGP6pH0VtazYUcJVM0f47Z6F7pgxKpnaRjdbD9l8sqZ7EmKdtZsuPnGo35uFOxqN1p2kYwnH9Emv5O1FFa6eOSKkccwY6Xw5WLOa6a7mPpsHLprk96a0Vas6bjZrTjqrVnXufNaHY/ocj0dZtHoPc8enMTI14fgHBNDI1H6kJsaybk8FN87JDGksJjKtLiwnY4BzI7G/dWa6mvnz7cZPY9qVW1DK3vJarpk1MtShICLMGJnM2t02NNp0T15RObMyUyNi9VhLOKbPWbR6D/3jo7lgqh8XqOqB6SOT2Xm4msraxlCHYiLMgcpa9lXUBnU4f09YwjF9SmVtI+98dpDLpw8jPiYq1OEAtMzs+/Dbm4I6tY6JfKsLnc/LrNGWcIwJO2+u3099k4drZ3Vt4ahAUu/kOv/M28uCv+Ra0jGdlldUTr+YKCYPHRDqUDrFEo7pU/6xeg+ThvTnhOHh8we6YW8l4KyNHuz53ExkW11UxvSRycQEaR2nnoqMKI3xg80HjrBhbyXXzBoZVh2sc8amEe29Fyg6KrjzuZnIVVXfxKb9R5gdIc1pEMYJR0RSReQ1EakWkSIRuaGd/e4Xkc9E5KiI7BKR+1ttLxSRWhGp8j7eD847MOFm0eo9xEQJV8wYHupQjjEzM4U/XDMNgJtOy4yYDmATWut2V+BRmBlBK8aGbcIBHgUagAxgAfCYiExtYz8BbgZSgAuBu0Xkulb7XKqqSd7H+YEM2vhPXlE5jy7b4Zc+jdyCEhZ+upvZmamkJgZvobXOumz6cMamJ7LlgM04YDpndVEZIp/POh4JwjLhiEgicBXwE1WtUtUVwJvATa33VdXfquoaVW1S1a3AG8Dc4EZs/O2TglKufeJjfv/+1h53pL+z8QA3PPUJdY0eVheVhW2n/PlThpBbUEpljQ2PNseXV1TOxIz+DIiPCXUonSaqevy9gkxEZgArVTXBp+w+4CxVvbSD4wRYAzyhqo97ywqBfjjJdS1wv6qub+PY24HbAdLT02cuWrTIf28oQlVVVZGU5J/1NXaUu9lS5mZSahTjUzoejtzkUX6yspYD1c5n0wVcOSGGL43r2pVJVYPy1s4GFhc10Tw1ZnfO5c966MiOCjcP59Zx+0lxnD4svCYBCVYdhLtwqQePKncuqeH0YdHcPDUuqK/dUR3Mnz8/T1VntXdseH2qP5cEHGlVVgn0P85xD+J8p/zVp2wBThIS4NvAeyIySVWPmbxKVZ8EngSYOHGiZmdndzf2XiMnJwd/1MOnu0r59fuf4FElNtrd4QSDjW4Pd724hgPVNUS5BLdHERGuP3d2p/o28orKWbH9MMVH63lz/X6q65vInpTOyh2lNLk9xES7On2uZv6qh+M506M8kf9v9moK2dkzA/56XRGsOgh34VIP+fsrqXtvBZedfgLZQe6T7EkdhGvCqQJaj1sdALTbwC0id+P05cxT1frmclVd6bPbr0TkFmAe8Jb/wjUdeXrFrpb1OhqaPOQWlLT5hd/o9vCtl9by/qZDPHjpFE4ckcx/v7eF3IIy9nfibuq8onKue/JjGt3Oa83KTOGXV55IVkZ/8orKyS0oZc7YtLDtlHe5hPOmZPD62n3UNbrD5sZUE36am4XD9bPcnrDswwG2AdEiMsGnbBqQ39bOIvJ14AHgHFXde5xzK87VjgkCt0fZsLeypcI9Cit3lFJR03DMfo1uD/csXMu7+Qf56Zem8NW5Y5iZmcLz3ziVk0cl85+vbmR3aU27r+PxKL95d3NLsnEJzJ80mKwM56J4ZmYKd80fH/Z/oOdPyaCmwc1HO0tCHYoJY4GcsDOQwjLhqGo18CrwkIgkishc4HLg+db7isgC4JfAeapa0GrbKBGZKyKxIhLvHTI9CFjZ+jwmMBZvOsiByjq+e14W952fxQ2njuLTXWVc8MiHfLDtMOCsUvidl9fxzmcH+fElk/n6GWNajo+JcvG/180AgW+9vJbGNhYqa2jycO+idXy6q5wolxAlEBuAhaiC4bRxaSTFRfN+/qFQh2LCWCRN2OkrXJvUAO4EngGKgVLgDlXNF5F5wDuq2txr9TCQBqzyqfwXVPWbOH0+jwHjgDpgHXCRqtqt3EGgqjz2QQGjUhO4a/74loXObjhlFN/9+zpueeZTLjphCLtKqtly8Cg/vmQyt84b+4XzjExN4DdXncSdL67hd+9v5YcXTW7ZdrSukW++kMfKHaXcf8FE5oxJJXdXWVg3nXUkLjqK7InpLNl8CLdHQ7o4nAlP+yucCTtvnTfm+DuHmbBNOKpaBlzRRvlynEEFzc/brXVVzQdOCkiA5rg+2VXG+j0V/OKKE4754jxh+EDe+tYZ3PeP9by94QAA0S5pmcSyLRefOJTrTxnFEx8UMHfcIM7MSufQkTpueeZTdhRX8furp3GVdzG1SLoRri3nTcng7Q0HWLennJmZkf1ejP+t9vbfzIrAz0ZYNqmZ3uHxD3YyKCm2zVU1470TDjbnIVU97hxiP/3SFLIykvjWwjX84JX1XPLH5ewpq+GZr85uSTa9wfxJg4mJEmtWM23KKywjITaKyUOPN2g3/FjCMQGx+cARcrYe5qunj253tNWcsWnERruIEojpRJ9Lv9go7sweT2VtE39ftZeSqgYevGwqZ2alB+IthMyA+BjmjE3jvfyDhON9cia0VheVM31kMtERMmGnr8iL2ESEJz8sICE2qsNlk2dmpvDirXO49/yJHd6b42tfRW3LiDeXQPHR+g73j1TnTx1CYWkNO4qrQh2KCSNV9U1sPnCEWRHYPwmWcEwA7C2v4c31+7n+lFEkJ3R8R39XhyvPGZtGXIwrokeidcZ5kzMAeH+TNauZz0XihJ2+wnbQgIlcT6/YhQDfOMP/o2iar4rC/SbOnhoyMJ5pI5N5P/8gd80fH+pwTJiIxAk7ffn9CkdE/igib7dRPkBEHhSRyT5l3xGRjSJiV1q9RHl1Ay9/uofLpg9jWHJgbkqLlJs4e+r8KRms31vJwcq6UIdiwsSyLcUMSoxl+6HIbGr16xe9iIwDvokzp1lrs4CfAb5Tmz4BpAO3+DMOEzrP5xZR2+jmP84cF+pQIt4FU51mtcWbrVnNwKrCMtbvreRwVUPELkXu7yuL7wDrVXV1G9tmAPXApuYCVa0FngPu83McJgQ+3lnCYzk7mZmZwsQhkTdkM9yMS09i2MB4nl5eEJFfLsa/Xl+7r+XfkboUeacSjoiMF5FGEXmoVflj3pU2Z4lIHHAj8FIbx28GfgfEAY0ioiLyinfzy8AUETm9R+/EhFReUTk3Pf0ptY1uNu6ttC9IP1izu4JDR+spLK3hhqci8xet8Z+aBjdAp28jCEedSjiqugP4C/AdEUkDEJGfAl8Hvuy9opkDJAPL2zjFzUABzgzNp3kf93q3rcOZBfrC7r8NE2q5BaUtM0K7PZH56yvc5BaUttyHU9/k4f38gyGOyITSxn2VnDh8QJduIwg3XWlSewiIAh4QkVtx+mNuUtUl3u1zcGZi3tDGseuBEcBSVc31PooAVNXj3T6nm+/BhIHmu56FyP31FW6ab4xtno3hrfX7Ka3qnfcdmY7tKqlmR3EVV548IqIHzHR6WLSqHhCRR4DveY+7R1V9l8UcBhxR1YY2Dp8KxOIshNaWw0BWZ2Mx4edoXRMAN87J5IoZwyP2DyKc+A4BH9gvhl+8vYmvPbuKl26bQ1Kc3dHQlyzx3o91rvf+rEjV1U/tdpx+mBWq+mirbfE4gwLacjLO1c+6drbX4iwDbSLU8u0lJCfE8OBlU22GYz+amZnSkryHJcdz23N53P7cav76tdnERdsCbX3F4s2HmDSkPyNTE0IdSo90uklNRM7BGcb8MTBXRFrPwlyK04fTlhnATlVtvWx0s1TAVpyKUKrKiu0lzB03yJJNAJ09KYP//spJfLSzlO+8vA63x+ZZ6wvKqhtYXVjG+VMi++oGOj9K7WTgNZyBA9nAbuBXrXbbAsSKSFvT9k7BZzh0G8YAWzsTiwk/O4qrOHikjnkTBoU6lF7vypNH8ONLJvPOZwf55gt5PLpsu41e6+WWbinGo3DelCGhDqXHjptwRGQ88A7wPvAtbx/Nz4GLReRMn10/9P73lDZOUwFME5ELRGRO80g37/mTcfpvPmzjOBMBlm93Lk7PsIQTFLfOG8uXZwxn8aZD/Pd727j2iY9Z+OluGpqc1VDzisp5dNkOS0S9xJJNhxgyIJ4Thg8IdSg91mEfjogMwUk0m4EF3hFl4Nys+X3g18DpAKpaKCKfApfiLA/t66fA08DrOH0984AV3m2XAA04V1AmAi3ffpgxgxIZkRLZ7cuRZPzgRASnY7TJo/zw1Y38/K18xqUnsvVgFW6PEhPl4sdfmuxdd0hwCWw/dJTP9h9hxsgUThwxkJgoIcolxES52HTgCPn7Kjlt3KBjBn3kFZXz9s4G+o8pt8EgQVbX6ObD7Ye58uThEbecdFs6TDiqehD4wpq/quoGJn/xCB4D/ldE7lLVGp/9PwNObedlbgT+0XrZZxFJxUlS5+P07/xQVdu6qVRwEt+t3qK/AA+o9wYGEZnuPc9knMT5DVVtb/CC6aL6Jje5BWVcPav3LIAWCeaMHURczA4amzxER7n49jnjKalq5P9t3N9yP1SD28NP38hv8/jnPi7q4OzbGJ+eyNj0JFwiLctdv12YG7H3f0Sqj3aWUNPg7hXNaeD/2aJfAH4A3Ikzs0CHvMngbJxh0609inPlkwFMB/4lIuu9y0b7uh1nKeppOD/4FgO7gMdFJBZ4A3gE+DPwH8AbIjKhneHbpovWFFVQ2+jmjPHWnBZM7c2afclJQ1nwVC4Nbg/RLhf/eckkxqf3x63Km+v28eqafSjOWkIXnziU+RMH4/YoSzYfYvGmQzQPQ2jyKEWlNRSVVbcksObpVCzhBM/iTcUkxUUzZ2xkLkfQml8Tjqo2icjXcIZBd8YQ4KvemQxaiEgicBVwgqpWAStE5E3gJuCBVue4Bfi9qu71Hvt74DbgcZwBDtHAI94rnj+KyH04Se7dbrxF08qKHYeJcgmnjbMbPYPNd8i0b9mLt7W9fENSXDT/2niAxiYPMdEuvjZ3TMv2cYOT+HD74ZZtv79mOjMzU8grKuf6p3JpaPLgErEbeoPI4/0hcFZWeu8ZAq+qYffAGUZd06rsPuCtNvatBE71eT4LOOr993eBd1rt/zbwvTbOczuwGlidkJCgOFdL9jjOY8hNf9CMBb8JeRz26NwjdtgkHTDnao0dNqnT22KHTdLhdz2vQ7/+aMjj70uP2KFZmvmDtzVxSnbIY+nCY3VH3+3hug5NEtD6np1KoK0piJO823z3S/L27bTe1u55VPVJVZ2lqrNGjBgR8qQbDo9ly5Z1uL2sqp744Vk88NUrQh5rKOshkh71+zZT+fEi6vdt7vS2+n2buXFmBrHpmWw7eCTk76GvfBYe/usbRLmEvaveC/n77mwdHE+4JpwqoPUYwAE4k3web98BQJU6774r5zFd9NHOUlRh3oT0UIdiAuz0YdFEu4R/5u0NdSh9xuJNhzhldOpxl2mPJOGacLYB0SIywadsGtDWkJt877a29ssHTpJjxxOe1M55TBct336Y/vHRTBsxMNShmAAbECfMnzSYV9fuo8ntOf4BpkeKSqvZdqiKc3vB7AK+wjLhqGo1zr08D4lIoojMBS4Hnm9j9+eAe0VkuIgMw5lc9FnvthzADdwjInEicre3fGkg4+8LVJXl20s4fVwa0VFh+TEyfnb1zBEcPlrPB9sOhzqUXm+xd7LO3jCdja9w/qa4E2dCz2JgIXCHquaLyDwR8V3Q+1YbvHIAACAASURBVAmcdXY2Ap8B//KWoc7Q5ytw1uOpwFm/5wq1IdE9tqukmn0VtZxhzWl9xvxJg0lLjOUfq61ZLdCW9JLJOlsL2znOVbUMJ1m0Ll+OMxig+bnizHrw/XbOsxaYGaAw+6wVO5zpbObZ/Td9RkyUiytmDOe5jwspq24gNbH39C2Ek4qaBlYVlnPHWeNCHYrfhfMVjgljH24rYWRqPzLTetcvMNOxq2eNoNGtvL52X6hD6bWeWbELt0cZmdr7VmyxhGO6rNHt3HF+xvj0XjG/k+m8SUMGcOLwgTZaLUDyisr5v2XOffA/ezO/103AagnHdNn6PRVU1Tdxps0O3SddPWuEM9Hn/ta3uJmeWrH9MM3LHDVPJdSbWMIxXfbh9hJcAqePs4TTF102bRixUS4bPBAA8THOFDYugZhoV6+bSsgSjumyFdsPc+KIZAYmxIQ6FBMCyQmxnDclgzfW7WtZg8f4R8HhahJiovjOuVm9cmZuSzimSz7cdpi1uyuYMDgx1KGYEPrKrBGU1zTy782HQh1Kr9E8a/e5UzK455wJvS7ZgCUc0wV5ReV842+rUODNdQd6XYem6bwzJ6STMSCOf/SywQMdrZbavBBdoD73a3aXU1rdwPlTe9fNnr7C9j4cE35yC0ppcjs9mm6PrY3Sl0W5hCtPHsHjOTv57btbOGdyRsR/FvKKylnwVC71TR6io4RvnzOBMYOcW/4KDlfxx6XbaXIHbiG69z47SGyUi+yJg/163nBiCcd02pyxaTSva9wbOzRN10wZ2h8FHsvZyTMrd0V8n0NuQQl13j6pRrfyu/e3tblfIBaiU1Xe33SIuePTSIrrvV/LvfedGb+bMnQALmDWmFS+f+GkiP5yMT23u6wWcBZB6Q2rgR6pbQKc31Sx0S5+feVJTBk2ABHYtP8I9/9zPY1uRQKwEN3WQ0fZXVbDHdm9b3YBX5ZwTKet21OBW+E/zhob0V8sxj/mjE0jyiW4PUpMVGRf8RaVVvN8bhEnDh/ABVOHcNq4Qcd8xrMynHnN7n0hl91HPThp1n/ezz+ECJwzufc2p4ENGjBdkFdUBsDJoyzZGGc56598aTIA3z43ckdVuT3Kff9YT5QIT9w0i7vPbvu9zMxM4YFT4xmR2o9vv7yOo3WNfovhvfyDnDwqhcH94/12znBkCcd02qrCcrIyknrVglCmZ244JZPE2Cj2lteGOpRue3pFAasKy3nwsqkMS+54/rJ+0cIj187gQGUdP3vDP8tq7S2vIX//ES7oxaPTmlnCMZ3i9ihrisqZNTo11KGYMBIb7eL08YPI2Xq4U0sMh5utB4/yu/e2cf6UDK48eXinjpmZmcK3zh7Pq2v38ca6nk9i2rz2zXlThvT4XOHOEo7plK0Hj3K0vonZoyOz2cQETvbEdPZV1LLzcNXxdw4jDU0e7l20jv7x0fzyyhO7NBHt3fPHc/KoZH78+mfsLa/pURzv5x8iKyOJMYN6/83UlnBMp6z29t/MyrQrHHOs5vtGcrZG1kqg/7d0O/n7j/BfXz6RQUlxXTo2OsrFI9fOQBXu/ft63J7uXd2VVzfwaWEZ5/eBqxuwhGM6aXVhOUMGxDMipfet0WF6ZnhyPyYMToqopaf/vmo3f1q2g7OyBnHhCd37sh+VlsBDl0/l08IyvvrXT7s1A8G/txTj9mivnl3AV9glHBFJFZHXRKRaRIpE5IYO9r1fRD4TkaMisktE7m+1vVBEakWkyvt4P/DvoHdaXVjGzNEptv6NadNZWel8UlBGTUNTqEM5rk93lfLAKxtRhdyCsh5NVZOZmoBLYPn2Eq594mM+6eJyAu/nH2TowHhOHD6w2zFEkrBLOMCjQAOQASwAHhORqe3sK8DNQApwIXC3iFzXap9LVTXJ+zg/UEH3ZvsqatlfWcfsCB32agIve+JgGtwePt4Z/uu3vLJmb8tdNE3unq05k7urrOXfTR7lrpfWsGFvRaeOrW1w8+H2w5w/JaPP/JALq4QjIonAVcBPVLVKVVcAbwI3tbW/qv5WVdeoapOqbgXeAOYGL+K+YXWht//GRqiZdswek0JCbFRE9ONU1TlXYVF+WHNmztg0YqNdzrminJtgv/znj/jtu1uoa3R3eOyH2w9T1+jh/Kl9o/8GQMJpKKOIzABWqmqCT9l9wFmqeulxjhVgDfCEqj7uLSsE+uEk1rXA/aq6vp3jbwduB0hPT5+5aNGinr+hDuwod7OlzM2k1CjGp0QF9LW6q6qqiqSkJJ7Lr+ej/U08ek4CUa6+8UvMV3M99GWdqYNH8urYV+Xht2f2C9tf7KrK/R/WkhwnTE+P6vLfX1v14Pu3PDTJxctbGli+r4lhicIFo2M42qBtvs5TG+pZW9zEH89OIDqC/q46+izMnz8/T1VntXdsuE1tkwQcaVVWCfTvxLEP4iSWv/qULcBJQgJ8G3hPRCap6heueVX1SeBJgIkTJ2p2dnZXY2/Th1sP86/P9jN0YD+S4qIpPlrPlgNHWLGjBI9CbLSbhbeF56SHOTk5ZGdn8+t1HzJ7bBznnH1qqEMKieZ66Ms6Uwd74ov4yeufkXnCbMamh2eC3nzgCCXvLefeC0/ghlNHdfn4tuohu9U+l5wHOVuL+d6i9fw1vwGAKGni0mnDOHHEQNL7xzEoKZb1y1YzOn0AKeNOCMu///b05O8hqAlHRHKAs9rZvBL4FjCgVfkA4Ohxzns3Tl/OPFWtby5X1ZU+u/1KRG4B5gFvdS3y7vl/Gw5w50trjimLi3YRH+NqWbe8ocnD31ftDtsPXGVtI1sPHeXiE4eGOhQT5rKz0gFneHS4Jpwl3psszw3wnGXZEwdz45xR/PHfO1DArcqb6/fxeqsbRTcfOMKCvwRmuYNwFNQ+HFXNVlVp53EGsA2IFpEJPodNA9qdQ0JEvg48AJyjqsdbDUpxrnYCrqy6gR+9vrHluUvgnrPHs+UXF/LMV08hPsZp9xXg1TX7eGfjgWCE1WVrdpejCrPshk9zHCNTExibnkhOGA+PXrz5ENNHJjN4QODnLDszazBx3r/z+BgX//iP01j30/NY/N0zuXbWiOaVPlpm2u4LwmrQgKpWA68CD4lIoojMBS4Hnm9rfxFZAPwSOE9VC1ptGyUic0UkVkTivUOmB+FcSQVUbYObrz+7iqr6JmKjnA9cbLSLsyYORkSYmZnCi7fO4d7zJ/K3r53CtJHJ3PXSGhZ+ujvQoXXZ6sIyolzC9JHJoQ7FRIDsrMHkFpRS29Bxh3koHKysY8PeSs6bEpx7Xnz/zl+8dQ4zR6eSnBDLhIz+XDN7VEsy6ktrS4VbHw7AncAzQDFQCtyhqvkAIjIPeEdVm6/XHwbSgFU+nZQvqOo3cfp9HgPGAXXAOuAiVQ3oT4kmt4dvLVzL+r0VPLZgJun948gtKGXO2LRjLplnZqa0PJ81JoU7X1zDD1/dSHlNA3ecNS5sOl1XFZZzwrABJMSG40fFhJvsiek8s3IXubtKmR9mK1cu3uw0p50fpIQDx/6dty5/8dY5bX439GZh9y2iqmXAFe1sW44zsKD5+ZgOzpMPnOT3ADugqvzkjXyWbD7EQ5dPbbmD+XgfpoTYaJ66eRb3/WM9v313K1sOHGHikP7MGTsopB/EJo+yfk8FN87JDFkMJrKcMiaV+BgXH2w9HH4JZ9MhMtMSGD84PPqX2ktGvVnYJZxI9n9Ld7Dw093cmT2Om08b3aVjY6Jc/M8102lo8vDm+gPI+gPExewIaWdi4REP9U0em7DTdFp8TBSnjU0jZ2sx0N792sF3tK6Rj3eWcMtpo8Om9aAvCqs+nEiVV1TOHS/k8fvF27hyxnDuv2Bit87jcgknDHcG6YVDZ+L2cmd995k2YafpguyJgyksraGwpDrUobT4cFsJjW4NWv+NaZtd4fRQXlE51z+ZS4Pbg0vg2tkje/QLas7YQcRF76C+yROQtdO7Ylu5mzGDEknv37WZdE3flj3RGR79wbbDjA6TKfcXbzpISkJMn2vCCjd2hdNDy7YU0+B2rgQEWN2DiQDBadd96bY5ZKYmkNwvJmSjw1SV7eVu+wM1XZaZlsjQgfH87aPCHk2M6S+Nbg9LtxRz9qQMoqPsKy+UrPZ7QFVZVeg0ebn8OLxxZmYKP7x4EiXVDSzdUtzj83XHzsPVVDVi/Temy/KKyik+Wk9BSTULnsoNedJZtauMI3VNnDclvAYx9EWWcNpQWa+d+iNZtHoPn+wq5+bTMvle81h7P10RnDs5g4wBcbyQW+SX83VVXpFN2Gm6J7egtGW56fowuKlx8eZDxEa7mDchPaRxGEs4bSqvV244zi+zgsNVPPjmJk4fl8aDl07lrvnj/dr8FB3l4rrZo/hw+2F2l/ZsCdvuePezg8S6oKK6IeivbSJb8wzKzUJ5layqLN50iDPGDyIxzrqsQ80STjvqmzy8vnZfm9sa3R6+8/d1xEa7+MM103EFaKbX608ZhUuEFz8N7lVOXmEZOVsP0+CBBU9/EvImERNZmm9qvGL6MBQoqQrdj5YtB4+yt7zWRqeFCUs47RBg4ardLFq15wvbHlmyjQ17K/n1lScyZGDg5mQaMjCecycP5h+r91LfFLypQt7acKBlgapQD802kWlmZgq/v2Y6mWkJPLW84PgHBMhi72Sd5wR4sk7TOZZw2pASJzzztdnMGZPG91/ZwI9e20hDkzMS7ZOCUv6cs5NrZ43koiDMoHzjnEzKqht4Z+PBgL9Ws+Zlgl30rXmejH9FuYRvnDGGtbsrWvoEg21J82Sd/QM/Wac5Pks4bRgYJ8yfOJhnvzab/zhrLC9+spvrn8rlzXX7uO251WT0j+Onl04JSixzxw1idFpCUAcPbNhbyeSh/blyQkyfmTbdBMZXZo5gYL8Y/rJ8V9Bfe3H+ITbsrWTKsNYrnphQsYTTgegoFz+8aDKP3nAyn+2r5J6X13Gkromymka2HOxwiR6/cbmEBadmsrqonC0HW69N53+7SqrZcvAoV88cyZfGxVqyMT2SEBvNglNH8V7+waAOfskrKueOF/MAeCVvr/VDhglLOJ1wyUlDueGUz1cHdLuD26/xlZkjiI12BeUq5718p+nughP6zjrrJrBuOX00US7hmZXBu8rJLSilybvKYVOQ/15N+yzhdNKXpg1rWTQt2P0aKYmxfOmkoby2Zh9V9U0Bfa13PzvISSMGMjy5X0Bfx/QdGQPiuXTaMBat3kNlTWNQXrN5hg7B+iHDiSWcTvrCYkpBbmq6cU4m1Q3udodq+8OBylrW7anggql2dWP869YzxlLT4OalIC0y2PzD7JpZI6wfMozYnVBdEMr1K2aMTGbK0AE89WEBlbUNAVkr573PnOa0i6w5zfjZlGEDmDs+jWc/2sU3zhhzzI2hgbB0czH946L5xRUnBvy1TOfZ/4kIISKcOWEQRWU1/P79bSz4i//nqHo3/yBZGUmMTQ+PBapM73LrvLEcOlLPvzbuD+jreDzK0q3FnDkx3ZJNmAm7/xsikioir4lItYgUicgNHez7oIg0ikiVz2Osz/bpIpInIjXe/04PzrsIjPjYKAA86v8bMkur6vl0VxkXWnOaCZCzJqQzfnAS/7tkO48u2x6wkWMb91Vy+Gg950yymz3DTdglHOBRoAHIABYAj4lIR0sH/l1Vk3weBQAiEgu8AbwApAB/A97wlkekeRPSiYlyptFxufy7Vs6SzYfwqI1OM4HjcgnnTXYWZwvUVTrAv7cU4xJnITgTXsIq4YhIInAV8BNVrVLVFcCbwE3dOF02Th/VI6par6p/xBm0cra/4g02Z+DCqaQmxjIoKY6TRgz027nf/ewgI1P7MWWo3SRnAic+JnBX6c2WbjnEyaNSSE2M2N+WvVa4DRrIAppUdZtP2XrgrA6OuVREyoADwP+p6mPe8qnABm2eJ92xwVv+buuTiMjtwO0A6enp5OTkdPtNBNrNE4VH1tTx8xf+zbmZMT0+X02j8uG2Gs7LjOaDDz5oKa+qqgrreggWqwf/1UHiUTcucRKOSyCuooicnL09D9CrvM7DZ/tq+UpWTED+n9lnoWd1EG4JJwlofTt9JdC/nf0XAU8Ch4BTgVdEpEJVF3rPVdnZc6nqk95zMXHiRM3Ozu5O/EFxliqfVn7Cv4qO8P1r5zIgvmdJ5411+3DrOm676JRjRr7l5OQQzvUQLFYP/quDbGDwmH3c8/I6rjslk1uvOKHH5/T10ie7gY3cfslpZGW097XRffZZ6FkdBLVJTURyRETbeawAqoDWbToDgDbnkVHVTaq6X1XdqvoR8L/AV7ybu3SuSCIi/OfFk6mobeTPy3b2+HzvfnaQwf3jmBGi5axN33LZ9OHMHp3Cyh0lHNsA0XP/3nyIESn9mDDYRlqGo6AmHFXNVlVp53EGsA2IFpEJPodNA/I7+xI4/TR4jzlJRHwXqzmpC+cKaycMH8iXZwznmZW72Fve/Tmqahvc5Gw9zAVThwRsXR9jWrtm1kgKSqpZVei/QQO1DW5W7Cjh3MkZHPtnb8JFWA0aUNVq4FXgIRFJFJG5wOXA823tLyKXi0iKOE4B7sEZmQaQA7iBe0QkTkTu9pYvDeibCKL7zp+IAL97b2u3z/Hh9sPUNrq50EanmSC65KShJMVF8/Iq/8088HFBCfVNHs624dBhK6wSjtedQD+gGFgI3KGq+QAiMk9Eqnz2vQ7YgdNM9hzwG1X9G4CqNgBXADcDFcDXgSu85b3CsOR+3DpvDK+v28+GvRXdOse7nx0kOSGGU8ak+jk6Y9qXEBvNZdOH8f82HuBInX/mV1uyuZjE2ChOHWuf5XAVdglHVctU9QpVTVTVUar6ks+25aqa5PP8elVN895/M8k79Nn3XGtVdaaq9lPVk1V1bTDfSzB886xxpCXG8l//2tzl9vBPCkr518YDnDwymZiosPsomF7u2lkjqWv08Oa6ns88oKos3VzMvAnpxEVH+SE6Ewj2LRPh+sfH8J3zsvhkVxn3Llrf6Rvp8orKufHpT2ho8rB8R4mtF2KC7qQRA5k0pD9/b2MZ967K33+Eg0fqONuWkg5rlnB6gYkZSQjw2tp9XPvEx3y0o6TD/TftP8L3Fq2j0e1cEXk8auuFmKATEa6dPZKN+yrJ39/6DoauWbqlGBGYb7MLhDVLOL3AqsJymgflNHmUrz27it++u4XiI3XH7Hegspb7/rGeS/60nMNH64l2SUjW9zGm2ZdnDCc22sWiHl7l/HtLMdNGJJPeP85PkZlACLcbP003zBmbRmy0i8YmD1FRLqaPTOaxD3by1PICLps2nFmjU3hj3T7WFJUDwm3zxnJX9nh2HK4it6CUOWPTbL0QExLJCbFcOHUIr63dxw8vntwy9U1XFB+tY/2eCr53XlYAIjT+ZAmnF2heHM43eRSVVvPMil0s/HQPr6xxpg5xCTx6wwwuOnFoy3GWaEyoXTt7JG+u3897+Qe5fPrwLh+fs+UwAOdMzvB3aMbPLOH0Eq2TR2ZaIj+//AQG9Ivh/5buaLkjtqCkOmQxGtOW08amMTK1Hy9/uqdbCeefeXvoHxdNbUNgl183PWd9OL1c9sTBxMW4rK/GhC2XS7hm5kg+LiilqLRrP4g+3lnCp4XlHK1vYsHTn9hoyzBnCaeXa25uu/f8iba2uwlbX5k1AgF+8MqGLiWN373/+cTygVruwPiPJZw+YGZmCnfNH2/JxoSt/RV1iEBuQVmnF2Z76ZPd5BWVE2WjLSOG9eEYY0Iut6CU5oky6ho9LN9+uMMfSB/tLOGnb3zGWVnp3DV/HKsKy220ZQSwhGOMCbk5Y9OIi3FR3+hBgbc3HOCmOZmkJX3xvppdJdXc8cIaRg9K5E83zGBAfAynjLErm0hgTWrGmJBr7mu874KJ/ODCiewpq+Erj3/M7tJjl96orG3kG39bhUvg6Vtm9XjxQRNclnCMMWGhua/xjuzxvHTbqZTXNHDlYyvZuNeZ9qbJ7eHul9awp6yGx2+cSWZaYogjNl1lCccYE3ZmZqbyz2+eTlx0FNc9+TFPryjgmic+Zvn2Ev7rihM51QYHRCRLOMaYsDR+cBKv3nk6g5Li+MXbm1mzu4IolzDOlo+OWJZwjDFhK2NAPJfPGPZ5gdrM5pHMEo4xJqydlTWYeJsto1cIu4QjIqki8pqIVItIkYjc0MG+74hIlc+jQUQ2+mwvFJFan+3vB+ddGGP8xWbL6D3C8T6cR4EGIAOYDvxLRNaran7rHVX1It/nIpIDLG2126WquiRAsRpjgsBmNu8dwuoKR0QSgauAn6hqlaquAN4EburEsaOBecBzgYzRGGNM94RVwgGygCZV3eZTth6Y2oljbwaWq2phq/IXReSwiLwvItP8FKcxxpguCrcmtSTgSKuySqB/J469GXi4VdkCYA3OUjDfBt4TkUmqWtH6YBG5HbgdID09nZycnK5F3gtVVVVZPWD1AFYHzaweelgHqhq0B5ADaDuPFcAMoKbVMd8D3jrOec8AqoCk4+y3BadPp8M4s7Ky1KguW7Ys1CGEBasHq4NmVg8d1wGwWjv4bg3qFY6qZne03duHEy0iE1R1u7d4GvCFAQOt3AK8qqpVxwsB52rHGGNMkIVVH46qVgOvAg+JSKKIzAUuB55v7xgR6QdcAzzbqnyUiMwVkVgRiReR+4FBwMqAvQFjjDHtCquE43Un0A8oBhYCd6h3SLSIzBOR1lcxVwAVwLJW5f2Bx4ByYB9wIXCRqtptysYYEwLhNmgAVS3DSSJtbVuOM7DAt2whTmJqvW8+cFIgYjTGGNN14XiFY4wxpheyhGOMMSYoLOEYY4wJCks4xhhjgsISjjHGmKCwhGOMMSYoLOEYY4wJCks4xhhjgsISjjHGmKCwhGOMMSYoLOEYY4wJCks4xhhjgsISjjHGmKCwhGOMMSYoLOEYY4wJCks4xhhjgsISjjHGmKCwhGOMMSYowi7hiMjdIrJaROpF5NlO7P9dETkoIkdE5BkRifPZNlpElolIjYhsEZFzAxq8McaYdoVdwgH2Aw8DzxxvRxG5AHgAOAfIBMYCP/fZZSGwFkgDfgT8U0TS/R2wMcaY4wu7hKOqr6rq60BpJ3a/BXhaVfNVtRz4BfBVABHJAk4Gfqaqtar6CrARuCowkRtjjOlIdKgD6KGpwBs+z9cDGSKS5t1WoKpHW22f2taJROR24Hbv03oR+SwA8UaaQUBJqIMIA1YPVgfNrB46roPMjg6M9ISTBFT6PG/+d/82tjVvH97WiVT1SeBJABFZraqz/Btq5LF6cFg9WB00s3roWR0EtUlNRHJERNt5rOjGKauAAT7Pm/99tI1tzduPYowxJuiCmnBUNVtVpZ3HGd04ZT4wzef5NOCQqpZ6t40Vkf6ttud3/x0YY4zprrAbNCAi0SISD0QBUSISLyLtNf09B3xDRKaISDLwY+BZAFXdBqwDfuY9x5eBk4BXOhHGkz19H72E1YPD6sHqoJnVQw/qQFTVn4H0mIg8CPysVfHPVfVBERkFbAKmqOpu7/73Aj8A+uEkk2+qar1322icBHQqsBu4S1WXBP5dGGOMaS3sEo4xxpjeKeya1IwxxvROlnCMMcYEhSUcHyKSKiKviUi1iBSJyA2hjikYOpq/TkTO8c5DV+Odl67DG7silYjEicjT3v/vR0VknYhc5LO9r9TDCyJywDs34TYRudVnW5+oA18iMkFE6kTkBZ+yG7yfk2oReV1EUkMZYyB5b2WpE5Eq72Orz7Yu14MlnGM9CjQAGcAC4DERaXNmgl6mzfnrRGQQ8CrwEyAVWA38PejRBUc0sAc4CxiIM+JxkXcC2L5UD78CRqvqAOAy4GERmdnH6sDXo8Cq5ife74MngJtwvidqgD+HJrSguVtVk7yPidD9erBBA14ikgiUAyd4h1QjIs8D+1T1gZAGFyQi8jAwQlW/6n1+O/BVVT3d+zwRZ0qLGaq6JWSBBomIbMCZDDaNPlgPIjIRyAG+DSTTx+pARK4DrsQZGTteVW8UkV/iJOQbvPuMAzYDaa2m0eoVRCQHeEFV/9KqvFv1YFc4n8sCmpqTjVe7c6/1EVNx6gAAVa0GdtIH6kREMnA+E/n0sXoQkT+LSA2wBTgA/D/6Xh0MAB4C7m21qXU97MRpFckKXnRB9ysRKRGRlSKS7S3rVj1YwvlcEnCkVVklzrxsfVV789H16joRkRjgReBv3l/vfaoeVPVOnPc2D6cZrZ4+Vgc4M88/rap7W5X3tXr4Ac6yL8Nxbvh8y3s10616sITzOZt77Yv6XJ2IiAt4HufX2t3e4j5XD6rqVtUVwAjgDvpQHYjIdOBc4H/a2Nxn6gFAVT9R1aOqWq+qfwNWAhfTzXqI9Nmi/WkbEC0iE1R1u7esr8+9lo+z5hDQ0m4/jl5aJyIiwNM4naAXq2qjd1OfqodWovn8vfaVOsgGRgO7nY8ESTjTbE0B3sVn/kYRGQvE4Xx/9AUKCK3msex0PaiqPbwP4GWcVUITgbk4l4hTQx1XEN53NBCPM0Lpee+/o4F0bx1c5S37DZAb6ngDWA+PA7lAUqvyPlEPwGDgOrxfsMAFQDXOaLU+UQfeekgAhvg8fgf801sHU3Ga3ud5vydeAF4OdcwBqodk72eg+ftggffzkNXdegj5mwqnB85wz9e9lbobuCHUMQXpfT/o/eXi+3jQu+1cnM7jWpwRS6NDHW+A6iDT+77rcJoLmh8L+ko9eL9QPwAqvF8mG4HbfLb3+jpop14exBmp1fz8Bu/3QzXOApCpoY4xgJ+HVTjNZBXeH2Pn9aQebFi0McaYoLBBA8YYY4LCEo4xxpigsIRjjDEmKCzhGGOMCQpLOMYYY4LCEo4xxpigsIRjjDEmKCzhGBMhRGSAiDwoIpNDHYsx3WEJx5jIMQv4GRAT6kCM6Q5LOMZEjhk4SwVsCnUgxnSHTW1jTAQQkc3ApFbFPOd6cwAAAOJJREFUr6rqVaGIx5jusIRjTAQQkdk4s5nnA7/0Fh9Q1aLQRWVM19h6OMZEhvU4i6H9SVVzQx2MMd1hfTjGRIapQCywJtSBGNNdlnCMiQwn46zXsy7UgRjTXZZwjIkMM4Cdqnok1IEY012WcIyJDFOw4dAmwtmgAWMiQwVwsohcAFQC21W1NMQxGdMlNizamAggIicATwMnAfHAPFVdEdqojOkaSzjGGGOCwvpwjDHGBIUlHGOMMUFhCccYY0xQWMIxxhgTFJZwjDHGBIUlHGOMMUFhCccYY0xQWMIxxhgTFP8fNzUEombBA5UAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
},
{
"output_type": "stream",
"text": [
"time: 951 ms\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3U8et4jTxCe9",
"colab_type": "text"
},
"source": [
"[![](https://raw.githubusercontent.com/BackProp-fr/meetup/master/images/LogoBackPropTranspSmall.png)](https://www.backprop.fr)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3nl5ELHYw1w7",
"colab_type": "text"
},
"source": [
"---\n",
"\n",
"# Le code à partir d'ici n'est plus commenté\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5osLh-XOxEBh",
"colab_type": "text"
},
"source": [
"[![](https://raw.githubusercontent.com/BackProp-fr/meetup/master/images/LogoBackPropTranspSmall.png)](https://www.backprop.fr)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "kboffBHd1GMO",
"colab_type": "text"
},
"source": [
"## <font color=\"teal\">TimeDistributed expliqué - Référence</font>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xDTUXpJp1ecs",
"colab_type": "text"
},
"source": [
"Le site de Jason Brownlee, Machine Learning Mastery est une bible à consulter régulièrement. Les exemples sont de très bonne qualité, sans erreur(s) et pertinents"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Ky_Wxqok02y4",
"colab_type": "text"
},
"source": [
"- How to Use the [TimeDistributed](https://machinelearningmastery.com/timedistributed-layer-for-long-short-term-memory-networks-in-python/) Layer in Keras de Jason Brownlee "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "tjrc2ti5zoVa",
"colab_type": "text"
},
"source": [
"TimeDistributed est utilisé presqu'à chaque fois dans les modèles LSTM mais pourquoi ?"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ugDvfwnA2ioa",
"colab_type": "text"
},
"source": [
"Le code est entièrement celui de Jason"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "KR94CWpJ3bas",
"colab_type": "text"
},
"source": [
"### <font color=\"orange\">Imports</font>"
]
},
{
"cell_type": "code",
"metadata": {
"id": "M2qTsW2f2MkO",
"colab_type": "code",
"colab": {}
},
"source": [
"from numpy import array\n",
"import tensorflow as tf"
],
"execution_count": 1,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "gWQl3akF3CHt",
"colab_type": "code",
"colab": {}
},
"source": [
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import Dense\n",
"from tensorflow.keras.layers import LSTM"
],
"execution_count": 2,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "BcLn9FU73heJ",
"colab_type": "text"
},
"source": [
"### <font color=\"orange\">Jeu de données</font>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HajaF14W3not",
"colab_type": "text"
},
"source": [
"L'exercice consiste tout simplement à prédire x lorsque x est présenté !"
]
},
{
"cell_type": "code",
"metadata": {
"id": "zaTYRc6b2QZA",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"outputId": "0740472b-017e-4399-a3a5-bfff79fdcc1a"
},
"source": [
"# prepare sequence\n",
"length = 5\n",
"seq = array([i/float(length) for i in range(length)])\n",
"seq"
],
"execution_count": 3,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([0. , 0.2, 0.4, 0.6, 0.8])"
]
},
"metadata": {
"tags": []
},
"execution_count": 3
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "r5nxCAvI37Eb",
"colab_type": "text"
},
"source": [
"Etant donné qu'on utilise un LSTM il faut que les données en entrée soient en 3D"
]
},
{
"cell_type": "code",
"metadata": {
"id": "VifCCW_I3O0b",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"outputId": "c3d35c15-4367-4628-bec5-16e3b056f342"
},
"source": [
"X = seq.reshape(len(seq), 1, 1)\n",
"X.shape"
],
"execution_count": 4,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(5, 1, 1)"
]
},
"metadata": {
"tags": []
},
"execution_count": 4
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "1_b1W1Sy3ImZ",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 186
},
"outputId": "cd337a1a-6222-4f77-f9d2-02e6970cdda8"
},
"source": [
"X"
],
"execution_count": 5,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[[0. ]],\n",
"\n",
" [[0.2]],\n",
"\n",
" [[0.4]],\n",
"\n",
" [[0.6]],\n",
"\n",
" [[0.8]]])"
]
},
"metadata": {
"tags": []
},
"execution_count": 5
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "_cUjY9bc3RWD",
"colab_type": "code",
"colab": {}
},
"source": [
"y = seq.reshape(len(seq), 1)"
],
"execution_count": 6,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "eMb0T_4S4GLU",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"outputId": "68a27436-0a7f-46de-ff81-599cd5da202b"
},
"source": [
"y.shape"
],
"execution_count": 7,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(5, 1)"
]
},
"metadata": {
"tags": []
},
"execution_count": 7
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "fXHkkyxJ4K4V",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 120
},
"outputId": "fa5af16a-7d8d-4eb3-a63f-db638acd58e9"
},
"source": [
"y"
],
"execution_count": 8,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[0. ],\n",
" [0.2],\n",
" [0.4],\n",
" [0.6],\n",
" [0.8]])"
]
},
"metadata": {
"tags": []
},
"execution_count": 8
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "M9JzBmNV4Rl_",
"colab_type": "text"
},
"source": [
"### <font color=\"orange\">Hyperparamètres</font>"
]
},
{
"cell_type": "code",
"metadata": {
"id": "J5OrkYn_4Xz5",
"colab_type": "code",
"colab": {}
},
"source": [
"# define LSTM configuration\n",
"n_neurons = length\n",
"n_batch = length\n",
"n_epoch = 1000"
],
"execution_count": 9,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "rOMMjSJK5Baz",
"colab_type": "text"
},
"source": [
"### <font color=\"orange\">Model 1</font>"
]
},
{
"cell_type": "code",
"metadata": {
"id": "GA14K-BgG3xR",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"outputId": "a15cca36-36f2-4e80-80af-0929000eb3e0"
},
"source": [
"X.shape, y.shape"
],
"execution_count": 10,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"((5, 1, 1), (5, 1))"
]
},
"metadata": {
"tags": []
},
"execution_count": 10
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "k_zvUx3N5Ild",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "74fa8eec-d56e-4abd-ab6f-5ff8757e37a9"
},
"source": [
"# create LSTM\n",
"model = Sequential()\n",
"model.add(LSTM(n_neurons, input_shape=(1, 1)))\n",
"model.add(Dense(1))\n",
"model.compile(loss='mean_squared_error', optimizer='adam')\n",
"print(model.summary())"
],
"execution_count": 11,
"outputs": [
{
"output_type": "stream",
"text": [
"Model: \"sequential\"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"lstm (LSTM) (None, 5) 140 \n",
"_________________________________________________________________\n",
"dense (Dense) (None, 1) 6 \n",
"=================================================================\n",
"Total params: 146\n",
"Trainable params: 146\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n",
"None\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "XfcICmvq5UFa",
"colab_type": "text"
},
"source": [
"### <font color=\"orange\">Apprentissage</font>"
]
},
{
"cell_type": "code",
"metadata": {
"id": "v-axQg8D5h1q",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"outputId": "3d25cfe9-a91e-4c7a-c841-6cd2eee6954b"
},
"source": [
"# train LSTM\n",
"model.fit(X, y, epochs=n_epoch, batch_size=n_batch, verbose=0)"
],
"execution_count": 25,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<tensorflow.python.keras.callbacks.History at 0x7fe9cd0f2e48>"
]
},
"metadata": {
"tags": []
},
"execution_count": 25
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "QVwiC7vc5YE7",
"colab_type": "text"
},
"source": [
"### <font color=\"orange\">Prédictions</font>"
]
},
{
"cell_type": "code",
"metadata": {
"id": "kgbqR5Y750bn",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 120
},
"outputId": "08f3f9ca-c004-473a-851d-93ab294e95a9"
},
"source": [
"# evaluate\n",
"result = model.predict(X, batch_size=n_batch, verbose=0)\n",
"for value in result:\n",
"\tprint('%.1f' % value)"
],
"execution_count": 28,
"outputs": [
{
"output_type": "stream",
"text": [
"0.0\n",
"0.2\n",
"0.4\n",
"0.6\n",
"0.8\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "K9hjAQXRB7RR",
"colab_type": "text"
},
"source": [
"### <font color=\"orange\">Model 2</font>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "pFXMNRKpCLhh",
"colab_type": "text"
},
"source": [
"Ce qui change dans ce modèle est en entrée un input (5,1) et en sortie un Dense(5)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "NeQdqglSCH4z",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"outputId": "1a2f21e4-0b53-47da-85e9-5c9814e9ed1f"
},
"source": [
"length"
],
"execution_count": 39,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"5"
]
},
"metadata": {
"tags": []
},
"execution_count": 39
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "s4v6vDbxGkIJ",
"colab_type": "code",
"colab": {}
},
"source": [
"# prepare sequence\n",
"length = 5\n",
"seq = array([i/float(length) for i in range(length)])\n",
"X = seq.reshape(1, length, 1)\n",
"y = seq.reshape(1, length)"
],
"execution_count": 54,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Io3CyCIpGlzS",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"outputId": "5291d63c-f754-49cc-df75-7d1221c4a3b4"
},
"source": [
"X.shape, y.shape"
],
"execution_count": 55,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"((1, 5, 1), (1, 5))"
]
},
"metadata": {
"tags": []
},
"execution_count": 55
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "V5EB3tQ9B_D2",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "76330cb6-06d1-46e2-fb64-f1debbaf8f44"
},
"source": [
"# create LSTM\n",
"model = Sequential()\n",
"model.add(LSTM(5, input_shape=(5, 1)))\n",
"model.add(Dense(length))\n",
"model.compile(loss='mean_squared_error', optimizer='adam')\n",
"print(model.summary())"
],
"execution_count": 56,
"outputs": [
{
"output_type": "stream",
"text": [
"Model: \"sequential_3\"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"lstm_3 (LSTM) (None, 5) 140 \n",
"_________________________________________________________________\n",
"dense_3 (Dense) (None, 5) 30 \n",
"=================================================================\n",
"Total params: 170\n",
"Trainable params: 170\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n",
"None\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "IJR6o5MNCVvH",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 87
},
"outputId": "052c98f5-fc65-4d6c-eb3b-578cc9451661"
},
"source": [
"# train LSTM\n",
"model.fit(X, y, epochs=500, batch_size=1, verbose=0)"
],
"execution_count": 40,
"outputs": [
{
"output_type": "stream",
"text": [
"WARNING:tensorflow:Model was constructed with shape (None, 5, 1) for input Tensor(\"lstm_2_input:0\", shape=(None, 5, 1), dtype=float32), but it was called on an input with incompatible shape (1, 1, 1).\n",
"WARNING:tensorflow:Model was constructed with shape (None, 5, 1) for input Tensor(\"lstm_2_input:0\", shape=(None, 5, 1), dtype=float32), but it was called on an input with incompatible shape (1, 1, 1).\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<tensorflow.python.keras.callbacks.History at 0x7fe9cc674c88>"
]
},
"metadata": {
"tags": []
},
"execution_count": 40
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "fHKs7T4qChjc",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 137
},
"outputId": "8ca85f78-586a-4a8e-b263-c4e4f631e636"
},
"source": [
"# evaluate\n",
"result = model.predict(X, batch_size=n_batch, verbose=0)\n",
"for value in result[0,:]:\n",
"\tprint('%.1f' % value)"
],
"execution_count": 41,
"outputs": [
{
"output_type": "stream",
"text": [
"WARNING:tensorflow:Model was constructed with shape (None, 5, 1) for input Tensor(\"lstm_2_input:0\", shape=(None, 5, 1), dtype=float32), but it was called on an input with incompatible shape (5, 1, 1).\n",
"0.0\n",
"0.0\n",
"0.0\n",
"0.0\n",
"0.0\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "dWcRPW7LDXqd",
"colab_type": "text"
},
"source": [
"### <font color=\"orange\">Model 3 - Many-to-Many LSTM for Sequence Prediction</font>"
]
},
{
"cell_type": "code",
"metadata": {
"id": "n9OrNEcFElG1",
"colab_type": "code",
"colab": {}
},
"source": [
"from tensorflow.keras.layers import TimeDistributed"
],
"execution_count": 43,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "tlrpGBg6FwfB",
"colab_type": "code",
"colab": {}
},
"source": [
"# prepare sequence\n",
"length = 5\n",
"seq = array([i/float(length) for i in range(length)])\n",
"X = seq.reshape(1, length, 1)\n",
"y = seq.reshape(1, length, 1)"
],
"execution_count": 48,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "dsIizkTXF1rG",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"outputId": "992b9fa6-97de-42c1-b11d-d6f9d9b4bc96"
},
"source": [
"X.shape, y.shape"
],
"execution_count": 49,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"((1, 5, 1), (1, 5, 1))"
]
},
"metadata": {
"tags": []
},
"execution_count": 49
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "cGLgokSJGEja",
"colab_type": "code",
"colab": {}
},
"source": [
"# define LSTM configuration\n",
"n_neurons = length\n",
"n_batch = 1\n",
"n_epoch = 1000"
],
"execution_count": 50,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "LfsO401CGHgW",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 253
},
"outputId": "b04e3dd4-5f3c-4502-e5c1-1c356748a413"
},
"source": [
"# create LSTM\n",
"model = Sequential()\n",
"model.add(LSTM(n_neurons, input_shape=(length, 1), return_sequences=True))\n",
"model.add(TimeDistributed(Dense(1)))\n",
"model.compile(loss='mean_squared_error', optimizer='adam')\n",
"print(model.summary())"
],
"execution_count": 51,
"outputs": [
{
"output_type": "stream",
"text": [
"Model: \"sequential_2\"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"lstm_2 (LSTM) (None, 5, 5) 140 \n",
"_________________________________________________________________\n",
"time_distributed_2 (TimeDist (None, 5, 1) 6 \n",
"=================================================================\n",
"Total params: 146\n",
"Trainable params: 146\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n",
"None\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "bAagNkQzGKuo",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"outputId": "32905239-f2e8-4e3d-986c-c6b1d54fab72"
},
"source": [
"# train LSTM\n",
"model.fit(X, y, epochs=n_epoch, batch_size=n_batch, verbose=0)"
],
"execution_count": 52,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<keras.callbacks.callbacks.History at 0x7fe9cb27cc18>"
]
},
"metadata": {
"tags": []
},
"execution_count": 52
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "teCH8KItGOz4",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 120
},
"outputId": "274ce0a2-855a-4999-a266-973802f50db2"
},
"source": [
"# evaluate\n",
"result = model.predict(X, batch_size=n_batch, verbose=0)\n",
"for value in result[0,:,0]:\n",
"\tprint('%.1f' % value)"
],
"execution_count": 53,
"outputs": [
{
"output_type": "stream",
"text": [
"0.1\n",
"0.2\n",
"0.4\n",
"0.6\n",
"0.8\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6cIMcHy_JCJs",
"colab_type": "text"
},
"source": [
"## Forecasting Several Steps Ahead"
]
},
{
"cell_type": "code",
"metadata": {
"id": "MHGUOoe5uH3b",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"outputId": "bf83cc67-5a79-4044-c25f-773561224b82"
},
"source": [
"n_steps"
],
"execution_count": 96,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"50"
]
},
"metadata": {
"tags": []
},
"execution_count": 96
},
{
"output_type": "stream",
"text": [
"time: 4.32 ms\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "3UrKgWOHJCJt",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"outputId": "10852c4b-f435-4a73-bd1d-e966c0dc9aea"
},
"source": [
"np.random.seed(43) # not 42, as it would give the first series in the train set\n",
"series = generate_time_series(1, n_steps + 10)"
],
"execution_count": 97,
"outputs": [
{
"output_type": "stream",
"text": [
"time: 1.83 ms\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "DOn5x46mt1gf",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"outputId": "c95e4896-1112-44b1-93f4-62cbfe6df4b1"
},
"source": [
"X_new, Y_new = series[:, :n_steps], series[:, n_steps:]\n",
"X = X_new"
],
"execution_count": 98,
"outputs": [
{
"output_type": "stream",
"text": [
"time: 1.78 ms\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "vHhw0aIfuRm-",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"outputId": "c777ddd7-380b-4907-a78f-6436fe140eab"
},
"source": [
"X_new.shape, Y_new.shape"
],
"execution_count": 99,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"((1, 50, 1), (1, 10, 1))"
]
},
"metadata": {
"tags": []
},
"execution_count": 99
},
{
"output_type": "stream",
"text": [
"time: 3.27 ms\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "blgIhNhPt4K1",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"outputId": "760a92af-127e-4f72-ce86-17f8fee9c5a1"
},
"source": [
"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)"
],
"execution_count": 100,
"outputs": [
{
"output_type": "stream",
"text": [
"time: 608 ms\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "_6PLIuHJt6za",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"outputId": "44cb5648-0dfd-4e50-8192-899e7b686be2"
},
"source": [
"Y_pred = X[:, n_steps:]"
],
"execution_count": 101,
"outputs": [
{
"output_type": "stream",
"text": [
"time: 932 µs\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "QajhKDpdJCJv",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"outputId": "81551052-50a7-4770-9417-5024899e1a0d"
},
"source": [
"Y_pred.shape"
],
"execution_count": 102,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(1, 10, 1)"
]
},
"metadata": {
"tags": []
},
"execution_count": 102
},
{
"output_type": "stream",
"text": [
"time: 3.3 ms\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "VLTHqs6UJCJx",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 351
},
"outputId": "219df1fe-f976-48e3-b8b1-74d497430baa"
},
"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], \"ro-\", label=\"Actual\")\n",
" plt.plot(np.arange(n_steps, n_steps + ahead), Y_pred[0, :, 0], \"bx-\", 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()"
],
"execution_count": 103,
"outputs": [
{
"output_type": "stream",
"text": [
"Saving figure forecast_ahead_plot\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
},
{
"output_type": "stream",
"text": [
"time: 766 ms\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "137SKJqJJCJ0",
"colab_type": "text"
},
"source": [
"Now let's use this model to predict the next 10 values. We first need to regenerate the sequences with 9 more time steps."
]
},
{
"cell_type": "code",
"metadata": {
"id": "9ZCljYM3JCJ0",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"outputId": "cccc6722-dc3d-4dce-e971-dc919b3dc09a"
},
"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]"
],
"execution_count": 104,
"outputs": [
{
"output_type": "stream",
"text": [
"time: 63.4 ms\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Xkf7JdsgJCJ2",
"colab_type": "text"
},
"source": [
"Now let's predict the next 10 values one by one:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "exE0deg1JCJ3",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"outputId": "6cd31d3f-4ae1-48b3-acdf-551843ee3412"
},
"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]"
],
"execution_count": 105,
"outputs": [
{
"output_type": "stream",
"text": [
"time: 7.78 s\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Ydh6VWysJCJ5",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"outputId": "4bbc9efe-bf37-4a21-a5eb-da7d708cc4f0"
},
"source": [
"Y_pred.shape"
],
"execution_count": 106,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(2000, 10)"
]
},
"metadata": {
"tags": []
},
"execution_count": 106
},
{
"output_type": "stream",
"text": [
"time: 3.53 ms\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "EBEdwAISJCJ7",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"outputId": "facca569-ff3f-4192-af80-b48386d1e6cf"
},
"source": [
"np.mean(keras.metrics.mean_squared_error(Y_valid, Y_pred))"
],
"execution_count": 107,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0.027510816"
]
},
"metadata": {
"tags": []
},
"execution_count": 107
},
{
"output_type": "stream",
"text": [
"time: 5.58 ms\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6HLc_TsiJCJ9",
"colab_type": "text"
},
"source": [
"Let's compare this performance with some baselines: naive predictions and a simple linear model:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "cG8Uy0usJCJ-",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"outputId": "3d76ae3b-a473-4fab-e4a4-4d27f51ec498"
},
"source": [
"Y_naive_pred = Y_valid[:, -1:]\n",
"np.mean(keras.metrics.mean_squared_error(Y_valid, Y_naive_pred))"
],
"execution_count": 108,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0.22278848"
]
},
"metadata": {
"tags": []
},
"execution_count": 108
},
{
"output_type": "stream",
"text": [
"time: 6.43 ms\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "HDJdpmoLJCKA",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 715
},
"outputId": "036818fc-b3ad-4e64-c24b-8f507208ce3a"
},
"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))"
],
"execution_count": 109,
"outputs": [
{
"output_type": "stream",
"text": [
"Epoch 1/20\n",
"219/219 [==============================] - 1s 3ms/step - loss: 0.1343 - val_loss: 0.0606\n",
"Epoch 2/20\n",
"219/219 [==============================] - 1s 3ms/step - loss: 0.0496 - val_loss: 0.0425\n",
"Epoch 3/20\n",
"219/219 [==============================] - 1s 3ms/step - loss: 0.0385 - val_loss: 0.0353\n",
"Epoch 4/20\n",
"219/219 [==============================] - 1s 3ms/step - loss: 0.0331 - val_loss: 0.0311\n",
"Epoch 5/20\n",
"219/219 [==============================] - 1s 3ms/step - loss: 0.0298 - val_loss: 0.0283\n",
"Epoch 6/20\n",
"219/219 [==============================] - 1s 3ms/step - loss: 0.0273 - val_loss: 0.0264\n",
"Epoch 7/20\n",
"219/219 [==============================] - 1s 3ms/step - loss: 0.0256 - val_loss: 0.0249\n",
"Epoch 8/20\n",
"219/219 [==============================] - 1s 3ms/step - loss: 0.0244 - val_loss: 0.0237\n",
"Epoch 9/20\n",
"219/219 [==============================] - 1s 3ms/step - loss: 0.0234 - val_loss: 0.0229\n",
"Epoch 10/20\n",
"219/219 [==============================] - 1s 3ms/step - loss: 0.0227 - val_loss: 0.0222\n",
"Epoch 11/20\n",
"219/219 [==============================] - 1s 3ms/step - loss: 0.0220 - val_loss: 0.0216\n",
"Epoch 12/20\n",
"219/219 [==============================] - 1s 3ms/step - loss: 0.0215 - val_loss: 0.0212\n",
"Epoch 13/20\n",
"219/219 [==============================] - 1s 3ms/step - loss: 0.0210 - val_loss: 0.0208\n",
"Epoch 14/20\n",
"219/219 [==============================] - 1s 3ms/step - loss: 0.0207 - val_loss: 0.0207\n",
"Epoch 15/20\n",
"219/219 [==============================] - 1s 3ms/step - loss: 0.0203 - val_loss: 0.0202\n",
"Epoch 16/20\n",
"219/219 [==============================] - 1s 3ms/step - loss: 0.0200 - val_loss: 0.0199\n",
"Epoch 17/20\n",
"219/219 [==============================] - 1s 3ms/step - loss: 0.0197 - val_loss: 0.0195\n",
"Epoch 18/20\n",
"219/219 [==============================] - 1s 3ms/step - loss: 0.0193 - val_loss: 0.0192\n",
"Epoch 19/20\n",
"219/219 [==============================] - 1s 3ms/step - loss: 0.0191 - val_loss: 0.0189\n",
"Epoch 20/20\n",
"219/219 [==============================] - 1s 3ms/step - loss: 0.0188 - val_loss: 0.0187\n",
"time: 13.7 s\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0PQwNzMeJCKC",
"colab_type": "text"
},
"source": [
"Now let's create an RNN that predicts all 10 next values at once:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "bFZ8YWR0JCKC",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"outputId": "502dad1b-62d8-4007-8b79-2e22d01b17ab"
},
"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))"
],
"execution_count": 112,
"outputs": [
{
"output_type": "stream",
"text": [
"time: 189 ms\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "gunT5guSJCKF",
"colab_type": "code",
"colab": {}
},
"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]"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "v9y8DP9UJCKH",
"colab_type": "code",
"colab": {},
"outputId": "43624d4a-1230-44d8-ad45-65d71312e749"
},
"source": [
"plot_multiple_forecasts(X_new, Y_new, Y_pred)\n",
"plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3f2a86MxJCKJ",
"colab_type": "text"
},
"source": [
"Now let's create an RNN that predicts the next 10 steps at each time step. That is, instead of just forecasting time steps 50 to 59 based on time steps 0 to 49, it will forecast time steps 1 to 10 at time step 0, then time steps 2 to 11 at time step 1, and so on, and finally it will forecast time steps 50 to 59 at the last time step. Notice that the model is causal: when it makes predictions at any time step, it can only see past time steps."
]
},
{
"cell_type": "code",
"metadata": {
"id": "Jh6xyedmJCKK",
"colab_type": "code",
"colab": {}
},
"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:]"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "eSH8kbvWJCKM",
"colab_type": "code",
"colab": {},
"outputId": "4dbe4cb1-5ece-4c15-ddfd-b4f304f7269f"
},
"source": [
"X_train.shape, Y_train.shape"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"((7000, 50, 1), (7000, 50, 10))"
]
},
"metadata": {
"tags": []
},
"execution_count": 37
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "j8g2rcQdJCKO",
"colab_type": "code",
"colab": {},
"outputId": "dabeed74-ce1a-4891-b504-eab4e6018310"
},
"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(lr=0.01), metrics=[last_time_step_mse])\n",
"history = model.fit(X_train, Y_train, epochs=20,\n",
" validation_data=(X_valid, Y_valid))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Train on 7000 samples, validate on 2000 samples\n",
"Epoch 1/20\n",
"7000/7000 [==============================] - 5s 732us/sample - loss: 0.0498 - last_time_step_mse: 0.0388 - val_loss: 0.0416 - val_last_time_step_mse: 0.0321\n",
"Epoch 2/20\n",
"7000/7000 [==============================] - 4s 572us/sample - loss: 0.0388 - last_time_step_mse: 0.0281 - val_loss: 0.0332 - val_last_time_step_mse: 0.0223\n",
"Epoch 3/20\n",
"7000/7000 [==============================] - 4s 575us/sample - loss: 0.0321 - last_time_step_mse: 0.0208 - val_loss: 0.0304 - val_last_time_step_mse: 0.0196\n",
"Epoch 4/20\n",
"7000/7000 [==============================] - 4s 579us/sample - loss: 0.0294 - last_time_step_mse: 0.0182 - val_loss: 0.0278 - val_last_time_step_mse: 0.0165\n",
"Epoch 5/20\n",
"7000/7000 [==============================] - 4s 597us/sample - loss: 0.0271 - last_time_step_mse: 0.0153 - val_loss: 0.0262 - val_last_time_step_mse: 0.0141\n",
"Epoch 6/20\n",
"7000/7000 [==============================] - 4s 573us/sample - loss: 0.0245 - last_time_step_mse: 0.0120 - val_loss: 0.0235 - val_last_time_step_mse: 0.0111\n",
"Epoch 7/20\n",
"7000/7000 [==============================] - 4s 587us/sample - loss: 0.0227 - last_time_step_mse: 0.0103 - val_loss: 0.0236 - val_last_time_step_mse: 0.0112\n",
"Epoch 8/20\n",
"7000/7000 [==============================] - 4s 589us/sample - loss: 0.0211 - last_time_step_mse: 0.0085 - val_loss: 0.0197 - val_last_time_step_mse: 0.0071\n",
"Epoch 9/20\n",
"7000/7000 [==============================] - 4s 590us/sample - loss: 0.0204 - last_time_step_mse: 0.0080 - val_loss: 0.0199 - val_last_time_step_mse: 0.0080\n",
"Epoch 10/20\n",
"7000/7000 [==============================] - 4s 562us/sample - loss: 0.0198 - last_time_step_mse: 0.0076 - val_loss: 0.0187 - val_last_time_step_mse: 0.0066\n",
"Epoch 11/20\n",
"7000/7000 [==============================] - 4s 526us/sample - loss: 0.0195 - last_time_step_mse: 0.0074 - val_loss: 0.0189 - val_last_time_step_mse: 0.0069\n",
"Epoch 12/20\n",
"7000/7000 [==============================] - 4s 527us/sample - loss: 0.0191 - last_time_step_mse: 0.0070 - val_loss: 0.0186 - val_last_time_step_mse: 0.0071\n",
"Epoch 13/20\n",
"7000/7000 [==============================] - 4s 526us/sample - loss: 0.0187 - last_time_step_mse: 0.0068 - val_loss: 0.0179 - val_last_time_step_mse: 0.0058\n",
"Epoch 14/20\n",
"7000/7000 [==============================] - 4s 533us/sample - loss: 0.0191 - last_time_step_mse: 0.0073 - val_loss: 0.0184 - val_last_time_step_mse: 0.0068\n",
"Epoch 15/20\n",
"7000/7000 [==============================] - 4s 533us/sample - loss: 0.0193 - last_time_step_mse: 0.0076 - val_loss: 0.0201 - val_last_time_step_mse: 0.0082\n",
"Epoch 16/20\n",
"7000/7000 [==============================] - 4s 536us/sample - loss: 0.0186 - last_time_step_mse: 0.0067 - val_loss: 0.0188 - val_last_time_step_mse: 0.0071\n",
"Epoch 17/20\n",
"7000/7000 [==============================] - 4s 534us/sample - loss: 0.0186 - last_time_step_mse: 0.0069 - val_loss: 0.0174 - val_last_time_step_mse: 0.0059\n",
"Epoch 18/20\n",
"7000/7000 [==============================] - 4s 566us/sample - loss: 0.0184 - last_time_step_mse: 0.0067 - val_loss: 0.0172 - val_last_time_step_mse: 0.0058\n",
"Epoch 19/20\n",
"7000/7000 [==============================] - 4s 588us/sample - loss: 0.0181 - last_time_step_mse: 0.0064 - val_loss: 0.0180 - val_last_time_step_mse: 0.0067\n",
"Epoch 20/20\n",
"7000/7000 [==============================] - 4s 597us/sample - loss: 0.0180 - last_time_step_mse: 0.0065 - val_loss: 0.0190 - val_last_time_step_mse: 0.0078\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "YJuyM5FXJCKR",
"colab_type": "code",
"colab": {}
},
"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]"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "GiwaTwc_JCKT",
"colab_type": "code",
"colab": {},
"outputId": "294d3b49-1e5b-42f3-8520-ba4e2e018457"
},
"source": [
"plot_multiple_forecasts(X_new, Y_new, Y_pred)\n",
"plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "GsjI4NeRJCKV",
"colab_type": "text"
},
"source": [
"# Deep RNN with Batch Norm"
]
},
{
"cell_type": "code",
"metadata": {
"id": "XCQiRQNpJCKV",
"colab_type": "code",
"colab": {},
"outputId": "e2f0b752-88ff-4bbc-b41e-a5add0d9a882"
},
"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))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Train on 7000 samples, validate on 2000 samples\n",
"Epoch 1/20\n",
"7000/7000 [==============================] - 5s 765us/sample - loss: 0.1936 - last_time_step_mse: 0.1913 - val_loss: 0.0901 - val_last_time_step_mse: 0.0863\n",
"Epoch 2/20\n",
"7000/7000 [==============================] - 4s 607us/sample - loss: 0.0531 - last_time_step_mse: 0.0441 - val_loss: 0.0559 - val_last_time_step_mse: 0.0469\n",
"Epoch 3/20\n",
"7000/7000 [==============================] - 4s 599us/sample - loss: 0.0470 - last_time_step_mse: 0.0374 - val_loss: 0.0453 - val_last_time_step_mse: 0.0354\n",
"Epoch 4/20\n",
"7000/7000 [==============================] - 4s 550us/sample - loss: 0.0437 - last_time_step_mse: 0.0337 - val_loss: 0.0423 - val_last_time_step_mse: 0.0320\n",
"Epoch 5/20\n",
"7000/7000 [==============================] - 4s 541us/sample - loss: 0.0414 - last_time_step_mse: 0.0310 - val_loss: 0.0402 - val_last_time_step_mse: 0.0301\n",
"Epoch 6/20\n",
"7000/7000 [==============================] - 4s 541us/sample - loss: 0.0390 - last_time_step_mse: 0.0283 - val_loss: 0.0377 - val_last_time_step_mse: 0.0261\n",
"Epoch 7/20\n",
"7000/7000 [==============================] - 4s 551us/sample - loss: 0.0368 - last_time_step_mse: 0.0255 - val_loss: 0.0369 - val_last_time_step_mse: 0.0255\n",
"Epoch 8/20\n",
"7000/7000 [==============================] - 4s 613us/sample - loss: 0.0354 - last_time_step_mse: 0.0240 - val_loss: 0.0369 - val_last_time_step_mse: 0.0244\n",
"Epoch 9/20\n",
"7000/7000 [==============================] - 4s 629us/sample - loss: 0.0342 - last_time_step_mse: 0.0227 - val_loss: 0.0361 - val_last_time_step_mse: 0.0233\n",
"Epoch 10/20\n",
"7000/7000 [==============================] - 5s 645us/sample - loss: 0.0332 - last_time_step_mse: 0.0216 - val_loss: 0.0340 - val_last_time_step_mse: 0.0223\n",
"Epoch 11/20\n",
"7000/7000 [==============================] - 5s 654us/sample - loss: 0.0324 - last_time_step_mse: 0.0210 - val_loss: 0.0338 - val_last_time_step_mse: 0.0229\n",
"Epoch 12/20\n",
"7000/7000 [==============================] - 5s 677us/sample - loss: 0.0316 - last_time_step_mse: 0.0202 - val_loss: 0.0332 - val_last_time_step_mse: 0.0214\n",
"Epoch 13/20\n",
"7000/7000 [==============================] - 5s 674us/sample - loss: 0.0309 - last_time_step_mse: 0.0192 - val_loss: 0.0307 - val_last_time_step_mse: 0.0187\n",
"Epoch 14/20\n",
"7000/7000 [==============================] - 5s 671us/sample - loss: 0.0305 - last_time_step_mse: 0.0189 - val_loss: 0.0322 - val_last_time_step_mse: 0.0198\n",
"Epoch 15/20\n",
"7000/7000 [==============================] - 5s 675us/sample - loss: 0.0304 - last_time_step_mse: 0.0185 - val_loss: 0.0297 - val_last_time_step_mse: 0.0177\n",
"Epoch 16/20\n",
"7000/7000 [==============================] - 5s 710us/sample - loss: 0.0296 - last_time_step_mse: 0.0177 - val_loss: 0.0291 - val_last_time_step_mse: 0.0168\n",
"Epoch 17/20\n",
"7000/7000 [==============================] - 5s 728us/sample - loss: 0.0293 - last_time_step_mse: 0.0173 - val_loss: 0.0285 - val_last_time_step_mse: 0.0163\n",
"Epoch 18/20\n",
"7000/7000 [==============================] - 5s 727us/sample - loss: 0.0288 - last_time_step_mse: 0.0169 - val_loss: 0.0284 - val_last_time_step_mse: 0.0159\n",
"Epoch 19/20\n",
"7000/7000 [==============================] - 5s 729us/sample - loss: 0.0286 - last_time_step_mse: 0.0166 - val_loss: 0.0284 - val_last_time_step_mse: 0.0156\n",
"Epoch 20/20\n",
"7000/7000 [==============================] - 5s 720us/sample - loss: 0.0282 - last_time_step_mse: 0.0162 - val_loss: 0.0323 - val_last_time_step_mse: 0.0214\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "iGwWLfkcJCKY",
"colab_type": "text"
},
"source": [
"# Deep RNNs with Layer Norm"
]
},
{
"cell_type": "code",
"metadata": {
"id": "HkuIwOHjJCKY",
"colab_type": "code",
"colab": {}
},
"source": [
"from tensorflow.keras.layers import LayerNormalization"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "ZCwQBAibJCKa",
"colab_type": "code",
"colab": {}
},
"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]"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "TwfOHwbcJCKc",
"colab_type": "code",
"colab": {},
"outputId": "12db8a83-9712-4786-e83a-42f0760ffb72"
},
"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))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Train on 7000 samples, validate on 2000 samples\n",
"Epoch 1/20\n",
"7000/7000 [==============================] - 13s 2ms/sample - loss: 0.1539 - last_time_step_mse: 0.1506 - val_loss: 0.0701 - val_last_time_step_mse: 0.0641\n",
"Epoch 2/20\n",
"7000/7000 [==============================] - 10s 1ms/sample - loss: 0.0627 - last_time_step_mse: 0.0555 - val_loss: 0.0568 - val_last_time_step_mse: 0.0495\n",
"Epoch 3/20\n",
"7000/7000 [==============================] - 11s 2ms/sample - loss: 0.0540 - last_time_step_mse: 0.0463 - val_loss: 0.0502 - val_last_time_step_mse: 0.0423\n",
"Epoch 4/20\n",
"7000/7000 [==============================] - 11s 2ms/sample - loss: 0.0472 - last_time_step_mse: 0.0385 - val_loss: 0.0434 - val_last_time_step_mse: 0.0336\n",
"Epoch 5/20\n",
"7000/7000 [==============================] - 11s 2ms/sample - loss: 0.0419 - last_time_step_mse: 0.0320 - val_loss: 0.0394 - val_last_time_step_mse: 0.0292\n",
"Epoch 6/20\n",
"7000/7000 [==============================] - 10s 1ms/sample - loss: 0.0382 - last_time_step_mse: 0.0272 - val_loss: 0.0363 - val_last_time_step_mse: 0.0246\n",
"Epoch 7/20\n",
"7000/7000 [==============================] - 10s 1ms/sample - loss: 0.0362 - last_time_step_mse: 0.0248 - val_loss: 0.0345 - val_last_time_step_mse: 0.0231\n",
"Epoch 8/20\n",
"7000/7000 [==============================] - 10s 1ms/sample - loss: 0.0343 - last_time_step_mse: 0.0226 - val_loss: 0.0345 - val_last_time_step_mse: 0.0223\n",
"Epoch 9/20\n",
"7000/7000 [==============================] - 10s 1ms/sample - loss: 0.0333 - last_time_step_mse: 0.0216 - val_loss: 0.0338 - val_last_time_step_mse: 0.0219\n",
"Epoch 10/20\n",
"7000/7000 [==============================] - 10s 1ms/sample - loss: 0.0326 - last_time_step_mse: 0.0208 - val_loss: 0.0319 - val_last_time_step_mse: 0.0206\n",
"Epoch 11/20\n",
"7000/7000 [==============================] - 10s 1ms/sample - loss: 0.0319 - last_time_step_mse: 0.0200 - val_loss: 0.0306 - val_last_time_step_mse: 0.0188\n",
"Epoch 12/20\n",
"7000/7000 [==============================] - 10s 1ms/sample - loss: 0.0310 - last_time_step_mse: 0.0190 - val_loss: 0.0303 - val_last_time_step_mse: 0.0185\n",
"Epoch 13/20\n",
"7000/7000 [==============================] - 10s 1ms/sample - loss: 0.0306 - last_time_step_mse: 0.0185 - val_loss: 0.0298 - val_last_time_step_mse: 0.0175\n",
"Epoch 14/20\n",
"7000/7000 [==============================] - 10s 1ms/sample - loss: 0.0302 - last_time_step_mse: 0.0180 - val_loss: 0.0300 - val_last_time_step_mse: 0.0181\n",
"Epoch 15/20\n",
"7000/7000 [==============================] - 10s 1ms/sample - loss: 0.0297 - last_time_step_mse: 0.0174 - val_loss: 0.0290 - val_last_time_step_mse: 0.0164\n",
"Epoch 16/20\n",
"7000/7000 [==============================] - 11s 2ms/sample - loss: 0.0294 - last_time_step_mse: 0.0170 - val_loss: 0.0286 - val_last_time_step_mse: 0.0166\n",
"Epoch 17/20\n",
"7000/7000 [==============================] - 11s 2ms/sample - loss: 0.0286 - last_time_step_mse: 0.0160 - val_loss: 0.0286 - val_last_time_step_mse: 0.0158\n",
"Epoch 18/20\n",
"7000/7000 [==============================] - 11s 2ms/sample - loss: 0.0284 - last_time_step_mse: 0.0158 - val_loss: 0.0277 - val_last_time_step_mse: 0.0144\n",
"Epoch 19/20\n",
"7000/7000 [==============================] - 11s 2ms/sample - loss: 0.0277 - last_time_step_mse: 0.0149 - val_loss: 0.0274 - val_last_time_step_mse: 0.0141\n",
"Epoch 20/20\n",
"7000/7000 [==============================] - 11s 2ms/sample - loss: 0.0273 - last_time_step_mse: 0.0144 - val_loss: 0.0267 - val_last_time_step_mse: 0.0132\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xqFfM9r7JCKe",
"colab_type": "text"
},
"source": [
"# Creating a Custom RNN Class"
]
},
{
"cell_type": "code",
"metadata": {
"id": "cKimHYf1JCKe",
"colab_type": "code",
"colab": {}
},
"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",
" return [tf.zeros([self.cell.state_size], dtype=inputs.dtype)]\n",
" @tf.function\n",
" def call(self, inputs):\n",
" states = self.get_initial_state(inputs)\n",
" n_steps = tf.shape(inputs)[1]\n",
" if self.return_sequences:\n",
" sequences = tf.TensorArray(inputs.dtype, size=n_steps)\n",
" outputs = tf.zeros(shape=[n_steps, 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 sequences.stack()\n",
" else:\n",
" return outputs"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Kdtv_sXHJCKg",
"colab_type": "code",
"colab": {},
"outputId": "a96bae26-55b5-4e0d-b6a3-aada9e20de6a"
},
"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))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Train on 7000 samples, validate on 2000 samples\n",
"Epoch 1/20\n",
"7000/7000 [==============================] - 12s 2ms/sample - loss: 0.2208 - last_time_step_mse: 0.2084 - val_loss: 0.0874 - val_last_time_step_mse: 0.0761\n",
"Epoch 2/20\n",
"7000/7000 [==============================] - 10s 1ms/sample - loss: 0.0724 - last_time_step_mse: 0.0604 - val_loss: 0.0628 - val_last_time_step_mse: 0.0508\n",
"Epoch 3/20\n",
"7000/7000 [==============================] - 10s 1ms/sample - loss: 0.0577 - last_time_step_mse: 0.0439 - val_loss: 0.0537 - val_last_time_step_mse: 0.0389\n",
"Epoch 4/20\n",
"7000/7000 [==============================] - 10s 1ms/sample - loss: 0.0506 - last_time_step_mse: 0.0363 - val_loss: 0.0485 - val_last_time_step_mse: 0.0327\n",
"Epoch 5/20\n",
"7000/7000 [==============================] - 10s 1ms/sample - loss: 0.0460 - last_time_step_mse: 0.0317 - val_loss: 0.0444 - val_last_time_step_mse: 0.0298\n",
"Epoch 6/20\n",
"7000/7000 [==============================] - 10s 1ms/sample - loss: 0.0428 - last_time_step_mse: 0.0287 - val_loss: 0.0416 - val_last_time_step_mse: 0.0281\n",
"Epoch 7/20\n",
"7000/7000 [==============================] - 10s 1ms/sample - loss: 0.0403 - last_time_step_mse: 0.0266 - val_loss: 0.0394 - val_last_time_step_mse: 0.0270\n",
"Epoch 8/20\n",
"7000/7000 [==============================] - 10s 1ms/sample - loss: 0.0382 - last_time_step_mse: 0.0248 - val_loss: 0.0372 - val_last_time_step_mse: 0.0240\n",
"Epoch 9/20\n",
"7000/7000 [==============================] - 10s 1ms/sample - loss: 0.0367 - last_time_step_mse: 0.0232 - val_loss: 0.0355 - val_last_time_step_mse: 0.0218\n",
"Epoch 10/20\n",
"7000/7000 [==============================] - 10s 1ms/sample - loss: 0.0347 - last_time_step_mse: 0.0214 - val_loss: 0.0340 - val_last_time_step_mse: 0.0201\n",
"Epoch 11/20\n",
"7000/7000 [==============================] - 10s 1ms/sample - loss: 0.0333 - last_time_step_mse: 0.0199 - val_loss: 0.0327 - val_last_time_step_mse: 0.0192\n",
"Epoch 12/20\n",
"7000/7000 [==============================] - 10s 1ms/sample - loss: 0.0323 - last_time_step_mse: 0.0186 - val_loss: 0.0320 - val_last_time_step_mse: 0.0181\n",
"Epoch 13/20\n",
"7000/7000 [==============================] - 9s 1ms/sample - loss: 0.0313 - last_time_step_mse: 0.0178 - val_loss: 0.0304 - val_last_time_step_mse: 0.0170\n",
"Epoch 14/20\n",
"7000/7000 [==============================] - 9s 1ms/sample - loss: 0.0304 - last_time_step_mse: 0.0171 - val_loss: 0.0303 - val_last_time_step_mse: 0.0162\n",
"Epoch 15/20\n",
"7000/7000 [==============================] - 9s 1ms/sample - loss: 0.0304 - last_time_step_mse: 0.0173 - val_loss: 0.0297 - val_last_time_step_mse: 0.0168\n",
"Epoch 16/20\n",
"7000/7000 [==============================] - 9s 1ms/sample - loss: 0.0296 - last_time_step_mse: 0.0168 - val_loss: 0.0290 - val_last_time_step_mse: 0.0158\n",
"Epoch 17/20\n",
"7000/7000 [==============================] - 9s 1ms/sample - loss: 0.0297 - last_time_step_mse: 0.0168 - val_loss: 0.0293 - val_last_time_step_mse: 0.0175\n",
"Epoch 18/20\n",
"7000/7000 [==============================] - 9s 1ms/sample - loss: 0.0287 - last_time_step_mse: 0.0163 - val_loss: 0.0284 - val_last_time_step_mse: 0.0156\n",
"Epoch 19/20\n",
"7000/7000 [==============================] - 9s 1ms/sample - loss: 0.0283 - last_time_step_mse: 0.0158 - val_loss: 0.0279 - val_last_time_step_mse: 0.0148\n",
"Epoch 20/20\n",
"7000/7000 [==============================] - 9s 1ms/sample - loss: 0.0282 - last_time_step_mse: 0.0157 - val_loss: 0.0277 - val_last_time_step_mse: 0.0150\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "DRovj0EHJCKi",
"colab_type": "text"
},
"source": [
"# LSTMs"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "moSPtKf7r-_V",
"colab_type": "text"
},
"source": [
"La fonction suivante est reprise de plus haut"
]
},
{
"cell_type": "code",
"metadata": {
"scrolled": true,
"id": "KTDZ3p45JCKj",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"outputId": "0f28a70f-2784-4001-dc3c-05ba91a19cc7"
},
"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))"
],
"execution_count": 113,
"outputs": [
{
"output_type": "stream",
"text": [
"time: 1.04 s\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "SJHFcBKWJCKl",
"colab_type": "code",
"colab": {},
"outputId": "fe9313e8-d0c2-4646-876e-533d606aef89"
},
"source": [
"model.evaluate(X_valid, Y_valid)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"2000/2000 [==============================] - 0s 234us/sample - loss: 0.0240 - last_time_step_mse: 0.0086\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[0.024016654595732687, 0.00855141]"
]
},
"metadata": {
"tags": []
},
"execution_count": 48
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "zo25dmh7JCKn",
"colab_type": "code",
"colab": {},
"outputId": "14d55bc8-4b80-4be3-80f3-20134b1f1fc2"
},
"source": [
"plot_learning_curves(history.history[\"loss\"], history.history[\"val_loss\"])\n",
"plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Tkb2vGzpJCKq",
"colab_type": "code",
"colab": {}
},
"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]"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"scrolled": true,
"id": "glycuWwXJCKr",
"colab_type": "code",
"colab": {},
"outputId": "388ea312-2911-4784-9a44-3da26ec25192"
},
"source": [
"plot_multiple_forecasts(X_new, Y_new, Y_pred)\n",
"plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "mfmxmMm_JCKt",
"colab_type": "text"
},
"source": [
"# GRUs"
]
},
{
"cell_type": "code",
"metadata": {
"id": "TkAh0qmJJCKu",
"colab_type": "code",
"colab": {},
"outputId": "7bcd1b21-5d35-4114-bd81-84127eca561b"
},
"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))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Train on 7000 samples, validate on 2000 samples\n",
"Epoch 1/20\n",
"7000/7000 [==============================] - 10s 1ms/sample - loss: 0.0742 - last_time_step_mse: 0.0663 - val_loss: 0.0523 - val_last_time_step_mse: 0.0421\n",
"Epoch 2/20\n",
"7000/7000 [==============================] - 8s 1ms/sample - loss: 0.0476 - last_time_step_mse: 0.0367 - val_loss: 0.0441 - val_last_time_step_mse: 0.0327\n",
"Epoch 3/20\n",
"7000/7000 [==============================] - 8s 1ms/sample - loss: 0.0418 - last_time_step_mse: 0.0305 - val_loss: 0.0391 - val_last_time_step_mse: 0.0271\n",
"Epoch 4/20\n",
"7000/7000 [==============================] - 8s 1ms/sample - loss: 0.0373 - last_time_step_mse: 0.0249 - val_loss: 0.0343 - val_last_time_step_mse: 0.0205\n",
"Epoch 5/20\n",
"7000/7000 [==============================] - 8s 1ms/sample - loss: 0.0327 - last_time_step_mse: 0.0179 - val_loss: 0.0313 - val_last_time_step_mse: 0.0158\n",
"Epoch 6/20\n",
"7000/7000 [==============================] - 8s 1ms/sample - loss: 0.0308 - last_time_step_mse: 0.0155 - val_loss: 0.0297 - val_last_time_step_mse: 0.0143\n",
"Epoch 7/20\n",
"7000/7000 [==============================] - 8s 1ms/sample - loss: 0.0296 - last_time_step_mse: 0.0146 - val_loss: 0.0290 - val_last_time_step_mse: 0.0140\n",
"Epoch 8/20\n",
"7000/7000 [==============================] - 8s 1ms/sample - loss: 0.0284 - last_time_step_mse: 0.0134 - val_loss: 0.0278 - val_last_time_step_mse: 0.0128\n",
"Epoch 9/20\n",
"7000/7000 [==============================] - 8s 1ms/sample - loss: 0.0278 - last_time_step_mse: 0.0131 - val_loss: 0.0278 - val_last_time_step_mse: 0.0133\n",
"Epoch 10/20\n",
"7000/7000 [==============================] - 8s 1ms/sample - loss: 0.0272 - last_time_step_mse: 0.0126 - val_loss: 0.0272 - val_last_time_step_mse: 0.0139\n",
"Epoch 11/20\n",
"7000/7000 [==============================] - 8s 1ms/sample - loss: 0.0267 - last_time_step_mse: 0.0122 - val_loss: 0.0269 - val_last_time_step_mse: 0.0123\n",
"Epoch 12/20\n",
"7000/7000 [==============================] - 8s 1ms/sample - loss: 0.0264 - last_time_step_mse: 0.0121 - val_loss: 0.0267 - val_last_time_step_mse: 0.0132\n",
"Epoch 13/20\n",
"7000/7000 [==============================] - 8s 1ms/sample - loss: 0.0260 - last_time_step_mse: 0.0117 - val_loss: 0.0259 - val_last_time_step_mse: 0.0120\n",
"Epoch 14/20\n",
"7000/7000 [==============================] - 8s 1ms/sample - loss: 0.0257 - last_time_step_mse: 0.0116 - val_loss: 0.0265 - val_last_time_step_mse: 0.0132\n",
"Epoch 15/20\n",
"7000/7000 [==============================] - 8s 1ms/sample - loss: 0.0255 - last_time_step_mse: 0.0116 - val_loss: 0.0256 - val_last_time_step_mse: 0.0119\n",
"Epoch 16/20\n",
"7000/7000 [==============================] - 8s 1ms/sample - loss: 0.0252 - last_time_step_mse: 0.0112 - val_loss: 0.0249 - val_last_time_step_mse: 0.0110\n",
"Epoch 17/20\n",
"7000/7000 [==============================] - 8s 1ms/sample - loss: 0.0249 - last_time_step_mse: 0.0111 - val_loss: 0.0246 - val_last_time_step_mse: 0.0107\n",
"Epoch 18/20\n",
"7000/7000 [==============================] - 8s 1ms/sample - loss: 0.0248 - last_time_step_mse: 0.0112 - val_loss: 0.0251 - val_last_time_step_mse: 0.0124\n",
"Epoch 19/20\n",
"7000/7000 [==============================] - 8s 1ms/sample - loss: 0.0244 - last_time_step_mse: 0.0108 - val_loss: 0.0241 - val_last_time_step_mse: 0.0104\n",
"Epoch 20/20\n",
"7000/7000 [==============================] - 8s 1ms/sample - loss: 0.0242 - last_time_step_mse: 0.0106 - val_loss: 0.0241 - val_last_time_step_mse: 0.0103\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "tuGqRiL3JCKw",
"colab_type": "code",
"colab": {},
"outputId": "fe00064c-f8bd-4e4a-aa45-9ddc1e1cf2c1"
},
"source": [
"model.evaluate(X_valid, Y_valid)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"2000/2000 [==============================] - 0s 201us/sample - loss: 0.0241 - last_time_step_mse: 0.0103\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[0.02407100349664688, 0.010298318]"
]
},
"metadata": {
"tags": []
},
"execution_count": 53
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "T-e4mhJ_JCKz",
"colab_type": "code",
"colab": {},
"outputId": "873c339b-ebb5-4d93-977e-e33490ec26f9"
},
"source": [
"plot_learning_curves(history.history[\"loss\"], history.history[\"val_loss\"])\n",
"plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "taykTyJ7JCK0",
"colab_type": "code",
"colab": {}
},
"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]"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"scrolled": true,
"id": "X-2JhiJ2JCK3",
"colab_type": "code",
"colab": {},
"outputId": "c6987184-7738-449c-baaa-1b5522aa8ca0"
},
"source": [
"plot_multiple_forecasts(X_new, Y_new, Y_pred)\n",
"plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "j0b-l8AsJCK4",
"colab_type": "text"
},
"source": [
"## Using One-Dimensional Convolutional Layers to Process Sequences"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1qsQ4rybJCK5",
"colab_type": "text"
},
"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",
"metadata": {
"id": "ZrUUVvwNJCK5",
"colab_type": "code",
"colab": {},
"outputId": "9ebc7eca-7bb8-465d-ae43-9833d3f1049a"
},
"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]))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Train on 7000 samples, validate on 2000 samples\n",
"Epoch 1/20\n",
"7000/7000 [==============================] - 7s 938us/sample - loss: 0.0683 - last_time_step_mse: 0.0605 - val_loss: 0.0482 - val_last_time_step_mse: 0.0405\n",
"Epoch 2/20\n",
"7000/7000 [==============================] - 4s 630us/sample - loss: 0.0416 - last_time_step_mse: 0.0342 - val_loss: 0.0368 - val_last_time_step_mse: 0.0283\n",
"Epoch 3/20\n",
"7000/7000 [==============================] - 5s 670us/sample - loss: 0.0334 - last_time_step_mse: 0.0251 - val_loss: 0.0307 - val_last_time_step_mse: 0.0220\n",
"Epoch 4/20\n",
"7000/7000 [==============================] - 5s 651us/sample - loss: 0.0273 - last_time_step_mse: 0.0172 - val_loss: 0.0251 - val_last_time_step_mse: 0.0141\n",
"Epoch 5/20\n",
"7000/7000 [==============================] - 4s 639us/sample - loss: 0.0243 - last_time_step_mse: 0.0134 - val_loss: 0.0238 - val_last_time_step_mse: 0.0128\n",
"Epoch 6/20\n",
"7000/7000 [==============================] - 4s 639us/sample - loss: 0.0230 - last_time_step_mse: 0.0121 - val_loss: 0.0226 - val_last_time_step_mse: 0.0116\n",
"Epoch 7/20\n",
"7000/7000 [==============================] - 5s 654us/sample - loss: 0.0224 - last_time_step_mse: 0.0116 - val_loss: 0.0220 - val_last_time_step_mse: 0.0110\n",
"Epoch 8/20\n",
"7000/7000 [==============================] - 5s 643us/sample - loss: 0.0218 - last_time_step_mse: 0.0111 - val_loss: 0.0216 - val_last_time_step_mse: 0.0107\n",
"Epoch 9/20\n",
"7000/7000 [==============================] - 5s 663us/sample - loss: 0.0214 - last_time_step_mse: 0.0107 - val_loss: 0.0211 - val_last_time_step_mse: 0.0103\n",
"Epoch 10/20\n",
"7000/7000 [==============================] - 5s 667us/sample - loss: 0.0211 - last_time_step_mse: 0.0105 - val_loss: 0.0209 - val_last_time_step_mse: 0.0102\n",
"Epoch 11/20\n",
"7000/7000 [==============================] - 5s 661us/sample - loss: 0.0207 - last_time_step_mse: 0.0103 - val_loss: 0.0206 - val_last_time_step_mse: 0.0099\n",
"Epoch 12/20\n",
"7000/7000 [==============================] - 5s 666us/sample - loss: 0.0206 - last_time_step_mse: 0.0102 - val_loss: 0.0203 - val_last_time_step_mse: 0.0097\n",
"Epoch 13/20\n",
"7000/7000 [==============================] - 5s 665us/sample - loss: 0.0202 - last_time_step_mse: 0.0098 - val_loss: 0.0200 - val_last_time_step_mse: 0.0095\n",
"Epoch 14/20\n",
"7000/7000 [==============================] - 5s 668us/sample - loss: 0.0200 - last_time_step_mse: 0.0096 - val_loss: 0.0207 - val_last_time_step_mse: 0.0105\n",
"Epoch 15/20\n",
"7000/7000 [==============================] - 5s 660us/sample - loss: 0.0197 - last_time_step_mse: 0.0095 - val_loss: 0.0198 - val_last_time_step_mse: 0.0093\n",
"Epoch 16/20\n",
"7000/7000 [==============================] - 5s 661us/sample - loss: 0.0194 - last_time_step_mse: 0.0091 - val_loss: 0.0191 - val_last_time_step_mse: 0.0087\n",
"Epoch 17/20\n",
"7000/7000 [==============================] - 5s 670us/sample - loss: 0.0191 - last_time_step_mse: 0.0089 - val_loss: 0.0188 - val_last_time_step_mse: 0.0085\n",
"Epoch 18/20\n",
"7000/7000 [==============================] - 5s 659us/sample - loss: 0.0187 - last_time_step_mse: 0.0085 - val_loss: 0.0183 - val_last_time_step_mse: 0.0077\n",
"Epoch 19/20\n",
"7000/7000 [==============================] - 5s 662us/sample - loss: 0.0181 - last_time_step_mse: 0.0078 - val_loss: 0.0178 - val_last_time_step_mse: 0.0074\n",
"Epoch 20/20\n",
"7000/7000 [==============================] - 5s 669us/sample - loss: 0.0175 - last_time_step_mse: 0.0072 - val_loss: 0.0173 - val_last_time_step_mse: 0.0067\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "nF2nABp9JCK7",
"colab_type": "text"
},
"source": [
"## WaveNet"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Vyes4ByZJCK7",
"colab_type": "text"
},
"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",
"metadata": {
"id": "-NybSOsIJCK7",
"colab_type": "code",
"colab": {},
"outputId": "0e103fbd-3798-4d1b-e831-ef953c3ab27b"
},
"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))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Train on 7000 samples, validate on 2000 samples\n",
"Epoch 1/20\n",
"7000/7000 [==============================] - 2s 275us/sample - loss: 0.0684 - last_time_step_mse: 0.0550 - val_loss: 0.0387 - val_last_time_step_mse: 0.0248\n",
"Epoch 2/20\n",
"7000/7000 [==============================] - 1s 176us/sample - loss: 0.0342 - last_time_step_mse: 0.0216 - val_loss: 0.0307 - val_last_time_step_mse: 0.0182\n",
"Epoch 3/20\n",
"7000/7000 [==============================] - 1s 194us/sample - loss: 0.0293 - last_time_step_mse: 0.0172 - val_loss: 0.0275 - val_last_time_step_mse: 0.0152\n",
"Epoch 4/20\n",
"7000/7000 [==============================] - 1s 186us/sample - loss: 0.0270 - last_time_step_mse: 0.0149 - val_loss: 0.0259 - val_last_time_step_mse: 0.0140\n",
"Epoch 5/20\n",
"7000/7000 [==============================] - 1s 182us/sample - loss: 0.0254 - last_time_step_mse: 0.0133 - val_loss: 0.0248 - val_last_time_step_mse: 0.0126\n",
"Epoch 6/20\n",
"7000/7000 [==============================] - 1s 182us/sample - loss: 0.0245 - last_time_step_mse: 0.0125 - val_loss: 0.0237 - val_last_time_step_mse: 0.0116\n",
"Epoch 7/20\n",
"7000/7000 [==============================] - 1s 187us/sample - loss: 0.0236 - last_time_step_mse: 0.0117 - val_loss: 0.0231 - val_last_time_step_mse: 0.0112\n",
"Epoch 8/20\n",
"7000/7000 [==============================] - 1s 180us/sample - loss: 0.0231 - last_time_step_mse: 0.0113 - val_loss: 0.0229 - val_last_time_step_mse: 0.0112\n",
"Epoch 9/20\n",
"7000/7000 [==============================] - 1s 181us/sample - loss: 0.0228 - last_time_step_mse: 0.0111 - val_loss: 0.0222 - val_last_time_step_mse: 0.0105\n",
"Epoch 10/20\n",
"7000/7000 [==============================] - 1s 197us/sample - loss: 0.0224 - last_time_step_mse: 0.0107 - val_loss: 0.0221 - val_last_time_step_mse: 0.0104\n",
"Epoch 11/20\n",
"7000/7000 [==============================] - 1s 192us/sample - loss: 0.0219 - last_time_step_mse: 0.0102 - val_loss: 0.0217 - val_last_time_step_mse: 0.0099\n",
"Epoch 12/20\n",
"7000/7000 [==============================] - 1s 206us/sample - loss: 0.0217 - last_time_step_mse: 0.0101 - val_loss: 0.0219 - val_last_time_step_mse: 0.0102\n",
"Epoch 13/20\n",
"7000/7000 [==============================] - 1s 179us/sample - loss: 0.0214 - last_time_step_mse: 0.0099 - val_loss: 0.0214 - val_last_time_step_mse: 0.0099\n",
"Epoch 14/20\n",
"7000/7000 [==============================] - 1s 176us/sample - loss: 0.0213 - last_time_step_mse: 0.0098 - val_loss: 0.0211 - val_last_time_step_mse: 0.0094\n",
"Epoch 15/20\n",
"7000/7000 [==============================] - 1s 181us/sample - loss: 0.0211 - last_time_step_mse: 0.0096 - val_loss: 0.0209 - val_last_time_step_mse: 0.0092\n",
"Epoch 16/20\n",
"7000/7000 [==============================] - 1s 183us/sample - loss: 0.0208 - last_time_step_mse: 0.0093 - val_loss: 0.0208 - val_last_time_step_mse: 0.0092\n",
"Epoch 17/20\n",
"7000/7000 [==============================] - 1s 193us/sample - loss: 0.0206 - last_time_step_mse: 0.0091 - val_loss: 0.0206 - val_last_time_step_mse: 0.0091\n",
"Epoch 18/20\n",
"7000/7000 [==============================] - 1s 202us/sample - loss: 0.0205 - last_time_step_mse: 0.0089 - val_loss: 0.0203 - val_last_time_step_mse: 0.0090\n",
"Epoch 19/20\n",
"7000/7000 [==============================] - 1s 200us/sample - loss: 0.0203 - last_time_step_mse: 0.0088 - val_loss: 0.0198 - val_last_time_step_mse: 0.0083\n",
"Epoch 20/20\n",
"7000/7000 [==============================] - 1s 213us/sample - loss: 0.0200 - last_time_step_mse: 0.0084 - val_loss: 0.0200 - val_last_time_step_mse: 0.0083\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "83o7b3NlJCK9",
"colab_type": "text"
},
"source": [
"Here is the original WaveNet defined in the paper: it uses Gated Activation Units instead of ReLU and parametrized skip connections, plus it pads with zeros on the left to avoid getting shorter and shorter sequences:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "obZmatA2JCK-",
"colab_type": "code",
"colab": {}
},
"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"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "u3HbN1jiJCK_",
"colab_type": "code",
"colab": {}
},
"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"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "n6UnkgVFJCLA",
"colab_type": "code",
"colab": {}
},
"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])"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "qaz-cTZ6JCLC",
"colab_type": "code",
"colab": {},
"outputId": "c0381897-3d54-46ee-8476-448c8feee448"
},
"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))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Train on 7000 samples, validate on 2000 samples\n",
"Epoch 1/2\n",
"7000/7000 [==============================] - 3s 443us/sample - loss: 0.1299 - last_time_step_mse: 0.1258 - val_loss: 0.1229 - val_last_time_step_mse: 0.1199\n",
"Epoch 2/2\n",
"7000/7000 [==============================] - 2s 271us/sample - loss: 0.1222 - last_time_step_mse: 0.1178 - val_loss: 0.1218 - val_last_time_step_mse: 0.1190\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cMXBVEcwJCLD",
"colab_type": "text"
},
"source": [
"In this chapter we explored the fundamentals of RNNs and used them to process sequences (namely, time series). In the process we also looked at other ways to process sequences, including CNNs. In the next chapter we will use RNNs for Natural Language Processing, and we will learn more about RNNs (bidirectional RNNs, stateful vs stateless RNNs, Encoder–Decoders, and Attention-augmented Encoder-Decoders). We will also look at the Transformer, an Attention-only architecture."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Te91cLKmJCLE",
"colab_type": "text"
},
"source": [
"# Exercise solutions"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "MpobmJ0gJCLE",
"colab_type": "text"
},
"source": [
"## 1. to 8."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "YX1QZzmvJCLE",
"colab_type": "text"
},
"source": [
"See Appendix A."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "N99RnollJCLE",
"colab_type": "text"
},
"source": [
"## 9. Tackling the SketchRNN Dataset"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "lnbG-uWZJCLF",
"colab_type": "text"
},
"source": [
"_Exercise: Train a classification model for the SketchRNN dataset, available in TensorFlow Datasets._"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "YgSgECtHJCLF",
"colab_type": "text"
},
"source": [
"The dataset is not available in TFDS yet, the [pull request](https://github.com/tensorflow/datasets/pull/361) is still work in progress. Luckily, the data is conveniently available as TFRecords, so let's download it (it might take a while, as it's about 1 GB large, with 3,450,000 training sketches and 345,000 test sketches):"
]
},
{
"cell_type": "code",
"metadata": {
"id": "TlzfletoJCLF",
"colab_type": "code",
"colab": {}
},
"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)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "hdWU19k5JCLH",
"colab_type": "code",
"colab": {}
},
"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-*\")])"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "yfGoVIydJCLI",
"colab_type": "code",
"colab": {},
"outputId": "df9cf563-81b1-4087-ddae-70bcf742e194"
},
"source": [
"train_files"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"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']"
]
},
"metadata": {
"tags": []
},
"execution_count": 65
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "gMVB7ClOJCLK",
"colab_type": "code",
"colab": {},
"outputId": "a689c008-5753-44e9-e508-dd4e18f2a334"
},
"source": [
"eval_files"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"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']"
]
},
"metadata": {
"tags": []
},
"execution_count": 66
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "h7EcJG4cJCLM",
"colab_type": "code",
"colab": {}
},
"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()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "mNbezPXMJCLX",
"colab_type": "code",
"colab": {}
},
"source": [
"assert train_classes == test_classes\n",
"class_names = [name.strip().lower() for name in train_classes]"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "nKu4DxYbJCLY",
"colab_type": "code",
"colab": {},
"outputId": "0c54ee6e-4adb-48ab-b7bb-c7af316cd8e5"
},
"source": [
"sorted(class_names)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"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']"
]
},
"metadata": {
"tags": []
},
"execution_count": 69
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "9Q3VV2dwJCLb",
"colab_type": "code",
"colab": {}
},
"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"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "nLVM4ObJJCLh",
"colab_type": "code",
"colab": {}
},
"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)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "rnndirwTJCLj",
"colab_type": "code",
"colab": {}
},
"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:])"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "3qsdaeBuJCLk",
"colab_type": "code",
"colab": {},
"outputId": "d4e603b9-f3c4-467f-f7ec-652afa5edd82"
},
"source": [
"for sketches, lengths, labels in train_set.take(1):\n",
" print(\"sketches =\", sketches)\n",
" print(\"lengths =\", lengths)\n",
" print(\"labels =\", labels)"
],
"execution_count": null,
"outputs": [
{
"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"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "3XrgGewyJCLm",
"colab_type": "code",
"colab": {},
"outputId": "d918bc9b-b628-4a86-91dd-917771be7933"
},
"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)"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
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