From e37e50d592bc6e097a89092153112350640e1f71 Mon Sep 17 00:00:00 2001 From: hossein20s Date: Fri, 18 Oct 2019 02:09:01 +0000 Subject: [PATCH 1/2] Created using Colaboratory --- 23_Time_Series_Prediction.ipynb | 3927 +++++++++++++++++++++++++++++++ 1 file changed, 3927 insertions(+) create mode 100644 23_Time_Series_Prediction.ipynb diff --git a/23_Time_Series_Prediction.ipynb b/23_Time_Series_Prediction.ipynb new file mode 100644 index 0000000..9a962da --- /dev/null +++ b/23_Time_Series_Prediction.ipynb @@ -0,0 +1,3927 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + }, + "colab": { + "name": "23_Time-Series-Prediction.ipynb", + "provenance": [], + "collapsed_sections": [ + "v2HIO-L7wPdy", + "5t6DXkeYwPeU", + "pylQQ42VwPej", + "2agyuXRywPgq", + "b5npmuR6wPhg", + "ByUxlvhgwPhh", + "q8qVbNWtwPhh" + ], + "include_colab_link": true + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2eJGvDw0wPc1", + "colab_type": "text" + }, + "source": [ + "# TensorFlow Tutorial #23\n", + "# Time-Series Prediction\n", + "\n", + "by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n", + "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rpJpupAqwPc4", + "colab_type": "text" + }, + "source": [ + "## Introduction\n", + "\n", + "This tutorial tries to predict the future weather of a city using weather-data from several other cities.\n", + "\n", + "Because we will be working with sequences of arbitrary length, we will use a Recurrent Neural Network (RNN).\n", + "\n", + "You should be familiar with TensorFlow and Keras in general, see Tutorials #01 and #03-C, and the basics of Recurrent Neural Networks as explained in Tutorial #20." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6SUvmh-9wPc6", + "colab_type": "text" + }, + "source": [ + "## Location\n", + "\n", + "We will use weather-data from the period 1980-2018 for five cities in [Denmark](https://en.wikipedia.org/wiki/Denmark):\n", + "\n", + "* **[Aalborg](https://en.wikipedia.org/wiki/Aalborg)** The weather-data is actually from an airforce base which is also home to [The Hunter Corps (Jægerkorps)](https://en.wikipedia.org/wiki/Jaeger_Corps_(Denmark).\n", + "* **[Aarhus](https://en.wikipedia.org/wiki/Aarhus)** is the city where [the inventor of C++](https://en.wikipedia.org/wiki/Bjarne_Stroustrup) studied and the [Google V8 JavaScript Engine](https://en.wikipedia.org/wiki/Chrome_V8) was developed.\n", + "* **[Esbjerg](https://en.wikipedia.org/wiki/Esbjerg)** has a large fishing-port.\n", + "* **[Odense](https://en.wikipedia.org/wiki/Odense)** is the birth-city of the fairytale author [H. C. Andersen](https://en.wikipedia.org/wiki/Hans_Christian_Andersen).\n", + "* **[Roskilde](https://en.wikipedia.org/wiki/Roskilde)** has an old cathedral housing the tombs of the Danish royal family." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QDE1GCf4wPc7", + "colab_type": "text" + }, + "source": [ + "The following map shows the location of the cities in Denmark:\n", + "\n", + "![Map of Denmark](https://github.com/hossein20s/TensorFlow-Tutorials/blob/master/images/Denmark.jpg?raw=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KST-d6LQwPc8", + "colab_type": "text" + }, + "source": [ + "The following map shows the location of Denmark within Europe:\n", + "\n", + "![Map of Europe](https://github.com/hossein20s/TensorFlow-Tutorials/blob/master/images/Europe.jpg?raw=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vXPIxHP4wPc-", + "colab_type": "text" + }, + "source": [ + "## Flowchart\n", + "\n", + "In this tutorial, we are trying to predict the weather for the Danish city \"Odense\" 24 hours into the future, given the current and past weather-data from 5 cities (although the flowchart below only shows 2 cities).\n", + "\n", + "We use a Recurrent Neural Network (RNN) because it can work on sequences of arbitrary length. During training we will use sub-sequences of 1344 data-points (8 weeks) from the training-set, with each data-point or observation having 20 input-signals for the temperature, pressure, etc. for each of the 5 cities. We then want to train the neural network so it outputs the 3 signals for tomorrow's temperature, pressure and wind-speed." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "sPKsP4KfwPc_", + "colab_type": "text" + }, + "source": [ + "![Flowchart](https://github.com/hossein20s/TensorFlow-Tutorials/blob/master/images/23_time_series_flowchart.png?raw=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8l8H5QRQwPdA", + "colab_type": "text" + }, + "source": [ + "## Imports" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "DodpoewhwPdB", + "colab_type": "code", + "colab": {} + }, + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import tensorflow as tf\n", + "import numpy as np\n", + "import pandas as pd\n", + "import os\n", + "from sklearn.preprocessing import MinMaxScaler" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "l0c-1RabwPdH", + "colab_type": "text" + }, + "source": [ + "We need to import several things from Keras." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Ba3n0QNOwPdI", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# from tf.keras.models import Sequential # This does not work!\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Input, Dense, GRU, Embedding\n", + "from tensorflow.keras.optimizers import RMSprop\n", + "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, TensorBoard, ReduceLROnPlateau" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "K3zgR_60wPdM", + "colab_type": "text" + }, + "source": [ + "This was developed using Python 3.6 (Anaconda) and package versions:" + ] + }, + { + "cell_type": "code", + "metadata": { + "scrolled": false, + "id": "E9SAs5wqwPdN", + "colab_type": "code", + "outputId": "d893f0c5-c653-4794-9abd-24dc395f2c5f", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + } + }, + "source": [ + "tf.__version__" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'1.15.0-rc3'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 4 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "scrolled": true, + "id": "3eZxKhdYwPdR", + "colab_type": "code", + "outputId": "4ca51614-3db9-45e7-c857-2a0a0bf3cb86", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + } + }, + "source": [ + "tf.keras.__version__" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'2.2.4-tf'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 5 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "bQqcfRlnwPdV", + "colab_type": "code", + "outputId": "b4fada10-5236-48ae-9672-42d3f447a9fa", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + } + }, + "source": [ + "pd.__version__" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'0.24.2'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 6 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5WEvg-wLwPdY", + "colab_type": "text" + }, + "source": [ + "## Load Data\n", + "\n", + "Weather-data for 5 cities in Denmark will be downloaded automatically below.\n", + "\n", + "The raw weather-data was originally obtained from the [National Climatic Data Center (NCDC), USA](https://www7.ncdc.noaa.gov/CDO/cdoselect.cmd). Their web-site and database-access is very confusing and may change soon. Furthermore, the raw data-file had to be manually edited before it could be read. So you should expect some challenges if you want to download weather-data for another region. The following Python-module provides some functionality that may be helpful if you want to use new weather-data, but you will have to modify the source-code to fit your data-format." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "0EKf--E8wPda", + "colab_type": "code", + "outputId": "9cbcd30e-3d97-41d7-a4a7-99f16d1ccb31", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 63 + } + }, + "source": [ + "!git clone https://github.com/hossein20s/TensorFlow-Tutorials\n", + "!cp TensorFlow-Tutorials/*.py .\n", + "\n", + "import weather\n" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "fatal: destination path 'TensorFlow-Tutorials' already exists and is not an empty directory.\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JHuYxfRBwPde", + "colab_type": "text" + }, + "source": [ + "Download the data-set if you don't have it already. It is about 35 MB." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "JLAhzMSfwPdf", + "colab_type": "code", + "outputId": "dff49710-18ed-459a-a62f-55fea77f8522", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + } + }, + "source": [ + "weather.maybe_download_and_extract()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Data has apparently already been downloaded and unpacked.\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Q85DXu_IwPdi", + "colab_type": "text" + }, + "source": [ + "List of the cities used in the data-set." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "fK-oHbPGwPdk", + "colab_type": "code", + "outputId": "299e689b-c391-4670-e93b-f961c46a54f4", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + } + }, + "source": [ + "cities = weather.cities\n", + "cities" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "['Aalborg', 'Aarhus', 'Esbjerg', 'Odense', 'Roskilde']" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 9 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IpjwHOh8wPdn", + "colab_type": "text" + }, + "source": [ + "Load and resample the data so it has observations at regular time-intervals for every 60 minutes. Missing data-points are linearly interpolated. This takes about 30 seconds to run the first time but uses a cache-file so it loads very quickly the next time." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "_hU0BoMbwPdp", + "colab_type": "code", + "outputId": "91f03c36-d22a-4f24-9222-fa83820e5fc1", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 53 + } + }, + "source": [ + "%%time\n", + "df = weather.load_resampled_data()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "CPU times: user 37.5 ms, sys: 64.1 ms, total: 102 ms\n", + "Wall time: 325 ms\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "D_Vetst4wPdt", + "colab_type": "text" + }, + "source": [ + "These are the top rows of the data-set." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "UTv8npHowPdu", + "colab_type": "code", + "outputId": "97297b18-303e-40f5-83fd-2f7d3120fa23", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 476 + } + }, + "source": [ + "df.head()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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AalborgAarhusEsbjergOdenseRoskilde
TempPressureWindSpeedWindDirTempPressureWindSpeedWindDirTempPressureWindSpeedWindDirTempPressureWindSpeedWindDirTempPressureWindSpeedWindDir
DateTime
1980-03-01 11:00:005.0000001007.76666710.2280.0000005.01008.30000015.4290.06.083333NaN12.383333310.0000006.1428571011.06666712.585714290.05.000000NaN11.466667280.000000
1980-03-01 12:00:005.0000001008.00000010.3290.0000005.01008.60000013.4280.06.583333NaN12.883333310.0000007.0000001011.20000011.300000290.05.000000NaN12.466667280.000000
1980-03-01 13:00:005.0000001008.0666679.7290.0000005.01008.43333315.4280.06.888889NaN13.244444309.4444447.0000001011.30000012.118182290.05.166667NaN13.133333278.333333
1980-03-01 14:00:004.3333331008.13333311.1283.3333335.01008.26666714.9300.06.222222NaN12.911111306.1111116.8571431011.40000012.742857290.05.833333NaN12.300000270.000000
1980-03-01 15:00:004.0000001008.20000011.3280.0000005.01008.10000017.0290.05.555556NaN12.577778302.7777786.0000001011.50000012.400000290.04.833333NaN12.300000270.000000
\n", + "
" + ], + "text/plain": [ + " Aalborg ... Roskilde \n", + " Temp Pressure ... WindSpeed WindDir\n", + "DateTime ... \n", + "1980-03-01 11:00:00 5.000000 1007.766667 ... 11.466667 280.000000\n", + "1980-03-01 12:00:00 5.000000 1008.000000 ... 12.466667 280.000000\n", + "1980-03-01 13:00:00 5.000000 1008.066667 ... 13.133333 278.333333\n", + "1980-03-01 14:00:00 4.333333 1008.133333 ... 12.300000 270.000000\n", + "1980-03-01 15:00:00 4.000000 1008.200000 ... 12.300000 270.000000\n", + "\n", + "[5 rows x 20 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 11 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "v2HIO-L7wPdy", + "colab_type": "text" + }, + "source": [ + "### Missing Data\n", + "\n", + "The two cities Esbjerg and Roskilde have missing data for the atmospheric pressure, as can be seen in the following two plots. \n", + "\n", + "Because we are using resampled data, we have filled in the missing values with new values that are linearly interpolated from the neighbouring values, which appears as long straight lines in these plots.\n", + "\n", + "This may confuse the neural network. For simplicity, we will simply remove these two signals from the data.\n", + "\n", + "But it is only short periods of data that are missing, so you could actually generate this data by creating a predictive model that generates the missing data from all the other input signals. Then you could add these generated values back into the data-set to fill the gaps." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "nROi76ntwPdz", + "colab_type": "code", + "outputId": "d43aa3eb-e5ea-4ce9-c756-42e7dfa18cdd", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 321 + } + }, + "source": [ + "df['Esbjerg']['Pressure'].plot()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 12 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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WTH17BYZ0LMGMyQNQmJe+aTg0AkArKUci2QeQyASOZBE3AYUIrVBoP1bValqm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\naBYRrRd/NxG3k3jtNhDRciLqJzvWZHH/9UQ02e2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TlxwftQjKSA0FULUEgN8O61xVnmAQ2rr3cMhSMQyTibRvXCdqEZSRIgpAdwMV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\nEH9xcgesv/8slSK6JnFvm1W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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GLCLX_yawPd7", + "colab_type": "text" + }, + "source": [ + "Before removing these two signals, there are 20 input-signals in the data-set." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Df_g04tCwPd8", + "colab_type": "code", + "outputId": "4cc9b80d-07b0-4c8a-ed8b-5410d975f4a3", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 55 + } + }, + "source": [ + "df.values.shape" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(333109, 20)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 14 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mPVBWmUbwPeB", + "colab_type": "text" + }, + "source": [ + "Then we remove the two signals that have missing data." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "fcTXI8k1wPeB", + "colab_type": "code", + "colab": {} + }, + "source": [ + "df.drop(('Esbjerg', 'Pressure'), axis=1, inplace=True)\n", + "df.drop(('Roskilde', 'Pressure'), axis=1, inplace=True)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hxpuF6zlwPeH", + "colab_type": "text" + }, + "source": [ + "Now there are only 18 input-signals in the data." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "-SnarcOpwPeH", + "colab_type": "code", + "outputId": "3b4e34d3-13fd-4c4a-c816-457c7119fc18", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 55 + } + }, + "source": [ + "df.values.shape" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(333109, 18)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 16 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "L3MaiEsEwPeO", + "colab_type": "text" + }, + "source": [ + "We can verify that these two data-columns have indeed been removed." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "VJUpPewGwPeP", + "colab_type": "code", + "outputId": "9f55c594-d442-45f3-b12a-b9b775e7083c", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 196 + } + }, + "source": [ + "df.head(1)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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1980-03-01 11:00:005.01007.76666710.2280.05.01008.315.4290.06.08333312.383333310.06.1428571011.06666712.585714290.05.011.466667280.0
\n", + "
" + ], + "text/plain": [ + " Aalborg ... Roskilde \n", + " Temp Pressure WindSpeed ... Temp WindSpeed WindDir\n", + "DateTime ... \n", + "1980-03-01 11:00:00 5.0 1007.766667 10.2 ... 5.0 11.466667 280.0\n", + "\n", + "[1 rows x 18 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 17 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5t6DXkeYwPeU", + "colab_type": "text" + }, + "source": [ + "### Data Errors\n", + "\n", + "There are some errors in this data. As shown in the plot below, the temperature in the city of Odense suddenly jumped to almost 50 degrees C. But the highest temperature ever measured in Denmark was only 36.4 degrees Celcius and the lowest was -31.2 C. So this is clearly a data error. However, we will not correct any data-errors in this tutorial." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Z5Cu5qXlwPeW", + "colab_type": "code", + "outputId": "c43f6cb8-698e-462a-cf34-5ae9dc9d612f", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 332 + } + }, + "source": [ + "df['Odense']['Temp']['2006-05':'2006-07'].plot()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 18 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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vuC1Z6HRjS0IfRcnKqYZWTutvBtQhxkvqjbiOeBDpofUchoKMmwb85Mk144kp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\n0Kz5DbsGE0IXx/2Vm9LVQRRz/PyvG+WYBwyPvUq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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s1eYnGtzwPeY", + "colab_type": "text" + }, + "source": [ + "This can also be confirmed to be an error by considering the temperatures in some of the other cities in Denmark for that period, which was only around 10 degrees. Because the country is so small, it is not possible for one city in Denmark to have 50 degrees while another city only has 10 degrees." + ] + }, + { + "cell_type": "code", + "metadata": { + "scrolled": true, + "id": "xWME0HGxwPeZ", + "colab_type": "code", + "outputId": "d0aa6883-f8ad-46f3-f030-5730a1906070", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 332 + } + }, + "source": [ + "df['Aarhus']['Temp']['2006-05':'2006-07'].plot()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 19 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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dh940hZTmOjn+4Xu346vXPIS7dk4fwhEZSG52IaDjSXPv4DQs111oL9Nts5Kz\ngJkUuQEHROq/CjOwycDL3qrhaiOcrKrURlleDJRNqkXMRGvt6CPeJ5TOQRwZD50aYJPhNccSrkLo\nuLLcNImmQll0XcaasU0sKkpBZdpuxjx0uk+jCg+9bWWKkaLa6CaHIo4itJJYTFx9T4s/pkG/qJRa\nDeBrAP5Yax3kymtzlit3q7X+pNb6LK31WRs3HnozALrBucKBIFNzpYf+3v++BdsOzuO+PTMleoYm\nitqgHz7gxo74dOJxycvhHHqr4ekEDu5195OG8ebi3kOP7XZyR98spKt9oOBYII3Di0/x1nEUHIys\n+iPNQw9da+M8RcwyUI9RIPTQAdhqi1GQjQqI1HkhA6w616/42E9wyru+W3ksWmtcff8+p+t22xWB\nTrovGmz1QRw6JQs1WONoWRokjkxRsrwogubY5PlPNGN08gKd3DegllJJOk5eTVGxzwmkZ6cVBHWH\nSiLlZJT8+AfFQAZdKdWAMeZf0Fp/3b69Uyl1nP38OACjba4I4OvXPoRL7zJePZ2oqqa8suKeCyZo\n/57ZRnhDdCwHSJ/VODzAjR33MClT1GQHoqxyEZP29BCUS4fdH55DNx7cbCd3D//UiLIgF+qhc6Pp\nKyqaKoRaU30WNumxoJ1CqJ2OlOeII+U5dMBz6tK7DhuLhAa9SsFzS496St+4biv+57/8DP91w7bA\nIcsKjaJgJXHtdiloSzp5KoxVaB0Y1qoS2s4xyLUPFNtjI0pmrpP50gnkoSehYyhXAkqseABT4yaO\nlOtelNv4xSrRwH6kQVFlRvIZALdprc9lH/0XgNfbv18P4FtD7Hcg/OmXb8DrPnMlAH+DVy3XpEGn\nC0vcE53A2XYWzHaUeADUHvrhBH4LBCVTbeArtvU7nA69m8qFedP9lvt8FVhFudA9thDHQGYgLgR8\nW9zIkFcbKQRxhlaDGyCZOa2cISx76JGjPDj45GlS9KtlgBJb9s6W3nvAvnfP7pnAIaPVRsIkl2ZM\noU4+Uj6dP+DQK6wfedadvHDKFEe5WCM+08m7euh0j/FzRKfyDU/fDABYze4bOp9c5bKq5KFXn6sq\nDOKhPwPA6wA8Vyl1vf33UgDvB/ACpdRdAJ5vXy8a6IaoStCXTQ7oZqKbkgx8rnUww9ONbb5TG/SV\njI9ffA9O+5vvAQivNwUhW0lsg6JwKo1SFmSJQ2ceej+DLu4bgHvomV8NLuA+GhWHrrXoSpSHHnph\n64tEqhwUBcLqiua199ClQY8iFRhJgmz918kL971eQdF7dpdjEFw7zifW3OrQZdp9ELQVOnQ++VSV\n+2jYrNcsLwIpJ+AzSmcrPHRc59v+AAAgAElEQVTarlO5xNygm7/H7e8niKqzHjpVYswKjThWbtuE\nYe6EpN8XtNaXoXsLzucNsa8FgUuAAODgbIpbth/E0x+7oZRmzAvzAyxz1C7R+DZJjjXKFlk1Ro9/\nsHLV2U4WPNTcQ08d5YKhZYv9KJeAu7XWe7V9MOfS3K0GFyTJI+MQqQUZdJdS3ogxnxaBnI906EpQ\nLk1m0FWJIlAB5SI59Koy1WH5WUNbTDQTHJxLKyc9MrpVyV4NFvSkc9+IfcGthvCOOYfOg6IkaVUK\njnYqjSNW6OQ5Ch3WiAc85TLTzl0Galry0P01JNCfjznGVHM8ZdMa7JxsY66TufOX2USjWFWcz1Fz\n6MsByXfyUp4A8JYvXovf+NQVmJxPw76SuS7dML5ui/TQ/SxNVM7P7tmLcy+4c5SHUmOE2DfTEUFR\nn+6dccolUr7aYiN8MAmByqWPh86pOtoOr98xihIStA3ypA8VND5JGTQT0eeSZYNyDl0qWaSH3hKB\nxUYF5cKp0dwGRcnD7VRMenTsMxVB5dt3TLnjokvfiCNXl0YqTAIOnQVFSXvukqQqJqIkUqw3qaBc\nmIdu6sBHTOXinUclKCuaOF79lBPwjf/9dPzqk4+327EeehwG9PmK59g1rZEnFi0LSlFzpukEgC37\nDK+2Z6pdav7qqi1q7d6jbfDnhKL9gA98vOU/rsVHL7xrQWqFGouHLNciKOopA18+16pcBPcp28Xx\nLM9+Bp3vk9Qx3pAUuOp+0892mMp43fZBXPehQsYOuLSOPPTIeqqEVuChhxptpYAGJRYphbXjviBZ\nNw+dyg0cvarpqiIS5SAnPX6sMxWZt9+4bisAE+SmiZWCl5xyoevrdeks9V9rd29E7FgkkihiNJ6g\nXJiH3ohN4F1mp5Lsk1M7tJskjnDmo9a7+3G2kzuVEOnk6TWh1YhGzqEvC+RFd5SL9dHX26JIu6fa\npRKqssARGWvy0OkEkw4U8BPGXls8Z3Lu0NUKNRaO+TTH/XtmSu9LHrWXh+6ph2oPPSi6NATlQsZK\neobASvHQw4ksaFxc+NgRN8Shh64COkIxAxVFPlUeCDNHOcjTbiVmcuowD13SSVWTJcdpx5nCVmvH\nGr7sQuJ7dpIBlAFJCjJSobGcFW4zx1JBuUTKG3Rx34xzDz0Kcx047ROpag6dQBPFbCdDHFPpBM+h\n898mUXRkFOeSF51uUjo362wW196ZTihlYim1tAne8IIXJqIkFKD8QC+kCH+NhePPv3ojnv3Bi0uZ\nl6nw0LmOOrNJNMSbElqsNCyHzEm4ZdtBfNN6g+b7Bb505RbnPREomCpTzqv2MQycQY8jLCRG786J\n60pUli2CGW0lDJBM/VdAoENvJbHjk5MulAs36LKPqQxOh40eyh76+lXmWZ9hiYHUx5Q/z7y+OCG2\nOm+qthixFUWFPUcSK1f7ppWE5y/g0OPIBN5FdioV/WowL1vupukMeu4zWXPP8fPzmURqKA+9b1B0\nucAfWl7mlp5TOkmprVJGSAtWnMv+7zh07Xk003/Sz9LyQayDpMuLn92zBwBwYDbFI47y3mNWiOvt\njFXs0tplYIkeIEm5yJyE13/2KuyZbuNXn3w8okjhi1c9iL/+5s2YS/PA+HvKxeyD875VstpBwRNj\n5isS6AYF3butpEy5zKVmrCRbBFA6X1XFuehYaRJYN9HA3ME8MJAcNOm1kth1Floz1gyOk8ANfFXp\nBDqe2ba/Do2EmlawJsyCQwcQtqCz9wYNt2rcccQNutChN72Bb8SUvFbuY0pNP/j546DvznVytCYi\nJLZ0Qq6JcmEeehwtTqboUiNsXOCNtBLzHQnyCVprt+yV1RbzwixeaPaUHhpHXU53eUF0wb/+7P7g\n/TQPA9tcdufK50Zh6noSleuNAOH1b2cF9kybbFC69mSot+6fC+4xSblMtXnzhhF46IdIuRyY7ZgV\nqg4NOtU4N2oKc2xcmmgMeGiAZFCPaA0ygkS7xKpatkiTXqsRuaAor6mza2oeJ7/zfFy3ZT9yoVEv\nCh1QL3Q9ptteHtqwbe/CAG9ZYRJbrrsoiGLlHnq1bHGuE3roFM9bzRJ+mrHphOQC5Ix+M5RLqOvn\noO2Sh55Eke3PYCYj7qE3bOejQbFiDbrUlpN3RdeAPqVlF6HQvnaFnNmo9kNMQRTtl9K9+NUaSw/y\nUP/54nuC9w2l5l/TkreVRK4xgSnO5b9DD025M71/2DhtQgaEEjxmOnngRVLAnBKLZDeeO3dOYe+0\nLxUwKBYSFM3yAmf+3QV421ducM8D70pErdjo2YiYEVcQ2nKFUmYjGW0y9GTQW40okDwSKLhJlQRT\nli6fa43L792HrND49GX3hT06c42/P/82nPY333c13ena8PiJCfCabVGHJTplVbJKquXCVxTdPHTJ\noZOBXzPGDHpiKBfeco7GH0XKBZHp/HHQeZhLfWIRKbLiKKS/qsbYCyvWoMsEgm6JRdJDL4pQaVCI\nv3kQRWtdUsIQ6vroy4tTNpna1I86eiJ431BlVZQL1ejOSxRCElfX7C60LgUOAW9A6J39M53g/qCA\nOSUWTQeUi8YLP3wJXvjhS4Y8YjhPtREP76F3rIf3reu3eSUIqzoYRcaYpc4xYnEGIbMLPgMZdAqK\nmvfJuLWSuFTrG+Acummv1skKNJPIJdHwEreyWurnf3Y/AOC1tpMUV6k52WJitmOMdDh+aRAj5Vcm\nMTu2/hy6vacsrcJT8ptJHJQ8kBx6qHKpplxofwlTZMXW+SBEajgOfcUadNm0QiYW+cy8Kg891MAS\nZO2HvIDz9qT3Vnvoy4sT1xtD/txTjw3eT4siXL2JWiS8wh6hW1cdnjzSycoSRvIyD86lwf1w0Bl0\ns8+qfpmklhoGdFxUxmAYVFXn4702qQKl99D9KkYhTIM31RXZa3hOmIwgTYStJCplNgK+eYj30I0R\npzR38n4vuHVnKauUJuNbt0/aY/Meeu5WHxFyDUu5hEHcRHLokadGYlYauFstF1odtkTzaUm5RKy8\nhKRcGr04dJ6tGinEsef/iR4krJ9oHCEqF6FckYlF7ntMdw7YbiNdai87mROrzkaTgUx2qDn0xcfB\nudQZRwk6/7w8LhAm8QDlaoGpbXIsC0jFVhVx/54Zt6SuSkoBgLt3mfRz8jKpwBNh0hpwesA5hz5I\npmheaOw4OF96n1YezUOkXAj06PAEG1czxHHo3qtVQsKoEGaKRsxDp2GRER9rxEEdGMIM59CtQW/E\n1kPPNd777Vvdd2Utpl95kkm8+Y1ffBQAf07zgiVFRcxDFyuy8G/iuu1x96FckihyHjrJXakJTmDQ\nE5oowsSiNCuCmIM5nyH4+ZJB0CgKvftGfITo0AuxDHMPXAWHnksPnXFyQS/D3PYgjMjL757hV3vo\ni49f/Psf4sl/+4PKz8hLlmoPnv4NlA06YLywQOkQR4gj4OBcB8/+4MX462/eDMBcfzJU3KiQZJWM\nUl5obFzd8p/bSWj1WAWHPoDK5QPfvwNPPedC7JoKjTpvGjws5cInJKcEsfc51f03lAvz0DmHrrgR\nhKBcVPDM0BjNZ6j20O3qphlTUNSca/LQ72M5BrJ/JxnacSG7pIA4GXDXclBQRoFKhI6bUy721qhM\n/Y/KFSjJAVg9JoKikXIKnCZllRaFNcrcSIf7kB66XFHw8Ss1+hZ0ywLZWizrwqEXOqzPwoOiQBjs\n8h66D874oGht0JcavaR59BBLGVtHyFR5aVgCdaInkHTtwKwxxN++cRsAE29x6ftsm2SM6H9yKGjS\ncJRLs8yhV9Xrl7jodlNpep+gZXidkmHVj1UGPWz0ECFWrIQGky1Weeiccqny0OnbeaErOfRpyaHn\nBRqJ8T5LiUW8TID9LmDoGN5nNWMeeWK15UWhbf327hw6V6ME/VGrPHT2Wx68BKSHHgW1XHjqv6Rc\npDIvvFcrPHRxLQ5blcs1D+zDCz/8Y8x2snJbuRKHrt1noYceGmNZhzlMLGLZpOImq3Xoywvvoeel\nRtCVOvQ49NBLgTGmXqAHmtfH5k7AtC2z/J9XPwjAx3Ao05EM+kQzhlKhQR+28TSH16EfgodeQbm4\nCoCZyZCMIsGh+5hoGIgTKxyucqHn5Y+e9zg885SN+NUnHx+cawKXLaa24XHL8s7lhtLsejIBxJZ9\ns/jhbbt8UDT3md7k6WsdGmmgmkPvMMrF6+/L55Gv9Oh6k8qFG3RabcjUf+rJmojzF+7DT4BxFDnV\nHY1Xlg04bD30933nNty5cxq3bpsUVRHLskX3mVA9aK1LSzi/HUu50M3JJgPZRWUhCSI1Fg5Oucig\nWZgZbGWLjJeMBYdOy1haEURsQqcuNZwqme1kweohy80YJqxHTpRMqxGjEUWYspSLUt6b64W9M0bS\nSIdx1vt+iOd+8OKQcrF88aCoqqVOlIspI2u55MJz6D5TVAVGBQgDczz1n7a9ae0Y/vVNZ+OY1a3K\nUqwztkb8DQ8dxJTVjzsOXTxbIYdeBM/i7ql2qRYTUS6u+5KSvDn7O5aUi5/Qqzj08aY32r4Yl7mm\nE03poZdVLkC5CYikdhrSgPPxKl/87LTj1kIdzioXLxMqSuqUbrJFSiYhSJVLQLkI2SKna0qlBgbo\nb1hj8eA7VOWCM9dCh1720GVgidK0yUOP2CovikwyCe+ENd3Ow16l9h4jVcvknDHgY4nx0qbbllNv\nJs6b64U90x3xuo1798wEQVFguF6S4YSUu+MGvGwx5h56xDn0crnXWBgkz6GX973Bxhdec9Yj/Rja\npt7Jo5nsNImpYbMcezhBcydMKVa6wxVfM2MqrOxY5h2UdOgqrPNCn0s5IYCgucSEMOjcEDcTah1X\nYdAjlLxsDl6OWHLoUWTqoX/+TWfjU68/CwqL0IJuqeCkZ1KbypbZ9HbGIt8h3y40xSXKhcsW/W9N\njYvQcNRYPngOXXjoIigq+4YCZQ+d3nOUi/PQfYCNZybOtLPAwOeW8iPvbS7N3dI4YWn6q1rJQB46\n327wmiUWVX3eC5wy4uocwAYaLc/MqUtnR3oYcPuxq7BYVeNo/aombvibF+L9r34SfvKO55oxdHI0\n4gh//+onuu9tOzCHJK7y0PkzWoS9VYvwmSQO3RTcAuPUfZA2DPBSzRVGuZCHXmHQuRfeSmJHSzWT\nKJgAKCgqJaLu/PWQLQJAy17jJFJBn1M67886ZSNOWDd+eFMuTiYkamfkhXZLRadKKViygfTQ2Q0S\n6nND2SIV7KHtcsNRyxaXF45yyfIgNVxeb7pOQcedSJWW07xGx76ZDs45/zbX6CGJVVBKd/9sJwjG\npjZTuRH57u1jrDgVYVUrDn73pvOu6nmM3YpUkfdWaI2tB+bwxHd/HzdvPTjwtkidQyUuKNklTOdn\nskXIxKKQQ4+UwmM2mOYM3WSmR000EEUKm9Z4NVAjjrC6leDlTzoOgElGiiPl6pu7sbP0eSrkJc8J\n/e05dOO5kowxiAcISoPLFnnSWRXlMiE8dPK0ZZNnolzca9EJqRGcv9JunBGPI4VmoIgJv2w89PLv\nu2FFGXRe40AGRXPmkQNcmxrSM7qHysXXTy4rG2Rn8oX2dKzRG/xcVy0pnQ49LUoTdFW1RakckHV8\nIqUCr/sTl9zr+NckUkGziz3Tbffdhk36oCqO4yyhBvBL68QulbmH/qPbe/dN71Yimnvo/3X9Nky1\nM3zFBmgH2RZRBGSwKB1deuHkcZJKhD4ueejK0ypnPfronuNI4shth47jo79+Jv7uFafjj573OCQV\nBp2Ou9WIbPnr8Jn1f9vaLZEviSt16EqF1QqJYqHbgSchVTjOrj0cYCZtikOQB06/IQ+dwL1spSAC\nxRVqGvvbJFLhvSsGpZQaKrFoRVVbpJMg24xRUAqo9tBDDj0MaPKbgzh0fqP7feiS8a+xeOCGLy90\nSSlB16Kd5YJSC+uh+8qCIeUiKTOuLyZQgC2Jo0CdMt3OnPxwopm4e6zViDDeiLEfaclDH2/EZhXQ\nh0O//N69fuxijL58rqUEtcaUpTjW2fr/3ZBWUC4u2aUogiAoEPLk9HYSRSwFPTToR69q4nt//MvY\nbNuo9UIrMRMbHUcUKbzuaZvtdss+JD1rY40YeVEgzf2+fe0W5SgXoo9oxR0EeBF6y6RDd8dtOXV5\njIRVjHIZb5CHnpfq1TSTKDC+MigaUjDlc0T3jSzGJcd0WHvoBClFNHSIpVxIrsg8dknPZD05dB8U\nDYy9lku9mnJZTHBqomry9EFRWS43vDekbAyg8sjm/fW2bj7J3DgCD51LD1lQdFUztm0NNeIocsoH\nUtXQRNQig96HQ99+cC44xv1Mi85VLgBc2VmguqwsB1/FzImgaJZTrMB/XyGULQLemJQ5dPP3qY9Y\nW5lEJOHPTdm8VDXDcB66rWlf5aGPN2JXSZJqzRTaGDvFPPRIKTSSUPEik6Yc5VLhoo8zyqURe8NM\nRpd+YYpz+d81hUEPZYvl/ZCqKI7Q06BDHQkGXWiNM2akSc5FD7LxnvxvtctKMydG8nE8KMof8LzQ\nwcNYe+iLi/lO+aHdsnfWvUfXrRATtKmH7reTuqy+8KGg37znV09370kU1jiYoKi59usnGphuZ0Gg\nkzz0hFEuxLXSgzvejGw/yj6GVxzLV67xVIr3rD3lcu9uk1HZjbuu2q6rAMjuc5l8o7hXq1Tw/ZLK\nZUgrQdeiqqxuVTIP99BTERSlbYw3Y1ct1fD//jiD1H8VGkjZIo9nyFaNhXvoihlmyaE3BOXSTPj5\nkkHR0m5YOeKwWqWc8GRSUj+sSIOeCq2xDFgWmnnotlt28Bmr5ia9cM2Coqkw9tygL6SnY43+CCbP\nvMB3b9qOZ37gIlx0h+GdyaDnOlyByWJs9PCHsjHFgqXG8FZ5Y4Z/Nb/1Br2J2U4eVNjLqT1YpLDX\nSg7HBeUylsSVSTPlfYarDU6DkBfuDLrWuNDy8P06aPH9toVBB8IgqHkdyhYB7zVWZY4Og6Yz6IN5\n6HStjRcedjei60jUV1F4Hry6FLBI/Y/D4CWXLVYZWh4UNb9XwTERWiIomoggqKRgJGgMSaRKklsO\npQ5j2SIhF1pj46ELGVkXD51S/yvbgwkPnWeH5loH3lXtoS8uJOVCgbLrthwAEBZkkjrlvGArMFv7\nI2g7pnwRKtdpp+JOp0p9XLa4flUTM53MGe6jVzXtPk0H+R2Tpv4Kecz0YBpFRH/DJ4+FgyY5unf5\nKoa07xfdvgvfun4rJKpyLxJhVKTXLTl0lxIvvPkqw9cLNIlW1UnnY3j+z5lKmnQvjDdjdGxW6cnH\nmvLJvPsSyYzJgBPNxAO+SoUUh+ks5PfPz0M/lQvgPXNZ+mDNWKNrUFRSVlXzYcLGEFAuwvjft2cG\n2yoKuXXDijToqQh0kodEKLR21RGlyoX4dq5pd7+zS7a4gkPPC5S0xzUWD3xZ/c3rtrq06hmbdk+f\ny3r3ma3lQg+atl4295DiiDdG9vIwCa9hVq5U7vqJBrQGrntwPwDg0cdMuNITcaTwy4/bAMDXoaHt\nrx1vVAb8JEIqsQiCmXOdIkiz3zPjm2TsnjJ/v/G8q/DWL11f2i53TjpZOJkBoRdrXium8hAGTnjz\nVRxwLzgFUMU55++dsmkNAF/NcLwRo5Obc+Ibe4eUWicrDE3GVkNBfRYx1sTq0Plx9yqfy2ueA95D\nl/TRUeONMCjKVUKRKk2mEpzmkvEfjmse2F/6bS+sKINOHotMHpFKlowFSaUuWVs6hvf4478rdFjq\nklCUJpHaoC8m+HV533duY8WNvJEzBZgk7xwWWAPMAxNwmKrcSabqoSJNM29qcNxR4wCMZ6SULy5F\nHPrvPeuxALzRpAdw7Vij0oBJSEntP3zvdvd63iYs0cTwjWu9J37HzilHR1Vvt6zskoG5ULkSNok2\n32ceutClD4NWD8qFj4GM57xTFMVIbep/i1UvNNukRiTUrAMh5WJ3JYeaxDIo2rsF3bikXIhDt8f0\nlEetAwA8cv14ST1D92SkypOpxG221nsci9Wl+PIbn7G5/OMeWFEGnSf5SOMqa1WQMS5919ZycZQL\nTyySssWSt1R76EsFKdmjhyNn3vl4wz/EBKJcEuYRlZatUTklu8rYeg/d//a049cCMM2pm3Fke5EW\nVoceYe1YIxhTg3GsVQZC8p/SUeGYS3NEygde/+3yB4LP/0W04+PIKjx0GZiTbeYcTWHfi93kF1JU\nVcfVC70olyARqxmWpx1vxEhzc/19g2avUadjowmnKigqVxNJFAYv4whuYvzmdWXqasMqo7d/3VMf\nDcBfX7qPPvLrZ+JLv/tUjDXicKJQvu1cuR569/MnOXRp0J/+2A1df1uFFWXQfWcSEQiTgc/CN42W\n3rtUPUgPnScW8WV/octpxjUWDzKphoxGXpTT+YPqmTZgHjQqUOESN46US8XoRbmAyRYJJHM8MJu6\neh1amzEkkXK9NI9ickgaf9WkIe8jWcaAY65jPHSe3AL4Nny9uiBVSXVlKzNJuTiGxXnoJM0Lvdoh\nHXRnyKvOB60+IuWvb5saSjTjoBkG4OWYTfbM+tR/7cYvA7yEJC4fN5kW3piEcNREA1f91fPx7l85\nzf4+VLmcePQEnnrSMfZYmEGPfQq/bJxddev9v7/0GPfdXpQL3W+DYkUZ9MwZ6aLkoYfetA+K8rIA\nQFmXHMgWtUwsCj1y/vANE1lebMi62UcCqhJ/AOCLV25x16VVdQ3tBB70hpRB0UjhH3/tDPz20x6N\nJ55wlPlOV5VLKDGjmiUH5jpoJb6ueicvEMcKx68bw6vPPAHve8UTAIQrgLgiKFruVVtdlgLwTYNX\nCx73s284y4xtrHseYNDUpdJDL9c45w0uAD45lWu7DINelAudz2biE2rms3BFVmiUKNPQQw/zCkIP\nPdxfQ3Sb4n93O58b17SCDGAarwSnXGIV1pMJVgoVp2/9KpMo1sl1z6Do2vHhcj9XqEEXXndRQOp3\n6WOuUQf8zVwlW8zzsB56JmgcWbVxJeDK+/bhKX93Ab53847lHsoh4d7d07hnt2npdtlde/Ddm7YD\nCK/LMavCLMgZ1o8S8DI8gFZrYWOAWC5blcKjj1mF977iCT0VDaRD554seUTzqeFxyaNsp4UryHXu\nr52Bp5+8Idgub0ww3ohdk2upZOG3VVYU2LDaH/t8miOJo5LS4uRj1+DFpz8iqLsuERako4BwyIPL\nLkTdOHSosAXd0By6NcyNHiqXsUbsxkfqsvEGT+ohA04qFy9jLK82WF2aCspFTmwnHm3iJP/0G0/p\neyw0jqrJqSEmimaXeE3VhMjtU6/EovEBErmCfQ317UWGkyLmVTp0/1DzWtV5UQRej+xgw1OZjYdO\nN7NXBjRtF5VM6NlXAm540Mj4rrp/3zKP5NDw3A/9GM/70I+RFxq/9Zkr8PtfuBa375gMzvVTH3tM\nMJnus+qOqlVWmhPl4j0kk2rdXcvb7T3SofMHnqfYH7O66bzadpZXTgo+QcQblVWt2KW6d/IC379l\nh0v55/eVUXPEeOR6Y2CIQ+fJLWusF7l2PHHSxSqEskWq5dJDtsi9Wvae+b+c+j8MaCKWyThmTH7S\no3PHZYsECnJnRXm1xukiAEHTCjnWJJaFsvwEzifTbkgEh86xnjkiVELCjCf8Xi8des7iffx9QtXK\noBf6flsp9Vml1C6l1M3svfcopbYqpa63/1461F67wKtchA49DymXeVEHJGW65Lbj0EPZIulYeZPZ\nlMm7clbUi/ogrgTQxDaAgGJFg3vkOw7Ou2DX6lZis0HLmutK2oxRLt77Rs9la7f3SIfOuV7OWT5y\n/bgzijx/gcMXWfLBtySKgn6eb/63a/Drn7wcD+6bLfVDLQrtvDBSuXDp3E3veZEbV69s0aqgKPcg\nue4cMPcTnRLyanmvzYXIFntx6HwVQ8aSKJexCg9dFl9rZ0Up8YmPV+5RZnTKlV0/0DiqDOuxrLIk\nz4WQBrzq9NH25jp5z6Bo1aTYC4N8+zwAL654/8Na6zPsv/OH2msXBAW3etRnkQY9sx1ZlAoNOBDe\nEIUO6yk7pUJiuqy7AEwSrRgOnZ7TYXnMlYCgiBYz2HOdnMnRolKMpC1os8BDp+QS/mCq3tIvoJvK\nxXj6nHLhS9wnnHBU2E2mQmfOZZH0dxL7FcO/M6XKP198D7SVSkbKOypkyGY7ZhWwqlVeZk80e9da\nD1P/rexTSDvLQdFqakBBqly67rYSjkPvQbm0GOVCz/NEsxfl4p9nXimSxh13MaZJKb4S1rjpB6di\nqoiPHLvWG3QjVaSArzyv5e1SoHvjmlbPe3fkHrrW+hIAS7Le50HRoNqieOBlRmeaaxfNlkFR8gQb\nceR6iFJAiCiXhq3DzIsjrZTaXOTRDeslrQT8+xXemMmenSnzynhdegAu7V4Gxhq2OUJRERQNHvAh\nKBcVGIPwgXreqZuC1708zjjyDzJvWvCZy+7z+ys0C8RGTrHDPXReopeDt9irQlhiuBwULevQy5mi\n/HxK4z8MnGyxKijKZJ4lyqXCQ8+K8orbrDb8NkMOPdxfI45KE5vLeRiAV6UxVlEuJGE1+1XMoIff\nq3p2f/lxG/CJ1/08/vB5J4cKLbXIBr0H3qKUutFSMuu7fUkp9btKqauVUlfv3r275wbJs0jzikxR\nm+IN+KCZ+cx41o3YlLMkCZQMijbjyGaK+ps7zcLPMhZMWilBUX0YUy57Was17qFPtzP3MFV66CIL\nk7eZc5RL1D2Fe1DKhToW+ZrX4eNgKJc+nr/jTRUeZ9PVT9m0xm2TTwKksoqUbeaQhzkTaW4C9lUG\nYCwJjbxcQaa5yZ7lq9R+tVw85eK/U3Wsw956vTJFabsNtsLqWBqFGy/yiKsyRcvNOvhkJTz0WPTs\njBTe/EyTHLb5mAn0Q7daLkDY3Yh/t6oEroRSCi86/RFB2WcAJaXUYlAuVfg4gMcCOAPAdgAf6vZF\nrfUntdZnaa3P2rhxY/tTTeQAACAASURBVM+NktZcBkXJwI+7pakPDmW5McTmQWAdbBqhQoIaFQD+\nZqbZ33Ho3ENfIQad7NznfnL/so3hB7fswLM+cFFJgtcPm9aOub95avr0fBaspHKRDSo99A4LdBdF\n2DoOKHuQVYa3ykOnsgHH2OYN8nerWsnAHnoSKbzhGY/B3f/fS/CJ1/18qfYH4Kt9Upek1JYxkJUi\nAeDNzzoJn/rts9z70kPPCo0vX/0g/ufHf2peU00b1h+1pEPvyjuHhqicKXpoHHrVE0RjSmI/nnZW\nWGrEj9dx6Jl/JgHzfKsKFY5U7Pj9iYC5UnjZk47D/e9/Wd8a84CvvljloUtqzJXYLXHog5+/qgYX\nd77vJQP//pAaXGitd7IdfgrAfx/KdiRcULSU6l/Yno4xpttZoMsm/XgjDrtwOw+ddeV2xXwUZRN6\nyoUX/GpaemYlgCaWYXpVjhrv+ubN2DXVxq6pNk5YNz7w7zpZWFGRMN3J3IMy1oht2YXuSiX+Otdl\nD13a2SqDXuXo0MTAVSYSgXdXwaM6r4zoFpK5UYEtdg6o2mekjGEjR6VK5fCXL/m5YD+Shslyjb/4\n6o0AfJ5GEivbVLm6lktQD12VJ0Iy7AtVudDKbF1FUozz0FmwspOVC6y5VUulyqUiKOoCuuH+mnFc\nki0OA+oWtLbiWGTdl0bc5X4cxqBX3LvD0C6H5KErpY5jL18F4OZu3x0G5CGneVhwy3jhhePO//bb\ntwIwD5sx9oXl0ENvjrYFeJ4c8BwhD5jyZX8ziVech76cIM9jsk9Nbgmeict10tPzGdLcLrPtZMo9\n+G5BUZp4cw3beaYL5VJxV1c9VHlhApTrbNZn1SXv56G7olDiM6JcdMlD147HpU5cvWRrhJM2rg5e\np8EEmAe0I93H3Wq3mNcoSf34/3wYwxpBKmD2S48rp61XGnQraqj00N2KW+rQ2fiYgocmpd9+mknd\nP37dmCjcNtyxEP3XqjCqq5rSoJOEtf+KsRuGHZ9EXw9dKfVFAM8GsEEp9RCAdwN4tlLqDJhV1f0A\n3rygUVh4HbovuDXWMG2xskKXEitMyypPuVQGRXO/ZJOpwhn30APZoloRhhRYGZ2T6Fzunx0uYzUN\nlBf+OGbaGdaMNdCITNeXXARFKUjWYstsep0X2gZFQ4oAMBMAGQeJKoVKXhjjKpfeLzhtk+NXQw69\nvA3ZHIJQ5VW1syLITqXCcrJlWhUoUYnAKaot+2bdM1DErCGypSGrKlIGqf/u+LpQLpUj6o7nnHos\nbnrPC7FmrLuHnrAJmTT+gUFPQiUKr65JLejCYwknp3f/yul4+4tPRWLr8fj9D3csb3zGY/Cj23fh\nBadtKn0mSzT4TNEFGPQFih/6GnSt9Wsr3v7MgvbaBeSlmfK55r2xhqnAlhUarzzjeHzz+m0481Hr\ncN2WA5huZ5huZzhh/bihXCLVM6BGEwIt0fjSlOp1KGWWzSulwQVPoloukJHZPzOch87LEfO/p9sZ\nsrxwadlpWggOvbp8Q5NVPqwKiq5f1cDOyXapYp75Tnl8RgJXpgY4dx00S+jxYEoPvapV23yam+xU\nmPuzk3kq0Y+zeh8yAMcnyPd++1Ycd9S4W8HM52ZCJJ480+UWdFx/73Toihu+Q6cpAFQac8CfQ0m5\nmCxcRrmwevdAGENQqhzgLRUai7yev18p21447fi1uOavX1D5mfTQKRmqHNMZfH9VtN4wWIjKZeRw\nDaAZ5dJKIqSZoVwo6UPyiffvmXWUS6mYDy3XEx4UJdliaPypAFOkVk7qf1UwZqlxt03dH4THv3Xb\nJB7cZ1rJcaMzy1ZXc2luzrWNe8gs3bbo3OOVSkZ9xPtKAt740EMuFSFAdVCUOgat7xEc62bkCPSx\n9KyqJpX5NLcqK6ARRS64z+/nqtVFFbYe8L1J1441kBeFm+RocuS0SlW6PBk6epd/NzjWhdmYAHwS\npr/blkNv9qJc2DXtpamvCkDKFP1RQW6Lrl05pjP4M7xQD335rQVDKiiXSJkLSx66CXyWDUtql9mR\n8lQJ3Zx++emDoqZBrgo06oC/scx2VoZBX22Xdc86pbdCaDFBp2IQlctLP3opfvn/XAQgTAii1ZFS\nlFiknZdW6C6JRRUqF8r2jZWCrHWu3P/lcVU9KJlVTBCHXgXuNVZ56D5dXhj0Cg/9MRtWmaCoNWYk\nv+XGf9Bn/+CsXy2dsH4cqQ2K8qJV1AjC/F3m1N2xKToW/z8/nBHaQLYqQE8P3enQ8zCxCEBQ9oHG\n1604F7AwD30YOA5deuhD7HPY/q2l3y/s56MFXbzUdiGiWZs49CQ2yQiShphLczRiI2UiL5zuDbpZ\nYoWQQ49C2SJgXlPCymIyLg/um8XL/+lS14WmF2TnneUEN9DDfp96dq4da2AuNU0MGrb5QF5o5FWU\ni1hltZI4ULlID/3c1zwZZ28+Go84ysslCVWemfHQzZieecpG/OFzT674Xe+AWrdHdf1Es2QIj10z\n5jj0OPI5E4N66Kc+Yo37m5d+nZ63FJao/c0NXVm2CFfjxHnobGJcSOp/L8Rsn4HKJZayxdAh4zEJ\nGRTlSVPVBp0d9yhnJwCvPftR+CN739CY5fkaxkgPukLrhuW3EgxkcKnGBRVdSvPCpveHDwJhaj6z\ntTT8NmK2ZKOHX7asksYyzbQt9OPb1W1+x3eCrjKjwL9d/gBu3jqJr17zUN/v0g29EoKjw+rQg0Co\npRfWjCWYT3O34oojVSqBXNKhCzUSqVx46j8A/OJJx+DLv/e0vp1y3PiKwhm6f33T2XjbCx9f+k7Y\ncLg75SJXdOPNGBtWt4L3qNtSZKmQuYqU916UAF/FzDCD3slNDII8dAI3fFEP3lmz99x3hfEfFeia\nFtqPJyu0rX8TGu04UqVSHkA565UoVKC6mQTf7kIpDYlzXv1E/Km9b7plig5jpBd6rleUQU8LT7mQ\nF9ZIzEUttM/6ogfhD57zWPdbSv0nr5sunNOoRxUcukuRtss7O4kQ5UKG5OM9OsUcCo5iNbf7gY51\nuZpW84zE9kI89DbvSlMYNYo1QJT6T8aTVmBl2aIKVC6O8x3gLq4uztX/AQo59O7qmapiblK7TLWE\nqNonHScPoFbJ49zvRXIWYT7NLeUSCVol9NClsf+mbTZ97+4Z+57/oayVMipQlyJytAj0rBN8TIwo\nF86hh2PqpUOnbbnvLqLF65YpOsw+F7oaWjEGPbdp+YDNDLU8aSOOnFGjJgIka3vkep+623ABNrjv\nApwXR6hDj7hs0VMuhj80lAs1Dh413M1YYaP3TrfxV9+4yXmpZAiXq8lFqCU3f3/pyi34wS3967N3\nKjz0pi2ElpPUNKKgqC9SJT30NlO5UO/XOOquQ68CGf/TjluLP3+R98T7PUD9OHRSY1QVepKNKrKc\nVftU5TZ5gGlK3Q18UueUSzsrkBeWh+YGHWFrNqlDf2DvbLD9bh76KDHexaCbsUtaRfWgXEIj3a0e\nOhAmWI3aQ+cgB6QkW1zEfUocUqboYoCrHKg4l+lEE2Eu9YHNJFIuwCb1u0qxcrMsKEoadc+hG2PA\nJY303djy7Vprt6wdpPnvMKDssyoC9pzv3o6vXvMQztq8Hq8685GYtZPXzsn5kY5hUHAvm/5+x9dv\nAgDc//6X9fwtpyHmmZzU1zQx16HQpvlIK4kw3fbJHDI5rCl+26sBtIRLaEkibFzNq+T1/u2gtVyq\n6CipRS/Y2CMWvOS8bje5HxC2SCQPnVNYdJ/7Y+tFo5SPJfDmF8lDJ/llJ9elcxs+z8ZBa1dQLuZY\nwF6rntz4QhKLhgFd77LKpf8+P/LrZ2DbgYU/4yvGQw86EpGHbi/yfIdkbMbjIOPAtankoVNyUsIM\nuuPQc5/6z+9RL5Hycrhca9fDUXaQWSi0s+flC00PLX2H5H6LtVroB9lcgqNfULeq5LGjTViRNJIt\nkodO6fKScmkmZgUmg6KDGBynrkBZIdEL3LBV1nLpUblP0icP7J1h5ZsR3I+EXvcad3qm20blsnas\n4WJMprJgNU9eolwi4P/+xpnB9ulQFUKaYJQO5ljDl+QoeeiCGuGf82e9snyufV3V3F2qexYLLRcf\nMGMgZdogqfuvOOME/P6zH9v3e/2wgjx0nxFmihb5jLrZ1Bi1WCzLOK9GfGxeVHvoSoFx6OHN7R7K\n3KhclFIoCmC/NeiyZsOo0OvecgbdTmZzae4M2VKC0yadrAg49d1TbWxc06r6GYDw4SIahZJfAF/G\nmCgXV1BNeOgdmwCURBFyq4AKmkQPcE7IGGuEhrmf6qGfh04Klan5ctKVDM7unGxj3UTTGSua8PiE\nJBOIOPh8SqvUNWOJa6qexGXP2pfE9d2P6DNePI0fX5XOe1Sge0KOVWaKGg/d/07q0EsBXvsMV8mN\ne7V4GyVkdvonXvfzuGvndM9rOmqsIA+dAkSRU7nEkbkYVMNFBn24B0QNLig4xYOisQ2Yps6ghzdp\nIwiKms8LrYcOAg4LBWDPdBtv/NyVOGDT6uXtxitLzi9Rga67dk7hLf9xLTpZEVIuuQ4kozOdDNdu\n2Y+3f/XGysxaHiic63iDnhWayffggqKUENTOQm6ZijfRhE310GXqfy8EWmQROOz9O+5Flh+Xlz/p\neBx/1Bhee/ajSp/J0qe5LV+xqpmAJ7aF92L3Ab3vlae7v6cs5bK6lSArCld6VyYEcQ6d1++Wz0Aw\nDhWuTEZpAumOWNVMgutgarlwAx9eJ6lDL8kz7Xir0keSJeLQadJJnS2L8cRHHrVo+6vCyjHo1iCM\nN2MU2iheYqXQTJhBF0EffpGbiVWnWHsTi5uFe+8KIedGN5Ipnu+VF7TErVrGLQRk/JQCPn3pfbjo\njt344pUPAijHSWdFM4+lwJ9/9Ub8943bcfO2gyUOnbdBm2ln+J3zrsJ/Xv0g9s12SkY9L7RbYnsO\nXdlGD9qpMEzFzMJ9l7x5XsvFJ3zB0XENpyrof0zOWLAm4cAAHLrwIiU2rmnhp3/5PJx+fPnBlUvt\nyfkUM+0Mq1sJeC0hvtley/MXP+E4fPstvwTAyxZXjyVIM22Dor106GECk2mOHR4PPxXDTHrD4JdO\n3oA3PH0z3vvK00uSUBWc6yg493xyNOVzEbx2EswqDz0KJ4PFAl27UduLYbDiDDrxqGRcm0Llwm+0\nZlJeopERlkXtOeVCgU9C6KF7w0EP3GJmjeYV4wW8YQ8M+pA68Pk0xwW37uz/RQFeergtOHSutplp\n5245uXuqHWjJzXi1+3yeUy7aNxohiaipCx566DxTlOcZFJaO61bdrgpSQeH/7v27KmpuUEjKpZ0V\nmGlnrs46z4uoGmcVaAzT7QxKGTowLbrr0HkVQm4US+n9fBzidh8l79yII7znV0/HsWvGetJZvACX\nUggotrIE04+9ypYunYdeG3QHMlbjDf9QkxdG50d2HwkMehxmeMqAi6zJUUm55CRbNEbclSIY8QWi\nrSlUKx04ZjuZO5Z+48gLjQ98/3bsnTbBynPOvw3/61+vxnVb9g81PtIDt7MimFA6mTDoncxJ83ZN\ntYMgqEkACturAYb68GVkvYdO9byTqKLAmquBzeuhd+/hWIVu8sN+v12IQkJ622leYNp56P59bsN7\nUS788+n5DK3ENKPOcpOYJRsic645UuG2FaooF/P/UpW9kM+o/IzecrXm2WojlC16A1819sCgLwGH\nvlw5I8AKMugUSBjjBl2VAyU8EMYfNlK5ELiB5DcHfVZFuaS5drXStfYXZhQz7p07p/Cyj16Ku3ZO\nBTcd0RRchQEA7/mvWwAYD32tDWb1u1EuuWs3PnbRPfgb+9t7bMLI1HzW62cl3Lx10v2OlCxJZDTB\ne2e8ssV4m+Z67RYGfbaTBc0bnObfGnCtvVHhJZDjSDlvnlMuJn7iJwoeRBvkIaXvavH9ft4nN7bD\nyldlY+Es18ygV08q/Uo80D0/1c4w1ohZJjWdP05NgHm53b13gjeKwxzloSOks8Lj5nkGdAx8Ai/J\nM1nQW6IRnJPF59CHXUmPEivGoJPRdB56mpuqdEJr7stvhuVADR0Tvg4+U/K1/y5vSEuSMuJ1AW90\n98908ND+MBljUHz7hm24Zdskzr9pB3YcNHrTiVbsArXy4ZpuZ5hpZ2hnhVOS9LtRSN5JXZryLtvu\nh0dY9cPkfOoSgtavaqKTF0Gf0CzXTgG0a2o+kNXNdYw22tE3hS+zUOiwjZz30E2TA5rvOOVC3iYF\nRSOlnMEfZBndTdnS79QsxEOXq66sKDDTzkut7YYy6IxyGUti02w6D1c4BKW4V1tu2yZ3RZ9XZb0u\nBiLxjHIYmtQ6b/Q/o2C6rUSqOPRR12/phmGaTy8WVoxBJ+NJgZv5zCwhQ1rFe+iNKApu0ER66IJi\nKelWo/JDlObacXeGcrEeur1JXvSPl+CX/uGiQzo+8sqV8hyxtgk1NEaJbbZEKmXEphXZiBx0I3lt\n9OCewiV37sbdu0yZ3DF7DQ7OpW5SmGjGJcqFrzR2TbaDG3m2kyMvCi/lYmWMATMZOIOuKahXfV06\ntpAXPZhZEQZFB3lgnYeuh6NcZHB9GMhtp7m2NFXc9V7tx9PTGLQ2irBGbLt2sYqjfLu+JK4Ym3By\n+HeqjOJiwdXjEccdR2VZqm/xJlbcyjt3y2hL3bWpOXTwoKhVRXTyiuwxT7M0kjAK3hBGW8rNuDdS\n5tCJcilcNyOtvUdMdnGXpR8ODNC5J8sLvOubN7na4D6ZyD8whaUPaEzuCxYP7TcG/bEbV9lz1NtA\n+4CwOVgKJrezHBffsQufvvTerr/97c9eieef+2P7ezOIybk0iG108gKT86m7RrnWjmPfPdUO6593\ncuRFdStAwNbtsee6KIyB50Ya8JQLGXC63uSxkwxvEDlnt76S/T30Q+dfqygarU0gs9sqYVAPHYCn\nXDIfFJWTFX1dTnoUv+Cg87uUzV1oDPJc8cmdPqL7ulfHouUse90ryWypsOQG/R++dzte95krSu+T\n2oNnC8qO3XGknIEo0ShxFChXZK2HMuVS9rzIa4wUghrdcgm6ZV9/2uXaLQfw75dvwdu+cgMA7zko\n5Xm++Swvz+bsJcn3Vrcabny9QNJAurGI/55PC7zhc1fhfd+5re+4zX7Mdg7Ope4cTDRNUS0jLzTX\nSGsEBp2Pby7NkBdFYJSBsM51ZJf9uT3XPKgnl9UmCzKUMR5vG1YPUuemm1qlP4fenRboB/5bnoC1\nekwGRcvORTdwPrjVMA2Q06I6KKrQvU645KHNd4hy6X1co4SjzeSEE3lpovTU5fPM75Xl7DRGY3hY\neegfv/geXHrXHvd6y95Z3LdnxtEJXBURR+UKbGRMGnEk0vfLShb+d7eoOMD6FxZe78w5dGqqQNgz\n3b+OOc1DtA2q30KFvwDgqvv3O0P3F1+9sXQjEDVDgcd+M/9f2horjcgUwNo5SQZ9uISkjr0WN2+b\ndEZ6VSuxiUbapzgX2iU+SQ59ljh00aSCl2TgvDj196TJVdIv3HunjFm6FwYp68sdA2kMev9u4R76\nnzz/FLz8Sb6v+mw771orZSgPPTElZzMbFI3FOeOGrmy8Kzx0CiwuoZdLzZ9LHLpi5ZEF5SLrtVcF\neJcDg6rRFhPLRrmQkXnmBy7Ccz54sTMc3qAbTlAW7KHlPtV18Z+prt6UQpiIEAsOrukoF1+rmuvQ\n6TNCe4A+nxS1J4+BnhFt+WLAlNFNmc77cz+5L/AYyQiSlnvQ6HkcK3zpqgfd62H7knbsymDbgTmf\n8NWIkeZmkqNoPqdcdk2VOXTeANk3ExGaf3shOrZEg08WKgeyZYBcev+90K03aD8OfUyUlxgG3mMr\nggqPB+fSHpRL731UUS6FVWQ1mEFXKlS2lDj0CiO4HJ6l99DLKhff0s3SMqzeeBAUZR77MlLobvX0\nsFS58IxDwD/wFBSdS8udwJNYOYOfxFFQR9pQLtXeFPHihDIF43leEywyhreq6QIQ1jfpBzI2ZJw7\nmW+I3MkKV5MDMFRO7gyfrzQ34UqOhrfrj27f6bxyiVWsI/mwHjqNeb6Tu5uTgqIZo1Hm0hz7Zjpo\nxhFmO3nQFo0qAMr6Fk4JYOMVvkSD6Vrjltcq5IOTWN4Lkav9Msgym74iV2f9HDspfx0GNP5cmwSr\nH//5s7FpbQuvOevEYL9qCA+dUy5jjbAYFxcGdPufUEW50P0mG3MsJlqMQuWQkzv/TqRUqViXuzZd\nboVXP+UE/MnzTxnl0EsgNiHpcw0XE8tWnEsaGTJyPFO09BBH/kI24ijo1t6Iw2ptUmvM7xepBmgw\nrbTPFNWu56gZL8+Y7G9A3m214OT10PG288IFbjpZEawcmlaCRvuQBv21n7ocSgH3nWPK1r7pvKsB\nmK4pAPCkRx6FGx86iKPGG8HKph8lIZfYNObpThaojzq5oVyot+vBWaOC+bkT1uDmrZMBl22CoroU\nFPXlZk1lS1I3dETeQVxacYXGq8not0EcSyofMdHorjDph2FVLuR10gT56GNW4Yp3Pt9+Vn2v9jMG\nxC0blUscePQ8KErv8louwXYUSrPZn73wFKxuxXjpE4/DUoFWe3KybCY+ZuIMOlO5lMvpWg69C110\n7mvOGO3AK/D4TWvwluecjF/7hRMXfV/dsGxTSVboIINRpv4DxoDL9H564EnGtmF1033W7UGNhIce\nRyLgRrwu6ymaFWEneu5JD8LZ3vDggeC4iJowXq416HmBE3mTjiQKlSJtX4CJoDVw89aDwb7Iiz7x\n6An3mq8iOCVRtay+8aFwe7yE7/7ZFJEyk00nM0FRilfQPtbYoC1fdc2RQRf1wnk390h66Mx7loXY\nkkgonmJPxw1CFWxaa7zOZz1+41BBUY5hPXQabl7hAHRT2siCXlUgL30sicM+nFHEZIp+pSP3AZR5\naAA4ZnULf/Wy0wYq9zoqNLt46M0kKnHoNKEqVa6+SBPbcsoWlVL4sxc93j2Hy4HlM+i5dq2vAEa5\nMIMeiwpsSewfarrYz378se733bweBZlM4jl1Hhwy9UXMMtAYL393zAxp0P1xmu+ShLCTFc4AtbM8\nMLxKbJu60rTYOQHKnWbmKZGowrs3YwgzOAHgk5fcg1u3Tdr3wtUSbwe3f7ZjYhkJlTXWznum80Nl\nWSdZCVkq90utxOi7FK/ICx2oMKhaoOdJQ4MjV2u89vcgUrXHbVqDS//iOXjzM08aSrbIMS6uQz9I\nD52jm/Nx9Kpm3+3ScRvKJVSBOQ+d3d9yH+Z1mAW7XHAcuogd8JgJ3QeNrh66/+5yKkxWApbtkqa2\npySBOObxZkixNMVD7D1066W4Cn1F6IUHSoYy5cK5Wpl11mrEaFtvlBAa9P43zSPXG0ndyceaTu1z\nzEOftKn4N2+dxMG5FCcfuxprWglm2lmw7Xt3zyBSIX8PsI5HFmSkSV6Z5mHZW95g+oYHD0Jrjb8/\n/3a89KOX4t9+dj+aSfgw5YV2fU8n5zNEkTnfxKG7QmjkoY+VPfTZTmb04yqsUx966GVlUuK8MZn5\nG1IuPCg+qPb4xKMnSlUGh6Fc+gUsJRyHXmFkZIyn6v1uIGM21ojd6pLGV1a1VHvoK0UZ0k3lEgR0\nhYceqXAlzw36MMl0RyKWlXLhnCRx1IGHXsGh+zZP5iI/ZsNqAKZzC1+tRhH3TspBUb8UVaXAlwv6\nMc91PuMcev+b5nHHmnGRMSUP/Y6dU46OAYD7986gGUdYO97AdDsPtv3A3hk0k6hUIF/q0ek1GY4s\n18F2uIe4Z7odTBp//a1bShNUrjUmbFB1tp25MsYZyQttUJkmDeehz/lJb65DNe0FdSI59KjaoPNE\nIvO7couyXl1qemGYoCjHsHVAesnY+KaGmVQAr3pqNeJSoFjqzun4yhz6yjDodE1pNXMs0+vTWXPF\nubiHzoKiSmFFUC4rAcsWFM0FRUJBQ8mhN9jSqplEJRXAb/7io3DCujE899RN+MGtvnExbyUHyFrL\n/jU37uZ33gPivHnQim2Axhc0QZEhJ1rjrp3Twff2zXSw+ZhVWN1KMN1O0ckLnLBuHFsPzGHPdBvN\nOMIZJ67D597wC9gxOY+//PpNpQnFGXL7f1oUgZEzpWkjtLMCc2leUunICSLPNSYa5taY6YRqo/m0\nwNrxJODQqXgYL9w1l2auvkiQ0cvKEihVVpHwoKjk0KXiiZJ1Xvak4zEMhinOBZga3pfdvafv9yRo\ncqpS4UhF1qV/8ZyBqbyQcgnPkfTQu1EuXGG0nKDVIZ2r7771l7Hd1jqiVS6tPJ3KJRKUi7g3Hs5Y\nNoOeiiaxZPhCDj1MBedSJqIdxhoxXvwEE5UPEkZgbtgc2soWEWyHrn8sZnuesNLVoA/w4FHFQGpw\nTRPWnFD3HJhN0dxkKIzpdoZ2WuDoVU1sPTCH/bOpK5T1nFOPxfaDphSA9PgKVkoAIA/dlxRIcxOc\nbGcF5jp5cCwASnXMuYc+Z8v3cqniuomGpVyIQzeUy/k37XD7pKCo9AQTxitHqmzYuESNGhlobb13\ncS+sm2jixve8EKuHbPHVrTBWN3zujb9QOmfD7KfKQ5erhGECaVQgrRQUZbJFx6F3oVxkv9PlAnnm\ndD6OWd3CMVY2eeojDF3pmrUzHXpY272/3PPhgmUz6JT6TSB6Q3ro/MZrJL5YV9XSqjJLLq+gXBh3\na2b7kI+jfXLevJNzHbrZ+e+cdxXWTTTxodc8uTQW8tCpAiJvJSfRSiK0kghT8xnaWR6kifO/fYmC\nbh66eZ+aBgMmmzAvCrPSaRuDLDl5qcLIC41V1kjOCg99zr42QdGQciFsWN20maJhwS0ZgOZFlQBY\n2aKnXMwxmwkpicPVGX3O26oNimGDoo24vDIcBBSHWD9RHmNY+XA4T/mpJx2DH9620yUWEThl1d9D\n77/Pj/3GU3Bgrn9ZhYWAx0wkSN1Fq9tulSM5h/5wx/J56EURcMPkwfI2WXEUPkgNljlapTiQ6cCN\nWGEuNV5KqRQAH6cMcQAAIABJREFU42qbImJOgZrbd0y596s89Atv3wUAlQa9bY+HPPXZdvfknqbl\nPi+9aw9OWDfujCkArB33f/O67RzkoV9x3z73OZeBZky1Mp+WPXQZSMoL7a7DbCfHRDN252g+zV0t\nnFQERQmPWDuGuTRHoUOvW3rrpaAoTyxigbA0z4PVmTkXh/4AH2pQdFi84LRNOOfVT8Srzjyh9FkQ\n7xlyDPT1sUYUOjysIqU05L128Qub11e+/7InLb4e3ckrKww6lWaeY81RgOp4wCByz4cD+p4FpdRn\nlVK7lFI3s/eOVkpdoJS6y/5ffUf0QJ6H9VHI8PFU60YpO9DL2laPleeioA8i/EOvVFlJQF+NSxKo\n6uVoJ5ABFn3rXZBscK5jAp1T7dBD//GfP9v93Uwi/MhODlsPzGGC6c7HG/5vOvZ/+fE9wf4vu2sP\n9s10XHmBrCicjDOJlesQBFQb9Kqg6Cpn0LPAKFOlQ8Oh28JdrXBybTVi36Eo6p79yZsYu89FMgnJ\n+JKo2kM/FCxWv0wJpRRee/ajglUnIaCahjXo9v+xRhw4QEnktdvu9hCG/fk/d2ywrYv+7Nk4741n\nD7X/UaKXh06ODd2vDecYhN+LFIKaTw9nDDKtnQfgxeK9dwC4UGv9OAAX2td9wW1gVhQBbUIUxZjg\ns6XKxSkrWmWDLo0Fj4pLeRzvhiI1rVUPINd1d/IwZb8KnDP/i6/eGHz2iLVjeMRRY+51M4nw9hef\n6l6vYg/pRDOkoABTN2WbDRwBwHu+fWtA6WTU8CBS1sPVjpYxlEt3D72w3YRoUil0mLhBrQENh+4L\nbp29+WgAwA//9JlBH9iInWsZBJUeO6cM6BoQ5VRVy+VQsVQeei+MYlKJVLhSTSqknIp9FwA+/fpf\nwH3nvNT95jEbVjlPeDnQK4lJOgoyacq9X1MuDn3Pgtb6EgD7xNuvAPB5+/fnAbxykJ1x/XSa60A/\nTEaG89lSh66UcskrVTdh6KGzFnUiKBozDs545GEglhv4M05cByCs35LmGgdmw1o0Es6gd3J847qt\nwWfrJhpoJT5te7wR43898yT3OaeiuEHnN61UTvDJshO0JFPIC5+dOtcpeqpcSFGwqhmeEwpedVy9\nG0+5xErhy7/3NNz//pfh5GPXIImVL+Ub+fR+mSwkYxu8IBNdA2fQYxEgX8ADLAs7LQe6dVAaBHTK\nKP3fb5NJRKl2DXNc/O9XjjdLRnumU6YkV4tnnA6h7KGroMbNwxmHehY2aa232793ANjU7YtKqd9V\nSl2tlLp6z5697n1OAwCecx5r+rK4sUgmAYDNx5hmD888ZWNpX9JYNHp46Of99H4ARjbYiMPMUW7Q\nqcgVr7CY5UWpuJjEfGaKWHGFwwm2fjcFy4h7psnprEcb5mo180zGhWEllJKN+OrHZnQmNpsys23b\ngG4culfEuFaAbFLhrf8An4zFlTQcjThyQe5gNSQoFiV+y/dD1+D049cCMPr5UXnow6pcFgN8Qhr2\nUEi5ohHeH1wiSs9W5O7rlWPEOXzwvbzi5c4MwOMB4bGoqKZcCAue1rQhc7sSylrrT2qtz9Jan3X0\nMce491NWpAoIPfTjrFTPSNPCgNuLTt+Ey97+HDyryqALL8QFURDeBHGknCSKElx4oX3usTsej3vo\nRW8PnQzqCVZHCwBvfuZJeMbJ5viPGg+Pibxhpy/uQrlwzHW60yaZreVOBpIaKwPAd27aXjbozNOW\nvV0B493Jzj1K+XNSKqwUR27C4QHofkHRKFJMY232/4TjjwJg7o9RdW+XWcTLATlBDgPuofPrxIOi\nvLokMPyksVSg61ilBN6wuoU1Ywlee7YpdlVTLv1xqGdhp1LqOACw/+8a5Echh67BxRXtLHcXl7yO\nOFKlLEmllOuxKSF16LyuBXf0kzjCn7wgLKVJRtxw6NxDDwMzgDGAveRc5L1vWOUlh888ZaPbB01S\nm48xx7HWGnhPN3CDXs1vSj07XwmkuXYtyWLLofNz/f+3d+ZRclV1Hv/+3lJV3Z3uTkLInkwSAklI\nQhZIEAQJe9gHGFl0hCgjDorjAmeOox4FHZWj43jkcJRBh0FnXI6OO8dRwEHBbWTfUZSgwEEgakgn\nnV6q6s4f79737r2vllddr+pVVf8+5/TpWl69d6te1e/+3vf+lmqLoo4TSS56GnmwWGkaU0NyiXno\npuRinAftG2dniuo6uZpg1SX57P5crB3hVGmkSXSrMJs5NzaI956+BietmYfjVx9oaujaoqhylsL1\nC3SmRa/VOq7gu3joA6fgo+eul9sGj9u226Hg8zxn40J86sJ4xNl0YqqrId8FcCmA6+T/7yR5kR6Z\nUbQ8dNWgGYgMW6M/WtNYRJqznd7va3XVFXpJAV2XVB6yadBre+iq2bKesDR/uBAaKRU7vW7RMO7/\nw268euUcANEaw6AR5VLFQ7cMuh1WqUoreKGGHj1vy0XKK3eIjKbVvoyQcTQNHdAkl2I1gx4tirpa\nFUx7P7aB1z14Nakd/lez8PYTVuKCI5bE0tzTICvJxW1Cclkyux+fv/QIAOZaiqd56LZ5bGYCbCVh\nRcoqOftGJnEo3cU9dCLCpy/a1JpBdhFJwha/AuAXAFYR0XNEdBkCQ34yET0F4CR5vyGCRVH9flnT\nzqVnKL3C9YuGE+0ztiiqWlbBaiLgODEpIx8adLPinfryKIPpUCC5jIxVj3JRaeJ6XeT5Q4VwolCT\nx1WnrMLNO44I5R/1pdZjz6tLLubxdQ+9WJJFtGSN+GLZ/Kzv/b25xn3XUy8DkJKLlmYdpeHDKAKl\nanJX1dA9J3wvrhNJSW6dRVE1XiCaDPOei6tOCUqS2pmiaZCVnTM+zyYmlVjNeGXQVfawPO8dKqHX\n9NBj21Y5WVlNyp1IXQ9dCHFxladObPRg+ikrCzMOfaIY1XZRi5lq5fobVxyd6IQb2qijdXch84fr\nOPHQxHwVD13VJ1F6ccF3ZQ/H6qngu/ZOoM93ceZhC7F56SwUfBcDeS+UkpTRHO7zccLqaD35wi1L\n8PTL+7Bq/lD4WF8Vg/6T37xs3J+0onCisMUo1POkNXNxxxMvhanjCtXjdWS8GF5dOFqEkV2R0lXF\nucKwRdMv0KURVyuiRWRWUKykqdthizp6JENahaWy0tBda4JMA3NRNHhMiOjqqxNppPa6em+2KejQ\ni49MaOtKgiG5WF7jRKmsVVUzk0tynlPxB25jR1BUi0MH4p5vqKHLfTx27am4eOsSXHLUsmB84aKt\nE8Z52+9r73gRv3t5L3btHQ8bKiyc2Rd6/GrSqFYX5NxNi/Gr952E5XMGtHGac+6bj10OIN7X1Kyu\nGEw4KowtLKIltXpltC/eujQ2hnd89QEAZmVLu46K7Wnb6ofdv1NfmLMXro2Kio4TSjKVStVW6xnb\nDFkZOr8JyaXqPrWwxchDj9ZHOpHXHr4EF29dineceHDdbaMF3+A9LZS5HJ06WWVBWzMK9JlVj7wA\non6egJb2PcWmvEA8ysX+0dier7rEV9sN5D187LzDQuOnonAKvhurZlgsC/guYcs/3xFqxytl+Vwd\nux1bEuyJ54ptK/G5u3fGPFQVJjiQc41FUc9xsGcykGdUdM3Tu4LGIkMVsm2jYk5m5UPdC1eSi8Ju\n8KtXyNQjZHQPHIh3nncdvXVabQuUnoaeym4aphWhk74XTbyR1BLcT1LDPwv6cm7YQrEe6nNSduNb\nb3s1HvjD7tSucHqB9nro2m27CcNkUcQkl0Yvqw3JBZFOSRT/0diLjXrYoo4yQKbkIizNOsrCtF+n\nExbhb+DHFZt45DjVBLNBJj6phc7VC4aiRVEZh65CCO1wSbuoFhBNbHqly3jFRCuu3/ps7XjxcFHU\nOg+2x+46TuhZ1gvl634PPX2DPnsgFwvfu+CIYB0n6TpUJ6PemloInjdUwPZ18zMcUefRZoNu1ui2\nIzOiQj1Ti3Kx08prSS5xQxmFLeoog6MSn/JhKzY9Lr2MO580IzcrxcWqx+xMzVpUW7xVFehUQwBl\n0PtzasKJ4tDV8ezMOxWSmXMdrDgwkHkWyMtYuza5frXkOJYWbr3VnBXiGFVbrBCHbnnoJSshphrp\naeip7KZhXOOKp7l9ffb1m7F97XzkPTdWh+iogw7Azo+djjULhqq8unuIFlAzHkgHk5nkUiwLI9tx\nolQOvWblWde77LbRv8xEenW2SkbHfKAQSi7mL9yp4KHvGy/GPPQbf/I783UVDIXyyhqprb1guM+4\n77lBJMN+KaMoL1uFUfb5rtTQo9T/iSrhherzniiVcdSKA/DK6KTW+9MxNXQ7bFGPGrI+S6OIlmbE\nHadCpqihxTuxhJhqdLuH7qXooZ+2fgFOWx9URqy0yNipWaKNQqFBZ4tejfYulVgaum7Y9CgX9WVv\n9EdrL5z6mmdYrx505KGb+9S70gfbOUYqvRr7MTKW/JqzDgUQFNCyUZNIIxq6LZOoMSgPXcWs/2U0\niFwZyHtRlIvU0MerGHT9x+/LEr6qBnzBd8Px2tp3kClq3tcxDLqVoBRbFLXijMthuGMdg97lceit\nKhDWy2VkwwgedtGrkpnkUrQ60+uSS9gMtkGDruvihoeO+l5K6KFbx9TLxgbbuaFGrRidKGJkvIiC\n74Sa9gtaNUTFVDT0SuR9N6yvriJX1PFm9QcRNUHdcqmhT9Y36KpZgqoimfcdQ3KJp+xH+7EnXrvL\nlL4oatest2vs2BmO1UhLcslqPc1YZE5xDPkE0WDdSpiExB56VTKMcjEXRVUzBCD63+hMrOvihGhR\nj6h6xqWiroYeGvQgbLFkGPQS9uyfxFDBrxjdolDRH41o6JXH6mBUSi4q6/THvw40/ANmRAZdpe+H\nNVeI8P4z1uDBZ3dj+ZwBw5ub1e/DdQgjspplwXeNBdJqvSvV88b7tOqW600WKnaVUvt1KazpUc9p\nTUtyyayWi6t/DuyhJ0F9Tk3+fHqa9hp07XaxLGKGzZZcKvVirIXdZzD09ImM7M9KFKywxXBMVqZo\nQWnU2tj2jhexZ2wSgwUvXHhcocWSK6aioVceqxsaXlWNUPUNVesIY5PlsL54mNrvEP7u2BXhfvTk\npFkDOXguYd94tPirJsGgMYUZimhkeNphi5aG7mtJW6anH0/9Dz30Oga7+z301hy4kUSdbkP9bnr5\nPTZLdouiJRFGjijUd1x92WtlY1bC9nr01P85M+IG3XMIC2YGUR1Kf48vigb/Q4PuuUbDCCCQXPaO\nlzCj4IOI8MN3viZs7qyzYk7gvZ++vrnWXnnPwYtSQ895Dmb2+9g9OilrhgcD3j9ZgudGiTpAXMbQ\n78/uzxkRMXo3HNcxG2n351xjgqyU+q/wXEK+Sk0dW3LxNMmlnq6cVnW9rGKY7UkwLTql+XMrUCV2\nB6pkTzNt99DNZJyJUhkOaaU+rUzRyQY9dP1HTtp9quKhP/Hh7WEii/oh2PpcXEN3ZK0U3UMvYXS8\nGH7RVsnaLDbzhwt48sPbE/3orjx+JZ54YU/F5/RFUcchDOQ87B6dhO9G9eP3T5RidcztRC19cXb1\ngkHDyBR8NwyZdBwyWgMO5DzM7I8+T9vbNMMWo2gZoniVQVu6set4VyMtO9wJiUVp0ssG/cItS/DV\ne57FmRsWZj2UjqW9vaekDQyq+JUxPhmUx1ULcVFjYKmVNeqhW/qsrqHbMdjBOOLV++yqb3EN3cVk\nWaBULmNGPhj7vvEi9o4XsWSgcllfnSQlDADg6lNXVX1OX/jSS9v6toduNQixvV59Ulo8q99YzCwY\nkou50DmQ90LDEYSE1tDQiYxKlvqkYSd8uQ5hzowgrn62Vnq4Emnpzllp6JVKG6SBXnq511g5dxCP\nXHNq1sPoaDLR0PNe0IleIOgurwy6HeXSqIbuG3HotvEI9r28grYdHNMMT1SEGrqsBum70kMvidCg\nj02WMDpRatuloO6F6R2BfNcxDIXvxsMNdRZKuekMKQHpz+d9N+xapMqTKvrzbmi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YQ0/z3P0gZc8vp2dS4dlX2P/gZYvGqGj4jjE80YRvOx3wCZ+rqqT0L9caD9gA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\nvsTAYF4j3qUMAH7xFxdi3Y7ekvWeferSuRrWfosZz9vaTK0dLZbW+kt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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "92asXtqAwPef", + "colab_type": "text" + }, + "source": [ + "### Add Data\n", + "\n", + "We can add some input-signals to the data that may help our model in making predictions.\n", + "\n", + "For example, given just a temperature of 10 degrees Celcius the model wouldn't know whether that temperature was measured during the day or the night, or during summer or winter. The model would have to infer this from the surrounding data-points which might not be very accurate for determining whether it's an abnormally warm winter, or an abnormally cold summer, or whether it's day or night. So having this information could make a big difference in how accurately the model can predict the next output.\n", + "\n", + "Although the data-set does contain the date and time information for each observation, it is only used in the index so as to order the data. We will therefore add separate input-signals to the data-set for the day-of-year (between 1 and 366) and the hour-of-day (between 0 and 23)." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Cu3pzeXUwPeg", + "colab_type": "code", + "colab": {} + }, + "source": [ + "df['Various', 'Day'] = df.index.dayofyear\n", + "df['Various', 'Hour'] = df.index.hour" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pylQQ42VwPej", + "colab_type": "text" + }, + "source": [ + "### Target Data for Prediction\n", + "\n", + "We will try and predict the future weather-data for this city." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "4UkYOSO2wPej", + "colab_type": "code", + "colab": {} + }, + "source": [ + "target_city = 'Odense'" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "g8OZBR2OwPen", + "colab_type": "text" + }, + "source": [ + "We will try and predict these signals." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "p9AEmONwwPen", + "colab_type": "code", + "colab": {} + }, + "source": [ + "target_names = ['Temp', 'WindSpeed', 'Pressure']" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dYg-Wcz7wPes", + "colab_type": "text" + }, + "source": [ + "The following is the number of time-steps that we will shift the target-data. Our data-set is resampled to have an observation for each hour, so there are 24 observations for 24 hours.\n", + "\n", + "If we want to predict the weather 24 hours into the future, we shift the data 24 time-steps. If we want to predict the weather 7 days into the future, we shift the data 7 * 24 time-steps." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "D_9t8dzkwPet", + "colab_type": "code", + "colab": {} + }, + "source": [ + "shift_days = 1\n", + "shift_steps = shift_days * 24 # Number of hours." + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JPWaNU87wPew", + "colab_type": "text" + }, + "source": [ + "Create a new data-frame with the time-shifted data.\n", + "\n", + "**Note the negative time-shift!**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "9G2ebh5OwPex", + "colab_type": "code", + "colab": {} + }, + "source": [ + "df_targets = df[target_city][target_names].shift(-shift_steps)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5nrXKy7GwPez", + "colab_type": "text" + }, + "source": [ + "**WARNING!** You should double-check that you have shifted the data in the right direction! We want to predict the future, not the past!\n", + "\n", + "The shifted data-frame is confusing because Pandas keeps the original time-stamps even though we have shifted the data. You can check the time-shift is correct by comparing the original and time-shifted data-frames.\n", + "\n", + "This is the first `shift_steps + 5` rows of the original data-frame:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "9WiykanswPe1", + "colab_type": "code", + "outputId": "38c18bb4-0ee1-4ff4-b9a7-926f3d48f9dc", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + } + }, + "source": [ + "df[target_city][target_names].head(shift_steps + 5)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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TempWindSpeedPressure
DateTime
1980-03-01 11:00:006.14285712.5857141011.066667
1980-03-01 12:00:007.00000011.3000001011.200000
1980-03-01 13:00:007.00000012.1181821011.300000
1980-03-01 14:00:006.85714312.7428571011.400000
1980-03-01 15:00:006.00000012.4000001011.500000
1980-03-01 16:00:004.90909112.6181821011.688889
1980-03-01 17:00:003.95348812.6465121011.877778
1980-03-01 18:00:003.67441911.7255811012.066667
1980-03-01 19:00:003.39534910.8046511012.255556
1980-03-01 20:00:003.1162799.8837211012.444444
1980-03-01 21:00:002.8372098.9627911012.633333
1980-03-01 22:00:002.5581408.0418601012.822222
1980-03-01 23:00:002.2790707.1209301013.011111
1980-03-02 00:00:002.0000006.2000001013.200000
1980-03-02 01:00:002.0769237.7384621012.366667
1980-03-02 02:00:002.5384627.9692311011.533333
1980-03-02 03:00:003.0000008.2000001010.700000
1980-03-02 04:00:003.0000007.9272731010.100000
1980-03-02 05:00:002.9166677.6583331009.500000
1980-03-02 06:00:002.4166677.4083331008.900000
1980-03-02 07:00:002.0000007.1000001008.300000
1980-03-02 08:00:002.1428576.5428571007.700000
1980-03-02 09:00:003.0000006.2000001007.100000
1980-03-02 10:00:002.8333338.3500001006.466667
1980-03-02 11:00:002.0000006.8285711005.833333
1980-03-02 12:00:002.0000008.2000001005.200000
1980-03-02 13:00:000.1666679.2166671004.766667
1980-03-02 14:00:001.00000011.8857141004.333333
1980-03-02 15:00:001.00000012.4000001003.900000
\n", + "
" + ], + "text/plain": [ + " Temp WindSpeed Pressure\n", + "DateTime \n", + "1980-03-01 11:00:00 6.142857 12.585714 1011.066667\n", + "1980-03-01 12:00:00 7.000000 11.300000 1011.200000\n", + "1980-03-01 13:00:00 7.000000 12.118182 1011.300000\n", + "1980-03-01 14:00:00 6.857143 12.742857 1011.400000\n", + "1980-03-01 15:00:00 6.000000 12.400000 1011.500000\n", + "1980-03-01 16:00:00 4.909091 12.618182 1011.688889\n", + "1980-03-01 17:00:00 3.953488 12.646512 1011.877778\n", + "1980-03-01 18:00:00 3.674419 11.725581 1012.066667\n", + "1980-03-01 19:00:00 3.395349 10.804651 1012.255556\n", + "1980-03-01 20:00:00 3.116279 9.883721 1012.444444\n", + "1980-03-01 21:00:00 2.837209 8.962791 1012.633333\n", + "1980-03-01 22:00:00 2.558140 8.041860 1012.822222\n", + "1980-03-01 23:00:00 2.279070 7.120930 1013.011111\n", + "1980-03-02 00:00:00 2.000000 6.200000 1013.200000\n", + "1980-03-02 01:00:00 2.076923 7.738462 1012.366667\n", + "1980-03-02 02:00:00 2.538462 7.969231 1011.533333\n", + "1980-03-02 03:00:00 3.000000 8.200000 1010.700000\n", + "1980-03-02 04:00:00 3.000000 7.927273 1010.100000\n", + "1980-03-02 05:00:00 2.916667 7.658333 1009.500000\n", + "1980-03-02 06:00:00 2.416667 7.408333 1008.900000\n", + "1980-03-02 07:00:00 2.000000 7.100000 1008.300000\n", + "1980-03-02 08:00:00 2.142857 6.542857 1007.700000\n", + "1980-03-02 09:00:00 3.000000 6.200000 1007.100000\n", + "1980-03-02 10:00:00 2.833333 8.350000 1006.466667\n", + "1980-03-02 11:00:00 2.000000 6.828571 1005.833333\n", + "1980-03-02 12:00:00 2.000000 8.200000 1005.200000\n", + "1980-03-02 13:00:00 0.166667 9.216667 1004.766667\n", + "1980-03-02 14:00:00 1.000000 11.885714 1004.333333\n", + "1980-03-02 15:00:00 1.000000 12.400000 1003.900000" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 26 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "i1Zh_qtTwPe3", + "colab_type": "text" + }, + "source": [ + "The following is the first 5 rows of the time-shifted data-frame. This should be identical to the last 5 rows shown above from the original data, except for the time-stamp." + ] + }, + { + "cell_type": "code", + "metadata": { + "scrolled": false, + "id": "_2Ow9SdwwPe4", + "colab_type": "code", + "outputId": "cb8b093c-a14d-4b95-cde7-8efd632c546e", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 425 + } + }, + "source": [ + "df_targets.head(5)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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TempWindSpeedPressure
DateTime
1980-03-01 11:00:002.0000006.8285711005.833333
1980-03-01 12:00:002.0000008.2000001005.200000
1980-03-01 13:00:000.1666679.2166671004.766667
1980-03-01 14:00:001.00000011.8857141004.333333
1980-03-01 15:00:001.00000012.4000001003.900000
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" + ], + "text/plain": [ + " Temp WindSpeed Pressure\n", + "DateTime \n", + "1980-03-01 11:00:00 2.000000 6.828571 1005.833333\n", + "1980-03-01 12:00:00 2.000000 8.200000 1005.200000\n", + "1980-03-01 13:00:00 0.166667 9.216667 1004.766667\n", + "1980-03-01 14:00:00 1.000000 11.885714 1004.333333\n", + "1980-03-01 15:00:00 1.000000 12.400000 1003.900000" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 27 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XscDYRsEwPe6", + "colab_type": "text" + }, + "source": [ + "The time-shifted data-frame has the same length as the original data-frame, but the last observations are `NaN` (not a number) because the data has been shifted backwards so we are trying to shift data that does not exist in the original data-frame." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "ocI5cY3FwPe7", + "colab_type": "code", + "outputId": "66d35252-4ac9-407a-a21f-741db4dbba56", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 425 + } + }, + "source": [ + "df_targets.tail()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Temp WindSpeed Pressure\n", + "DateTime \n", + "2018-03-01 19:00:00 NaN NaN NaN\n", + "2018-03-01 20:00:00 NaN NaN NaN\n", + "2018-03-01 21:00:00 NaN NaN NaN\n", + "2018-03-01 22:00:00 NaN NaN NaN\n", + "2018-03-01 23:00:00 NaN NaN NaN" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 28 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "elB6-BRDwPe-", + "colab_type": "text" + }, + "source": [ + "### NumPy Arrays\n", + "\n", + "We now convert the Pandas data-frames to NumPy arrays that can be input to the neural network. We also remove the last part of the numpy arrays, because the target-data has `NaN` for the shifted period, and we only want to have valid data and we need the same array-shapes for the input- and output-data.\n", + "\n", + "These are the input-signals:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "EatdfsiowPfA", + "colab_type": "code", + "colab": {} + }, + "source": [ + "x_data = df.values[0:-shift_steps]" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "gNVuSAKrwPfD", + "colab_type": "code", + "outputId": "213153bd-d2b9-4db9-9a6e-9e54d9189f76", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 53 + } + }, + "source": [ + "print(type(x_data))\n", + "print(\"Shape:\", x_data.shape)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\n", + "Shape: (333085, 20)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YybPGRDBwPfF", + "colab_type": "text" + }, + "source": [ + "These are the output-signals (or target-signals):" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "tvJu12uhwPfG", + "colab_type": "code", + "colab": {} + }, + "source": [ + "y_data = df_targets.values[:-shift_steps]" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "scrolled": true, + "id": "92W5a11HwPfI", + "colab_type": "code", + "outputId": "584bef6c-6a79-4cc9-d99a-af35ea8c4b14", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 53 + } + }, + "source": [ + "print(type(y_data))\n", + "print(\"Shape:\", y_data.shape)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\n", + "Shape: (333085, 3)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JTbCXRFuwPfL", + "colab_type": "text" + }, + "source": [ + "This is the number of observations (aka. data-points or samples) in the data-set:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "b-e_mi8vwPfM", + "colab_type": "code", + "outputId": "f409355b-f1ce-4f58-ea41-7f43e484ae10", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + } + }, + "source": [ + "num_data = len(x_data)\n", + "num_data" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "333085" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 33 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hAeo1x6WwPfO", + "colab_type": "text" + }, + "source": [ + "This is the fraction of the data-set that will be used for the training-set:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "sv6jvNerwPfP", + "colab_type": "code", + "colab": {} + }, + "source": [ + "train_split = 0.9" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_Mw0nVxQwPfT", + "colab_type": "text" + }, + "source": [ + "This is the number of observations in the training-set:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "mcPQ4FqewPfV", + "colab_type": "code", + "outputId": "3e69a7ff-2653-462e-b10e-37cfb715774f", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + } + }, + "source": [ + "num_train = int(train_split * num_data)\n", + "num_train" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "299776" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 35 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4QkW4lk6wPfb", + "colab_type": "text" + }, + "source": [ + "This is the number of observations in the test-set:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Y9PuOxh4wPfc", + "colab_type": "code", + "outputId": "d4d9b0a3-864e-4ffb-9de1-9ff9c9d708d7", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + } + }, + "source": [ + "num_test = num_data - num_train\n", + "num_test" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "33309" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 36 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "L_VDTY4UwPff", + "colab_type": "text" + }, + "source": [ + "These are the input-signals for the training- and test-sets:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Z7tS9ziEwPff", + "colab_type": "code", + "outputId": "dc2e5b25-d564-4116-f347-e03af422a624", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + } + }, + "source": [ + "x_train = x_data[0:num_train]\n", + "x_test = x_data[num_train:]\n", + "len(x_train) + len(x_test)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "333085" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 37 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Fl6EGLHDwPfi", + "colab_type": "text" + }, + "source": [ + "These are the output-signals for the training- and test-sets:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "16gMrq2jwPfj", + "colab_type": "code", + "outputId": "fb8918de-2ca7-4d28-9770-c6d7a2b0013c", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + } + }, + "source": [ + "y_train = y_data[0:num_train]\n", + "y_test = y_data[num_train:]\n", + "len(y_train) + len(y_test)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "333085" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 38 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "M1paoApdwPfn", + "colab_type": "text" + }, + "source": [ + "This is the number of input-signals:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "J_WyDY4mwPfp", + "colab_type": "code", + "outputId": "108e5e93-c5c2-4468-b20d-dc39a1544781", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + } + }, + "source": [ + "num_x_signals = x_data.shape[1]\n", + "num_x_signals" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "20" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 39 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3iJ2wFFPwPfr", + "colab_type": "text" + }, + "source": [ + "This is the number of output-signals:" + ] + }, + { + "cell_type": "code", + "metadata": { + "scrolled": true, + "id": "6jDdI8nLwPfr", + "colab_type": "code", + "outputId": "4d5d211c-9c34-4b7f-ff81-18a7d96e8dba", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + } + }, + "source": [ + "num_y_signals = y_data.shape[1]\n", + "num_y_signals" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "3" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 40 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "m3xbZ-V_wPft", + "colab_type": "text" + }, + "source": [ + "### Scaled Data\n", + "\n", + "The data-set contains a wide range of values:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "vq9B0AKVwPfv", + "colab_type": "code", + "outputId": "6ce0d570-bb8f-4a7e-f776-9377f0b19af2", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 53 + } + }, + "source": [ + "print(\"Min:\", np.min(x_train))\n", + "print(\"Max:\", np.max(x_train))" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Min: -27.0\n", + "Max: 1050.8\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SV5Ug_T7wPfx", + "colab_type": "text" + }, + "source": [ + "The neural network works best on values roughly between -1 and 1, so we need to scale the data before it is being input to the neural network. We can use `scikit-learn` for this.\n", + "\n", + "We first create a scaler-object for the input-signals." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "wobXHcmswPfy", + "colab_type": "code", + "colab": {} + }, + "source": [ + "x_scaler = MinMaxScaler()" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zxhFPakqwPf0", + "colab_type": "text" + }, + "source": [ + "We then detect the range of values from the training-data and scale the training-data." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "cs5OfekSwPf1", + "colab_type": "code", + "colab": {} + }, + "source": [ + "x_train_scaled = x_scaler.fit_transform(x_train)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PEPsv87ewPf4", + "colab_type": "text" + }, + "source": [ + "Apart from a small rounding-error, the data has been scaled to be between 0 and 1." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "-Ou_tkj6wPf4", + "colab_type": "code", + "outputId": "da37806d-2a42-4390-a490-ad633a8efad7", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 53 + } + }, + "source": [ + "print(\"Min:\", np.min(x_train_scaled))\n", + "print(\"Max:\", np.max(x_train_scaled))" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Min: 0.0\n", + "Max: 1.0000000000000002\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UQZjSZhTwPf6", + "colab_type": "text" + }, + "source": [ + "We use the same scaler-object for the input-signals in the test-set." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "_-1OEYRowPf7", + "colab_type": "code", + "colab": {} + }, + "source": [ + "x_test_scaled = x_scaler.transform(x_test)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NWsSw3oFwPf9", + "colab_type": "text" + }, + "source": [ + "The target-data comes from the same data-set as the input-signals, because it is the weather-data for one of the cities that is merely time-shifted. But the target-data could be from a different source with different value-ranges, so we create a separate scaler-object for the target-data." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "QTmZCjblwPf-", + "colab_type": "code", + "colab": {} + }, + "source": [ + "y_scaler = MinMaxScaler()\n", + "y_train_scaled = y_scaler.fit_transform(y_train)\n", + "y_test_scaled = y_scaler.transform(y_test)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "abunH1OjwPgA", + "colab_type": "text" + }, + "source": [ + "## Data Generator\n", + "\n", + "The data-set has now been prepared as 2-dimensional numpy arrays. The training-data has almost 300k observations, consisting of 20 input-signals and 3 output-signals.\n", + "\n", + "These are the array-shapes of the input and output data:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "1R8JrXBPwPgB", + "colab_type": "code", + "outputId": "10f78c7e-ebf2-406a-a8ca-e3b523b4e33e", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 53 + } + }, + "source": [ + "print(x_train_scaled.shape)\n", + "print(y_train_scaled.shape)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "(299776, 20)\n", + "(299776, 3)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NDSzeIvqwPgD", + "colab_type": "text" + }, + "source": [ + "Instead of training the Recurrent Neural Network on the complete sequences of almost 300k observations, we will use the following function to create a batch of shorter sub-sequences picked at random from the training-data." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Uxq01eDNwPgD", + "colab_type": "code", + "colab": {} + }, + "source": [ + "def batch_generator(batch_size, sequence_length):\n", + " \"\"\"\n", + " Generator function for creating random batches of training-data.\n", + " \"\"\"\n", + "\n", + " # Infinite loop.\n", + " while True:\n", + " # Allocate a new array for the batch of input-signals.\n", + " x_shape = (batch_size, sequence_length, num_x_signals)\n", + " x_batch = np.zeros(shape=x_shape, dtype=np.float16)\n", + "\n", + " # Allocate a new array for the batch of output-signals.\n", + " y_shape = (batch_size, sequence_length, num_y_signals)\n", + " y_batch = np.zeros(shape=y_shape, dtype=np.float16)\n", + "\n", + " # Fill the batch with random sequences of data.\n", + " for i in range(batch_size):\n", + " # Get a random start-index.\n", + " # This points somewhere into the training-data.\n", + " idx = np.random.randint(num_train - sequence_length)\n", + " \n", + " # Copy the sequences of data starting at this index.\n", + " x_batch[i] = x_train_scaled[idx:idx+sequence_length]\n", + " y_batch[i] = y_train_scaled[idx:idx+sequence_length]\n", + " \n", + " yield (x_batch, y_batch)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "S-60stDGwPgI", + "colab_type": "text" + }, + "source": [ + "We will use a large batch-size so as to keep the GPU near 100% work-load. You may have to adjust this number depending on your GPU, its RAM and your choice of `sequence_length` below." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "M8r0o4s0wPgI", + "colab_type": "code", + "colab": {} + }, + "source": [ + "batch_size = 256" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8YD-qNsqwPgK", + "colab_type": "text" + }, + "source": [ + "We will use a sequence-length of 1344, which means that each random sequence contains observations for 8 weeks. One time-step corresponds to one hour, so 24 x 7 time-steps corresponds to a week, and 24 x 7 x 8 corresponds to 8 weeks." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "szHC0H-cwPgK", + "colab_type": "code", + "outputId": "11a9f463-7ed7-4da7-f753-bb5d5a7e8ee6", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + } + }, + "source": [ + "sequence_length = 24 * 7 * 8\n", + "sequence_length" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "1344" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 50 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vumvL2ZAwPgP", + "colab_type": "text" + }, + "source": [ + "We then create the batch-generator." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "saEtBnTBwPgP", + "colab_type": "code", + "colab": {} + }, + "source": [ + "generator = batch_generator(batch_size=batch_size,\n", + " sequence_length=sequence_length)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VmzbmR22wPgR", + "colab_type": "text" + }, + "source": [ + "We can then test the batch-generator to see if it works." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "54Ogaz3cwPgR", + "colab_type": "code", + "colab": {} + }, + "source": [ + "x_batch, y_batch = next(generator)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "v-9UB0cYwPgT", + "colab_type": "text" + }, + "source": [ + "This gives us a random batch of 256 sequences, each sequence having 1344 observations, and each observation having 20 input-signals and 3 output-signals." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "pJ-XI1nawPgV", + "colab_type": "code", + "outputId": "b9665182-f80f-4f5c-e8f9-ef4a1be17a92", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 53 + } + }, + "source": [ + "print(x_batch.shape)\n", + "print(y_batch.shape)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "(256, 1344, 20)\n", + "(256, 1344, 3)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QSeUlf4zwPgX", + "colab_type": "text" + }, + "source": [ + "We can plot one of the 20 input-signals as an example." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "PP4mNTIMwPgX", + "colab_type": "code", + "outputId": "b3121627-bf90-4885-8666-a6a249aa18a9", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 287 + } + }, + "source": [ + "batch = 0 # First sequence in the batch.\n", + "signal = 0 # First signal from the 20 input-signals.\n", + "seq = x_batch[batch, :, signal]\n", + "plt.plot(seq)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 54 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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3PLMFm/vSlNZanGBGtaTOa2evEOUCCXerME0HadN9yiU7LhAnMlNoFReszu1i\nMvu4gG0NULEe34dbBY2CAHOSmOypMAjQVQ6tymzJ0lEjJ7BtVSqrqYRBtumNbpBDhWlKSM/tTQX9\nzsGGLjYDgD21CE+91IffrN4GAJjTXQEAfGfpOn3clC6c5mg3++GIEmlxJgFGIRyoFIKxEGKt/CYK\nLZ1dCHG+EGKVEGKNEOLKnM8vE0JsFUI8pv79KfssZuNuL2aPvQD+YHqF0D46oVg/+LojAdgB5MX7\n9+rV7gVf+DWA1ELoqWTz3wGwZvTQfykv3rUQKk6WURqkNRTPALmM0iwbruiWrjWFcryqmR/LBbcQ\nWo0h0NtQCHRXXAtBsrRPmdmfbwe4dQjpZ2XHZVSPUuuLfPh9Qw0rqPzw82kPip+v3AzAWAhL1+7A\ntv5aapEJob+zREqrYRFhz3BDpdMGWDS3W4/vHkq/+/kzq+l8YiIsnAIWghAiBHAjgLcAOAHAe4QQ\nJ+Rs+n0p5anq39fY+BAbf0fOfh7jhCTnAcrjZPdoDfw+1prcx1ldJVz26sU4Yv9eAMDNH3411n72\nrfhzpRC2swDyKxfPw7nHzgcAbOuvq+ObFFW3D4BL8SCRZgmJnH4IlVJguXakZBaCG1QWdgyBB7bd\n6l8+xjGUQ8THryE9b6JrDuJEsthI2q7SPi93GdmKMS82wmMImY5p6v1APUJPpaTTSYcbsc5yAoAP\nnHO4Pm45FJY11zfUsLK6gNQ44S48whYVLyqrtNP/c36aSECcVj1V+/utx4me60ShFXV0JoA1Usq1\nUso6gJsAXDi+0/LoBMiM5Ss1rxDaB3eXNCMno0IrEjI9lZIVu3nziQsAAL/42GsBQCuEA5lfubdK\n6ZY2JUKW3x+K7dTO66dt3QIw3iYSMEyfQWCnqFqpr0x55GUU6X0a2X34NQCpAKyWzDXQcQKVjeOS\n+ZVLTqWy4xICDOtoiWVKmQY5tgIdVPxClAo6HCU6jgIAn37bCbjs1YsBpDUIJebG6a9FuqqZlFoj\nSnJ7HXzp/ues85NiI+pzCjRHikgv7RoXZo6zN9GKQlgEYD17v0GNuXinEOJxIcSPhBCHsPEuIcRy\nIcRSIcRFeScQQlyutlm+devW1mfvMSJo1cIfRq8Q2sdoFGuUSIShwBffexouPn0Rjpzfa31+xmHz\nsO6GC3DUATMBAEcdMBOvXDwXi/fv0cfvVStI+h437BwEYAqiuOATgNXBi1fZujTTVe0yMqv+tDDN\nFrLc/cOPW3YKvTiKYghuoR1nENX9CQKBajnAkHNe3sWMX1uRhUD3JBtUTrchC4Huw3AjVnEYo7DJ\nnVQtB9aqvX840gp4hso+GqjK9Jd8AAAgAElEQVSN3BuZFMecnjQWsW7bAACjEOJE6v1J+U0UOuWw\nug3AYinlyQDuBvAt9tlhUsolAN4L4F+FEEe6O0spvyKlXCKlXDJ//vwOTckjjxvfxxDaBxdAzVxG\nZCEcd+AsfO7dp1qrzCKkmUYNfXxaydNZ/+aWJwEA67aniiFmFkKgLQR7riT0JHP5kDDmWTt59Ne2\ncDer8jITukAxqylXmrxvQJQkWsjHidTHDkTqnrGb9yATQ+DVxfr4LIZA2xZxGQ3WUwuBMn9clxFg\nzkn1GYQ9tUjHGyjov6fWyHUZkX6hOZ24cBYA4JlNewAA3RXjCqPncioElTcC4Cv+g9WYhpRyu5Sy\npt5+DcAZ7LON6u9aAPcDOG0M8/UYBXSAcRxdRgO1aEq0BuwEWo3FSJkKuTAY3cM9t6fMFEKMajlI\nqRwc4U7gBVjUwSuPugKwA7JuDCENkmaDytxC4H57lz2Us7EO5SiRdD8z50SaLmSJlPqcYSB0lbC+\nRp5llGMZEEgRavcQc0W5pHnDjRhdpVBThtSiRFOAE6pMIZTZ95haCKnymKkqmPuHzTPw+8enrsAT\nDpqVmRMpkF2DDQSCkebFif49TfqgMoBlAI4WQhwuhKgAuASAlS0khDiIvX0HgKfV+FwhRFW93h/A\n2QBWdmLiHs1hLATu++6s8D7xmrtw+beXd/SYkxVF9/Hl3UNYcv3dWLMlXflxF8hoMKenovmMao2U\n14bTOR+9YKZ1XBKQUrkwLOoKZ8XJKRrIVUGrUkqjLDmFaflpp8xlpG4BDwJzJZIh4IOJa/HUVzon\nKQTLsmBB5bxgsnv8krZeWJZRXh1CaGoDao1Yuc3M8YxCsBXFS7uGsHOwga5yqAX8QN0ohP/5qkOx\n7oYLdEyIn58UyI7BOsphYLG4NqaKhSCljABcAeAupIL+B1LKp4QQ1wohKGvoo0KIp4QQKwB8FMBl\navx4AMvV+H0AbpBSeoWwl2DSEsc3qEwkbdMdRZbWz57YhG39dXx3acr2SYK6NMqMkTk9ZdSiBEP1\nGHVFdMbpKE45eDYA4MOvPwqAbSEIh8vIZeok4R0lUq+MeaCYKpX5qp7SI2kb+usKWK44hopcRk6w\nm1bHVCUMcAvBDoKT4IxYENwFDXEhW8Rl1FD8QjRej6VFf83vm1vE+ZyipDjuwJma42jPcKTnTNYE\nL0SjSmmKCdWj1GVG9zFKpL5XU6JSWUp5B4A7nLGr2eurAFyVs9+DAE4a4xw92oSbj83HPEYPO4aQ\nXT3TirtdC2GuCjo+vakPtUaMaim0Mofo9LR6JYHHqRRi2HNw3SWJNFlGz2/rR5xIzc3jVirvYKmx\nXCFUdFwi/YwrBP6aW1H0G9RMo+waSAmFQqTV2Y5lQRk9I7mMtIXA3Fkmy8jtmJbGQbRAjhOVPZUf\nQ+DoG04zhLrKIWYqAd/P3KZ0TFK6/PzlMEC1FKAWJSiXAu1StCyECXYZeeqKaQyXG999PVY066k7\n3WDVITDXCLXK/NWzW63tRhtD2K83VQgXf+lBAKnQDESWisF1ofC8eDcHP6+oi4TVV3/9fOo/11lG\ntsuI03Hz1p0VN4bA7oXFdhplXUaRI/h48DcIhKaeNvul47zaN29Ro/31uqcCU4pOYVpD8QvRyj1S\nSrHEvi+yqNznhaymQJgVP48hUFU0/+75qn9GtYRaVEclDCzXX22SWAieumIaw+0t674e8/H3LX1g\nCQe+iu1RBWTzlEBv10J4/XEHWO+rpSBdtTuuP1px8u/X7YcQOytx7rIhCwEAfrh8g3EZqZaQtG2t\nkbDUSEr5TDKV0kMFLqOaco3k1UdUmStLU1cL5TJyXE2mvaeJAbigLCZy+yTSVjS8/3EjTt1euo5A\nWQjcZbR5T0ohcvphc63z9GmFINBTCSEEWQi28jnriP30Pvy45Gaa01Nm7q0kYzlNFLxCmOIYqWlL\nnoXQSYWwr2QXEfj15rGRbldVxvSdhKNUCOUwwH9cukS/r5QCS5DlZdLQuFD0124Mwc0yihOJnqpR\nCEcvmKHTVt1WlbXY0GdwdwsvKgOMm0iIbNppRVk59BM0/YxDfQ1agYYC5RyXkUvNbS1wnBRUnXbK\nsozclNooSVAKAtX1DMptZscLTjskVQSXnnUYOPqGjUIQQmBGtaRiCHYdwf4zKnofXsfQyxYPRiFN\nnhiCVwhTGDc/ugFHfepnWL9jMPfzvIrOTsrwsTaMmWqwLASrX3A6vnHXEF7cPmiCym2wyp6gctWB\nVGgGASOsIzeQw++fKoSUgjrTgYwJSBqvlAI8e/1bcNh+PVg0p1ttywKy5B6KEh0cJQuBc/ZLRyHM\n6S471BWxVgixY7nkuoyECSrz6yDivYYTowHsTCuAWQiJ1L910y/aWBhkZZXU+cjtRjjryP2w6vrz\nsWTxPOv70S4jJTlnVktODMEOIAOwXFFkIcztqeRnGXkLwaNdEKXyuu0DuZ/TQzJeLqM4x3Sfziji\nMuKWw0u7h9iKd/SPF087TOsQhBUQBvKoK0xLR5phlJhiM8D2iVPwdmZXKXWXqKBy4Gxbj2J0OxZC\nSsCmMoQca6SnUrLuBWXTWO4aJ+00kU7aaZCv7HgMwSbKM6mzAFM0klNipPeVFHeDxUHKgdBBZbpX\n+v4z19qz178F5x47X8dLKAA9o6ukanFslxHvf8CzzShO1FUOrfoKYyFMj0pljwlAfy1djZFZ7yI3\ny6iDq/oiyt/pCivLiLN6Mv9zksgxWQhltkJM/e85DV9cOmgptQvDfOeJpqMAmIUgTTP7tBGM1NQV\nJsip6JhjE0OINC9WNqhMSqCnEuqmMoCqti6n+fbalaXm3OXw+ADKbcVSMekc6dxMDCHJtRDonpt7\no9NZhVA9JAz1BbcQImWljNQnhLOj0lyBNEhsWQjKZcSPxd1A+8+oqusPrBhCfZJYCD7LaApjqJ6m\nwBW5gXTGiRVD6Nz584J70xm2hWBcI404wezuMnYM1DEcxdq9MtoYApBnIWTTTitOHUAiObmdmWvJ\nWvWnAlIyX3k5SAO4un1lYFsIjUjqYiqyiBqJZFXG6Xb0++qphNij0jIBYyEEjv8e4BTeUp+PE8aZ\neEE6Xx5DsMj6Yje+woLK0iiaRizx7d++gEPm9oAoqQGodNYEUiJjIbjgbqBQWwhl9A01dNwoLwbA\nA8VzlYVQKdlZRlOmMM1j8kLnvxcUm+XGEDroMuKVu80sj5d3D+E9X1lqdQWbaohV68VAuBW5Ulet\nDtWTsVkIXCGUQtVjOH2fOKtg3iRHqP+5NRGGTCEwAakthJJAPU50HQN5K3iK6X5qRUuCPooTVEM7\nhsBX/dyVRkFl7jKK3DqEROrfUcoOa9NUUNFdUQyhoV1GdP+MkKXfZBAIzTD69Qeet7YrhSK1UqRE\nsyzhmUwh0FdLMYS64zKy9usylsWsLmp6JK0YwlSirvCYpKDHghpsZD7PUQidrB0oaoCSh3+//zn8\ndu123PLoxhG3m8zQrRd7KtjBFFvEupsNN2J9X9qxEPg+aR2CTTsBQLtVYin1ytiQ2xk/O6ffdvP9\ngVT5kKUTBtkso3qUaJ/3HpVdE7EmLm5coLdacjKxEl1LYSwXiiGYLCNOf+1aCFpZBdl6DGs72PEV\nqlQu+g7I7VYKVFA5sdlO80BpxYCJIVRLAdZs6cfvXtyZnj+HrZSsLMCkKNejRCt2n2W0F/HMpj4s\nvvJ2fOn+NRM9lY6DHrJiCyH924nCtFWb9uC9X12qS/cBx0LYB4rUSMgeOKsLL+8aApDe+9Vb+rFF\n5a1/4kcrdKbNaKkrXFRKaWokdw3ZgWImTAXsOgQnyyiR2dhGOQx0LIRz9vDOYl3lEL2VEH1D6Qq7\nzrKMSPbTb6q7EmaDyg4fU+RkGbnZQJqmgjGxBsL4+mmMYOIosK4tjU0Uu4HMPUhdRiMpD8JBc0zX\nM9p0j7I8bn/8ZXXcPAvBKAQqCgwCEy+JkwTfX74e5VBgP5auOhGY9grhf9/0GADgH+5cNcEz6Tx0\nAdEoXEbtxhB+sHw9HnxuO+57Zoses6kJRj4wTUE0WYVNZkRxSjmw/8wqdihW0tWK0G5bfx1HH5Dm\n9D/zch+AfOEwGpDLiGeLBcLm66HbHgRp+8lBRTQXKT85CcSYVe7SSrgcCt0EJ2CKhlxRaVWyQHel\npJVcIzapqLzQC0gzazgPUS1OUFH0G26GUDUv7TRATgyBEe+RQsixTKVyLYWsKjkZwQ1EK/FqKUSt\nkejzjAQrqKzmSS1RzXHNMf71f5yKy197hJWt9Jqj5+PCUxfi/WcvtmIIW/fUMLOrbG07EZj2CoGT\nTE03kDAotBD0Co6PtacRyGXQzTKaLDbMfcBCSFscBpjVVcJTG3db6YsA8PlLUmb31VtSK6qdGAIH\nuYz0ql9KFTzOFl8JkVa/En02WQg6yyjHT80tBMrV19uyrJdqKWUgjeIEiQS6Sm4MwWQZ1R0LwSXo\nc/Ptece0UmAybyJVi0BFdyHnMrLqahI1F+h6BcDEEFwhT+/IeuuphhioR7pP8kjg3zV9B2ccNlf3\nOQDsY1x02iL89VuPt44xf2YVn7/kNByzYKZRfirT6o1OpfpEYNorhHOO2h9Amv/708c24lsPrpvY\nCXUQI9EBA0UWQnuCe0CluPJTWR219oEitXqUdu+ipi7X3rZSC8C/ueB4HK76Jz+7ObUa2okhcOii\nLuZPD4VgZG3mngsIzO2ppAHOKNGpsDyo7DZhKYeBVuqhYyE0mGuHCOfIIuyq5NchdFdC1KOEWa5U\nmFZMXRGxNN0wgHVtPD5SUvUCQH7aaSLTrnE8lZO3xXRBwd+eStrDmYrzRkKFxQf4V0vf+2gRMuXV\nUNbnRGPiZzDOoB98PUrwFzc9hmtufWqCZ9Q50GqrSBZrVwOPIbSpEChLg/PMcB78pi4jmJXsVAVV\n6dK1/nr1Vi04j5w/A92VELO6Srq5+lhjCFUVQ6CvLK3aNatTYioFbLK1obri93cVQo6FQM1tAiG0\nQIoS1rAlDFAJA9SjWI91Oy4jnWXkFKyRBcVpIxpOHULMs4GY9RM511YKjYVgFaZR2ils+g2qQ3Br\nC2gFry2ESgkD9bilGEI5x0IAgL+7uD1CZ2P5pFxG5TEuIDqBaV+HQL7PgXrUZMupB3osioQ8L0Yi\ntLuQHyCFwNslRq27jHQMob3TTwpQXj0J3jwe++5KiH6VojlatlMX1XJouYw4rw+9T9gqmuoT6nGi\n+HrszCHXXVMOhZ5/GAjtChpu8A5eYWohRMxCKLsWQqJTQ2leIdJ02TAQlssoyqtU1haCsFJqdR2B\nGqdnmVu5bnqq2w+B3r/huANwL4t/UZZRaiFEmZ7KeeApoXzbmV1lPP6350G2UadJrrBIuSMnGhM/\ng3EGuTWms0ejKJU0l9yuzRtBFkK9wELYF4qWU7PeCFnePpSERf9wZCyEjsQQTIYOFZXxQDGPIZBA\n4eydPHOIr/oBZ8UbCKvHMLcmyGXE93dTXMuKLI7Olc5PWTRBjsuIVypLoxB4lhF3GVkxBCtrzsQQ\nuFJKSfOM4P73952OQ+f16IUhrca1hdBC2ql9v+zPZnWVMbunjNGiFAjEceqia4fqpNNoaQZCiPOF\nEKuEEGuEEFfmfH6ZEGKrEOIx9e9P2WeXCiFWq3+XdnLyrWAwxzKYbjz+RUI+rw6h3RgC+Z+5hWB1\ntppm9zQPdeYCAdK4istBM8CUZCfSToUQVnonCVh6T6tSIoYDUiGr6xCY8qjluIwIoRB61V6LEl3b\nUg6FchmZsYqjqKI4UZZAeiyeFUVuK/p9aJdRTpaRW6lsuYxYDIEnSdDxpGM96aCyusRqKcRh+/Vg\nsEYpwXkxhNEHlccKavzTSJIJ5zECWlAIQogQwI0A3gLgBADvEUKckLPp96WUp6p/X1P7zgNwDYDf\nA3AmgGuEEHNz9h038FJ6AuVUTxcUMUiYqlW2bZsWAvlq63F2dZaeo1kMYeqjHqVm/TvPOBgAcOoh\nc7SizDP3O2EhhIGt2PkqOpHSis1QDKBuWQjpsfKyjCpMAIUBHAvBBH/JZUTWtrFc0n0bij2UhKRR\nCMpPzl1G6n5pC4ErBIfLiLvD7MK0bLpzIlN3JLmcNuwc0i42QiU01NqkrHsrKstINhfyRS6jsaCk\n5iTlxBelAa1ZCGcCWCOlXCulrAO4CcCFLR7/zQDullLukFLuBHA3gPPbm+rosWn3MH6zZltm/NJv\nPIyla7fjY99/LNeCmGyQUuJf7n4WT6v89rzP80BCmq/e213IRzkWQjsFb0uf39HeBPYylq7djo/9\n4DFVeZzgM7c9hZd2D6FSCnDm4fMQBgIPr9uhkxbyGpuMNYZgyO2Y0BN2NlBeDKERJ5oaIdRCWuKJ\njbusubpBUuIo+u7SF6y000RKrNiwW3dQc+ko4kSirNxI6Tx5zAM2dYWTZcTTToNA6Fz/l3cNZ9xh\nJu3U3CNOfx0Ioef96VuezGQZVVlbS81KWilBytST0Ewe2/dr5G1bRRgILFPPxFgtyk6glV/sIgDr\n2fsNaszFO4UQjwshfiSEOGSU+44LHnzOKIMzDze85o+t34VLv/4wfvLoRqzatGdvTadtDNRjfP6e\n1Xjf1x7K/bw47VT97UCWETU2sRQCO1Sz4/aoFeHuwcaI200WfPR7j+Inv9uINVv68es12/CNB9Zh\n/Y4h1jAmvd6H1cNMBUUXnbpQH6NdC+HvLj4JV73lON30hn+PvDDNEqYFMQQSMo1EYsdAeu+Pmj8z\nnR93GQVCZwndt2orixeE+PXq9Dn64r1r9LW6dBRuRhP9DQV1YjPuJSCf7TQUAscfmOb0v7BjwMo+\nKrIQdCqqCioff1B6ba87Zr7OtCLYgeA0MaC3agLkzVb9MxiXUacKLLvLoa5byeNB2tvoVJbRbQC+\nJ6WsCSH+HMC3ALyh1Z2FEJcDuBwADj300A5NyWTG/O3bT8BlZx8OIFUS7/3qQ4a9cQowdhIhnPsb\npLfFaad5WUbtuoyyFgJXNM1cRrSSe0lRPkx2UGB4uBFb/q55vVVru12qYQoJlg+eeyRueewlAO0T\nlb3nTPMMEBsnYASslVqpV9HCVghSohIYzv1GlKARJ+ithDr4WbZcRsIq4swjW6s1eAzBfOeUMim0\nQki3jxOpUyvpOaPfAVE4UE0HzYHO14iM9UOxBU3LzZsTaQshPVdPpYT5M6tYOKcb/bXIUggrNuzS\nrxfNTWkouGU3Ev21ey/GWmNCmNNTxovKaJ4qFsJGAIew9werMQ0p5XYpZU29/RqAM1rdV+3/FSnl\nEinlkvnz57c696boU/GDS9gDNqvLzgSYCm0gqfqU0+9y2B3RsimmXFa3m2WkYwiM797ilGlyXLrP\nfTkxncmM4UZi5crOdTJJiMOGvpt5PYaLphNUxpUw0D0G8iqVDSWIESj1SOrCNC1g4yRT/NRrsXcK\nSzjyADKBFlFuDCFOiFk1fU8LESlTwUmVzgB3GZkYQsIUArHJfuu361j/YpWeyeJYrqUmYdw4lTBA\nI060RUWYy76bOco1xa2CZtQVHJ1KTJnD5tQpJTMWtPKLXQbgaCHE4UKICoBLANzKNxBCHMTevgPA\n0+r1XQDOE0LMVcHk89TYXkHfcAOVMLBWPpxoCrCbpU9WkEIgpkQXduVmknmd5CiJ0YIYLduNIUR6\nhTj57zfHcCPO5JwDwD++62RrOxKmsxjfTScqT8ulALWY0TMEdq49T82sWC6jtA6BLIS6UghcSVn8\n/gUWArci6LungjlSGo04SdNOHQuBYggVrhDYMdJrSKy0U9p/x0BdF5EKVTRH529EpnFPw3IZmaIz\n7jYjcBceucssl1ILAvm4A1OXVBF/2GjBey9PvDpoQSFIKSMAVyAV5E8D+IGU8ikhxLVCiHeozT4q\nhHhKCLECwEcBXKb23QHgOqRKZRmAa9XYXsGO/jrm9torukPn9Vjvi3iAJhMomDejms/LxGUxp6TO\nq0MYa5ZRkUJo5orS3aomoYvucz9fhcVX3p772XAUWw8quTredvJCaztOh0zoRBohpXwCNtEbkJOa\nyaifKahslIREPZJWYNTl97ctBDv4S8cAUgF/xPxePLsp9X1HOssIep40j0AIXccA5JHbwYoVcGzu\nSxlku8ohukqhznLiBHs2TXa6X5kshBaKzewYw4ibAjDdCTulEA7frz3ai/FCSzEEKeUdAO5wxq5m\nr68CcFXBvl8H8PUxzLFtLH1+u25ZRxBC4KRFs/HExt0ApojLaCjfQiDRmkcHzF+PtR+ClMbP266F\nQAJhMlpkX1DB0rxc9FojQVI110aCqLugbSkPNnYiSFgp2Y1hspXKJobAid3iJBXS2kKIEou6Gsi6\njPgKmQeVCcZCCHHsgpm4adl6HQMIg4DFEJiQVkqJUr2j2NBZC6EsBPXbcYPw9FPtrYToKptK5Xqc\naMFsKpXNvU8VgsxUH+cFgvn33YrL6AvvOQ1f/dVanLRodtNtWwG3KCcDr8vEh7XHCas27cH6HUM4\nZG5P5jPeAHtKKISB1ELodSwE46vNdxnlNhRpQyFYlMZsZTSagjftMppk9/txFmikFGT+mxhqxNb1\nd7HUxSWHjVxS04oLohm4hUAClvcNdqt5ASNk02I1k4raiOzipxmOy4iDGudwBTLEgsrUCvKx9TsR\nqaIqtw4hznEZNZLEuGuEsMjt3PtFv9Weagnd5VDTb9SjRCtkzWWkyO3Se5bvMsoDVwKtfF8Hz+3B\nZy58Rcf8/fz+Trw6mKYKYageY/kLqWfq8tcdkfm8i63ueFPwyYqdKoawdU/NGs8T+Dy422CrJ0I7\nHiOuZIothJGPwYn42nVbjQeWrdupXxOj6y6WGrtrsG5dcxfjq//hB88a9/lVmO9c90NgjKDaQgBs\nC0GmlcoUx3h+20DGQuAKwRWGVNFbKQV460kHAjBFnrO6SnjdMWnyR62R6E5yeXUIoRColAwtdsxI\n3CiVlLZ3hSy5klILwRTNNWITQ3Dpr4HU+kgzrezryhO4/POJCOrymM4kMBCml0IYqsf45gPP4/ir\n78Snbn4SAHDAzGpmuy6eOeFIsgef26Z9l5MFu4ZSC4ELLyA/i8gW2EaQAOkPvp0sI75CLqKraGYh\ncFfRZLLK+He9dls/fvnsVlz90yf12PaBOtZsMV3ieOB1bzT7KTMLQbuMGCOothCcYHMaQwi0wPnm\ng+tw7zNbrKY9M7qyTeP//LXpAupXq7cCSBXCP/3hKfq4s7pKKIV2ADkl0rPHeC8DbuVEbNVeixI8\nsXG31TGNg9Nq0yKOLLasy0hqgVoOBVZt2qP6IYx8f7mbaiKSfMqWhTDxGmFasZ3+50Mv4Prbn9bv\nu8pBJoYAAOu2D+jXnM55x0Ad7/3qQzhz8Tz8YC+s/lrFxp35uft5/Q4sayG2LQjedWo0iAqEuZ29\n1JrLCGieoro3sXqzKUz80n3PZSrbdw828I0H1un3B8xyY1LAYU6iwvEHzcK2ftuaaxfc3ULFU/mF\nabYrKU7SVbub+rp7yFg/3AVJxzxodhcA6GI04jIiEOWETjGF1H2WSSAnbgV1KWB1Pzar54PPbddF\no+4KnVx4vZWSXsTVGokVVHbpr4HUwtvWX9cFc4S3n7IQy1+wF1WBpRAm1kKYDJhWCqGP/djfveRg\n/PVbj8/tmMb94FzAkUvm4XWTi17hmYJqahLIRWmlrkupHAZtuozMTnlFQUBzSowop7p0MiBKJA6c\n1YVAIFeIN9g13vvx1+GI+TOsz1dd95aMqf+TD726Y2R/nH+HWkLyvsE0PSGE5UqKqDbAEbJvOmGB\nfs3bNdJ0K04Lx0oYWJYQPSO8CK2RSPQwq0FKbpVC1SHEem7ZeIWh4AaA973qUHx36YvaVdpTDbWr\nK02fldpllGchnHfCAjyzaQ8asX2uPz7rMFx8+iLrurlVMhEuI57F5V1GHQbnjvmTcw63ij6KwBUC\npXdOgvoQC7RScn8weS4jO8BsPyy82flo0OiAhVDkdppoDNQiHHlAL449cCZe3m27CmdWS5byyuuM\nVSkFGVKy7kpo+efHAkrZlMoaoGwgytCR2kKA5UqiOgQXrrvu3GPTWEBeIRpQ7Bbj8QI6Fz1+vMeB\ncNJOG7HM3C9q40nHvP6ik3DgrC59jN5KyarCjpJEWyou/TVgYoSNOLEEvhACM7vK1jWWJjqG4IPK\n44ehRoxKGGDdDRfguANnNd8B9oqXgokT3eiao6FWRKUg5Y7JE8J2to/ZlzcqT7tRtUdd0VoMYeRj\ncFcVF0rfX/YifvXs1lHPqVMYrMforZQwp6diuVMA4MDZXdb17o2YgYtKGEBKQ2RHq3DK0NEWAuws\noyjJz8F3v35aoeYVoo0EYw1IFlQ2riw6D9VCmBhCkqFoqEWxIsEz41xQdpdDU08RScSx1PN26a8B\nI+TrUdJUyFtB54n4ftl1HjYJahKml0KoR4X54Rz8oeB0zsQZxFMLJxqU6kcV1g1OOZ2jELiFYPLX\noTlwxhJD6KmETtDabNNqYVq6n9n2kz9+An/89YdHPadOob8Wobdayvxu7vn469BVDrWr6+LT9xon\nowVOac2pGMIg7THAXTOZOoQcYejWoZCLqKYriUf+Hm987+kAjEJIEhMX4G4kXjCXMqamvyMeVKbm\n9LUosYLdgFFU3eUQAcuWqscJGkmi+ZRc+ms+t3qcNE0lHW0dQqfBYwhnHbnfXj+/i8kj+TqAoUZs\n1RgUwa6+5C6jyWchUFcyKmDJ4yrKG+Pj5HvmzJmjAa3CXIXArZVmBW883jCZ0k4H6zF6q6G1SHjb\nyQfhyPkzUAqFJkg8+oCZEzK/Cisso34IgOm0xV0zPIZA/EIu3FtP1e/0HTfLADtwdlWdj44nTe8F\nCjRL1v5SCMv/HymaCwD4o1cdBiBVCG4NH+1Dge9yaFb9ZJGUGCV2UmQhNJHxFpfRBEjDydADgWNy\nzWaMSFd7zYX5V/54Cc1MTjgAACAASURBVD507pGY01O2HgByGUwGkikCKYQZrI8vga+OCHn9ZuNE\nsWSK9tJOaZXcXQktF5vlMmoSJ+a++MmUZUQWwq9XG7fVZ1XT9HIQ6EVCEW3IeIMLU87XQy0ltYXg\nUFpwC4GvQqXTquiT5x+HS886DG8/5SB9npHQpbOMWFDZaZCTSOCnivH1mU17LKVGNBeAodqoNeLM\n6lxbCMpy08dQVkYpDKwuahJGSYXsfM2eZTvtdGJdRpMBk2s2Y0TfUGSXghfg8P178cnzj7N8mwB0\ns5PJRMCmhbGbZmcJ4/zg7j/etUqPBcHYYwg95ZIlMEbDZRQl0mqK4l7DRICqXnsrJSsbjRhxw0Bg\np6oSb+V3NR6wLIQEtssosdtP8vaRRCcB2ELH1cVzeir4zIWv0FYxX7H+55/+XmY+9DukFX1Ka0Lk\ndnQOiQdU2uqK9buMUosS3doTMMI4tRBsYawtBEXXQu/pGS0Fdp9lyZWl+tuIk6ZxActlNMFZRpMB\nk2s2Y0CcSKzavCdDbz0SyiylD+A+9/YFVZLIDGFe33AD/bVI/5ibgW9H06MHwnDjs3NaMYT8OVF2\nSjvpkLQK666EdlC5wFWVfwyZyR2fiGyjgVqE7f01RHGiLcLeaik3blQKBbaTQhjF76qToO9912DD\nchntHGzgO0tfMPn+jNKC3FzaQmBCp5kSfpdqDwoAxyzIusloxU5FVFR5Hoa8H4IpHBusx/r8tShR\nQeX0PVkK67YPZDJsSEHRcUhRkcVMPE2c3I6OQdc9lGN5uOBWgbcQppFC2DVYx9Y9NezX2zzVlFBl\nBTOAUQhjEVR/9cMVOOZvfqbfJ4nEyX/7c7zimrtw3Kfv1IHrIjz64k4c9+k7cf+qLQCM0KUfTjN+\norznPZam2Xkr+mCgFuHmRzeYLleU/lcNrVWpfd5mFkLCGC7TbYcjo/huXfFS84mNEStf6sOJ19yF\nM67/Bf7yByuw5PpfAEiTCE45eA4AO8uGr5bnjeJ31UnQHN72xd9g52A9I7R48JYWuLc/kfZoIOXx\nysWGc+lIp46i6Hx8f47unMI0apDD6xDmqwK+4w+aZTKZYuP/B0z/4xe2D2b6ZGjGVGnPa1AphHIQ\nKAvBLOJ4wR6Q9rJo6jJi33cnuKdGC1+YNk7orZbwz394Cs4+av+W95nZXbaK2UgRjMVC+Mmjaf8f\nMmFdn+zmvtqI9RGPqErK+1dtxbnHHqB/3PRQ5TOYmv3zXDdxkm1DOBKuv/1pfO/hF7FoTg/OPHye\nVpTdZZXpFCcIg3BUfRaiRGJml8odpwe2bhTCvU9vxjtOWZi7b6fw5Eu79evbmAJ63THz8a4zDsYr\nFs3GW15xoB4nYdJdDnHywZ1htxwtuLzoG46wSAndUw6ejWc27bHaTwohsHB2lxbaNP+/u/hkXPLK\nXeitlpoS8lnnzlkxk5XHM4qiOFF0FlBjEovmpB3Jrr3wRKxYnxIIksuIrLGRWowu3q8Xv169DQtV\n5XRFK4RIX1saQ8gGlYeYhd1MyHOXzURkGXWiZ0YnMW0UQlc5xDuZudsK5nSXdTEaYHKxO8G104gl\nKiWR4U3vGx65pzA9xLr9pbYQ7MrMIiWQpxDS3rJ2s/ORQBW7O5S7hK5hVpdRCF2KfdIlKCtCFEtN\nDEfFRPzB3Rumc9Fq7KDZ3QgDYbWuBIy1sGTx3AnJUQdsN4aUhpvnhIWz8PLuYdNLQP1uTjp4Np7c\n2AfACNx5vRW8/rgDRn/unNtFApRnFFFcgAeV6Vma0VWyYwhxgpJKkBipZeRBc1JFcLBqdUnHIJdR\nOUz7RXOLmZ4d/vw2yzLiWYkTsVifbBbC5JrNXsbcnrLFbFnvQAyBQO4QN55AQcoi6OIibTIrhRCS\nhZBY4+5886aue/GK1iqVaRVIFMgU05ihFYJZlZHQaXbURpygqvvopmN7WyEUpfgVuRXIpdFKKvN4\ngc+tFiVa6JomMNntNqq+1WMNkvL9SRGQYuRFaGnWj7BSUelZqoSBnXaaSK1oR0q5/D3Fb/Sao+er\nbe3Vf5plFGhqEc5syl2+TS2EvUxW6KITTZQ6iWljIbSD7kpoCSXd1SuRVtZCOxhuxJjVVc5YG8NN\nOi2t2zYIIBsr0DGE3GI0sNc5LiNpevG2kkDV5WR0ELXATGYhAOl9qoRpHKZZDCFOJOujqywE5jLa\nG/nY7oq0uxyOWHBGAc2eFoodxwvcQqizbJySSondpNhaSe5xivSRXDKjPffdf/k6rN6yJ/NZnECl\nuHIuI6lp5cthkEk7JUWzYFaWeJJwxmHzsPLaN+umUORaoRgC9Wquqd8oZzblxXXNAsU8mWAiXEYT\nZXkWoaWnUAhxvhBilRBijRDiyhG2e6cQQgohlqj3i4UQQ0KIx9S/L3dq4p1AtRTqHxRgr+bHaiWQ\nEHUthGaZRl9/4HkArMlIYscQdO1BQaFXroVAhUM5WUZPbtyN57b2W2OGez6dO1kKM6pl65qoK1d6\n3uL7RW4FnXaaZyHsBYXgKq1GnGD2COmk1FGsu6CX9d4AX+E24oS1iUxf/NUPV6TbiWxGUTjGjm3c\nQjh0vx688XhDjEdyjBYHpUCwgjCp4kxp3Iq7jHiDnEPmZZtXcfAOgW4MoRwKVMuhXmBxlxFPG28m\n5CtNguj7Gpr+0oUQIYAbAbwJwAYAy4QQt0opVzrbzQTwFwAecg7xnJTy1A7Nt6MoyjICoMzg9o89\nzNr9cdRaTD2lFTMJfhIA9FsvTjvNsxCgK0ndz9/2xd8AANbdcIEeo1XTUCPGkxt349M/TZudz3As\nhERK/XCPZHlQ3EOnnaqNSWkCe6d6ue5kj6UBzuIvmYoBJ5LKhMsozmXk3i8SZpytdKwWwkjCNNDC\nXymEMECPul9DjUjRWdhKqhYlVsFctRTijccdgHue2dJ0LjrttEEKKEB3OdCJCTELKo/GZcRX6BOR\nZTTZ0Mov/UwAa6SUa6WUdQA3AbgwZ7vrAPw9gMnVXWYEcCZJwBYYY62mJUVDD8wph8yxxotAWRW0\nOqUVPT0QeS6jInI7PaY4cFrJMpJS4qu/Tq2UPcMNrTAAHlQ27izqfsXncOeTm/DTxzbq90VWDrcQ\nFucwiXYajZx7P3eEdFJSFmMVrGOBK5S1IHYWGiQMedbMWAXcSPvTJ7Q4KIcCvcq11l+LUYtM34Ns\n2mlW7Fz26sUjzoWsjaG6qbHoKoc6VpcwC+FdZxg34Gh89F4ftKYQFgFYz95vUGMaQojTARwipbw9\nZ//DhRCPCiF+KYR4TftT7TyqpZRJkgQcp1eIx1g0RUKPFMBHXn8UgOYuI1pl6TzsxFEIVJhW4CbK\nsxAoJa8VLqPNfcYH/cL2QeszCq7qhi2JoR/gp/3gdx/BX9z0mH5vmrM7CoHFEEZTtfzVX63FsnU7\nMFiP8JnbntLZUM2QR8uwIKejHoG8CRNJteEKZU3cFuUrhCJq547PS9iKKQyEXsQM1lILgdwxldD8\nbrjlAJhkhFbSxcuh0DGEUhigqxTq54lqbQDgqANm4g9VxmErZJdEsjcRMYTJhjE7R4UQAYDPAbgs\n5+OXARwqpdwuhDgDwC1CiBOllH3OMS4HcDkAHHrooTmHGR/w7IdKKbCCUY0x0lfQSn7dtrQ72yKV\nPveLp7fgijccjX+//zksXbsdX37fGfpHW4ti3a7RrTcwhWlQ4+ZcI1kIkpGPhU7a6advMa0ih+ox\nuish7mXm+38//rJ1LH6/6BrdGAJVydK5hTC54l2awz5rIbTiMvqP3zyP6/57ZWb8Gw+sw21XnIOT\nVK3Apt3DeNXf3YN/+sNTdOVtPUpw1U+eyOyb1+OAQPGFdvifOgU3KEoy3lUUJAx7rDTK8VQI6d9/\nuDOlRymFgaaZuNK5zzyG4DbIIaXVylQDIfDgc9vV+QS6KyGe3dyPddsGdDW+Pm5oakiaYb8ZRNjn\nFUIrFsJGAIew9werMcJMAK8AcL8QYh2AVwG4VQixREpZk1JuBwAp5SMAngNwjHsCKeVXpJRLpJRL\n5s+f396VtAHKeiG/Po8hcMHWDkjAURbIEfN7rQY3f3/nM/jls1uxfqdZhb+8y3jbjIWQvq+E9uq6\nqG2mG9xNJO+HkKadPvz8Dlz33ytx07IX9XbUVpTmc9YRNhUvcT8BdlCZxmgKPEBNq7nIdRnpSlKm\nEFqQuXnKgHDnU0Z5ff6eZwGYgCuQ9ksGgP16KzhHrUZff+x8HHVAceXuhacuwvtedSg++sajm09u\nnODG2mkV67pYaLur3nq8HhvPlEZXeJYDgVnd+etLoxDiTAvN6y96BS579WK89pjmzz0X7qVA6Nab\n5/7T/VixYbelaGgR0kqG2CfOOxYXnroQJyxsrYdKp/H5S07Fv733tAk5t4tWFMIyAEcLIQ4XQlQA\nXALgVvpQSrlbSrm/lHKxlHIxgKUA3iGlXC6EmK+C0hBCHAHgaABrO34VbcJd8TaSBHN60lXhzsGR\nC8iagX6QJDwrYYA3Hb8g4zIqYg/Ntr+0V+JcCYxUqcwthEDFEN79/36L//jN82jEEqeq2AYpwIFa\nhNndZXzv8lfh7apy+F//x6n40LlH6joCXq/hWgjcDbRLVYFTELnqWghtuow+ef5xmTEeoKaKao6X\nVG7+Vy9dgu/+6e9h3Q0X4BvvP3PEVWFXOcT1F52kV5ATAXd+9P6YBTPxikVGgNHqmFNsnD6KqmSO\nX3zsdfjcu08ZcRt3RR8GqUuSfk8cbh0Cd2UdMKsLf/uOE1tKO37byQfp16UgsN6nczLHpd9YKxbC\nSQfPxucvOW3EjLPxxIWnLsLbTh7fKv1W0fRbkFJGAK4AcBeApwH8QEr5lBDiWiHEO5rs/loAjwsh\nHgPwIwAflFJOmobFtGIlYRLFEvPVw7+zCedQM9APsq58qUKITN0DfU6wUkndGILjf+fy01IOzjwk\nVHaKZju1P99fXe+AEs4DtVhn19DjRc8ZWVTab5uYAOE//zx1HQyy66MiPFKOI3EZ8WsYbsT4+A9W\nYEtffn5CTyXEHR99DV57zHzcesXZWDSnG5tZ/n1eVtAexZUzZ4Ie+nbh+rW53OQpk3n59vPbVGRH\nHTADF58+ctW/e76yjhdk771Vh5Dk92loBTwBoBSKTJtSfmp6fiYyZXgqoqW7JaW8A8AdztjVBdue\ny17/GMCPxzC/cUXGQogTzFU8Q2N1GdGquBGZIFpPJdRuFALPfLF6HVAmj1Op7FYwAyOnnSaSevEq\nDv2GHRuZP9O+3oFapM3sYw+cCawAFsxKM58o1kEKIZGm6pQsKs5PRMd0XUb0vqgd590rN+PHv9uA\nWhTj31SHLo5Fc7pxwsJZ+PafnAkA2Nw3jNtWvITzTliAt5+yEAsVjw6Q9riY3V3WSr86gVXH7SAb\nQzDv7ZqDrJAdT5+4Oy/T4yB7Tru5jWmQM1pw909JWST8mbJcRk5Bp0dr2KfvlokhGJZTarCze6jR\ncvYKQeYI6Uac6NV9Vzm0BCadk5DHYJo4P+wkJ4ZgvVbynkjapDQNciiGwKEtBFII9Uhni3zwdUfi\npstfhVepeIIpWMtaCDQ3bgGRoo10llFoXUOcSH1dfFp0Hld5lkOBUw6Zg98/YYE1Tg//d5a+AMCO\nBRF5IRXXTTb++dGCC3nuZtnbOfTCuY3kBsrTQUIRK27YOYREjsxhNBJ4J0O69v2ZFWS5jNRzNZEp\nw1MRU/vpGCOMhaCCn3GCGYr3/lM3P4nTr7t7VMcbZqtvHUNgQbTuctZlVNRrmAtNgKedkssoP7NI\n0yEHtj81UORjbvCWK4R6lOChtTu0UgwDoZUBwCktsllGQJo1NMCEOFe0gHHl8E5ubrAcMH5fThXe\niBM0Yok3jkDS9vDzOxCppjd8ToBJ/53qCqHIZbS3UyYzFkKQVewccSI1E3C7QjrPIuId4PIshIno\ncTCVMbWfjjGChMParWmGTSOWGb/kaNDP3Ew6hhAZoVdiHZ4ItaKgMrMw+Fzd7CN+rvTz9C8JCCkN\nuV1epfJBqhBu2Qs7cclXfot6nGDHQH5AnVxGQ4w/hgulgXqEPcNZskAdVC7ZQeUoMe40fg20/RMb\nDV01WTC9Tb6ffpUDT3jkhZ0YbsRMIUwtl5GLIpfR3l4Iu6cjq47/vD5wzuG5+5ZaCCDngStz/bth\nK5zQCiobSg2P1rFPR1zogfrEjx5Hb7WERpJkeucON+IR6Q04eNzB+MkTfR5atfPgcZGFQPvTinum\nslxMHUJ+3IBe82YhiTSVnrHiFCIBub8qzLqd1RzUI9uKIbguNmK5NNcfo2/I3ANy0+h+ChVbIcSq\nsE04isrQkJuxAd1beuTvom8owmOKfx8ArvrJE3ho7XYcOq8HQkw+dsnRImjRZXTiOKdQuivvub12\nsP76i16B973qsNx9O2EhULEbJ4sMciyEdgPY+yq8haDw4f/8HaTMrkA/f8/qwv3/86EX8Lmfr9IC\nj1sIiZRIEolbV7yU6SHLrYSPfO9RnHj1nXj/Nx7GMy+bej1SGj9T3a+IaTSfusLMiV5q5kmkAliI\n1JcbJ9LqDzyaVLs0tZC18XSKjB7fsAtf/uVz+j0JdlI+pFiv/e+V2LhrCHGSaB59bh3l0Xs8pwr2\nenKyRrh8+bNvL8cvnra5cW557CV888F1uonMdEG5wGX0zHXn4+YPnz2u55ZOPhslY2wfSLO9RiIr\nbFshsGOWS+kxeBo3vwfU2+KEgyamtmCqYp9WCAfPybItulkJ/37/c5YbhCClxKdufhJfuHcNnlaC\nnP84o1jqYi8ykQO2aucYqMe4b9VWXHPrU3qMVtHLVQe1qhtULnQZ2cE0qbKM0krltIUmz8I5aHaX\n5lkifOjcozLXSygHgeEyktIS0J/44ePWtjVnpc9zwi/+0gOIVHVpKITlAnNpGQBg1aaUejmPIfNT\nF5xgttu8J/M5kHYbm0gKik7hFsYRVSnlE7N1lcNxz66plkIcMLOKI/bvxfEHzcJ8ZWk+uzlV3C43\n1QGMIqRtlxH7/ZBy+LuLT9JjfFHx1pMOwrobLtAZch6tYZ9WCLN7yrjtinOssXIQYMlhc/E3FxyP\nz/5B+mNzs10AWwiThVB33D/0/iNvSAUsrWCKaBqK6CgAE7QjoUafl0ORay0Y5cOyjIL0oamEAq8+\ncj+su+EC9FRK+On/MqvJdTdcYDVad1EKhWnSk6Sr1Js//OrM9QMsqByRy8j83Db31XRhWxDY15vH\nOURxi1fkuEI+cM7hFlsrANx2xTlYd8MFutEKALx7yeg66k1GcGVpuYz2suETBgIPf+r3ce9fnYuf\n/cVr9Fyuu/BEnLhwlq4iJvzfPzCCu12qc9tCSF9feOoivP7YtMr59ceOviuch419OoYAQGfUEMqh\nwI8+lAo4Yuzsr0VY4OzH/dv0uuGwpVKTEPohu93QRoK7mqXsTp19JM05n9m0J+VyCQSLIagdJS9M\nS5VHGkewJchfv/W4XBZKFzwwnloeKAzE8/oOAJlYjOFYElZcZShHAQ/UIlRLQcurS3Kx8XPO6524\niuNOgbtbLJfRJAme/tFZi/FHZy3OjPNiwZ4mcaAi2DGE7O9gqmeQTQbs83fQjRlwgUNkXdv21PCR\n7z1qVc7yVSwJvIaTMUTb0GpGtx1UPv0ilBhNdW8lxAfOOTyjTNxsIerVTCttnWUEqemvAyV4ebtB\nwuWvPRJ/UpAVwkGtGwFTh9BToBBqTk8IN8OHuPFDJ/uKB+dJUfD6iCJcd+GJ+jUpBO5SoLGpBPd3\nwn+ftoUwORRCEQ6abdyU7Xags7OMsqJrkt+CKYF9XiG4P06ehUIrmZuWrcdtK17CP961Sn/GTXde\nc0CIWT582anijJQLh/D5S07F6xi5VzkMkMi0jedQI0ZXOci4m0h+vvnE1HYh66R/2E7PlNJQA4dB\nSn/Nm7WPFqnLyNQRBEJgRk6gt1IKUCNXWk4NwJtPXKBjCNVyaAWSeXCexgdqcVNB8kdnLcatV5yN\n95+9WHP6LJzTjdMOTWMkrWaLTSaccNAs/MnZRlFzC6HCvsTJrhA4iWAe11QrqDatzG7rsB4M+7xC\ncN0dfOVBn92sCmp++MgGvTJv5FkITsc1ek/mLT20V/zX76xzXnjqIksRVUoB4kSiEacpo93lMBOQ\nNr2W7VTOLXtqCIQpOKNsp7QfQrodCfJ2kDY2520L810Aab9bO7ZimfylEImKIaR9mY2baA9TCBSo\nH6hFLdWInHzwHFzz9hOtbCJSphPZ+axdBIHA1W83QfNygYUwWVxGrcB107YKT0Mx/tjn77AQwvJH\ncpM8L8VxKIcqu+EwmwJQAp0sBDuGsHTtjkwcgT/QpBCeVRkz83qrORaCHZ+gc+0YqGNOT0VbI3GS\n9jM2WUZSVy63g0rJzjIKApE2Unce1j3DEb754Dps769h9eZ+lAKbjGy4Eat5BaiWA+veEREdYO73\nQD1q29VA97ZrihelATbtQ3kCC9PGgrznqhUUFRUeqNxRvZ7IbszY5xUCAKy89s14g6JEKLMnK29F\n+rYvpC0lLZoKIrJzg8rOypi7idy4Mg/oVsJUIWzancYsTlw4SwtwXeXr0EHw8XJo8u3P/Ow9eHHH\noOYyiqWElO1THZQCk2WUMNdXUUDvqZf68Py2ARx30EwrBkD9dUMBZSGY+8kpK7RCqMVNYwhF0Aph\nCrqMXIQFQeWpVF9BFPOjRZGFcPXbTsDn3n1KJrPJY/TwCgGpVZBHzpXnClm7bQA7B+r4xI9M8xWT\nZWRTWVOPZtdCyEPGQpCwFAp9vlkFtkkBkJAja6KRJCgFQYZaQIi0H0IiaWVfOJURUQoD3fSHty3k\nq3qO57b246Hnt2M/J8On1ogRqblWS7aFwKnHH31xFx55YSce37Cr7RWgUQhT9+ee16SnMkWrcNul\nIC9SCN2VEBeffvCUUoqTFVP3CekwqFsSUUQAxSbon3/3ETy+wfDsFMUQKMuGVs95CuGw/dJCKzud\nUCBOEsv3Tivxr/76eWzvr+msHBJyl3/nEazbNsAsBPs8YZC6FZJEZtoNjgaNOMGjL+5CkqSWRt5x\nFswywv8zt63EcCPBwjlpgdDSq96IVy6ei+EoQZKk96RaCm2FMNDAIfNSN8Bf/XAF3vnvD6bB8Ey3\nh9ZwsmqtOWuK9ULguOjUtIGKlZ7bpD/3ZMMZqmFPu4VpRK7oq4/HD97ppvChc4/Ea4+Zj1MPNlW7\nYSBw5/9+DR7fsFungn72jqfx8u4h9FZClEsBdg02cNdTm/CeMw+1+xkkErsV9TIJItdvf9mrF+Pj\n5x2jz0VIYwh2tzW+ot/WX9duG+4X3zlY17n9wrERoliiq5y6jNZuG8DxbXLdnHn4PKzZ0q+VlUtD\n8MtPnIu5vRX0D0d47T/chyiRmNtTxpVvSVs7Hji7C3N6Krh75WYcd+BMzJ9ZRRgIDNaNhbFzsI5L\nXnkonnxpNx5+3vRTOqeFRux5+MgbjsYbj1+A03K6eU0V6JRlphP7a1NLIXznA2di1xg6EZbCAD//\ny9daXeE8OgtvIShUSyFOP3RuRmgfd+AsvHvJIbj49IPxh0sOwdtOXoj1O4Yw1Ih1C7/7V23F6s17\nMuR0OwfrCAOBWSr/3fXbL5zTpS0SHiyshIFyOWUtBCBdpUeOyygdl7pnrWuM1KIEgRD6geRkdqPB\nsQtmAoDuFeHer8P268WsrjIWzunWLTjfcNwCizOJ+i4/s2lPmnZaCvS1DjdiDNZj7DejkqG6bneF\n31VOv9up7FLgZIUEtx3rZEdPpWTRprSDYxbMtHogeHQWLSkEIcT5QohVQog1QogrR9junUIIKYRY\nwsauUvutEkK8uROTnkg8sGYbgHSlNqfbrFT6hiNtzhOdxM7BBuZ0l7Ugci1l7m5xg4WxlLrQrRwG\n1ue1KGZ9is1BSVGUclxGtShuO5jHQcfY3l+35p3n36VUWjfNkDceKgUirVlQQXoqsJvVXc4Ekaey\nQB8r9puR/tZ4TcJUUwgekx9NXUZCiBDAjQDeBGADgGVCiFullCud7WYC+AsAD7GxEwBcAuBEAAsB\n/EIIcYyUcsr+ki84+SB88d41AOxsCSml1e4yiiV2DdZHFMLc3WJlGam0U24hcGE4WI91lhHPbKIm\n5mEQZFxGtSjBcQfOHPX1uqDUv1+t3grAWD1Lr3pjRkBRMD1TDR7YitCyEOqK9ygnI+gs1qxnX0NP\npZTha7rklYfi2799YYJm5DEd0YqFcCaANVLKtVLKOoCbAFyYs911AP4eAO+MfiGAm6SUNSnl8wDW\nqONNWbz1pIP0a+7CoIIvIdL88DhJsHOggTk93N9pC+kwyLcQqqwwDchy+A/UItOo3mFYTXvWikwH\nk3qUtMRV1AzEsElV2+QymtdbKXQHuOm7FE8A0uvmFgKxeXaVg0xW0PyZ3lXAccI49zzw2PfQioRY\nBGA9e79BjWkIIU4HcIiU8vbR7jvVwH32sxg3TqwKvkJF50wxhLnMQjjq/7d37kFSVWcC/33dPTPA\ngMDIQxDkjRFfqEhQo/GFIrq6DysFuqvksZQuLG7Y1EajcWvdsrJxtyizKROkEkxqS0Jcza6UYUMl\n2WS3UpsYxo0vUBAQFRYUMIIgzKP72z/uud2n73RP93Tf6e7B71c1Neeec2/z9aHnfH3O9xqTnxI4\nkbdD6BmY1uEZlX2OdaTpcgrBl6ErnaE7HRwZHY24gXalMxXXsvWJ5pDp7SVDr6toQNmNnlLNehm5\ne1f9dDsQGMvHDLPUxYZRS6r2MhKRBLAKWFLFaywFlgKcccYZ1YrUr/hHGX7EpWoucjeZEN469BGv\n7/8wzyNi+phhnD5iMHs/OA7kG5mjNoSMKkeOdzFsUKrH2fmxzpy94sbzxvPVZ4M6CoENIcOQVIp9\nh4/nPZPWwgnB+kpUOSV7ec3QS6pnAsF8Rdjs4hD+e/uBbP+gpiRzp7TxrdsvZOwpLXnuwIZh9A/l\nrBB7gYne9QTXYYLL/QAAD9ZJREFUFzIMOAf4pYjsBuYBG5xhudSzAKjqGlWdo6pzRo8eHR1uKPxj\nDH+hSzv//qQIqYTwK2d8/p+dh/Kev+Gc07LtRJEjo//74Dj7Dp9g2/4Ps5WofI525Iq9+MdJnd05\no3LU1zudycSS76YpFd0hFH/NQoVxIGpDSLiSnmlWPvVitj+c54XnjuOiSW3MHFu9/cMwjN4pZ4ew\nGZghIlMIFvNFwG3hoKoeBrIO4iLyS+BLqtouIseBdSKyisCoPAP4bXzi155iR0ad3RnSLtDKr+M6\namj+gu4vqMV2CJt3B1XSfr3rUI9qZgCP/CSXddW3C3Q7u0OhSOWgmE31CiEaiNbbpiPcHUW9jERy\n9Z2TiVx+pINHc95HgyvMW/Rx49lll/H2+x/VWwzjJKGkQlDVbhFZDmwCksBaVd0iIg8B7aq6oZdn\nt4jIU8BWoBtYNpA9jCBQCPcvPIsPT3TlpfTtTGfySlWGrP7Ti/KeL5ahslgmz7YSrqKppPC9z17M\nkic209GV5sjxLs4aN6xHVbaMq11QLdE6DL1FPP/l1dOZPKqVS6f1DChLZRVCosdx0NIrpvKJ08xg\nWg7nTxxR8EuDYVRCWTYEVd0IbIz0PVjk3isj1w8DD1coX0Py51dM7dHXGSZrczaEkGhNVz//jH9f\nseIthY6MfJqSCS6fMZqEwKFjnRw61sGprc157qhAVllVSzR5RG+vOeaUQXy+SNGdJpfQLpWQPMP7\nvKltfGXhWQWfMQyjf7FI5ZjYfehYYFQWyfsmHs0CWmyHcEoRo+nIMsL0kwlh1NAW3n7/I050ZRgx\npDkbuBaSVo3FqBy1GVSqZELDcjIhsQTMGYZRPaYQYuLRn71BOh2ciUfzEvk05+Ww93cIuUXxxys+\nlW2PLLJYfvWmWXxz8QXe8ykOHu0AAiNutCZz2pXRDHnm7kvKeVs9CBPFhVSqEBKSUwgXTcqlLfbj\nPAzDqC2W3C5G0uriEPKCzKIlOovsEAbn/ivOHj+cmWOHsv3do3k7hNf/fkHw72S0hytnUzKRjT1o\nTiWyye+ysnmBbgDnT6js3FmcF1WocCqtqxAms0slhOGDm9j60PWc6Mo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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WGMhZH9SwPgZ", + "colab_type": "text" + }, + "source": [ + "We can also plot one of the output-signals that we want the model to learn how to predict given all those 20 input signals." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "EBrOTA2VwPgZ", + "colab_type": "code", + "outputId": "b73e26e1-114c-45f7-bbc5-5e69ba64d432", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 287 + } + }, + "source": [ + "seq = y_batch[batch, :, signal]\n", + "plt.plot(seq)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 55 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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PwKcB/NF4zyGl/IKUcrOUcnN/f/9Eb8lhCpA3c9fBaAgXrVUOYfvhIQxXY20Y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\nwNFjxyspNrKajGIzVz2TUd33DKuheANyX3eRF+68fFKzmRCiPVFyuwz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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OC0T5Z39wPgb", + "colab_type": "text" + }, + "source": [ + "### Validation Set\n", + "\n", + "The neural network trains quickly so we can easily run many training epochs. But then there is a risk of overfitting the model to the training-set so it does not generalize well to unseen data. We will therefore monitor the model's performance on the test-set after each epoch and only save the model's weights if the performance is improved on the test-set.\n", + "\n", + "The batch-generator randomly selects a batch of short sequences from the training-data and uses that during training. But for the validation-data we will instead run through the entire sequence from the test-set and measure the prediction accuracy on that entire sequence." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "pMbKCkTvwPgc", + "colab_type": "code", + "colab": {} + }, + "source": [ + "validation_data = (np.expand_dims(x_test_scaled, axis=0),\n", + " np.expand_dims(y_test_scaled, axis=0))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YpSU3TQ0wPgd", + "colab_type": "text" + }, + "source": [ + "## Create the Recurrent Neural Network\n", + "\n", + "We are now ready to create the Recurrent Neural Network (RNN). We will use the Keras API for this because of its simplicity. See Tutorial #03-C for a tutorial on Keras and Tutorial #20 for more information on Recurrent Neural Networks." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "x_ITVhJBwPge", + "colab_type": "code", + "colab": {} + }, + "source": [ + "model = Sequential()" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FznFRMVwwPgg", + "colab_type": "text" + }, + "source": [ + "We can now add a Gated Recurrent Unit (GRU) to the network. This will have 512 outputs for each time-step in the sequence.\n", + "\n", + "Note that because this is the first layer in the model, Keras needs to know the shape of its input, which is a batch of sequences of arbitrary length (indicated by `None`), where each observation has a number of input-signals (`num_x_signals`)." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "MO0GMLJfwPgg", + "colab_type": "code", + "outputId": "68d03930-f2ff-4a38-8ea4-ca867fb73e64", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 131 + } + }, + "source": [ + "model.add(GRU(units=512,\n", + " return_sequences=True,\n", + " input_shape=(None, num_x_signals,)))" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "If using Keras pass *_constraint arguments to layers.\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "H9x9wsPPwPgi", + "colab_type": "text" + }, + "source": [ + "The GRU outputs a batch of sequences of 512 values. We want to predict 3 output-signals, so we add a fully-connected (or dense) layer which maps 512 values down to only 3 values.\n", + "\n", + "The output-signals in the data-set have been limited to be between 0 and 1 using a scaler-object. So we also limit the output of the neural network using the Sigmoid activation function, which squashes the output to be between 0 and 1." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "1MjMPjL4wPgi", + "colab_type": "code", + "colab": {} + }, + "source": [ + "model.add(Dense(num_y_signals, activation='sigmoid'))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XsVeIPNcwPgl", + "colab_type": "text" + }, + "source": [ + "A problem with using the Sigmoid activation function, is that we can now only output values in the same range as the training-data.\n", + "\n", + "For example, if the training-data only has temperatures between -20 and +30 degrees, then the scaler-object will map -20 to 0 and +30 to 1. So if we limit the output of the neural network to be between 0 and 1 using the Sigmoid function, this can only be mapped back to temperature values between -20 and +30.\n", + "\n", + "We can use a linear activation function on the output instead. This allows for the output to take on arbitrary values. It might work with the standard initialization for a simple network architecture, but for more complicated network architectures e.g. with more layers, it might be necessary to initialize the weights with smaller values to avoid `NaN` values during training. You may need to experiment with this to get it working." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "PIAzoMoewPgm", + "colab_type": "code", + "colab": {} + }, + "source": [ + "if False:\n", + " from tensorflow.keras.initializers import RandomUniform\n", + "\n", + " # Maybe use lower init-ranges.\n", + " init = RandomUniform(minval=-0.05, maxval=0.05)\n", + "\n", + " model.add(Dense(num_y_signals,\n", + " activation='linear',\n", + " kernel_initializer=init))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2agyuXRywPgq", + "colab_type": "text" + }, + "source": [ + "### Loss Function\n", + "\n", + "We will use Mean Squared Error (MSE) as the loss-function that will be minimized. This measures how closely the model's output matches the true output signals.\n", + "\n", + "However, at the beginning of a sequence, the model has only seen input-signals for a few time-steps, so its generated output may be very inaccurate. Using the loss-value for the early time-steps may cause the model to distort its later output. We therefore give the model a \"warmup-period\" of 50 time-steps where we don't use its accuracy in the loss-function, in hope of improving the accuracy for later time-steps." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "RGS8_v3nwPgq", + "colab_type": "code", + "colab": {} + }, + "source": [ + "warmup_steps = 50" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "EFqGNn3gwPgs", + "colab_type": "code", + "colab": {} + }, + "source": [ + "def loss_mse_warmup(y_true, y_pred):\n", + " \"\"\"\n", + " Calculate the Mean Squared Error between y_true and y_pred,\n", + " but ignore the beginning \"warmup\" part of the sequences.\n", + " \n", + " y_true is the desired output.\n", + " y_pred is the model's output.\n", + " \"\"\"\n", + "\n", + " # The shape of both input tensors are:\n", + " # [batch_size, sequence_length, num_y_signals].\n", + "\n", + " # Ignore the \"warmup\" parts of the sequences\n", + " # by taking slices of the tensors.\n", + " y_true_slice = y_true[:, warmup_steps:, :]\n", + " y_pred_slice = y_pred[:, warmup_steps:, :]\n", + "\n", + " # These sliced tensors both have this shape:\n", + " # [batch_size, sequence_length - warmup_steps, num_y_signals]\n", + "\n", + " # Calculate the MSE loss for each value in these tensors.\n", + " # This outputs a 3-rank tensor of the same shape.\n", + " loss = tf.losses.mean_squared_error(labels=y_true_slice,\n", + " predictions=y_pred_slice)\n", + "\n", + " # Keras may reduce this across the first axis (the batch)\n", + " # but the semantics are unclear, so to be sure we use\n", + " # the loss across the entire tensor, we reduce it to a\n", + " # single scalar with the mean function.\n", + " loss_mean = tf.reduce_mean(loss)\n", + "\n", + " return loss_mean" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RzXJSehHwPgt", + "colab_type": "text" + }, + "source": [ + "### Compile Model\n", + "\n", + "This is the optimizer and the beginning learning-rate that we will use." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "CwHgXL48wPgv", + "colab_type": "code", + "colab": {} + }, + "source": [ + "optimizer = RMSprop(lr=1e-3)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Kbjo6pKpwPgy", + "colab_type": "text" + }, + "source": [ + "We then compile the Keras model so it is ready for training." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "EBvqgad5wPgz", + "colab_type": "code", + "outputId": "309dd36c-a47d-4b6b-e554-a5c303152183", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 111 + } + }, + "source": [ + "model.compile(loss=loss_mse_warmup, optimizer=optimizer)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/losses/losses_impl.py:121: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use tf.where in 2.0, which has the same broadcast rule as np.where\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zdta74cgwPg2", + "colab_type": "text" + }, + "source": [ + "This is a very small model with only two layers. The output shape of `(None, None, 3)` means that the model will output a batch with an arbitrary number of sequences, each of which has an arbitrary number of observations, and each observation has 3 signals. This corresponds to the 3 target signals we want to predict." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "LJZDyU8bwPg3", + "colab_type": "code", + "outputId": "5e7ca8b1-4dd8-43ba-b2bf-d53fc3af625d", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 233 + } + }, + "source": [ + "model.summary()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Model: \"sequential\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "gru (GRU) (None, None, 512) 818688 \n", + "_________________________________________________________________\n", + "dense (Dense) (None, None, 3) 1539 \n", + "=================================================================\n", + "Total params: 820,227\n", + "Trainable params: 820,227\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ScJ_6rTcwPg4", + "colab_type": "text" + }, + "source": [ + "### Callback Functions\n", + "\n", + "During training we want to save checkpoints and log the progress to TensorBoard so we create the appropriate callbacks for Keras.\n", + "\n", + "This is the callback for writing checkpoints during training." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "rAQm73XzwPg5", + "colab_type": "code", + "colab": {} + }, + "source": [ + "path_checkpoint = '23_checkpoint.keras'\n", + "callback_checkpoint = ModelCheckpoint(filepath=path_checkpoint,\n", + " monitor='val_loss',\n", + " verbose=1,\n", + " save_weights_only=True,\n", + " save_best_only=True)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bwiOt1PJwPg6", + "colab_type": "text" + }, + "source": [ + "This is the callback for stopping the optimization when performance worsens on the validation-set." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "C0iiIbiUwPg6", + "colab_type": "code", + "colab": {} + }, + "source": [ + "callback_early_stopping = EarlyStopping(monitor='val_loss',\n", + " patience=5, verbose=1)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dxeFQ-cpwPg8", + "colab_type": "text" + }, + "source": [ + "This is the callback for writing the TensorBoard log during training." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "DxI8rw6uwPg8", + "colab_type": "code", + "colab": {} + }, + "source": [ + "callback_tensorboard = TensorBoard(log_dir='./23_logs/',\n", + " histogram_freq=0,\n", + " write_graph=False)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hLn058WVwPg-", + "colab_type": "text" + }, + "source": [ + "This callback reduces the learning-rate for the optimizer if the validation-loss has not improved since the last epoch (as indicated by `patience=0`). The learning-rate will be reduced by multiplying it with the given factor. We set a start learning-rate of 1e-3 above, so multiplying it by 0.1 gives a learning-rate of 1e-4. We don't want the learning-rate to go any lower than this." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "hPovMktNwPg-", + "colab_type": "code", + "colab": {} + }, + "source": [ + "callback_reduce_lr = ReduceLROnPlateau(monitor='val_loss',\n", + " factor=0.1,\n", + " min_lr=1e-4,\n", + " patience=0,\n", + " verbose=1)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "u5FGAYmxwPhA", + "colab_type": "code", + "colab": {} + }, + "source": [ + "callbacks = [callback_early_stopping,\n", + " callback_checkpoint,\n", + " callback_tensorboard,\n", + " callback_reduce_lr]" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dTv6qPB_wPhD", + "colab_type": "text" + }, + "source": [ + "## Train the Recurrent Neural Network\n", + "\n", + "We can now train the neural network.\n", + "\n", + "Note that a single \"epoch\" does not correspond to a single processing of the training-set, because of how the batch-generator randomly selects sub-sequences from the training-set. Instead we have selected `steps_per_epoch` so that one \"epoch\" is processed in a few minutes.\n", + "\n", + "With these settings, each \"epoch\" took about 2.5 minutes to process on a GTX 1070. After 14 \"epochs\" the optimization was stopped because the validation-loss had not decreased for 5 \"epochs\". This optimization took about 35 minutes to finish.\n", + "\n", + "Also note that the loss sometimes becomes `NaN` (not-a-number). This is often resolved by restarting and running the Notebook again. But it may also be caused by your neural network architecture, learning-rate, batch-size, sequence-length, etc. in which case you may have to modify those settings." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "xCvfjb4FwPhE", + "colab_type": "code", + "outputId": "18891ff6-48e5-429d-bfd9-5e1a71ca6c08", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 613 + } + }, + "source": [ + "%%time\n", + "model.fit_generator(generator=generator,\n", + " epochs=5,\n", + " steps_per_epoch=100,\n", + " validation_data=validation_data,\n", + " callbacks=callbacks)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Epoch 1/5\n", + " 99/100 [============================>.] - ETA: 5s - loss: 0.0082 Epoch 1/5\n", + " 1/100 [..............................] - ETA: 22:06 - loss: 0.0051\n", + "Epoch 00001: val_loss improved from inf to 0.00506, saving model to 23_checkpoint.keras\n", + "100/100 [==============================] - 569s 6s/step - loss: 0.0081 - val_loss: 0.0051\n", + "Epoch 2/5\n", + " 99/100 [============================>.] - ETA: 5s - loss: 0.0044 Epoch 1/5\n", + " 1/100 [..............................] - ETA: 21:42 - loss: 0.0031\n", + "Epoch 00002: val_loss improved from 0.00506 to 0.00307, saving model to 23_checkpoint.keras\n", + "100/100 [==============================] - 557s 6s/step - loss: 0.0044 - val_loss: 0.0031\n", + "Epoch 3/5\n", + " 99/100 [============================>.] - ETA: 5s - loss: 0.0036 Epoch 1/5\n", + " 1/100 [..............................] - ETA: 21:20 - loss: 0.0030\n", + "Epoch 00003: val_loss improved from 0.00307 to 0.00304, saving model to 23_checkpoint.keras\n", + "\n", + "Epoch 00003: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n", + "100/100 [==============================] - 557s 6s/step - loss: 0.0036 - val_loss: 0.0030\n", + "Epoch 4/5\n", + " 99/100 [============================>.] - ETA: 5s - loss: 0.0027 Epoch 1/5\n", + " 1/100 [..............................] - ETA: 21:14 - loss: 0.0024\n", + "Epoch 00004: val_loss improved from 0.00304 to 0.00235, saving model to 23_checkpoint.keras\n", + "100/100 [==============================] - 559s 6s/step - loss: 0.0027 - val_loss: 0.0024\n", + "Epoch 5/5\n", + " 99/100 [============================>.] - ETA: 5s - loss: 0.0026 Epoch 1/5\n", + " 1/100 [..............................] - ETA: 21:05 - loss: 0.0023\n", + "Epoch 00005: val_loss improved from 0.00235 to 0.00230, saving model to 23_checkpoint.keras\n", + "\n", + "Epoch 00005: ReduceLROnPlateau reducing learning rate to 0.0001.\n", + "100/100 [==============================] - 554s 6s/step - loss: 0.0026 - val_loss: 0.0023\n", + "CPU times: user 59min 31s, sys: 8min 14s, total: 1h 7min 46s\n", + "Wall time: 46min 36s\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 71 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "aCVSsGh9W4-G", + "colab_type": "code", + "colab": {} + }, + "source": [ + "!cp *checkpoint* drive/My\\ Drive/models/" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RJxmdCA4GLE8", + "colab_type": "text" + }, + "source": [ + "# New Section" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_w6vqHJIwPhH", + "colab_type": "text" + }, + "source": [ + "### Load Checkpoint\n", + "\n", + "Because we use early-stopping when training the model, it is possible that the model's performance has worsened on the test-set for several epochs before training was stopped. We therefore reload the last saved checkpoint, which should have the best performance on the test-set." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "aAgKGrA5wPhI", + "colab_type": "code", + "colab": {} + }, + "source": [ + "try:\n", + " model.load_weights(path_checkpoint)\n", + "except Exception as error:\n", + " print(\"Error trying to load checkpoint.\")\n", + " print(error)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9OVvL6XTwPhK", + "colab_type": "text" + }, + "source": [ + "## Performance on Test-Set\n", + "\n", + "We can now evaluate the model's performance on the test-set. This function expects a batch of data, but we will just use one long time-series for the test-set, so we just expand the array-dimensionality to create a batch with that one sequence." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "fbVU1hThwPhK", + "colab_type": "code", + "outputId": "ffd553dd-42cd-4e8b-b16f-3410ce3703b1", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + } + }, + "source": [ + "result = model.evaluate(x=np.expand_dims(x_test_scaled, axis=0),\n", + " y=np.expand_dims(y_test_scaled, axis=0))" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\r1/1 [==============================] - 13s 13s/sample - loss: 0.0023\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "QWHucwySwPhO", + "colab_type": "code", + "outputId": "5ce56306-e5b1-4288-e1d9-ada493ac007c", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + } + }, + "source": [ + "print(\"loss (test-set):\", result)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "loss (test-set): 0.0023025174159556627\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "zV4xlTZwwPhQ", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# If you have several metrics you can use this instead.\n", + "if False:\n", + " for res, metric in zip(result, model.metrics_names):\n", + " print(\"{0}: {1:.3e}\".format(metric, res))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "b_zSk9INwPhT", + "colab_type": "text" + }, + "source": [ + "## Generate Predictions\n", + "\n", + "This helper-function plots the predicted and true output-signals." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "yMQudxPYwPhU", + "colab_type": "code", + "colab": {} + }, + "source": [ + "def plot_comparison(start_idx, length=100, train=True):\n", + " \"\"\"\n", + " Plot the predicted and true output-signals.\n", + " \n", + " :param start_idx: Start-index for the time-series.\n", + " :param length: Sequence-length to process and plot.\n", + " :param train: Boolean whether to use training- or test-set.\n", + " \"\"\"\n", + " \n", + " if train:\n", + " # Use training-data.\n", + " x = x_train_scaled\n", + " y_true = y_train\n", + " else:\n", + " # Use test-data.\n", + " x = x_test_scaled\n", + " y_true = y_test\n", + " \n", + " # End-index for the sequences.\n", + " end_idx = start_idx + length\n", + " \n", + " # Select the sequences from the given start-index and\n", + " # of the given length.\n", + " x = x[start_idx:end_idx]\n", + " y_true = y_true[start_idx:end_idx]\n", + " \n", + " # Input-signals for the model.\n", + " x = np.expand_dims(x, axis=0)\n", + "\n", + " # Use the model to predict the output-signals.\n", + " y_pred = model.predict(x)\n", + " \n", + " # The output of the model is between 0 and 1.\n", + " # Do an inverse map to get it back to the scale\n", + " # of the original data-set.\n", + " y_pred_rescaled = y_scaler.inverse_transform(y_pred[0])\n", + " \n", + " # For each output-signal.\n", + " for signal in range(len(target_names)):\n", + " # Get the output-signal predicted by the model.\n", + " signal_pred = y_pred_rescaled[:, signal]\n", + " \n", + " # Get the true output-signal from the data-set.\n", + " signal_true = y_true[:, signal]\n", + "\n", + " # Make the plotting-canvas bigger.\n", + " plt.figure(figsize=(15,5))\n", + " \n", + " # Plot and compare the two signals.\n", + " plt.plot(signal_true, label='true')\n", + " plt.plot(signal_pred, label='pred')\n", + " \n", + " # Plot grey box for warmup-period.\n", + " p = plt.axvspan(0, warmup_steps, facecolor='black', alpha=0.15)\n", + " \n", + " # Plot labels etc.\n", + " plt.ylabel(target_names[signal])\n", + " plt.legend()\n", + " plt.show()" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FnqILZQtwPhV", + "colab_type": "text" + }, + "source": [ + "We can now plot an example of predicted output-signals. It is important to understand what these plots show, as they are actually a bit more complicated than you might think.\n", + "\n", + "These plots only show the output-signals and not the 20 input-signals used to predict the output-signals. The time-shift between the input-signals and the output-signals is held fixed in these plots. The model **always** predicts the output-signals e.g. 24 hours into the future (as defined in the `shift_steps` variable above). So the plot's x-axis merely shows how many time-steps of the input-signals have been seen by the predictive model so far.\n", + "\n", + "The prediction is not very accurate for the first 30-50 time-steps because the model has seen very little input-data at this point.\n", + "The model generates a single time-step of output data for each time-step of the input-data, so when the model has only run for a few time-steps, it knows very little of the history of the input-signals and cannot make an accurate prediction. The model needs to \"warm up\" by processing perhaps 30-50 time-steps before its predicted output-signals can be used.\n", + "\n", + "That is why we ignore this \"warmup-period\" of 50 time-steps when calculating the mean-squared-error in the loss-function. The \"warmup-period\" is shown as a grey box in these plots.\n", + "\n", + "Let us start with an example from the training-data. This is data that the model has seen during training so it should perform reasonably well on this data." + ] + }, + { + "cell_type": "code", + "metadata": { + "scrolled": true, + "id": "dR92llmWwPhV", + "colab_type": "code", + "outputId": "92186356-84f4-4063-e945-cf48f7faf421", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 955 + } + }, + "source": [ + "plot_comparison(start_idx=100000, length=1000, train=True)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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nCbUB13CmmGG6kGG5dWlCNCOBJw6eIFz5DAu84hjhZwDeSujg9QTePhF4wo6g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Nd/J3D+q/j8/X8Ll6rYu/+o8HFsYx/a4RZzOJwOvkluBuUSTUU3MaAmdC+n4ul0EYMg1gRdNPNM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EB1gs+hbNINDoxncDcOaWHwJIpSqeyR3Fogvrsp9zo9Ehr6QLfQF61tcT/giZ6pnECZrQ69Xf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\nco0o0Ty/yUo731XeSGo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jJwfnijvAG5vNdOw4MmUlRqrUNuQjwJuYswK8M1cPLfhePKwjootAgVmlrB8D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oUxbfpeTWF6rRWvPS9+nYNNxsT3H1qsJ00xqi3zjzPH6SqRyfvbH916gqhvI8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RrAAQdswMY2xb3KTbKqZat3nd8f5441kkbVKjPS9/16+TUz996ZOSNfjeaZ55zn+VBI9e2c5n1h\ncKikH9Vy+zp91F9JMmOWm0J79DVo7kc7ngH29DuuN9+7g2X5Gmuh/BAmax7XLx7PhgOl7OgwvXnr\n0VPsLaritpWTuphy6f0OFu0IPoCWJpehH4YKtP0SnQCXfAmy5nZ9TMok915Sti/gy8Q+VD8G9/7Z\nUAk7H3FFcvwZShER6ZKCwhBgfe6DXrt1ha0t+FqbaO1lptBvSW48OUlRPLWnbe2atZYfvVrMIztP\n890NxZ2nexmfq4RXtN1NWWyuc0EhwLhzSa89xI3jT5McH+WCxn3PuvVMEVHuj3t8ugsUdzzs+pF9\nbg986En3zf7Tn3YZ0IrjbVOAMrwPMGPGMZosmZxKRV0Th0p78SE6c477pnuIm3t/7YmdHCmr4Uc3\nn0tGUh/OveOb3TkT1bcvMQZScnwUiyem8vc9wafptePvgVl5wpWqX3iLK5oR2By+J2/+AdZ9vdPm\nf+xz1XxXz+pFw/jWVji+pa38f0fLPuqyKis+cWY6d6iY6vVcPFTajyquUfHufae3QWGVN7U3KQem\nrnbvUbu7bXM7JJ7eXsiyyankJMd13lm6162Hy5rLLSsmMjYxhut+9io/fvEATS3u9/oPrx8lKTaS\na87t4r0wa567LO6iPW/xTleYK1glz3AVEemC3KIdbs36mPEQFeTn2x3/FPz133YtjvxrI0VEpEsK\nCkOANZ2DQtPivgXva6bQZwxXz05mZ3E9h8vdYzy3v5L1edXMy4plR1EdLxwIMmUrZ6H7I3xgHQDF\nqYvYdvQUGxLWcNom8J2ST7o/sMc3uUqis9/tDdS4tU/7n3Ml9c+91QV7Uy+GS78Ge55yaw6Ld7UV\nC0j3PsAMc8uFobZsspsOu+XIqZ4PzpjtCu74p08Ngd0nKnnszQL+efV0zp/WxXSxlubOGZrmBjeF\nuKvAZgitmZPFzoJKCk73UEAm0QvYjmx0GYWpq2Hi+W1TqHtSWw5P3g2v/qhTsZp/7CthdnYS2cm9\n+N09ddi1lchdGny/MXD5v8OV/z341Vz7KDcljphIX+++5OjIGIhOOtOnr0eVJ9x087hUV0Eydwk8\ncVdbAathcKC4in3FVV1nhI9sdJe5i8lNieOFz6ziqvk5fH/dfq772atsPFDG33YWcuOS8cRHd5EB\njk9zQVFRF0Hhm39wQc+MNWf/gkLJhBVuSvrR12DCsr7f35+JrDrhMuzR8QM7PhGREUhBYSjwMoWB\nvQp9La7vYF8zhQBXzBhDVIThexuL+d6GYu7fVMqicXH8z5XjmZsZy6+3lFFR36ES3rRL3Qe0F7/J\nEd94Vty/nxt+/hq3/fUk1zb+Jw3jVsDGH8Cmn7sPIdMD1miM9/5op89qP33wvLtdm4WnP+umf+V4\nFSn960Oa+9ZbMdxNHhtPemI0W4/2YnpjpvdN97qvwQ8XDElD+z9sOkpslI+PXdRFcRlr4c+3ulYk\nVUVt20+85foFDuN6Qr/LvZYp63YVdX9gbLILMo5scLczvHO37EDv+hfuebLtesGbZ65W1Tex9cgp\nLpndiywhtBURCcNMj89nmJqRSF5/gkJwU0h7nSkscllCY1w2+tr73TkX+P8wxJ7aXogxcFVXQeG+\nZ12mz5v6mJoQzY9vWcQvPriYwtP1fPC3m2lqscF7+gXKnhd8+mhrq5seOefaEbX+GnCZvcZqqC5y\n62r7Ki4FPvwszL0OVt878OMTERmBFBSGgDPTRwMqkBovYOqqeX13xsRG8L55qZTXtrCtoJZpaTHc\ne3E2ET7Dpy/IpKaxle9vLG7XuuJVu+DM9d0JK/nqu8/hd3cs48E7l/PLT11HzI2/cNUP9z7t/tAG\nliefdz3MuAI+8HD7NYI+H9z0e1jzn3DdL2HVPW577hJXnnzNt/r82sKZMYYlk1LZ2ttMIbgpcqeP\nDnphjcr6Jv76VgHXLsx104SD2fgDt7bt9DF45CPQ6n2xsPUBNx1w0gWDOsbemJqRyPTMRNb1NIXU\nGJctPHXE3R47w/tCw3adlQkUWKnU39MT2HigjOZWy+qZQQooBeMPCsO0J+e0jIT+TR8FLyjsZaGZ\nqkIXFPplnuN+R4ZpCmlLq+XRbfmcP20smR16E9JYA398v6viPOvKTvddOy+HdZ+7mOsX5/LBlROD\nVy0NlDXPFV1p6vAl2smDUF/hZmWMNJMvbLve36mfky+A9/2u10VyRERGu9CpWjCKBVtT6PNPH+1H\nUAjw4SVj+fCSsZ22T06N4ePLM/j55lJ++GoJn1yZwW+2lPH03gruTr6LD85s4ap3fyVI/8BkuPI+\nWP9fsOoL7XelTHQVQ4OJT4PzP9V+W0wifGJDv15XuFs2OY3ndxVTUlnf+cNkoLgUmH8T7PB+rjsf\ngQXv6/0TlR1wl71s6/HYtnzqmlq47bxJLiP48IfdB86bfu/OhaOvw4vfhHOugRmXw5Ofgu/OdFnu\n6iL3fxwi2YrL52Txq1fyqKht6jrABbeu8PQxGJPrzkl/lrvwbZjYQ9azpgTi0txU6d1PwIWfBWP4\n+54SkuOiWDIpSNGYYE4ecpVQQ+Rn11fTMhJ5Zkch9U0trl1NX8Qk9T4DXnnC9a8LNPMKeP1nbvpu\nX9ecnaX1e0soOF3HV999Tvsdu5+AZ+5xRYsuvhcu/EzQ+6clRPP9m3rZyzN7vitCVrq3ff/Pgm3u\nMndJP15BiEvKhrvecAF2zoKejxcRkbOmTGEICL6m0Js+Gtn36aM9uW5uCh88N43nD1Ry658P88ze\nCj564RTuvuc/yL7hv4M3lAdY8U/whYOQHp5ZjVCw1FtXuPVoL7KF1/0SPvYSLPmwyzr0ZQ3mT5e6\nf71greUPm46ycEIK83KTXVZy918hb71bMwcuOI2Kh+t+AYtucx9Ea8tcsZDVX4ZLvtL7sQ2yNXOy\naGm1vLSvh2yhvwKpP0s3ZhwkjWuX+etSdYnrsbn0ThdEfjOFlrI81u8r4ZJZGURG9PKt9eTBsM0S\nAkzNSMBaOHqytu93jkns3fRRa9umjwaadIFbx5y/te/PfZb+sOkoWWNieNecgD6rp4+5L1OSx7tC\nW5d8aWCC1a4qkBZsc/02w6XyaF9lzOr8RYCIiAwaBYWhwGtJYVrb2kWY5v4Vmumt2xalcdP8VLIT\nI/neVeP56tVzevdNf4gVuwg3c8eNITbKx8tehcqOdhZUUN/kTcv0+VzwlXOuy9qdOty7JwkMHntR\ntv/B145wqLSG2/xrmw684LJXOefC0Veh7jTsfMxNhYtOcFMvr/+1WzN69xZY/a9DnqnpzsLxKWQm\nxfD33d30dgNYfLsLLBbe7G4b4zKEx3oRFNaUQmKGq1o6zTWUL3vpJ5TXNHLZOVk93DlAeR6Mndb7\n40OMf+pjv4rN9HZNYf1pV220Y7XiCSsA44qRDKGjJ2t4eX8ptyyfSFRg8H/wRVdt9LpfwJSLBu4J\nU6e4lkEdK5AW7XDT8H19zNCKiIgEoaAwBNhuC80MTlBojOGjy9L5xXWTmJcdOh/oR7qoCB83LB7P\nw9uOt2uy3tzSyn8+vZurf7KR/3q2Q39C/5SxE2/37kmqAta7dbM+rrG5lS89toN/e2o3q2dl8J6F\nXibm5EGXfZh8oSui8tJ/uLVfFwRMhRs7Da74lsv2hBifz7By6ljePn66+wNnXA53POsq5vpNWAmV\n+a6vZneqSyAh01U1vO1xmHcjyfv+QryvmYtn9XI9YVOdqy4b5plCOItehb0JCv3rN5Oy22+PS3HZ\npBNvdr7PIPp/m48R4TPcsrxDQ/W89S7TPNCZO5/PVdMM/F221rUPypw9sM8lIiKjloLCEGC9b3rb\nFZo5yzWFErq+eMVs0hKi+crjO2hptRScruP2/32D32w8TG5KHA9tOU5ZdUCGL3OOyyafeKt3T1C2\nv+16Nx+Yv/HkLv70xjH+efU0fnv7MmIivYxD2QG3FnHCCldcaMtvXOAURmt75uWOoeB0HeU1jX27\no7+SbkEPgUZ1SVtbC4CFNxPbUs2dOYcZE9vLbHp5nrsM40xhfHQkuSlx/cwUjuldS4oq1yCepCC9\n/LLn964w0ACprG/ioS3HWTMni6zANcHWwpFXXdGXYE3oz1b2PCje0TYLoKrIzR7IOKf7+4mIiPSS\ngsIQELTQTHP/W1JIaEuOj+JrV8/hnfwKPvy/b3DJd//BliOnuO/GBTx453IaW1p58LUjbXeIjHGN\n4Y9t6t0TlAYEhSV7gh5SWFHHI9uOc9vKSXxx7WwifN4H2YYqVzhm7DTX+yzLW8900ef7/kKH0bxx\nyQDtsrG9kjUHTAQUbe/6mMYa10MyoS0jeGTMUk7bBK6J6UPW6uQhd5kWvkEhuGxhXlnfK5A2+OKw\nDVWutUJ3/O1PxgRp/ZA1z2V2a3vR5mUA/PqVPCrqmrjrkg7Z3VNH3BrbwerVmTXPBYH+vqWl3u91\nGLYyERGR0KSgMBQECQr727xewsM1C8dx0Yx0Xj1YxjULx7H+ntXctHQC0zMTWTMniwdfO0J1Q9v5\nwKTzXdavsRZammD9t9v3CgxUts9lYcYthvJDQQ95YONhWi18fFWHnoRnWiTMcP3gPvYSfGYnpHXR\nuzBEzfUHhSf6GBRGxblWB4XvdH1MtbdWMSBT+Pf9p9jROoVJLcd7/1xnftbhHRROy0jkUEk1tg+F\nkEqrGnhg60kMlmffDH6OnnFm+miQoNBfhKXjertBUFrVwG82HObqBTmuIFOgM5VAe1fcqc/GLXKX\n/vWu/qnkmcoUiojIwFBQGAL81UcJkim0yhSOSMYYfnnbEjb866V8930LyU1pW9f5iYunUVnfzJ/f\nONZ2h0kXuPPj8Cvwzp/g5e+49iDBlO5z65rGToOTeZ12V9Q28cfNx7h6QQ4T0uLb7/Rnr/zr3CKj\nIWXC2bzUYZEcH8WEtDh2FfSyD16gnIXuQ3dXQU6NVyQosa2gzMv7S6mIzSWm6mjvn6f8kHuMwJ6f\nYWhaRgI1jS2cqKjv+WCgoq6JDz3wBoX1bprtNx/ZxH3P7e06qKw64dp/BKvEnOOtt83f0p+h98lP\nXzpAY0srn1/jZedqy2Hfc1BzEo5scNV5M+cMzpPnnAvx6bD/OXj5PtceJndp+ynMIiIiZ0FBYQgI\nNn20rSWFMoUjlX89VkeLJqayfEoav3/9aNsH5ckXQlwq/On9rkcgtAUnHZXtd9PK0qa56WYdml7/\n3+aj1DS28ImLg2Soyg4AJuwyg8HMz03ue6YQ3BTAmpK2Xo8d+TOF3vTRusYWNh8uJy5zGtSehPpe\nBqIV+ZAcfgF3RwsnpACw9UjPUzjrGlv4yO+2cLCkipsvnAvA9fOS+dk/DrGzqwC+qqhz5VG/hLEu\nEDuysV9j7638U7X88Y1jvH/ZBKaku+I6PPN59/t4/3J46//B3OshYpBa//p8rjDSzkdg/bfcNn/V\nXBERkQGgoDAE2GAtKc4UmlGmcDS6edkEjpXXsuWI188wOh7e9W/uepzXGD3YFMe601Bd3JYpxLr1\nTp4Tp+v41St5rJ6VwTk5QfpRnjzgMoNR4f9lxNxxyRw9WUtFXVPPBweadom7PPRS8P3VXv9DL0vz\nxpFyGptbyZ7sVYIM+Hl3q7Iw+Dq5MDMnZwxJMZFsPtx9UNjY3Mon/m8bbx47xY9uXsScyS7Q+/hy\nF1y/cqCLLzkqTwSfOuo3+UK33ra5j0WF+uDhrfk0t9r2awn9XxrUlrlWPZd9fdCeH3DVfzPOgYnn\nwY0PuP6lIiIiA0RBYQg4kym07aePtvqiXXNwGXXWzssmITqCR7YFrFFb8mH4Whn86xFY+x2oLGhb\nb+W363F3mT6zrYCJt66wqaWVf/nTWzS3tPKN98wN/sQnD7r1hCOAf93X7hN9nEKaOtllSnc8DK0t\nnff7M7RepvCV/aVER/qYNstb39bboLCqMHhFzTATGeFj2ZQ0NuWd7PKY1lbL5/7yNi/vL+Xb18/n\nqvk5Z6bNpkbUMydnDC/v7yIorCrs3I4i0LRLoakWDr98Ni+jS62tlkffzOfC6entM/unj8GSO+DK\n++CuNyCpD/0p+yNzNty1Ce74G8y7QT1jRURkQCniCAVB+hSalgZlCUex+OhIrpqfw7M7iqhtDCg4\n4yOFZYEAACAASURBVP8gmLvEXQa2nDi2GZ7+jFtrNGUVjHVTQCsL9nG8vJb7ntvL1qOn+PYNC9qm\nwAWy1q0pDOO+eYHmjnOZ0D5XIAVY9UUo2Aqbf9l5X3WJy9Z6/xev7C9lxZQ0YjO8Kbene7GusKHa\n9X4cAZlCgBVT0sgrraGkMvi6wpf2lvD09kK+uHYW71/m9ffzr6VsqGLVzAzePHqKqvoOWd2WZvfz\n7mr6KMC0y9z/x/aHBuCVdPbOjnewp45xw+LxbRvrTkFDhcvGr/inoV13OxgtL0REZNQbtKDQGPOA\nMabEGLMzYFuaMWadMeaAd5nqbZ9tjHndGNNgjLmnw+OsNcbsM8YcNMbcO1jjHU7+QjOmQ/N6VR4d\n3W5YMp7qhmae3xWkymj2fPdlgr/qobXwty/CmFz40BOuqXxcKrWRyTy9fgMX3beeX284zC3LJ3LN\nwi4+YJfnub5xI6SiYXpiDDnJsf1bV3juLTD5Inj9p67aa6Aar3E9rrXHgZJqLpqRDrHJrthIx+xt\nMFXeMWNy+z62ELRy6liALqeQPrX9BKnxUXzsooC1qv6gsLGaVTPTaW61bMrrcP/qYsB2P300Mhrm\nvBf2PuOC7YFUtIN5f72cV2M/zXu23dk2ZfS0VwQqZdLAPp+IiMgwGcxM4e+AtR223Qu8aK2dAbzo\n3QYoB/4F+G7gwcaYCOB+4EpgDnCLMWaQyrsNn6CFZprr1aNwlFs+OY0JaXE8uq2g886oOMia2xYU\n7vsbFL4Nl3zFBYTAC7uK2NuYwdIxp/ifGxdw/62L+eY1XUwbBTjulbufsGKAX8nwmZebzKa8k3z6\nz2+x5gcvc//6g9Q3BZkSGsz5n3JTdP1Tcv0CGtdv2F8GwKqZGS6Dk5TTFvB1p9L7P+0u2Akjc8eN\nITEmMugU0vqmFv6+u5i187KJigj4kxPdlilcMimVuKgIXuk4hfRM8NzDNNsF73dTSPc+cxavorOW\n575MlG2kLHo8Efmb4OE73JTiU142OGXigD6fiIjIcBm0oNBa+wou2At0LfCgd/1B4L3esSXW2i1A\nx4oQy4GD1to8a20j8GfvMUaWYGsKWxqUKRzlfD7D9YvG8+qhMkqqgkzLy10CBW+55t+bfwHJE92H\nY1y1xHsefofTsROZEVHM+5ZO4N0LcoiO7OZX/tgml+3KmD1Ir2joLZucSnFlA68ePElSbBT/8/w+\n3vX9l3lme2HPffWmX+7WZr72k/btKQKCwpcPlJKZFMOsLC/A6XVQeMJd9hTshInICB9LJ6cGDQrX\n7y2hprGFqxd0eK3elxc0VBITGcF508Z2Ljbj/zl1t6YQ3BcZyRNhx1/6+QqCKNpBxJFX+HbTLeTd\nsgGu/zUU73BrF0v3umNSJw/c84mIiAyjoV5TmGWt9X9iKgJ6WpmfCwR2g873to0oNtiaQmUKBbhy\nfjbWwot7SjrvzF3i1jXlveQ+qC6+DSIisdby+b+8g7WwePFSTNUJaKzp/okaqmH/866yoW/kLDW+\n44IpvPyF1Wz5ymU8+snz+eNHV5AYE8ldf3yTjz64NXiw7efzuUIiRdtdaw+/mlJIyKSl1fLqwTIu\nmpGB8a/zGtPLoLBkD0REj6hM08qpYzlUWkNpVUO77U/vKGRsQjQrpqS1v0NkDETEQEMVAKtmpHP0\nZC1HTwacq1Xe1OmeCvL4fDDvesj7h+sbOAAKNzxIk41gV/a1LJucCudcA7Eprv3E/udh3GKISxmQ\n5xIRERluw/bpz7qv6Xv4qr5vjDEfN8ZsNcZsLS3topJdCGprSdG+T6GN7NzDTkaXWVlJTEyL54Vg\n6wr9xWbWfcNdelnCZ3cUsflwOfdeNZuUSQvcvpK93T/Rpp9DdRFc+LkBGnloiIrwMWlswpmg7fzp\n6TzzLxfx9avnsPFgGWt/uIHndgb52fpNvsBdHvOm1jbWunWXiRnsKKjgdG0Tq2amtx2flO3WFPaU\nhSza7vrrjaAKkv6g7+GAirm1jc28tKeEK+dnExkR5M9NTNKZdYAXzfS3pihr21+6FyLjIH5szwOY\nd737Ym3XY/1/EZ6d+aexOx9na+QifnTHpe78iYqFBTe5XoEFW2HWVWf9PCIiIqFiqIPCYmNMDoB3\nGST90U4BEFjWbby3LShr7a+stUuttUszMjLOerBDJdiaQl9zPa0Rmj462hljuHxOFq8eOkl1Q3P7\nnekzIToRindC5lxInUR9Uwv/9eweZmcncfOyiW7dIUDJrq6fpKUZtj7gqjhOHDnrCbsS4TPceeEU\nnv7UhYxLieUT/7eNex5+p3PlS3A/1+hEOL7J3a7xN67P5JX9pRgDF80IeK9JGgctDa46ZVesdT0m\ncxYO3IsKAQvGp7BqZgb3PbePf3tyF3uLKvneC/upawoyddQvJvFMpnBqegK5KXFt6wpbmmHPkzBz\nTe+y19kLXAGmbQ/2HJR3o6y6gf/83WOMM2XMvvRWxiYGzNg49wNt1xfc1O/nEBERCTVDHRQ+Cdzu\nXb8deKKH47cAM4wxU4wx0cDN3mOMKMGqjxpVHxXPmjlZNDa3di7C4YuAhbe461NWAfDbjYcpOF3H\n198zhwifgZTJEJUAxd0EhYdfhqoTsOT2ro8ZgWZkJfHYJy/g7kum89ib+Vz14w3sKezQ0zAiEsYv\nbcsUVrug0CZm8te3C1g8MZW0hOi24/0tJrqbQnrsdRc05iwYwFcz/CJ8hgduX8pHL5zC7147wtof\nbuC3Gw9z/rSxLJucFvxOMUlngkJjDKtmZvD6oZM0tbS6gLCmFObd2LsBGANL73Tr/grf6ddrcP0U\n32Fuw9sApM69vP0BOQvh4nvhtschVZVHRURk5BjMlhR/Al4HZhlj8o0xHwG+A1xujDkAvMu7jTEm\n2xiTD3wO+Kp3/BhrbTNwN/A8sAf4i7W2m0+3YarLTKHWFAosmZRKanwU63YXd9659jtw7f2w+l8p\nrqzn/vUHuWJuFudP86Y0+nyuxUR3QeGxTWB8LlM4ykRH+rjnilk8/InzaGxu5fqfvcbrhzqsSZuw\n0mVa6yvPBIXbT0WTV1rDB1Z0WBPorybaVVuKutPw0G2QOgXmXj/Ar2b4RUb4+OrVc3jwzuXcd8MC\nXr33Uv74sZXuC4pgYsacCQoBLp6ZTnVDM+/s3gfPfxmy5vdtmuY517pzuZ9VSH/xyiFe2V/Kh3OO\nuf+jjms+jYFLvgTTLu3X44uIiISqyMF6YGvtLV3s6vTJ01pbhJsaGuxxngWeHcChhR5jsCYCrDKF\n0llkhI/LzsnihV1FNLW0ti/rHxEJiz4IwH1PvUNzi+XLV3XoM5g+0xXg6ErBVre+zV8NchRaMimN\npz51ITf/chP3PPwOL3x2FQkx3tvjxBVgWyF/y5npo3/e00BqfBRXze/QUiKph0zhhu9B7Un44KMQ\n30X2bAS4eGYvp+/HJLX7WV00I4PoSB9pL9ztgvBbH3LneG8ljHXFkvY8BZd8uU+N3vNP1fK9F/Zz\n9YIcxhfuhSkX9/55RUREwtzIKTMY5qwvsnOmUIVmxHP5nCwq65vZ0kVz8O35p3n0zXzuuHAyk8Ym\ntN+ZNsVND22q63xHa12vQ3/RmlEsMymW+25cwImKOv7n+X1tO8Yvc9mn45uh2k3hfXx/Izctm0Bs\nVET7B/G3TugYFNZXwCMfgdd+DOfeCuPOHcRXEkaiE9tlChNiIrlySgSTq7Zhz/9U/9Zdzn8flO6B\nIxv6dLfH3iygpdXypUvHYaoKIXPktGYRERHpiYLCEGFNQFBoLb6WeqwKzYhn1YwMYqN8vBBkCqm1\nln9/ajfpiTHcfcn0zndOneIu/Q23A5085AIWBYUALJ2cxu3nTebB14+w7agXgMckuYI9xzdDTQn1\nkWNosBF8YHmQNWWRMa5SZseg8B/fcVUroxLg0q8O+usIGwFrCv1uTdmDD8vh9NX9e8yFt0BChiue\n1EvWWh59M5/zpo4lt8mrnpo+q3/PLyIiEoYUFIYKXySm1VU/NC2uz1erpo+KJy46ggunZ7Bud3Gn\npusv7ilh69FT3LNmJkmxQVocpPmDwsOd9xVsdZfjlw7wiMPXF66YxbjkOL74yHbqm1rcxgkrIX8r\nTafyOdE8hotnZjBxbHzwB0jKab+msLkBtvwGzv0g3HtsxDSsHxBBgsIFrXsos2N4qqgXbSiCiYqF\nSedD4fZe32Xr0VMcPVnLjUvGQ5mXJc5QUCgiIqOHgsIQYX2RZ5rXmxbXUFuZQgm0Zm4WBafr2HWi\nfYXMh7YeJz0xxn2gDcafKSwPFhRuc9mrDE2V80uIieRb183jUGkN31+3322cuNL1Jzz0Eodasvjc\n5TO7foCkDg3sS/ZASyPMeFff1seNBjFjoLkeWtragcRVHqYwahKbjwSfKt0rmXOgPM/1leyFR7fl\nEx8dwdp52VC6DyJiIHVy/59fREQkzCgoDBHWF4nxCs34ml1QqEyhBLpsdiY+Q7sppCerG1i/t4Tr\nFo0L3hwcXEGTmDFQfqjzvvytMG6Ra28hZ6yelckHVkzkV6/ksX5vCUxw/RujbCOx4+awYHxK13dO\nym4fFBZ5GavskdWCYkD4ixsFZgtPHqA6cRKHy2r6/7iZ5wC2LevXjbrGFp7eXshV83NccaGK45Ay\nQb8TIiIyqigoDBGBhWZMszKF0tnYxBiWTkpr15riyXdO0NxquaGrLCG4CowZs6Fkb/vtzQ1QtAPG\naz1hMF+7eg6zs5P4p//bxm2PnqDEuOmMS5ed1/0d06ZAdXFbA/vCdyA6qS1jK21iktylPyisLYfa\nk7SkTaewop7axuau79udzDnusnh3j4e+sLuI6oZmbljs/Q5VFMCY3P49r4iISJhSUBgqAoJCX4sy\nhRLc5XOy2FNYyfFyNy3u0TfzmZ+bzOzsMd3fMfMcKNntqo36Fe2A1iYVmelCbFQED3x4Gbcun0hR\nRT2bm2cAEDduTvd3HLfYXZ54y13mb3VVNH16u+2kY1B40mWzY7Lc9NwjZb2b/tlJ6hTX//XkwR4P\nfWRbPrkpcayY4rUIqVRQKCIio48+pYQIawLWFCpTKF24fE4WAD996SDP7ypiZ0ElNyzuxQfYzDlQ\nV36m+Trg1hMC5KrITFfGpcTxb9fMZd3nLuaK6+90lUV7qko5bpG7PPQSrP82FL4NU9XzLih/UNhY\n7S69Ykip490a135PIY2IhOQJcOpIt4cVVtSx8WAZNywZj89noKXZTf1NVlAoIiKji6oehAirTKH0\nwuT0BK6Ym8VDW4/z0NbjREUYrjm3N0Gh19C+ZDckucCSw6+4jIg+APdK9Lk3wbk39dwQPS4Fxs6A\n137Stm3apYM7uHAV3SFTWOHaQYybNB0o4nBZdf8fO3Vy8Iq7AR5/qwBraftipboYbKsyhSIiMuoo\nKAwRWlMovfXL25ZyvLyWjQfLSI6LIi0huuc7jfX6F5bnwbRLXG/CA+tg6R2DO9iRpKdgMNDVP3B9\nCetOQ1VRW/ZQ2vNnCusr3GXlCYhNIT4xmXHJseSVnkWxmbQpsOvxLndba3lkWz7LJ6cxaWyC9/wF\n7jK5mzW6IiIiI5CCwhBhfVFt1UeVKZQeTEiL55blE3t/h6QciIiG014D+z1PQ0sDzH/f4AxwtJty\nkfsn3Yv3ehHWeu0nKgrOBGRTMhLIO5sKpKmTXbGfutMue9vB28dPk1dawz+tmtq2sSLfXaqXpIiI\njDJaUxgqfEHWFCoolIHi87k1VqePuds7H3EfmlVkRoZT/FjwRUHVCXe7Mv9MQDYlPYG80mpsYHGk\nvvBXe/V/EdLB718/SmyUj6vm57Rt9GcKNX1URERGGQWFIcKaIGsKNX1UBlLKRDh11P3L+wfMu7Fv\nUyJFBprP5/V1LHK3A9pBTElPpLK+mVO1Td08QDcSvbWz1aWddj29/QSPv1XAHRdMISk2qm1H5QmI\nToTY5P49p4iISJhSUBgigq4pVKZQBlLqJJc1ee5eiIzTekIJDUnZruJnU52rkOsVPpqa7tb59bvY\nTEK6u6wta7f5SFkN9z66g8UTU/jc5TPb36ci3wWl+rJERERGGQWFocIX2W5NocVgfb0oICLSWymT\noPYk7HsWLvqcimlIaEjKgcrCtp6C3rTPKV5QeKi/xWb86xVr2oJCay2ffuhtInyGn9y6mKiIDn8C\nKwtUjVdEREYlBYUhwnZYU2gjY/VttQysude1XV/+seEbh0igpBw3fbR4t7udOQeA8alxREWY/vcq\njE12a7UDMoV7i6p45/hp7lkzk9yUuM73qShQkRkRERmVVH00RFgTgWl1a2d8LfVaTygDL20KfHq7\n6wmnNVMSKsbkQEMF5G9xRWfSZwAQGeFjYlo8h/ubKTTGZQsDMoXP7yrCGFg7L6fz8c2Nrk/hGGXQ\nRURk9FFQGCJsRFS7NYVaTyiDInXScI9ApL3kCe5y7zOQMQsi2gq/TElP7H+mECA+va3dBfDcziKW\nTUojIymm87GVBYDVtGoRERmVNH00RLSrPtqsTKGIjBLjFrnLqhOQPb/drqkZCRw+WUNLaz/bUiSM\nPTN99EhZDXuLqrhiXnbwY8sPucux0/r3XCIiImFMQWGoCCg0Y1qUKRSRUSItoHn8jMvb7ZqRmUhj\ncytHT/a32Ez6memjz+9ybS/WzMkKfuzJPG88CgpFRGT0UVAYIgILzfia62lVUCgio0FgQa3p72q3\na1Z2EgD7i6v699gJ6Wcyhc/tKmJe7hgmpMUHP/bkQYhOgsTM/j2XiIhIGFNQGCLa9Slsqcdq+qiI\njBZ3Pg/v+XGnAkgzMpMwxlUN7Zex06G+grKju3jr2GnWzu1i6ii46aNjp6rqs4iIjEqDFhQaYx4w\nxpQYY3YGbEszxqwzxhzwLlO97cYY82NjzEFjzHZjzOKA+7QYY972/j05WOMdbp3WFCpTKCKjxcSV\nsOT2TpvjoiOYlBbf/0zh7HcDcGzjQwCs7Wo9YX0FFLwJ6TOD7xcRERnhBjNT+DtgbYdt9wIvWmtn\nAC96twGuBGZ4/z4O/DzgPnXW2nO9f9cM4niHlY2IwdgWaG1WplBExDMzK6n/mcLk8ZC9gOhjLzM1\nI4HpmUnBj1v/X1B3Clb+c/8HKiIiEsYGLSi01r4ClHfYfC3woHf9QeC9Adt/b51NQIoxJkgjqZGr\nNcqtc/E11ylTKCLimZ2dxJGyGuqbWvp1/8aUaSTVF3Y9dfTEW/DGr2DpnZC7OPgxIiIiI9xQrynM\nstYWeteLAH8ZuFzgeMBx+d42gFhjzFZjzCZjzHvphjHm496xW0tLSwd04IPNX23U11ynTKGIiGdm\ndhKtFg6VVvfr/oebUsnmJGvnBikgU1sOf7kdErPhsq+d5UhFRETC17AVmrHWWqA3zacmWWuXArcC\nPzTGdFkv3Fr7K2vtUmvt0oyMjIEa6pBojYgDwChTKCJyxmyvAum+fk4h3Xo6gRjTzPyUps47n7gL\nqgrh/X+AuNSzGaaIiEhYG+qgsNg/LdS7LPG2FwATAo4b723DWuu/zAP+ASwaqsEOpdZIFxRGNFZh\nbIsyhSIiwKSxCURH+NjXj2IzNQ3NbCyJAcBUFrTfWXcK9v0Nzrsbxi8diKGKiIiEraEOCp8E/CXm\nbgeeCNj+Ia8K6UqgwlpbaIxJNcbEABhj0oELgN1DPOYhYf1BYcMpAGUKRUSAqAgfUzMS+pUp/Me+\nUo41exnAjkHhsc2AhWmXnv0gRUREwlzkYD2wMeZPwGog3RiTD3wD+A7wF2PMR4CjwE3e4c8CVwEH\ngVrgDm/7OcAvjTGtuAD2O9baERkUnskU1rug0CooFBEB3BTSNw53rFvWs+d3FVEflwOtQEXHoPA1\n8EUpSygiIsIgBoXW2lu62HVZkGMtcFeQ7a8B8wd4aCHpTFDYcNrd1vRRERHAFZv569snqKxvYkxs\nVK/u09Dcwkt7S7h6/nTYnwjlee0POPo6jFsEUXGDMGIREZHwMmyFZqS9M9NH68u92woKRUQAZmW5\nYjP7+zCF9LWDJ6luaOaK+TmQMQvK9rkdRTugshBOvAmTzh+M4YqIiIQdBYUhwr+GsC1TGDOcwxER\nCRmz/BVI+1Bs5rmdRSTGRHL+tLGQMRtK90FjLfziQvj+bGhtVlAoIiLiUVAYIvzTRyPrXH/F1qjE\n4RyOiEjIyE2JIzEmstfFZlpaLev2FHPp7ExiIiMgfaZrPbHv2baDYsbAhBWDNGIREZHwMmhrCqVv\n/C0oomqLAWiNHjOcwxERCRnGGGZmJfY6KNxypJzymkbWzst2GzJmu8t1X4eIaLj+1zDxPIhLGaQR\ni4iIhBdlCkOFL4LWiBgia13rxpbopGEekIhI6JiVncT+4ipcXbLuPbeziJhIHxfPzHAbpqyClEmu\nLcWc98Lc90JS1iCPWEREJHwoKAwhrZFx+Jrr3HVlCkVEzpiZlcSp2iZKqxq6Pa6l1fK3nYWsmplB\nQow3GSY6Hm57HK74Nlx7/xCMV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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "a3src-BKwPhX", + "colab_type": "text" + }, + "source": [ + "The model was able to predict the overall oscillations of the temperature quite well but the peaks were sometimes inaccurate. For the wind-speed, the overall oscillations are predicted reasonably well but the peaks are quite inaccurate. For the atmospheric pressure, the overall curve-shape has been predicted although there seems to be a slight lag and the predicted curve has a lot of noise compared to the smoothness of the original signal." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "k9If6_z7wPhX", + "colab_type": "text" + }, + "source": [ + "### Strange Example\n", + "\n", + "The following is another example from the training-set.\n", + "\n", + "Note how the temperature does not oscillate very much within each day (this plot shows almost 42 days). The temperature normally oscillates within each day, see e.g. the plot above where the daily temperature-oscillation is very clear. It is unclear whether this period had unusually stable temperature, or if perhaps there's a data-error." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "sAo1mu0RwPhY", + "colab_type": "code", + "outputId": "3f8e4b9f-ac71-4bc9-e6af-8e2754d29a9b", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 955 + } + }, + "source": [ + "plot_comparison(start_idx=200000, length=1000, train=True)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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DM4GlWVCWBZN+2fn28GHqY/FuCQLFaUfebnQjqQkUQoi+s72gisr6Zspqmyhw1BplHKrB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R0WDAejeP5AH5bVu/NnMfVyR71PUqOvxOgeDgLOGuDGxwCTRQX8jko16XTH3erK2/4APPAx1Qt4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\nSUVORK5CYII=\n", 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rtsOYPOwbC7JZEc0qMsWg8wFPEJU2M5x2JWBzWM3YuLg25f2tZuUFEorE9Ncf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\nXeelW2UK2LwdSpfHfqJFVfCxh8XQZPeD8V9Qd6DQyp0Vf5sYyI9j+tP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Pv95khVMC2tWT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\nAFyR98zphfjiSBecHh+UQ1TRJLITg1oBm9OLbQ098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bPgsKlVJ/UkpVK6XyXPYlKKXeUkodc2zjHfu/pJQ6qJQ6pJTaoZRa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\n6mf2lvLnvBIevCKbpaMZOC781iXZcdhti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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KfBwg7RFwPhZ", + "colab_type": "text" + }, + "source": [ + "As a check, we can plot this signal directly from the resampled data-set, which looks similar." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "p0YKXucRwPha", + "colab_type": "code", + "outputId": "fbdd9ef2-41a6-4057-87a7-1757593c3b5e", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 336 + } + }, + "source": [ + "df['Odense']['Temp'][200000:200000+1000].plot()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 79 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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FjQ5su+NCfOvSeQCA949KE7lTK21qsxkA2HUiNHn7wPojCR+joAX/3tcPodRs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\n2oVHtfAD0Sz89At+nWNswU+kWTrDZBt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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7kkYv112wPhd", + "colab_type": "text" + }, + "source": [ + "We can plot the same period from the original data that has not been resampled. It also looks similar.\n", + "\n", + "So either the temperature was unusually stable for a part of this period, or there is a data-error in the raw data that was obtained from the internet weather-database." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "LZFhxWkAwPhd", + "colab_type": "code", + "outputId": "4dd86a20-644d-4406-ce37-f4a626caeb5c", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 307 + } + }, + "source": [ + "df_org = weather.load_original_data()\n", + "df_org.xs('Odense')['Temp']['2002-12-23':'2003-02-04'].plot()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 80 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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F9y+eO+Cznzt9Kltaurnl8XfC62rLSvLLaSsi54nIuyKySUS+mcxnk5nIYLyy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\nJotYTtsdbT2IwOzm+H6mXKEFfh4T1vA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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7jWbXouawPhe", + "colab_type": "text" + }, + "source": [ + "### Example from Test-Set\n", + "\n", + "Now consider an example from the test-set. The model has not seen this data during training.\n", + "\n", + "The temperature is predicted reasonably well, although the peaks are sometimes inaccurate.\n", + "\n", + "The wind-speed has not been predicted so well. The daily oscillation-frequency seems to match, but the center-level and the peaks are quite inaccurate. A guess would be that the wind-speed is difficult to predict from the given input data, so the model has merely learnt to output sinusoidal oscillations in the daily frequency and approximately at the right center-level.\n", + "\n", + "The atmospheric pressure is predicted reasonably well, except for a lag and a more noisy signal than the true time-series." + ] + }, + { + "cell_type": "code", + "metadata": { + "scrolled": false, + "id": "4Nx8832CwPhe", + "colab_type": "code", + "outputId": "87b2a769-a066-40fd-bcc1-fa889bf5c1d8", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 955 + } + }, + "source": [ + "plot_comparison(start_idx=200, length=1000, train=False)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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GLJ6C+ADfOmVy//MzfOqhE3z1pCsw+8TFG0xGgS34jXg5Sl5j+C5ZcCufDqai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SPvd/vRC//4brcWQXVbtec8MMfud1x/DLrxCzjY1kk90JlbqL0Zyeen6ZoSo4p+2j/4gPcW6s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\nN2nkyMPSX/0n6fEv6MWRNytVCnc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0lIiIiCpMSonrntyBC298sdJDIaIqwUDQaSwNJSIiogqLJXgdQkTpGAg6jaWhREREVGFj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\nOx+f6p1nVzAFjKlZ0+23Wu78BZwa6sygvPD7l6atDmp5+IY+tDXW4d2Bk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5tMdRUNXInpwqLpuW2P3O7dUUwmOj4YnpurT1xHodtPoGOvfxDdQXeKwKAosZ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w78/ldioM3nCQHh/fDizOEoxuGyZkyJ9oPK2jxhdyvNzmfcRHOzUtmz0QjUZg+sKcqKQm2tiymU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\nwB1/pr5p4GaHg8qfCo0H6dixm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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "b5npmuR6wPhg", + "colab_type": "text" + }, + "source": [ + "## Conclusion\n", + "\n", + "This tutorial showed how to use a Recurrent Neural Network to predict several time-series from a number of input-signals. We used weather-data for 5 cities to predict tomorrow's weather for one of the cities.\n", + "\n", + "It worked reasonably well for predicting the temperature where the daily oscillations were predicted well, but the peaks were sometimes not predicted so accurately. The atmospheric pressure was also predicted reasonably well, although the predicted signal was more noisy and had a short lag. The wind-speed could not be predicted very well.\n", + "\n", + "You can use this method with different time-series but you should be careful to distinguish between *causation and correlation* in the data. The neural network may easily discover patterns in the data that are only temporary correlations which do not generalize well to unseen data.\n", + "\n", + "You should select input- and output-data where a *causal* relationship probably exists. You should have a lot of data available for training, and you should try and reduce the risk of over-fitting the model to the training-data, e.g. using early-stopping as we did in this tutorial." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ByUxlvhgwPhh", + "colab_type": "text" + }, + "source": [ + "## Exercises\n", + "\n", + "These are a few suggestions for exercises that may help improve your skills with TensorFlow. It is important to get hands-on experience with TensorFlow in order to learn how to use it properly.\n", + "\n", + "You may want to backup this Notebook before making any changes.\n", + "\n", + "* Remove the wind-speed from the target-data. Does it improve prediction for the temperature and pressure?\n", + "* Train for more epochs, possibly with a lower learning-rate. Does it improve the performance on the test-set?\n", + "* Try a different architecture for the neural network, e.g. higher or lower state-size for the GRU layer, more GRU layers, dense layers before and after the GRU layers, etc.\n", + "* Use hyper-parameter optimization from Tutorial #19.\n", + "* Try using longer and shorter sequences for the batch-generator.\n", + "* Try and remove the city \"Odense\" from the input-signals.\n", + "* Try and add last year's weather-data to the input-signals.\n", + "* How good is the model at predicting the weather 3 or 7 days into the future?\n", + "* Can you train a single model with the output-signals for multiple time-shifts, so that a single model predicts the weather in e.g. 1, 3 and 7 days.\n", + "* Explain to a friend how the program works." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "q8qVbNWtwPhh", + "colab_type": "text" + }, + "source": [ + "## License (MIT)\n", + "\n", + "Copyright (c) 2018 by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n", + "\n", + "Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n", + "\n", + "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n", + "\n", + "THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE." + ] + } + ] +} \ No newline at end of file From 07c79574828d6828eba525ab39a0e16cbe1aec5d Mon Sep 17 00:00:00 2001 From: hossein20s Date: Fri, 18 Oct 2019 03:44:54 +0000 Subject: [PATCH 2/2] compatible with TF 2.0 --- .gitignore | 2 +- 23_Time-Series-Prediction.ipynb | 280 +- 23_Time_Series_Prediction.ipynb | 7831 +++++++++++++++---------------- 3 files changed, 3924 insertions(+), 4189 deletions(-) diff --git a/.gitignore b/.gitignore index fc3ae95..46ced8d 100644 --- a/.gitignore +++ b/.gitignore @@ -6,7 +6,7 @@ data/ inception/ vgg16/ checkpoints/ -checkpoints* +*checkpoints* logs/ summary/ diff --git a/23_Time-Series-Prediction.ipynb b/23_Time-Series-Prediction.ipynb index 69a35b5..d7b6161 100644 --- a/23_Time-Series-Prediction.ipynb +++ b/23_Time-Series-Prediction.ipynb @@ -86,16 +86,7 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", - " from ._conv import register_converters as _register_converters\n" - ] - } - ], + "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", @@ -143,7 +134,7 @@ { "data": { "text/plain": [ - "'1.5.0'" + "'2.0.0'" ] }, "execution_count": 3, @@ -165,7 +156,7 @@ { "data": { "text/plain": [ - "'2.1.2-tf'" + "'2.2.4-tf'" ] }, "execution_count": 4, @@ -185,7 +176,7 @@ { "data": { "text/plain": [ - "'0.22.0'" + "'0.25.1'" ] }, "execution_count": 5, @@ -210,7 +201,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -226,7 +217,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -250,7 +241,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -259,7 +250,7 @@ "['Aalborg', 'Aarhus', 'Esbjerg', 'Odense', 'Roskilde']" ] }, - "execution_count": 8, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -278,15 +269,15 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "CPU times: user 16 ms, sys: 16 ms, total: 32 ms\n", - "Wall time: 30.1 ms\n" + "/opt/src/TensorFlow-Tutorials/weather.py:169: FutureWarning: supplying multiple axes to axis is deprecated and will be removed in a future version.\n", + " df_res = df.dropna(axis=[0, 1], how='all')\n" ] } ], @@ -304,250 +295,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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AalborgAarhusEsbjergOdenseRoskilde
TempPressureWindSpeedWindDirTempPressureWindSpeedWindDirTempPressureWindSpeedWindDirTempPressureWindSpeedWindDirTempPressureWindSpeedWindDir
DateTime
1980-03-01 11:00:005.0000001007.76666710.2280.0000005.01008.30000015.4290.06.083333NaN12.383333310.0000006.1428571011.06666712.585714290.05.000000NaN11.466667280.000000
1980-03-01 12:00:005.0000001008.00000010.3290.0000005.01008.60000013.4280.06.583333NaN12.883333310.0000007.0000001011.20000011.300000290.05.000000NaN12.466667280.000000
1980-03-01 13:00:005.0000001008.0666679.7290.0000005.01008.43333315.4280.06.888889NaN13.244444309.4444447.0000001011.30000012.118182290.05.166667NaN13.133333278.333333
1980-03-01 14:00:004.3333331008.13333311.1283.3333335.01008.26666714.9300.06.222222NaN12.911111306.1111116.8571431011.40000012.742857290.05.833333NaN12.300000270.000000
1980-03-01 15:00:004.0000001008.20000011.3280.0000005.01008.10000017.0290.05.555556NaN12.577778302.7777786.0000001011.50000012.400000290.04.833333NaN12.300000270.000000
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" - ], - "text/plain": [ - " Aalborg Aarhus \\\n", - " Temp Pressure WindSpeed WindDir Temp \n", - "DateTime \n", - "1980-03-01 11:00:00 5.000000 1007.766667 10.2 280.000000 5.0 \n", - "1980-03-01 12:00:00 5.000000 1008.000000 10.3 290.000000 5.0 \n", - "1980-03-01 13:00:00 5.000000 1008.066667 9.7 290.000000 5.0 \n", - "1980-03-01 14:00:00 4.333333 1008.133333 11.1 283.333333 5.0 \n", - "1980-03-01 15:00:00 4.000000 1008.200000 11.3 280.000000 5.0 \n", - "\n", - " Esbjerg \\\n", - " Pressure WindSpeed WindDir Temp Pressure \n", - "DateTime \n", - "1980-03-01 11:00:00 1008.300000 15.4 290.0 6.083333 NaN \n", - "1980-03-01 12:00:00 1008.600000 13.4 280.0 6.583333 NaN \n", - "1980-03-01 13:00:00 1008.433333 15.4 280.0 6.888889 NaN \n", - "1980-03-01 14:00:00 1008.266667 14.9 300.0 6.222222 NaN \n", - "1980-03-01 15:00:00 1008.100000 17.0 290.0 5.555556 NaN \n", - "\n", - " Odense \\\n", - " WindSpeed WindDir Temp Pressure WindSpeed \n", - "DateTime \n", - "1980-03-01 11:00:00 12.383333 310.000000 6.142857 1011.066667 12.585714 \n", - "1980-03-01 12:00:00 12.883333 310.000000 7.000000 1011.200000 11.300000 \n", - "1980-03-01 13:00:00 13.244444 309.444444 7.000000 1011.300000 12.118182 \n", - "1980-03-01 14:00:00 12.911111 306.111111 6.857143 1011.400000 12.742857 \n", - "1980-03-01 15:00:00 12.577778 302.777778 6.000000 1011.500000 12.400000 \n", - "\n", - " Roskilde \n", - " WindDir Temp Pressure WindSpeed WindDir \n", - "DateTime \n", - "1980-03-01 11:00:00 290.0 5.000000 NaN 11.466667 280.000000 \n", - "1980-03-01 12:00:00 290.0 5.000000 NaN 12.466667 280.000000 \n", - "1980-03-01 13:00:00 290.0 5.166667 NaN 13.133333 278.333333 \n", - "1980-03-01 14:00:00 290.0 5.833333 NaN 12.300000 270.000000 \n", - "1980-03-01 15:00:00 290.0 4.833333 NaN 12.300000 270.000000 " - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.head()" ] @@ -3028,7 +2778,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.1" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/23_Time_Series_Prediction.ipynb b/23_Time_Series_Prediction.ipynb index 9a962da..5c2e241 100644 --- a/23_Time_Series_Prediction.ipynb +++ b/23_Time_Series_Prediction.ipynb @@ -1,3927 +1,3912 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "anaconda-cloud": {}, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.1" - }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "view-in-github" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "2eJGvDw0wPc1" + }, + "source": [ + "# TensorFlow Tutorial #23\n", + "# Time-Series Prediction\n", + "\n", + "by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n", + "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "rpJpupAqwPc4" + }, + "source": [ + "## Introduction\n", + "\n", + "This tutorial tries to predict the future weather of a city using weather-data from several other cities.\n", + "\n", + "Because we will be working with sequences of arbitrary length, we will use a Recurrent Neural Network (RNN).\n", + "\n", + "You should be familiar with TensorFlow and Keras in general, see Tutorials #01 and #03-C, and the basics of Recurrent Neural Networks as explained in Tutorial #20." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "6SUvmh-9wPc6" + }, + "source": [ + "## Location\n", + "\n", + "We will use weather-data from the period 1980-2018 for five cities in [Denmark](https://en.wikipedia.org/wiki/Denmark):\n", + "\n", + "* **[Aalborg](https://en.wikipedia.org/wiki/Aalborg)** The weather-data is actually from an airforce base which is also home to [The Hunter Corps (Jægerkorps)](https://en.wikipedia.org/wiki/Jaeger_Corps_(Denmark).\n", + "* **[Aarhus](https://en.wikipedia.org/wiki/Aarhus)** is the city where [the inventor of C++](https://en.wikipedia.org/wiki/Bjarne_Stroustrup) studied and the [Google V8 JavaScript Engine](https://en.wikipedia.org/wiki/Chrome_V8) was developed.\n", + "* **[Esbjerg](https://en.wikipedia.org/wiki/Esbjerg)** has a large fishing-port.\n", + "* **[Odense](https://en.wikipedia.org/wiki/Odense)** is the birth-city of the fairytale author [H. C. Andersen](https://en.wikipedia.org/wiki/Hans_Christian_Andersen).\n", + "* **[Roskilde](https://en.wikipedia.org/wiki/Roskilde)** has an old cathedral housing the tombs of the Danish royal family." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "QDE1GCf4wPc7" + }, + "source": [ + "The following map shows the location of the cities in Denmark:\n", + "\n", + "![Map of Denmark](https://github.com/hossein20s/TensorFlow-Tutorials/blob/master/images/Denmark.jpg?raw=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "KST-d6LQwPc8" + }, + "source": [ + "The following map shows the location of Denmark within Europe:\n", + "\n", + "![Map of Europe](https://github.com/hossein20s/TensorFlow-Tutorials/blob/master/images/Europe.jpg?raw=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "vXPIxHP4wPc-" + }, + "source": [ + "## Flowchart\n", + "\n", + "In this tutorial, we are trying to predict the weather for the Danish city \"Odense\" 24 hours into the future, given the current and past weather-data from 5 cities (although the flowchart below only shows 2 cities).\n", + "\n", + "We use a Recurrent Neural Network (RNN) because it can work on sequences of arbitrary length. During training we will use sub-sequences of 1344 data-points (8 weeks) from the training-set, with each data-point or observation having 20 input-signals for the temperature, pressure, etc. for each of the 5 cities. We then want to train the neural network so it outputs the 3 signals for tomorrow's temperature, pressure and wind-speed." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "sPKsP4KfwPc_" + }, + "source": [ + "![Flowchart](https://github.com/hossein20s/TensorFlow-Tutorials/blob/master/images/23_time_series_flowchart.png?raw=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "8l8H5QRQwPdA" + }, + "source": [ + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "DodpoewhwPdB" + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import tensorflow as tf\n", + "import numpy as np\n", + "import pandas as pd\n", + "import os\n", + "from sklearn.preprocessing import MinMaxScaler" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "l0c-1RabwPdH" + }, + "source": [ + "We need to import several things from Keras." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "Ba3n0QNOwPdI" + }, + "outputs": [], + "source": [ + "# from tf.keras.models import Sequential # This does not work!\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Input, Dense, GRU, Embedding\n", + "from tensorflow.keras.optimizers import RMSprop\n", + "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, TensorBoard, ReduceLROnPlateau" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "K3zgR_60wPdM" + }, + "source": [ + "This was developed using Python 3.6 (Anaconda) and package versions:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { "colab": { - "name": "23_Time-Series-Prediction.ipynb", - "provenance": [], - "collapsed_sections": [ - "v2HIO-L7wPdy", - "5t6DXkeYwPeU", - "pylQQ42VwPej", - "2agyuXRywPgq", - "b5npmuR6wPhg", - "ByUxlvhgwPhh", - "q8qVbNWtwPhh" - ], - "include_colab_link": true - }, - "accelerator": "GPU" + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "colab_type": "code", + "id": "E9SAs5wqwPdN", + "outputId": "d893f0c5-c653-4794-9abd-24dc395f2c5f", + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'2.0.0'" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tf.__version__" + ] }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "2eJGvDw0wPc1", - "colab_type": "text" - }, - "source": [ - "# TensorFlow Tutorial #23\n", - "# Time-Series Prediction\n", - "\n", - "by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n", - "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "rpJpupAqwPc4", - "colab_type": "text" - }, - "source": [ - "## Introduction\n", - "\n", - "This tutorial tries to predict the future weather of a city using weather-data from several other cities.\n", - "\n", - "Because we will be working with sequences of arbitrary length, we will use a Recurrent Neural Network (RNN).\n", - "\n", - "You should be familiar with TensorFlow and Keras in general, see Tutorials #01 and #03-C, and the basics of Recurrent Neural Networks as explained in Tutorial #20." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "6SUvmh-9wPc6", - "colab_type": "text" - }, - "source": [ - "## Location\n", - "\n", - "We will use weather-data from the period 1980-2018 for five cities in [Denmark](https://en.wikipedia.org/wiki/Denmark):\n", - "\n", - "* **[Aalborg](https://en.wikipedia.org/wiki/Aalborg)** The weather-data is actually from an airforce base which is also home to [The Hunter Corps (Jægerkorps)](https://en.wikipedia.org/wiki/Jaeger_Corps_(Denmark).\n", - "* **[Aarhus](https://en.wikipedia.org/wiki/Aarhus)** is the city where [the inventor of C++](https://en.wikipedia.org/wiki/Bjarne_Stroustrup) studied and the [Google V8 JavaScript Engine](https://en.wikipedia.org/wiki/Chrome_V8) was developed.\n", - "* **[Esbjerg](https://en.wikipedia.org/wiki/Esbjerg)** has a large fishing-port.\n", - "* **[Odense](https://en.wikipedia.org/wiki/Odense)** is the birth-city of the fairytale author [H. C. Andersen](https://en.wikipedia.org/wiki/Hans_Christian_Andersen).\n", - "* **[Roskilde](https://en.wikipedia.org/wiki/Roskilde)** has an old cathedral housing the tombs of the Danish royal family." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "QDE1GCf4wPc7", - "colab_type": "text" - }, - "source": [ - "The following map shows the location of the cities in Denmark:\n", - "\n", - "![Map of Denmark](https://github.com/hossein20s/TensorFlow-Tutorials/blob/master/images/Denmark.jpg?raw=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "KST-d6LQwPc8", - "colab_type": "text" - }, - "source": [ - "The following map shows the location of Denmark within Europe:\n", - "\n", - "![Map of Europe](https://github.com/hossein20s/TensorFlow-Tutorials/blob/master/images/Europe.jpg?raw=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "vXPIxHP4wPc-", - "colab_type": "text" - }, - "source": [ - "## Flowchart\n", - "\n", - "In this tutorial, we are trying to predict the weather for the Danish city \"Odense\" 24 hours into the future, given the current and past weather-data from 5 cities (although the flowchart below only shows 2 cities).\n", - "\n", - "We use a Recurrent Neural Network (RNN) because it can work on sequences of arbitrary length. During training we will use sub-sequences of 1344 data-points (8 weeks) from the training-set, with each data-point or observation having 20 input-signals for the temperature, pressure, etc. for each of the 5 cities. We then want to train the neural network so it outputs the 3 signals for tomorrow's temperature, pressure and wind-speed." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "sPKsP4KfwPc_", - "colab_type": "text" - }, - "source": [ - "![Flowchart](https://github.com/hossein20s/TensorFlow-Tutorials/blob/master/images/23_time_series_flowchart.png?raw=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "8l8H5QRQwPdA", - "colab_type": "text" - }, - "source": [ - "## Imports" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "DodpoewhwPdB", - "colab_type": "code", - "colab": {} - }, - "source": [ - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "import tensorflow as tf\n", - "import numpy as np\n", - "import pandas as pd\n", - "import os\n", - "from sklearn.preprocessing import MinMaxScaler" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "l0c-1RabwPdH", - "colab_type": "text" - }, - "source": [ - "We need to import several things from Keras." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "Ba3n0QNOwPdI", - "colab_type": "code", - "colab": {} - }, - "source": [ - "# from tf.keras.models import Sequential # This does not work!\n", - "from tensorflow.keras.models import Sequential\n", - "from tensorflow.keras.layers import Input, Dense, GRU, Embedding\n", - "from tensorflow.keras.optimizers import RMSprop\n", - "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, TensorBoard, ReduceLROnPlateau" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "K3zgR_60wPdM", - "colab_type": "text" - }, - "source": [ - "This was developed using Python 3.6 (Anaconda) and package versions:" - ] - }, - { - "cell_type": "code", - "metadata": { - "scrolled": false, - "id": "E9SAs5wqwPdN", - "colab_type": "code", - "outputId": "d893f0c5-c653-4794-9abd-24dc395f2c5f", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 35 - } - }, - "source": [ - "tf.__version__" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "'1.15.0-rc3'" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 4 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "scrolled": true, - "id": "3eZxKhdYwPdR", - "colab_type": "code", - "outputId": "4ca51614-3db9-45e7-c857-2a0a0bf3cb86", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 35 - } - }, - "source": [ - "tf.keras.__version__" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "'2.2.4-tf'" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 5 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "bQqcfRlnwPdV", - "colab_type": "code", - "outputId": "b4fada10-5236-48ae-9672-42d3f447a9fa", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 35 - } - }, - "source": [ - "pd.__version__" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "'0.24.2'" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 6 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "5WEvg-wLwPdY", - "colab_type": "text" - }, - "source": [ - "## Load Data\n", - "\n", - "Weather-data for 5 cities in Denmark will be downloaded automatically below.\n", - "\n", - "The raw weather-data was originally obtained from the [National Climatic Data Center (NCDC), USA](https://www7.ncdc.noaa.gov/CDO/cdoselect.cmd). Their web-site and database-access is very confusing and may change soon. Furthermore, the raw data-file had to be manually edited before it could be read. So you should expect some challenges if you want to download weather-data for another region. The following Python-module provides some functionality that may be helpful if you want to use new weather-data, but you will have to modify the source-code to fit your data-format." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "0EKf--E8wPda", - "colab_type": "code", - "outputId": "9cbcd30e-3d97-41d7-a4a7-99f16d1ccb31", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 63 - } - }, - "source": [ - "!git clone https://github.com/hossein20s/TensorFlow-Tutorials\n", - "!cp TensorFlow-Tutorials/*.py .\n", - "\n", - "import weather\n" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "stream", - "text": [ - "fatal: destination path 'TensorFlow-Tutorials' already exists and is not an empty directory.\n" - ], - "name": "stdout" - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "JHuYxfRBwPde", - "colab_type": "text" - }, - "source": [ - "Download the data-set if you don't have it already. It is about 35 MB." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "JLAhzMSfwPdf", - "colab_type": "code", - "outputId": "dff49710-18ed-459a-a62f-55fea77f8522", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 35 - } - }, - "source": [ - "weather.maybe_download_and_extract()" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Data has apparently already been downloaded and unpacked.\n" - ], - "name": "stdout" - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Q85DXu_IwPdi", - "colab_type": "text" - }, - "source": [ - "List of the cities used in the data-set." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "fK-oHbPGwPdk", - "colab_type": "code", - "outputId": "299e689b-c391-4670-e93b-f961c46a54f4", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 35 - } - }, - "source": [ - "cities = weather.cities\n", - "cities" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "['Aalborg', 'Aarhus', 'Esbjerg', 'Odense', 'Roskilde']" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 9 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "IpjwHOh8wPdn", - "colab_type": "text" - }, - "source": [ - "Load and resample the data so it has observations at regular time-intervals for every 60 minutes. Missing data-points are linearly interpolated. This takes about 30 seconds to run the first time but uses a cache-file so it loads very quickly the next time." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "_hU0BoMbwPdp", - "colab_type": "code", - "outputId": "91f03c36-d22a-4f24-9222-fa83820e5fc1", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 53 - } - }, - "source": [ - "%%time\n", - "df = weather.load_resampled_data()" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "stream", - "text": [ - "CPU times: user 37.5 ms, sys: 64.1 ms, total: 102 ms\n", - "Wall time: 325 ms\n" - ], - "name": "stdout" - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "D_Vetst4wPdt", - "colab_type": "text" - }, - "source": [ - "These are the top rows of the data-set." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "UTv8npHowPdu", - "colab_type": "code", - "outputId": "97297b18-303e-40f5-83fd-2f7d3120fa23", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 476 - } - }, - "source": [ - "df.head()" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "
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AalborgAarhusEsbjergOdenseRoskilde
TempPressureWindSpeedWindDirTempPressureWindSpeedWindDirTempPressureWindSpeedWindDirTempPressureWindSpeedWindDirTempPressureWindSpeedWindDir
DateTime
1980-03-01 11:00:005.0000001007.76666710.2280.0000005.01008.30000015.4290.06.083333NaN12.383333310.0000006.1428571011.06666712.585714290.05.000000NaN11.466667280.000000
1980-03-01 12:00:005.0000001008.00000010.3290.0000005.01008.60000013.4280.06.583333NaN12.883333310.0000007.0000001011.20000011.300000290.05.000000NaN12.466667280.000000
1980-03-01 13:00:005.0000001008.0666679.7290.0000005.01008.43333315.4280.06.888889NaN13.244444309.4444447.0000001011.30000012.118182290.05.166667NaN13.133333278.333333
1980-03-01 14:00:004.3333331008.13333311.1283.3333335.01008.26666714.9300.06.222222NaN12.911111306.1111116.8571431011.40000012.742857290.05.833333NaN12.300000270.000000
1980-03-01 15:00:004.0000001008.20000011.3280.0000005.01008.10000017.0290.05.555556NaN12.577778302.7777786.0000001011.50000012.400000290.04.833333NaN12.300000270.000000
\n", - "
" - ], - "text/plain": [ - " Aalborg ... Roskilde \n", - " Temp Pressure ... WindSpeed WindDir\n", - "DateTime ... \n", - "1980-03-01 11:00:00 5.000000 1007.766667 ... 11.466667 280.000000\n", - "1980-03-01 12:00:00 5.000000 1008.000000 ... 12.466667 280.000000\n", - "1980-03-01 13:00:00 5.000000 1008.066667 ... 13.133333 278.333333\n", - "1980-03-01 14:00:00 4.333333 1008.133333 ... 12.300000 270.000000\n", - "1980-03-01 15:00:00 4.000000 1008.200000 ... 12.300000 270.000000\n", - "\n", - "[5 rows x 20 columns]" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 11 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "v2HIO-L7wPdy", - "colab_type": "text" - }, - "source": [ - "### Missing Data\n", - "\n", - "The two cities Esbjerg and Roskilde have missing data for the atmospheric pressure, as can be seen in the following two plots. \n", - "\n", - "Because we are using resampled data, we have filled in the missing values with new values that are linearly interpolated from the neighbouring values, which appears as long straight lines in these plots.\n", - "\n", - "This may confuse the neural network. For simplicity, we will simply remove these two signals from the data.\n", - "\n", - "But it is only short periods of data that are missing, so you could actually generate this data by creating a predictive model that generates the missing data from all the other input signals. Then you could add these generated values back into the data-set to fill the gaps." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "nROi76ntwPdz", - "colab_type": "code", - "outputId": "d43aa3eb-e5ea-4ce9-c756-42e7dfa18cdd", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 321 - } - }, - "source": [ - "df['Esbjerg']['Pressure'].plot()" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 12 - }, - { - "output_type": "display_data", - "data": { - "image/png": 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WTH17BYZ0LMGMyQNQmJe+aTg0AkArKUci2QeQyASOZBE3AYUIrVBoP1bValqm\nmjmnorouSSNJ0QB4FBBHg//OL8dd763CyK7N8PQl/ZGf47/jV06gawHJMVKPjtYkBERNXSIRrHnD\nPG4CChC3ndJN93UtJ7AZu7oXCblqpqfdikqh0spfiv7hUUAcNWZ8uxF3vbcKY49tjmcuTZ3809E8\nLTQCwKiu/wvzN8f/FnwAwt+CAOAaQFBolK+vdGp1N0vXpCp39koo70WmiP7hUUAcJU98tQF//egn\nnNqrJZ68uD/ystO78pcIjwA4aryKbyBK1JpoohZQs4Z5qKiJWu73yvGG7Ij+Lae1CkrXpCrvLayF\nJJoSAoBrABwBxhgembUOD3+2Fmf1aY3HJvVFbnZwpt3gjMQAMx7ya0/sDCDZBNSsYT4AHgoaFCIG\neq6WGSddBf30+qxOGdkJQGLMkpmK+wA4gDD5T/90DR6bvR7nD2iLf57fx3AB5DfBGo0OZlo7Xn5C\nGQBBA5CiN5o3FIrAcT9AMDCycz7w8U+q281Mql4UY9t+QFsDKG6QI5w3Hv3DNQCOcD8wxnDfB6vx\n1Dcb8dsh7TH97OMtNXzxi9BEARlpAGOPbRG3q9XUxeKrsWaiAAhL28T6zGXDyixVPJTj16SaRcld\no/RMQJIAkPbnzl8OIGird7+/Cq8s3IIrhnfEnacd61oDF7cJjQDQas+XQGipFsmipCSjFo1EExDX\nANJO44IcWGh6BADIiRBqo8w3E1BudlZSQphWxjIANJI0AFHz8CNXgRN8bn9zOd5euh1Xjz4Gt53S\nLbCTPxAiE9DBo7W6fTClCSJXYWOTTEB7uA8gEFjVACS12S8nsPL+MaMBIK4BcNs/B3h76XbcfHJX\nR5P/f68Y5PKo1AmNAPhhy4HEA6eCpH3nKEqqFuZFUJgb4RpAQLD6QOSIKoMZ84obeQC5ivA8pQCQ\n31/5YjTH3ooa3P3eSuzjVWc5AKZO6I7rT+riaOWvN9e5SWgEwO7D1bop01LUj/IBJiKUFPFcgKBg\n9ZHIFidcLwus/W/KEHRvKfSLyDMI0SuS3YNSD9dpH67Gi/M3Y8dBe12ZOPWLP446xvExciJZuGxY\nmfPBGBB4AXDliI7xv4t0BIAUJZKr0AAiRCgpyo13heKkF6uLooikAXgYWjm4UwnyxBwSoxht+SJE\n+ii7eLnxjMdKMyIz5ESycPfphm3THRN4ATC8S6IpjL4GIPxWPsCRLEJJYR4vCBcACGTZTCOZXNyO\nsCkrKcCTF/fDx9ePEM4jruaVPgAAce0ASF6EBNi3x/GZ+RqNjOySEyFf7q/AC4AcmedXTwBIK8Qc\nxQNMBJQW5XITUEBQq66ph9wu+h3PAAAgAElEQVQEZDaMelBZU8N98nMiOLVXK/Ro3QhAwtmspgGc\n3CPR8louAKTQVCJ1wcHJHJ76ZqOrx8uJZPkSPRT4u9Zs8oS0QFTVAIpysa+ixnZjcU76yI47gWNJ\naV7tmjZI2Xf5toMAgDHdm1s+j7RwUBMAPVo1iv8tvx87lhaiMDeCFy4fZN25wQkcag2BzPDTjkP4\nZt1ux+eXz/fZPvUHDrwAkKdOV+t8QdLkrtQAIiSYgOpiDIdUCntx/MWyBiCFgcZYkvmoX/smKfvO\n+Nb8Kky5upL+zVZZcLQozk/Zr0NJAVo0yseq+8djZNdmfHFRD7DrJ3x6zkYUuNzP1y+NMvCJYPKw\nuyqdrmB18SggpQlI0AAAoTFM44JcD0bJMYtVH4C0AKhVfPdqNYU27D5i6piXDOkQLxsiIeUnqK28\n5A8jgfDt7WPiSWASvA1kZrJtfyXeX/YLLhtWhmfnbjJ+g0n8qhkUeA1ArnKb0QCUkjOSRSgt4uUg\ngoLVeVL6OmsUSVZqpkEpDp8IOKZZoeYxp53VE52aFSVtS2gAxo9Eu6YFvsVpc/zDjtFl5txyEIDf\nDe9ouK8VlPlMXhF4ASDPHFVr2ScRDwNVaABZhLgGwJPB0sOYbkIkF5H1cDmpjHeKBqAiAOQFA9+9\n5gR8N/VE0+eJawCK4zbIiSTZZnnkD0fiQGUNXlu0BWf2bo02jVN9Uk7IsVozxSaBFwBym/Gjk/rq\n7Cf8VibyZEey4hoALwmdHm4c2xUAMKZb8yQT0Igupfi9wcpJ6r0rlYKQ5mej4ICG+Tlo3bgB3rpq\nKJ66pL/hGKWjKU1ALRrlJTWp4QKAI/HSgs2orIliyqhOrh87y0Twy6SB7RyfJ/A+AKlP6/Fti9Gn\nXWPN/SQNQBIAvx3SHn8YeQyK8rLjqzieC5AeerdrjPLppwEAVu84GN9eXRtDjkHilSQAquMCgBBj\nzHR0WP8OTU05aCmuASSPp1nDPEuTfkFuBJW8+VAosaKbVtVG8fx35RjdrRm6t2xk/AYTWA37bNvE\nudYReA2gY6lgy71yhL6UVSaC5WdH0K5pAQBhtdi0gOcCBAH5XFwdjSXleaghmf2khYC0MopkEZ64\nqJ/qe5RHNPNcxX0ACg1gQs9WSe83smDpZatzgo2V6fetH7Zhz5Ea/GGk87IPdnEj7iDwd2vjgtz4\n6lEPyVQk9QSIKB7kkqJc7gMIAPIJtLo2ajraIcUERISebdxZeSUdVyGQLj+hDGt3Ho7/bxTG2qhB\nDi8NUc+JxhiembMRvdsWY0gn46RDr7AaUq1G4DUAs0jloCUTkNKZV1KYx30AAUDetaumLpaSt6GF\npAFI4Z+RiPWyEnpITmCl802plhutuhoaNL3nBBezFpjPV/2K8r2V+MOoY9Ja69+N+7/eCABJGkom\noIjiQeYaQDBI0gDqYqbD3aSyC9JEHSFKstnqhWWaeUilXZSaI4AkJ7CRobhhPg8PDStmJlTGGP7v\nm5/RoaQApxzX0vtBGYzFKfVGAEgagLTyV5qWS4vyeB5AAJDfstV1UdXMWzWkPACtjN0GOYlMTDuL\nMoprAITnLh+ouZ+R2s01gPrNwk37sGzbQVw5olPae/y64QOodwJAWvkrIz9KCnNxqKoubkrgpAnZ\nBFplIgpIQukEVobJMTBH6fjS0SJZWRjTTbuWkKEPgAuA0GJm4fDUNz+jpDAX5/Zv6/2AAEzoqa1l\nMEtxS+rUGwEgzfdaDURKxFwA3rUpvci/lZq6WLJ5RQdJAEjzr7IURIwBgzrad8jFfQBqJiDZJqNV\nV162uzVhOMFhza+H8NXa3bhsWBnyc9z/ntWehMc1It0ArgEkIWkA0oOsbCKeqAfEzUBBoSYaw2GT\nBfqkKCDJ7pmiATBnBbTMJpgZ2V39SuHn+M/TczaiQU4Elwzt4Ns59e4mN+60+iMAWLIPQKkBlHIB\nEAiU8+fBoxYFgPi/srk8Yyze1cusViEnnghmIESMVl1+FfHi+MsvB47i/R9/waRB7XwtKKl3u101\n2nkOQr25WyWbv7QyTNEACsVyEDwSKK3IV9DXn9jZtE8mxQSkuHNjjBn289UjplhAGO2nhVmnNidc\nzJy7CQzAFS4XfTNC725yI+Ks3ggASQOQNHClAChtyOsBBQHpW5k8tANuHtcN157YGb87wfihqhHD\nQOMmIKUGAON+vgDwypWDVbfHo8gMTDhGkXfpjgzhuM/Bylq8+v0WnHF8K7RtUuDrubOyCN//5STv\nju/ZkX0mvjIUl4ZRxZNamBtBXnYW1wDSjPS1SCaXxgW5uPsM4+bXNXXJ9XWUE20sZk4DGHZMqep2\nrgFwtEyHLy3cjIqaKKakqexD80b5xjvZJNQC4JIhqc4Y6QGMKpqIE5GYCxAcAZCJfWTNBC6c3bdN\nyrYaQx9AIgJH2TtAYkCH1C5iElItKaN+AMYagPff6fSze3l+jkxETfurqo3iuXnlGNm1Wbx/tFfY\nyV95+ffqGq1ZDO9WIppJRLuIaKVsW1MimkVE68XfTcTtRESPEdEGIlpORP1k75ks7r+eiCY7GrVI\nA5W4byk8UKkBAGI2MDcBpRXJhKN3s9dEY3jl94Px0fXD49tq64T3xTSigOQ+gGoVv8LsW0bhhd8N\n0jxnPIhANRM4+Tx6+KEBnN67tefncJtOOg16goJal7l3lm7HniPV+ONI90s+u8EJndU1WrOYWa48\nD2C8YttUALMZY10AzBb/B4AJALqIP1MA/AcQBAaAewAMBjAIwD2S0HCbxgWCY6RpYaqnvqSQl4MI\nCnqROjV1MQzrXIrjWhfHtyXCQIX/lQ8rA5CXIwmA1HLMxzQrQqFOpc5YPJHQoQnIhzBQbmTyB6no\nW682xRh6TEm6h+MJhgKAMTYHwD7F5okAXhD/fgHAWbLtLzKBBQAaE1ErAKcAmMUY28cY2w9gFlKF\niiuc3KMFHjrneNx8cteU10qK8nhJ6DST8AFo71MrM+Gc2kvIhIxHAYnb1aOAIkn7WkHSAIw6MWnN\n/y9dMRhf3DzKFw0glN2HQzjoWat3YuOeCvxhVCdT9aRevXIIPr9ppA8jcw+7BssWjLEd4t+/Amgh\n/t0GwFbZftvEbVrbXYeIcP7AdqqZeiVFudhTUeNKESVXyMClnJS+rvfRa2X+mycv7o+xx7aIN4SR\nUPoAYgy6JiAjojoaQHImsPq9M7xLKTo3L/LFBxDGZDOtJ+4PATWtSEXf2jctwHiTRd+GHlOCri0a\nOj63n4EEju9WJsymrs2oRDSFiBYT0eLdu3cbndvSsUsL81BTF8OR6jonQ3QNp1/zsBCqpWY0AOUK\nPjuLElpBPA9AJRFMEgC11gWANC4jE45hIpgPD29Qyk00F0OrzaD1rP751GPdGo6rLCrfjx+3HsCV\nIzr6ntznhxlRwu4n2ymadiD+3iVu3w5A3qiyrbhNa3sKjLGnGWMDGGMDmjVrZnN46iTKQdQPP0BZ\nabAca12aFxnuI02gyhW8HOVqP1tW+1/SIFIFQCIPQM0HYESimmzqI9GyONF6z8gHkEl5AFaiVrzW\nuU87vpXjY8jH+NQ3P6NpYS7O7e+8765ZJL+YXw3hAfsC4H0AUiTPZADvybZfKkYDDQFwUDQVfQZg\nHBE1EZ2/48RtrtG1hfHkIxWEC4ofoL41GO/TrjHOH6BfJTFewVDns9cpBIC8aYwyjyBx3MTK2JYJ\nSCcKqCgvG0+LjeWNlE67q7fWxd7FelulecM83HHqsfjx7pPRXyd01krJDSdW109vHIFTjmuh+fqw\nY0o024PaYd3Ow5i9ZhcmDy1TjTT0GrMVct3ATBjoqwDmA+hGRNuI6AoA0wGcTETrAYwV/weAjwFs\nBLABwDMArgYAxtg+ANMALBJ/7he3OUK6qQZ0aII3/jjMcP+SwvqlAQRNftx75nEYWKZfkTM+gRtE\nAcmRr6qlFXiECO2bFuA4MTY7xpgsCsi6AIgp+kkokcZgHAZq7+Ed1a052jR23uTbDXq1KcaVIzuh\ncUGubo9jv5Sd7i0boXED7fo7dVF39Qup6NulPhZ9AxKLIz99AIbFyxljF2q8lJKfLPoDrtE4zkwA\nMy2NziRdWhTpdoSSaJbh5SDaNW2ArfuOenb83Ows0yYQPe2nb/vGSf/LnZ7yKKBIFuGxC/vipH9+\nIyaCST4AGyaguAagPoFLJiuvSkHccFIXzFmn7/NKB25NrXb7157dzzhWRC3nxy47Dh7Fez9ux8WD\nO6CJSii5lzAT5lG3CXUq6ll922Bin9a4ZVw3U/s3Eav4BSUXwIwKfdmwMtfOd9sp3V07lhoE8+WU\ntfb67MaRuH9iz6Rtaqtq6SGRPyxOTEBGGkCWaQ3A3sPrt+ugo47/yOx0ms5+uHKUdb+c8Ny8csSY\n/0XfAFmSo4+XNdQCoCA3gkcn9UVpkblohNzsLBQ3yHHkA+je0nmYl4SZ56d5I+3PZvX5y3FwZ31y\nwwh8e/sYw/3aN9UvlmUUBdStZcOUEF65UElUA01t/SlpAE7yADQFgLjZaK5R6ylsloDMp0m4FTLt\n9DB63a/MCIB/XdDbcJ9DR2vxysItOK1XK7QzuI+9QMu/5SWhFgB2LpSUC6DFmG7uRh7pYWb0jAEt\nNYpBWX2onNxXx7ZqhHZNC9ChRP/B6Nu+CZ7X6akrDdmKA1Et7j3eGlL2ofJ1MoGNkPzOWk7cSNwE\n5FEpCAqmANAjKONV9v5Q4zd9tYMT+okmx5cXbsaR6jpMCWhuglkuP6HM9L7hFgA23lNamLnZwFql\nEPQiLJRM6GkcbtdNR0uS7KpqpTq0ULPLSxOyfBKSwjUvHVpm+tgSMZ0wUOE85kxATsJA/Qz/M7uy\n19utm4WkJykE2y56C4ZzDPwEZi0EM+eVY0SXUvRsU2y8swfEfQAOb4Nrx3Q2vW+oBYAdSor06wEZ\nPRZuJhGb1WC01F+zzqLbTumGf1/QR7NnrrzmjhvoXaOLBrXHP87rjckWfBtqq+qIigZQlJeN8umn\nWTq2RLyfhGEUkP5xcmwmDREIMyYPsPVeOY9f1BePTupjuF/53krH5xrepRRf3DzKcL+vbh2Ntk28\ni3BqotOh69ReLfHBdSfovl/6SqMxhj+kqeQzkFhc2OloZ5dQCwA7KqhQEVRHAASkSoQcp2Nq07gB\nzlIpsSzhpyYfySKc27+tpZWy2qpc+u7dipiQHj6tCTzhA/BGA2iYn41OzYrw6pVDbL0fEMZ4+vGt\nMbGPsyorcu1Az/ZOADqbSP7TczibHpPOOPS+k0cn9UWrYn3hIwn1VsX5OKFz+rLrE6XO/TtnuAWA\njamrpDAP+ytrUpKNJCb0bKkbj203nE0NN75no9rwj5zfGxP7uFc+WO9BTOzjLmp2+Ug8CsidcxhV\nA5V8DoaJYDYGlJ+TFXd8G11frWzr/h2aYI4JJ71VgrggUqI3RrVv479XJJcFX7b1AADg9yPMFX3z\nirgGwJ3A3lFalAvGgH2V6lrABQPb6dbYcfV5MPk9690Pkwa1x9BO2uM9o3drxzeUFaeSF6hFecT7\nAbj0rMSrgWo4gbN88AEAQCOdPq9tGjfALA2Ty6COTS21K2yok+Alv190J9eAeIGtLsq0khXNlDLx\nEjN1stwm1ALAnglIvzk8EelGFbhZSdTp9zy8i9AMQu86mBmu0XW854zjEvumIf9YLapHLQ/ACVJH\nMK3jRUwKALuZwBI92xRrdi7T+6hWr0KzRnl4449D0Sg/VRCYvcct1QLyUJOwmgagVikY8DcBSx3j\nSrluE2oBYAepHISeI1jvRvD7JmGMaT48Q8SVv96QzKyO3Ho4vVoRVqlU95QW2m59H0ZN4aV53TAP\nwIYGoBSqJx1rPirLLgRhJbz83lN09zNj8vMS6dro3aN697iVezLddfx4HoAPlJooBzGkk3Y9m14m\nQ8RKTYS9mfmizZS4cLoqb+1yDRq3+y2oaQBqiWBOkExAWgIlvt2HctB+PP96917vdolSHH74AIY7\nbGto1gT09a2j8dmN2g1b0m3SkhYXXAMwiZ3vq7RQEAB6BeG0mj83ys9GQZ571QHNhCteNFi7IJX0\n+fUKdpl5Nnq2KcZzGslbD517vPEBPEatvr80Ibv10EpOYE0TkNlSEC7Uctc6gpWPOsigKJ8e153Y\nJf633qd1a6J6yWFj8/N0SjbLx1hWWqibo5JuC5CkbfFaQB7SqEE2srNINxlML0lp6oRjcdPYrqbK\nT+tRPv00DNaIy5djxqTwoE4kkNnVkVZSz/kDzNdDl1b+bq8aq1RKOyTCQN05h1EegNkwUKc+AMDe\nRGT1kusFOpg2Y6V7xhRxq2Rzuj+N5IfiTmCT2Fn9EZFhMlir4gaaNW2K8rJxw9guvkhpszHJelUL\n5RNDXnYEE3qaa2+nRTqeebU4crVEMCec008oFVCgMZkkooD0j+NlQxi3HPBXDO+Iu07vYW7nEISB\n6mHl9shKsxMg3i7VxKDdylcItwCw+b6SwjzDktBGNW+McGMV/MLlg4x3MhxH8kBuPSW1cqqVh6Sn\nTtawV3PF9SemprYn8gDceWjvOPVYrLrvFMcRInZ8AMpDa030/7rAOMPXDGUlBbYzluVY+aRhyCdI\ntwZQIpqnhxtM7kvvOtmVuQEIoQCYNNB5i7aSotxQNIWRauA4eXaU73V6k7vRes8q2ZGseMesJy/u\nh6tGHxPPQHVLI8nKIs1aSYD5lb2TaqASWp9JvzuX0YYEVu4ntSggaXxuaoNeNcOxYiVItxO4ZXE+\nvr19DP40Xr9se4PciGt9ikMnAKafc3w8Wcfu91VaZKwB+IFa0s+4Hu6GADKF+VztJm8VoHaERnQo\nKcCfxnePfw6/HGZmT+NnNyc/+NtvUv1LdhZhfzFo/m4UOeaHAhEEl0a7pgW+NqEPnQAAnNtCSwpz\nseewtxqAmRu2V9tiPHOpcQEwzagQU+NIHonyPbeO64qG+Tmatm8reDn5JeqkJJ/Dq1O+8cehSZOf\nWQ3AjhPYjY/g1QTZRSU4wExbTyXtbZpUnUzKUydYa4AUgPnfFG4KqlAKAAm7gqCkKA9Ha6OorKlz\neUTWOdnEit/JF65cWGkdq3FBruOiXX6o0Cn2co/OObCsKS4a3D7+vxMfwLK7x1k6t53PpFdCQolT\ne7zUeEerbIab3KbiszLLH0dZq+yZ/kxgc7g5TsOewEHG7nWQapNbbQ3pZntGNzDzHCv30bt5urds\niE17KhyNCfDW4eeXBmB0Xs39FANq1jAPxQX6k/O/J/VN+t/OR2rUwL9H+dZTuqEwL1u3wqwbvHP1\nMLTQaIbkBWERAG6OMtQagF2kLN09FhvD3GzQe/jUXs5CLAF1oeZkQm2gEdWSOB/J/rZ/Hr9QTvh+\nOe7sChqj727ssS1StEBbeQA+Rtk0zM/B7eO7xyOJ3GyTKqdv+4TT248yzWG4/wHte76JwUJDjVAL\nALvfV6lGQTinNmwiwqOT+uCdq4c5Oo6bKJNk9AuKufMEeFE/Jh11UuR4Gd+vJCwrUQmvInjk/KZv\nWyy962RPjv3WVcPQp11jU70NggDXABxegXhFUEUk0GMXJlRxuyuqiX3aJK1cvMTOGMM2uUikI01e\njn0BYP1LOn9AO+eLEZ3X3K7V1NlhVrxZ9BIeAWDxnWPxgw0h0b9DE7x7zQmaOSBBYFTXRK9yrUfA\nzuIo1D4Au4JAqgi650gNerctxrJtBwEknFv1Gd17xKW51UtzRLrEl1MN4Ox+bTCmW3Nc9+rSpO1q\n30eD3Aj+dnYv3P7m8vi2Zg3N9bU1g9FEapVbx3XDU99sTNn+4XXDXfEpmcVs798w8sLvBiEaY6iq\njWpO9HYEe6hnPLsmi/ycCIrysrH3SE28pLISI8edEdKX0a6pc/XYzUWv3jWzc5qGKvXkvSR8GoDw\nvkfO74Mzeqd2ZjMb4qu1nzxSyQwXDmqPM1XGIfHSFdYLs2llFfdsU6z6mdUIQaJw2okYJCvaIdQC\nwAlCb+BkE5B8blFLgLFDrsWkDjUh3q2lenVSOWbnRb15zFZtJcvvcEa6LFgR2yfWn9rMHtatz31W\nH/0OcXrVMjnBxs7zG2oB4OShKClMLQgnn3yLG+RgTLdmcIobTstSLZVdY27R7SHg8gTql1NW+m7S\nVbDLKydwkwL179bpdbX79nQ1gAmnZypYZKAJyD4lRXmWw0CNkI+HqWyzi5uPpNsmILWbzospJJEJ\n7MHBTeCVoDNdldMA5TV3mtTnN2bvGWVDd44zQikA3HgUS1UKwlkRoGb3tTpv2J1nzL5N3wRk79wA\nMPbY5vbfbIF09CR2gtF9Ytam21IjIUrratxzxnGYeZlxmZHU4wX7+o7o4lwrr69koAnI/s1aUpiH\nfRXVphum+IXaA6j1KeXquvxT6KmCetfMztWUjue0rZ/58/lymrSj/JgzJqt3bNP6pvNzIjixe2qZ\nkXRXvNRCr6sdxxwZZwJyQklRLmIM2F9ZG9/mpiiQJkSpxrdZ1Gyw7pqAFP+7Ffop/9tDoRrM6Usb\nu1dC+b0YhYH6cV2ev3wgnrtMXRA55bnLB+IOg4qhalgNsuAkE+qr59QHAEC3NaTuuQ1O/o/zeuPr\nW0djRFd/VsZm0c0EttVhzcFgLBAwRS1wmL08xmWXtV8f3a05xnT3xtTXtkkBrhzZyZNjZwoZaAKy\n/14psmZvRcIPYGXlarRrfk4EZaWFlm2qavs3b2SsRZg9i/L48s/hZC5Xc4Bz7GPWNxA2jYgTLEIp\nANxYdZY2VK8HlA6MyurefHJX1e3yydvspEt63zifTXTp3bYYt45T/y60OLd/W1vn8qpJj9EqMehO\nYLeQCkJmOuEUAC7cpIlyEAkTkNOVq9rDZSauetV949FNbLyhNmHkZUdQaKNhi9ok4rYPQO3tjfXy\nEGwT70KSNt67djiuPbGL6f3XTBuPqQbt/ezSqVkhRnTxx7z4xc0j8egk/X7EWosUq6z963hXjmME\nNykKOBIARHQDEa0kolVEdKO4rQ8RLSCiH4loMRENErcTET1GRBuIaDkR9XPjA9ilcUEusgiorosZ\n72yC4gY5mDLCng0zNzsLOdnCzNakUJg83XBu5arUNtKPArI+u0rlpuXPU0lRHr6beqLlY5khTCvU\n/JxISuKa2bh/o8/55S2j8d8rBmPysDI0zM/Gyce620pUTufmDTGxj37t/+tP6uJK2ZO8bOOFTu+2\nxY7PE7ToPzdoJvo1rSQt2o69IqKeAK4EMAhADYBPiehDAA8BuI8x9gkRnSr+PxrABABdxJ/BAP4j\n/raNk8kgkkVoWmi/OXy+YkW+7B5rXZ+MsJqRSUhMwnrvdDOR6p4zemDTngq8OH9zymutfSgRLNGi\nUR5O9Mg56RW/6dsG153Y2fFxurZoiBX3nuLCiAQCGiWaxNtXn4BojKHrnZ/g9vH2OoZdObITHvp0\nrcsjSw8zLh2AReX78LvhHTFn3W401sguV8PJMvNYAAsZY5WMsToA3wA4G8L8IxWvKQbwi/j3RAAv\nMoEFABoTUSs7J3brJrUaoinnPxf3w01j3VF7AXdXtpIZSe2IyvPI/7d6XS8/oWP83V7PGw+dezy6\nt2yo2vRi4V/G4sGzj/d4BO7SuCAHnZppl1H2YiJu37QAx7XWrit12yndQlFRM5JFyM3OQvn00/B7\nm1p3icsVUdPJ2B4t8OdTj0WLRvk4b0A7S+91IgBWAhhBRCVEVADgVADtANwI4GEi2grgHwD+LO7f\nBsBW2fu3iduSIKIpoulo8e7du1VP7NazUeLAEdS6cQPcMNa8PdgLmMrf6/46AZcM6aD5Hv2GMMHl\nxO4t8OmNI5Ed8rhvL3MkjJhz+xjV6KILB7VHl+ZFuGaMc42EEy5sm4AYYz8R0d8BfA6gAsCPAKIA\nrgJwE2PsLSI6H8CzAMZaOO7TAJ4GgAEDBnj6tJQoVjtBMgvaHUsWGdj5PSoFwUmQEyHURg2qgAZI\n3D54tjuVb+X3bCb01qgPOMq/Zow9C2GCBxH9DcKq/kEAN4i7vAFghvj3dggagkRbcZt9HD5DwVQD\n1T9UcYMcVNREk7bJV5NyH4D+0V12AucKt1BegLsp+c2iO8aiqlY/uMDIx8OFsbfwmkICTqOAmou/\n20Ow/78CweY/StzlRADrxb/fB3CpGA00BMBBxtgOm+d1Muw4qbHAQVAB1Mfwvz8MNS2w9GsB2RqU\nJjec1AU3n9zVdrx7faRxQS5aOozjD5KGYJYwCS0/gxSCjFM97S0iWg3gAwDXMMYOQIgM+icRLQPw\nNwBTxH0/BrARwAYAzwC42u5J3fMBeO/wsls1VPm2dk0LcMlQbdu+2mnUBKVeRy07D3CD3AiuP6mL\nZlcojjp+TfBTJ3iTh2BE3/aN03JejjWcmoBGqGybC6C/ynYG4Bon53Mb5Yo6GD4A8xNDkUo7RiLS\n1WP0jh6mFVx9x63vYsqITpj+yRp3DmaA9Pw8eXE/35LUOM4I57LNpYej1MVG226jZsaRrxofPvf4\npKQZ07WAdHfkEqC+kQ6h3qtNMRrme5ENznGbcAoACYcr9lJFHkA6FQAzz6n8Yba/ylfkAfA5Py0Y\nXXf+vXD8IJQCIAh5AN5hTgwpNQS1nACr8EknOITRCcwJH6EUABJOG1gXKMo5pNUHYHH2NTNWq1PI\n4I5NLb6DY5Vg+Jm8oT5/tvpKKAWAZMZwesMFtT0eYLyKb19SkPR/Uj1+C9dF/r6JfdrEq5JyvMXo\nzisr1S/bUB9ow0Mx005IBYA3x+3cXLs2i9eccpxQzbFVsfBQqE3i0scee2wLDDvGRJSFjevktlls\nUJk5rWLSwHYY0KGJq+cOM3nZEXx0fUqQXb2ibRMuANIN78QssuzucShWKTQmoVZa2U2uGnUMLh7c\nAY1UQjuVdGtpUlCZ0ASUuzhNYJKz6r5TTOcHTD8nXMXc7PKbfm3w7o/bcdkJZekeStoJsAKeMYRS\nA5Bw0+SoN/kDwOnH2zInTfwAAA1hSURBVCpcahoiQrFBIxXpgVHTDh48uxeaFuaaKvf8rwt6a742\nbWLPePMPp6WjC/OyPRecYaO0KA8fXT8CbZsUGO9cz+GO7vQTSg1Aum3SWVlR4qPrh2PTngpfzqXn\ns7hgYHtcMLC94g3q+57YrYXmLoV52ZjYpw3qogy92/FsTg6nPhNOASA5gdM8DgA4rnUxjmvtvEOR\nhDuOaf0rYyZ66hxe24fjMdwElH64fl6P4c8XJ0j0adcYgzs2jZsg75/YM80j4oRSA0gLAVA3zurb\nBs/N24TzTXb9MVUe2qaUcKMvK8c/hnYqSfcQ8O41J8T//k1frmEGgVAKgIQPIK3D8JQzerdO2dam\ncQMsvvNkx8d2et02/u1Urr6HBCLCrJtG+lr+mN8b4SGUAqB14wbYW1GDbDc7nAeIpXedrFrp0ype\nXZ2senrd6ytdQpbcd9PYrvj10NF0DyMjCKUAmHnZQMzfuBdNAtnRyzlOP5fRCp+v0DhBJt29tjOJ\nUDqBmzXMw5kqJpIgks5Q1SCXuuDUP4IQls2xRig1AI4z+HPKqc+88cehaMT7EZiCCwAOh+MKQdE4\nB5qsP/XalCGoqo16PJpgwwWAT4zoUorF5ftxNMNvOA4nKAwJQGhsugmlDyAd2LWaZIvF0Pq2b4Kf\npo13b0A6WBkrr8fCcQvuAwgfXAPwmMuGlWHP4Wr8cVQn38/Np3ZOOgiKKYhjDNcAdOhQUuj4GPk5\nEdx5eg8U5HJZy7FO84Z5xjtxODbhs5IOfz61O/YcqcaHy3ekeyiewRdrwebD64dj677KdA+DU0/h\nAkCHvOwI/nFeb+TnRHD7+G7pHo5rJDWQ52bbQNO8YT6aN3SvSY+X8FspfHABYEB+jiAEwgSf1Dnp\nhCuV4SHjfQC3j++Gdk3rZ29Sbt7hcDh6ZLwGcPXozrh6dOd0D8NX5HKBCwkOJ3PJeA2gPmLU8Ytb\niDhecPfpPdC0MBelRTxyKSxkvAbA4XDcYUKvVpjQq1W6h8GxANcAOBwOJ0PhGoDPtGiUh5OObeHL\nuXiZBw6HowcXAD6z8C9j0z0EXrOFw+EA4CageomV+b1XG97cncPJVLgAqMdohXjKi3UN5iVxOZyM\nhQuADEQyATUp4F2TOJxMxpEAIKIbiGglEa0iohtl268jojXi9odk2/9MRBuIaC0RneLk3Bzn8LK9\nHE5mY9sJTEQ9AVwJYBCAGgCfEtGHANoBmAigN2Osmoiai/v3ADAJwHEAWgP4goi6MsZ4iywOh8NJ\nA040gGMBLGSMVTLG6gB8A+BsAFcBmM4YqwYAxtgucf+JAF5jjFUzxjYB2ABBeHBchgf5cDgcMzgR\nACsBjCCiEiIqAHAqhNV/V3H7QiL6hogGivu3AbBV9v5t4rYkiGgKES0mosW7d+92MDyOFoV5guI3\naWC7NI+Ew+GkE9smIMbYT0T0dwCfA6gA8COAqHjMpgCGABgI4HUiMt0PkTH2NICnAWDAgAF8LWuD\nSJZg28/Piai+np8TwfoHJiA7i/sAOJxMxpETmDH2LGOsP2NsJID9ANZBWNm/zQS+BxADUApgOwQN\nQaKtuI3jMl1bFOHGsV3w5MX9NPfJiWRxJzCHk+E4jQKSHLztIdj/XwHwLoAx4vauAHIB7AHwPoBJ\nRJRHRB0BdAHwvZPzc9QhItw4titaN66ffQ44HI47OC0F8RYRlQCoBXANY+wAEc0EMJOIVkKIDprM\nhMDzVUT0OoDVAOrE/XkEEIfD4aQJRwKAMTZCZVsNgN9q7P8AgAecnJPD4XA47sAzgTkcDidD4QKA\nw+FwMhQuADgcDidD4QKAw+FwMhQuADgcDidD4QKAw+FwMhQKcntAIjoMYK3OLsUADhocxmgfN44B\nAO0BbHHhOPVtvGb24eP1dp+wjdfMPny8+vu0ZIw1NNhPaA4S1B8Aiw1ef9rEMXT3ceMY4j67XTpO\nvRqvi5+JjzdDxmvyM/Hx6uxjNHdKP2E3AX3gwj5uHAMADrh0nPo2XjP78PF6u0/YxmtmHz5eZ/sA\nCL4JaDFjbEC6x2GGMI0V4OP1Gj5eb+Hjded8QdcAnk73ACwQprECfLxew8frLXy8Lpwv0BoAh8Ph\ncLwj6BoAh8PhcDyCCwANiGgmEe0Sy1pL23oT0XwiWkFEHxBRI3F7DhG9IG7/iYj+LHtPubj9RyJa\nHKAx5xLRc+L2ZUQ0Wvae/uL2DUT0GHnUOcbF8X5NRGvFa/yj1KfC5bG2I6KviGg1Ea0iohvE7U2J\naBYRrRd/NxG3k3jtNhDRciLqJzvWZHH/9UQ02e2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TlxwftQjKSA0FULUEgN8O61xVnmAQ2rr3cMhSMQyTibRvXCdqEZSRIgpAdwMV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\nEH9xcgesv/8slSK6JnFvm1W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" - ] - }, - "metadata": { - "tags": [] - } - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "GLCLX_yawPd7", - "colab_type": "text" - }, - "source": [ - "Before removing these two signals, there are 20 input-signals in the data-set." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "Df_g04tCwPd8", - "colab_type": "code", - "outputId": "4cc9b80d-07b0-4c8a-ed8b-5410d975f4a3", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 55 - } - }, - "source": [ - "df.values.shape" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "(333109, 20)" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 14 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "mPVBWmUbwPeB", - "colab_type": "text" - }, - "source": [ - "Then we remove the two signals that have missing data." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "fcTXI8k1wPeB", - "colab_type": "code", - "colab": {} - }, - "source": [ - "df.drop(('Esbjerg', 'Pressure'), axis=1, inplace=True)\n", - "df.drop(('Roskilde', 'Pressure'), axis=1, inplace=True)" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "hxpuF6zlwPeH", - "colab_type": "text" - }, - "source": [ - "Now there are only 18 input-signals in the data." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "-SnarcOpwPeH", - "colab_type": "code", - "outputId": "3b4e34d3-13fd-4c4a-c816-457c7119fc18", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 55 - } - }, - "source": [ - "df.values.shape" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "(333109, 18)" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 16 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "L3MaiEsEwPeO", - "colab_type": "text" - }, - "source": [ - "We can verify that these two data-columns have indeed been removed." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "VJUpPewGwPeP", - "colab_type": "code", - "outputId": "9f55c594-d442-45f3-b12a-b9b775e7083c", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 196 - } - }, - "source": [ - "df.head(1)" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "
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1980-03-01 11:00:005.01007.76666710.2280.05.01008.315.4290.06.08333312.383333310.06.1428571011.06666712.585714290.05.011.466667280.0
\n", - "
" - ], - "text/plain": [ - " Aalborg ... Roskilde \n", - " Temp Pressure WindSpeed ... Temp WindSpeed WindDir\n", - "DateTime ... \n", - "1980-03-01 11:00:00 5.0 1007.766667 10.2 ... 5.0 11.466667 280.0\n", - "\n", - "[1 rows x 18 columns]" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 17 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "5t6DXkeYwPeU", - "colab_type": "text" - }, - "source": [ - "### Data Errors\n", - "\n", - "There are some errors in this data. As shown in the plot below, the temperature in the city of Odense suddenly jumped to almost 50 degrees C. But the highest temperature ever measured in Denmark was only 36.4 degrees Celcius and the lowest was -31.2 C. So this is clearly a data error. However, we will not correct any data-errors in this tutorial." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "Z5Cu5qXlwPeW", - "colab_type": "code", - "outputId": "c43f6cb8-698e-462a-cf34-5ae9dc9d612f", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 332 - } - }, - "source": [ - "df['Odense']['Temp']['2006-05':'2006-07'].plot()" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 18 - }, - { - "output_type": "display_data", - "data": { - "image/png": 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vuC1Z6HRjS0IfRcnKqYZWTutvBtQhxkvqjbiOeBDpofUchoKMmwb85Mk144kp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\n0Kz5DbsGE0IXx/2Vm9LVQRRz/PyvG+WYBwyPvUq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" - ] - }, - "metadata": { - "tags": [] - } - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s1eYnGtzwPeY", - "colab_type": "text" - }, - "source": [ - "This can also be confirmed to be an error by considering the temperatures in some of the other cities in Denmark for that period, which was only around 10 degrees. Because the country is so small, it is not possible for one city in Denmark to have 50 degrees while another city only has 10 degrees." - ] - }, - { - "cell_type": "code", - "metadata": { - "scrolled": true, - "id": "xWME0HGxwPeZ", - "colab_type": "code", - "outputId": "d0aa6883-f8ad-46f3-f030-5730a1906070", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 332 - } - }, - "source": [ - "df['Aarhus']['Temp']['2006-05':'2006-07'].plot()" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 19 - }, - { - "output_type": "display_data", - "data": { - "image/png": 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dh940hZTmOjn+4Xu346vXPIS7dk4fwhEZSG52IaDjSXPv4DQs111oL9Nts5Kz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YQ0/z3P0gZc8vp2dS4dlX2P/gZYvGqGj4jjE80YRvOx3wCZ+rqqT0L9caD9gA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\nvsTAYF4j3qUMAH7xFxdi3Y7ekvWeferSuRrWfosZz9vaTK0dLZbW+kt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" - ] - }, - "metadata": { - "tags": [] - } - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "92asXtqAwPef", - "colab_type": "text" - }, - "source": [ - "### Add Data\n", - "\n", - "We can add some input-signals to the data that may help our model in making predictions.\n", - "\n", - "For example, given just a temperature of 10 degrees Celcius the model wouldn't know whether that temperature was measured during the day or the night, or during summer or winter. The model would have to infer this from the surrounding data-points which might not be very accurate for determining whether it's an abnormally warm winter, or an abnormally cold summer, or whether it's day or night. So having this information could make a big difference in how accurately the model can predict the next output.\n", - "\n", - "Although the data-set does contain the date and time information for each observation, it is only used in the index so as to order the data. We will therefore add separate input-signals to the data-set for the day-of-year (between 1 and 366) and the hour-of-day (between 0 and 23)." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "Cu3pzeXUwPeg", - "colab_type": "code", - "colab": {} - }, - "source": [ - "df['Various', 'Day'] = df.index.dayofyear\n", - "df['Various', 'Hour'] = df.index.hour" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "pylQQ42VwPej", - "colab_type": "text" - }, - "source": [ - "### Target Data for Prediction\n", - "\n", - "We will try and predict the future weather-data for this city." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "4UkYOSO2wPej", - "colab_type": "code", - "colab": {} - }, - "source": [ - "target_city = 'Odense'" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "g8OZBR2OwPen", - "colab_type": "text" - }, - "source": [ - "We will try and predict these signals." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "p9AEmONwwPen", - "colab_type": "code", - "colab": {} - }, - "source": [ - "target_names = ['Temp', 'WindSpeed', 'Pressure']" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "dYg-Wcz7wPes", - "colab_type": "text" - }, - "source": [ - "The following is the number of time-steps that we will shift the target-data. Our data-set is resampled to have an observation for each hour, so there are 24 observations for 24 hours.\n", - "\n", - "If we want to predict the weather 24 hours into the future, we shift the data 24 time-steps. If we want to predict the weather 7 days into the future, we shift the data 7 * 24 time-steps." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "D_9t8dzkwPet", - "colab_type": "code", - "colab": {} - }, - "source": [ - "shift_days = 1\n", - "shift_steps = shift_days * 24 # Number of hours." - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "JPWaNU87wPew", - "colab_type": "text" - }, - "source": [ - "Create a new data-frame with the time-shifted data.\n", - "\n", - "**Note the negative time-shift!**" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "9G2ebh5OwPex", - "colab_type": "code", - "colab": {} - }, - "source": [ - "df_targets = df[target_city][target_names].shift(-shift_steps)" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "5nrXKy7GwPez", - "colab_type": "text" - }, - "source": [ - "**WARNING!** You should double-check that you have shifted the data in the right direction! We want to predict the future, not the past!\n", - "\n", - "The shifted data-frame is confusing because Pandas keeps the original time-stamps even though we have shifted the data. You can check the time-shift is correct by comparing the original and time-shifted data-frames.\n", - "\n", - "This is the first `shift_steps + 5` rows of the original data-frame:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "9WiykanswPe1", - "colab_type": "code", - "outputId": "38c18bb4-0ee1-4ff4-b9a7-926f3d48f9dc", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - } - }, - "source": [ - "df[target_city][target_names].head(shift_steps + 5)" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "
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TempWindSpeedPressure
DateTime
1980-03-01 11:00:006.14285712.5857141011.066667
1980-03-01 12:00:007.00000011.3000001011.200000
1980-03-01 13:00:007.00000012.1181821011.300000
1980-03-01 14:00:006.85714312.7428571011.400000
1980-03-01 15:00:006.00000012.4000001011.500000
1980-03-01 16:00:004.90909112.6181821011.688889
1980-03-01 17:00:003.95348812.6465121011.877778
1980-03-01 18:00:003.67441911.7255811012.066667
1980-03-01 19:00:003.39534910.8046511012.255556
1980-03-01 20:00:003.1162799.8837211012.444444
1980-03-01 21:00:002.8372098.9627911012.633333
1980-03-01 22:00:002.5581408.0418601012.822222
1980-03-01 23:00:002.2790707.1209301013.011111
1980-03-02 00:00:002.0000006.2000001013.200000
1980-03-02 01:00:002.0769237.7384621012.366667
1980-03-02 02:00:002.5384627.9692311011.533333
1980-03-02 03:00:003.0000008.2000001010.700000
1980-03-02 04:00:003.0000007.9272731010.100000
1980-03-02 05:00:002.9166677.6583331009.500000
1980-03-02 06:00:002.4166677.4083331008.900000
1980-03-02 07:00:002.0000007.1000001008.300000
1980-03-02 08:00:002.1428576.5428571007.700000
1980-03-02 09:00:003.0000006.2000001007.100000
1980-03-02 10:00:002.8333338.3500001006.466667
1980-03-02 11:00:002.0000006.8285711005.833333
1980-03-02 12:00:002.0000008.2000001005.200000
1980-03-02 13:00:000.1666679.2166671004.766667
1980-03-02 14:00:001.00000011.8857141004.333333
1980-03-02 15:00:001.00000012.4000001003.900000
\n", - "
" - ], - "text/plain": [ - " Temp WindSpeed Pressure\n", - "DateTime \n", - "1980-03-01 11:00:00 6.142857 12.585714 1011.066667\n", - "1980-03-01 12:00:00 7.000000 11.300000 1011.200000\n", - "1980-03-01 13:00:00 7.000000 12.118182 1011.300000\n", - "1980-03-01 14:00:00 6.857143 12.742857 1011.400000\n", - "1980-03-01 15:00:00 6.000000 12.400000 1011.500000\n", - "1980-03-01 16:00:00 4.909091 12.618182 1011.688889\n", - "1980-03-01 17:00:00 3.953488 12.646512 1011.877778\n", - "1980-03-01 18:00:00 3.674419 11.725581 1012.066667\n", - "1980-03-01 19:00:00 3.395349 10.804651 1012.255556\n", - "1980-03-01 20:00:00 3.116279 9.883721 1012.444444\n", - "1980-03-01 21:00:00 2.837209 8.962791 1012.633333\n", - "1980-03-01 22:00:00 2.558140 8.041860 1012.822222\n", - "1980-03-01 23:00:00 2.279070 7.120930 1013.011111\n", - "1980-03-02 00:00:00 2.000000 6.200000 1013.200000\n", - "1980-03-02 01:00:00 2.076923 7.738462 1012.366667\n", - "1980-03-02 02:00:00 2.538462 7.969231 1011.533333\n", - "1980-03-02 03:00:00 3.000000 8.200000 1010.700000\n", - "1980-03-02 04:00:00 3.000000 7.927273 1010.100000\n", - "1980-03-02 05:00:00 2.916667 7.658333 1009.500000\n", - "1980-03-02 06:00:00 2.416667 7.408333 1008.900000\n", - "1980-03-02 07:00:00 2.000000 7.100000 1008.300000\n", - "1980-03-02 08:00:00 2.142857 6.542857 1007.700000\n", - "1980-03-02 09:00:00 3.000000 6.200000 1007.100000\n", - "1980-03-02 10:00:00 2.833333 8.350000 1006.466667\n", - "1980-03-02 11:00:00 2.000000 6.828571 1005.833333\n", - "1980-03-02 12:00:00 2.000000 8.200000 1005.200000\n", - "1980-03-02 13:00:00 0.166667 9.216667 1004.766667\n", - "1980-03-02 14:00:00 1.000000 11.885714 1004.333333\n", - "1980-03-02 15:00:00 1.000000 12.400000 1003.900000" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 26 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "i1Zh_qtTwPe3", - "colab_type": "text" - }, - "source": [ - "The following is the first 5 rows of the time-shifted data-frame. This should be identical to the last 5 rows shown above from the original data, except for the time-stamp." - ] - }, - { - "cell_type": "code", - "metadata": { - "scrolled": false, - "id": "_2Ow9SdwwPe4", - "colab_type": "code", - "outputId": "cb8b093c-a14d-4b95-cde7-8efd632c546e", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 425 - } - }, - "source": [ - "df_targets.head(5)" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "
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TempWindSpeedPressure
DateTime
1980-03-01 11:00:002.0000006.8285711005.833333
1980-03-01 12:00:002.0000008.2000001005.200000
1980-03-01 13:00:000.1666679.2166671004.766667
1980-03-01 14:00:001.00000011.8857141004.333333
1980-03-01 15:00:001.00000012.4000001003.900000
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" - ], - "text/plain": [ - " Temp WindSpeed Pressure\n", - "DateTime \n", - "1980-03-01 11:00:00 2.000000 6.828571 1005.833333\n", - "1980-03-01 12:00:00 2.000000 8.200000 1005.200000\n", - "1980-03-01 13:00:00 0.166667 9.216667 1004.766667\n", - "1980-03-01 14:00:00 1.000000 11.885714 1004.333333\n", - "1980-03-01 15:00:00 1.000000 12.400000 1003.900000" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 27 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "XscDYRsEwPe6", - "colab_type": "text" - }, - "source": [ - "The time-shifted data-frame has the same length as the original data-frame, but the last observations are `NaN` (not a number) because the data has been shifted backwards so we are trying to shift data that does not exist in the original data-frame." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "ocI5cY3FwPe7", - "colab_type": "code", - "outputId": "66d35252-4ac9-407a-a21f-741db4dbba56", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 425 - } - }, - "source": [ - "df_targets.tail()" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " Temp WindSpeed Pressure\n", - "DateTime \n", - "2018-03-01 19:00:00 NaN NaN NaN\n", - "2018-03-01 20:00:00 NaN NaN NaN\n", - "2018-03-01 21:00:00 NaN NaN NaN\n", - "2018-03-01 22:00:00 NaN NaN NaN\n", - "2018-03-01 23:00:00 NaN NaN NaN" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 28 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "elB6-BRDwPe-", - "colab_type": "text" - }, - "source": [ - "### NumPy Arrays\n", - "\n", - "We now convert the Pandas data-frames to NumPy arrays that can be input to the neural network. We also remove the last part of the numpy arrays, because the target-data has `NaN` for the shifted period, and we only want to have valid data and we need the same array-shapes for the input- and output-data.\n", - "\n", - "These are the input-signals:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "EatdfsiowPfA", - "colab_type": "code", - "colab": {} - }, - "source": [ - "x_data = df.values[0:-shift_steps]" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "gNVuSAKrwPfD", - "colab_type": "code", - "outputId": "213153bd-d2b9-4db9-9a6e-9e54d9189f76", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 53 - } - }, - "source": [ - "print(type(x_data))\n", - "print(\"Shape:\", x_data.shape)" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "stream", - "text": [ - "\n", - "Shape: (333085, 20)\n" - ], - "name": "stdout" - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "YybPGRDBwPfF", - "colab_type": "text" - }, - "source": [ - "These are the output-signals (or target-signals):" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "tvJu12uhwPfG", - "colab_type": "code", - "colab": {} - }, - "source": [ - "y_data = df_targets.values[:-shift_steps]" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "scrolled": true, - "id": "92W5a11HwPfI", - "colab_type": "code", - "outputId": "584bef6c-6a79-4cc9-d99a-af35ea8c4b14", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 53 - } - }, - "source": [ - "print(type(y_data))\n", - "print(\"Shape:\", y_data.shape)" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "stream", - "text": [ - "\n", - "Shape: (333085, 3)\n" - ], - "name": "stdout" - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "JTbCXRFuwPfL", - "colab_type": "text" - }, - "source": [ - "This is the number of observations (aka. data-points or samples) in the data-set:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "b-e_mi8vwPfM", - "colab_type": "code", - "outputId": "f409355b-f1ce-4f58-ea41-7f43e484ae10", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 35 - } - }, - "source": [ - "num_data = len(x_data)\n", - "num_data" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "333085" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 33 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "hAeo1x6WwPfO", - "colab_type": "text" - }, - "source": [ - "This is the fraction of the data-set that will be used for the training-set:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "sv6jvNerwPfP", - "colab_type": "code", - "colab": {} - }, - "source": [ - "train_split = 0.9" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "_Mw0nVxQwPfT", - "colab_type": "text" - }, - "source": [ - "This is the number of observations in the training-set:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "mcPQ4FqewPfV", - "colab_type": "code", - "outputId": "3e69a7ff-2653-462e-b10e-37cfb715774f", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 35 - } - }, - "source": [ - "num_train = int(train_split * num_data)\n", - "num_train" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "299776" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 35 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "4QkW4lk6wPfb", - "colab_type": "text" - }, - "source": [ - "This is the number of observations in the test-set:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "Y9PuOxh4wPfc", - "colab_type": "code", - "outputId": "d4d9b0a3-864e-4ffb-9de1-9ff9c9d708d7", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 35 - } - }, - "source": [ - "num_test = num_data - num_train\n", - "num_test" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "33309" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 36 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "L_VDTY4UwPff", - "colab_type": "text" - }, - "source": [ - "These are the input-signals for the training- and test-sets:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "Z7tS9ziEwPff", - "colab_type": "code", - "outputId": "dc2e5b25-d564-4116-f347-e03af422a624", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 35 - } - }, - "source": [ - "x_train = x_data[0:num_train]\n", - "x_test = x_data[num_train:]\n", - "len(x_train) + len(x_test)" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "333085" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 37 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Fl6EGLHDwPfi", - "colab_type": "text" - }, - "source": [ - "These are the output-signals for the training- and test-sets:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "16gMrq2jwPfj", - "colab_type": "code", - "outputId": "fb8918de-2ca7-4d28-9770-c6d7a2b0013c", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 35 - } - }, - "source": [ - "y_train = y_data[0:num_train]\n", - "y_test = y_data[num_train:]\n", - "len(y_train) + len(y_test)" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "333085" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 38 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "M1paoApdwPfn", - "colab_type": "text" - }, - "source": [ - "This is the number of input-signals:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "J_WyDY4mwPfp", - "colab_type": "code", - "outputId": "108e5e93-c5c2-4468-b20d-dc39a1544781", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 35 - } - }, - "source": [ - "num_x_signals = x_data.shape[1]\n", - "num_x_signals" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "20" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 39 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "3iJ2wFFPwPfr", - "colab_type": "text" - }, - "source": [ - "This is the number of output-signals:" - ] - }, - { - "cell_type": "code", - "metadata": { - "scrolled": true, - "id": "6jDdI8nLwPfr", - "colab_type": "code", - "outputId": "4d5d211c-9c34-4b7f-ff81-18a7d96e8dba", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 35 - } - }, - "source": [ - "num_y_signals = y_data.shape[1]\n", - "num_y_signals" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "3" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 40 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "m3xbZ-V_wPft", - "colab_type": "text" - }, - "source": [ - "### Scaled Data\n", - "\n", - "The data-set contains a wide range of values:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "vq9B0AKVwPfv", - "colab_type": "code", - "outputId": "6ce0d570-bb8f-4a7e-f776-9377f0b19af2", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 53 - } - }, - "source": [ - "print(\"Min:\", np.min(x_train))\n", - "print(\"Max:\", np.max(x_train))" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Min: -27.0\n", - "Max: 1050.8\n" - ], - "name": "stdout" - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "SV5Ug_T7wPfx", - "colab_type": "text" - }, - "source": [ - "The neural network works best on values roughly between -1 and 1, so we need to scale the data before it is being input to the neural network. We can use `scikit-learn` for this.\n", - "\n", - "We first create a scaler-object for the input-signals." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "wobXHcmswPfy", - "colab_type": "code", - "colab": {} - }, - "source": [ - "x_scaler = MinMaxScaler()" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "zxhFPakqwPf0", - "colab_type": "text" - }, - "source": [ - "We then detect the range of values from the training-data and scale the training-data." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "cs5OfekSwPf1", - "colab_type": "code", - "colab": {} - }, - "source": [ - "x_train_scaled = x_scaler.fit_transform(x_train)" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "PEPsv87ewPf4", - "colab_type": "text" - }, - "source": [ - "Apart from a small rounding-error, the data has been scaled to be between 0 and 1." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "-Ou_tkj6wPf4", - "colab_type": "code", - "outputId": "da37806d-2a42-4390-a490-ad633a8efad7", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 53 - } - }, - "source": [ - "print(\"Min:\", np.min(x_train_scaled))\n", - "print(\"Max:\", np.max(x_train_scaled))" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Min: 0.0\n", - "Max: 1.0000000000000002\n" - ], - "name": "stdout" - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "UQZjSZhTwPf6", - "colab_type": "text" - }, - "source": [ - "We use the same scaler-object for the input-signals in the test-set." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "_-1OEYRowPf7", - "colab_type": "code", - "colab": {} - }, - "source": [ - "x_test_scaled = x_scaler.transform(x_test)" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "NWsSw3oFwPf9", - "colab_type": "text" - }, - "source": [ - "The target-data comes from the same data-set as the input-signals, because it is the weather-data for one of the cities that is merely time-shifted. But the target-data could be from a different source with different value-ranges, so we create a separate scaler-object for the target-data." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "QTmZCjblwPf-", - "colab_type": "code", - "colab": {} - }, - "source": [ - "y_scaler = MinMaxScaler()\n", - "y_train_scaled = y_scaler.fit_transform(y_train)\n", - "y_test_scaled = y_scaler.transform(y_test)" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "abunH1OjwPgA", - "colab_type": "text" - }, - "source": [ - "## Data Generator\n", - "\n", - "The data-set has now been prepared as 2-dimensional numpy arrays. The training-data has almost 300k observations, consisting of 20 input-signals and 3 output-signals.\n", - "\n", - "These are the array-shapes of the input and output data:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "1R8JrXBPwPgB", - "colab_type": "code", - "outputId": "10f78c7e-ebf2-406a-a8ca-e3b523b4e33e", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 53 - } - }, - "source": [ - "print(x_train_scaled.shape)\n", - "print(y_train_scaled.shape)" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "stream", - "text": [ - "(299776, 20)\n", - "(299776, 3)\n" - ], - "name": "stdout" - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "NDSzeIvqwPgD", - "colab_type": "text" - }, - "source": [ - "Instead of training the Recurrent Neural Network on the complete sequences of almost 300k observations, we will use the following function to create a batch of shorter sub-sequences picked at random from the training-data." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "Uxq01eDNwPgD", - "colab_type": "code", - "colab": {} - }, - "source": [ - "def batch_generator(batch_size, sequence_length):\n", - " \"\"\"\n", - " Generator function for creating random batches of training-data.\n", - " \"\"\"\n", - "\n", - " # Infinite loop.\n", - " while True:\n", - " # Allocate a new array for the batch of input-signals.\n", - " x_shape = (batch_size, sequence_length, num_x_signals)\n", - " x_batch = np.zeros(shape=x_shape, dtype=np.float16)\n", - "\n", - " # Allocate a new array for the batch of output-signals.\n", - " y_shape = (batch_size, sequence_length, num_y_signals)\n", - " y_batch = np.zeros(shape=y_shape, dtype=np.float16)\n", - "\n", - " # Fill the batch with random sequences of data.\n", - " for i in range(batch_size):\n", - " # Get a random start-index.\n", - " # This points somewhere into the training-data.\n", - " idx = np.random.randint(num_train - sequence_length)\n", - " \n", - " # Copy the sequences of data starting at this index.\n", - " x_batch[i] = x_train_scaled[idx:idx+sequence_length]\n", - " y_batch[i] = y_train_scaled[idx:idx+sequence_length]\n", - " \n", - " yield (x_batch, y_batch)" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "S-60stDGwPgI", - "colab_type": "text" - }, - "source": [ - "We will use a large batch-size so as to keep the GPU near 100% work-load. You may have to adjust this number depending on your GPU, its RAM and your choice of `sequence_length` below." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "M8r0o4s0wPgI", - "colab_type": "code", - "colab": {} - }, - "source": [ - "batch_size = 256" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "8YD-qNsqwPgK", - "colab_type": "text" - }, - "source": [ - "We will use a sequence-length of 1344, which means that each random sequence contains observations for 8 weeks. One time-step corresponds to one hour, so 24 x 7 time-steps corresponds to a week, and 24 x 7 x 8 corresponds to 8 weeks." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "szHC0H-cwPgK", - "colab_type": "code", - "outputId": "11a9f463-7ed7-4da7-f753-bb5d5a7e8ee6", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 35 - } - }, - "source": [ - "sequence_length = 24 * 7 * 8\n", - "sequence_length" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "1344" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 50 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "vumvL2ZAwPgP", - "colab_type": "text" - }, - "source": [ - "We then create the batch-generator." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "saEtBnTBwPgP", - "colab_type": "code", - "colab": {} - }, - "source": [ - "generator = batch_generator(batch_size=batch_size,\n", - " sequence_length=sequence_length)" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "VmzbmR22wPgR", - "colab_type": "text" - }, - "source": [ - "We can then test the batch-generator to see if it works." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "54Ogaz3cwPgR", - "colab_type": "code", - "colab": {} - }, - "source": [ - "x_batch, y_batch = next(generator)" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "v-9UB0cYwPgT", - "colab_type": "text" - }, - "source": [ - "This gives us a random batch of 256 sequences, each sequence having 1344 observations, and each observation having 20 input-signals and 3 output-signals." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "pJ-XI1nawPgV", - "colab_type": "code", - "outputId": "b9665182-f80f-4f5c-e8f9-ef4a1be17a92", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 53 - } - }, - "source": [ - "print(x_batch.shape)\n", - "print(y_batch.shape)" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "stream", - "text": [ - "(256, 1344, 20)\n", - "(256, 1344, 3)\n" - ], - "name": "stdout" - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "QSeUlf4zwPgX", - "colab_type": "text" - }, - "source": [ - "We can plot one of the 20 input-signals as an example." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "PP4mNTIMwPgX", - "colab_type": "code", - "outputId": "b3121627-bf90-4885-8666-a6a249aa18a9", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 287 - } - }, - "source": [ - "batch = 0 # First sequence in the batch.\n", - "signal = 0 # First signal from the 20 input-signals.\n", - "seq = x_batch[batch, :, signal]\n", - "plt.plot(seq)" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "[]" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 54 - }, - { - "output_type": "display_data", - "data": { - "image/png": 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3PLMFm/vSlNZanGBGtaTOa2evEOUCCXerME0HadN9yiU7LhAnMlNoFReszu1i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\ncj+su+EC9FRK+On/MqvJdTdcYDVad1EKhWnSk6Sr1Js//Or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" - ] - }, - "metadata": { - "tags": [] - } - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "WGMhZH9SwPgZ", - "colab_type": "text" - }, - "source": [ - "We can also plot one of the output-signals that we want the model to learn how to predict given all those 20 input signals." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "EBrOTA2VwPgZ", - "colab_type": "code", - "outputId": "b73e26e1-114c-45f7-bbc5-5e69ba64d432", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 287 - } - }, - "source": [ - "seq = y_batch[batch, :, signal]\n", - "plt.plot(seq)" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "[]" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 55 - }, - { - "output_type": "display_data", - "data": { - "image/png": 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PwKcB/NF4zyGl/IKUcrOUcnN/f/9Eb8lhCpA3c9fBaAgXrVUOYfvhIQxXY20Y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\nwNFjxyspNrKajGIzVz2TUd33DKuheANyX3eRF+68fFKzmRCiPVFyuwz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" - ] - }, - "metadata": { - "tags": [] - } - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "OC0T5Z39wPgb", - "colab_type": "text" - }, - "source": [ - "### Validation Set\n", - "\n", - "The neural network trains quickly so we can easily run many training epochs. But then there is a risk of overfitting the model to the training-set so it does not generalize well to unseen data. We will therefore monitor the model's performance on the test-set after each epoch and only save the model's weights if the performance is improved on the test-set.\n", - "\n", - "The batch-generator randomly selects a batch of short sequences from the training-data and uses that during training. But for the validation-data we will instead run through the entire sequence from the test-set and measure the prediction accuracy on that entire sequence." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "pMbKCkTvwPgc", - "colab_type": "code", - "colab": {} - }, - "source": [ - "validation_data = (np.expand_dims(x_test_scaled, axis=0),\n", - " np.expand_dims(y_test_scaled, axis=0))" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "YpSU3TQ0wPgd", - "colab_type": "text" - }, - "source": [ - "## Create the Recurrent Neural Network\n", - "\n", - "We are now ready to create the Recurrent Neural Network (RNN). We will use the Keras API for this because of its simplicity. See Tutorial #03-C for a tutorial on Keras and Tutorial #20 for more information on Recurrent Neural Networks." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "x_ITVhJBwPge", - "colab_type": "code", - "colab": {} - }, - "source": [ - "model = Sequential()" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "FznFRMVwwPgg", - "colab_type": "text" - }, - "source": [ - "We can now add a Gated Recurrent Unit (GRU) to the network. This will have 512 outputs for each time-step in the sequence.\n", - "\n", - "Note that because this is the first layer in the model, Keras needs to know the shape of its input, which is a batch of sequences of arbitrary length (indicated by `None`), where each observation has a number of input-signals (`num_x_signals`)." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "MO0GMLJfwPgg", - "colab_type": "code", - "outputId": "68d03930-f2ff-4a38-8ea4-ca867fb73e64", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 131 - } - }, - "source": [ - "model.add(GRU(units=512,\n", - " return_sequences=True,\n", - " input_shape=(None, num_x_signals,)))" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "stream", - "text": [ - "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "If using Keras pass *_constraint arguments to layers.\n" - ], - "name": "stdout" - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "H9x9wsPPwPgi", - "colab_type": "text" - }, - "source": [ - "The GRU outputs a batch of sequences of 512 values. We want to predict 3 output-signals, so we add a fully-connected (or dense) layer which maps 512 values down to only 3 values.\n", - "\n", - "The output-signals in the data-set have been limited to be between 0 and 1 using a scaler-object. So we also limit the output of the neural network using the Sigmoid activation function, which squashes the output to be between 0 and 1." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "1MjMPjL4wPgi", - "colab_type": "code", - "colab": {} - }, - "source": [ - "model.add(Dense(num_y_signals, activation='sigmoid'))" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "XsVeIPNcwPgl", - "colab_type": "text" - }, - "source": [ - "A problem with using the Sigmoid activation function, is that we can now only output values in the same range as the training-data.\n", - "\n", - "For example, if the training-data only has temperatures between -20 and +30 degrees, then the scaler-object will map -20 to 0 and +30 to 1. So if we limit the output of the neural network to be between 0 and 1 using the Sigmoid function, this can only be mapped back to temperature values between -20 and +30.\n", - "\n", - "We can use a linear activation function on the output instead. This allows for the output to take on arbitrary values. It might work with the standard initialization for a simple network architecture, but for more complicated network architectures e.g. with more layers, it might be necessary to initialize the weights with smaller values to avoid `NaN` values during training. You may need to experiment with this to get it working." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "PIAzoMoewPgm", - "colab_type": "code", - "colab": {} - }, - "source": [ - "if False:\n", - " from tensorflow.keras.initializers import RandomUniform\n", - "\n", - " # Maybe use lower init-ranges.\n", - " init = RandomUniform(minval=-0.05, maxval=0.05)\n", - "\n", - " model.add(Dense(num_y_signals,\n", - " activation='linear',\n", - " kernel_initializer=init))" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "2agyuXRywPgq", - "colab_type": "text" - }, - "source": [ - "### Loss Function\n", - "\n", - "We will use Mean Squared Error (MSE) as the loss-function that will be minimized. This measures how closely the model's output matches the true output signals.\n", - "\n", - "However, at the beginning of a sequence, the model has only seen input-signals for a few time-steps, so its generated output may be very inaccurate. Using the loss-value for the early time-steps may cause the model to distort its later output. We therefore give the model a \"warmup-period\" of 50 time-steps where we don't use its accuracy in the loss-function, in hope of improving the accuracy for later time-steps." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "RGS8_v3nwPgq", - "colab_type": "code", - "colab": {} - }, - "source": [ - "warmup_steps = 50" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "EFqGNn3gwPgs", - "colab_type": "code", - "colab": {} - }, - "source": [ - "def loss_mse_warmup(y_true, y_pred):\n", - " \"\"\"\n", - " Calculate the Mean Squared Error between y_true and y_pred,\n", - " but ignore the beginning \"warmup\" part of the sequences.\n", - " \n", - " y_true is the desired output.\n", - " y_pred is the model's output.\n", - " \"\"\"\n", - "\n", - " # The shape of both input tensors are:\n", - " # [batch_size, sequence_length, num_y_signals].\n", - "\n", - " # Ignore the \"warmup\" parts of the sequences\n", - " # by taking slices of the tensors.\n", - " y_true_slice = y_true[:, warmup_steps:, :]\n", - " y_pred_slice = y_pred[:, warmup_steps:, :]\n", - "\n", - " # These sliced tensors both have this shape:\n", - " # [batch_size, sequence_length - warmup_steps, num_y_signals]\n", - "\n", - " # Calculate the MSE loss for each value in these tensors.\n", - " # This outputs a 3-rank tensor of the same shape.\n", - " loss = tf.losses.mean_squared_error(labels=y_true_slice,\n", - " predictions=y_pred_slice)\n", - "\n", - " # Keras may reduce this across the first axis (the batch)\n", - " # but the semantics are unclear, so to be sure we use\n", - " # the loss across the entire tensor, we reduce it to a\n", - " # single scalar with the mean function.\n", - " loss_mean = tf.reduce_mean(loss)\n", - "\n", - " return loss_mean" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "RzXJSehHwPgt", - "colab_type": "text" - }, - "source": [ - "### Compile Model\n", - "\n", - "This is the optimizer and the beginning learning-rate that we will use." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "CwHgXL48wPgv", - "colab_type": "code", - "colab": {} - }, - "source": [ - "optimizer = RMSprop(lr=1e-3)" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Kbjo6pKpwPgy", - "colab_type": "text" - }, - "source": [ - "We then compile the Keras model so it is ready for training." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "EBvqgad5wPgz", - "colab_type": "code", - "outputId": "309dd36c-a47d-4b6b-e554-a5c303152183", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 111 - } - }, - "source": [ - "model.compile(loss=loss_mse_warmup, optimizer=optimizer)" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "stream", - "text": [ - "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/losses/losses_impl.py:121: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "Use tf.where in 2.0, which has the same broadcast rule as np.where\n" - ], - "name": "stdout" - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "zdta74cgwPg2", - "colab_type": "text" - }, - "source": [ - "This is a very small model with only two layers. The output shape of `(None, None, 3)` means that the model will output a batch with an arbitrary number of sequences, each of which has an arbitrary number of observations, and each observation has 3 signals. This corresponds to the 3 target signals we want to predict." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "LJZDyU8bwPg3", - "colab_type": "code", - "outputId": "5e7ca8b1-4dd8-43ba-b2bf-d53fc3af625d", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 233 - } - }, - "source": [ - "model.summary()" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Model: \"sequential\"\n", - "_________________________________________________________________\n", - "Layer (type) Output Shape Param # \n", - "=================================================================\n", - "gru (GRU) (None, None, 512) 818688 \n", - "_________________________________________________________________\n", - "dense (Dense) (None, None, 3) 1539 \n", - "=================================================================\n", - "Total params: 820,227\n", - "Trainable params: 820,227\n", - "Non-trainable params: 0\n", - "_________________________________________________________________\n" - ], - "name": "stdout" - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ScJ_6rTcwPg4", - "colab_type": "text" - }, - "source": [ - "### Callback Functions\n", - "\n", - "During training we want to save checkpoints and log the progress to TensorBoard so we create the appropriate callbacks for Keras.\n", - "\n", - "This is the callback for writing checkpoints during training." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "rAQm73XzwPg5", - "colab_type": "code", - "colab": {} - }, - "source": [ - "path_checkpoint = '23_checkpoint.keras'\n", - "callback_checkpoint = ModelCheckpoint(filepath=path_checkpoint,\n", - " monitor='val_loss',\n", - " verbose=1,\n", - " save_weights_only=True,\n", - " save_best_only=True)" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "bwiOt1PJwPg6", - "colab_type": "text" - }, - "source": [ - "This is the callback for stopping the optimization when performance worsens on the validation-set." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "C0iiIbiUwPg6", - "colab_type": "code", - "colab": {} - }, - "source": [ - "callback_early_stopping = EarlyStopping(monitor='val_loss',\n", - " patience=5, verbose=1)" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "dxeFQ-cpwPg8", - "colab_type": "text" - }, - "source": [ - "This is the callback for writing the TensorBoard log during training." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "DxI8rw6uwPg8", - "colab_type": "code", - "colab": {} - }, - "source": [ - "callback_tensorboard = TensorBoard(log_dir='./23_logs/',\n", - " histogram_freq=0,\n", - " write_graph=False)" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "hLn058WVwPg-", - "colab_type": "text" - }, - "source": [ - "This callback reduces the learning-rate for the optimizer if the validation-loss has not improved since the last epoch (as indicated by `patience=0`). The learning-rate will be reduced by multiplying it with the given factor. We set a start learning-rate of 1e-3 above, so multiplying it by 0.1 gives a learning-rate of 1e-4. We don't want the learning-rate to go any lower than this." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "hPovMktNwPg-", - "colab_type": "code", - "colab": {} - }, - "source": [ - "callback_reduce_lr = ReduceLROnPlateau(monitor='val_loss',\n", - " factor=0.1,\n", - " min_lr=1e-4,\n", - " patience=0,\n", - " verbose=1)" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "u5FGAYmxwPhA", - "colab_type": "code", - "colab": {} - }, - "source": [ - "callbacks = [callback_early_stopping,\n", - " callback_checkpoint,\n", - " callback_tensorboard,\n", - " callback_reduce_lr]" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "dTv6qPB_wPhD", - "colab_type": "text" - }, - "source": [ - "## Train the Recurrent Neural Network\n", - "\n", - "We can now train the neural network.\n", - "\n", - "Note that a single \"epoch\" does not correspond to a single processing of the training-set, because of how the batch-generator randomly selects sub-sequences from the training-set. Instead we have selected `steps_per_epoch` so that one \"epoch\" is processed in a few minutes.\n", - "\n", - "With these settings, each \"epoch\" took about 2.5 minutes to process on a GTX 1070. After 14 \"epochs\" the optimization was stopped because the validation-loss had not decreased for 5 \"epochs\". This optimization took about 35 minutes to finish.\n", - "\n", - "Also note that the loss sometimes becomes `NaN` (not-a-number). This is often resolved by restarting and running the Notebook again. But it may also be caused by your neural network architecture, learning-rate, batch-size, sequence-length, etc. in which case you may have to modify those settings." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "xCvfjb4FwPhE", - "colab_type": "code", - "outputId": "18891ff6-48e5-429d-bfd9-5e1a71ca6c08", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 613 - } - }, - "source": [ - "%%time\n", - "model.fit_generator(generator=generator,\n", - " epochs=5,\n", - " steps_per_epoch=100,\n", - " validation_data=validation_data,\n", - " callbacks=callbacks)" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Epoch 1/5\n", - " 99/100 [============================>.] - ETA: 5s - loss: 0.0082 Epoch 1/5\n", - " 1/100 [..............................] - ETA: 22:06 - loss: 0.0051\n", - "Epoch 00001: val_loss improved from inf to 0.00506, saving model to 23_checkpoint.keras\n", - "100/100 [==============================] - 569s 6s/step - loss: 0.0081 - val_loss: 0.0051\n", - "Epoch 2/5\n", - " 99/100 [============================>.] - ETA: 5s - loss: 0.0044 Epoch 1/5\n", - " 1/100 [..............................] - ETA: 21:42 - loss: 0.0031\n", - "Epoch 00002: val_loss improved from 0.00506 to 0.00307, saving model to 23_checkpoint.keras\n", - "100/100 [==============================] - 557s 6s/step - loss: 0.0044 - val_loss: 0.0031\n", - "Epoch 3/5\n", - " 99/100 [============================>.] - ETA: 5s - loss: 0.0036 Epoch 1/5\n", - " 1/100 [..............................] - ETA: 21:20 - loss: 0.0030\n", - "Epoch 00003: val_loss improved from 0.00307 to 0.00304, saving model to 23_checkpoint.keras\n", - "\n", - "Epoch 00003: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n", - "100/100 [==============================] - 557s 6s/step - loss: 0.0036 - val_loss: 0.0030\n", - "Epoch 4/5\n", - " 99/100 [============================>.] - ETA: 5s - loss: 0.0027 Epoch 1/5\n", - " 1/100 [..............................] - ETA: 21:14 - loss: 0.0024\n", - "Epoch 00004: val_loss improved from 0.00304 to 0.00235, saving model to 23_checkpoint.keras\n", - "100/100 [==============================] - 559s 6s/step - loss: 0.0027 - val_loss: 0.0024\n", - "Epoch 5/5\n", - " 99/100 [============================>.] - ETA: 5s - loss: 0.0026 Epoch 1/5\n", - " 1/100 [..............................] - ETA: 21:05 - loss: 0.0023\n", - "Epoch 00005: val_loss improved from 0.00235 to 0.00230, saving model to 23_checkpoint.keras\n", - "\n", - "Epoch 00005: ReduceLROnPlateau reducing learning rate to 0.0001.\n", - "100/100 [==============================] - 554s 6s/step - loss: 0.0026 - val_loss: 0.0023\n", - "CPU times: user 59min 31s, sys: 8min 14s, total: 1h 7min 46s\n", - "Wall time: 46min 36s\n" - ], - "name": "stdout" - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 71 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "aCVSsGh9W4-G", - "colab_type": "code", - "colab": {} - }, - "source": [ - "!cp *checkpoint* drive/My\\ Drive/models/" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "RJxmdCA4GLE8", - "colab_type": "text" - }, - "source": [ - "# New Section" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "_w6vqHJIwPhH", - "colab_type": "text" - }, - "source": [ - "### Load Checkpoint\n", - "\n", - "Because we use early-stopping when training the model, it is possible that the model's performance has worsened on the test-set for several epochs before training was stopped. We therefore reload the last saved checkpoint, which should have the best performance on the test-set." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "aAgKGrA5wPhI", - "colab_type": "code", - "colab": {} - }, - "source": [ - "try:\n", - " model.load_weights(path_checkpoint)\n", - "except Exception as error:\n", - " print(\"Error trying to load checkpoint.\")\n", - " print(error)" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "9OVvL6XTwPhK", - "colab_type": "text" - }, - "source": [ - "## Performance on Test-Set\n", - "\n", - "We can now evaluate the model's performance on the test-set. This function expects a batch of data, but we will just use one long time-series for the test-set, so we just expand the array-dimensionality to create a batch with that one sequence." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "fbVU1hThwPhK", - "colab_type": "code", - "outputId": "ffd553dd-42cd-4e8b-b16f-3410ce3703b1", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 35 - } - }, - "source": [ - "result = model.evaluate(x=np.expand_dims(x_test_scaled, axis=0),\n", - " y=np.expand_dims(y_test_scaled, axis=0))" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "stream", - "text": [ - "\r1/1 [==============================] - 13s 13s/sample - loss: 0.0023\n" - ], - "name": "stdout" - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "QWHucwySwPhO", - "colab_type": "code", - "outputId": "5ce56306-e5b1-4288-e1d9-ada493ac007c", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 35 - } - }, - "source": [ - "print(\"loss (test-set):\", result)" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "stream", - "text": [ - "loss (test-set): 0.0023025174159556627\n" - ], - "name": "stdout" - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "zV4xlTZwwPhQ", - "colab_type": "code", - "colab": {} - }, - "source": [ - "# If you have several metrics you can use this instead.\n", - "if False:\n", - " for res, metric in zip(result, model.metrics_names):\n", - " print(\"{0}: {1:.3e}\".format(metric, res))" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "b_zSk9INwPhT", - "colab_type": "text" - }, - "source": [ - "## Generate Predictions\n", - "\n", - "This helper-function plots the predicted and true output-signals." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "yMQudxPYwPhU", - "colab_type": "code", - "colab": {} - }, - "source": [ - "def plot_comparison(start_idx, length=100, train=True):\n", - " \"\"\"\n", - " Plot the predicted and true output-signals.\n", - " \n", - " :param start_idx: Start-index for the time-series.\n", - " :param length: Sequence-length to process and plot.\n", - " :param train: Boolean whether to use training- or test-set.\n", - " \"\"\"\n", - " \n", - " if train:\n", - " # Use training-data.\n", - " x = x_train_scaled\n", - " y_true = y_train\n", - " else:\n", - " # Use test-data.\n", - " x = x_test_scaled\n", - " y_true = y_test\n", - " \n", - " # End-index for the sequences.\n", - " end_idx = start_idx + length\n", - " \n", - " # Select the sequences from the given start-index and\n", - " # of the given length.\n", - " x = x[start_idx:end_idx]\n", - " y_true = y_true[start_idx:end_idx]\n", - " \n", - " # Input-signals for the model.\n", - " x = np.expand_dims(x, axis=0)\n", - "\n", - " # Use the model to predict the output-signals.\n", - " y_pred = model.predict(x)\n", - " \n", - " # The output of the model is between 0 and 1.\n", - " # Do an inverse map to get it back to the scale\n", - " # of the original data-set.\n", - " y_pred_rescaled = y_scaler.inverse_transform(y_pred[0])\n", - " \n", - " # For each output-signal.\n", - " for signal in range(len(target_names)):\n", - " # Get the output-signal predicted by the model.\n", - " signal_pred = y_pred_rescaled[:, signal]\n", - " \n", - " # Get the true output-signal from the data-set.\n", - " signal_true = y_true[:, signal]\n", - "\n", - " # Make the plotting-canvas bigger.\n", - " plt.figure(figsize=(15,5))\n", - " \n", - " # Plot and compare the two signals.\n", - " plt.plot(signal_true, label='true')\n", - " plt.plot(signal_pred, label='pred')\n", - " \n", - " # Plot grey box for warmup-period.\n", - " p = plt.axvspan(0, warmup_steps, facecolor='black', alpha=0.15)\n", - " \n", - " # Plot labels etc.\n", - " plt.ylabel(target_names[signal])\n", - " plt.legend()\n", - " plt.show()" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "FnqILZQtwPhV", - "colab_type": "text" - }, - "source": [ - "We can now plot an example of predicted output-signals. It is important to understand what these plots show, as they are actually a bit more complicated than you might think.\n", - "\n", - "These plots only show the output-signals and not the 20 input-signals used to predict the output-signals. The time-shift between the input-signals and the output-signals is held fixed in these plots. The model **always** predicts the output-signals e.g. 24 hours into the future (as defined in the `shift_steps` variable above). So the plot's x-axis merely shows how many time-steps of the input-signals have been seen by the predictive model so far.\n", - "\n", - "The prediction is not very accurate for the first 30-50 time-steps because the model has seen very little input-data at this point.\n", - "The model generates a single time-step of output data for each time-step of the input-data, so when the model has only run for a few time-steps, it knows very little of the history of the input-signals and cannot make an accurate prediction. The model needs to \"warm up\" by processing perhaps 30-50 time-steps before its predicted output-signals can be used.\n", - "\n", - "That is why we ignore this \"warmup-period\" of 50 time-steps when calculating the mean-squared-error in the loss-function. The \"warmup-period\" is shown as a grey box in these plots.\n", - "\n", - "Let us start with an example from the training-data. This is data that the model has seen during training so it should perform reasonably well on this data." - ] - }, - { - "cell_type": "code", - "metadata": { - "scrolled": true, - "id": "dR92llmWwPhV", - "colab_type": "code", - "outputId": "92186356-84f4-4063-e945-cf48f7faf421", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 955 - } - }, - "source": [ - "plot_comparison(start_idx=100000, length=1000, train=True)" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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nCbUB13CmmGG6kGG5dWlCNCOBJw6eIFz5DAu84hjhZwDeSujg9QTePhF4wo6g\nvt454oyRg9cMq4FX9vc2QbLpVMIQzbbOvxuI4CI/I1U0hcSIwJsQRuDgqCEHD11Js5jQwSv4TTpm\nec39jlmiFDRHvGJ9gpXXOa3mmC72JzCrqAVIt5GgF17YZLNOgVv3VkjltQCdNhM4eEEArQXqphaY\npWwaTItubhf7WBirF97BueKq38eZnHuLoKf+I3zsHfDx76Xy5Ed6j8Xh+VN1pozws4kcvNwUOE3w\nky2mVoYE3qUM0fTCWBPRd4Jw5bNeiGbSIitBXYfQn1Nh4a/KfmieA//S5QYLwlZQ67irKmj38lQT\n5eCFAq8eCrzqASjvg8YZsqaRzMHzuv38u4j8tAg8ITEi8CaE4Tt0lUXOMlbdH6TzFI1kDl5JNXDS\nawWenS5Tpt0ryRsH1TjLOTXN7fv6IZrZshZ4TjPBhBE6UXVV5PZ9Fd3rDZhN23TiiqnuCiifmlHF\nMKAQVhx1SvvYb4wn8Aqmzz76YavjhmhW8xY8/QmdWzJ1Lamjj1LOpWOPKVCKKYYFXvieJ+wXNRiO\nWc5ZTBcsWo6frBLXmIiDJwg7h3rH7ZV/h35+UaICWwCNMzjKZJnwulTZBygt8gThCqbe9VZtgmTT\nKVJG0hw8fb1MNU7pnLlcRTt4bpuZdDdZDp7b6effRWSK4LRGP18Q1kEE3oTQAm9tiKZK5xJX0cxi\n45mFNfd7VomK0aIbd6GvFJa9QjNV5ob5fk5fvjIHgN9KHqJZrs7oPjFhkZXZdAIHz9bhpQ3yFCyz\nl9TsTx3iYOrsWALvx07/Io/l3sc/+2ZdZXScEKRm1+NAugavPw5v/mG44bvgzLNUEwg8P1AjHLxI\n4CUL04wqZu6fynNgOs9UUffluRR5eG64eZBgD0EQhC3kS68u8D8/8BSen2yDxw8UDXv14tVMGRQz\nZmIHL9U8y3mmAQOllHYnQMI0hSue+lAOnmEYFLPphDl4CsMAo3aqV1eA8l4A9qaWk1fRtIYcvExR\nh24KQgJE4E0K38FW6TUhmoGZJ08yBy+nHAIzs+Z+L12mQptu3GO5HdLKIVOZW1V6Pxc6eH4Sy79b\nwybDDft0/hzZKESzSzuug+doAdRUuVX9Ao25m9lnLNFqJMxX69b4pu5XAPjA3Vr8aOGYTOB1PZ+7\n/Of1Lze9A/a+Cewab8guJRJ408MCLz/VG2cSam2X//723Xz5/d9JzjJ11VLghTPj9wqMi+tFRVa2\nVuEppfjtz7/CaxeShRwL4/OJx0/wN8ckUf9K4ne+8Co/fP/j/PlzZ/jG6WTnf31EdWDQYZpJi6yY\nnQUWlN6w6rh+6OABUUiaIFzCsVRrAAAgAElEQVSh1IZcbtCRQEnWEF6gSKcMqJ/sVQaPzpE9xlLC\nIiudfg+8iEypt34ShLiIwJsUvTYJww5ellwCB0/5HhnD67VYGCTIlkkbAU435okeltXNVuZX3W1E\nAqSTQHh0azQoMFcKKztZOUjnmE614+902XrcDZUjm+4LPGv3G/QPi4fjjwfg+GP9n1//GgCplEEx\nk6aZYPfNdgNucV8Eqwi73wh77gTgjekTiUI0q+uGaCZ18Bym8n2Bf/e101imwVdfS5AzOSZecGmq\naC62HH7786/ynb/xV1v7hwQAgkDx8//lOd790a9s91CEmASB4jc+90rv96Tn/6j2L6AXr0kdPNOp\nU1N6o6lpe1DUUSC0t35OEoStouv62F6w5hwpZtOJ8viDQOlN9OZ5KOloosjB281ywhy8DqSHBV5B\nQjSFxIjAmxSeg41FfjgHz8yRUzZuoGLlUPluF9DCcBiV0fkPXifmTm5YVjfIVlffHzpLqQTCQ3VX\nWFFFqoWBiTA/zZTRSuDghSGawWoHL7/vVgCs5YQCb+k1ANpmGV74M/jib4DdoJg1adrxwz27ns+N\n9gtw4C1gpmH2BgCuM87FFngdx6ditFEYPXeTXHIHTynFcstlaqAoTj5jcueBKb762ta7L44XhWhu\nrcIbLCSz1Lo0BWSuZs41ur2ff/aPn+3lWgqXL9EC8wPffSvXzxcnJ/ByVuI+eGmnRh2dNtDsemPN\nbYJwuRH1Aq7k0qvu12HM8TeJvUBhGoYuRleI2iRpoTejViYToulIiKaQDBF4kyJwcEa2SciTUTZA\nrDBNz9ZNLtVwFSX6As/vxmyVEDp4fm5m9f3pLF2ymE78kJ+gvUJNFVY5S+SmqNJM7ODVgyy5gVBW\nc/YGfGVQbByNPR4Alo/RUHn+Zu8Pwumvwxd+CZ7+ROL4eddx2Gcfgf1v0XfkpyBXZZ+KL/BajqfD\nZ82S7lsDfQevE19I17sejh8wX1ot8G/ZW+bE4tbv4EUO3lbn4A2+ry+d3frQ06udowv9784fPfE6\nr56TcJ/LnchlK2XT3LqnwvGlZAu8nsArrBZ45WyaZjdZvrPlNlY7eFYOzKwIPOGKpt7R59hwiGYx\nYYimHyjyKVe7b9Hmh5WHlEXZaE+gyEpJHztI3itYuHoRgTchDN9d2+gcLdR6Ai9GmKYbOnhryuRC\nr7AJ3XgLYi8qohKFDA7QTpWwnPgXZ7+zQl0Veg3Ao+NWVDNxDl4tyJIbCNEknaFulMk4ycoAB4uv\ncVzt5vlDPwbf9jP6zlc+qxcwCSbnkruIia8raEZMH2TeO0ut7cbKR2t0PSpGm05qoG3DGFU0LzT0\nd2VuSOBV8xb1rrfluXG9Ruds7d8Z7Hm40BQHb6sZFHhA/HNW2DaiPLlSLk0lbyXuE7pRiGaiKppK\nkfUa1NFz2//4+1/V0Si5qgg84Yqm5+BNQODNpPTmfG+9ZRiQq1JSrYtvkxAJPim0IiRABN6EMHwH\nW1mrnCnQffCsQIu2ONUv/dDBY0QOXiqnHTwVU+C5jbB9QGFmzWMds0zWS9A0vVOjTpGp/GqBV1KN\nBFU0tcBb9rLkLHPVQ00jmeAEUEtHOaF2Ucxl4Ts/AG/7STj2RaqZINHkPOuf1z9E1a8Apq5jxjmD\n4wexito0be3gtQcFXqYIqXSiRdBCc32B5wcq/ns9Ju4l6oM36OAthKJW2DqODQm8cSrWCpeWerfv\n4FXzFrVOvM2miPUEXj5j0k3YeNlUXs/Bazs+p1Y6IvCEK57oHBmsoglhkZWEjc7XVNEGyFUpqtYY\nIZpa0B1fbPF9v/cY5+0whFTy8IQEiMCbEKkgzMFLr+2Dlw4Fnu1tfnH2QgfPsNbm4KXz2hEynHjC\nzGvqnA2zMLvmMccqk/PjCzzDrlNXhaEcvCkKfoN2XDEVjntlhMBrmWWyboJQvSAgVXudE2o35Why\nnr8FfIf9Zi22g6eUYsYPc1uqAwJv+iCV7mkMgliL4WbXo2y0aTEg8MIdvCRFVnoCr7y6imp0Adrq\nhfml6oO3SuA1ReBtNccX29y8u8T7/84tgAi8K4FoDivntMBzfaUrWMZkjcA79wI89XEKhpusp2Yo\n4lSuwg/cc40+VL2rQ9lF4F09dGvwZ+/VhUR2COtVmi1kzIRtEgKmjVB8RdWzAXJVCkkdvIEcvC8f\nXuSJ48v81l+F1WpF4AkJEIE3IVLK1Tl4w20S0jnSgYNBgONvvmhWTiTw8mseS+d1iGYqpsDzW4u0\nVJZ8Ye2x/HSRXBDT7lcKM0yyX5WDl5+m4Ndpuz5BnKQtuwlmhqafWuN0dsxkgpPOEkbgcEbNUIoS\npMt7ANiTii/wbC9grxEKvEEHb/o6TOWyi5V4Ai908BrGUOx8LtkiaGGDEE3QLRS2Es+/NH3wogvr\nbDEjAu8ScHK5w4HpAu9+iy7hLQLv8qcXopm1+ud/gs+t3nHJpFN6M8334Pe+BT71k9zdfHQsgeek\ny/zYvYcAON+w9eZVgvxi4Qrn6U/A0w/oYmY7hOg6VMmvLrKSs0zsBJspfgBTPYE36OBVKPhNugmO\nNejgnV7REV1LbihAReAJCRCBNwmUIh04o3PwwlDLPE6sXRzf0wIvNVxFCcjldbNyP+ZJrtrLrFAi\nP+SWAQRWkZzqxmue67RIKZ+aKg7l4E1hBV0s5cYL+XGakClhu8EaB89OlykkEXgtHX66qCqUhwTe\nLmM5doim7QbsMxZxzEI/Zw6gohfC+4zF2AKvTJuGKvDHT57sFw5JGMa00HRIGTBdWO3gRQu8esLi\nCElx/EvTB6/WcSlkTPZUc5KDdwk4tdJh/1S+l2sytsBzu/DipyXZ/xIQVQIuhQ4eJPvcah13wL17\nHsK82n32kWQCLxRxjlVlV1lfl87XuxKiebVx9jl9u3hke8cxQaIw6OEQTctM4SbY5fSDYN0QzVzQ\n7G2cbopSYZsEfZ6dCAsrtQjXgyLwhASIwJsEysdA6Ry8NSGakcCzY4VoKkfv2IwSeKaVwcXE78Zz\n3pTdoK4KFDLptQ9mShSNbjynK7yI1xkSeGG1qCqteOEMdhOyJTquv8bBc6wqxSBBZb/WBQAWqXD9\nnBa+lLTAm1UrscMrbM9nj7FEO7dHh1RGhOGae4wlVtqbC5Cm7VExWtRUgZ9+8Bne+dtf1A8k3OVe\naNrMlrKrGtMDF78wj4l3CXPwqnmLuVJWHLwtpml71Dou+6fzWGaKYsYc/3v02L+HP/oheOgnJztI\nYQ1RIZTyoMBL4OCvEnivP65v8zPs7h7tbeTEIpz/zUKVSj5NNp3qO3gi8K4ejobXtOOPgb8zIgBq\nHZds5HIPYJkGrh/E3uj0AkWVUHzlVodo5rwmXqDiHct3QAW9RueRwGurMKJHBJ6QABF4E8DwtQBw\nSI9odB4KPMPBjrGLE3h6sWuOCNEE6Bp5/FAEbjouu06T/Kqec73HsiWKdHtlgjckvIi3jSE3MNyp\nqhoxK2k6TciU6br+GlfRy1Qp04Qg5sIjFHhBfo7dlXDyK8xCKs1MsIjjB7ESm7tuwLyxgpNf3Qw+\nCtfcayxtuhhWStHsupTosOQNCfOEeSoLTZvZYmbN/ePs4PdonIXP/QIsvLrpUy9lDl5P4EmRlS3l\n1LKeL/ZP6TklKtgxFi98St8+/8c7ZpF3uRJtvhUzE3DwTnxVz2k3vYP5zmu4fswFJ/Tmr2xpBsMw\n2FXJ6hy8SOBt9W6QsP24HaidgN1vBLcF51/Y7hFNhHrHXVNBE7SDp5SujhmHQCkqNMFI9fvgAuSq\nZH29ce3FOZYbru3C9d/rS22uny/Sjhw8VwSeEB8ReBPACLTAC1KZNc5LYPYdvFi7puEJbmZHtEkA\nXCMX+yQ3nAZNlaeYXSvwUrkyRTrUOzHC48ILvJ+tYAy6XKHAm6ER08FroDJFuq6/ZsfMy1RJocCO\nWWglDNHcvXd/f0ypFBR3UQ2LpsQZU9fzmaKFlxluBj+NSufZGyNEs+34ZIMOpqE46wyJs4RFVlba\n7prwTOg7eElLpQM6b+LLH4b736FzcTbgUgg8zw94/lSNSt5iTzXL+YadLEdhgNeX2tIofRNOrehd\n4P3TetEwTsl9AGon4dxzsO/Neqf5wsuTHKYwRKPrUcyYmCljbIFXyaX1ptnRv4KD98GuWyk756nQ\niu/ihfN/rqyLde0u5zhft7VTEbj9Ramwc2me07dveJe+PfnE9o1liHrX5dhCa6y0gkbX66d4DGCZ\nemkcS5ShI1+qNPU5kRpYVmerOo0FL55Y9PptshaaNosth7uumaKFOHhCckTgTYBU6OAF5tqFuUrr\nRVXcEE38yMEbLfC8dJ6U1401WZhOkyZ5CtbaCSydL2MaimYrRt5bJFByQyJo+iAA16XOxXbwVKZI\noFgj8IIwrCFox+uF5zcvECiD6w5cs/qB8m7KbiTwNh+T7QZUjRZBduj/ZhhQ2cdeY2nTxXCt41JB\nL6KjUuI9oiIrMS8+q3bdByhn0xjGmALv9Nf1bWcJjn95w6deijYJv/X5Vzhd6zJfynL3tdN4geLr\nJ5IXa/ja0SXu+7VH+Fu/8gXp67YBp1b0ouGiHbwLL+nbu39U30Y5OcKW0Ox6vQJSURGIsRy8s89A\nexFu/C6o6tziXcZy7Dw8p6Xn5HJVt9vZXc1xutYZ6PMphVZ2PFHlzANvhcIcnHpqe8czwPf8zpf4\n9l9/lM9+42zi13Zcn8KICCfL1JvGcTdB/EBRot3vVRwRniNl2r3N0w0JI7hI5/iL584A8A/efICO\nkhw8ITki8CZA5OCp1NqFeT8Hz4kl8IxwB8fKFkY+Hpg58nRZ6W7ueJhuk4YaHaJphRU5240Yjlm4\ng5vKrxV4gZnlZuNkvP5sdhM/rfPlskOhrEboBnYbi5sfB/Dq51mmxGx5KJS1tJuCoxu8x2nm2/V8\nqrRQg3Hz0Ziq+zlgbh6iWeu4lA0t8BpqaDy5qnY7op25TVhP4KVSBuVseryF+emn9c6rVYQXHtrw\nqV4QOXjJ/0xcji/q9+r/+Lu38U0HZ0gZ8NXX4n3uq4+jL3aOH/DpZ85MdIw7iZXQ4Yyc4Wreihea\nPczia/r25ndCOg9nn53UEIURNG2PUlYLu6gVTJINnt5ccizc1Ln+26G0G4B5oxZb4HXaTVxlMlvV\nc/dteyscX2zTiioG2wlyp4Urk8jBK++GfXfpTYPLAKUUx8LryTMnk+eDdpy16SIw4ODFLI7iK0WB\nDmTKqx+IBJ7Rjnes0CzAzPDnz57hlj1l/tb1M+LgCWMhAm8CGGEuijHKwQtDNIupLnaMHZy+wBvt\n4GEVKBg2F1qbL9Asr0mTwsgQzWxRC7xuK8buay/JfqhhesrEmbqRm4xT8XrhOU08Sztcw6LTKGiB\nZccUeH7zPIuqQmmo+hW5KpmwgXucRqVOp0XWcDHyawUepT3sMmqsxHLw9MRbZ9jBC0VxzEIrtY67\nutfgANWC1av6FZvWItReh2vfBofug6N/veHTo0XfVlbRXGo5vOW6afZUc1TzFm/YU+Hrryd3AaLQ\nzH9V+EtSX/7NSQ9zx1DruOQtk0y4qVLJW+NVY106ApkSlPfC/Bv6jp6wJTQGBJ6ZMijn0rHPfz9Q\nNLqeFni1k3rhWdrVF3isxHYnuu0mHTLMlfT17c3X6rnySC2cIxwReDueSOCVdmsXuH55bKg1BtYd\nr55L/j3semvTRaAv8GK5bujzraA6kC2tfiB09Cq044V79gSexYWmzQ27SqTNFNlcuK5wY7a2EgRE\n4E2EyMEbJfAiB6+ccnHiOHi+ja3SZNJrJx0ArDx5bBqb5Zf5HlbQpaHy5EYcKxcKPLsd38HLlNaK\nIH/2Zm5KxXfwPFNPVMNjisSjE1PgqdYCS1R6C6Ae2QppVwu8OBVC/SgkdJTAy1Uo0Ynp4Ok8lDUO\nXnTcGIVWuq6P7QUjHTzQpZwTO3hLoesydzMc/FZYfFUXXVmH6CK0lWUTllrOqjzDfdXcWIVWltoO\nGTPF+4KP830rH+s7FcIqhl3hTDrVC8VNxOIRmLlehy/P3QQLhyc4SmGYhYauqBtRyqZj9/dsdKP+\nXhY0zvRayFDSxaSSOHhOp0mXbK83550HpkgZ8NJi+HpxFXY+jXO6gEhxXlerbi9cFkWWlgZa7Lx6\nPkGbpZCOMyDwjjwCzz4IQDoK0Yx5jni+Iq86egNskLCfXR67Fx2z8YGiEM0sjhf0Ip1mSjlcw4od\nCSQIIAJvIkRVNEmPysHTAq9kxquimfJtumRJGcbIxw2rQJEuTXuTySIsVmKbRVKptcfKFbXwcNtx\ncvBqtFWWyoiG6ey6hf3GIs5mQlEpcJq63xxrc/CskhZ4XiteDp7RXWFFldYmSOcqmG4TULFy8IKO\n/nupwvTaB7NlCqpFbZM2CaMcvN5bniBPpd90dR0Hb5zcqfqp8MX7tcADOPaldZ/uXYIiK8ttZ1Wl\n0JliZqxCKUtNh9nCwOf/8l9MYng7jnp3tcBLp4x4i41hFg/D7A3659mbtDMsBTa2jHP1Lrsr/UiO\nYjYdu79nNE9U85be0Kns1Q/kpvBTViKB53VbtFWW+XK2N45rZwoca4STnAi8nU/znM69S5k6TBP6\neXnbyGJ43XjztVOcWGrTibPRPECvovfxx+Dj3wt/+s+gcY5MwiIrvlLk6a518CKBZzgxQzTD67tp\nYQ8IvGrBwiGr+5AKQkxE4E2ASOAZ6eyax6IqmhUjXqPzlN/FZq1Q7P2tTJ68YdPYbCKztXBz08WR\nD5u5cCKKEV7jt5epDffAC7Hm9ILPrB3f+CBeF5Q/IPBWf/UyPYG3tOl4AFJOg4bKrxV42QqG8nXV\n0hjvt+poZy09UuBVSOPT7Wy8gKmvcvD0/y8dXiDIhceN4eCtWpSNoDpO9cP6aX1b2Q+779ANVKOi\nKyNwwovQyOtatw7/6e/BiceTjWEApZR28IYFXttJHBa63Ha4vjDw2axs8h28Shl28MyUgZ/UwfNd\nWDkBM6HAm7sRUDuq6fHlhO35LLYc9gwJvLgO3mqBd1qH1QIYBk5unnljJdb1CCBw2nTIrHLdd5Vz\nnOmEm3QSornzaZ7vhfdG/WZpJi9qMmmijcG3XDuNUv2KwXHpuoFeiwxuej7zhz0HL0mIpnbwhtZb\nYbG8HHbCEM3IwdPnWN4ysY2MboIuCDERgTcBjEBfTM0RDl6Q1gv+UsruLZ43wvRtfSKv93gmroMX\nCbzS6MfDUAIjxsXZbS1TVwWqI8r3W/M3ApBrbLK4DhPx7VRYVXTIwSuVynSV1XPUNiOqELo2RFMn\nOZfpxFvAhLlxZmF0iCZA0NlYnNU6LlVDC40G+v9nRg5sghy8zQTeWCGa9VO6IEZ+Gsy07mN0+ul1\nnx45eCPF1hMfg9cehU/9L8nGMEDT9nB9tcrBmy5mcLyAdsLd16WWw82Z/oaAWj429rh2MrWOt8oV\ntswUblIHb/k4KB9m9fnO7E36dnHz3opCci6EIcu9Hp9AMWPGPkeiIjrVXFo7eFGIJuDm5hLl4Blu\nG9vI9nI4AebLWU61w9/Fwdv5dJYg2gSNvkuNc9s3npDFpj5Pbt6jr/sLzWSRIJ3IwVs8ojdB97wJ\nDn++l4MX2+UOFLmgvbbISujg5XB619YNCauoY2awPb93zuUsEwdLHDwhESLwJoDRa22w1sFTvRy8\neG0S0oGNs6GDV6CATTOmg+evJ/CykcDb/OIctFeoU2BqhPAwZg4BUGi+vvFBHD2ebkrvcGWHBN58\nOUuNYrwQTaVIe00a5HtlxHsMVK2yY/RWS9lhfmFxZu2D2Ujg1Td0l2odl7m0jTKzPfe1t/NXnNO3\nYWP2jVgj8F79PHz9gd7j1cKYAq+yT+dNQVgB7dl1G8q7PYE34sGvf1zfLrysF/xjsNzS4x928IDE\nYZpLLYfr0lrgPebfNvaYdjr1UQ5e0jKpS6FTF4Vohi1SqJ28+AEKazhX1wu53dWLC9GcNlvaFYgc\nPMDLzzNv1HBjLl5Tfgcntbro13w5y8mWCLyrBrvZb+BdvnwcvChE8+bdWlgtJhR4vZ68S2F+8YG3\nwplnyIRf7bghmoEfkFOjQjTDDW3DiZf3HIZoKjOzKkQzm07RJSM5eEIiROBNgMjBS41w8DBSBGaO\ngmHHqqJpBjaOsVYoRgTpPBnDo9XdZCILBZ43nPQbEd6fcjd38FS3Rk2NDtEkP8UKZcqdTQRe6OC1\nCKuKDlX2nC1mWFEliNMHz2lioGioAuXs0JjCi1BcBy8V5sZZ5fUFXl61N9w5r3dcZtJdjIE+gV6g\ntFjKVcHM9quQbcAqgVc/DQ/8Q3jovb33rprXcfmJmoLXTun8u4i9d+r8zJVjI5/u9kI0hy5GvgdL\nR+G279W/v/rf4o9hgMWWjYnPTLH/uc0Uxhd4B9B5IF8NbsOw6xDTAb6aGA7RtFJG7IVLj8WwoEoU\nopkta2c4xvdaSM7Zmt40HAzRTFJkpSfw/LBo1YDA8/MzVI1mbAfP9Lp4IwTeuW64uZY0RNOzdbsW\ncdyvHJxGv4BIcRdgbFis61Kx1HIoZsxej8/FVvxiXUGgsL1AC7zFI3rzav/dYNcpt44B8UM0zaBL\nimBtkZVemyw7ZqNzPX7PSKNUv51UzjJF4AmJEYE3AQxv/Rw8gMAqUKIb08Hr4qU2FngAnr3JrmlY\nZCUKWVxDOBGZ3ua7r4Zdo06RqfxoZ/FMag9T3U128sNFwLKn/28zQ+GeaTNF2yxj2DF62YTitW0U\n1uTyRWGVZaMdS+CZjv572dKIHLyBY23knNU6LtOpzpomp23H185ZaVcsB68+KPC+/gf9B175DACV\n0K1MVOK+fhrK+/q/b+K8uOsVWamf1CF6N3wnTB+Cw5+PP4YBjCNf4EjuR9hnH+3dNxOWX1/apJjN\n8DjrXY95lnCsKi8r3cBZXLzVeH5A0/aGHLwUSpHMxVs8ojcrolYp0ff6Mii0sBM5Gzp4q3Pw4odo\nRvNV2Q9Dw6NIAoBshQrt2OFnab+DZ64usDVfzuJj6hzzpALvs/8a/vOP6lvhysBu9NcSZlrPAzGu\naVvN+YbNTCnDdMHCMJKFaHY9fS5VjZYOQZ25AfbdDcDUyjcAYrvcGT9cR63JwYtCNN14YfGhg+eg\nr/WZQQdPWVLUSkiECLwJEISJsSMdPOg5eHFy8NKBg7uBwFOhwPPtTZKJQxG0Jia894cyuKQxvc2T\nko1unSYF9k+PqKIJrJizFN1NnJPQhVpy9Xs0NSKfz05XyDhx+vJp8eqmSxjD1UbDi1DV6MRawJhu\nk7bKYpgj3MmBfL6NBF6j61Ex2pCr8hPfdn1/mJHTVtoVy+mIGrOXc2k48RWYv1Xnzr32KNCvrhm7\n0EoQ6DCayoDAi3by19l99dYrshIJp+mDcMN36KpjY1TaLJ94GIC9h/+wd18k9pcTOHgrbf0eTPlL\neIV5zqtQoIvgWEXUN62S74cyRwUEElXSrJ+G6rX9UF+I/b0WkrPSdkgZrIqaKGbiO3hN28VMGWTd\ncKMvP7CBlZ+iZHRxnXjnmxV08UcIPAA/XUgeohnOZxz70rqh4sJlht1cHX6YrfSuw9uF6wd86dUL\n3H3tNGkzxXQh08vJi0PX1d+9GS8UqtUDYQi6QaGlI5LcmJtg2SAUXsMb6uksCoOcYcesoqnH76LP\n+6jISs4y6Sir30ZBEGIgAm8CBK4+6QxrtMBTVoEC3ViOUkbZBObmDp7vbLKTE150jeGY8AG6Rh5r\nEwfP9TyyfoPZuV29XKk1x0lXyPubTPZhDt4Fx6KcTa9K2I/wMlVyfoy2DVF+oTXi/xaGVU6bHWxv\n891u023RNtZpKp+N5+A1bY8yLchW+Ll33cpvff+dAP3d9tLuWMKj5fhkzBQWAbz+NTh4L1Sv6S2i\nIxem1onZ7Lx1AQJvSOBFCfKjBV4vJGX4WhRVqJy+ThfaGDMcMsp5LB37XO++noOXQOBFn0fJW0YV\nd7FM+F3oxKvCerUwqnBPOuzhEWvBEdFe5IxX4G+ODby/Mb/XQnKi/lyDG1jFbBrHC2KFjXWcgFw6\nhRG1Z8n1i0hFoeSBHW+Bbim7t7EYMR/2xHPNfDKBZzfDdhs36tYx556L/9pLwCe/doKf/MRTHB6j\np9pW0LQ9fum/vpAsamMdfv+vj/D8qRgRMsN4NgTu6vDDXLUfJbRNPP7aEsttl3fdoTctZ4uZRDl4\nnXADtkL4WRdmIZ2F0i5yLV19Oq6Dl/XDjfLhEE3DIDBz5HHibaiFZoGttLAbdPA6gVTRFJIhAm8C\nBF7k4K0TopnOk6cby8GzlNNrrbDesQCUu4nzFobNpPMbCLxUgYy/8XFOnbtACsWBPXvWfY6fm6IY\nbHJBDB28847VW9CvIV/d/DgAoUgIspW1j4VhklUznqBOey06jHYmBx28yDEaRdvxKap2729HFUJ7\nPXlihrJ1HI9C1tRFTJwmXPPN4SJaC7zEDl4jbJEwkH9DtqwvQusJvGCdHLzl42CYUDkAU9eF9x0l\nKaWmfo3ZONWrCFbORqGnMYUrfeGSdxYwSrtYVuHOaVsE3iArYdjroBNkRgIvSYhme5Enzhu8+6Nf\n6d8nDt6W0fX8Nb1Ci+F5EqfQSu/1UfXe/IDAy2uBp2JU9gV08Qhr9Rx5cK5IzkpRD7LJQjTPPQ8o\neNtP6t+P/nX8114Cfufhw3z62TN89hvb/L1WCv7svZz/w/fysS+/xq/8+YsXdTjXD/iVv3iJv/s7\n6/dAXZfw2r3KncpVYrX+2UpePqfXCvdcp93p2VImUQ5eFGFTijano/Dz6gEy7VDgxczBy/QcvLXr\nrSCdD6toxi+yYgf6XO8VWbFM2iqNkiqaQgJE4E2ASOCl1wvRTOfJYdONkYOXVboa47p/KxJ4mzp4\nTToqQy6z/rGcVKE/Ma1DY0WHL+TKI3LUQspTc+SxWa5tIM7CRcDZbnpVP6VBjMIMRbq4ziaTdOjg\nGaPyC8OQ1Cmjg+1uPiRnMYQAACAASURBVDlbXotOqjD6wV7BlvaGoqplexSCZq+CZz6jJ+doh5Di\nLmgvQLCxo9hyfAqWCRde1nfsujUUePoz6Dt4cQWeFnG//bUGJxYHhHxpNzTOjHxJtGM5LPDOvP4q\njcy8zr+I8viS5rspRaV1jIYKF4thE3bDMMimU7GqnkZEn0e2u0i6uoc6BQJS4uANEbmiM8X+PBCV\nAI9VtjtEdZZYUkPnW2k3tBf7zXmFidF1g7WtZMLCVK0YeXi96oDdFUhZvVwg6LeEMeKE2AUBWRxU\nZvUcWcqm+e479nG2Y+J3kwo84Ka3w9zNl53Aa4ROWeJqxZPmG38KTz/A9cf+iO9OPc4jL4/vlH/9\nxDI//eAz449lRD6/a1U4c/48p1a2z1Hquj7vT3+Cqf/wVggCZkvZRDl40QZsMYgE3qy+rR4g0wwF\nXsxNsHwQOXhr1yQqnSOPHc/BC0MwbSKBZ4a3KV2hW4qsrOHoQot/8fEn+dQzp7d7KJcdIvAmgApP\nylF98ABUukBedelsIjgcPyCH02utsN6xAEy/s9ZlGXye3aJFjkLGXPc5jpnvhxasQ7Omd3lzo4qQ\nhMzOaXfv5eMbVNIMdwFPt811Qz2tghZItZVNFunhwkSNcvBSKciUqabihWhafpuusY6DZ6ZRVpGS\nsXEOXsvxyAWtftXNUQ6eCjZNSu84PoVsut88euZ6KM1D6zwEQa+peyNuuE7Y5PwPX/RWX+DLe9fP\nwQsigbf6/heOnOBYJ/xeTocOXtLG4q0Fsn6Lx4Lbw/Gd6j2Us8xE1UFrHZc8XUyvhVXZTSpl0k2X\nxcEbIhJ4g30HIwcvdpEV34POCssMC7xd+ra1cNHjFFbTcX2yQwWkCpn4Dp4dNXDuLGv3biDU0wzd\nvFgFrcJIEcNauwn2bTfP0QiyOJ0E4Yy1k1pwlvfBoW/TubyXyQZBVLgJoLZBxMYl4eW/hNJuOtY0\n32l+nXN1O7abNMynnz3DQ09fxOI3cmgHwg+fOudDt8Znn9++Spptx+NfpD+NsXIcXnyImUKG5QSF\nuqLrTcGLXO7QwascIN08Daj4IZoqFLrDRVbQdRPyRlwHLwzRDEYVWcmIgzeCf/r//Q2f+cZZfuqT\nX09ciXunIwJvAijfwVZpMtZoMRWkc+SUDtHcqPBH2wnIYfdK6653LIAC3Q0dwcDWxUMK2fUFnmsW\nyKmNBV67ofOsCpX1Bd7eMHzz2Oun1n0OTgPSeRbb/voCL68FUrOxycIjdPCC9VpA5CpUjHhtEjJB\nG3s9Bw8gW6aygcDz/ADH9cgE3Z7Ai0R12wkXYr3CJqNds4iW4+nXLh7WoZCZonZJAg+6K72QrVgN\n3MO/p4wUC1Q5uTzwOZf3rDuWKIx4+Js1ZTRZUSUtCrJlfTFM6uCFf/M542b9+0Alz5yV6iW9x6HW\ncZkz9PfEKO2mmrdomRVpkzBEdMEb7DtomQlDNLsrGKh+GGxEtOPdFoE3aWzXJ5cedvD0oi9OoZWO\nOxCimV89d5tF/bsZI4fKDt05M7t24VrJW7TJoewEDl79NFT26o24A/do8bD0WvzXbyGDYfjb7uCd\newH23sWp4u28ydDvTzNBCPsg5xurI2L+2X98IlkF3V6IZv96+/yijmwZrnF2Kek4AXUVXru//gdU\n8xb1jksQ8/8WXW9ybk073Fa47qoeIOV1mKIZW1Tngg0EnpUnixNvvu3l4PWFHegNUBtLcvCGsD2f\n1xZa3DBfRKnkrZZ2OiLwJoDyHBwsMubo2S5IF8gqvfPScDYSeC4Zw8ewNhJ4ekIrGPaGjqDfbWoH\nbx3RCeClizq/YgO6Tb27Va6uL/CKVV2Cu1PfYKEXVuFaajsbOHhaILWb8QSeWq9CaLZCKWYVzazf\nxjHXF3hGpkDVdNa94LddnwLhexiGMRWHF2JRkZP6xruobdsPBd6r/YbSkUvSPNeb7JMIvE5mDh+T\n8w2bn/vT5/itz72iBd46uVNer9H56ovRFE1qlPrvw9S1UNuk9+EwYR7iq+mb9O+1IQcvhuMaUe+4\nzBN+T0q7qOYtmkZZQjSHWGo7ZNIpigNOvpmKQjRjLvJCVzQSeL3vRrSYcTavxCskoxs5cANE80rb\nThiiOVBgBfqRElGLmI1oN/Vcm86unSMruTQtchhugiIr9dNQCftyRv05L4N+arB6cbitAs9zdB72\n7tt4LXMzNxinKdLpVVlOyvn66mv85188Fz+PG/oOXriBWeu41FWBktGl3t5GwdFZ1tWrAQ5/nn/+\n9D+krJo0nXjvU5RCkXNrffcOetfrvcZS7BBNi1BEW2ujgaIQzVhi0XcgZfVaamWGGp0bUkVzFRfC\nzYsbd+nNh9jRTVcJIvAmge/gkN5A4OV7uW7NDS7O3Y5+TmoDgRdVMyuwcchnYDdpk+uF9YwcdrpI\nXm08QbvtsE9ccWr9J4U7xGl7g6R9p0mQKdF1g3Vz8HIFvYDsbCrw6rTIY6XX+b/lKpSJ1wcvF3Q2\nFHhYRcqmw8p6As/2yfcmd32cNbly0YJmM4HnehQz6bDKXCjwin2Blwlzp2LnqtXPULe0y+IFij/8\n2gk+/IVXOdrJ6dCrEQvz9frgVY0WK6rYXwSV90AjYSGClhZ4i+ndUJhbJRBz6eQhmvutMDSsOE8l\nb1FDQjSHWW45zBQyq6oxWknbJLR1s+ylMESzlwMWOehJFvhCLDquTz4zXGRF/x7Hweu6vg4V76ys\nKrACkA5z8Exn89DKTks/x8ytjZYo5yw6Kktqs4Jfg9RP9Te8YkY2XCqiua2QMbdX4C2+qqM2dr+R\nF1M3kjIUtxnHx66kOezgAYk203o5eOH5fmq5Qx19retudq3eQsrtMILkLf8EgGrndd5uPhk7vDa6\n3mScFSgMbGCHxVamjGbsEE0rCK+Lo6KvrDw5w43nmvoupLO9tcuqRucqgxG4m+byX01E3+0b5iOB\nN94myE5FBN4k8CMHb/TbqdJ50oGNQUBzAwfPtrXYMjObV9EsYNPeQOApp0lL5dYsElYN2ypSpLNh\nSEMk8KLS2iMJFxCWu8FkbzdxU3rs+6ZG///yJf037NbmAq9JfmSrBQCyFYqqHSsHL6/auObasIoe\nmQKl1PoOXtP2KBjhBTR0NKKG5L3XFOd13sk6zcUj2rbPtNnR1cmiQial3eEfutAvRpLAwVtMza25\n+9Vm+P6PCK1zoxDNwa+EUkzRZIVSX+CNU0ExfH47M6sXeQMLu3FCNK/JhDvLYYjmkipJiOYQSy1n\nVXgmjFFFMxR4y0pfRJeiQgZRXlbSPmjCpnRHhGhGBRfizGs9B3CEg2dkKwTKIO3GEHhtfY5lcmvn\nyHIuTZssqbhhY0rpTa5I2EVz22Ui8KL8rYOzxbEFnh8o/vc/fma8dgQRC6/q27mbednV79F1qXMX\n4eB1yKD/P9fP688xyVw7HKJ5crlNIxR4biteJdatoNo+oX946z+Fe34CgHeknoj92UUOXtpeWe3g\nhRvWVVrxq2iqDQReOq+LrMSJmPBsMK0BgdcvstINe+NJs/M+5+si8DZCBN4k8B1stXGIpoEih0Nj\nAwfP6YYCbyMHz9SNM/ObhGjitGiT6+36jhxXprhpfz4VlUIeVbEyInLwnA1yOpwmnbCYyTUzox2z\nQjkUeO1NFh7dOg0Kvd2tNWTLFFVrcyEU+OSw8dIbCDyrQNGw1w/RdDwKrBZ4aTNFKZvuvyaV0nkn\nmzl4js9+IxRd1Wv0bTEUaKEYSyrwzqnpXnGWiP+fvTeNtuQ6qwT3iTniju++9zLz5ZzK1GjLkowG\nsLEx2KbAhoLu1Q1Fsdyq6mKmVjM0TRcF9OpVUKsaKIoquuimAZuyMW67sQ2YwjYWHmTjSciyrCkl\npZTKOfONd4454vSPcyLuFMOJpyElkd9aWnp577vx4t4bcc63v72//Z13+eefYY4xYfCmHvRHUEiM\nHp0GePuYaUxUYUEdbcIlBohWY+9r6u/ruzBZ2S/z6622gpapYjuuXWPw5mJn5OF7yReAL/5uitor\nz8FzZiWaW+PZ6/2aRPOFj1RiORXJeiciPXeDCLoqT0xWpkOSMCIW1KC8B891GHjXjcU1u2GocKBB\njty5ilBOOF0gdPHhUxT//I8fYIBBb77sJJrHVmvVJIxT8dzWGFce+mt86mtP7f5Ekn2idRCn3DYi\nSDhM1nclPxuNR3gv/jd8Uf+f0MQIv/B2Jo+vstbOm6xc6Dpp71tkX72CWt3jzqKd64B3/CbWT/wT\n3CU9JfzdJZ+B7HUn/cRAms+0yUi4CKbBQ0Rk5jI9F0RjYxIC0Tl4sp4WcbSZHjxeqLsm00xjY8jk\nx8evSTQz40UDeISQQ4SQzxJCniCEPE4I+Rn+eIcQch8h5BT/f35z1yskCJdo6koewGOArQavkMEL\nOIOn6DmujgBACCLZQK1Eokn8McbQYar5Ek2q1qCRCK5TkKB5Q8QggFoAgvQWYhDohQzeECPKPofD\nOQCv3mCJSFDmyuYNMaRmPsAzmjCpXT4mgW9cUdF702qw4OVuGiMvnPTgTTnNtUx1FhQ2D8y4RmbF\n2A+xL+abVjJrzmgBREpZFF2VhSr4CBzA6eJC2MbthyYJ3nJNw7PjhMHbXnhZmDJ4UxsbZ8X6mJZo\n7gVAS51BZ2K0jr7UYYlrbXWGQWQ9eNUYvD3ygG3MsoqWqWAjrDG54LUNMI2V4Un81M5vAPf9KrDx\nBABA4T14wkYLfF5aIstKGbwU4FUw2bgWQuEG8YKLZjLeIhAA5m4QwVIIcxw2F7fYMUyoYfn35vGi\no24u7kk1TYYHHRLi1ByiMPj69+lLCj77FF83CgyfXupI1rajyxaGXljNiITHuTPP4H3ab+DnH3o7\ncPK/7u5EBhcZE2QuYdOJsSPvwWGysSt2Yvjk53Cn9DRWSR+PGD+Gt378LThKLlcDeN4iwPMV9nPs\n7HLYOaXA538L+KO3p67YVcMMunCJPlmHlo9jiYwwHogV+ZJCieTOGRFxNq+NkVAxBQBUGiCUcgrz\nqgWD+IISTR+QtfTv6nM9eACuGa1MxcbAg0TYPQtcY/Dm48Vk8EIA/zOl9BYA3wzgpwkhtwD4VwA+\nTSm9HsCn+b9f0UHioNRkBQBM4mLoFQA8nwEFVSsAeAAixYIFD06Qv2BIwQhjahaOSUhYOc/JB2bE\nG8KVLMZC5f4xCWNSgxEWMXhj9GI2tmE5z2TFYOcTlgA86g0wiIslmmY8LgdCngDAUy0YKGDwvGhB\nogkwh7lBBYBHKYXtR1hNAR5n8CSZbT4JwBNl8HjSdNprYK012XjecGIFTw75558BzjJ78BKAR2sT\nG+o6H3w/qlB9H2+gK7VZb5A1y+AZFefg9Z0AK+inPYpNQ8XlgN8311i8NPa4Uw6F578KAJD5OiVU\nUQZSAEe4ROvJK/w+TwoaVXqwXuAYugGe3XwZAczT9zPr/+cZaQ/dVCTrnYhszA1jtCQbAF2QaAKA\nT3RIUbnluuey3zGNxT2JEIKItwwIyXQ5M3WFTsnhCka2vNRxseugaShY5jMjd8Pijc48NPnHh354\nd+z28DLQWENEuaGJeYADvOrns7M1Oz9PdzfwenKqmkQzsAFZT9mpSz0HRoN9h0KjNrLi3JeBz/w6\ncOEB4Mm/3tUh6mEXQ2kCzJSVYwCAaOeM0OtZoYSCeAM2uD0J1QRkHUuSmESTUgqdegil7JnDRDW5\nyYoowFNzevASiea1UQlJbAxdrNR1NA0VhFxj8ObjRQN4lNLLlNKH+M9DACcBHADwfQDey3/tvQC+\n/8U6h5cq5MiFAy0X4CXGKIzBy09iA74ZZMlhpiNWDFjELezBk0K7VKKZVOT8cTagGnsh4A8RqTnj\nCKZ/V2rACIsHnXcDDYc71ozhw0zwBDIus912hxiimMHTqI84KGFyeOIaF70/jTmg9p1gwVkS4KMN\n5kxWAKBlKrOgsLZSCDz8KEYUUyyH66x6W1udPGkt7wLgsaTpGbeJWw+2sWSxzeHGvfVJD16WRDNO\nXDSnHhux856RaDYSgCc+gDcermOLNllvUG1lxujFUGVx6SlY8tWh3dRltGWq2In593itDw8AAwkH\nw3OIiMqq0ucfAACoVRk8bwSXmDiwVMMdh9v4+KM8IU8ZvKvTgxfFFPe+5wF893/6Au57Yh1eGF39\n+WWf+hVEH/1xxLucWZZElkQzMccRlWi2E4fBeYkmOMATGJrs8aKjmcHgAQCUBOQLsAoc4F2mHazU\neZGpUS5dfynC8SN8/LHL+Pab9iyaZFUIaf0RxJTg14MfZg+sP179ZLjT6MAJQCngNw7j0C4ZvEdP\nsz61wfHvxfD67wMA7Cfb1UxWQnemt2zshyDJDFqvwgzE6Tj5XwFZY3vbYx/e1SHqUQ9jZeINYO5h\nxmRS74zQ68Moho6AGZdMt6AQAphL6JCxkEQziikzUZGyC9dEs2DCTx2qi0/KmzNZ4T14qjQl0RQH\neN2xn5m3vFpie+Rjua5DkgjqmpLOsbwWLF6SHjxCyFEAdwD4KoC9lNJEk3EFwN6X4hxezFAiBzY1\nchmlWGGVnbYSYFTE4Hnsxi0akwCwYeeMwcs+1kZ3CCX2MaY6Wmb2ogMAEgdUQY7M4un1IRpwJot5\nQdhyA1ZUwOB5I2wGKg4uFbCTiokYpHSuEvX6GFKrkMEDUC5B4n+H6gUAT61BjV1EMc10rxtPu2hO\nMXgLEk2jxdzIchywEuvztn8FaB2cGUzMAB4DWboil0tPgalq+RK+5boOPvVz34a//fk348hyDUOY\niCU102QlkWhOM3j+kIHLHurYGvH3moxvqFB939jcwHnX5BLN2d5CZrJSjcFrxr0ZgJcO4n41jUoI\nXOBTv7orlmNz6OEGcgHD+jHgyBsmDB7vwRMenOwP4UiMMX/nrWt44vIAz2wMGbusGFcN4H32yQ08\ndK6HOKb40fc9iBt/5ZO47d98alfSuhcqqL0FuX8O/+l9H9j9MSiFGy6OSUgkmn7J90YphRNEaIGv\nfxkMXiQbQgxewBk8y8wpOiZqE0GAF0PCJtqTxHnpCFM2XGVZ9RdObWLohviBOw+hw8HnhW51KVxn\ncBJnsA+fiO5mD1z5RvWT4bMCk/0jah7CKhnAcaox1WEU4/wltm40f/AP0Pjh9yE0V3CAbFVSSyB0\nJzPiwIoHlBeHpWAX7DmlwFN/DVz3FuA1/w0rPO0ChDTjPsbqhA0291wHAFAH54ReH0QxGuDf8XyO\nYy6hLYlJNCNKocNHlMPgSaoBHYEYwIsCQFbTv5v24CnyxGRFEOA5foQ7fu0+/PJfPCb0+6/EcMMo\nVak1DOWaRHMuXnSARwipA/gIgJ+llM4gAMpKC5l3NiHkxwghDxJCHtzcrNDncxVCiV3Y0KGXMHgd\nNcCwgMGLfLbYULkY4EE12ZiEnMVnu8cYjNddtx+rjexFBwCkEknkyctD1GGnc5OKwpGbqMU51TxK\nUwYvbwYeOyEJHimfq0S8IUYw0+rWQvDFWouKjxMm2v8iBk81oUYOAJo5RNP2p1w0c3rwzmyNEev8\nM3SzJS0233Br4c5E/pjENIOnSmI9eJxZC809OL5ax2pDx4k9Dbz5hlUYqoyh3AbGiz14QbTI4IX8\n93q0Pkl66vsAkErVdwsubBhMepYwlFwmalQwWQmiGGM/QiPYTp34WqaKPuUA+9Uk0Xz6k8CXfhfx\nR3+88kvXBy6uJxfhd24ADt3NBkqPNlMmqAqD58CCJkv4vtsPQJEIPvjAeZzdHoNqtasG8C712bX4\nkZ98Axr6pNf4qkk2KU2vvaVn/1IsocuIIKKMFZhb3zRZTKLpRzEoBZrg30sGgxfJOjNHKTsXLgfT\n9ew9SaoyKmNwCdtkCRHkicFP5zhAY6B7tvz1L2Jc4bPibtjbwD3HOmjoCj76ULHrcVYcCc9gp34D\n+vo+jOUmcLkiwKOUSTSb+9P9Q+JqCVpBLQGwPaVGx4iIks5nCxsHcYBsVZRouohkA12+/zlBhJjP\noNUjR2w/mo6Nk0D3DHDjO4A9N7PCZ0l/ela0aQ+eNpFoErONHuqwxmLzWf2IoiPz/WzeJdxcwpKg\ni2YUUxgIEOXkbZJqQiIUcSjACOeYrMwweIIumj2HfV8f+Oo54eHvr7TwgjhVcjUM9ZpEcy5eVIBH\nCFHBwN2fUko/yh9eJ4Ss8efXAGSuWpTSP6CU3kkpvXN1dTXrV142oUUOHOhpZXw+Yn7jt9RiBi/i\ncphYzgdlAGPwapKfy+D53IXyyNqewuPIHLiFdrbV8VNXBmhJLrR6wQw8Hq7SRC3OSaz8MQCK7ZBp\npYvCk0zIRclCFIIEdjGDx/X0WgmDF9gc4GXMeEpDYw6oOgJ848IiOBt5Icx00Pkig7c+cPHW/3A/\nHtvh14ab/VnbnB00g346hyeN3Ug0nS5iEKysrM5IYlumin/0mn24EtQKxyRMM3ghl2h6agPndrjs\nS9EYuBoIJkGUogZWCDGSHjwgBZkM4IklHQMnQA0O1NhNgWLLVFOXx1cLg/c79z2N+z/5IQAAfe5+\n+P1qYyk2Bg7WyDakpSPAoXvYgxcemIxJEHXR9EewCWPwVhs63nbzXrzvy2fxbb/1OQwj7ar14CUF\nl1v2N/Evv+NE+vijGffpSxKBDRK6iCnBO+Wv4OGzi/eXSCTyufkRN5JEoEiklFVI7qM6zWfwYpmN\n7imLkO9JRMnek2RdXKJJBxdxKWLnkrKQHca6YOd0zqtemkiupbalwtIUvPN1a/jk4xVZ89DHPrqB\nvnUU33vbfjwaHgJdf6LaMZwuS/Lr+1KAp7QZwJOqGFoBcP0ITYwRqI1UEUKbB5hEsxKD5+B0N8Qd\nv3YfO24QQ9F0xERBndjoVZVFP8V77m78bmDPLeznjZPVjkEplugArja7V+6QDnRPbP0Po5iNJQIW\nXcLNJbTISGiNDGPO4OXkbelcYxFzlCmTFVUm6VptKDK8pAcvEmO7x1OKo5NXdmmG8zIPL5wGeNcY\nvPl4MV00CYB3AzhJKf0PU099DMC9/Od7Afzli3UOL1WosQuPFA0n5wBPDgqdLymvltKsWSpTESsG\n6gU9eIkLpWwUjDYAYDZZkp30WM1H1w7QkhyQohEJPDy1iQbNYfB4r1s/0tP+hrzwJQtqVJAw8uG8\nI5i5cwcTBs+i40KWIuTSVKno/XEDllU9xAPPLTJe2yMfyyrf4OYAnhvEeG6LncNlj3+neQweZ3Z1\nv5sP8ChlEk0RgOf2MEINy43Fa+nQkoVebIB6i4t+yuBNPRbZXThUw7F9K9gcenASFrp1sHS2XxI0\ncCATCpsavAeP21JPjX9ww0ioX6DvBNhH+DXLhyY3TRVdcKC+WwYvjieOcS+D+M+ffhI3DL6CU/EB\nyKCwH/iTSq/v7mxCITHM9l5g7XbW8/LgH0Plib3wHDxvBBsTCfo913XSBH0Y61eNwdsZ+2iZKlRZ\nwj+56zB+6i3HQQjw6POZQfZ8ghdhPhvfjlUyQHz6/l0dJkm+dXVRoaDKUimrkMjv6knBLcNFkyoG\ntAoADzkAT9HFjXbC3gVcoh2s1HUEUczu9ZcRwEuuJQDY3zZh+5G4jBkAumcgI0a/dgS37G/hVLQG\nunWqmvww2R/Mdgrw9DZb4zSnGoPnBBEaxEGgTskPW4cYg+dXSIQDdyIPRNIfqiBSaqjBxfqgounH\nkx8HDtzJ+rhXb2KPVQR41O1DIyECfXnmcUeqlRZ2kwiiGE2J3wPzEk1rCS06Evr+45hCJwHiHIlm\neu+IALPIBxQNXhjP5DeMwUskmmIAbzQ1kqvf6wG/81rggT8Ueu0rJbwwSpVcDUPB0LvG4E3Hi8ng\nvRHAuwB8ByHkYf7fOwD8HwDeTgg5BeBt/N+v6NCoB0/K7y1LGLym7GFcBPC4tpqWMnhmYQ9eIrlU\nipgpAPU2A3jhODsh9sMYNTjFM/B4BGoLDYxYkjwfPGkeUQMtqxjgBbIJLS6odPGm7iHMBRvxNPj5\nNmAXVruTz0ku6jHUWAJz1wEDf//connHlYGLFS1iLmPSJCFrWUxOcXqTJb9bIb8+nGwGb2vkgSCG\n6vdnZ/IA7N9xCHgDxuCJVF+dHvqoYaW+eC01DAVDaiJ2FwF5Vg8edbrooY6b19jneqHLk7n2IaAn\nJocJeP/IGDr7TmqzPXyGKjMlr8CG2ncCrKUA7wAADqihMyez3TJ4n/hfgH93AAgFLN9fgnin9BWs\nkR38VvgDeDC+AcbJamYEdpcxfmZ7L+uhecsvAc/ch72P/QEAIBJ20RzCniqo3HpgImeiqnVVAV4i\n+W5ZKn7xu27CrQdaV0+iyQHeR6M3YUBNLJ/+2K4O4/JROkaGQkFTpFI3vmSAs5VI5jMkmlQ1odLy\nRDFKjKrk7HVb0nlRS4DBk4ZXcIV2sL9tgFIuEbY6gN4Cdp4tff2LGdPXEoC0r8cuaKlYiO1nAACj\n+lEc7lg4Tdcgef1qo2SSopveTAGe1WEAz3CrMcJOwBi8SJvs31L7IEziI84pNGZG6E4s+sEYPEOT\nQfUG6sTFlX4FgDe4DFx6CLjpHezfVoftBVtPix8DgD9gn2lozhZDPdkqbc1IjxFRtKVEojmXAxht\nNDAW2o/CmM04zpNopgY1Iu6XoQfI2mSOZXI6igy/IsCzpxi81tMfAfrngY//wqtqULoXTsbJ1A0V\no2sM3ky8mC6af0cpJZTS11FKb+f/fZxSuk0pfSul9HpK6dsopa9sPVUcMYAnwODVpaDQ+ZJwgBeX\nMnjFYxJClyU4ilkMzFodlmTHOYyHH8WoUXuxupX1u1oLMmjKsM0+yR4bwyxl8ELFgkELFiDeNzek\nVj6DxxfrBpxCgBdxgCOZRT14HODt1/HU+jDtQ0hifeBiSQtSIJjEHt77+PQ6+xsbQcLgZQO8C10H\nDdggNErn8KQxZWiiq7JQ43fsdNGNrcwezIahYggLdG7+EKU03dCmc3/idNGnNdy0j32uqUwzYfAE\nKtQul8PaMNC1jv9wngAAIABJREFUA+aYanaAHmuIT2QWIjJNBvA4m9riAI8XDly1Bexm+G4cAX//\nR+xnnqhdzYh3zuJX1ffjqfgg7ou/CV+Nb4LWPVUJfLpc0inVuRz2TT8PnHgblp54PxSEYrbdAJth\nOcXg3bzWTD2AXGJcNYlm1/YXenoNVa7GuryQwQHeZdrB30R34dD6p3NNlYoiT6IJMAavjMFP7iEr\nGrLCk5ox4kAxoQs4+6VOxHlFx2TdKxsHEIWQgyF6tI4675cMY8qkg6s3VpfovcDRtf3UaRiYfPZO\nBYAXbZ0CALgc4D1LGTADf1woEuBlTABeY3kfYhDUw2qpkuNHaBIbsTbZv9U6Kx6SCkWwOHBSeWAU\nU8bgKTKIXkcdDtaHFQxyTv0N+/+N75g81tgHjKrJzz2+tkXmyszjvlyDEYutR2EUoy3x350vYusN\nmHARheWAIeIAL7e1JsnnRIAZN1lx/NkxKZJEEMt8rROZOQnMmMLtO/PRyRNnvyj0+ldCTPfgGaLt\nK/+A4iVx0Xw1B+GN6kHekEtMGLw6ye+bo5SCcAq/jMGLFQMmXAy87M2HcpZLs4qBmaHrGFITJIdV\nigIfBjwhgBdprKpPs8Cil7A3BpoiAE+YwcszWWHnUifFDeDUGyKiBEqRiyaXXd62jy2uf39m9v1d\n6btoK8HCIPgE4J28zIDNpUSimfNZX+ja2KvwzWaewVu5nv1/80nhHrxwzEBZHoM3ouaCxXWenFXy\nGBt40z62CZ7bsbExcGGba0x2kjFuYT6SfscxNSaz9JaOpAAvsYQXYSf7ToD92AYFARosiaprCiQC\n2HJzdwwed5gEsDtr8xc4/M/+Bmpw8VPBz4BCwtPxQUg0rMR00MREx5pKgm77ISj2Om4gFyqZrIym\nXIJruoJ7jrEihE31qzbofHvkY8maBXiKRMR7C1/o4GtfFw18jd7AmIRdmEckEs15kxUA0GRSCmDT\n10eDTPYOAIhmwoSPcc4ekkSUFBTkbHMsWROUaHLg0kctBXgpO7J2G3D5kWz1x0sU2yMfndpkrZww\neOKMQLT9HLq0Dqm2hANtE6dTgFeBnUqKbkYLAyeApkgwdB0juY1mVYAXRGjCBp0yEJG4NF7KKTRm\nReQ7KYPXs30u0ZSgmE3UiYP1Kgze5tOAYk6kmQArYFY0kAkH7Pfp3F7JAJ4YgxdEMZokx0UzcQkN\ny48VxaxHPx/gsc+OiEo0ZR22Hy2MuJLTXj6xz3vMr10dPpb6J4Hb/il7ontG6PWvhJiWaFYxaluI\nCw8C7/5HwGMfeQHP7urHNYD3PEPijbNhAcBLAFtN8mH72ZuYF1Fo1ENIVIAUfy1UYcO3Lw+yF4yY\nM3iaWQ7MhqQBycte7KXE7ERAohnzRv5wnHEsP5FoljN4kVJHDU5+EsMlLCNa1IM3kWgGBUls7A4x\nhgGjaBg8Z/CuX5KgKRIeeG6yyYZRjK2Rh6bsLzB4e5vsenjyCgNRl1yeIOVIYy50HdzQ5MnUfA/e\nyo0ACLDxJDRFzEUzLpFojmBCmkvMpxmdaYmm5PbQo3UcW6mhpsk4t2Pjf3zv3+N9T/Dz6JfLNBPj\nHxsG3nCcA472YaDH3PMSgCfC4A04gxfXVtPNU5IImqaKkdTcXQ/elUcnP69fZVtpdwDt5Efxsehb\n8CxlDOUz9CB7rgLTobn8c6hNAbzVGwEAR8mVCmMSRhhSY2bu5Ad/7FvwTUeWMKL67oY5vwDBGLzZ\n9USWiHhv4QsdnMHboQ0MrUPssV30liX3wPwcPIBJNMtNVngPXzjMNFgBGDAz4GPoFrMBNPQQQQak\n7LWW8HUvLrsGOKDo0xrqBgN4QTgF8PzhVe3Dm7+WTJWdYxWJZjzawhZtwVBlVgxpHmT7eZX3lewP\nXKKZ7JdDdRntuNq65gYRGsROC54AUnWI7IqrHKYB3ubIQxhTmKoMojfQlr3UgVQo+ufYuj89Bqi2\np5qMFUA44r9fmzXgC9Q6LCq2HgURRSMP4PEcQhEYA8Hm4PlpIX8hOINHBFxrEwbPDiKYmjLzlKzy\nvVxQxZEUb15HTrPi4M3fw87lVQXwJgyesAFdVjz1CeD8V4CP/cyuRna8XOMawHuekQC8QC6Y7ybJ\niCUNFvHhRTQzARl5MQz4rIeoJGI+dsFzHYwzNiDKe2L0WjkwG0sNqH42wFOSZuV5fXrWOfFNxB9l\nsDlTDF4pwNOaaBI77SNZPNaUyUqei6aiIZJ0NIg9SSKywh9hDDMzkUqDAzwtcnH7ofYMg7c18hFT\noC55MyMSAKTAKpHZXLYlQFJyJZoXew6uqyUAb47B0yzGdm2e5D145YsYcXsY0BpWG4uV94ahYkhN\nSLE/0xcQTFXQpwEenC4ivY09TQOHOhae3RzjyctDfH2HH1ug+urzfscfetPN+PE3c2OF9hHWwxdP\nZn6JDODtOwH2k20Q3n+XRMtUMUA99zMujI2TLBne+9qrz+Bd/gak0MEn4nvSh56h+xljufmU8GH0\ngCdx09cTN7U4Rq6IMXhRCIQuhrGxUFBpmSqGsXZVevAopeiOgxnWBWASxqs2B8/eRgwJgdqA2zjK\nHtsFaElYI1NbXN+ETFaSGVp+P9NgBQBknVm3j+ySZDj0EOYMcAYAmfd5R17JNcDvyQGslMFLC0pr\nt7H/X364+BhZ4Y+f91iUmWvpIz8CfPwXUwYvdx/KCnsbO2ikrz24XMOmtCJsRAVg0oNntGYAnq12\n0I6rSc8dP0YTNog5DfDY9aD64seigZta9F/usf3CUGVAr6MlVTRZ6Z1jvdvTUV9lAK9CYj3aZqOU\nm8trM4+HSh0WXCFpdBDFDOCpFiDPgqkE4EkC4z+SHrxcczxuskKEJJo+l2iGsObyEqkqg8clmndI\nXCJ88G62577KAF6SvyUM3q4Gu2/zz8gfpkXnV0NcA3i7DUpxwwfuwp4H/z0AQNIKAB5YH57J56Vl\n9eENvYg16ooAPN5TYcHDpUGGaxBnZnSrHOA5ShN6kG2hm1avBBi8yGKVtGiQYS2dbO60VgrwqN5A\nAzbcvMopr3AOqDXDKMxHoLLjhAWyH+KPMJ5jJhYilSCNcc+xDh67NEi17Unl0oKXSjrSlykSlqf6\ng7btgAGIXImmg8MGryZmJWWrNwMbJ8VcNCmF4vdzGbwmZ/AAzMg0p8FwkiO7QQQzHKT9moc6Fr5w\nahNhTPH4gG8443KAF3BWeaXTgZSME1k6wiSeo/VUjiYiseg7AfZLO5BaiwCvR2u5n3FhbD7JZENL\nR3clq3tBgzNB63RyHXjQMNL3At3nhA9TC3usN3i6B0urIaqv4Zh0pZDdToP3zw5iI3UYTKJpKOhf\npTEJIy+EH8WZDN7V68HbgSM30DR1oLHGTBF2AfASq+9GxkgZZrJS/P4SgKv4/VyJZuJ+aY9LGIrI\nR0yU3KdVVUdESTnAcyYMXi0FePx9JBL0qonn+hPAb98E+n+/Eb/64Qfxpt/8DDar9IPxSK6lvboP\nPPpnwAP/D5ZcpkqoZLLibKNLG2nv1OGOhQtxRYDnzpqsJPulp7bRynOpzgnPdWARD/I0wOPqEM2v\nYrIy6cG72GN7lKFKgN6o7qLZOw+05gHeXgZsKhTm1q9cwIiauOO62ZmxUTLTVkA2zgad53gM8MfU\nChJNmuM0mzB4kkjvXBwAsoaxNxngnUQq0aw4JuFGfRsjucWA9NLRVw3AC6OYffZTDF5MK7hDT8fW\nM5P5w5e+/gKe5dWNawBvt8GrKLXLXwIAkDkGZz5i2YAJdoNnyTTdMOY0vwDA42yhRdxMgJcMCida\nsYsmAPhqC1aUDfD0kG8oAgAv5L1QcS9jM5uq3pYDvCYMEsBxc/rwphi8ImAWqnVmEV3Qj0P8EUYw\nhBg8+DbuPtZBFFM8dJZVPy/yod8GdRckmgBmDE76TgBqtNmco7kIohg7Yx97Fb6ZzDN4ALDvtcDW\nKdSIDz+KiweXBg7kOECf1tA2F6vvdUPBkPLznRqVkCyMhEyKqWfXt2ESH50VNlD8cMdKn9ukPHEQ\nYPAS2bA6PbojsUjfPFlJopm6aLYOzjzeMlV0Y6s6g0cp4vWT+MxOB315qXLD/wseU1I/gA241hSJ\nbdIV2Ip61MNYXSwWxJ3jOEYuIxIBQpx978f6AmPeMlX0Qo0lUy+xrCWZvdXO6MG7agye28NIqqNl\nqmjXDFwg+4AdcUCeRFJAahiLwErEZCVMAd4gV6KpGqxn2LHzE2HWF+4jKmDwDE2BAx2RJyjRRH2K\nwePvQzXZeQ4vFx9jPh75IOANQIaXQL/+fpzfcfDQueoGS8m1dNyZyLT3XPgkAMCp0IMnOzvYofVU\n8n+4Y+FMsAQq6DQMgBUw1RqciODRC30cW2Hfk6ctoY1hJXZCGrK/S5YOTx40WohBKgE8acpF8xIH\neLoqA1oDFrVTlUppeCPWHz3P4CWOyiNxmabdXcdYXVrYu2mSq3jlYDiIKGqws/Mb3pevCoxciKIY\nJhFg8EQlmpIKJ4hg6bP3v6rpiEEqjUmoaTL2SgP0Zd72sXQU2DnzqpAhJutg4qI5ySEq9uHFMett\nv+UfA5IKXPzaC3qeVzOuAbzdxlzPQToPKCeoYjDDEiDTaMWLKMwiq93pY00xeBdzAJ4HNdfaejoC\nvZU7oFxL3KgETFZUo44urYMMLi0+6fYREB2KtsgCLJw7l4N6o5wk3RsgJjIcLCac0xFpDTTgFBou\nSMEIY1oi0Uxm2wU2Xn94CbJEUpnm2R0uhaXugkQTmPThJRGak4Hl05FskG0MASIDU03xaazdDtAI\n+z3m8Fho35wmUzUYGTKvhqFmMnhJb48mS2kiMe6x81XrbIM43Jm8Tw8aRrDg9soHAkdpX+jUZnrw\nLvZ+z3wxbSgfCyRU3qjLxnfMSTSbpoqtyGLFlypW0OMtSF4Pf9dbxnsettl3FFWfp/PEpQF+77PP\n34HT7rNEp9ZmrLily4x1laoZyDSiPtwMgEeXjuEwWReqdH74S08CAJNoZgI8FaCxcNLxQkVyz8wX\njORdALz3f+UsfuJPvpYaIu06nC6GYABvua7hYtQB3QUbPHTZe6vriwBPE5BoJu9f9vIZPD0BeOP8\nRNgLY+gkRFwA8HRFggO9vAdvisFLgOtM8a25n1noV4mzXwYO3oXL6mG8TfkGAODJy9VYLmByLR3b\n+hxzC1UMmA5b04QZPEohu110MWHwDnUsXMQKMLoi7n7r9QGjhb9+9DKGXogfuJOBoVBvo0ls+L74\nfaYPGMBTlo9NHpRkjEgdRiheBJNjPwV4/9fnmMmTySWaWuyk12tpJL3a7SOzj9d5H52AEiQJ099B\naHQWHo85gzfvEJ0VQcTaYrKKswno04pm8vKI+F4T57XpVGHwkh68DImmqSkIoIqPSfBD1HQFq+hh\nh/B1oLnGVBmvglEJKcDj6p8E6FXuwxtcZDnDnluAY28GHvmzl82opOcb1wDebmNOm10G8GJZh07z\nJZp+yHXcFRi8fbqPS8PFC1EObDgoB4oAEOttNGh2BV5P5AkCAM9QJFymyyDDjITG6WEsNTLlgvNB\nOLgJ7DyAN0Sg1ACQ9MbOilhtoEHsQiAkB2NmspI3Tw+YYvDGqOkKXru/ia9yo5Vz2zaWaxqkwJkZ\ncp7E97xuDbesNfGdtzD2y9OXM5vJB4kddjxgEprpBvQk9t8BAFizWQ9WYR+ekzCmtUwjmpomY5wB\n8JKEX1OktAfPGTKAp9UYUHjjiRXcfqiNH7jzIL79xlVsxE08fqoc1MRcMqPVpq4lvcHe15kvpHK0\ngUA1WB3zRDBDorkRFM8bzAw+FuE0XcMm2EYYDKqzeO/43S/gt/7mqWJ2VSB6W1cwpCa+63aWCFmq\njIahok/EDWTCKEYbA3hahty3fQirZABasslTSvEBDvDsjIJK01QxpsmMp5dWppkH8FRZqizRefff\nPYdPPn4Ff/SF6mzbTDg9dGkNLUvF8dUautSCP6rOKI3cEIQANS0D4AnMwYtiChkR5CDfZKXRZPfh\nZjefyXGDCCpC0IJCoa7IcKiW9n3nH2ziopm8rxmg2lgDhhnFwbwIHCalOvwteDJcw83GNo6t1PDk\nleogve8EOE4u4uCZjwCvfxewfAKaXRHgeQNINMTOnETzIl0BARWXfbt9wGjikQs9NAwFdx1l928C\nZrzhYoEwL/QxA1Ta8tGZx8dSA2Yo+DnFERTK5Id3HpmsJawHrwEJMahvixVVEqVHfe/s4ymDJ7bm\n+mGMJQzgaYsAD1wh4tvlDCUDeIv98wCmAF65RJMmo0RyJZrscUnYRVOF7UcLY1JMTUZA1EpjEuq6\ngiXawxbf19JcznuexayXQaRmUumYhF0yeMl119wP3PMTrCBz6lMv2HlezbgG8HYbcxuaZiwm+NNB\nFQMaB3jjDImmF8UwiZdP809FYrJyqBZlSjTl0IZLinsCk9Bbe6CSCM9dWNyAjFi8B09XZVyiHSij\nbAZvAAtHlotBMABIvF8gyHLjBAB3AF9mVbpyBs8unPMkB2Mu9RSQaPJk+O5jHTx8vgc3iHB222bv\nyR9lbhL//Z2H8PGfeRP+6T1MImNrnUw5Y5Ks1qKMIedJNPcDtVXsHT4BAMVOmpzBc+UGSAZYJIRM\nht9OLfRJwpVo2QHAGbHnjTrbIE7sqeMvfvqN+M3/7jb88T+/G2prL/zeennvC5f6GdacbPi6twAX\nHkQ7YonLUGBQaVJdR3NRorleMm8wM6YA3htuuxkA8MP/8a921c8DMLOaxy728ct//ihsP8TPfPDr\nqbRJJKi9jR6t40Cb3cOmJqNhKOihLszgjf0IHTJEkFHllngPjOkUJ1RuEEPhSYkLbaFY0DRV2OBJ\nzUs8KqGIwSub7TYfyTX3kYcu4H//2PMw2HF72IksLNc0vP2WfRiihiivUFUQAzdkYz+kxXtXlUmp\ni2YYx2iC7085JisWvw/PXskHDG4QQ0MIWiTRVCXYMAQAXg8h0eBBS3vwZopvzbVKDJ5/7u+BOMD6\n0uvxdLCKZf8ybt5b2xUL23cC3Ct/ClRSgLf8EtDcD5XvY8Jz8Lgyo0snJiuHOxYuUb6eCwO8AaA3\nsTP2sVrX0/U7Ntj3GAzFh53XxhfgURVKa//M42O5CTOnLWMhktm8koE/+B/uTB82VCntO6/DFRsw\nzdfk3/3y5mwRLNnzBItXjh9hmQwQGIt7JeEAxh+LADwKnfqTOXXTwd+bLgDw4qRQpuZJNDmDF5fs\nJ3EM0AhUUjPHJJgqH3ZewWTF0iS04y5OjS188IFzE3WQAMP5co95ieauGbwp51ocupv9fBUdfV/I\nuAbwdhtzkhS9BODFsgG1gMHzQgpdkMFLJJprRpgp0VQiG54kBvBOHGPyjS8+vGi/ng4MFQB4CYOn\njjI2abeH7cjEoU45wFMtBiSivAqcN4Qvs8+6qAcv1hqoE6ewmq9ENsa0hMGTJDa3hzO2dx3twA9j\nPHqxj3M7No4s1xh7kSXz4JEkoWO5zRL0aHYzTJJVM+wvDjlPghBg/x1YGbLvqXAR4+yVq+QzrzQZ\nfjttshJNJA+JRDOphFr17GM1V/ZjhfTx+adL+if8McZUh6nNsQG3/RBAI7Se+jMAwEBA7lN3OTCZ\nY/CWLBU7Mb8PM3odc2PnWcREwUW6gptOMMOHWrCNr53dnTvfpZ6Ld737q/jTr57De790Fn/58CX8\n9qfEZ2FJDnPj299miUFNV9AwFGzHdbYZReXJ1NgNsIwBoowkSGonAK+YMRm6AXTCvg+Pqgv3W8tU\nYScM3m5GJYy3gU//G+DcV8t/dy7yAJ6yizEJnufgr+r/Dt8iPY7/8qUzu2ZgqdPFZmhiqaahU9Ng\nNTvQwmHlfpeRF2b23wFiLppRTNEiCcDLZvCSpPPCRv41PmHwiiSaMgP5ZQDf6cFT2D6S9uBNr2GN\n/UyiJ3BtA8CVRz4DAPjJz6s4S/dCoT5uro9xucpMNh7D0QjfL38R7vXfw0aKNPdD4v2AwgweByc7\naKSS/05Nw5DwvVN0PfIGgNHCztjH0pRJFzXZfVwF4NWdi7hEVhZGXNhyEzVRgMddliNZnxkEb6hy\nqloxiSe0bieJ9AcfGWJ7PMVCJdeoYFHO9n10MEBkZqxtvMUjEGTw9BIGTxcYmp4qIUrGJMhlDF7M\nPsOIKIhiCmuOwTdVGR5VxMck+BFWVB8a9bBJW/hXH310ksu9Chi8pMidSjR3y+BNOdfCaLHv62r3\n4b9AcQ3g7TbmJJq6Wd6Dp8b5PXg+78GDUg7MUommEaDrRAuAUasA8NqrLEn+xlOTBHToBvje//Pv\noEVjxJAy5YfzYWkKA3h+b3GAtt3FTmTN9G/lhVJjFaY4T2LnDeAKMHjUaKIBp1CiqYZjjMsYPICB\nN57Avp7LVB54bgeX+g6OLGlMMlFgaJMkoX2ZV9PtySZNKU2b/DWvtzgDbzr234Hm8FmYcIsXMb5R\nJglVVjRb7O+cuzxZyBLpl6ZIaU6aDCi36hl9gQCaywewKvXx+VMlAC8Yw4aRypfSWDkBHLwbyjOf\nhCoTIQavFayz67I+66DWqenMRROoLNEc1w4hggy1xY65Snri5gFz8e3//nPo8u90a8Tu+aJrdT5k\nt4cubWBfkzN4qoyGrmIr4u9NIBGyxwMYJFgYBAwA0hIDeF/5+jcKTSkGbsB6VMAZvCyAlzJ41Ucl\n7Nz3m8AXfhv0vd9T+fW5AE+u1oMXRDEOh+dwa/go3lP7PQBiLPJCxDHg9rFDa+gkxi9mGwqiyvLV\noRuwWXFffz/wwB/OPKcpUnH/LZjUupUweDkSzSSpHY2H6I6zE0Y3jKAhyB1yDjAmZ0RNEAEGz5Eb\n0BQpvY4evzTAve95gK1lzTXWyynYh9XafBBPxofw6I6Es5RJ/A7iCrwwrpzgKVtPoklsSDd/D3ug\neRDE3kJdCWEHgtfCFIOXSOsIIYh1/vmLrkfeCNBq2Bn76EwBPML3hWgsLtFseOtYJ3sWHveVOkwB\n4AJgwhYpxowaxFTl1J3XgpcaAxVG2jpgoWdPXXOKzq5Hwc/IGexAITEic2XhOZnP/o0csR48nXqz\nLsPpgVQERBf6nEQlmqWDznnfd0DZ9TO/V5qazLwVKjB4awrLxTZpGw1deVVJNJM2ldRFc9cMXgLw\nmqyQXt97DeD9g4+5Dc0sGUkQywaUuECiGfKGX1VkTALbnPfofMbaHIunRQ58uRxMAUj18M7OZZze\nZFXYLz+7jUcv9tGAzdiyrJ6wudjb0vEo5c3c5x+YeS6yexhADOAlDF5uk7Q3gCtbIIRV63NDa6BB\nHIR5G3ToQ6EBHGJALjoOO6k0SVup6zjcsfCxhy+BUuC6Fpn8Tk4kSWgXHCRN9eH98RfP4Gc/xOY/\nKX63GOCt3Q6CGLeQsyUMHkvafS0blAHAL/+3dwEALq5PFrJE2qbJkx68kJujSEb29U1qy2hjhCvd\n4gRPCsb5Iyn23w6ycRItXS7twQujGO1oh8ld52YXdWoq+hAHQWlsP4thjfW7kTpLiFbQ3zXAm44r\nnFFYALYFofnMrCFxYbU0GTVdwUbEiwgCUiavzxJlUsuQ/Db2I6YE+7FdKEkcuCF0cAYP6mIPnqHC\nSQCewLwo+GPgQ+8CHv0wG+b+yJ9iSE2QyAee+3z566ei7wRQJLJgJa5I1Xrwxl6I6wlz/pVplB67\ncngDEBqjT2tpYk5SOVQFS3okDJ4K/OVPAx//BTY7jIcmlw86F2LwuJxMh58WIebDDWJoJMxPXJEw\neEb5vDB3AEeuQZclqDJbMz/z5Abuf3oTF7rOpFgzLDdsAgBr6zF8PT6BIKI4S9lr94WMkRbp450O\no8eKm/r+17IHmkzSeFTti0s0+ZrbR23mXicpOyV4DYQMcHRtf1IowOQ+jisAPD0awpYWC4+BXBMe\nBp5KNOdkjIYqASpbay24YkURt4eQShjBxM58UaFghNB8JP3RpJYF8Ng9JwbwKGubyQJ4AHzZEgN4\n3PSE5N0nXJVFy4AZ763zwPa1eYmmocpwqXgP3tgLsVdin8Phw0cgy2Qy0/jVJNHkBfpd9+AlYDcB\nv419wuvQyz2uAbzdxhTAu9f/X9EwihM4qhiQIxeKBPS9xQvQCylM4ucvEnPHAoBlld3oFwezN7wW\nOwiLBq9PB29wXiF9fOIxdlEngKdBHG5oUh4rNR2PkpsQERk483czzxG3jz6tCQE8o85ZrrwKkzeE\nIzHzkKz+ssmBFiWIM8EBgC2Vy0+hWjPf9x2H23hqnR33aKJcLJBoNjnA26J8s58CeB9/NJG0UkjO\nTn4PHgDsvx0A8FrpTPEAXqeHGARRAYN3dG8HPpUhTUmrphm8JEeOk88vj6HkDIEUFLvXyYENhxiZ\nfUXY+xrAH+GE3i1NFAZuiA4Zws9osO/UdPRpRYlmHAM7pzGwWJ+kopugRgt7pRcG4J3bYQlCktSK\nhBH0MJZb6QZv6QosTcZmxK+xDCfW+QiH7BqTaquLTyoaNtDGAbKVKwUEGJNlkCkGT55d41rWlMmK\nCAN37svAyY8BH/kXwG8chRmP8c/8X0So1ICn/6b89VORzAibXwOq9uAN3RA3StxtMHIhId7d956O\ngpkCeDy5pxXnMg7dEPuUqc/zwfekP4pINMOYol3G4HGliAkf4xwQ4wURNISFDJ6uShiLADx/BJfU\noKtS6qR8vsvujZEXTtY9kR7TwIXqd3GRsgT/El0GlVQs+6zPTUguOBWNwTPwoYAkY1uabHj2YaVX\nwWSFrX9Das24Mitmk1nbixacQgdUMRYkmjIHeLQSwLNTtcvMn1BrsCDYE8zlh3ROfqgrcrrnmcTD\nyBOTaA5gASApwPvDz5/GL/zZN1ghQvAzCgZsbSP1xbVN5S7NsQCgDqOYA7zsvdtX6rBgl46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Nd/J3D+q/j8/X8Ll6rYu/+o8HFsYx/a4RZzOJwOvkluBuUSTUU3MaAmdC+n4ul0EYMg1gRdNPNM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EB1gs+hbNINDoxncDcOaWHwJIpSqeyR3Fogvrsp9zo9Ehr6QLfQF61tcT/giZ6pnECZrQ69Xf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\nco0o0Ty/yUo731XeSGo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jJwfnijvAG5vNdOw4MmUlRqrUNuQjwJuYswK8M1cPLfhePKwjootAgVmlrB8D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oUxbfpeTWF6rRWvPS9+nYNNxsT3H1qsJ00xqi3zjzPH6SqRyfvbH916gqhvI8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RrAAQdswMY2xb3KTbKqZat3nd8f5441kkbVKjPS9/16+TUz996ZOSNfjeaZ55zn+VBI9e2c5n1h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\nQow3GSY6Hm57HK74Nlx7/xCMV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" - ] - }, - "metadata": { - "tags": [] - } - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "a3src-BKwPhX", - "colab_type": "text" - }, - "source": [ - "The model was able to predict the overall oscillations of the temperature quite well but the peaks were sometimes inaccurate. For the wind-speed, the overall oscillations are predicted reasonably well but the peaks are quite inaccurate. For the atmospheric pressure, the overall curve-shape has been predicted although there seems to be a slight lag and the predicted curve has a lot of noise compared to the smoothness of the original signal." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "k9If6_z7wPhX", - "colab_type": "text" - }, - "source": [ - "### Strange Example\n", - "\n", - "The following is another example from the training-set.\n", - "\n", - "Note how the temperature does not oscillate very much within each day (this plot shows almost 42 days). The temperature normally oscillates within each day, see e.g. the plot above where the daily temperature-oscillation is very clear. It is unclear whether this period had unusually stable temperature, or if perhaps there's a data-error." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "sAo1mu0RwPhY", - "colab_type": "code", - "outputId": "3f8e4b9f-ac71-4bc9-e6af-8e2754d29a9b", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 955 - } - }, - "source": [ - "plot_comparison(start_idx=200000, length=1000, train=True)" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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DM4GlWVCWBZN+2fn28GHqY/FuCQLFaUfebnQjqQkUQoi+s72gisr6Zspqmyhw1BplHKrB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R0WDAejeP5AH5bVu/NnMfVyR71PUqOvxOgeDgLOGuDGxwCTRQX8jko16XTH3erK2/4APPAx1Qt4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\nSUVORK5CYII=\n", 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rtsOYPOwbC7JZEc0qMsWg8wFPEJU2M5x2JWBzWM3YuLg25f2tZuUFEorE9Ncf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\nXeelW2UK2LwdSpfHfqJFVfCxh8XQZPeD8V9Qd6DQyp0Vf5sYyI9j+tP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Pv95khVMC2tWT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\nAFyR98zphfjiSBecHh+UQ1TRJLITg1oBm9OLbQ098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bPgsKlVJ/UkpVK6XyXPYlKKXeUkodc2zjHfu/pJQ6qJQ6pJTaoZRa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\n6mf2lvLnvBIevCKbpaMZOC781iXZcdhti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" - ] - }, - "metadata": { - "tags": [] - } - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "KfBwg7RFwPhZ", - "colab_type": "text" - }, - "source": [ - "As a check, we can plot this signal directly from the resampled data-set, which looks similar." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "p0YKXucRwPha", - "colab_type": "code", - "outputId": "fbdd9ef2-41a6-4057-87a7-1757593c3b5e", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 336 - } - }, - "source": [ - "df['Odense']['Temp'][200000:200000+1000].plot()" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 79 - }, - { - "output_type": "display_data", - "data": { - "image/png": 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FjQ5su+NCfOvSeQCA949KE7lTK21qsxkA2HUiNHn7wPojCR+joAX/3tcPodRs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\n9wGA2yH5uR+D9D88BuC/hBC9UXeSx8T6fkKI54n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- "text/plain": [ - "
" - ] - }, - "metadata": { - "tags": [] - } - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "7kkYv112wPhd", - "colab_type": "text" - }, - "source": [ - "We can plot the same period from the original data that has not been resampled. It also looks similar.\n", - "\n", - "So either the temperature was unusually stable for a part of this period, or there is a data-error in the raw data that was obtained from the internet weather-database." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "LZFhxWkAwPhd", - "colab_type": "code", - "outputId": "4dd86a20-644d-4406-ce37-f4a626caeb5c", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 307 - } - }, - "source": [ - "df_org = weather.load_original_data()\n", - "df_org.xs('Odense')['Temp']['2002-12-23':'2003-02-04'].plot()" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 80 - }, - { - "output_type": "display_data", - "data": { - "image/png": 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F9y+eO+Cznzt9Kltaurnl8XfC62rLSvLLaSsi54nIuyKySUS+mcxnk5nIYLyy\nuZUub4BzZmc2vd764bxFZNJJ12+QCyxfSnRPVo0mFmPrytjT3hsWxpnAYWr4PnMfoZAiUX2kwuPi\nVx85hqPH1QJw9LhaypOs4Z9RgS8iTuDXwPnAbOAKEYnftieKdN1tn1q7z0ismpp6YlUilJpPD/am\nCoVOTxL2wXwjHJKpY/A1CTC+vpxASLE3g1VvHY5Ik05QqXCoZiK4nA4+dsIEAFq7vFQk2YM301fC\nccAmpdQWpZQP+BtwcaIfXrO7Y8hCPxhSPL1uP6fNGDGkxKpEsEw6f162g/2Hi6NUcjHY8Acr7avR\nWFh1gjIZoGAJ90AohFKK9h5/0n5Kq4javo4+yt15pOEDY4CdtuVd5rq4rLJF6Hz2L28y57tPDukA\n3tpxiINdvpTr5CRDbVm/o/bZIumAZTfpLNvSmsMjSR7LpKNt+JpEsBrHb2/rPsKWqWPl//mCiodX\n7gaSzxGZbLabPGX6CMrzTMM/IiJyrYisEJEVLS0tvLnj0IBthhIq9eTafZQ4hdNnxCk1l0aOGlvD\nfZ86nhFVHpaYfT4LHW+g355ZaE8tWsPXJMPo6lLcTgc7WnsyljzpCEfphDjUbSRXJZvlX11awiOf\nO5EfX3Z00kEomb4SdgPjbMtjzXVhlFJ3KaUWKqUWjhgxgj3tvZSWOCIcbSu2DbwJJIJSiqfW7Wfx\nlEaqshAmKSIsntLIhfOaeGnTwYiU6ULFXlCq0By4voAunKZJHKdD8Lgc3PniFr58/6qM7QMMG75V\nTyeVrP+jx9VSX+HGlaQyk2mB/zowTUQmiYgb+DDw6GAf6Oj1U1NWwoOfXdw/yLa2lHb+7v5Otrf2\ncG4WzDl2zp49Cm8gxEubDmZ1v5nAZ9PwMx2ylm60hq9JFsv0+9BbuyMqWqYLS8P3h0Lh7mGOIeQa\nJavMZPRKUEoFgM8DTwLvAPcrpdbG2/7t3R3s7eijpqyEcXX92bCvbI60He/t6OVwAkXK/rxsO26n\ng7OTaAGWDhZNrKfK4yoKs44vEAr3/iw0DT9cKVMLfE2C3PqBefz4sqMB2HigM+3jhwV+oF/DH0rU\ncLL+qYxfCUqpx5VS05VSU5RStwy6LUb54pqykohQujW7O9hk+/Ivv3MZR938FG/v7og7VmuXl3+s\n2MX7j2lmRFX6Wr0lgtvl4PSZI3n87b0F39jcFwhR7nHidjoKTuAHdC18TZKUOB0cN9HIzE7VlJwI\ngVAo3ARlaBp+ngn8ZGnt9hkC33aRuhzC3183gn1u+fc6drQZYVN3R9WVsPPnZTvwBkLh9ORs86Wz\npxMIKr7xz9UZr565u72XTQd1SKyvAAAgAElEQVS62HawO+03mG5fAKcI5R5n3mXdKqUGreLp03H4\nmhQYV19GY6WHN7enX+BbWr0/qMJBEEOpHlPwAh8ML7RVv/y4SfWcPXsU/3xzN7vbe7l76dbwdpsO\ndMX8fJ8/yB9f3cYZM0fmrC3bpMYKvnXBTF7Y0MJ9y3dkbD9v7jjEibc+y1k/fYHTfvw87/m/Z3g5\njb6Dx1bvZU9HH+UlzrzT8H+3dCvH/OBpth6MHUanwzI1qSAizB9Xw+pBLAipYgn8zj5/OEFzKBq+\n25VHNvxksQ7dU+LA4RAev+Fk7rl6EZcvGkdbt4+v3L8SMLq0X3LMGDa3dEU0ErD4+TMbae328elT\ncqPdW3z0+AkcN6meXz27KWNa/oHD/XWxb7/8aEZVe/jlsxsH+URqlHtceafhP7nWaAl3sMsb833L\naRvdEFqjORJzmmvY3NKV9nPecgS32xqPD0Xgu5Js7JNXAt+KKbUeU2Y3V1PpcXHytBE015SybEsb\n00dV8rVzZ7JoYj19/tCA6nY7Wnu484XNXL5wHMdPzmwphSPhcAgfXDCWvR19rM1Q+0N72OQlx4zl\nUydPZtmWtpj5DEOhwu1MqgxrNokXTaGjdDSpMndMDUrBO3vTe91aep/9RjIUp22y5sq8uhKsCzP6\nAnU6hF/917F8/bwZ/PRD8wGYOtJIL97UEmnWWbmrnZCCjy2emPkDToAzZo5EBJ55JzOZt5ZQ++1H\njwXgiuPGU1Xq4u/Ldw72saQpczvzNq8gEKfhjBVSqk06mmSxWg2mW1GzlBO7eXQoJeDzKiwzWaI1\nfDvHjq/js6dNDTfxtQT+5ig7/oZ9nTgdwpSRFRk+2sRorPRwzLhalryTmRBNS9jNG2tU0KvwuDhu\nYj0rtqeWuxBNY6Wb9x3dTIXblbd1daxmEtFYPUq1hq9JltHVpTRUuAeNBEyFcGvDJLpUDUbehWUm\ng3VhJlKetL7CTX2Fe4DjdsP+TiY2lGe8UFoynDV7FGt2d2QkRNMXI/Tw2Al1bG5JTx9aEaGy1EWZ\nO/+cthb+QOzzxa/DMjUpIiKMbyhnT3t6r1nLl5eua6mgo3SsC/NAZ2wnXDRTR1bGFPgzRucmMice\nZ80yEr+eWZ9+LT9Wk48FE+oAeGunYccPhVTKTmN/MITb6aDCnX6nbWuXd0jObKvOT7yLJ1xaQYdl\nalJgRKWHlzYd5L7XhhZld99rO/jWg6uBfpNOvAjDZClogW+ZdEZVJ5YoNXVkJZtausJCo9cXZHtb\nD9NzFIoZj2kjKxlbV8bSDekvtWDV7rY/0Rw9thanQ3jDjCOefOPjfPfRuAnOg+ILhHA5zDj8NDlt\n93X08T+PrWPB/yzh58+kHlG0xnzctncAshN++kkykkGjAWiqKQXgxofWsCPFkslv7jjEjQ+t4a/L\nd3LfazuIdjedOn1oRR2TfXrNqxaHFW4Xt3/0WE6Y0pjQ9lNHVNLe46e120djpYe3dh5CKZjdVJ3h\nI00OEWFCQzktccIHU2VPey9/fHUbF8wbTZmtTGqZ28mc5mre2H4ofDP846vbY7ZNG4xQSNHjC1Lu\ncXHq9BHMaa5Jy3F/9R+rwnWGhlJ7vMQp+IOKsjhtMPt8QUTAozV8TQp87vSpvLqllQ37uzjQ2Reu\nl58M21v7c0R+88Imdrb1RxXe9sGjuOSYQavFHxERCffSToS8uxLOm9tETVli1ePCkTrm49Fjq/dS\n7nZy8rTMl0JOlipPCZ0J1P9JhPYeH3c8v4kr7l6GUnDjBbMGbHPs+DpW7mwfknOox/xslcfFggl1\nfHDBWAB2tvWkXEjNFwhF1NU/3Jv6d2JZg+KFZXb7gpSXOMNdhjSaZBhZXcr33mcoSf44kWBH4nBv\n/3Wys62XxkoPo6o9fPvCWVy2cFzS1S5jUZFEE5S8E/jJYAn8jfs78QdD/GfNXs6aNSpC280Xqstc\n4R//X6v2sG4I4V4/eWoDP3riXUZWefjNR49lbN1AzWPBhDr6/CFW7Uw9yqCrzzjeytL+E0opxSV3\nvMyvntuU0phv7jhEIKS4+6qFLJ7SQHuKAj8UUuEoHF8cJ3+PL0hZkh2BNBo7ViarP8U+t9EKzZ1X\nHstrN57FJ9NY8iUZeVfQAr+pppTx9eX8v5e28sw7BzjU4+eio5pyfVgxqSrt1/BvfHANd764OeWx\n9rT3Mrupmn98ZjFnzIxdCdRy3A6lzEKXqcVX2Bold3oDHOzysS1OOYMjsXRjCy6H8J7J9dSWl9CR\nosC3h2LGuxh7fAEqPPl389cUDpZTNGWB3+ePMLksmFCfluOyM2w0fBHhhx84im2tPXzl/pVUeVyc\nmoXOVqlQXVpCty9IlzdApzcwJNv1wS4vjUeoANpcW8a8MTX8adn28LpHV+3BG0jcxGMJ/CqbwN9v\nOon3pdj9aunGgxwzvpaq0hJqykoiUsyTwf6IHW+Mbm8w6Z6fGo2doQr8Pn8oro8pXby7P/EyzgUt\n8AFOmNLAx0+cSLcvyNlzRuVV/L2dKtMsstH8cayKn6lwsMtHY6X7iNtdvXhihAZ9w1/f4uYkonUs\nk45dw7cE/f4kcgo6+/z4AiHaun2s2d0R9rHUlLk53OunvccX0WglGqXUgJDQ6Nj7A50Dj6ej15d0\nz0+Nxo4VBZOqL6zXHwx3ucoHCl7gA3z93Jl8eNE4PnlSboulDUZ9hSGgL7njFQDaun0pOXGVUrR0\neROq8X/R0U0DTra/Lt+ZcJ0dS8OvtAt8U9Af6PTGLFwXzQNv7GLezU8x/dv/4RfPbEQpOHmaEYXV\nUOHGFwwx//tPM/3b/4k7xtceWM3s/36StXv6/RHff2xdxDbH3fLMgGN/fduhvLrYNIWH5QP60t9T\na3n4wBu76C8LmXuKQuCXuZ3c+oGjmN2cX+GYds6ePYpJjZHlHlIx6xzuC+ALhBhReWSB73E5efC6\nxVy2YCxfO3cGiyYadv0XN7QktK9YAt+q4R0IKVoTyOTdYqt1dO8r26hwOznKLANx2cKxfP/iOeH3\n40XbWGUp7FmPD71ltEa+cF6/z8Z+A7KyjOePqz3iMWo08RhTWzbkMWaOruK5r57GE188OQ1HNJAX\nvnZawtsWhcAvBCo8Lj5zauQTyM4UzDpWKeDGBAQ+GM2Ob7vsaD53+lT+8ZnFzBxdFU7IOhJW6KU9\nSme/rRzz/gTs+NFZsJNGVIS17tpyN1edMJGvnzcDiP/YbIVf2s0+lx5rxC9//oyp/fuyfd6yuc7J\nYyVAU9xY5+sJUxqY1FjBzNGZORcnNCReN0wL/Cwysqo0Ynl7KgK/MzmBH82CCXWs3NGeUIPm/iid\nfjv4vsN94XKuidQGira9T6gfeHJaeRc9cWL7rRuEvXjbJPMkt7Ihod/nALqfrSZ9XHfalJgZraGQ\n4g+vbKMvjqJinfv55EfSV0MWsdvdq0td7Imq5Z8IB7t8A8ZKhgUT6uj0Bnh8zd4jbtvlDeB2OiIc\n4fsP94VLVyQSqdPtC0aEpcXKVrQuiO44NXGsiy1SoIcQMcJd7cdrf9/4rD7FNUOjwu3EH1QRT5hd\n3gBPrt3Hdx9dy0+eejfm56zzWQv8YcpIW42ghkoPbSlUs2wxo1ESidKJxVmzRzGnuZrr//oW//PY\nukGLl3X1BSLMOWBo9XOaa3A5hJ2HjvyE0usLRmROj6+PJfCNfcQrzmZ19em0CXxfUFHicEQ4Ze3Z\nvz7d7UqTJqzz0+oH8evnNnHUzU9y54tGT217uQQ7vWENP39Cg7XAzyINFf0Cv77CnZLAP9jlw+kQ\n6spTE/jVpSU8+NnFXL5wHL97aeugDR66vAMTl75/8Rw+cvx4jh5Xy7LNrUeM1OnyBqgt6z/WCTEE\nfkVY4MfW8C1tvcvrj1gXHYFjF/h+3fxEkyYsDf2JtXvZdKCL2558l5qyElbubAeImzxodYjLp+Q/\nfTVkEbuAqisfKPDnf/8pvvHA6kHHONjlpb7CPaT6MB6Xk2tOngTA5pb4ZVq7vIEBWXznzW1iwYQ6\nTp7WyKpdHZx1+wuD7mv51jbKbSd8LJOOlaNwt6kxRWPZSO0a/gsxIo3sJh3d/ESTLkrNxKlv/HMN\nNz20BoB7P35c+P1Xt7Ty8yUDq75e84fXAa3hD2se+MwJPP/V02iIoeG39/j5+4rBWxO2dHoTCsk8\nEuPryxGBbQfjm2X6/MG49kcrln5LS/wSC5a5aJTNWd1UMzDMbe6YGk6bMYKn39nP6l3tA9636t7b\nBX59uTusOf3ksqOBSKdurMYwGk0qzGzqL7f+2tY2JjdWcPS4Wn7/8UXh9bcv2TDgc5a/bd6Y9FSZ\nTQda4GeZhRPrmdhYQV2Fm0M9vqQbgCRSViERSkucNNeUsa01vsDu8wfD2k00R489cny7Jajnje0/\n4WMlQjkdwi+uOIaGCg///cjaCDORUqpf4EfZ6Geb5ZpPnm7cfCKcugHttNWkh5mjq9nyvxeEly1l\n5/QZI3nv0c1xPzd/XC1Hja2JyFTPNfpqyBENFW78QRUWYom0dYTEyyokwsTGcrYMUgStzx+KK/Bd\nTgefPnXyoLXmLVNMRQJRCtWlJXzz/Jms3NnOP9/cFV7vtUVG2DOTjU5cxs2jymM4hbu89jh846bh\n1rXwNWnA4RC+9745XL14Ip84aVJ4vdc8x+M9Sdam6GvLFBm7GkTkZhHZLSIrzb8Ljvyp4UOdWWqh\nzXzs60mgVke4rEIaTDoAExsqBq162ecPDirQSxyOQYtKWYlUiZZvvfSYMRwzvpYfPrGew6Zwt8c4\nR4dlWtE7pSUOHBLltLWidHRpBU2a+Njiidz8vjkRiU7Ww2isevnBkMq78y/T6s/tSqn55t/jGd5X\nQdFgCfweU+An0D4wXFYhDSYdgEmNFXT0+uM2O+8LxDfpgGEuCSni1gSywtgGG8OOwyF8/31zae32\nhZ1gkRp+ZGKV1atWRKjwuCKctj4dh6/JAp5Buk0FQirvajnpqyFH1Edr+Ak0CE+2rMKRmGhqKlvj\n2PENk078U8TS4K/5w4pB309U4INh7//wovHc+8o2NuzvDGv4deUlA0w69sfoSo8rQsMPaJOOJgt4\nbApF9DUcDIWGnYb/eRFZLSL3iEhdhvdVUNRHafiPrT5y5utQyypEM2mEIfAvveOVmD4Ew6QTX1hb\nwnT51jZuf3pglIIlrJOtB/61c2dQXuLknNtf5NTbngeMMNZuX5BgyMh43HWoF6f0X0wVHldElI7O\ntNVkgzF1/VFn//1IZOnxotPwRWSJiLwd4+9i4DfAFGA+sBf4SZwxrhWRFSKyoqUlsSqOxUBY4Jvm\nlEM9/WaVeMlMVphXY1V6HEGTGyuYO8Yo6BSr1aB3EKctwJXvmcAp043a9vbSxRa9PkPolrmd3PfJ\n43n2K6cmdFz1FW5+97GFEeuONqtednkD4e+s3ua8rvC4okw+OixTk3muP2MaVy+eCMDIKFNr0dnw\nlVJnKaXmxvh7RCm1XykVVEqFgLuB4+KMcZdSaqFSauGIEfnZrSoTlLuduF2OsP2815ZlGq9qpFVW\nIV1OWxHh6sVGxEG0DyEYUviCg5t0RlR5+OMnjmPRxLqYWbK9Ng1/8dRGJo+oTPjYjp/cwM3vnR1e\nHmdqUl3eQDjjdk5zf7hnpccZs7SC1vA1mcTtcnDz++ZQ5XENuG4DQYXTkV/nXyajdOzNZS8B3s7U\nvgoREaGhwh2uKW8vHNYdp2rk3sN9uIZQViEWVshkjz9yn1YrxETs7+VuV8zCZ6nY8O3YbxBWVFNn\nn5/DfQNbLxo2fFtYZkBn2mqyR7nHGVNpyrfTL5MZAT8SkfmAArYBn87gvgqSunJ3WMO3lwbu8gYY\nGWP7lTvamdNcPaSyCtFYIZPRGnqf39CQSxNwepa7nTErf/b5kgvLjKa2vL/ommUC6+oLhI+1ylbY\nLTpKJxAK4ZDYiV4aTbqpcLsGhFYHVf5p+BkT+EqpKzM1drHQUNmv4dsFbizziD8YYtWudq44bnxa\njyFcqdIbLfCT0/CPZNJJBXuVTUvgd/T6w6Ga9kqelVFO218+uymlfWo0qVDmdg7o51B0NnzN0LAX\nUOvxBfrrvscw6byz9zB9/hALJqQ32Kk8rOFH7jM5ge+MGVY6VIFvr7JptZrbf9gbDs+0t15sri2j\nvcfPo6v2pLQvjWYoVLgjFQ4wsufz7QlTC/wcUl9hmHRaOr2s2tURztZr6fQO2NZqS5hugW+ZW679\n0xtcfuer4fVhk84gTluLco8ztg3fXDdYtu5g2E02Y+rKcAjs7egNR+PYm59cvXgix02q5yv3r+S5\n9QdS2p9GkyrlHmdE4AVoDV8TRX2Fm05vIKKpeHNNKXcv3TIgNPON7YcYU1sWs9rkULCXP16+rS38\nus902noS0M4r3C58gdCAWH6j+JojZZ+DwyHc9sGjuPXSeXhcTkZWlbKnvS9mc/XSEid3X7WQiQ0V\nfPHvK4H86jSkKW7K3QOVnkBI4cyzsGAt8HOIZZe2ujJ99D3j+co5M1i9q4PHoloQvrH9EMemWbuH\nSIeqUv0ljcMmnUESryzCZqEop1WvP5iyOcfisoXj+LDpt2iqLWVvRy9dfQEq3M4Bj8s1ZSWcMn1E\nuCHFty6YNaR9azSJUu52aQ1fMziWwLfCDM+ZPZr3HzOGWU3V3Pbk+nBo5J72XvZ29LFg/JFLEidL\ntBZsOUS9yZh0BnH8DlXg22muKWNvRx+dMVovWtjNQPl2sWmKF0PD77fhK6XMTNv8ErH5dTTDDEvg\nHzCbgZc4jR6tN5wxlZ1tvazcYTQD6bff16f9GKLj1C37eLJOWxjo+O09QqZusjTVlLKnvZdOrz/C\nnGOn2mbXzzeHmaZ4KXe7IhQeyyJrL/+RD2iBn0Osipn7TYHvdhknx9SRRsLRAdN5a3WVmj468UzV\nVLGSvsI2/ATj8GFgOGmvb/Bqm8nSXFuGNxBiZ1tvhMPWjtbwNbngrR2H8AVDbDXLjQdCZnlubcPX\nWFjZo+v2Go3ELW3bKn9sResc6Oyjrrxk0EJm6cJyiPZH6STgtPXEbkLe5w+mnHQVi+Zao1XixgOd\nEYLdTpXW8DU54LWtRsCDFSFmyvu8Owe1wM8h9eVu5o6p5u3dkQK/pqyEEqfQ0mUJfC8jbX1hM4kl\n8L1JmHQsoR4dh5wOp60dK0Kpzx+KK/Cry/rX59vFpile6syscCv3xNLwtUlHE8bhEP71+ZO475PH\n843zZjJjlNEsWUQYUekJa/gtnV5GVqenYFosfn/1Ir5/8RzAbtJJ3GnrNm9U/kBkWGa6TTpNtf03\nvURs+Nqko8kWv/7IsQBMMes/WTk1+VatNX+66w5TRITFUxtZPLUxYv2IqkiBP3lERayPp4XTZ45k\nc0sXYDfpJB6W6TC1mFBUQ/Z0m3QaKzyUOAV/UFHpiW3Dj3Taan1Gkx2suvjdUT2qS/KsAU9+HY0m\njCXwlVKGhp9hk45VedJuw3c7E0uaskwn0T1UDJNO+k4xh0MYXWN8D/Ft+Nppq8k+lh/LMmvma3nu\n/DoaTZgRVR4OdHpp7/HjC4YGNFdIN9YJ22ULyxysX6cdS65Ga/jptuFDvx0/nsC3x+drG74mW1RG\nKUyWScetBb4mEUZUemjr9rK3wwjZzKQNH4zQSpH+R1LvERqY27EEq+Wosuj1BSlNc3mDZlPDj2fD\nL3E6wmGiWsPXZAuPy8ih6Q4LfK3ha5JgRJWHkIL1+4wInkybdESESreLTptJJxGHLUBDZWQYKRht\nGr2BUPo1/FpLw49twzfeM24GWsPXZAsRocLtDDfh8QV0HL4mCaxY/LV7DkcsZ5IKjytCw0807r+m\nrITqUhc72/qboFiJW+kW+GENP45JB/odt/l2sWmKm0pbE56NBzqB/IvS0QI/T7EE/Lo9loafDYHv\ntMXhh5Iqa1xb7g7XqYf+0sjpDMsEmNRohL0N9n30a/j69NZkD7vCZJUomdiQuei6VNBXRJ4yotLQ\nZNfu6aDC7Qw7VTNJZWkJXdYjaTCEOwmB73E5wpEJMPTmJ/E4cWoDj37+RGY1VcfdxjL35FvSi6a4\nKbdp+JbAT3c586GiBX6e0ljVX0lzZHV2smwrPc5+k06SGr7b5QjbLcEWx59mp62IcNTYwauGVput\nEbUNX5NN7NfP69vaGFNblrAfLFvk19FowpS7XeFIlGzY78G0QZqaiTcYwp1E7R6PyxEurQz9tXjS\nreEngmXS0TZ8TTapMHs7H+r28dLGg1x0VBOSZ0+ZWuDnMZagz4b9HgwbpL2WTrIavl3gZ8qkkwiW\n01Zr+JpsYjltn1l/gEBIcdFRzbk+pAFogZ/HjKi0BH62TDr9Aj95G74zUuCbTtsyd/ZPsbCGrwW+\nJotYTtsdbT2IwOzm+H6mXKEFfh4T1vA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" - ] - }, - "metadata": { - "tags": [] - } - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "7jWbXouawPhe", - "colab_type": "text" - }, - "source": [ - "### Example from Test-Set\n", - "\n", - "Now consider an example from the test-set. The model has not seen this data during training.\n", - "\n", - "The temperature is predicted reasonably well, although the peaks are sometimes inaccurate.\n", - "\n", - "The wind-speed has not been predicted so well. The daily oscillation-frequency seems to match, but the center-level and the peaks are quite inaccurate. A guess would be that the wind-speed is difficult to predict from the given input data, so the model has merely learnt to output sinusoidal oscillations in the daily frequency and approximately at the right center-level.\n", - "\n", - "The atmospheric pressure is predicted reasonably well, except for a lag and a more noisy signal than the true time-series." - ] - }, - { - "cell_type": "code", - "metadata": { - "scrolled": false, - "id": "4Nx8832CwPhe", - "colab_type": "code", - "outputId": "87b2a769-a066-40fd-bcc1-fa889bf5c1d8", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 955 - } - }, - "source": [ - "plot_comparison(start_idx=200, length=1000, train=False)" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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GLJ6C+ADfOmVy//MzfOqhE3z1pCsw+8TFG0xGgS34jXg5Sl5j+C5ZcCufDqai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SPvd/vRC//4brcWQXVbtec8MMfud1x/DLrxCzjY1kk90JlbqL0Zyeen6ZoSo4p+2j/4gPcW6s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\nJD0F4KXs3zu4THHz3Ag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0lIiIiCpMSonrntyBC298sdJDIaIqwUDQaSwNJSIiogqLJXgdQkTpGAg6jaWhREREVGFj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\nOx+f6p1nVzAFjKlZ0+23Wu78BZwa6sygvPD7l6atDmp5+IY+tDXW4d2Bk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5tMdRUNXInpwqLpuW2P3O7dUUwmOj4YnpurT1xHodtPoGOvfxDdQXeKwKAosZ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w78/ldioM3nCQHh/fDizOEoxuGyZkyJ9oPK2jxhdyvNzmfcRHOzUtmz0QjUZg+sKcqKQm2tiymU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\nwB1/pr5p4GaHg8qfCo0H6dixm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- "text/plain": [ - "
" - ] - }, - "metadata": { - "tags": [] - } - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "b5npmuR6wPhg", - "colab_type": "text" - }, - "source": [ - "## Conclusion\n", - "\n", - "This tutorial showed how to use a Recurrent Neural Network to predict several time-series from a number of input-signals. We used weather-data for 5 cities to predict tomorrow's weather for one of the cities.\n", - "\n", - "It worked reasonably well for predicting the temperature where the daily oscillations were predicted well, but the peaks were sometimes not predicted so accurately. The atmospheric pressure was also predicted reasonably well, although the predicted signal was more noisy and had a short lag. The wind-speed could not be predicted very well.\n", - "\n", - "You can use this method with different time-series but you should be careful to distinguish between *causation and correlation* in the data. The neural network may easily discover patterns in the data that are only temporary correlations which do not generalize well to unseen data.\n", - "\n", - "You should select input- and output-data where a *causal* relationship probably exists. You should have a lot of data available for training, and you should try and reduce the risk of over-fitting the model to the training-data, e.g. using early-stopping as we did in this tutorial." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ByUxlvhgwPhh", - "colab_type": "text" - }, - "source": [ - "## Exercises\n", - "\n", - "These are a few suggestions for exercises that may help improve your skills with TensorFlow. It is important to get hands-on experience with TensorFlow in order to learn how to use it properly.\n", - "\n", - "You may want to backup this Notebook before making any changes.\n", - "\n", - "* Remove the wind-speed from the target-data. Does it improve prediction for the temperature and pressure?\n", - "* Train for more epochs, possibly with a lower learning-rate. Does it improve the performance on the test-set?\n", - "* Try a different architecture for the neural network, e.g. higher or lower state-size for the GRU layer, more GRU layers, dense layers before and after the GRU layers, etc.\n", - "* Use hyper-parameter optimization from Tutorial #19.\n", - "* Try using longer and shorter sequences for the batch-generator.\n", - "* Try and remove the city \"Odense\" from the input-signals.\n", - "* Try and add last year's weather-data to the input-signals.\n", - "* How good is the model at predicting the weather 3 or 7 days into the future?\n", - "* Can you train a single model with the output-signals for multiple time-shifts, so that a single model predicts the weather in e.g. 1, 3 and 7 days.\n", - "* Explain to a friend how the program works." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "q8qVbNWtwPhh", - "colab_type": "text" - }, - "source": [ - "## License (MIT)\n", - "\n", - "Copyright (c) 2018 by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n", - "\n", - "Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n", - "\n", - "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n", - "\n", - "THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE." - ] + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "colab_type": "code", + "id": "3eZxKhdYwPdR", + "outputId": "4ca51614-3db9-45e7-c857-2a0a0bf3cb86", + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'2.2.4-tf'" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tf.keras.__version__" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "colab_type": "code", + "id": "bQqcfRlnwPdV", + "outputId": "b4fada10-5236-48ae-9672-42d3f447a9fa" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'0.25.1'" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.__version__" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "5WEvg-wLwPdY" + }, + "source": [ + "## Load Data\n", + "\n", + "Weather-data for 5 cities in Denmark will be downloaded automatically below.\n", + "\n", + "The raw weather-data was originally obtained from the [National Climatic Data Center (NCDC), USA](https://www7.ncdc.noaa.gov/CDO/cdoselect.cmd). Their web-site and database-access is very confusing and may change soon. Furthermore, the raw data-file had to be manually edited before it could be read. So you should expect some challenges if you want to download weather-data for another region. The following Python-module provides some functionality that may be helpful if you want to use new weather-data, but you will have to modify the source-code to fit your data-format." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 63 + }, + "colab_type": "code", + "id": "0EKf--E8wPda", + "outputId": "9cbcd30e-3d97-41d7-a4a7-99f16d1ccb31" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cloning into 'TensorFlow-Tutorials'...\n", + "remote: Enumerating objects: 12, done.\u001b[K\n", + "remote: Counting objects: 100% (12/12), done.\u001b[K\n", + "remote: Compressing objects: 100% (10/10), done.\u001b[K\n", + "remote: Total 483 (delta 3), reused 5 (delta 2), pack-reused 471\u001b[K\n", + "Receiving objects: 100% (483/483), 63.64 MiB | 2.92 MiB/s, done.\n", + "Resolving deltas: 100% (225/225), done.\n", + "Checking connectivity... done.\n" + ] + } + ], + "source": [ + "!git clone https://github.com/hossein20s/TensorFlow-Tutorials\n", + "!cp TensorFlow-Tutorials/*.py .\n", + "\n", + "import weather\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "JHuYxfRBwPde" + }, + "source": [ + "Download the data-set if you don't have it already. It is about 35 MB." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "colab_type": "code", + "id": "JLAhzMSfwPdf", + "outputId": "dff49710-18ed-459a-a62f-55fea77f8522" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data has apparently already been downloaded and unpacked.\n" + ] + } + ], + "source": [ + "weather.maybe_download_and_extract()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Q85DXu_IwPdi" + }, + "source": [ + "List of the cities used in the data-set." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "colab_type": "code", + "id": "fK-oHbPGwPdk", + "outputId": "299e689b-c391-4670-e93b-f961c46a54f4" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['Aalborg', 'Aarhus', 'Esbjerg', 'Odense', 'Roskilde']" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cities = weather.cities\n", + "cities" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "IpjwHOh8wPdn" + }, + "source": [ + "Load and resample the data so it has observations at regular time-intervals for every 60 minutes. Missing data-points are linearly interpolated. This takes about 30 seconds to run the first time but uses a cache-file so it loads very quickly the next time." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 53 + }, + "colab_type": "code", + "id": "_hU0BoMbwPdp", + "outputId": "91f03c36-d22a-4f24-9222-fa83820e5fc1" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/src/TensorFlow-Tutorials/weather.py:169: FutureWarning: supplying multiple axes to axis is deprecated and will be removed in a future version.\n", + " df_res = df.dropna(axis=[0, 1], how='all')\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 2min 20s, sys: 34.3 s, total: 2min 55s\n", + "Wall time: 2min 56s\n" + ] + } + ], + "source": [ + "%%time\n", + "df = weather.load_resampled_data()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "D_Vetst4wPdt" + }, + "source": [ + "These are the top rows of the data-set." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 476 + }, + "colab_type": "code", + "id": "UTv8npHowPdu", + "outputId": "97297b18-303e-40f5-83fd-2f7d3120fa23" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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AalborgAarhusEsbjergOdenseRoskilde
TempPressureWindSpeedWindDirTempPressureWindSpeedWindDirTempPressureWindSpeedWindDirTempPressureWindSpeedWindDirTempPressureWindSpeedWindDir
DateTime
1980-03-01 11:00:005.0000001007.76666710.2280.0000005.01008.30000015.4290.06.083333NaN12.383333310.0000006.1428571011.06666712.585714290.05.000000NaN11.466667280.000000
1980-03-01 12:00:005.0000001008.00000010.3290.0000005.01008.60000013.4280.06.583333NaN12.883333310.0000007.0000001011.20000011.300000290.05.000000NaN12.466667280.000000
1980-03-01 13:00:005.0000001008.0666679.7290.0000005.01008.43333315.4280.06.888889NaN13.244444309.4444447.0000001011.30000012.118182290.05.166667NaN13.133333278.333333
1980-03-01 14:00:004.3333331008.13333311.1283.3333335.01008.26666714.9300.06.222222NaN12.911111306.1111116.8571431011.40000012.742857290.05.833333NaN12.300000270.000000
1980-03-01 15:00:004.0000001008.20000011.3280.0000005.01008.10000017.0290.05.555556NaN12.577778302.7777786.0000001011.50000012.400000290.04.833333NaN12.300000270.000000
\n", + "
" + ], + "text/plain": [ + " Aalborg Aarhus \\\n", + " Temp Pressure WindSpeed WindDir Temp \n", + "DateTime \n", + "1980-03-01 11:00:00 5.000000 1007.766667 10.2 280.000000 5.0 \n", + "1980-03-01 12:00:00 5.000000 1008.000000 10.3 290.000000 5.0 \n", + "1980-03-01 13:00:00 5.000000 1008.066667 9.7 290.000000 5.0 \n", + "1980-03-01 14:00:00 4.333333 1008.133333 11.1 283.333333 5.0 \n", + "1980-03-01 15:00:00 4.000000 1008.200000 11.3 280.000000 5.0 \n", + "\n", + " Esbjerg \\\n", + " Pressure WindSpeed WindDir Temp Pressure \n", + "DateTime \n", + "1980-03-01 11:00:00 1008.300000 15.4 290.0 6.083333 NaN \n", + "1980-03-01 12:00:00 1008.600000 13.4 280.0 6.583333 NaN \n", + "1980-03-01 13:00:00 1008.433333 15.4 280.0 6.888889 NaN \n", + "1980-03-01 14:00:00 1008.266667 14.9 300.0 6.222222 NaN \n", + "1980-03-01 15:00:00 1008.100000 17.0 290.0 5.555556 NaN \n", + "\n", + " Odense \\\n", + " WindSpeed WindDir Temp Pressure WindSpeed \n", + "DateTime \n", + "1980-03-01 11:00:00 12.383333 310.000000 6.142857 1011.066667 12.585714 \n", + "1980-03-01 12:00:00 12.883333 310.000000 7.000000 1011.200000 11.300000 \n", + "1980-03-01 13:00:00 13.244444 309.444444 7.000000 1011.300000 12.118182 \n", + "1980-03-01 14:00:00 12.911111 306.111111 6.857143 1011.400000 12.742857 \n", + "1980-03-01 15:00:00 12.577778 302.777778 6.000000 1011.500000 12.400000 \n", + "\n", + " Roskilde \n", + " WindDir Temp Pressure WindSpeed WindDir \n", + "DateTime \n", + "1980-03-01 11:00:00 290.0 5.000000 NaN 11.466667 280.000000 \n", + "1980-03-01 12:00:00 290.0 5.000000 NaN 12.466667 280.000000 \n", + "1980-03-01 13:00:00 290.0 5.166667 NaN 13.133333 278.333333 \n", + "1980-03-01 14:00:00 290.0 5.833333 NaN 12.300000 270.000000 \n", + "1980-03-01 15:00:00 290.0 4.833333 NaN 12.300000 270.000000 " + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "v2HIO-L7wPdy" + }, + "source": [ + "### Missing Data\n", + "\n", + "The two cities Esbjerg and Roskilde have missing data for the atmospheric pressure, as can be seen in the following two plots. \n", + "\n", + "Because we are using resampled data, we have filled in the missing values with new values that are linearly interpolated from the neighbouring values, which appears as long straight lines in these plots.\n", + "\n", + "This may confuse the neural network. For simplicity, we will simply remove these two signals from the data.\n", + "\n", + "But it is only short periods of data that are missing, so you could actually generate this data by creating a predictive model that generates the missing data from all the other input signals. Then you could add these generated values back into the data-set to fill the gaps." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 321 + }, + "colab_type": "code", + "id": "nROi76ntwPdz", + "outputId": "d43aa3eb-e5ea-4ce9-c756-42e7dfa18cdd" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df['Roskilde']['Pressure'].plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "GLCLX_yawPd7" + }, + "source": [ + "Before removing these two signals, there are 20 input-signals in the data-set." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 55 + }, + "colab_type": "code", + "id": "Df_g04tCwPd8", + "outputId": "4cc9b80d-07b0-4c8a-ed8b-5410d975f4a3" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(333109, 20)" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.values.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "mPVBWmUbwPeB" + }, + "source": [ + "Then we remove the two signals that have missing data." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "fcTXI8k1wPeB" + }, + "outputs": [], + "source": [ + "df.drop(('Esbjerg', 'Pressure'), axis=1, inplace=True)\n", + "df.drop(('Roskilde', 'Pressure'), axis=1, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "hxpuF6zlwPeH" + }, + "source": [ + "Now there are only 18 input-signals in the data." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 55 + }, + "colab_type": "code", + "id": "-SnarcOpwPeH", + "outputId": "3b4e34d3-13fd-4c4a-c816-457c7119fc18" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(333109, 18)" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.values.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "L3MaiEsEwPeO" + }, + "source": [ + "We can verify that these two data-columns have indeed been removed." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 196 + }, + "colab_type": "code", + "id": "VJUpPewGwPeP", + "outputId": "9f55c594-d442-45f3-b12a-b9b775e7083c" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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AalborgAarhusEsbjergOdenseRoskilde
TempPressureWindSpeedWindDirTempPressureWindSpeedWindDirTempWindSpeedWindDirTempPressureWindSpeedWindDirTempWindSpeedWindDir
DateTime
1980-03-01 11:00:005.01007.76666710.2280.05.01008.315.4290.06.08333312.383333310.06.1428571011.06666712.585714290.05.011.466667280.0
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" + ], + "text/plain": [ + " Aalborg Aarhus \\\n", + " Temp Pressure WindSpeed WindDir Temp Pressure \n", + "DateTime \n", + "1980-03-01 11:00:00 5.0 1007.766667 10.2 280.0 5.0 1008.3 \n", + "\n", + " Esbjerg Odense \\\n", + " WindSpeed WindDir Temp WindSpeed WindDir Temp \n", + "DateTime \n", + "1980-03-01 11:00:00 15.4 290.0 6.083333 12.383333 310.0 6.142857 \n", + "\n", + " Roskilde \\\n", + " Pressure WindSpeed WindDir Temp WindSpeed \n", + "DateTime \n", + "1980-03-01 11:00:00 1011.066667 12.585714 290.0 5.0 11.466667 \n", + "\n", + " \n", + " WindDir \n", + "DateTime \n", + "1980-03-01 11:00:00 280.0 " + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head(1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "5t6DXkeYwPeU" + }, + "source": [ + "### Data Errors\n", + "\n", + "There are some errors in this data. As shown in the plot below, the temperature in the city of Odense suddenly jumped to almost 50 degrees C. But the highest temperature ever measured in Denmark was only 36.4 degrees Celcius and the lowest was -31.2 C. So this is clearly a data error. However, we will not correct any data-errors in this tutorial." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 332 + }, + "colab_type": "code", + "id": "Z5Cu5qXlwPeW", + "outputId": "c43f6cb8-698e-462a-cf34-5ae9dc9d612f" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df['Odense']['Temp']['2006-05':'2006-07'].plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "s1eYnGtzwPeY" + }, + "source": [ + "This can also be confirmed to be an error by considering the temperatures in some of the other cities in Denmark for that period, which was only around 10 degrees. Because the country is so small, it is not possible for one city in Denmark to have 50 degrees while another city only has 10 degrees." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 332 + }, + "colab_type": "code", + "id": "xWME0HGxwPeZ", + "outputId": "d0aa6883-f8ad-46f3-f030-5730a1906070", + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df['Roskilde']['Temp']['2006-05':'2006-07'].plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "92asXtqAwPef" + }, + "source": [ + "### Add Data\n", + "\n", + "We can add some input-signals to the data that may help our model in making predictions.\n", + "\n", + "For example, given just a temperature of 10 degrees Celcius the model wouldn't know whether that temperature was measured during the day or the night, or during summer or winter. The model would have to infer this from the surrounding data-points which might not be very accurate for determining whether it's an abnormally warm winter, or an abnormally cold summer, or whether it's day or night. So having this information could make a big difference in how accurately the model can predict the next output.\n", + "\n", + "Although the data-set does contain the date and time information for each observation, it is only used in the index so as to order the data. We will therefore add separate input-signals to the data-set for the day-of-year (between 1 and 366) and the hour-of-day (between 0 and 23)." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "Cu3pzeXUwPeg" + }, + "outputs": [], + "source": [ + "df['Various', 'Day'] = df.index.dayofyear\n", + "df['Various', 'Hour'] = df.index.hour" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "pylQQ42VwPej" + }, + "source": [ + "### Target Data for Prediction\n", + "\n", + "We will try and predict the future weather-data for this city." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "4UkYOSO2wPej" + }, + "outputs": [], + "source": [ + "(between 1 and 366) and the hour-of-day (between 0 and 23).\n", + "\n", + "\n", + "df['Various', 'Day'] = df.index.dayofyear\n", + "df['Various', 'Hour'] = df.index.hour\n", + "Target Data for Prediction\n", + "We will try and predict the future weather-data for this city.target_city = 'Odense'" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "g8OZBR2OwPen" + }, + "source": [ + "We will try and predict these signals." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "p9AEmONwwPen" + }, + "outputs": [], + "source": [ + "target_names = ['Temp', 'WindSpeed', 'Pressure']" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "dYg-Wcz7wPes" + }, + "source": [ + "The following is the number of time-steps that we will shift the target-data. Our data-set is resampled to have an observation for each hour, so there are 24 observations for 24 hours.\n", + "\n", + "If we want to predict the weather 24 hours into the future, we shift the data 24 time-steps. If we want to predict the weather 7 days into the future, we shift the data 7 * 24 time-steps." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "D_9t8dzkwPet" + }, + "outputs": [], + "source": [ + "shift_days = 1\n", + "shift_steps = shift_days * 24 # Number of hours." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "JPWaNU87wPew" + }, + "source": [ + "Create a new data-frame with the time-shifted data.\n", + "\n", + "**Note the negative time-shift!**" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "9G2ebh5OwPex" + }, + "outputs": [], + "source": [ + "df_targets = df[target_city][target_names].shift(-shift_steps)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "5nrXKy7GwPez" + }, + "source": [ + "**WARNING!** You should double-check that you have shifted the data in the right direction! We want to predict the future, not the past!\n", + "\n", + "The shifted data-frame is confusing because Pandas keeps the original time-stamps even though we have shifted the data. You can check the time-shift is correct by comparing the original and time-shifted data-frames.\n", + "\n", + "This is the first `shift_steps + 5` rows of the original data-frame:" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "colab_type": "code", + "id": "9WiykanswPe1", + "outputId": "38c18bb4-0ee1-4ff4-b9a7-926f3d48f9dc" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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TempWindSpeedPressure
DateTime
1980-03-01 11:00:006.14285712.5857141011.066667
1980-03-01 12:00:007.00000011.3000001011.200000
1980-03-01 13:00:007.00000012.1181821011.300000
1980-03-01 14:00:006.85714312.7428571011.400000
1980-03-01 15:00:006.00000012.4000001011.500000
1980-03-01 16:00:004.90909112.6181821011.688889
1980-03-01 17:00:003.95348812.6465121011.877778
1980-03-01 18:00:003.67441911.7255811012.066667
1980-03-01 19:00:003.39534910.8046511012.255556
1980-03-01 20:00:003.1162799.8837211012.444444
1980-03-01 21:00:002.8372098.9627911012.633333
1980-03-01 22:00:002.5581408.0418601012.822222
1980-03-01 23:00:002.2790707.1209301013.011111
1980-03-02 00:00:002.0000006.2000001013.200000
1980-03-02 01:00:002.0769237.7384621012.366667
1980-03-02 02:00:002.5384627.9692311011.533333
1980-03-02 03:00:003.0000008.2000001010.700000
1980-03-02 04:00:003.0000007.9272731010.100000
1980-03-02 05:00:002.9166677.6583331009.500000
1980-03-02 06:00:002.4166677.4083331008.900000
1980-03-02 07:00:002.0000007.1000001008.300000
1980-03-02 08:00:002.1428576.5428571007.700000
1980-03-02 09:00:003.0000006.2000001007.100000
1980-03-02 10:00:002.8333338.3500001006.466667
1980-03-02 11:00:002.0000006.8285711005.833333
1980-03-02 12:00:002.0000008.2000001005.200000
1980-03-02 13:00:000.1666679.2166671004.766667
1980-03-02 14:00:001.00000011.8857141004.333333
1980-03-02 15:00:001.00000012.4000001003.900000
\n", + "
" + ], + "text/plain": [ + " Temp WindSpeed Pressure\n", + "DateTime \n", + "1980-03-01 11:00:00 6.142857 12.585714 1011.066667\n", + "1980-03-01 12:00:00 7.000000 11.300000 1011.200000\n", + "1980-03-01 13:00:00 7.000000 12.118182 1011.300000\n", + "1980-03-01 14:00:00 6.857143 12.742857 1011.400000\n", + "1980-03-01 15:00:00 6.000000 12.400000 1011.500000\n", + "1980-03-01 16:00:00 4.909091 12.618182 1011.688889\n", + "1980-03-01 17:00:00 3.953488 12.646512 1011.877778\n", + "1980-03-01 18:00:00 3.674419 11.725581 1012.066667\n", + "1980-03-01 19:00:00 3.395349 10.804651 1012.255556\n", + "1980-03-01 20:00:00 3.116279 9.883721 1012.444444\n", + "1980-03-01 21:00:00 2.837209 8.962791 1012.633333\n", + "1980-03-01 22:00:00 2.558140 8.041860 1012.822222\n", + "1980-03-01 23:00:00 2.279070 7.120930 1013.011111\n", + "1980-03-02 00:00:00 2.000000 6.200000 1013.200000\n", + "1980-03-02 01:00:00 2.076923 7.738462 1012.366667\n", + "1980-03-02 02:00:00 2.538462 7.969231 1011.533333\n", + "1980-03-02 03:00:00 3.000000 8.200000 1010.700000\n", + "1980-03-02 04:00:00 3.000000 7.927273 1010.100000\n", + "1980-03-02 05:00:00 2.916667 7.658333 1009.500000\n", + "1980-03-02 06:00:00 2.416667 7.408333 1008.900000\n", + "1980-03-02 07:00:00 2.000000 7.100000 1008.300000\n", + "1980-03-02 08:00:00 2.142857 6.542857 1007.700000\n", + "1980-03-02 09:00:00 3.000000 6.200000 1007.100000\n", + "1980-03-02 10:00:00 2.833333 8.350000 1006.466667\n", + "1980-03-02 11:00:00 2.000000 6.828571 1005.833333\n", + "1980-03-02 12:00:00 2.000000 8.200000 1005.200000\n", + "1980-03-02 13:00:00 0.166667 9.216667 1004.766667\n", + "1980-03-02 14:00:00 1.000000 11.885714 1004.333333\n", + "1980-03-02 15:00:00 1.000000 12.400000 1003.900000" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[target_city][target_names].head(shift_steps + 5)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "i1Zh_qtTwPe3" + }, + "source": [ + "The following is the first 5 rows of the time-shifted data-frame. This should be identical to the last 5 rows shown above from the original data, except for the time-stamp." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 425 + }, + "colab_type": "code", + "id": "_2Ow9SdwwPe4", + "outputId": "cb8b093c-a14d-4b95-cde7-8efd632c546e", + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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TempWindSpeedPressure
DateTime
1980-03-01 11:00:002.0000006.8285711005.833333
1980-03-01 12:00:002.0000008.2000001005.200000
1980-03-01 13:00:000.1666679.2166671004.766667
1980-03-01 14:00:001.00000011.8857141004.333333
1980-03-01 15:00:001.00000012.4000001003.900000
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TempWindSpeedPressure
DateTime
2018-03-01 19:00:00NaNNaNNaN
2018-03-01 20:00:00NaNNaNNaN
2018-03-01 21:00:00NaNNaNNaN
2018-03-01 22:00:00NaNNaNNaN
2018-03-01 23:00:00NaNNaNNaN
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" + ], + "text/plain": [ + " Temp WindSpeed Pressure\n", + "DateTime \n", + "2018-03-01 19:00:00 NaN NaN NaN\n", + "2018-03-01 20:00:00 NaN NaN NaN\n", + "2018-03-01 21:00:00 NaN NaN NaN\n", + "2018-03-01 22:00:00 NaN NaN NaN\n", + "2018-03-01 23:00:00 NaN NaN NaN" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_targets.tail()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "elB6-BRDwPe-" + }, + "source": [ + "### NumPy Arrays\n", + "\n", + "We now convert the Pandas data-frames to NumPy arrays that can be input to the neural network. We also remove the last part of the numpy arrays, because the target-data has `NaN` for the shifted period, and we only want to have valid data and we need the same array-shapes for the input- and output-data.\n", + "\n", + "These are the input-signals:" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "EatdfsiowPfA" + }, + "outputs": [], + "source": [ + "x_data = df.values[0:-shift_steps]" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 53 + }, + "colab_type": "code", + "id": "gNVuSAKrwPfD", + "outputId": "213153bd-d2b9-4db9-9a6e-9e54d9189f76" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Shape: (333085, 20)\n" + ] + } + ], + "source": [ + "print(type(x_data))\n", + "print(\"Shape:\", x_data.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "YybPGRDBwPfF" + }, + "source": [ + "These are the output-signals (or target-signals):" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "tvJu12uhwPfG" + }, + "outputs": [], + "source": [ + "y_data = df_targets.values[:-shift_steps]" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 53 + }, + "colab_type": "code", + "id": "92W5a11HwPfI", + "outputId": "584bef6c-6a79-4cc9-d99a-af35ea8c4b14", + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Shape: (333085, 3)\n" + ] + } + ], + "source": [ + "print(type(y_data))\n", + "print(\"Shape:\", y_data.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "JTbCXRFuwPfL" + }, + "source": [ + "This is the number of observations (aka. data-points or samples) in the data-set:" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "colab_type": "code", + "id": "b-e_mi8vwPfM", + "outputId": "f409355b-f1ce-4f58-ea41-7f43e484ae10" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "333085" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "num_data = len(x_data)\n", + "num_data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "hAeo1x6WwPfO" + }, + "source": [ + "This is the fraction of the data-set that will be used for the training-set:" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "sv6jvNerwPfP" + }, + "outputs": [], + "source": [ + "train_split = 0.9" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "_Mw0nVxQwPfT" + }, + "source": [ + "This is the number of observations in the training-set:" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "colab_type": "code", + "id": "mcPQ4FqewPfV", + "outputId": "3e69a7ff-2653-462e-b10e-37cfb715774f" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "299776" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "num_train = int(train_split * num_data)\n", + "num_train" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "4QkW4lk6wPfb" + }, + "source": [ + "This is the number of observations in the test-set:" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "colab_type": "code", + "id": "Y9PuOxh4wPfc", + "outputId": "d4d9b0a3-864e-4ffb-9de1-9ff9c9d708d7" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "33309" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "num_test = num_data - num_train\n", + "num_test" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "L_VDTY4UwPff" + }, + "source": [ + "These are the input-signals for the training- and test-sets:" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "colab_type": "code", + "id": "Z7tS9ziEwPff", + "outputId": "dc2e5b25-d564-4116-f347-e03af422a624" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "333085" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_train = x_data[0:num_train]\n", + "x_test = x_data[num_train:]\n", + "len(x_train) + len(x_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Fl6EGLHDwPfi" + }, + "source": [ + "These are the output-signals for the training- and test-sets:" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "colab_type": "code", + "id": "16gMrq2jwPfj", + "outputId": "fb8918de-2ca7-4d28-9770-c6d7a2b0013c" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "333085" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" } - ] -} \ No newline at end of file + ], + "source": [ + "y_train = y_data[0:num_train]\n", + "y_test = y_data[num_train:]\n", + "len(y_train) + len(y_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "M1paoApdwPfn" + }, + "source": [ + "This is the number of input-signals:" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "colab_type": "code", + "id": "J_WyDY4mwPfp", + "outputId": "108e5e93-c5c2-4468-b20d-dc39a1544781" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "20" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "num_x_signals = x_data.shape[1]\n", + "num_x_signals" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "3iJ2wFFPwPfr" + }, + "source": [ + "This is the number of output-signals:" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "colab_type": "code", + "id": "6jDdI8nLwPfr", + "outputId": "4d5d211c-9c34-4b7f-ff81-18a7d96e8dba", + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "3" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "num_y_signals = y_data.shape[1]\n", + "num_y_signals" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "m3xbZ-V_wPft" + }, + "source": [ + "### Scaled Data\n", + "\n", + "The data-set contains a wide range of values:" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 53 + }, + "colab_type": "code", + "id": "vq9B0AKVwPfv", + "outputId": "6ce0d570-bb8f-4a7e-f776-9377f0b19af2" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Min: -27.0\n", + "Max: 1050.8\n" + ] + } + ], + "source": [ + "print(\"Min:\", np.min(x_train))\n", + "print(\"Max:\", np.max(x_train))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "SV5Ug_T7wPfx" + }, + "source": [ + "The neural network works best on values roughly between -1 and 1, so we need to scale the data before it is being input to the neural network. We can use `scikit-learn` for this.\n", + "\n", + "We first create a scaler-object for the input-signals." + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "wobXHcmswPfy" + }, + "outputs": [], + "source": [ + "x_scaler = MinMaxScaler()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "zxhFPakqwPf0" + }, + "source": [ + "We then detect the range of values from the training-data and scale the training-data." + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "cs5OfekSwPf1" + }, + "outputs": [], + "source": [ + "x_train_scaled = x_scaler.fit_transform(x_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "PEPsv87ewPf4" + }, + "source": [ + "Apart from a small rounding-error, the data has been scaled to be between 0 and 1." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 53 + }, + "colab_type": "code", + "id": "-Ou_tkj6wPf4", + "outputId": "da37806d-2a42-4390-a490-ad633a8efad7" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Min: 0.0\n", + "Max: 1.0000000000000002\n" + ] + } + ], + "source": [ + "print(\"Min:\", np.min(x_train_scaled))\n", + "print(\"Max:\", np.max(x_train_scaled))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "UQZjSZhTwPf6" + }, + "source": [ + "We use the same scaler-object for the input-signals in the test-set." + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "_-1OEYRowPf7" + }, + "outputs": [], + "source": [ + "x_test_scaled = x_scaler.transform(x_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "NWsSw3oFwPf9" + }, + "source": [ + "The target-data comes from the same data-set as the input-signals, because it is the weather-data for one of the cities that is merely time-shifted. But the target-data could be from a different source with different value-ranges, so we create a separate scaler-object for the target-data." + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "QTmZCjblwPf-" + }, + "outputs": [], + "source": [ + "y_scaler = MinMaxScaler()\n", + "y_train_scaled = y_scaler.fit_transform(y_train)\n", + "y_test_scaled = y_scaler.transform(y_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "abunH1OjwPgA" + }, + "source": [ + "## Data Generator\n", + "\n", + "The data-set has now been prepared as 2-dimensional numpy arrays. The training-data has almost 300k observations, consisting of 20 input-signals and 3 output-signals.\n", + "\n", + "These are the array-shapes of the input and output data:" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 53 + }, + "colab_type": "code", + "id": "1R8JrXBPwPgB", + "outputId": "10f78c7e-ebf2-406a-a8ca-e3b523b4e33e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(299776, 20)\n", + "(299776, 3)\n" + ] + } + ], + "source": [ + "print(x_train_scaled.shape)\n", + "print(y_train_scaled.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "NDSzeIvqwPgD" + }, + "source": [ + "Instead of training the Recurrent Neural Network on the complete sequences of almost 300k observations, we will use the following function to create a batch of shorter sub-sequences picked at random from the training-data." + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "Uxq01eDNwPgD" + }, + "outputs": [], + "source": [ + "def batch_generator(batch_size, sequence_length):\n", + " \"\"\"\n", + " Generator function for creating random batches of training-data.\n", + " \"\"\"\n", + "\n", + " # Infinite loop.\n", + " while True:\n", + " # Allocate a new array for the batch of input-signals.\n", + " x_shape = (batch_size, sequence_length, num_x_signals)\n", + " x_batch = np.zeros(shape=x_shape, dtype=np.float16)\n", + "\n", + " # Allocate a new array for the batch of output-signals.\n", + " y_shape = (batch_size, sequence_length, num_y_signals)\n", + " y_batch = np.zeros(shape=y_shape, dtype=np.float16)\n", + "\n", + " # Fill the batch with random sequences of data.\n", + " for i in range(batch_size):\n", + " # Get a random start-index.\n", + " # This points somewhere into the training-data.\n", + " idx = np.random.randint(num_train - sequence_length)\n", + " \n", + " # Copy the sequences of data starting at this index.\n", + " x_batch[i] = x_train_scaled[idx:idx+sequence_length]\n", + " y_batch[i] = y_train_scaled[idx:idx+sequence_length]\n", + " \n", + " yield (x_batch, y_batch)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "S-60stDGwPgI" + }, + "source": [ + "We will use a large batch-size so as to keep the GPU near 100% work-load. You may have to adjust this number depending on your GPU, its RAM and your choice of `sequence_length` below." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "M8r0o4s0wPgI" + }, + "outputs": [], + "source": [ + "batch_size = 256" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "8YD-qNsqwPgK" + }, + "source": [ + "We will use a sequence-length of 1344, which means that each random sequence contains observations for 8 weeks. One time-step corresponds to one hour, so 24 x 7 time-steps corresponds to a week, and 24 x 7 x 8 corresponds to 8 weeks." + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "colab_type": "code", + "id": "szHC0H-cwPgK", + "outputId": "11a9f463-7ed7-4da7-f753-bb5d5a7e8ee6" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1344" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sequence_length = 24 * 7 * 8\n", + "sequence_length" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "vumvL2ZAwPgP" + }, + "source": [ + "We then create the batch-generator." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "saEtBnTBwPgP" + }, + "outputs": [], + "source": [ + "generator = batch_generator(batch_size=batch_size,\n", + " sequence_length=sequence_length)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "VmzbmR22wPgR" + }, + "source": [ + "We can then test the batch-generator to see if it works." + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "54Ogaz3cwPgR" + }, + "outputs": [], + "source": [ + "x_batch, y_batch = next(generator)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "v-9UB0cYwPgT" + }, + "source": [ + "This gives us a random batch of 256 sequences, each sequence having 1344 observations, and each observation having 20 input-signals and 3 output-signals." + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 53 + }, + "colab_type": "code", + "id": "pJ-XI1nawPgV", + "outputId": "b9665182-f80f-4f5c-e8f9-ef4a1be17a92" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(256, 1344, 20)\n", + "(256, 1344, 3)\n" + ] + } + ], + "source": [ + "print(x_batch.shape)\n", + "print(y_batch.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "QSeUlf4zwPgX" + }, + "source": [ + "We can plot one of the 20 input-signals as an example." + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 287 + }, + "colab_type": "code", + "id": "PP4mNTIMwPgX", + "outputId": "b3121627-bf90-4885-8666-a6a249aa18a9" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "batch = 0 # First sequence in the batch.\n", + "signal = 0 # First signal from the 20 input-signals.\n", + "seq = x_batch[batch, :, signal]\n", + "plt.plot(seq)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "WGMhZH9SwPgZ" + }, + "source": [ + "We can also plot one of the output-signals that we want the model to learn how to predict given all those 20 input signals." + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 287 + }, + "colab_type": "code", + "id": "EBrOTA2VwPgZ", + "outputId": "b73e26e1-114c-45f7-bbc5-5e69ba64d432" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "seq = y_batch[batch, :, signal]\n", + "plt.plot(seq)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "OC0T5Z39wPgb" + }, + "source": [ + "### Validation Set\n", + "\n", + "The neural network trains quickly so we can easily run many training epochs. But then there is a risk of overfitting the model to the training-set so it does not generalize well to unseen data. We will therefore monitor the model's performance on the test-set after each epoch and only save the model's weights if the performance is improved on the test-set.\n", + "\n", + "The batch-generator randomly selects a batch of short sequences from the training-data and uses that during training. But for the validation-data we will instead run through the entire sequence from the test-set and measure the prediction accuracy on that entire sequence." + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "pMbKCkTvwPgc" + }, + "outputs": [], + "source": [ + "validation_data = (np.expand_dims(x_test_scaled, axis=0),\n", + " np.expand_dims(y_test_scaled, axis=0))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "YpSU3TQ0wPgd" + }, + "source": [ + "## Create the Recurrent Neural Network\n", + "\n", + "We are now ready to create the Recurrent Neural Network (RNN). We will use the Keras API for this because of its simplicity. See Tutorial #03-C for a tutorial on Keras and Tutorial #20 for more information on Recurrent Neural Networks." + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "x_ITVhJBwPge" + }, + "outputs": [], + "source": [ + "model = Sequential()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "FznFRMVwwPgg" + }, + "source": [ + "We can now add a Gated Recurrent Unit (GRU) to the network. This will have 512 outputs for each time-step in the sequence.\n", + "\n", + "Note that because this is the first layer in the model, Keras needs to know the shape of its input, which is a batch of sequences of arbitrary length (indicated by `None`), where each observation has a number of input-signals (`num_x_signals`)." + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 131 + }, + "colab_type": "code", + "id": "MO0GMLJfwPgg", + "outputId": "68d03930-f2ff-4a38-8ea4-ca867fb73e64" + }, + "outputs": [], + "source": [ + "model.add(GRU(units=512,\n", + " return_sequences=True,\n", + " input_shape=(None, num_x_signals,), reset_after=False))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "H9x9wsPPwPgi" + }, + "source": [ + "The GRU outputs a batch of sequences of 512 values. We want to predict 3 output-signals, so we add a fully-connected (or dense) layer which maps 512 values down to only 3 values.\n", + "\n", + "The output-signals in the data-set have been limited to be between 0 and 1 using a scaler-object. So we also limit the output of the neural network using the Sigmoid activation function, which squashes the output to be between 0 and 1." + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "1MjMPjL4wPgi" + }, + "outputs": [], + "source": [ + "model.add(Dense(num_y_signals, activation='sigmoid'))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "XsVeIPNcwPgl" + }, + "source": [ + "A problem with using the Sigmoid activation function, is that we can now only output values in the same range as the training-data.\n", + "\n", + "For example, if the training-data only has temperatures between -20 and +30 degrees, then the scaler-object will map -20 to 0 and +30 to 1. So if we limit the output of the neural network to be between 0 and 1 using the Sigmoid function, this can only be mapped back to temperature values between -20 and +30.\n", + "\n", + "We can use a linear activation function on the output instead. This allows for the output to take on arbitrary values. It might work with the standard initialization for a simple network architecture, but for more complicated network architectures e.g. with more layers, it might be necessary to initialize the weights with smaller values to avoid `NaN` values during training. You may need to experiment with this to get it working." + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "PIAzoMoewPgm" + }, + "outputs": [], + "source": [ + "if False:\n", + " from tensorflow.keras.initializers import RandomUniform\n", + "\n", + " # Maybe use lower init-ranges.\n", + " init = RandomUniform(minval=-0.05, maxval=0.05)\n", + "\n", + " model.add(Dense(num_y_signals,\n", + " activation='linear',\n", + " kernel_initializer=init))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "2agyuXRywPgq" + }, + "source": [ + "### Loss Function\n", + "\n", + "We will use Mean Squared Error (MSE) as the loss-function that will be minimized. This measures how closely the model's output matches the true output signals.\n", + "\n", + "However, at the beginning of a sequence, the model has only seen input-signals for a few time-steps, so its generated output may be very inaccurate. Using the loss-value for the early time-steps may cause the model to distort its later output. We therefore give the model a \"warmup-period\" of 50 time-steps where we don't use its accuracy in the loss-function, in hope of improving the accuracy for later time-steps." + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "RGS8_v3nwPgq" + }, + "outputs": [], + "source": [ + "warmup_steps = 50" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "EFqGNn3gwPgs" + }, + "outputs": [], + "source": [ + "def loss_mse_warmup(y_true, y_pred):\n", + " \"\"\"\n", + " Calculate the Mean Squared Error between y_true and y_pred,\n", + " but ignore the beginning \"warmup\" part of the sequences.\n", + " \n", + " y_true is the desired output.\n", + " y_pred is the model's output.\n", + " \"\"\"\n", + "\n", + " # The shape of both input tensors are:\n", + " # [batch_size, sequence_length, num_y_signals].\n", + "\n", + " # Ignore the \"warmup\" parts of the sequences\n", + " # by taking slices of the tensors.\n", + " y_true_slice = y_true[:, warmup_steps:, :]\n", + " y_pred_slice = y_pred[:, warmup_steps:, :]\n", + "\n", + " # These sliced tensors both have this shape:\n", + " # [batch_size, sequence_length - warmup_steps, num_y_signals]\n", + "\n", + " # Calculate the MSE loss for each value in these tensors.\n", + " # This outputs a 3-rank tensor of the same shape.\n", + " loss = tf.losses.mean_squared_error(y_true=y_true_slice,\n", + " y_pred=y_pred_slice)\n", + "\n", + " # Keras may reduce this across the first axis (the batch)\n", + " # but the semantics are unclear, so to be sure we use\n", + " # the loss across the entire tensor, we reduce it to a\n", + " # single scalar with the mean function.\n", + " loss_mean = tf.reduce_mean(loss)\n", + "\n", + " return loss_mean" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "RzXJSehHwPgt" + }, + "source": [ + "### Compile Model\n", + "\n", + "This is the optimizer and the beginning learning-rate that we will use." + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "CwHgXL48wPgv" + }, + "outputs": [], + "source": [ + "optimizer = RMSprop(lr=1e-3)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Kbjo6pKpwPgy" + }, + "source": [ + "We then compile the Keras model so it is ready for training." + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 111 + }, + "colab_type": "code", + "id": "EBvqgad5wPgz", + "outputId": "309dd36c-a47d-4b6b-e554-a5c303152183" + }, + "outputs": [], + "source": [ + "model.compile(loss=loss_mse_warmup, optimizer=optimizer)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "zdta74cgwPg2" + }, + "source": [ + "This is a very small model with only two layers. The output shape of `(None, None, 3)` means that the model will output a batch with an arbitrary number of sequences, each of which has an arbitrary number of observations, and each observation has 3 signals. This corresponds to the 3 target signals we want to predict." + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 233 + }, + "colab_type": "code", + "id": "LJZDyU8bwPg3", + "outputId": "5e7ca8b1-4dd8-43ba-b2bf-d53fc3af625d" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_2\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "gru_3 (GRU) (None, None, 512) 818688 \n", + "_________________________________________________________________\n", + "dense_1 (Dense) (None, None, 3) 1539 \n", + "=================================================================\n", + "Total params: 820,227\n", + "Trainable params: 820,227\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "ScJ_6rTcwPg4" + }, + "source": [ + "### Callback Functions\n", + "\n", + "During training we want to save checkpoints and log the progress to TensorBoard so we create the appropriate callbacks for Keras.\n", + "\n", + "This is the callback for writing checkpoints during training." + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "rAQm73XzwPg5" + }, + "outputs": [], + "source": [ + "path_checkpoint = '23_checkpoint.keras'\n", + "callback_checkpoint = ModelCheckpoint(filepath=path_checkpoint,\n", + " monitor='val_loss',\n", + " verbose=1,\n", + " save_weights_only=True,\n", + " save_best_only=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "bwiOt1PJwPg6" + }, + "source": [ + "This is the callback for stopping the optimization when performance worsens on the validation-set." + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "C0iiIbiUwPg6" + }, + "outputs": [], + "source": [ + "callback_early_stopping = EarlyStopping(monitor='val_loss',\n", + " patience=5, verbose=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "dxeFQ-cpwPg8" + }, + "source": [ + "This is the callback for writing the TensorBoard log during training." + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "DxI8rw6uwPg8" + }, + "outputs": [], + "source": [ + "callback_tensorboard = TensorBoard(log_dir='./23_logs/',\n", + " histogram_freq=0,\n", + " write_graph=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "hLn058WVwPg-" + }, + "source": [ + "This callback reduces the learning-rate for the optimizer if the validation-loss has not improved since the last epoch (as indicated by `patience=0`). The learning-rate will be reduced by multiplying it with the given factor. We set a start learning-rate of 1e-3 above, so multiplying it by 0.1 gives a learning-rate of 1e-4. We don't want the learning-rate to go any lower than this." + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "hPovMktNwPg-" + }, + "outputs": [], + "source": [ + "callback_reduce_lr = ReduceLROnPlateau(monitor='val_loss',\n", + " factor=0.1,\n", + " min_lr=1e-4,\n", + " patience=0,\n", + " verbose=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "u5FGAYmxwPhA" + }, + "outputs": [], + "source": [ + "callbacks = [callback_early_stopping,\n", + " callback_checkpoint,\n", + " callback_tensorboard,\n", + " callback_reduce_lr]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "dTv6qPB_wPhD" + }, + "source": [ + "## Train the Recurrent Neural Network\n", + "\n", + "We can now train the neural network.\n", + "\n", + "Note that a single \"epoch\" does not correspond to a single processing of the training-set, because of how the batch-generator randomly selects sub-sequences from the training-set. Instead we have selected `steps_per_epoch` so that one \"epoch\" is processed in a few minutes.\n", + "\n", + "With these settings, each \"epoch\" took about 2.5 minutes to process on a GTX 1070. After 14 \"epochs\" the optimization was stopped because the validation-loss had not decreased for 5 \"epochs\". This optimization took about 35 minutes to finish.\n", + "\n", + "Also note that the loss sometimes becomes `NaN` (not-a-number). This is often resolved by restarting and running the Notebook again. But it may also be caused by your neural network architecture, learning-rate, batch-size, sequence-length, etc. in which case you may have to modify those settings." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 613 + }, + "colab_type": "code", + "id": "xCvfjb4FwPhE", + "outputId": "18891ff6-48e5-429d-bfd9-5e1a71ca6c08" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/5\n", + " 99/100 [============================>.] - ETA: 5s - loss: 0.0082 Epoch 1/5\n", + " 1/100 [..............................] - ETA: 22:06 - loss: 0.0051\n", + "Epoch 00001: val_loss improved from inf to 0.00506, saving model to 23_checkpoint.keras\n", + "100/100 [==============================] - 569s 6s/step - loss: 0.0081 - val_loss: 0.0051\n", + "Epoch 2/5\n", + " 99/100 [============================>.] - ETA: 5s - loss: 0.0044 Epoch 1/5\n", + " 1/100 [..............................] - ETA: 21:42 - loss: 0.0031\n", + "Epoch 00002: val_loss improved from 0.00506 to 0.00307, saving model to 23_checkpoint.keras\n", + "100/100 [==============================] - 557s 6s/step - loss: 0.0044 - val_loss: 0.0031\n", + "Epoch 3/5\n", + " 99/100 [============================>.] - ETA: 5s - loss: 0.0036 Epoch 1/5\n", + " 1/100 [..............................] - ETA: 21:20 - loss: 0.0030\n", + "Epoch 00003: val_loss improved from 0.00307 to 0.00304, saving model to 23_checkpoint.keras\n", + "\n", + "Epoch 00003: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n", + "100/100 [==============================] - 557s 6s/step - loss: 0.0036 - val_loss: 0.0030\n", + "Epoch 4/5\n", + " 99/100 [============================>.] - ETA: 5s - loss: 0.0027 Epoch 1/5\n", + " 1/100 [..............................] - ETA: 21:14 - loss: 0.0024\n", + "Epoch 00004: val_loss improved from 0.00304 to 0.00235, saving model to 23_checkpoint.keras\n", + "100/100 [==============================] - 559s 6s/step - loss: 0.0027 - val_loss: 0.0024\n", + "Epoch 5/5\n", + " 99/100 [============================>.] - ETA: 5s - loss: 0.0026 Epoch 1/5\n", + " 1/100 [..............................] - ETA: 21:05 - loss: 0.0023\n", + "Epoch 00005: val_loss improved from 0.00235 to 0.00230, saving model to 23_checkpoint.keras\n", + "\n", + "Epoch 00005: ReduceLROnPlateau reducing learning rate to 0.0001.\n", + "100/100 [==============================] - 554s 6s/step - loss: 0.0026 - val_loss: 0.0023\n", + "CPU times: user 59min 31s, sys: 8min 14s, total: 1h 7min 46s\n", + "Wall time: 46min 36s\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 71, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "%%time\n", + "model.fit_generator(generator=generator,\n", + " epochs=5,\n", + " steps_per_epoch=100,\n", + " validation_data=validation_data,\n", + " callbacks=callbacks)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "aCVSsGh9W4-G" + }, + "outputs": [], + "source": [ + "!cp *checkpoint* drive/My\\ Drive/models/" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "RJxmdCA4GLE8" + }, + "source": [ + "# New Section" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "_w6vqHJIwPhH" + }, + "source": [ + "### Load Checkpoint\n", + "\n", + "Because we use early-stopping when training the model, it is possible that the model's performance has worsened on the test-set for several epochs before training was stopped. We therefore reload the last saved checkpoint, which should have the best performance on the test-set." + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "aAgKGrA5wPhI" + }, + "outputs": [], + "source": [ + "try:\n", + " model.load_weights(path_checkpoint)\n", + "except Exception as error:\n", + " print(\"Error trying to load checkpoint.\")\n", + " print(error)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "9OVvL6XTwPhK" + }, + "source": [ + "## Performance on Test-Set\n", + "\n", + "We can now evaluate the model's performance on the test-set. This function expects a batch of data, but we will just use one long time-series for the test-set, so we just expand the array-dimensionality to create a batch with that one sequence." + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "colab_type": "code", + "id": "fbVU1hThwPhK", + "outputId": "ffd553dd-42cd-4e8b-b16f-3410ce3703b1" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "1/1 [==============================] - 19s 19s/sample - loss: 0.0026\n" + ] + } + ], + "source": [ + "result = model.evaluate(x=np.expand_dims(x_test_scaled, axis=0),\n", + " y=np.expand_dims(y_test_scaled, axis=0))" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "colab_type": "code", + "id": "QWHucwySwPhO", + "outputId": "5ce56306-e5b1-4288-e1d9-ada493ac007c" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss (test-set): 0.002629196271300316\n" + ] + } + ], + "source": [ + "print(\"loss (test-set):\", result)" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "zV4xlTZwwPhQ" + }, + "outputs": [], + "source": [ + "# If you have several metrics you can use this instead.\n", + "if False:\n", + " for res, metric in zip(result, model.metrics_names):\n", + " print(\"{0}: {1:.3e}\".format(metric, res))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "b_zSk9INwPhT" + }, + "source": [ + "## Generate Predictions\n", + "\n", + "This helper-function plots the predicted and true output-signals." + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "yMQudxPYwPhU" + }, + "outputs": [], + "source": [ + "def plot_comparison(start_idx, length=100, train=True):\n", + " \"\"\"\n", + " Plot the predicted and true output-signals.\n", + " \n", + " :param start_idx: Start-index for the time-series.\n", + " :param length: Sequence-length to process and plot.\n", + " :param train: Boolean whether to use training- or test-set.\n", + " \"\"\"\n", + " \n", + " if train:\n", + " # Use training-data.\n", + " x = x_train_scaled\n", + " y_true = y_train\n", + " else:\n", + " # Use test-data.\n", + " x = x_test_scaled\n", + " y_true = y_test\n", + " \n", + " # End-index for the sequences.\n", + " end_idx = start_idx + length\n", + " \n", + " # Select the sequences from the given start-index and\n", + " # of the given length.\n", + " x = x[start_idx:end_idx]\n", + " y_true = y_true[start_idx:end_idx]\n", + " \n", + " # Input-signals for the model.\n", + " x = np.expand_dims(x, axis=0)\n", + "\n", + " # Use the model to predict the output-signals.\n", + " y_pred = model.predict(x)\n", + " \n", + " # The output of the model is between 0 and 1.\n", + " # Do an inverse map to get it back to the scale\n", + " # of the original data-set.\n", + " y_pred_rescaled = y_scaler.inverse_transform(y_pred[0])\n", + " \n", + " # For each output-signal.\n", + " for signal in range(len(target_names)):\n", + " # Get the output-signal predicted by the model.\n", + " signal_pred = y_pred_rescaled[:, signal]\n", + " \n", + " # Get the true output-signal from the data-set.\n", + " signal_true = y_true[:, signal]\n", + "\n", + " # Make the plotting-canvas bigger.\n", + " plt.figure(figsize=(15,5))\n", + " \n", + " # Plot and compare the two signals.\n", + " plt.plot(signal_true, label='true')\n", + " plt.plot(signal_pred, label='pred')\n", + " \n", + " # Plot grey box for warmup-period.\n", + " p = plt.axvspan(0, warmup_steps, facecolor='black', alpha=0.15)\n", + " \n", + " # Plot labels etc.\n", + " plt.ylabel(target_names[signal])\n", + " plt.legend()\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "FnqILZQtwPhV" + }, + "source": [ + "We can now plot an example of predicted output-signals. It is important to understand what these plots show, as they are actually a bit more complicated than you might think.\n", + "\n", + "These plots only show the output-signals and not the 20 input-signals used to predict the output-signals. The time-shift between the input-signals and the output-signals is held fixed in these plots. The model **always** predicts the output-signals e.g. 24 hours into the future (as defined in the `shift_steps` variable above). So the plot's x-axis merely shows how many time-steps of the input-signals have been seen by the predictive model so far.\n", + "\n", + "The prediction is not very accurate for the first 30-50 time-steps because the model has seen very little input-data at this point.\n", + "The model generates a single time-step of output data for each time-step of the input-data, so when the model has only run for a few time-steps, it knows very little of the history of the input-signals and cannot make an accurate prediction. The model needs to \"warm up\" by processing perhaps 30-50 time-steps before its predicted output-signals can be used.\n", + "\n", + "That is why we ignore this \"warmup-period\" of 50 time-steps when calculating the mean-squared-error in the loss-function. The \"warmup-period\" is shown as a grey box in these plots.\n", + "\n", + "Let us start with an example from the training-data. This is data that the model has seen during training so it should perform reasonably well on this data." + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 955 + }, + "colab_type": "code", + "id": "dR92llmWwPhV", + "outputId": "92186356-84f4-4063-e945-cf48f7faf421", + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_comparison(start_idx=100000, length=1000, train=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "a3src-BKwPhX" + }, + "source": [ + "The model was able to predict the overall oscillations of the temperature quite well but the peaks were sometimes inaccurate. For the wind-speed, the overall oscillations are predicted reasonably well but the peaks are quite inaccurate. For the atmospheric pressure, the overall curve-shape has been predicted although there seems to be a slight lag and the predicted curve has a lot of noise compared to the smoothness of the original signal." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "k9If6_z7wPhX" + }, + "source": [ + "### Strange Example\n", + "\n", + "The following is another example from the training-set.\n", + "\n", + "Note how the temperature does not oscillate very much within each day (this plot shows almost 42 days). The temperature normally oscillates within each day, see e.g. the plot above where the daily temperature-oscillation is very clear. It is unclear whether this period had unusually stable temperature, or if perhaps there's a data-error." + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 955 + }, + "colab_type": "code", + "id": "sAo1mu0RwPhY", + "outputId": "3f8e4b9f-ac71-4bc9-e6af-8e2754d29a9b" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_comparison(start_idx=200000, length=1000, train=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "KfBwg7RFwPhZ" + }, + "source": [ + "As a check, we can plot this signal directly from the resampled data-set, which looks similar." + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 336 + }, + "colab_type": "code", + "id": "p0YKXucRwPha", + "outputId": "fbdd9ef2-41a6-4057-87a7-1757593c3b5e" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df['Odense']['Temp'][200000:200000+1000].plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "7kkYv112wPhd" + }, + "source": [ + "We can plot the same period from the original data that has not been resampled. It also looks similar.\n", + "\n", + "So either the temperature was unusually stable for a part of this period, or there is a data-error in the raw data that was obtained from the internet weather-database." + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 307 + }, + "colab_type": "code", + "id": "LZFhxWkAwPhd", + "outputId": "4dd86a20-644d-4406-ce37-f4a626caeb5c" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 99, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df_org = weather.load_original_data()\n", + "df_org.xs('Odense')['Temp']['2002-12-23':'2003-02-04'].plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "7jWbXouawPhe" + }, + "source": [ + "### Example from Test-Set\n", + "\n", + "Now consider an example from the test-set. The model has not seen this data during training.\n", + "\n", + "The temperature is predicted reasonably well, although the peaks are sometimes inaccurate.\n", + "\n", + "The wind-speed has not been predicted so well. The daily oscillation-frequency seems to match, but the center-level and the peaks are quite inaccurate. A guess would be that the wind-speed is difficult to predict from the given input data, so the model has merely learnt to output sinusoidal oscillations in the daily frequency and approximately at the right center-level.\n", + "\n", + "The atmospheric pressure is predicted reasonably well, except for a lag and a more noisy signal than the true time-series." + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 955 + }, + "colab_type": "code", + "id": "4Nx8832CwPhe", + "outputId": "87b2a769-a066-40fd-bcc1-fa889bf5c1d8", + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_comparison(start_idx=200, length=1000, train=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "b5npmuR6wPhg" + }, + "source": [ + "## Conclusion\n", + "\n", + "This tutorial showed how to use a Recurrent Neural Network to predict several time-series from a number of input-signals. We used weather-data for 5 cities to predict tomorrow's weather for one of the cities.\n", + "\n", + "It worked reasonably well for predicting the temperature where the daily oscillations were predicted well, but the peaks were sometimes not predicted so accurately. The atmospheric pressure was also predicted reasonably well, although the predicted signal was more noisy and had a short lag. The wind-speed could not be predicted very well.\n", + "\n", + "You can use this method with different time-series but you should be careful to distinguish between *causation and correlation* in the data. The neural network may easily discover patterns in the data that are only temporary correlations which do not generalize well to unseen data.\n", + "\n", + "You should select input- and output-data where a *causal* relationship probably exists. You should have a lot of data available for training, and you should try and reduce the risk of over-fitting the model to the training-data, e.g. using early-stopping as we did in this tutorial." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "ByUxlvhgwPhh" + }, + "source": [ + "## Exercises\n", + "\n", + "These are a few suggestions for exercises that may help improve your skills with TensorFlow. It is important to get hands-on experience with TensorFlow in order to learn how to use it properly.\n", + "\n", + "You may want to backup this Notebook before making any changes.\n", + "\n", + "* Remove the wind-speed from the target-data. Does it improve prediction for the temperature and pressure?\n", + "* Train for more epochs, possibly with a lower learning-rate. Does it improve the performance on the test-set?\n", + "* Try a different architecture for the neural network, e.g. higher or lower state-size for the GRU layer, more GRU layers, dense layers before and after the GRU layers, etc.\n", + "* Use hyper-parameter optimization from Tutorial #19.\n", + "* Try using longer and shorter sequences for the batch-generator.\n", + "* Try and remove the city \"Odense\" from the input-signals.\n", + "* Try and add last year's weather-data to the input-signals.\n", + "* How good is the model at predicting the weather 3 or 7 days into the future?\n", + "* Can you train a single model with the output-signals for multiple time-shifts, so that a single model predicts the weather in e.g. 1, 3 and 7 days.\n", + "* Explain to a friend how the program works." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "q8qVbNWtwPhh" + }, + "source": [ + "## License (MIT)\n", + "\n", + "Copyright (c) 2018 by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n", + "\n", + "Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n", + "\n", + "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n", + "\n", + "THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE." + ] + } + ], + "metadata": { + "accelerator": "GPU", + "anaconda-cloud": {}, + "colab": { + "collapsed_sections": [ + "v2HIO-L7wPdy", + "5t6DXkeYwPeU", + "pylQQ42VwPej", + "2agyuXRywPgq", + "b5npmuR6wPhg", + "ByUxlvhgwPhh", + "q8qVbNWtwPhh" + ], + "include_colab_link": true, + "name": "23_Time-Series-Prediction.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +}