{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# V0.1.6 - Building NARX Neural Network using Sysidentpy\n", "\n", "Example created by Wilson Rocha Lacerda Junior" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Series-Parallen Training and Parallel prediction\n", "\n", "Currently *SysIdentPy* support a Series-Parallel (open-loop) Feedforward Network training\n", "process, which make the training process easier. We convert the NARX network from Series-Parallel to the Parallel (closed-loop) configuration for prediction. \n", "\n", "Series-Parallel allows us to use Pytorch directly for training, so we can use all the power of the Pytorch library to build our NARX Neural Network model! \n", "\n", "![](figures/narxnet.png)\n", "\n", "The reader is referred to the following paper for a more in depth discussion about Series-Parallel and Parallel configurations regarding NARX neural network:\n", "\n", "[Parallel Training Considered Harmful?: Comparing series-parallel and parallel feedforward\n", "network training](https://arxiv.org/pdf/1706.07119.pdf)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Building a NARX Neural Network\n", "\n", "First, just import the necessary packages" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pip install sysidentpy" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "tags": [] }, "outputs": [], "source": [ "from torch import nn\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from sysidentpy.metrics import mean_squared_error\n", "from sysidentpy.utils.generate_data import get_siso_data\n", "from sysidentpy.neural_network import NARXNN" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Getting the data\n", "\n", "The data is generated by simulating the following model:\n", "\n", "$y_k = 0.2y_{k-1} + 0.1y_{k-1}x_{k-1} + 0.9x_{k-1} + e_{k}$.\n", "\n", "If *colored_noise* is set to True:\n", "\n", "$e_{k} = 0.8\\nu_{k-1} + \\nu_{k}$,\n", "\n", "where $x$ is a uniformly distributed random variable and $\\nu$ is a gaussian distributed variable with $\\mu=0$ and $\\sigma=0.1$" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "x_train, x_valid, y_train, y_valid = get_siso_data(n=1000,\n", " colored_noise=False,\n", " sigma=0.01,\n", " train_percentage=80)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Choosing the NARX parameters, loss function and optmizer\n", "\n", "One can create a NARXNN object and choose the maximum lag of both input and output for building the regressor matrix to serve as input of the network.\n", "\n", "In addition, you can choose the loss function, the optmizer, the optinional parameters of the optmizer, the number of epochs.\n", "\n", "Because we built this feature on top of Pytorch, you can choose any of the loss function of the torch.nn.functional. [Click here](https://pytorch.org/docs/stable/nn.functional.html#loss-functions) for a list of the loss functions you can use. You just need to pass the name of the loss function you want.\n", "\n", "Similarly, you can choose any of the optimizers of the torch.optim. [Click here](https://pytorch.org/docs/stable/optim.html) for a list of optimizers available.\n", "\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "narx_net = NARXNN(ylag=2,\n", " xlag=2,\n", " loss_func='mse_loss',\n", " optimizer='Adam',\n", " epochs=200,\n", " verbose=True,\n", " optim_params={'betas': (0.9, 0.999), 'eps': 1e-05} # optional parameters of the optimizer\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since we have defined our NARXNN using $ylag=2$ and $xlag=2$, we have a regressor matrix with 4 features. We need the size of the regressor matrix to build the layers of our network. Our input data(x_train) have only one feature, but since we are creating an NARX network, a regressor matrix is built behind the scenes with new features based on the xlag and ylag. \n", "\n", "You can check the size the of the regressor matrix by using the following line of code:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "4" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(narx_net.regressor_code) # the number of features of the NARX net" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Building the NARX Neural Network\n", "\n", "The configuration of your network follows exactly the same pattern of a network defined in Pytorch. The following representing our NARX neural network." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "class NARX(nn.Module):\n", " def __init__(self):\n", " super().__init__()\n", " self.lin = nn.Linear(4, 10)\n", " self.lin2 = nn.Linear(10, 10)\n", " self.lin3 = nn.Linear(10, 1)\n", " self.tanh = nn.Tanh()\n", "\n", " def forward(self, xb):\n", " z = self.lin(xb)\n", " z = self.tanh(z)\n", " z = self.lin2(z)\n", " z = self.tanh(z)\n", " z = self.lin3(z)\n", " return z" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have to pass the defined network to our NARXNN estimator." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "narx_net.net = NARX() " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Numpy array to Tensor\n", "\n", "If your inputs are numpy array, you can transform them to tensor using our *data_transform* method. Our function return a Dataloader object that we use to iterate on our training function. You can transform your inputs mannualy using TensorDataset and Dataloader using Pytorch.\n", "\n", "Since our inputs are numpy arrays, we transform them using the following:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "train_dl = narx_net.data_transform(x_train, y_train)\n", "valid_dl = narx_net.data_transform(x_valid, y_valid)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fit and Predict\n", "\n", "Because we have a fit (for training) and predict function for Polynomial NARMAX, we create the same pattern for the NARX net. So, you only have to fit and predict using the following:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "tags": [ "outputPrepend" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "s: 0.001731661903172414 | Validation metrics: 0.0022086017445256613\n", "01-07 15:36:58 - INFO - Train metrics: 0.0016636334337471496 | Validation metrics: 0.0021071012550494587\n", "01-07 15:36:58 - INFO - Train metrics: 0.001608394492501603 | Validation metrics: 0.0020541409442126\n", "01-07 15:36:58 - INFO - Train metrics: 0.0015560130035518704 | Validation metrics: 0.0019808202179976636\n", "01-07 15:36:59 - INFO - Train metrics: 0.0015087127656370103 | Validation metrics: 0.0019156069193750319\n", "01-07 15:36:59 - INFO - Train metrics: 0.0014678872695757558 | Validation metrics: 0.0018612849126297115\n", "01-07 15:36:59 - INFO - Train metrics: 0.0014256512215781144 | Validation metrics: 0.0018047122990317416\n", "01-07 15:36:59 - INFO - Train metrics: 0.0013885882456686563 | Validation metrics: 0.0017545426433736627\n", "01-07 15:36:59 - INFO - Train metrics: 0.0013531953940182028 | Validation metrics: 0.00170409430942564\n", "01-07 15:36:59 - INFO - Train metrics: 0.0013194922629141605 | Validation metrics: 0.001659289845547667\n", "01-07 15:36:59 - INFO - Train metrics: 0.001288092994530286 | Validation metrics: 0.001616562396814727\n", "01-07 15:36:59 - INFO - Train metrics: 0.0012572611840255092 | Validation metrics: 0.0015728269971794252\n", "01-07 15:36:59 - INFO - Train metrics: 0.0012282944520127803 | Validation metrics: 0.0015344962717570138\n", "01-07 15:36:59 - INFO - Train metrics: 0.0012000104080404814 | Validation metrics: 0.0014961352924618757\n", "01-07 15:36:59 - INFO - Train metrics: 0.001172495915736527 | Validation metrics: 0.0014590207202773956\n", "01-07 15:36:59 - INFO - Train metrics: 0.0011457135573491677 | Validation metrics: 0.0014235058263169997\n", "01-07 15:36:59 - INFO - Train metrics: 0.0011193126963479514 | Validation metrics: 0.0013889921170593513\n", "01-07 15:36:59 - INFO - Train metrics: 0.0010933511462155963 | Validation metrics: 0.0013554305357933796\n", "01-07 15:36:59 - INFO - Train metrics: 0.0010675421585083793 | Validation metrics: 0.001322374117062098\n", "01-07 15:36:59 - INFO - Train metrics: 0.0010419307360635664 | Validation metrics: 0.0012901194177266926\n", "01-07 15:37:00 - INFO - Train metrics: 0.0010163768318325356 | Validation metrics: 0.0012583352926403585\n", "01-07 15:37:00 - INFO - Train metrics: 0.0009908489074749419 | Validation metrics: 0.0012270198591674368\n", "01-07 15:37:00 - INFO - Train metrics: 0.0009653006164172669 | Validation metrics: 0.0011960408600950332\n", "01-07 15:37:00 - INFO - Train metrics: 0.0009397179094600704 | Validation metrics: 0.001165375923690847\n", "01-07 15:37:00 - INFO - Train metrics: 0.0009141063568868527 | Validation metrics: 0.001135000465155551\n", "01-07 15:37:00 - INFO - Train metrics: 0.0008884904444285372 | Validation metrics: 0.0011049109544002949\n", "01-07 15:37:00 - INFO - Train metrics: 0.0008629150414386563 | Validation metrics: 0.0010751176935931046\n", "01-07 15:37:00 - INFO - Train metrics: 0.000837442199326118 | Validation metrics: 0.0010456533237087607\n", "01-07 15:37:00 - INFO - Train metrics: 0.000812152301411805 | Validation metrics: 0.0010165641016110477\n", "01-07 15:37:00 - INFO - Train metrics: 0.000787136252452491 | Validation metrics: 0.0009879041639523525\n", "01-07 15:37:00 - INFO - Train metrics: 0.0007624904084472186 | Validation metrics: 0.0009597220471025341\n", "01-07 15:37:00 - INFO - Train metrics: 0.0007383046182453688 | Validation metrics: 0.0009320481795573024\n", "01-07 15:37:00 - INFO - Train metrics: 0.0007146476112355135 | Validation metrics: 0.0009048672644609605\n", "01-07 15:37:00 - INFO - Train metrics: 0.0006915545015173374 | Validation metrics: 0.0008781056932286557\n", "01-07 15:37:00 - INFO - Train metrics: 0.0006690152468834361 | Validation metrics: 0.0008516084729703237\n", "01-07 15:37:00 - INFO - Train metrics: 0.0006469790450294962 | Validation metrics: 0.0008251471003997281\n", "01-07 15:37:00 - INFO - Train metrics: 0.0006253891108081417 | Validation metrics: 0.0007984626031659469\n", "01-07 15:37:01 - INFO - Train metrics: 0.000604254945343533 | Validation metrics: 0.0007713593082794111\n", "01-07 15:37:01 - INFO - Train metrics: 0.0005837606822250219 | Validation metrics: 0.0007438592805593002\n", "01-07 15:37:01 - INFO - Train metrics: 0.0005643597430838996 | Validation metrics: 0.0007163561831200213\n", "01-07 15:37:01 - INFO - Train metrics: 0.0005467670616917872 | Validation metrics: 0.0006896820797754282\n", "01-07 15:37:01 - INFO - Train metrics: 0.0005317439026383678 | Validation metrics: 0.0006649664596118258\n", "01-07 15:37:01 - INFO - Train metrics: 0.0005196704395596209 | Validation metrics: 0.000643233294749275\n", "01-07 15:37:01 - INFO - Train metrics: 0.0005101100817371748 | Validation metrics: 0.0006249137922434719\n", "01-07 15:37:01 - INFO - Train metrics: 0.0005017474233458767 | Validation metrics: 0.000609600875347928\n", "01-07 15:37:01 - INFO - Train metrics: 0.0004929706351720684 | Validation metrics: 0.0005963277442126789\n", "01-07 15:37:01 - INFO - Train metrics: 0.00048280607117981856 | Validation metrics: 0.0005841869496354702\n", "01-07 15:37:01 - INFO - Train metrics: 0.00047144858727644765 | Validation metrics: 0.0005727274604423931\n", "01-07 15:37:01 - INFO - Train metrics: 0.00045991072811671813 | Validation metrics: 0.0005618223739374015\n", "01-07 15:37:01 - INFO - Train metrics: 0.0004491909847794367 | Validation metrics: 0.0005513809828882604\n", "01-07 15:37:01 - INFO - Train metrics: 0.00043975135701286014 | Validation metrics: 0.0005413844324225051\n", "01-07 15:37:01 - INFO - Train metrics: 0.000431464614249173 | Validation metrics: 0.0005319890447991025\n", "01-07 15:37:01 - INFO - Train metrics: 0.00042384439493753573 | Validation metrics: 0.0005233570453569745\n", "01-07 15:37:01 - INFO - Train metrics: 0.00041646369329401595 | Validation metrics: 0.0005154269985558295\n", "01-07 15:37:01 - INFO - Train metrics: 0.0004092682510985524 | Validation metrics: 0.0005079414472077983\n", "01-07 15:37:02 - INFO - Train metrics: 0.0004024409947515393 | Validation metrics: 0.0005006585352480261\n", "01-07 15:37:02 - INFO - Train metrics: 0.00039609244097967196 | Validation metrics: 0.0004935704178713037\n", "01-07 15:37:02 - INFO - Train metrics: 0.0003901623657882391 | Validation metrics: 0.00048684502535527855\n", "01-07 15:37:02 - INFO - Train metrics: 0.000384576486865348 | Validation metrics: 0.0004805675828288488\n", "01-07 15:37:02 - INFO - Train metrics: 0.00037935416256182624 | Validation metrics: 0.0004746544126637581\n", "01-07 15:37:02 - INFO - Train metrics: 0.0003745247159715751 | Validation metrics: 0.0004690212369284997\n", "01-07 15:37:02 - INFO - Train metrics: 0.0003700512901204113 | Validation metrics: 0.00046368835187009114\n", "01-07 15:37:02 - INFO - Train metrics: 0.0003659015368666724 | Validation metrics: 0.00045867834382483526\n", "01-07 15:37:02 - INFO - Train metrics: 0.00036209422599274506 | Validation metrics: 0.00045396444814590115\n", "01-07 15:37:02 - INFO - Train metrics: 0.0003586352259687435 | Validation metrics: 0.00044953846108083695\n", "01-07 15:37:02 - INFO - Train metrics: 0.00035550363915027086 | Validation metrics: 0.0004454151112257004\n", "01-07 15:37:02 - INFO - Train metrics: 0.00035270072473388137 | Validation metrics: 0.0004415890240731339\n", "01-07 15:37:02 - INFO - Train metrics: 0.0003502291812063113 | Validation metrics: 0.0004380522292892853\n", "01-07 15:37:02 - INFO - Train metrics: 0.00034807488263320917 | Validation metrics: 0.0004348048812126496\n", "01-07 15:37:02 - INFO - Train metrics: 0.0003462253565279146 | Validation metrics: 0.00043183250994788426\n", "01-07 15:37:02 - INFO - Train metrics: 0.0003446713955763653 | Validation metrics: 0.00042912383320402694\n", "01-07 15:37:02 - INFO - Train metrics: 0.0003433932633697053 | Validation metrics: 0.0004266660746934852\n", "01-07 15:37:02 - INFO - Train metrics: 0.0003423689350918785 | Validation metrics: 0.00042443517407383583\n", "01-07 15:37:03 - INFO - Train metrics: 0.00034157379203882154 | Validation metrics: 0.0004224079505850871\n", "01-07 15:37:03 - INFO - Train metrics: 0.0003409782914155371 | Validation metrics: 0.00042055938963195066\n", "01-07 15:37:03 - INFO - Train metrics: 0.0003405487415629362 | Validation metrics: 0.0004188546321749913\n", "01-07 15:37:03 - INFO - Train metrics: 0.0003402484420402195 | Validation metrics: 0.00041726137443229256\n", "01-07 15:37:03 - INFO - Train metrics: 0.00034003955925368984 | Validation metrics: 0.0004157432948234444\n", "01-07 15:37:03 - INFO - Train metrics: 0.0003398798201449057 | Validation metrics: 0.00041426217237562694\n", "01-07 15:37:03 - INFO - Train metrics: 0.00033972921628355605 | Validation metrics: 0.000412780060691552\n", "01-07 15:37:03 - INFO - Train metrics: 0.0003395463792370273 | Validation metrics: 0.00041126176736063576\n", "01-07 15:37:03 - INFO - Train metrics: 0.0003392930249951985 | Validation metrics: 0.00040967008210671596\n", "01-07 15:37:03 - INFO - Train metrics: 0.00033893313118546064 | Validation metrics: 0.0004079763000275977\n", "01-07 15:37:03 - INFO - Train metrics: 0.00033843556896346927 | Validation metrics: 0.000406151450170919\n", "01-07 15:37:03 - INFO - Train metrics: 0.0003377746710119615 | Validation metrics: 0.0004041758521737512\n", "01-07 15:37:03 - INFO - Train metrics: 0.00033693126016786615 | Validation metrics: 0.00040203488973966525\n", "01-07 15:37:03 - INFO - Train metrics: 0.0003358934163337662 | Validation metrics: 0.00039971908824894617\n", "01-07 15:37:03 - INFO - Train metrics: 0.0003346567829798926 | Validation metrics: 0.0003972305255387016\n", "01-07 15:37:03 - INFO - Train metrics: 0.0003332244779361426 | Validation metrics: 0.00039457451349895713\n", "01-07 15:37:03 - INFO - Train metrics: 0.0003316070257019681 | Validation metrics: 0.0003917661828877912\n", "01-07 15:37:04 - INFO - Train metrics: 0.0003298224794782259 | Validation metrics: 0.000388825174381091\n", "01-07 15:37:04 - INFO - Train metrics: 0.00032789161502334635 | Validation metrics: 0.00038577436211970496\n", "01-07 15:37:04 - INFO - Train metrics: 0.00032584223707199546 | Validation metrics: 0.0003826463847265889\n", "01-07 15:37:04 - INFO - Train metrics: 0.00032370364209426974 | Validation metrics: 0.0003794713923002057\n", "01-07 15:37:04 - INFO - Train metrics: 0.00032150641128649574 | Validation metrics: 0.0003762811826861868\n", "01-07 15:37:04 - INFO - Train metrics: 0.0003192806537648229 | Validation metrics: 0.00037310941372951023\n", "01-07 15:37:04 - INFO - Train metrics: 0.00031705561982710523 | Validation metrics: 0.0003699857492888854\n", "01-07 15:37:04 - INFO - Train metrics: 0.0003148579673420283 | Validation metrics: 0.000366940232160774\n", "01-07 15:37:04 - INFO - Train metrics: 0.000312712335656222 | Validation metrics: 0.00036400119565907073\n", "01-07 15:37:04 - INFO - Train metrics: 0.00031063908993798705 | Validation metrics: 0.00036118950723021317\n", "01-07 15:37:04 - INFO - Train metrics: 0.0003086567220609532 | Validation metrics: 0.00035852927454709633\n", "01-07 15:37:04 - INFO - Train metrics: 0.0003067817662832068 | Validation metrics: 0.00035604153879720604\n", "01-07 15:37:04 - INFO - Train metrics: 0.0003050283885842194 | Validation metrics: 0.00035374529037690447\n", "01-07 15:37:04 - INFO - Train metrics: 0.00030340990619115383 | Validation metrics: 0.00035166010734474643\n", "01-07 15:37:04 - INFO - Train metrics: 0.0003019396569007975 | Validation metrics: 0.0003498065361231001\n", "01-07 15:37:04 - INFO - Train metrics: 0.00030063297930710427 | Validation metrics: 0.00034820923353592404\n", "01-07 15:37:04 - INFO - Train metrics: 0.00029950742105040745 | Validation metrics: 0.0003468939905746304\n", "01-07 15:37:05 - INFO - Train metrics: 0.00029858445178572657 | Validation metrics: 0.00034589410613695477\n", "01-07 15:37:05 - INFO - Train metrics: 0.0002978932745445445 | Validation metrics: 0.0003452510963960767\n", "01-07 15:37:05 - INFO - Train metrics: 0.00029746715617214416 | Validation metrics: 0.0003450102084010576\n", "01-07 15:37:05 - INFO - Train metrics: 0.0002973488906782382 | Validation metrics: 0.00034522839570229856\n", "01-07 15:37:05 - INFO - Train metrics: 0.0002975911986669829 | Validation metrics: 0.0003459710260909615\n", "01-07 15:37:05 - INFO - Train metrics: 0.00029825481786310934 | Validation metrics: 0.00034731354727880146\n", "01-07 15:37:05 - INFO - Train metrics: 0.0002994102863962517 | Validation metrics: 0.00034933612209208536\n", "01-07 15:37:05 - INFO - Train metrics: 0.00030113068485999 | Validation metrics: 0.0003521199443382258\n", "01-07 15:37:05 - INFO - Train metrics: 0.0003034896462371475 | Validation metrics: 0.0003557415553274555\n", "01-07 15:37:05 - INFO - Train metrics: 0.0003065504268643897 | Validation metrics: 0.0003602598318148103\n", "01-07 15:37:05 - INFO - Train metrics: 0.0003103553375111365 | Validation metrics: 0.0003657060395455872\n", "01-07 15:37:05 - INFO - Train metrics: 0.00031491034818267015 | Validation metrics: 0.0003720622543819399\n", "01-07 15:37:05 - INFO - Train metrics: 0.0003201741682024752 | Validation metrics: 0.0003792558869610381\n", "01-07 15:37:05 - INFO - Train metrics: 0.00032604858498054003 | Validation metrics: 0.0003871450396466323\n", "01-07 15:37:05 - INFO - Train metrics: 0.0003323779083417266 | Validation metrics: 0.0003955262940085371\n", "01-07 15:37:05 - INFO - Train metrics: 0.00033895951041596986 | Validation metrics: 0.0004041436830337978\n", "01-07 15:37:05 - INFO - Train metrics: 0.00034556976386187203 | Validation metrics: 0.0004127231468281925\n", "01-07 15:37:05 - INFO - Train metrics: 0.00035198645385852865 | Validation metrics: 0.0004209970527403809\n", "01-07 15:37:06 - INFO - Train metrics: 0.00035801112466844217 | Validation metrics: 0.0004287305049306591\n", "01-07 15:37:06 - INFO - Train metrics: 0.0003634966399437774 | Validation metrics: 0.0004357517167168547\n", "01-07 15:37:06 - INFO - Train metrics: 0.0003683413602096638 | Validation metrics: 0.00044194520115993464\n", "01-07 15:37:06 - INFO - Train metrics: 0.0003724785012929446 | Validation metrics: 0.00044723639983185236\n", "01-07 15:37:06 - INFO - Train metrics: 0.00037584468482510376 | Validation metrics: 0.00045155896158475016\n", "01-07 15:37:06 - INFO - Train metrics: 0.0003783659104877537 | Validation metrics: 0.00045483452626598076\n", "01-07 15:37:06 - INFO - Train metrics: 0.00037994287105132463 | Validation metrics: 0.0004569590141681597\n", "01-07 15:37:06 - INFO - Train metrics: 0.000380452053360571 | Validation metrics: 0.00045780174115512786\n", "01-07 15:37:06 - INFO - Train metrics: 0.00037974666345708146 | Validation metrics: 0.0004572026970128369\n", "01-07 15:37:06 - INFO - Train metrics: 0.00037770371562871326 | Validation metrics: 0.00045503041415353014\n", "01-07 15:37:06 - INFO - Train metrics: 0.0003742375489141101 | Validation metrics: 0.0004511912808414887\n", "01-07 15:37:06 - INFO - Train metrics: 0.00036934043533451167 | Validation metrics: 0.0004456781672262069\n", "01-07 15:37:06 - INFO - Train metrics: 0.0003631047045673129 | Validation metrics: 0.0004385934423565902\n", "01-07 15:37:06 - INFO - Train metrics: 0.00035573762964356907 | Validation metrics: 0.0004301694324098979\n", "01-07 15:37:06 - INFO - Train metrics: 0.00034753898804059676 | Validation metrics: 0.00042074181038340684\n", "01-07 15:37:06 - INFO - Train metrics: 0.00033888767800471255 | Validation metrics: 0.0004107383476109291\n", "01-07 15:37:06 - INFO - Train metrics: 0.0003301888848870834 | Validation metrics: 0.0004006208410378395\n", "01-07 15:37:07 - INFO - Train metrics: 0.00032183875777364187 | Validation metrics: 0.0003908440565356439\n", "01-07 15:37:07 - INFO - Train metrics: 0.0003141747541926372 | Validation metrics: 0.00038180198676818325\n", "01-07 15:37:07 - INFO - Train metrics: 0.000307454180306776 | Validation metrics: 0.00037380107066998575\n", "01-07 15:37:07 - INFO - Train metrics: 0.0003018472653686449 | Validation metrics: 0.00036705005473005724\n", "01-07 15:37:07 - INFO - Train metrics: 0.00029743212544508654 | Validation metrics: 0.00036165240571147414\n", "01-07 15:37:07 - INFO - Train metrics: 0.00029421645693100505 | Validation metrics: 0.0003576285224596057\n", "01-07 15:37:07 - INFO - Train metrics: 0.0002921518077111026 | Validation metrics: 0.00035493213105993815\n", "01-07 15:37:07 - INFO - Train metrics: 0.0002911497094349838 | Validation metrics: 0.0003534661443678267\n", "01-07 15:37:07 - INFO - Train metrics: 0.00029109258612881607 | Validation metrics: 0.0003530971833500033\n", "01-07 15:37:07 - INFO - Train metrics: 0.0002918515325509908 | Validation metrics: 0.00035367595682137015\n", "01-07 15:37:07 - INFO - Train metrics: 0.0002932857009693093 | Validation metrics: 0.00035503833740479265\n", "01-07 15:37:07 - INFO - Train metrics: 0.00029525715819230875 | Validation metrics: 0.0003570222323454653\n", "01-07 15:37:07 - INFO - Train metrics: 0.00029763081642031146 | Validation metrics: 0.0003594691965686665\n", "01-07 15:37:07 - INFO - Train metrics: 0.0003002734052018498 | Validation metrics: 0.00036222303187418163\n", "01-07 15:37:07 - INFO - Train metrics: 0.000303068487988995 | Validation metrics: 0.0003651494852318946\n", "01-07 15:37:07 - INFO - Train metrics: 0.0003059041764549818 | Validation metrics: 0.0003681185201393685\n", "01-07 15:37:07 - INFO - Train metrics: 0.0003086917151499745 | Validation metrics: 0.0003710295434693119\n", "01-07 15:37:07 - INFO - Train metrics: 0.0003113492739498895 | Validation metrics: 0.0003737892165916473\n", "01-07 15:37:08 - INFO - Train metrics: 0.000313812609978445 | Validation metrics: 0.0003763259046903877\n", "01-07 15:37:08 - INFO - Train metrics: 0.00031603422490903235 | Validation metrics: 0.0003785862577985031\n", "01-07 15:37:08 - INFO - Train metrics: 0.0003179821597451909 | Validation metrics: 0.0003805355591619519\n", "01-07 15:37:08 - INFO - Train metrics: 0.00031963711208255407 | Validation metrics: 0.0003821539364501157\n", "01-07 15:37:08 - INFO - Train metrics: 0.00032098655173739415 | Validation metrics: 0.00038342721345175924\n", "01-07 15:37:08 - INFO - Train metrics: 0.0003220335433649408 | Validation metrics: 0.0003843609378273326\n", "01-07 15:37:08 - INFO - Train metrics: 0.00032279200669270674 | Validation metrics: 0.00038497080271499175\n", "01-07 15:37:08 - INFO - Train metrics: 0.000323276493927688 | Validation metrics: 0.0003852736230732666\n", "01-07 15:37:08 - INFO - Train metrics: 0.00032350804020982133 | Validation metrics: 0.0003852935221558907\n", "01-07 15:37:08 - INFO - Train metrics: 0.00032351338539452276 | Validation metrics: 0.00038506118804122313\n", "01-07 15:37:08 - INFO - Train metrics: 0.00032331781356214993 | Validation metrics: 0.00038460367465053093\n", "01-07 15:37:08 - INFO - Train metrics: 0.00032295610488422755 | Validation metrics: 0.0003839613025481201\n", "01-07 15:37:08 - INFO - Train metrics: 0.0003224537220759023 | Validation metrics: 0.00038316201729079086\n", "01-07 15:37:08 - INFO - Train metrics: 0.0003218434518057255 | Validation metrics: 0.000382242205134132\n", "01-07 15:37:08 - INFO - Train metrics: 0.0003211552868474667 | Validation metrics: 0.00038123582348679054\n", "01-07 15:37:08 - INFO - Train metrics: 0.0003204153512078186 | Validation metrics: 0.00038017172012431077\n", "01-07 15:37:08 - INFO - Train metrics: 0.00031964892459062294 | Validation metrics: 0.0003790780728461804\n", "01-07 15:37:09 - INFO - Train metrics: 0.0003188795963434134 | Validation metrics: 0.00037798168098864454\n", "01-07 15:37:09 - INFO - Train metrics: 0.0003181256487047099 | Validation metrics: 0.0003769028475602167\n", "01-07 15:37:09 - INFO - Train metrics: 0.00031740170288457534 | Validation metrics: 0.0003758576984347945\n", "01-07 15:37:09 - INFO - Train metrics: 0.0003167228451708243 | Validation metrics: 0.00037486222161055364\n", "01-07 15:37:09 - INFO - Train metrics: 0.0003161003193432899 | Validation metrics: 0.00037393078850017804\n", "01-07 15:37:09 - INFO - Train metrics: 0.0003155389118514824 | Validation metrics: 0.00037306778790250494\n", "01-07 15:37:09 - INFO - Train metrics: 0.0003150440879425216 | Validation metrics: 0.00037227901677140083\n", "01-07 15:37:09 - INFO - Train metrics: 0.0003146204099883057 | Validation metrics: 0.0003715702697089074\n", "01-07 15:37:09 - INFO - Train metrics: 0.0003142647122360887 | Validation metrics: 0.0003709373063751205\n", "01-07 15:37:09 - INFO - Train metrics: 0.00031397614688180705 | Validation metrics: 0.0003703798263244368\n", "01-07 15:37:09 - INFO - Train metrics: 0.00031375447059117894 | Validation metrics: 0.0003698968473760731\n", "01-07 15:37:09 - INFO - Train metrics: 0.00031359104951960326 | Validation metrics: 0.0003694792682624826\n", "01-07 15:37:09 - INFO - Train metrics: 0.00031348388841350035 | Validation metrics: 0.00036912421329003393\n" ] } ], "source": [ "narx_net.fit(train_dl, valid_dl)\n", "yhat = narx_net.predict(x_valid, y_valid)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.00045161987584446645\n" ] }, { "data": { "image/png": "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", "image/svg+xml": "\r\n\r\n\r\n\r\n \r\n \r\n \r\n \r\n 2021-01-07T15:37:10.892992\r\n image/svg+xml\r\n \r\n \r\n Matplotlib v3.3.2, https://matplotlib.org/\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print(mean_squared_error(y_valid, yhat))\n", "\n", "ee, ex, extras, lam = narx_net.residuals(x_valid, y_valid, yhat)\n", "narx_net.plot_result(y_valid, yhat, ee, ex)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Note\n", "\n", "If you built the net configuration before calling the NARXNN, you can just pass the model to the NARXNN as follows:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "tags": [ "outputPrepend" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "345595\n", "01-07 15:37:12 - INFO - Train metrics: 0.002210366904950306 | Validation metrics: 0.002455801884126332\n", "01-07 15:37:12 - INFO - Train metrics: 0.0021219287828581975 | Validation metrics: 0.0023316882052129566\n", "01-07 15:37:12 - INFO - Train metrics: 0.002046096711979717 | Validation metrics: 0.0022346086404991874\n", "01-07 15:37:12 - INFO - Train metrics: 0.0019744825730319683 | Validation metrics: 0.0021774639737688834\n", "01-07 15:37:12 - INFO - Train metrics: 0.0019032668919281516 | Validation metrics: 0.002106761924376843\n", "01-07 15:37:12 - INFO - Train metrics: 0.0018340980591495196 | Validation metrics: 0.002022272586173406\n", "01-07 15:37:12 - INFO - Train metrics: 0.0017689100868140994 | Validation metrics: 0.0019626052817329764\n", "01-07 15:37:12 - INFO - Train metrics: 0.0017043289429109012 | Validation metrics: 0.0018915892531624948\n", "01-07 15:37:13 - INFO - Train metrics: 0.0016417102330950157 | Validation metrics: 0.0018259573638476807\n", "01-07 15:37:13 - INFO - Train metrics: 0.0015812843858864262 | Validation metrics: 0.0017622544329067825\n", "01-07 15:37:13 - INFO - Train metrics: 0.0015218620588839577 | Validation metrics: 0.0016989622293322375\n", "01-07 15:37:13 - INFO - Train metrics: 0.0014640140057749168 | Validation metrics: 0.00163681558780184\n", "01-07 15:37:13 - INFO - Train metrics: 0.0014075388263019367 | Validation metrics: 0.001576707757200406\n", "01-07 15:37:13 - INFO - Train metrics: 0.0013521265376821383 | Validation metrics: 0.001516439957835834\n", "01-07 15:37:13 - INFO - Train metrics: 0.0012979850781040458 | Validation metrics: 0.0014579323569613725\n", "01-07 15:37:13 - INFO - Train metrics: 0.0012448962304022228 | Validation metrics: 0.001400122808226657\n", "01-07 15:37:13 - INFO - Train metrics: 0.0011929273792078024 | Validation metrics: 0.0013431158208410548\n", "01-07 15:37:13 - INFO - Train metrics: 0.0011420865399636945 | Validation metrics: 0.0012873870137175827\n", "01-07 15:37:13 - INFO - Train metrics: 0.001092387077674027 | Validation metrics: 0.0012324887840547646\n", "01-07 15:37:13 - INFO - Train metrics: 0.001043939299795095 | Validation metrics: 0.0011789030901090515\n", "01-07 15:37:13 - INFO - Train metrics: 0.0009968030836286122 | Validation metrics: 0.0011265738055843747\n", "01-07 15:37:13 - INFO - Train metrics: 0.0009511197031064794 | Validation metrics: 0.0010757269137868224\n", "01-07 15:37:13 - INFO - Train metrics: 0.0009070111998534273 | Validation metrics: 0.0010265195201329812\n", "01-07 15:37:13 - INFO - Train metrics: 0.0008646274523233019 | Validation metrics: 0.0009791560464002418\n", "01-07 15:37:14 - INFO - Train metrics: 0.0008241186790252314 | Validation metrics: 0.0009338094892845761\n", "01-07 15:37:14 - INFO - Train metrics: 0.0007856253506545734 | Validation metrics: 0.0008906729094862863\n", "01-07 15:37:14 - INFO - Train metrics: 0.0007492795107795164 | Validation metrics: 0.0008499134941064197\n", "01-07 15:37:14 - INFO - Train metrics: 0.0007151842755113815 | Validation metrics: 0.0008116572597910734\n", "01-07 15:37:14 - INFO - Train metrics: 0.0006834126757702938 | Validation metrics: 0.0007760080995715477\n", "01-07 15:37:14 - INFO - Train metrics: 0.0006539987930899631 | Validation metrics: 0.0007430137019849034\n", "01-07 15:37:14 - INFO - Train metrics: 0.0006269339446088108 | Validation metrics: 0.00071267569625769\n", "01-07 15:37:14 - INFO - Train metrics: 0.0006021666910935819 | Validation metrics: 0.0006849424492548963\n", "01-07 15:37:14 - INFO - Train metrics: 0.0005796052672186012 | Validation metrics: 0.000659718575083058\n", "01-07 15:37:14 - INFO - Train metrics: 0.0005591229958994253 | Validation metrics: 0.0006368633029239271\n", "01-07 15:37:14 - INFO - Train metrics: 0.0005405650888149507 | Validation metrics: 0.000616204720860667\n", "01-07 15:37:14 - INFO - Train metrics: 0.0005237590711223041 | Validation metrics: 0.0005975483459269986\n", "01-07 15:37:14 - INFO - Train metrics: 0.00050852393231385 | Validation metrics: 0.0005806897169524672\n", "01-07 15:37:14 - INFO - Train metrics: 0.0004946778986175874 | Validation metrics: 0.0005654229128714463\n", "01-07 15:37:14 - INFO - Train metrics: 0.00048204737392728 | Validation metrics: 0.0005515509048674369\n", "01-07 15:37:14 - INFO - Train metrics: 0.00047047132813490757 | Validation metrics: 0.0005388907862432076\n", "01-07 15:37:15 - INFO - Train metrics: 0.0004598066910405767 | Validation metrics: 0.000527283152673544\n", "01-07 15:37:15 - INFO - Train metrics: 0.00044992888964278467 | Validation metrics: 0.0005165869943360149\n", "01-07 15:37:15 - INFO - Train metrics: 0.00044073427725060466 | Validation metrics: 0.000506690369079341\n", "01-07 15:37:15 - INFO - Train metrics: 0.0004321383971010188 | Validation metrics: 0.0004975018035643969\n", "01-07 15:37:15 - INFO - Train metrics: 0.00042407548744791655 | Validation metrics: 0.0004889531201574801\n", "01-07 15:37:15 - INFO - Train metrics: 0.0004164969634946277 | Validation metrics: 0.00048099654359799445\n", "01-07 15:37:15 - INFO - Train metrics: 0.0004093697850993907 | Validation metrics: 0.0004736019978699547\n", "01-07 15:37:15 - INFO - Train metrics: 0.0004026738148186132 | Validation metrics: 0.00046675305224418867\n", "01-07 15:37:15 - INFO - Train metrics: 0.0003964002204785838 | Validation metrics: 0.0004604417310952388\n", "01-07 15:37:15 - INFO - Train metrics: 0.00039054716255674534 | Validation metrics: 0.0004546645581322449\n", "01-07 15:37:15 - INFO - Train metrics: 0.0003851151530325114 | Validation metrics: 0.000449414277976059\n", "01-07 15:37:15 - INFO - Train metrics: 0.00038009956308388454 | Validation metrics: 0.0004446664786864674\n", "01-07 15:37:15 - INFO - Train metrics: 0.00037547992702858304 | Validation metrics: 0.00044036932692938306\n", "01-07 15:37:15 - INFO - Train metrics: 0.0003712077397225252 | Validation metrics: 0.0004364242324502104\n", "01-07 15:37:15 - INFO - Train metrics: 0.00036719218671104795 | Validation metrics: 0.000432672272459839\n", "01-07 15:37:15 - INFO - Train metrics: 0.00036329191761910637 | Validation metrics: 0.0004288858661752646\n", "01-07 15:37:15 - INFO - Train metrics: 0.0003593162855076228 | Validation metrics: 0.000424773776856712\n", "01-07 15:37:16 - INFO - Train metrics: 0.000355045432674192 | Validation metrics: 0.00042001037027056547\n", "01-07 15:37:16 - INFO - Train metrics: 0.0003502772683201867 | Validation metrics: 0.00041429699349188894\n", "01-07 15:37:16 - INFO - Train metrics: 0.0003448881396795051 | Validation metrics: 0.00040744224944236606\n", "01-07 15:37:16 - INFO - Train metrics: 0.0003388940997393732 | Validation metrics: 0.0003994420427132887\n", "01-07 15:37:16 - INFO - Train metrics: 0.00033247737436588006 | Validation metrics: 0.000390525759701062\n", "01-07 15:37:16 - INFO - Train metrics: 0.0003259517162726646 | Validation metrics: 0.00038112781270444767\n", "01-07 15:37:16 - INFO - Train metrics: 0.0003196649546740824 | Validation metrics: 0.00037178658642759077\n", "01-07 15:37:16 - INFO - Train metrics: 0.0003138825001649064 | Validation metrics: 0.0003629967760214714\n", "01-07 15:37:16 - INFO - Train metrics: 0.00030871234210686556 | Validation metrics: 0.0003550866782792251\n", "01-07 15:37:16 - INFO - Train metrics: 0.00030411414652981126 | Validation metrics: 0.0003481725597697677\n", "01-07 15:37:16 - INFO - Train metrics: 0.0002999637847215423 | Validation metrics: 0.00034217520662809187\n", "01-07 15:37:16 - INFO - Train metrics: 0.00029612632220631986 | Validation metrics: 0.00033688323332391906\n", "01-07 15:37:16 - INFO - Train metrics: 0.00029249390492969683 | Validation metrics: 0.0003320344349233001\n", "01-07 15:37:16 - INFO - Train metrics: 0.00028899830140472204 | Validation metrics: 0.00032740421569670995\n", "01-07 15:37:16 - INFO - Train metrics: 0.0002856122061842469 | Validation metrics: 0.0003228759989283527\n", "01-07 15:37:16 - INFO - Train metrics: 0.0002823380386062377 | Validation metrics: 0.00031845411344553636\n", "01-07 15:37:16 - INFO - Train metrics: 0.0002791852770369724 | Validation metrics: 0.0003142011557080115\n", "01-07 15:37:17 - INFO - Train metrics: 0.00027615357295990474 | Validation metrics: 0.00031015706998813484\n", "01-07 15:37:17 - INFO - Train metrics: 0.000273234122065141 | Validation metrics: 0.00030630247313482217\n", "01-07 15:37:17 - INFO - Train metrics: 0.000270418022154716 | Validation metrics: 0.00030258539246837374\n", "01-07 15:37:17 - INFO - Train metrics: 0.0002677034881207532 | Validation metrics: 0.0002989757322762726\n", "01-07 15:37:17 - INFO - Train metrics: 0.00026509135799309737 | Validation metrics: 0.0002954797469422861\n", "01-07 15:37:17 - INFO - Train metrics: 0.00026257888857160873 | Validation metrics: 0.00029211435813898917\n", "01-07 15:37:17 - INFO - Train metrics: 0.00026016112425443917 | Validation metrics: 0.0002888771449568484\n", "01-07 15:37:17 - INFO - Train metrics: 0.00025783595154913873 | Validation metrics: 0.0002857526046085181\n", "01-07 15:37:17 - INFO - Train metrics: 0.0002556058174386658 | Validation metrics: 0.00028273304295255754\n", "01-07 15:37:17 - INFO - Train metrics: 0.00025347360954818346 | Validation metrics: 0.00027982454254254325\n", "01-07 15:37:17 - INFO - Train metrics: 0.0002514370482964651 | Validation metrics: 0.0002770292250183176\n", "01-07 15:37:17 - INFO - Train metrics: 0.0002494940384265437 | Validation metrics: 0.0002743391638487634\n", "01-07 15:37:17 - INFO - Train metrics: 0.0002476448472705962 | Validation metrics: 0.0002717535297217017\n", "01-07 15:37:17 - INFO - Train metrics: 0.0002458896601740691 | Validation metrics: 0.00026927336376052197\n", "01-07 15:37:17 - INFO - Train metrics: 0.00024422906125594573 | Validation metrics: 0.0002668986886015369\n", "01-07 15:37:17 - INFO - Train metrics: 0.00024266293424678813 | Validation metrics: 0.00026462765071291777\n", "01-07 15:37:17 - INFO - Train metrics: 0.00024119106440555145 | Validation metrics: 0.00026245941931259527\n", "01-07 15:37:18 - INFO - Train metrics: 0.00023981378237800883 | Validation metrics: 0.0002603952186723061\n", "01-07 15:37:18 - INFO - Train metrics: 0.00023853087918552802 | Validation metrics: 0.00025843343632225174\n", "01-07 15:37:18 - INFO - Train metrics: 0.00023734061786190685 | Validation metrics: 0.00025657294779625543\n", "01-07 15:37:18 - INFO - Train metrics: 0.00023624280744517003 | Validation metrics: 0.0002548134482390426\n", "01-07 15:37:18 - INFO - Train metrics: 0.00023523576005903495 | Validation metrics: 0.00025315438144407566\n", "01-07 15:37:18 - INFO - Train metrics: 0.00023431759219696012 | Validation metrics: 0.00025159269518388265\n", "01-07 15:37:18 - INFO - Train metrics: 0.00023348614091186652 | Validation metrics: 0.0002501277271258372\n", "01-07 15:37:18 - INFO - Train metrics: 0.00023273826816894188 | Validation metrics: 0.0002487561432644725\n", "01-07 15:37:18 - INFO - Train metrics: 0.00023207113430234264 | Validation metrics: 0.00024747689791973867\n", "01-07 15:37:18 - INFO - Train metrics: 0.00023147950080494142 | Validation metrics: 0.00024628423235492045\n", "01-07 15:37:18 - INFO - Train metrics: 0.00023095952174048592 | Validation metrics: 0.0002451757430051914\n", "01-07 15:37:18 - INFO - Train metrics: 0.00023050584656304277 | Validation metrics: 0.00024414669697036536\n", "01-07 15:37:18 - INFO - Train metrics: 0.00023011214059414598 | Validation metrics: 0.00024319141018416056\n", "01-07 15:37:18 - INFO - Train metrics: 0.00022977211197224308 | Validation metrics: 0.00024230558027729692\n", "01-07 15:37:18 - INFO - Train metrics: 0.00022947878861580428 | Validation metrics: 0.00024148205755574799\n", "01-07 15:37:18 - INFO - Train metrics: 0.0002292252248482788 | Validation metrics: 0.00024071514178443737\n", "01-07 15:37:18 - INFO - Train metrics: 0.0002290042617838517 | Validation metrics: 0.00023999807337825798\n", "01-07 15:37:19 - INFO - Train metrics: 0.0002288077868574315 | Validation metrics: 0.00023932430294643371\n", "01-07 15:37:19 - INFO - Train metrics: 0.00022862890257061293 | Validation metrics: 0.00023868687129273747\n", "01-07 15:37:19 - INFO - Train metrics: 0.0002284599159202914 | Validation metrics: 0.0002380787057454011\n", "01-07 15:37:19 - INFO - Train metrics: 0.000228295092394209 | Validation metrics: 0.00023749505238480526\n", "01-07 15:37:19 - INFO - Train metrics: 0.00022812758248975094 | Validation metrics: 0.00023692854088637038\n", "01-07 15:37:19 - INFO - Train metrics: 0.0002279525375014689 | Validation metrics: 0.0002363751959314363\n", "01-07 15:37:19 - INFO - Train metrics: 0.00022776462271182862 | Validation metrics: 0.00023582925187480268\n", "01-07 15:37:19 - INFO - Train metrics: 0.00022755977912119736 | Validation metrics: 0.0002352864998322206\n", "01-07 15:37:19 - INFO - Train metrics: 0.00022733577131468774 | Validation metrics: 0.00023474502497743062\n", "01-07 15:37:19 - INFO - Train metrics: 0.00022709002239976246 | Validation metrics: 0.0002342018268231039\n", "01-07 15:37:19 - INFO - Train metrics: 0.0002268206438368868 | Validation metrics: 0.00023365422649393705\n", "01-07 15:37:19 - INFO - Train metrics: 0.00022652796298944691 | Validation metrics: 0.00023310276373369486\n", "01-07 15:37:19 - INFO - Train metrics: 0.0002262118410489482 | Validation metrics: 0.00023254611299519022\n", "01-07 15:37:19 - INFO - Train metrics: 0.00022587281986452488 | Validation metrics: 0.00023198506155173586\n", "01-07 15:37:19 - INFO - Train metrics: 0.00022551252564005693 | Validation metrics: 0.00023142005228131746\n", "01-07 15:37:19 - INFO - Train metrics: 0.00022513274322812419 | Validation metrics: 0.00023085229607966887\n", "01-07 15:37:19 - INFO - Train metrics: 0.00022473569050107973 | Validation metrics: 0.00023028422517447987\n", "01-07 15:37:20 - INFO - Train metrics: 0.0002243238942768252 | Validation metrics: 0.0002297176566442964\n", "01-07 15:37:20 - INFO - Train metrics: 0.00022389947884321622 | Validation metrics: 0.00022915397938858304\n", "01-07 15:37:20 - INFO - Train metrics: 0.00022346559912112207 | Validation metrics: 0.0002285961042313526\n", "01-07 15:37:20 - INFO - Train metrics: 0.0002230254070407928 | Validation metrics: 0.00022804747733071146\n", "01-07 15:37:20 - INFO - Train metrics: 0.00022258167304617951 | Validation metrics: 0.0002275096447174108\n", "01-07 15:37:20 - INFO - Train metrics: 0.00022213605040261863 | Validation metrics: 0.00022698417226572266\n", "01-07 15:37:20 - INFO - Train metrics: 0.00022169324003520625 | Validation metrics: 0.00022647650136301914\n", "01-07 15:37:20 - INFO - Train metrics: 0.00022125456532939597 | Validation metrics: 0.00022598666772763763\n", "01-07 15:37:20 - INFO - Train metrics: 0.0002208239028205801 | Validation metrics: 0.00022551960107809253\n", "01-07 15:37:20 - INFO - Train metrics: 0.0002204027140097585 | Validation metrics: 0.0002250759424335991\n", "01-07 15:37:20 - INFO - Train metrics: 0.00021999442352059023 | Validation metrics: 0.00022465877180255836\n", "01-07 15:37:20 - INFO - Train metrics: 0.00021960241731751085 | Validation metrics: 0.00022427267436116857\n", "01-07 15:37:20 - INFO - Train metrics: 0.00021922933261603453 | Validation metrics: 0.0002239200131052804\n", "01-07 15:37:20 - INFO - Train metrics: 0.0002188774710258511 | Validation metrics: 0.00022360193454942694\n", "01-07 15:37:20 - INFO - Train metrics: 0.0002185489438146815 | Validation metrics: 0.00022332197657287724\n", "01-07 15:37:21 - INFO - Train metrics: 0.00021824759101048157 | Validation metrics: 0.0002230844800560406\n", "01-07 15:37:21 - INFO - Train metrics: 0.0002179754832158458 | Validation metrics: 0.0002228903159089951\n", "01-07 15:37:21 - INFO - Train metrics: 0.00021773601507529952 | Validation metrics: 0.00022274328541537427\n", "01-07 15:37:21 - INFO - Train metrics: 0.00021753267332255599 | Validation metrics: 0.00022264773313031354\n", "01-07 15:37:21 - INFO - Train metrics: 0.00021736769428427674 | Validation metrics: 0.00022260519259049549\n", "01-07 15:37:21 - INFO - Train metrics: 0.00021724345615917914 | Validation metrics: 0.00022261820861721655\n", "01-07 15:37:21 - INFO - Train metrics: 0.00021716334640700836 | Validation metrics: 0.00022269072507348175\n", "01-07 15:37:21 - INFO - Train metrics: 0.0002171307567910835 | Validation metrics: 0.00022282568056423293\n", "01-07 15:37:21 - INFO - Train metrics: 0.00021714792980165933 | Validation metrics: 0.00022302568017627404\n", "01-07 15:37:21 - INFO - Train metrics: 0.00021721531807988845 | Validation metrics: 0.00022328936602482855\n", "01-07 15:37:21 - INFO - Train metrics: 0.00021733591115081165 | Validation metrics: 0.00022362127713153535\n", "01-07 15:37:21 - INFO - Train metrics: 0.00021751444338340042 | Validation metrics: 0.00022402580582296872\n", "01-07 15:37:21 - INFO - Train metrics: 0.0002177492413074035 | Validation metrics: 0.0002244997850732144\n", "01-07 15:37:21 - INFO - Train metrics: 0.00021804048764235796 | Validation metrics: 0.0002250433676038878\n", "01-07 15:37:21 - INFO - Train metrics: 0.00021838909011412375 | Validation metrics: 0.00022565689075484195\n", "01-07 15:37:21 - INFO - Train metrics: 0.00021879392202516415 | Validation metrics: 0.00022633880746640228\n", "01-07 15:37:22 - INFO - Train metrics: 0.00021925447281843336 | Validation metrics: 0.0002270870607739051\n", "01-07 15:37:22 - INFO - Train metrics: 0.00021976654610817174 | Validation metrics: 0.00022789659983986482\n", "01-07 15:37:22 - INFO - Train metrics: 0.00022032778670884117 | Validation metrics: 0.00022876411476501762\n", "01-07 15:37:22 - INFO - Train metrics: 0.00022093321746425297 | Validation metrics: 0.0002296831841860437\n", "01-07 15:37:22 - INFO - Train metrics: 0.000221578333954873 | Validation metrics: 0.00023064866392741526\n", "01-07 15:37:22 - INFO - Train metrics: 0.00022225604972622373 | Validation metrics: 0.00023165088651979322\n", "01-07 15:37:22 - INFO - Train metrics: 0.00022295985317918564 | Validation metrics: 0.00023268227276716834\n", "01-07 15:37:22 - INFO - Train metrics: 0.00022368128450860487 | Validation metrics: 0.00023373297513862412\n", "01-07 15:37:22 - INFO - Train metrics: 0.00022441029202462616 | Validation metrics: 0.00023479109957480257\n", "01-07 15:37:22 - INFO - Train metrics: 0.00022514148542796377 | Validation metrics: 0.0002358496297007389\n", "01-07 15:37:22 - INFO - Train metrics: 0.000225862586038668 | Validation metrics: 0.00023689484100252616\n", "01-07 15:37:22 - INFO - Train metrics: 0.00022656387711623052 | Validation metrics: 0.0002379148129627786\n", "01-07 15:37:22 - INFO - Train metrics: 0.00022723678594138846 | Validation metrics: 0.00023889990412921766\n", "01-07 15:37:22 - INFO - Train metrics: 0.00022787378446921416 | Validation metrics: 0.0002398406861836768\n", "01-07 15:37:22 - INFO - Train metrics: 0.0002284668432884175 | Validation metrics: 0.00024072846104955357\n", "01-07 15:37:22 - INFO - Train metrics: 0.00022901042891470226 | Validation metrics: 0.00024155654763388965\n", "01-07 15:37:23 - INFO - Train metrics: 0.00022949470604045135 | Validation metrics: 0.00024231319078197233\n", "01-07 15:37:23 - INFO - Train metrics: 0.00022991774535159064 | Validation metrics: 0.00024299691075510863\n", "01-07 15:37:23 - INFO - Train metrics: 0.0002302746276442956 | Validation metrics: 0.0002436013185275681\n", "01-07 15:37:23 - INFO - Train metrics: 0.00023056636269018754 | Validation metrics: 0.00024412750578697094\n", "01-07 15:37:23 - INFO - Train metrics: 0.0002307933156730439 | Validation metrics: 0.0002445757659234464\n", "01-07 15:37:23 - INFO - Train metrics: 0.0002309524493273301 | Validation metrics: 0.0002449421161218932\n", "01-07 15:37:23 - INFO - Train metrics: 0.00023104809396019097 | Validation metrics: 0.0002452318872339233\n", "01-07 15:37:23 - INFO - Train metrics: 0.0002310840263589283 | Validation metrics: 0.0002454490578119558\n", "01-07 15:37:23 - INFO - Train metrics: 0.00023106519220528754 | Validation metrics: 0.0002455984874607318\n", "01-07 15:37:23 - INFO - Train metrics: 0.00023099538075964067 | Validation metrics: 0.0002456847397490602\n", "01-07 15:37:23 - INFO - Train metrics: 0.0002308801810515643 | Validation metrics: 0.00024571383872887856\n", "01-07 15:37:23 - INFO - Train metrics: 0.00023072619505523048 | Validation metrics: 0.00024569384160456296\n", "01-07 15:37:23 - INFO - Train metrics: 0.00023054031607488352 | Validation metrics: 0.0002456314673922448\n", "01-07 15:37:23 - INFO - Train metrics: 0.00023032854044784261 | Validation metrics: 0.00024553376392254397\n", "01-07 15:37:23 - INFO - Train metrics: 0.00023009750971927735 | Validation metrics: 0.0002454081957400165\n", "01-07 15:37:23 - INFO - Train metrics: 0.00022985101099777267 | Validation metrics: 0.000245258870894428\n", "01-07 15:37:23 - INFO - Train metrics: 0.000229597191714663 | Validation metrics: 0.00024509568013147346\n" ] }, { "data": { "image/png": "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", "image/svg+xml": "\r\n\r\n\r\n\r\n \r\n \r\n \r\n \r\n 2021-01-07T15:37:24.825472\r\n image/svg+xml\r\n \r\n \r\n Matplotlib v3.3.2, https://matplotlib.org/\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "class NARX(nn.Module):\n", " def __init__(self):\n", " super().__init__()\n", " self.lin = nn.Linear(4, 10)\n", " self.lin2 = nn.Linear(10, 10)\n", " self.lin3 = nn.Linear(10, 1)\n", " self.tanh = nn.Tanh()\n", "\n", " def forward(self, xb):\n", " z = self.lin(xb)\n", " z = self.tanh(z)\n", " z = self.lin2(z)\n", " z = self.tanh(z)\n", " z = self.lin3(z)\n", " return z\n", "\n", "narx_net2 = NARXNN(net=NARX(),\n", " ylag=2,\n", " xlag=2,\n", " loss_func='mse_loss',\n", " optimizer='Adam',\n", " epochs=200,\n", " verbose=True,\n", " optim_params={'betas': (0.9, 0.999), 'eps': 1e-05} # optional parameters of the optimizer\n", ")\n", "\n", "narx_net2.fit(train_dl, valid_dl)\n", "yhat = narx_net2.predict(x_valid, y_valid)\n", "\n", "ee, ex, extras, lam = narx_net2.residuals(x_valid, y_valid, yhat)\n", "narx_net2.plot_result(y_valid, yhat, ee, ex)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "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": "3.8.5-final" }, "orig_nbformat": 2 }, "nbformat": 4, "nbformat_minor": 2 }