From 11b4eef984be80f45ff07b4ec24949f026b7da6e Mon Sep 17 00:00:00 2001 From: Nidhi Shekhawat <146202184+Nidhi1314@users.noreply.github.com> Date: Mon, 8 Jul 2024 09:24:30 +0530 Subject: [PATCH 1/3] Dell Stock Prediction using lstm --- .../Data Sources/DELL_stock_action.csv | 9 + .../Data Sources/DELL_stock_dividends.csv | 7 + .../Data Sources/DELL_stock_history.csv | 1100 +++++++++++ .../Data Sources/DELL_stock_info.csv | 1000 ++++++++++ .../Data Sources/DELL_stock_spilts.csv | 3 + .../dell-stock-prediction-lstm.ipynb | 1723 +++++++++++++++++ 6 files changed, 3842 insertions(+) create mode 100644 Dell-Stock-Prediction-lstm/Data Sources/DELL_stock_action.csv create mode 100644 Dell-Stock-Prediction-lstm/Data Sources/DELL_stock_dividends.csv create mode 100644 Dell-Stock-Prediction-lstm/Data Sources/DELL_stock_history.csv create mode 100644 Dell-Stock-Prediction-lstm/Data Sources/DELL_stock_info.csv create mode 100644 Dell-Stock-Prediction-lstm/Data Sources/DELL_stock_spilts.csv create mode 100644 Dell-Stock-Prediction-lstm/dell-stock-prediction-lstm.ipynb diff --git a/Dell-Stock-Prediction-lstm/Data Sources/DELL_stock_action.csv b/Dell-Stock-Prediction-lstm/Data Sources/DELL_stock_action.csv new file mode 100644 index 00000000..7055abe5 --- /dev/null +++ b/Dell-Stock-Prediction-lstm/Data Sources/DELL_stock_action.csv @@ -0,0 +1,9 @@ +Date Dividends Stock Splits +2018-12-28 00:00:00-05:00 0 1.806 +2021-11-02 00:00:00-04:00 0 1.973 +2022-04-19 00:00:00-04:00 0.33 0 +2022-07-19 00:00:00-04:00 0.33 0 +2022-10-18 00:00:00-04:00 0.33 0 +2023-01-24 00:00:00-05:00 0.33 0 +2023-04-24 00:00:00-04:00 0.37 0 +2023-07-24 00:00:00-04:00 0.37 0 diff --git a/Dell-Stock-Prediction-lstm/Data Sources/DELL_stock_dividends.csv b/Dell-Stock-Prediction-lstm/Data Sources/DELL_stock_dividends.csv new file mode 100644 index 00000000..a7208929 --- /dev/null +++ b/Dell-Stock-Prediction-lstm/Data Sources/DELL_stock_dividends.csv @@ -0,0 +1,7 @@ +Date Dividends +2022-04-19 00:00:00-04:00 0.33 +2022-07-19 00:00:00-04:00 0.33 +2022-10-18 00:00:00-04:00 0.33 +2023-01-24 00:00:00-05:00 0.33 +2023-04-24 00:00:00-04:00 0.37 +2023-07-24 00:00:00-04:00 0.37 diff --git a/Dell-Stock-Prediction-lstm/Data Sources/DELL_stock_history.csv b/Dell-Stock-Prediction-lstm/Data Sources/DELL_stock_history.csv new file mode 100644 index 00000000..9198a3ab --- /dev/null +++ b/Dell-Stock-Prediction-lstm/Data Sources/DELL_stock_history.csv @@ -0,0 +1,1100 @@ +Date Open High Low Close Volume Dividends Stock Splits +2016-08-17 00:00:00-04:00 11.63646657 11.77021899 11.50271416 11.50271416 271519 0 0 +2016-08-18 00:00:00-04:00 11.7702178 11.7702178 11.36896059 11.43583679 1767366 0 0 +2016-08-19 00:00:00-04:00 11.42246199 11.63646603 11.40908657 11.63646603 4735900 0 0 +2016-08-22 00:00:00-04:00 11.50271414 12.19822707 11.39571166 11.67659283 2245909 0 0 +2016-08-23 00:00:00-04:00 11.70334255 12.27847848 11.63646635 12.03772449 1483020 0 0 +2016-08-24 00:00:00-04:00 13.37524663 13.37524663 12.06447263 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internationally. The company operates through two segments, Infrastructure Solutions Group (ISG) and Client Solutions Group (CSG). The ISG segment provides traditional and next-generation storage solutions, including all-flash arrays, scale-out file, object platforms, hyper-converged infrastructure, and software-defined storage; and rack, blade, tower, and hyperscale servers. This segment also offers networking products and services that help its business customers to transform and modernize their infrastructure, mobilize and enrich end-user experiences, and accelerate business applications and processes; attached software and peripherals; and support and deployment, configuration, and extended warranty services. The CSG segment provides desktops, workstations, and notebooks; displays, docking stations, and other electronics; and third-party software and peripherals, as well as support and deployment, configuration, and extended warranty services. The company is also involved in the provision of cybersecurity technology-driven security solutions to prevent security breaches, detect malicious activity, respond rapidly when a security breach occurs, and identify emerging threats; originating, collecting, and servicing customer financing arrangements; and infrastructure-as-a-service solutions, as well as in the resale of VMware products and services. The company was formerly known as Denali Holding Inc. and changed its name to Dell Technologies Inc. in August 2016. Dell Technologies Inc. was founded in 1984 and is headquartered in Round Rock, Texas. +fullTimeEmployees 133000 +companyOfficers [{'maxAge': 1, 'name': 'Mr. Michael Saul Dell', 'age': 57, 'title': 'Chairman & CEO', 'yearBorn': 1965, 'fiscalYear': 2023, 'totalPay': 2797308, 'exercisedValue': 0, 'unexercisedValue': 0}, {'maxAge': 1, 'name': 'Mr. Jeffrey W. 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Elias', 'age': 64, 'title': 'Chief Customer Officer and President of Services & Digital', 'yearBorn': 1958, 'fiscalYear': 2023, 'totalPay': 6171429, 'exercisedValue': 4068717, 'unexercisedValue': 0}] +auditRisk 7 +boardRisk 9 +compensationRisk 3 +shareHolderRightsRisk 10 +overallRisk 9 +governanceEpochDate 1696118400 +compensationAsOfEpochDate 1703980800 +maxAge 86400 +priceHint 2 +previousClose 67.76 +open 67.26 +dayLow 66.7 +dayHigh 68.645 +regularMarketPreviousClose 67.76 +regularMarketOpen 67.26 +regularMarketDayLow 66.7 +regularMarketDayHigh 68.645 +dividendRate 1.48 +dividendYield 0.0218 +exDividendDate 1698019200 +payoutRatio 0.5385 +beta 0.899 +trailingPE 26.115387 +forwardPE 9.956012 +volume 4065220 +regularMarketVolume 4065220 +averageVolume 4792907 +averageVolume10days 5914220 +averageDailyVolume10Day 5914220 +bid 0 +ask 0 +bidSize 1000 +askSize 800 +marketCap 49120694272 +fiftyTwoWeekLow 33.9 +fiftyTwoWeekHigh 72.82 +priceToSalesTrailing12Months 0.52470404 +fiftyDayAverage 64.3104 +twoHundredDayAverage 49.78645 +trailingAnnualDividendRate 1.4 +trailingAnnualDividendYield 0.020661157 +currency USD +enterpriseValue 68861665280 +profitMargins 0.02034 +floatShares 244315646 +sharesOutstanding 254312000 +sharesShort 9529693 +sharesShortPriorMonth 6485647 +sharesShortPreviousMonthDate 1693440000 +dateShortInterest 1695945600 +sharesPercentSharesOut 0.013200001 +heldPercentInsiders 0.03323 +heldPercentInstitutions 0.77109003 +shortRatio 1.48 +shortPercentOfFloat 0.0388 +impliedSharesOutstanding 723427008 +bookValue -3.829 +lastFiscalYearEnd 1675382400 +nextFiscalYearEnd 1706918400 +mostRecentQuarter 1691107200 +earningsQuarterlyGrowth -0.096 +netIncomeToCommon 1904000000 +trailingEps 2.6 +forwardEps 6.82 +pegRatio 13.51 +lastSplitFactor 1989:40:00 +lastSplitDate 1635811200 +enterpriseToRevenue 0.736 +enterpriseToEbitda 8.404 +52WeekChange 0.9686866 +SandP52WeekChange 0.18349409 +lastDividendValue 0.37 +lastDividendDate 1690156800 +exchange NYQ +quoteType EQUITY +symbol DELL +underlyingSymbol DELL +shortName Dell Technologies Inc. +longName Dell Technologies Inc. +firstTradeDateEpochUtc 1471440600 +timeZoneFullName America/New_York +timeZoneShortName EDT +uuid 04c74ad6-2535-3f70-9335-6e441ef69610 +messageBoardId finmb_266017 +gmtOffSetMilliseconds -14400000 +currentPrice 67.9 +targetHighPrice 89 +targetLowPrice 53 +targetMeanPrice 74.07 +targetMedianPrice 79 +recommendationMean 2.1 +recommendationKey buy +numberOfAnalystOpinions 14 +totalCash 8364000256 +totalCashPerShare 11.562 +ebitda 8193999872 +totalDebt 28010000384 +quickRatio 0.47 +currentRatio 0.766 +totalRevenue 93615996928 +revenuePerShare 129.393 +returnOnAssets 0.03716 +grossProfits 22686000000 +freeCashflow 5964624896 +operatingCashflow 8101000192 +earningsGrowth -0.074 +revenueGrowth -0.132 +grossMargins 0.23358999 +ebitdaMargins 0.08753 +operatingMargins 0.0508 +financialCurrency USD +trailingPegRatio 10.0779 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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1.806 +2021-11-02 00:00:00-04:00 1.973 diff --git a/Dell-Stock-Prediction-lstm/dell-stock-prediction-lstm.ipynb b/Dell-Stock-Prediction-lstm/dell-stock-prediction-lstm.ipynb new file mode 100644 index 00000000..5c105623 --- /dev/null +++ b/Dell-Stock-Prediction-lstm/dell-stock-prediction-lstm.ipynb @@ -0,0 +1,1723 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "079620cf", + "metadata": { + "_kg_hide-output": true, + "papermill": { + "duration": 0.00762, + "end_time": "2024-05-16T11:05:18.822959", + "exception": false, + "start_time": "2024-05-16T11:05:18.815339", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Dell Stock prediction - LSTM\n", + "#### 1. inputs data is the last 60 data of train data plus true test data\n", + "#### 2. inputs data is the last 60 data of train data only (not use true test data)" + ] + }, + { + "cell_type": "markdown", + "id": "1545818b", + "metadata": { + "papermill": { + "duration": 0.006846, + "end_time": "2024-05-16T11:05:18.837250", + "exception": false, + "start_time": "2024-05-16T11:05:18.830404", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### RNNs\n", + "RNNs are a type of neural network that is used for sequential data, such as time series or text. They are characterized by their ability to take in a sequence of inputs and produce a sequence of outputs, while maintaining a \"memory\" of past inputs. However, traditional RNNs suffer from the vanishing gradient problem, which occurs when the gradient of the loss function with respect to the weights becomes too small, making it difficult to learn long-term dependencies in the data.\n", + "### LSTM\n", + "LSTM stands for Long Short-Term Memory, and it is a type of Recurrent Neural Network (RNN) architecture that is designed to better handle the vanishing gradient problem that arises in traditional RNNs.\n", + "### The vanishing gradient problem \n", + "The vanishing gradient problem is a phenomenon that can occur during the training of neural networks, particularly deep neural networks with many layers. During backpropagation, the gradients of the loss function with respect to the weights in the earlier layers of the network can become extremely small, which means that the weights in these layers are updated very slowly or not at all.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "3acfaf1a", + "metadata": { + "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", + "_kg_hide-output": true, + "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5", + "execution": { + "iopub.execute_input": "2024-05-16T11:05:18.852694Z", + "iopub.status.busy": "2024-05-16T11:05:18.852082Z", + "iopub.status.idle": "2024-05-16T11:05:32.024136Z", + "shell.execute_reply": "2024-05-16T11:05:32.023264Z" + }, + "papermill": { + "duration": 13.182731, + "end_time": "2024-05-16T11:05:32.026935", + "exception": false, + "start_time": "2024-05-16T11:05:18.844204", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-05-16 11:05:21.380665: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "2024-05-16 11:05:21.380761: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "2024-05-16 11:05:21.517178: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n" + ] + } + ], + "source": [ + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from keras.models import Sequential\n", + "from keras.layers import Dense\n", + "from keras.layers import LSTM\n", + "from keras.layers import Dropout\n", + "from sklearn.preprocessing import MinMaxScaler" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "da3f9b10", + "metadata": { + "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0", + "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a", + "execution": { + "iopub.execute_input": "2024-05-16T11:05:32.047259Z", + "iopub.status.busy": "2024-05-16T11:05:32.046286Z", + "iopub.status.idle": "2024-05-16T11:05:32.070364Z", + "shell.execute_reply": "2024-05-16T11:05:32.069209Z" + }, + "papermill": { + "duration": 0.034694, + "end_time": "2024-05-16T11:05:32.072277", + "exception": false, + "start_time": "2024-05-16T11:05:32.037583", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1099\n", + "['Date', 'Open', 'High', 'Low', 'Close', 'Volume', 'Dividends', 'Stock Splits']\n" + ] + } + ], + "source": [ + "dataset = pd.read_csv(\"/kaggle/input/dell-stock-data-latest-and-updated/DELL_stock_history.csv\",sep='\\t')\n", + "print(len(dataset))\n", + "print(dataset.columns.tolist())" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c725310d", + "metadata": { + "_kg_hide-output": true, + "_uuid": "9de88d689da1a279477470f6cef999c0a45bf98a", + "execution": { + "iopub.execute_input": "2024-05-16T11:05:32.088331Z", + "iopub.status.busy": "2024-05-16T11:05:32.088004Z", + "iopub.status.idle": "2024-05-16T11:05:32.092638Z", + "shell.execute_reply": "2024-05-16T11:05:32.091804Z" + }, + "papermill": { + "duration": 0.014882, + "end_time": "2024-05-16T11:05:32.094628", + "exception": false, + "start_time": "2024-05-16T11:05:32.079746", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "dataset_train=dataset.iloc[-1000:-100]\n", + "dataset_test=dataset.iloc[-100:]" + ] + }, + { + "cell_type": "markdown", + "id": "992edcea", + "metadata": { + "papermill": { + "duration": 0.007141, + "end_time": "2024-05-16T11:05:32.109189", + "exception": false, + "start_time": "2024-05-16T11:05:32.102048", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# dataset_train" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c66afead", + "metadata": { + "_uuid": "99cca4c05240bd82c4492c6c3c200925c5c096e5", + "execution": { + "iopub.execute_input": "2024-05-16T11:05:32.124616Z", + "iopub.status.busy": "2024-05-16T11:05:32.124356Z", + "iopub.status.idle": "2024-05-16T11:05:32.133956Z", + "shell.execute_reply": "2024-05-16T11:05:32.133086Z" + }, + "papermill": { + "duration": 0.019685, + "end_time": "2024-05-16T11:05:32.135971", + "exception": false, + "start_time": "2024-05-16T11:05:32.116286", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[15.09262883],\n", + " [15.08728061],\n", + " [15.30930983],\n", + " [15.30128369],\n", + " [15.38420938]])" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "trainset = dataset_train.iloc[:,1:2].values #Open\n", + "display(trainset[0:5])" + ] + }, + { + "cell_type": "markdown", + "id": "8b6034ec", + "metadata": { + "papermill": { + "duration": 0.007157, + "end_time": "2024-05-16T11:05:32.150671", + "exception": false, + "start_time": "2024-05-16T11:05:32.143514", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## MinMaxScaler\n", + "MinMaxScaler is used to scale the values of data to a range of 0 to 1." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "4d087609", + "metadata": { + "_uuid": "1abc6fe47fb5c4443767a2e2053cf9999c8a9a5c", + "execution": { + "iopub.execute_input": "2024-05-16T11:05:32.170051Z", + "iopub.status.busy": "2024-05-16T11:05:32.169499Z", + "iopub.status.idle": "2024-05-16T11:05:32.176389Z", + "shell.execute_reply": "2024-05-16T11:05:32.175537Z" + }, + "papermill": { + "duration": 0.019588, + "end_time": "2024-05-16T11:05:32.178386", + "exception": false, + "start_time": "2024-05-16T11:05:32.158798", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.05429738]\n", + " [0.05402907]\n", + " [0.06516769]\n", + " [0.06476504]\n", + " [0.0689252 ]]\n", + "900\n" + ] + } + ], + "source": [ + "sc = MinMaxScaler(feature_range = (0,1))\n", + "training_scaled = sc.fit_transform(trainset)\n", + "print(training_scaled[0:5])\n", + "print(len(training_scaled))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "84cdb138", + "metadata": { + "_uuid": "d0d549e9a4af9ab516c3304fb44d650a815dcedc", + "execution": { + "iopub.execute_input": "2024-05-16T11:05:32.195115Z", + "iopub.status.busy": "2024-05-16T11:05:32.194846Z", + "iopub.status.idle": "2024-05-16T11:05:32.198483Z", + "shell.execute_reply": "2024-05-16T11:05:32.197622Z" + }, + "papermill": { + "duration": 0.014204, + "end_time": "2024-05-16T11:05:32.200337", + "exception": false, + "start_time": "2024-05-16T11:05:32.186133", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x_train = []\n", + "y_train = []" + ] + }, + { + "cell_type": "markdown", + "id": "502c87ee", + "metadata": { + "papermill": { + "duration": 0.007351, + "end_time": "2024-05-16T11:05:32.215097", + "exception": false, + "start_time": "2024-05-16T11:05:32.207746", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "* x_train is data during the past 60 days. \n", + "* y_data is the data of the day after the last day of x_train" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "634c2706", + "metadata": { + "_uuid": "4c4f8ba0c635e7da0ba1bc52abf0a42068acdc89", + "execution": { + "iopub.execute_input": "2024-05-16T11:05:32.231029Z", + "iopub.status.busy": "2024-05-16T11:05:32.230762Z", + "iopub.status.idle": "2024-05-16T11:05:32.238556Z", + "shell.execute_reply": "2024-05-16T11:05:32.237443Z" + }, + "papermill": { + "duration": 0.017873, + "end_time": "2024-05-16T11:05:32.240376", + "exception": false, + "start_time": "2024-05-16T11:05:32.222503", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(840, 60) (840,)\n" + ] + } + ], + "source": [ + "for i in range(60,len(training_scaled)):\n", + " x_train.append(training_scaled[i-60:i, 0])\n", + " y_train.append(training_scaled[i,0])\n", + "x_train,y_train = np.array(x_train),np.array(y_train)\n", + "print(x_train.shape,y_train.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "09b120f9", + "metadata": { + "papermill": { + "duration": 0.007582, + "end_time": "2024-05-16T11:05:32.255420", + "exception": false, + "start_time": "2024-05-16T11:05:32.247838", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "This code is reshaping the input data x_train into a 3D tensor with dimensions (samples, time steps, features) which is required as input for a Long Short-Term Memory (LSTM) neural network.\n", + "\n", + "The reshaping step is necessary because LSTMs require input data to be in a specific format, where:\n", + "\n", + "* samples refers to the number of input sequences (or samples) in the dataset\n", + "* time steps refers to the number of time steps in each input sequence\n", + "* features refers to the number of features (or variables) in each time step of the input sequence\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "7caeddc9", + "metadata": { + "_uuid": "316c5775b5c0a984b432f138792d2c0e9a484491", + "execution": { + "iopub.execute_input": "2024-05-16T11:05:32.271288Z", + "iopub.status.busy": "2024-05-16T11:05:32.271002Z", + "iopub.status.idle": "2024-05-16T11:05:32.275674Z", + "shell.execute_reply": "2024-05-16T11:05:32.274873Z" + }, + "papermill": { + "duration": 0.014753, + "end_time": "2024-05-16T11:05:32.277575", + "exception": false, + "start_time": "2024-05-16T11:05:32.262822", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(samples, time steps, features) = (840, 60, 1)\n" + ] + } + ], + "source": [ + "x_train = np.reshape(x_train, (x_train.shape[0],x_train.shape[1],1))\n", + "print('(samples, time steps, features) =',x_train.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "7cd71da2", + "metadata": { + "_uuid": "ed24d733423254641f134c0e84f44a8ce175589f", + "execution": { + "iopub.execute_input": "2024-05-16T11:05:32.293552Z", + "iopub.status.busy": "2024-05-16T11:05:32.293284Z", + "iopub.status.idle": "2024-05-16T11:05:33.541532Z", + "shell.execute_reply": "2024-05-16T11:05:33.540536Z" + }, + "papermill": { + "duration": 1.258825, + "end_time": "2024-05-16T11:05:33.543835", + "exception": false, + "start_time": "2024-05-16T11:05:32.285010", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.10/site-packages/keras/src/layers/rnn/rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", + " super().__init__(**kwargs)\n" + ] + } + ], + "source": [ + "regressor = Sequential()\n", + "regressor.add(LSTM(units = 50,return_sequences = True,input_shape = (x_train.shape[1],1)))\n", + "regressor.add(Dropout(0.2))\n", + "regressor.add(LSTM(units = 50,return_sequences = True))\n", + "regressor.add(Dropout(0.2))\n", + "regressor.add(LSTM(units = 50,return_sequences = True))\n", + "regressor.add(Dropout(0.2))\n", + "regressor.add(LSTM(units = 50))\n", + "regressor.add(Dropout(0.2))\n", + "regressor.add(Dense(units = 1))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "6c408daa", + "metadata": { + "_kg_hide-output": true, + "_uuid": "6681ff55d9a29b1ad0f37cdc82318fb2c8fbc0bd", + "execution": { + "iopub.execute_input": "2024-05-16T11:05:33.561006Z", + "iopub.status.busy": "2024-05-16T11:05:33.560706Z", + "iopub.status.idle": "2024-05-16T11:07:47.334674Z", + "shell.execute_reply": "2024-05-16T11:07:47.333741Z" + }, + "papermill": { + "duration": 133.78489, + "end_time": "2024-05-16T11:07:47.336729", + "exception": false, + "start_time": "2024-05-16T11:05:33.551839", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 17ms/step - loss: 0.0860\n", + "Epoch 2/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0174\n", + "Epoch 3/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0143\n", + "Epoch 4/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0132\n", + "Epoch 5/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0106\n", + "Epoch 6/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0099\n", + "Epoch 7/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0107\n", + "Epoch 8/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0094\n", + "Epoch 9/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0077\n", + "Epoch 10/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0078\n", + "Epoch 11/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0074\n", + "Epoch 12/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0067\n", + "Epoch 13/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0082\n", + "Epoch 14/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - loss: 0.0071\n", + "Epoch 15/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0063\n", + "Epoch 16/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0063\n", + "Epoch 17/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0067\n", + "Epoch 18/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0062\n", + "Epoch 19/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0063\n", + "Epoch 20/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0067\n", + "Epoch 21/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - loss: 0.0054\n", + "Epoch 22/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0053\n", + "Epoch 23/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0060\n", + "Epoch 24/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0055\n", + "Epoch 25/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0066\n", + "Epoch 26/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0068\n", + "Epoch 27/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0052\n", + "Epoch 28/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0049\n", + "Epoch 29/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0051\n", + "Epoch 30/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0049\n", + "Epoch 31/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0042\n", + "Epoch 32/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0046\n", + "Epoch 33/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0050\n", + "Epoch 34/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0044\n", + "Epoch 35/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0043\n", + "Epoch 36/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0042\n", + "Epoch 37/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0038\n", + "Epoch 38/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0044\n", + "Epoch 39/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0045\n", + "Epoch 40/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0039\n", + "Epoch 41/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0045\n", + "Epoch 42/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0037\n", + "Epoch 43/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0040\n", + "Epoch 44/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0037\n", + "Epoch 45/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0039\n", + "Epoch 46/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0036\n", + "Epoch 47/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0047\n", + "Epoch 48/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0040\n", + "Epoch 49/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0037\n", + "Epoch 50/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0034\n", + "Epoch 51/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0034\n", + "Epoch 52/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0042\n", + "Epoch 53/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0035\n", + "Epoch 54/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0035\n", + "Epoch 55/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0032\n", + "Epoch 56/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0033\n", + "Epoch 57/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0027\n", + "Epoch 58/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - loss: 0.0032\n", + "Epoch 59/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0044\n", + "Epoch 60/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0030\n", + "Epoch 61/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0031\n", + "Epoch 62/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0031\n", + "Epoch 63/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0036\n", + "Epoch 64/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0025\n", + "Epoch 65/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0031\n", + "Epoch 66/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0027\n", + "Epoch 67/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0030\n", + "Epoch 68/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0035\n", + "Epoch 69/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0031\n", + "Epoch 70/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0028\n", + "Epoch 71/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0034\n", + "Epoch 72/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0026\n", + "Epoch 73/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0028\n", + "Epoch 74/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0024\n", + "Epoch 75/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0025\n", + "Epoch 76/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0024\n", + "Epoch 77/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0024\n", + "Epoch 78/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0025\n", + "Epoch 79/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0020\n", + "Epoch 80/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0022\n", + "Epoch 81/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - loss: 0.0025\n", + "Epoch 82/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0025\n", + "Epoch 83/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0023\n", + "Epoch 84/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0025\n", + "Epoch 85/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0023\n", + "Epoch 86/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0023\n", + "Epoch 87/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0026\n", + "Epoch 88/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0026\n", + "Epoch 89/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0021\n", + "Epoch 90/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0021\n", + "Epoch 91/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0023\n", + "Epoch 92/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0027\n", + "Epoch 93/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0021\n", + "Epoch 94/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0021\n", + "Epoch 95/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0025\n", + "Epoch 96/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0018\n", + "Epoch 97/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - loss: 0.0020\n", + "Epoch 98/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0021\n", + "Epoch 99/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0021\n", + "Epoch 100/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0018\n", + "Epoch 101/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0018\n", + "Epoch 102/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0017\n", + "Epoch 103/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0020\n", + "Epoch 104/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0018\n", + "Epoch 105/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", + "Epoch 106/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0020\n", + "Epoch 107/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", + "Epoch 108/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0021\n", + "Epoch 109/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0017\n", + "Epoch 110/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0021\n", + "Epoch 111/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0018\n", + "Epoch 112/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0019\n", + "Epoch 113/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0021\n", + "Epoch 114/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0019\n", + "Epoch 115/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0017\n", + "Epoch 116/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", + "Epoch 117/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0017\n", + "Epoch 118/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 119/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0017\n", + "Epoch 120/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0018\n", + "Epoch 121/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0019\n", + "Epoch 122/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0017\n", + "Epoch 123/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0021\n", + "Epoch 124/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0017\n", + "Epoch 125/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0019\n", + "Epoch 126/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0019\n", + "Epoch 127/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0017\n", + "Epoch 128/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0017\n", + "Epoch 129/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0015\n", + "Epoch 130/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0017\n", + "Epoch 131/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 132/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 133/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0022\n", + "Epoch 134/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", + "Epoch 135/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0017\n", + "Epoch 136/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", + "Epoch 137/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", + "Epoch 138/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0018\n", + "Epoch 139/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", + "Epoch 140/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", + "Epoch 141/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", + "Epoch 142/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0017\n", + "Epoch 143/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 144/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0019\n", + "Epoch 145/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 146/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 147/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 148/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", + "Epoch 149/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 150/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 151/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 152/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0017\n", + "Epoch 153/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0018\n", + "Epoch 154/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0020\n", + "Epoch 155/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0019\n", + "Epoch 156/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 157/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 158/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", + "Epoch 159/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", + "Epoch 160/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 161/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 162/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 163/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 164/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 165/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0017\n", + "Epoch 166/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", + "Epoch 167/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 168/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 169/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", + "Epoch 170/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 171/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 172/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 173/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0012\n", + "Epoch 174/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 175/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 176/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0012\n", + "Epoch 177/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 178/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 179/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0011\n", + "Epoch 180/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 181/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 182/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 183/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", + "Epoch 184/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0012\n", + "Epoch 185/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 186/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0017\n", + "Epoch 187/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 188/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 189/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 190/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0018\n", + "Epoch 191/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0012\n", + "Epoch 192/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 193/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 194/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 195/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", + "Epoch 196/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0012\n", + "Epoch 197/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 198/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 199/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", + "Epoch 200/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 201/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 202/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0015\n", + "Epoch 203/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0016\n", + "Epoch 204/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 205/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0012\n", + "Epoch 206/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0012\n", + "Epoch 207/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0012\n", + "Epoch 208/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0012\n", + "Epoch 209/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 210/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 211/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 212/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0017\n", + "Epoch 213/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 214/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 215/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 216/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0012\n", + "Epoch 217/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", + "Epoch 218/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0012\n", + "Epoch 219/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 220/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 221/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 222/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 223/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0017\n", + "Epoch 224/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 225/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 226/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 227/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 228/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 229/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 230/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 231/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 232/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 233/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 234/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0017\n", + "Epoch 235/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 236/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0012\n", + "Epoch 237/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 238/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 239/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0017\n", + "Epoch 240/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 241/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 242/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 243/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 244/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0012\n", + "Epoch 245/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 246/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 247/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0012\n", + "Epoch 248/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0017\n", + "Epoch 249/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 250/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0011\n", + "Epoch 251/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 252/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0012\n", + "Epoch 253/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 254/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 255/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", + "Epoch 256/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 257/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0011\n", + "Epoch 258/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 259/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 260/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0011\n", + "Epoch 261/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0017\n", + "Epoch 262/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", + "Epoch 263/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 264/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 265/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", + "Epoch 266/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", + "Epoch 267/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0018\n", + "Epoch 268/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 269/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0012\n", + "Epoch 270/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 271/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 272/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 273/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 274/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 275/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", + "Epoch 276/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 277/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 0.0013\n", + "Epoch 278/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0012\n", + "Epoch 279/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 280/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 281/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", + "Epoch 282/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", + "Epoch 283/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", + "Epoch 284/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 285/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0012\n", + "Epoch 286/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 287/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - loss: 0.0016\n", + "Epoch 288/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0011\n", + "Epoch 289/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 290/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 291/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 292/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 293/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 294/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0012\n", + "Epoch 295/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 296/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 297/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 298/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 299/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 300/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0012\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "regressor.compile(optimizer = 'adam',loss = 'mean_squared_error')\n", + "\n", + "regressor.fit(x_train,y_train,epochs =300, batch_size = 32)" + ] + }, + { + "cell_type": "markdown", + "id": "94a9c3eb", + "metadata": { + "papermill": { + "duration": 0.215501, + "end_time": "2024-05-16T11:07:47.770140", + "exception": false, + "start_time": "2024-05-16T11:07:47.554639", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# dataset_test" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "7ab8b6a3", + "metadata": { + "_uuid": "6acd6a66dc0f9c013ea008c0d99734697757cae6", + "execution": { + "iopub.execute_input": "2024-05-16T11:07:48.208096Z", + "iopub.status.busy": "2024-05-16T11:07:48.207240Z", + "iopub.status.idle": "2024-05-16T11:07:48.218575Z", + "shell.execute_reply": "2024-05-16T11:07:48.217682Z" + }, + "papermill": { + "duration": 0.232795, + "end_time": "2024-05-16T11:07:48.220501", + "exception": false, + "start_time": "2024-05-16T11:07:47.987706", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "real_stock_price = dataset_test.iloc[:,1:2].values\n", + "\n", + "dataset_total = pd.concat((dataset_train['Open'],dataset_test['Open']),axis = 0)" + ] + }, + { + "cell_type": "markdown", + "id": "910f40c4", + "metadata": { + "papermill": { + "duration": 0.218258, + "end_time": "2024-05-16T11:07:48.656166", + "exception": false, + "start_time": "2024-05-16T11:07:48.437908", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## 1. inputs data is the last 60 data of train data plus true test data" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "56d6d971", + "metadata": { + "_kg_hide-output": true, + "_uuid": "31aa33ed70b08e71d0e8bfe4c1abae7901ab947d", + "execution": { + "iopub.execute_input": "2024-05-16T11:07:49.094325Z", + "iopub.status.busy": "2024-05-16T11:07:49.093941Z", + "iopub.status.idle": "2024-05-16T11:07:49.099584Z", + "shell.execute_reply": "2024-05-16T11:07:49.098698Z" + }, + "papermill": { + "duration": 0.227549, + "end_time": "2024-05-16T11:07:49.101537", + "exception": false, + "start_time": "2024-05-16T11:07:48.873988", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1000 100\n", + "(160,)\n" + ] + } + ], + "source": [ + "print(len(dataset_total),len(dataset_test))\n", + "inputs = dataset_total[len(dataset_total)-len(dataset_test)-60:].values\n", + "print(inputs.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "0572d772", + "metadata": { + "papermill": { + "duration": 0.217296, + "end_time": "2024-05-16T11:07:49.540387", + "exception": false, + "start_time": "2024-05-16T11:07:49.323091", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "In the context of a LSTM model, this reshaping may be necessary if the input data has only one feature (i.e., one time series) and the LSTM model expects a 3D input shape of (n_samples, n_time_steps, n_features). In this case, since there is only one feature, the shape does not change." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "9e7ae164", + "metadata": { + "_kg_hide-output": true, + "_uuid": "7d74568c439acc8dda77dc4365d4b413095508e1", + "execution": { + "iopub.execute_input": "2024-05-16T11:07:49.973818Z", + "iopub.status.busy": "2024-05-16T11:07:49.973439Z", + "iopub.status.idle": "2024-05-16T11:07:49.979427Z", + "shell.execute_reply": "2024-05-16T11:07:49.978363Z" + }, + "papermill": { + "duration": 0.224983, + "end_time": "2024-05-16T11:07:49.981319", + "exception": false, + "start_time": "2024-05-16T11:07:49.756336", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(160, 1)\n", + "(160, 1)\n" + ] + } + ], + "source": [ + "inputs = inputs.reshape(-1,1)\n", + "print(inputs.shape)\n", + "\n", + "#sc = MinMaxScaler(feature_range = (0,1))\n", + "inputs = sc.transform(inputs)\n", + "print(inputs.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "074d397d", + "metadata": { + "_kg_hide-output": true, + "_uuid": "ef076f0fdbe6ec72d85e7326c3e8aa73fcbad667", + "execution": { + "iopub.execute_input": "2024-05-16T11:07:50.460308Z", + "iopub.status.busy": "2024-05-16T11:07:50.459602Z", + "iopub.status.idle": "2024-05-16T11:07:50.466708Z", + "shell.execute_reply": "2024-05-16T11:07:50.465692Z" + }, + "papermill": { + "duration": 0.227565, + "end_time": "2024-05-16T11:07:50.468570", + "exception": false, + "start_time": "2024-05-16T11:07:50.241005", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(100, 60)\n", + "(100, 60, 1)\n" + ] + } + ], + "source": [ + "x_test = []\n", + "for i in range(60,len(inputs)):\n", + " x_test.append(inputs[i-60:i,0])\n", + "x_test = np.array(x_test)\n", + "print(x_test.shape)\n", + "\n", + "x_test = np.reshape(x_test, (x_test.shape[0],x_test.shape[1],1))\n", + "print(x_test.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "12042728", + "metadata": { + "_kg_hide-output": true, + "_uuid": "24730042bdad3a140e033a78ae9b170304f8cefb", + "execution": { + "iopub.execute_input": "2024-05-16T11:07:50.903905Z", + "iopub.status.busy": "2024-05-16T11:07:50.903530Z", + "iopub.status.idle": "2024-05-16T11:07:51.562192Z", + "shell.execute_reply": "2024-05-16T11:07:51.561434Z" + }, + "papermill": { + "duration": 0.880258, + "end_time": "2024-05-16T11:07:51.564333", + "exception": false, + "start_time": "2024-05-16T11:07:50.684075", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 102ms/step\n" + ] + } + ], + "source": [ + "predicted_price = regressor.predict(x_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "8e252ed5", + "metadata": { + "_kg_hide-output": true, + "_uuid": "4b06462fa06e7399373d8a552cd159176fbeef33", + "execution": { + "iopub.execute_input": "2024-05-16T11:07:51.999365Z", + "iopub.status.busy": "2024-05-16T11:07:51.998597Z", + "iopub.status.idle": "2024-05-16T11:07:52.005023Z", + "shell.execute_reply": "2024-05-16T11:07:52.004100Z" + }, + "papermill": { + "duration": 0.226239, + "end_time": "2024-05-16T11:07:52.006954", + "exception": false, + "start_time": "2024-05-16T11:07:51.780715", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(100, 1)\n" + ] + } + ], + "source": [ + "predicted_price = sc.inverse_transform(predicted_price)\n", + "print(predicted_price.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "878dcbd1", + "metadata": { + "_kg_hide-output": false, + "_uuid": "e8b30d578fafb1564da4448efb1f994537f6f588", + "execution": { + "iopub.execute_input": "2024-05-16T11:07:52.443983Z", + "iopub.status.busy": "2024-05-16T11:07:52.443107Z", + "iopub.status.idle": "2024-05-16T11:07:52.755016Z", + "shell.execute_reply": "2024-05-16T11:07:52.754063Z" + }, + "papermill": { + "duration": 0.53407, + "end_time": "2024-05-16T11:07:52.757169", + "exception": false, + "start_time": "2024-05-16T11:07:52.223099", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(real_stock_price[-100:],color = 'red', label = 'Real Price')\n", + "plt.plot(predicted_price[-100:], color = 'blue', label = 'Predicted Price')\n", + "plt.title('Dell Price Prediction')\n", + "plt.xlabel('Time')\n", + "plt.ylabel('Dell Price')\n", + "plt.ylim(0,80) \n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "211ef749", + "metadata": { + "papermill": { + "duration": 0.223993, + "end_time": "2024-05-16T11:07:53.200513", + "exception": false, + "start_time": "2024-05-16T11:07:52.976520", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## 2. inputs data is the last 60 data of train data only (not use true test data)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "d9ad3491", + "metadata": { + "_kg_hide-output": true, + "execution": { + "iopub.execute_input": "2024-05-16T11:07:53.670230Z", + "iopub.status.busy": 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regressor.predict(x)\n", + " inputs = np.append(inputs, pred)\n", + " inputs = inputs[1:]\n", + " new_predicted_price+=[pred.reshape(-1)]" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "32b0ab9a", + "metadata": { + "_kg_hide-output": true, + "execution": { + "iopub.execute_input": "2024-05-16T11:07:59.850844Z", + "iopub.status.busy": "2024-05-16T11:07:59.850021Z", + "iopub.status.idle": "2024-05-16T11:07:59.856205Z", + "shell.execute_reply": "2024-05-16T11:07:59.855399Z" + }, + "papermill": { + "duration": 0.24076, + "end_time": "2024-05-16T11:07:59.858113", + "exception": false, + "start_time": "2024-05-16T11:07:59.617353", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(100, 1)\n" + ] + } + ], + "source": [ + "new_predicted_price = sc.inverse_transform(np.array(new_predicted_price))\n", + "print(new_predicted_price.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "3146f0b6", + "metadata": { + "execution": { + "iopub.execute_input": "2024-05-16T11:08:00.370563Z", + "iopub.status.busy": "2024-05-16T11:08:00.370184Z", + "iopub.status.idle": "2024-05-16T11:08:00.609578Z", + "shell.execute_reply": "2024-05-16T11:08:00.608638Z" + }, + "papermill": { + "duration": 0.476895, + "end_time": "2024-05-16T11:08:00.611882", + "exception": false, + "start_time": "2024-05-16T11:08:00.134987", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", 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"sourceType": "notebook" + }, + "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": "3.10.13" + }, + "papermill": { + "default_parameters": {}, + "duration": 167.186816, + "end_time": "2024-05-16T11:08:03.256018", + "environment_variables": {}, + "exception": null, + "input_path": "__notebook__.ipynb", + "output_path": "__notebook__.ipynb", + "parameters": {}, + "start_time": "2024-05-16T11:05:16.069202", + "version": "2.5.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From a1712d09cb9f78bce3ddc05d71308c1a9e773641 Mon Sep 17 00:00:00 2001 From: Nidhi Shekhawat <146202184+Nidhi1314@users.noreply.github.com> Date: Tue, 9 Jul 2024 12:05:51 +0530 Subject: [PATCH 2/3] DellStockPrediction folder moved to NYSE --- .../Data Sources/DELL_stock_action.csv | 9 + .../Data Sources/DELL_stock_dividends.csv | 7 + .../Data Sources/DELL_stock_history.csv | 1100 +++++++++++ .../Data Sources/DELL_stock_info.csv | 1000 ++++++++++ .../Data Sources/DELL_stock_spilts.csv | 3 + .../dell-stock-prediction-lstm.ipynb | 1723 +++++++++++++++++ 6 files changed, 3842 insertions(+) create mode 100644 NYSE/Dell-Stock-Prediction-lstm/Data Sources/DELL_stock_action.csv create mode 100644 NYSE/Dell-Stock-Prediction-lstm/Data Sources/DELL_stock_dividends.csv create mode 100644 NYSE/Dell-Stock-Prediction-lstm/Data Sources/DELL_stock_history.csv create mode 100644 NYSE/Dell-Stock-Prediction-lstm/Data Sources/DELL_stock_info.csv create mode 100644 NYSE/Dell-Stock-Prediction-lstm/Data Sources/DELL_stock_spilts.csv create mode 100644 NYSE/Dell-Stock-Prediction-lstm/dell-stock-prediction-lstm.ipynb diff --git a/NYSE/Dell-Stock-Prediction-lstm/Data Sources/DELL_stock_action.csv b/NYSE/Dell-Stock-Prediction-lstm/Data Sources/DELL_stock_action.csv new file mode 100644 index 00000000..7055abe5 --- /dev/null +++ b/NYSE/Dell-Stock-Prediction-lstm/Data Sources/DELL_stock_action.csv @@ -0,0 +1,9 @@ +Date Dividends Stock Splits +2018-12-28 00:00:00-05:00 0 1.806 +2021-11-02 00:00:00-04:00 0 1.973 +2022-04-19 00:00:00-04:00 0.33 0 +2022-07-19 00:00:00-04:00 0.33 0 +2022-10-18 00:00:00-04:00 0.33 0 +2023-01-24 00:00:00-05:00 0.33 0 +2023-04-24 00:00:00-04:00 0.37 0 +2023-07-24 00:00:00-04:00 0.37 0 diff --git a/NYSE/Dell-Stock-Prediction-lstm/Data Sources/DELL_stock_dividends.csv b/NYSE/Dell-Stock-Prediction-lstm/Data Sources/DELL_stock_dividends.csv new file mode 100644 index 00000000..a7208929 --- /dev/null +++ b/NYSE/Dell-Stock-Prediction-lstm/Data Sources/DELL_stock_dividends.csv @@ -0,0 +1,7 @@ +Date Dividends +2022-04-19 00:00:00-04:00 0.33 +2022-07-19 00:00:00-04:00 0.33 +2022-10-18 00:00:00-04:00 0.33 +2023-01-24 00:00:00-05:00 0.33 +2023-04-24 00:00:00-04:00 0.37 +2023-07-24 00:00:00-04:00 0.37 diff --git a/NYSE/Dell-Stock-Prediction-lstm/Data Sources/DELL_stock_history.csv b/NYSE/Dell-Stock-Prediction-lstm/Data Sources/DELL_stock_history.csv new file mode 100644 index 00000000..9198a3ab --- /dev/null +++ b/NYSE/Dell-Stock-Prediction-lstm/Data Sources/DELL_stock_history.csv @@ -0,0 +1,1100 @@ +Date Open High Low Close Volume Dividends Stock Splits +2016-08-17 00:00:00-04:00 11.63646657 11.77021899 11.50271416 11.50271416 271519 0 0 +2016-08-18 00:00:00-04:00 11.7702178 11.7702178 11.36896059 11.43583679 1767366 0 0 +2016-08-19 00:00:00-04:00 11.42246199 11.63646603 11.40908657 11.63646603 4735900 0 0 +2016-08-22 00:00:00-04:00 11.50271414 12.19822707 11.39571166 11.67659283 2245909 0 0 +2016-08-23 00:00:00-04:00 11.70334255 12.27847848 11.63646635 12.03772449 1483020 0 0 +2016-08-24 00:00:00-04:00 13.37524663 13.37524663 12.06447263 12.19822502 2525979 0 0 +2016-08-25 00:00:00-04:00 12.37210414 12.37210414 12.06447396 12.14740086 371646 0 0 +2016-08-26 00:00:00-04:00 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The company operates through two segments, Infrastructure Solutions Group (ISG) and Client Solutions Group (CSG). The ISG segment provides traditional and next-generation storage solutions, including all-flash arrays, scale-out file, object platforms, hyper-converged infrastructure, and software-defined storage; and rack, blade, tower, and hyperscale servers. This segment also offers networking products and services that help its business customers to transform and modernize their infrastructure, mobilize and enrich end-user experiences, and accelerate business applications and processes; attached software and peripherals; and support and deployment, configuration, and extended warranty services. The CSG segment provides desktops, workstations, and notebooks; displays, docking stations, and other electronics; and third-party software and peripherals, as well as support and deployment, configuration, and extended warranty services. The company is also involved in the provision of cybersecurity technology-driven security solutions to prevent security breaches, detect malicious activity, respond rapidly when a security breach occurs, and identify emerging threats; originating, collecting, and servicing customer financing arrangements; and infrastructure-as-a-service solutions, as well as in the resale of VMware products and services. The company was formerly known as Denali Holding Inc. and changed its name to Dell Technologies Inc. in August 2016. Dell Technologies Inc. was founded in 1984 and is headquartered in Round Rock, Texas. +fullTimeEmployees 133000 +companyOfficers [{'maxAge': 1, 'name': 'Mr. Michael Saul Dell', 'age': 57, 'title': 'Chairman & CEO', 'yearBorn': 1965, 'fiscalYear': 2023, 'totalPay': 2797308, 'exercisedValue': 0, 'unexercisedValue': 0}, {'maxAge': 1, 'name': 'Mr. Jeffrey W. 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Elias', 'age': 64, 'title': 'Chief Customer Officer and President of Services & Digital', 'yearBorn': 1958, 'fiscalYear': 2023, 'totalPay': 6171429, 'exercisedValue': 4068717, 'unexercisedValue': 0}] +auditRisk 7 +boardRisk 9 +compensationRisk 3 +shareHolderRightsRisk 10 +overallRisk 9 +governanceEpochDate 1696118400 +compensationAsOfEpochDate 1703980800 +maxAge 86400 +priceHint 2 +previousClose 67.76 +open 67.26 +dayLow 66.7 +dayHigh 68.645 +regularMarketPreviousClose 67.76 +regularMarketOpen 67.26 +regularMarketDayLow 66.7 +regularMarketDayHigh 68.645 +dividendRate 1.48 +dividendYield 0.0218 +exDividendDate 1698019200 +payoutRatio 0.5385 +beta 0.899 +trailingPE 26.115387 +forwardPE 9.956012 +volume 4065220 +regularMarketVolume 4065220 +averageVolume 4792907 +averageVolume10days 5914220 +averageDailyVolume10Day 5914220 +bid 0 +ask 0 +bidSize 1000 +askSize 800 +marketCap 49120694272 +fiftyTwoWeekLow 33.9 +fiftyTwoWeekHigh 72.82 +priceToSalesTrailing12Months 0.52470404 +fiftyDayAverage 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+symbol DELL +underlyingSymbol DELL +shortName Dell Technologies Inc. +longName Dell Technologies Inc. +firstTradeDateEpochUtc 1471440600 +timeZoneFullName America/New_York +timeZoneShortName EDT +uuid 04c74ad6-2535-3f70-9335-6e441ef69610 +messageBoardId finmb_266017 +gmtOffSetMilliseconds -14400000 +currentPrice 67.9 +targetHighPrice 89 +targetLowPrice 53 +targetMeanPrice 74.07 +targetMedianPrice 79 +recommendationMean 2.1 +recommendationKey buy +numberOfAnalystOpinions 14 +totalCash 8364000256 +totalCashPerShare 11.562 +ebitda 8193999872 +totalDebt 28010000384 +quickRatio 0.47 +currentRatio 0.766 +totalRevenue 93615996928 +revenuePerShare 129.393 +returnOnAssets 0.03716 +grossProfits 22686000000 +freeCashflow 5964624896 +operatingCashflow 8101000192 +earningsGrowth -0.074 +revenueGrowth -0.132 +grossMargins 0.23358999 +ebitdaMargins 0.08753 +operatingMargins 0.0508 +financialCurrency USD +trailingPegRatio 10.0779 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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00:00:00-05:00 1.806 +2021-11-02 00:00:00-04:00 1.973 diff --git a/NYSE/Dell-Stock-Prediction-lstm/dell-stock-prediction-lstm.ipynb b/NYSE/Dell-Stock-Prediction-lstm/dell-stock-prediction-lstm.ipynb new file mode 100644 index 00000000..5c105623 --- /dev/null +++ b/NYSE/Dell-Stock-Prediction-lstm/dell-stock-prediction-lstm.ipynb @@ -0,0 +1,1723 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "079620cf", + "metadata": { + "_kg_hide-output": true, + "papermill": { + "duration": 0.00762, + "end_time": "2024-05-16T11:05:18.822959", + "exception": false, + "start_time": "2024-05-16T11:05:18.815339", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Dell Stock prediction - LSTM\n", + "#### 1. inputs data is the last 60 data of train data plus true test data\n", + "#### 2. inputs data is the last 60 data of train data only (not use true test data)" + ] + }, + { + "cell_type": "markdown", + "id": "1545818b", + "metadata": { + "papermill": { + "duration": 0.006846, + "end_time": "2024-05-16T11:05:18.837250", + "exception": false, + "start_time": "2024-05-16T11:05:18.830404", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### RNNs\n", + "RNNs are a type of neural network that is used for sequential data, such as time series or text. They are characterized by their ability to take in a sequence of inputs and produce a sequence of outputs, while maintaining a \"memory\" of past inputs. However, traditional RNNs suffer from the vanishing gradient problem, which occurs when the gradient of the loss function with respect to the weights becomes too small, making it difficult to learn long-term dependencies in the data.\n", + "### LSTM\n", + "LSTM stands for Long Short-Term Memory, and it is a type of Recurrent Neural Network (RNN) architecture that is designed to better handle the vanishing gradient problem that arises in traditional RNNs.\n", + "### The vanishing gradient problem \n", + "The vanishing gradient problem is a phenomenon that can occur during the training of neural networks, particularly deep neural networks with many layers. During backpropagation, the gradients of the loss function with respect to the weights in the earlier layers of the network can become extremely small, which means that the weights in these layers are updated very slowly or not at all.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "3acfaf1a", + "metadata": { + "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", + "_kg_hide-output": true, + "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5", + "execution": { + "iopub.execute_input": "2024-05-16T11:05:18.852694Z", + "iopub.status.busy": "2024-05-16T11:05:18.852082Z", + "iopub.status.idle": "2024-05-16T11:05:32.024136Z", + "shell.execute_reply": "2024-05-16T11:05:32.023264Z" + }, + "papermill": { + "duration": 13.182731, + "end_time": "2024-05-16T11:05:32.026935", + "exception": false, + "start_time": "2024-05-16T11:05:18.844204", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-05-16 11:05:21.380665: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "2024-05-16 11:05:21.380761: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "2024-05-16 11:05:21.517178: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n" + ] + } + ], + "source": [ + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from keras.models import Sequential\n", + "from keras.layers import Dense\n", + "from keras.layers import LSTM\n", + "from keras.layers import Dropout\n", + "from sklearn.preprocessing import MinMaxScaler" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "da3f9b10", + "metadata": { + "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0", + "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a", + "execution": { + "iopub.execute_input": "2024-05-16T11:05:32.047259Z", + "iopub.status.busy": "2024-05-16T11:05:32.046286Z", + "iopub.status.idle": "2024-05-16T11:05:32.070364Z", + "shell.execute_reply": "2024-05-16T11:05:32.069209Z" + }, + "papermill": { + "duration": 0.034694, + "end_time": "2024-05-16T11:05:32.072277", + "exception": false, + "start_time": "2024-05-16T11:05:32.037583", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1099\n", + "['Date', 'Open', 'High', 'Low', 'Close', 'Volume', 'Dividends', 'Stock Splits']\n" + ] + } + ], + "source": [ + "dataset = pd.read_csv(\"/kaggle/input/dell-stock-data-latest-and-updated/DELL_stock_history.csv\",sep='\\t')\n", + "print(len(dataset))\n", + "print(dataset.columns.tolist())" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c725310d", + "metadata": { + "_kg_hide-output": true, + "_uuid": "9de88d689da1a279477470f6cef999c0a45bf98a", + "execution": { + "iopub.execute_input": "2024-05-16T11:05:32.088331Z", + "iopub.status.busy": "2024-05-16T11:05:32.088004Z", + "iopub.status.idle": "2024-05-16T11:05:32.092638Z", + "shell.execute_reply": "2024-05-16T11:05:32.091804Z" + }, + "papermill": { + "duration": 0.014882, + "end_time": "2024-05-16T11:05:32.094628", + "exception": false, + "start_time": "2024-05-16T11:05:32.079746", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "dataset_train=dataset.iloc[-1000:-100]\n", + "dataset_test=dataset.iloc[-100:]" + ] + }, + { + "cell_type": "markdown", + "id": "992edcea", + "metadata": { + "papermill": { + "duration": 0.007141, + "end_time": "2024-05-16T11:05:32.109189", + "exception": false, + "start_time": "2024-05-16T11:05:32.102048", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# dataset_train" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c66afead", + "metadata": { + "_uuid": "99cca4c05240bd82c4492c6c3c200925c5c096e5", + "execution": { + "iopub.execute_input": "2024-05-16T11:05:32.124616Z", + "iopub.status.busy": "2024-05-16T11:05:32.124356Z", + "iopub.status.idle": "2024-05-16T11:05:32.133956Z", + "shell.execute_reply": "2024-05-16T11:05:32.133086Z" + }, + "papermill": { + "duration": 0.019685, + "end_time": "2024-05-16T11:05:32.135971", + "exception": false, + "start_time": "2024-05-16T11:05:32.116286", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[15.09262883],\n", + " [15.08728061],\n", + " [15.30930983],\n", + " [15.30128369],\n", + " [15.38420938]])" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "trainset = dataset_train.iloc[:,1:2].values #Open\n", + "display(trainset[0:5])" + ] + }, + { + "cell_type": "markdown", + "id": "8b6034ec", + "metadata": { + "papermill": { + "duration": 0.007157, + "end_time": "2024-05-16T11:05:32.150671", + "exception": false, + "start_time": "2024-05-16T11:05:32.143514", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## MinMaxScaler\n", + "MinMaxScaler is used to scale the values of data to a range of 0 to 1." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "4d087609", + "metadata": { + "_uuid": "1abc6fe47fb5c4443767a2e2053cf9999c8a9a5c", + "execution": { + "iopub.execute_input": "2024-05-16T11:05:32.170051Z", + "iopub.status.busy": "2024-05-16T11:05:32.169499Z", + "iopub.status.idle": "2024-05-16T11:05:32.176389Z", + "shell.execute_reply": "2024-05-16T11:05:32.175537Z" + }, + "papermill": { + "duration": 0.019588, + "end_time": "2024-05-16T11:05:32.178386", + "exception": false, + "start_time": "2024-05-16T11:05:32.158798", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.05429738]\n", + " [0.05402907]\n", + " [0.06516769]\n", + " [0.06476504]\n", + " [0.0689252 ]]\n", + "900\n" + ] + } + ], + "source": [ + "sc = MinMaxScaler(feature_range = (0,1))\n", + "training_scaled = sc.fit_transform(trainset)\n", + "print(training_scaled[0:5])\n", + "print(len(training_scaled))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "84cdb138", + "metadata": { + "_uuid": "d0d549e9a4af9ab516c3304fb44d650a815dcedc", + "execution": { + "iopub.execute_input": "2024-05-16T11:05:32.195115Z", + "iopub.status.busy": "2024-05-16T11:05:32.194846Z", + "iopub.status.idle": "2024-05-16T11:05:32.198483Z", + "shell.execute_reply": "2024-05-16T11:05:32.197622Z" + }, + "papermill": { + "duration": 0.014204, + "end_time": "2024-05-16T11:05:32.200337", + "exception": false, + "start_time": "2024-05-16T11:05:32.186133", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x_train = []\n", + "y_train = []" + ] + }, + { + "cell_type": "markdown", + "id": "502c87ee", + "metadata": { + "papermill": { + "duration": 0.007351, + "end_time": "2024-05-16T11:05:32.215097", + "exception": false, + "start_time": "2024-05-16T11:05:32.207746", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "* x_train is data during the past 60 days. \n", + "* y_data is the data of the day after the last day of x_train" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "634c2706", + "metadata": { + "_uuid": "4c4f8ba0c635e7da0ba1bc52abf0a42068acdc89", + "execution": { + "iopub.execute_input": "2024-05-16T11:05:32.231029Z", + "iopub.status.busy": "2024-05-16T11:05:32.230762Z", + "iopub.status.idle": "2024-05-16T11:05:32.238556Z", + "shell.execute_reply": "2024-05-16T11:05:32.237443Z" + }, + "papermill": { + "duration": 0.017873, + "end_time": "2024-05-16T11:05:32.240376", + "exception": false, + "start_time": "2024-05-16T11:05:32.222503", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(840, 60) (840,)\n" + ] + } + ], + "source": [ + "for i in range(60,len(training_scaled)):\n", + " x_train.append(training_scaled[i-60:i, 0])\n", + " y_train.append(training_scaled[i,0])\n", + "x_train,y_train = np.array(x_train),np.array(y_train)\n", + "print(x_train.shape,y_train.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "09b120f9", + "metadata": { + "papermill": { + "duration": 0.007582, + "end_time": "2024-05-16T11:05:32.255420", + "exception": false, + "start_time": "2024-05-16T11:05:32.247838", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "This code is reshaping the input data x_train into a 3D tensor with dimensions (samples, time steps, features) which is required as input for a Long Short-Term Memory (LSTM) neural network.\n", + "\n", + "The reshaping step is necessary because LSTMs require input data to be in a specific format, where:\n", + "\n", + "* samples refers to the number of input sequences (or samples) in the dataset\n", + "* time steps refers to the number of time steps in each input sequence\n", + "* features refers to the number of features (or variables) in each time step of the input sequence\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "7caeddc9", + "metadata": { + "_uuid": "316c5775b5c0a984b432f138792d2c0e9a484491", + "execution": { + "iopub.execute_input": "2024-05-16T11:05:32.271288Z", + "iopub.status.busy": "2024-05-16T11:05:32.271002Z", + "iopub.status.idle": "2024-05-16T11:05:32.275674Z", + "shell.execute_reply": "2024-05-16T11:05:32.274873Z" + }, + "papermill": { + "duration": 0.014753, + "end_time": "2024-05-16T11:05:32.277575", + "exception": false, + "start_time": "2024-05-16T11:05:32.262822", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(samples, time steps, features) = (840, 60, 1)\n" + ] + } + ], + "source": [ + "x_train = np.reshape(x_train, (x_train.shape[0],x_train.shape[1],1))\n", + "print('(samples, time steps, features) =',x_train.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "7cd71da2", + "metadata": { + "_uuid": "ed24d733423254641f134c0e84f44a8ce175589f", + "execution": { + "iopub.execute_input": "2024-05-16T11:05:32.293552Z", + "iopub.status.busy": "2024-05-16T11:05:32.293284Z", + "iopub.status.idle": "2024-05-16T11:05:33.541532Z", + "shell.execute_reply": "2024-05-16T11:05:33.540536Z" + }, + "papermill": { + "duration": 1.258825, + "end_time": "2024-05-16T11:05:33.543835", + "exception": false, + "start_time": "2024-05-16T11:05:32.285010", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.10/site-packages/keras/src/layers/rnn/rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", + " super().__init__(**kwargs)\n" + ] + } + ], + "source": [ + "regressor = Sequential()\n", + "regressor.add(LSTM(units = 50,return_sequences = True,input_shape = (x_train.shape[1],1)))\n", + "regressor.add(Dropout(0.2))\n", + "regressor.add(LSTM(units = 50,return_sequences = True))\n", + "regressor.add(Dropout(0.2))\n", + "regressor.add(LSTM(units = 50,return_sequences = True))\n", + "regressor.add(Dropout(0.2))\n", + "regressor.add(LSTM(units = 50))\n", + "regressor.add(Dropout(0.2))\n", + "regressor.add(Dense(units = 1))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "6c408daa", + "metadata": { + "_kg_hide-output": true, + "_uuid": "6681ff55d9a29b1ad0f37cdc82318fb2c8fbc0bd", + "execution": { + "iopub.execute_input": "2024-05-16T11:05:33.561006Z", + "iopub.status.busy": "2024-05-16T11:05:33.560706Z", + "iopub.status.idle": "2024-05-16T11:07:47.334674Z", + "shell.execute_reply": "2024-05-16T11:07:47.333741Z" + }, + "papermill": { + "duration": 133.78489, + "end_time": "2024-05-16T11:07:47.336729", + "exception": false, + "start_time": "2024-05-16T11:05:33.551839", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 17ms/step - loss: 0.0860\n", + "Epoch 2/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0174\n", + "Epoch 3/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0143\n", + "Epoch 4/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0132\n", + "Epoch 5/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0106\n", + "Epoch 6/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0099\n", + "Epoch 7/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0107\n", + "Epoch 8/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0094\n", + "Epoch 9/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0077\n", + "Epoch 10/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0078\n", + "Epoch 11/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0074\n", + "Epoch 12/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0067\n", + "Epoch 13/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0082\n", + "Epoch 14/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - loss: 0.0071\n", + "Epoch 15/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0063\n", + "Epoch 16/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0063\n", + "Epoch 17/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0067\n", + "Epoch 18/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0062\n", + "Epoch 19/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0063\n", + "Epoch 20/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0067\n", + "Epoch 21/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - loss: 0.0054\n", + "Epoch 22/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0053\n", + "Epoch 23/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0060\n", + "Epoch 24/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0055\n", + "Epoch 25/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0066\n", + "Epoch 26/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0068\n", + "Epoch 27/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0052\n", + "Epoch 28/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0049\n", + "Epoch 29/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0051\n", + "Epoch 30/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0049\n", + "Epoch 31/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0042\n", + "Epoch 32/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0046\n", + "Epoch 33/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0050\n", + "Epoch 34/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0044\n", + "Epoch 35/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0043\n", + "Epoch 36/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0042\n", + "Epoch 37/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0038\n", + "Epoch 38/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0044\n", + "Epoch 39/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0045\n", + "Epoch 40/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0039\n", + "Epoch 41/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0045\n", + "Epoch 42/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0037\n", + "Epoch 43/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0040\n", + "Epoch 44/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0037\n", + "Epoch 45/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0039\n", + "Epoch 46/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0036\n", + "Epoch 47/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0047\n", + "Epoch 48/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0040\n", + "Epoch 49/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0037\n", + "Epoch 50/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0034\n", + "Epoch 51/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0034\n", + "Epoch 52/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0042\n", + "Epoch 53/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0035\n", + "Epoch 54/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0035\n", + "Epoch 55/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0032\n", + "Epoch 56/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0033\n", + "Epoch 57/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0027\n", + "Epoch 58/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - loss: 0.0032\n", + "Epoch 59/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0044\n", + "Epoch 60/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0030\n", + "Epoch 61/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0031\n", + "Epoch 62/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0031\n", + "Epoch 63/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0036\n", + "Epoch 64/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0025\n", + "Epoch 65/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0031\n", + "Epoch 66/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0027\n", + "Epoch 67/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0030\n", + "Epoch 68/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0035\n", + "Epoch 69/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0031\n", + "Epoch 70/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0028\n", + "Epoch 71/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0034\n", + "Epoch 72/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0026\n", + "Epoch 73/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0028\n", + "Epoch 74/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0024\n", + "Epoch 75/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0025\n", + "Epoch 76/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0024\n", + "Epoch 77/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0024\n", + "Epoch 78/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0025\n", + "Epoch 79/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0020\n", + "Epoch 80/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0022\n", + "Epoch 81/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - loss: 0.0025\n", + "Epoch 82/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0025\n", + "Epoch 83/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0023\n", + "Epoch 84/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0025\n", + "Epoch 85/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0023\n", + "Epoch 86/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0023\n", + "Epoch 87/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0026\n", + "Epoch 88/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0026\n", + "Epoch 89/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0021\n", + "Epoch 90/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0021\n", + "Epoch 91/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0023\n", + "Epoch 92/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0027\n", + "Epoch 93/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0021\n", + "Epoch 94/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0021\n", + "Epoch 95/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0025\n", + "Epoch 96/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0018\n", + "Epoch 97/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - loss: 0.0020\n", + "Epoch 98/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0021\n", + "Epoch 99/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0021\n", + "Epoch 100/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0018\n", + "Epoch 101/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0018\n", + "Epoch 102/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0017\n", + "Epoch 103/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0020\n", + "Epoch 104/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0018\n", + "Epoch 105/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", + "Epoch 106/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0020\n", + "Epoch 107/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", + "Epoch 108/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0021\n", + "Epoch 109/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0017\n", + "Epoch 110/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0021\n", + "Epoch 111/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0018\n", + "Epoch 112/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0019\n", + "Epoch 113/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0021\n", + "Epoch 114/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0019\n", + "Epoch 115/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0017\n", + "Epoch 116/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", + "Epoch 117/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0017\n", + "Epoch 118/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 119/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0017\n", + "Epoch 120/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0018\n", + "Epoch 121/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0019\n", + "Epoch 122/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0017\n", + "Epoch 123/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0021\n", + "Epoch 124/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0017\n", + "Epoch 125/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0019\n", + "Epoch 126/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0019\n", + "Epoch 127/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0017\n", + "Epoch 128/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0017\n", + "Epoch 129/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0015\n", + "Epoch 130/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0017\n", + "Epoch 131/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 132/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 133/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0022\n", + "Epoch 134/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", + "Epoch 135/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0017\n", + "Epoch 136/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", + "Epoch 137/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", + "Epoch 138/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0018\n", + "Epoch 139/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", + "Epoch 140/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", + "Epoch 141/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", + "Epoch 142/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0017\n", + "Epoch 143/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 144/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0019\n", + "Epoch 145/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 146/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 147/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 148/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", + "Epoch 149/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 150/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 151/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 152/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0017\n", + "Epoch 153/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0018\n", + "Epoch 154/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0020\n", + "Epoch 155/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0019\n", + "Epoch 156/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 157/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 158/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", + "Epoch 159/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", + "Epoch 160/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 161/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 162/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 163/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 164/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 165/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0017\n", + "Epoch 166/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", + "Epoch 167/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 168/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 169/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", + "Epoch 170/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 171/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 172/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 173/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0012\n", + "Epoch 174/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 175/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 176/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0012\n", + "Epoch 177/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 178/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 179/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0011\n", + "Epoch 180/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 181/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 182/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 183/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", + "Epoch 184/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0012\n", + "Epoch 185/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 186/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0017\n", + "Epoch 187/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 188/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 189/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 190/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0018\n", + "Epoch 191/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0012\n", + "Epoch 192/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 193/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 194/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 195/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", + "Epoch 196/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0012\n", + "Epoch 197/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 198/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 199/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", + "Epoch 200/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 201/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 202/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0015\n", + "Epoch 203/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0016\n", + "Epoch 204/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 205/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0012\n", + "Epoch 206/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0012\n", + "Epoch 207/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0012\n", + "Epoch 208/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0012\n", + "Epoch 209/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 210/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 211/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 212/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0017\n", + "Epoch 213/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 214/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 215/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 216/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0012\n", + "Epoch 217/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", + "Epoch 218/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0012\n", + "Epoch 219/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 220/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 221/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 222/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 223/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0017\n", + "Epoch 224/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 225/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 226/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 227/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 228/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 229/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 230/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 231/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 232/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 233/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 234/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0017\n", + "Epoch 235/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 236/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0012\n", + "Epoch 237/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 238/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 239/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0017\n", + "Epoch 240/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 241/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 242/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 243/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 244/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0012\n", + "Epoch 245/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 246/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 247/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0012\n", + "Epoch 248/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0017\n", + "Epoch 249/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 250/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0011\n", + "Epoch 251/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 252/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0012\n", + "Epoch 253/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 254/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 255/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", + "Epoch 256/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 257/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0011\n", + "Epoch 258/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 259/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 260/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0011\n", + "Epoch 261/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0017\n", + "Epoch 262/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", + "Epoch 263/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 264/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 265/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", + "Epoch 266/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", + "Epoch 267/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0018\n", + "Epoch 268/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 269/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0012\n", + "Epoch 270/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 271/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 272/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 273/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 274/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 275/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", + "Epoch 276/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 277/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 0.0013\n", + "Epoch 278/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0012\n", + "Epoch 279/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 280/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 281/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", + "Epoch 282/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", + "Epoch 283/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", + "Epoch 284/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 285/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0012\n", + "Epoch 286/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 287/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - loss: 0.0016\n", + "Epoch 288/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0011\n", + "Epoch 289/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 290/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 291/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 292/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 293/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 294/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0012\n", + "Epoch 295/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 296/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - loss: 0.0014\n", + "Epoch 297/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", + "Epoch 298/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 299/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", + "Epoch 300/300\n", + "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0012\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "regressor.compile(optimizer = 'adam',loss = 'mean_squared_error')\n", + "\n", + "regressor.fit(x_train,y_train,epochs =300, batch_size = 32)" + ] + }, + { + "cell_type": "markdown", + "id": "94a9c3eb", + "metadata": { + "papermill": { + "duration": 0.215501, + "end_time": "2024-05-16T11:07:47.770140", + "exception": false, + "start_time": "2024-05-16T11:07:47.554639", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# dataset_test" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "7ab8b6a3", + "metadata": { + "_uuid": "6acd6a66dc0f9c013ea008c0d99734697757cae6", + "execution": { + "iopub.execute_input": "2024-05-16T11:07:48.208096Z", + "iopub.status.busy": "2024-05-16T11:07:48.207240Z", + "iopub.status.idle": "2024-05-16T11:07:48.218575Z", + "shell.execute_reply": "2024-05-16T11:07:48.217682Z" + }, + "papermill": { + "duration": 0.232795, + "end_time": "2024-05-16T11:07:48.220501", + "exception": false, + "start_time": "2024-05-16T11:07:47.987706", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "real_stock_price = dataset_test.iloc[:,1:2].values\n", + "\n", + "dataset_total = pd.concat((dataset_train['Open'],dataset_test['Open']),axis = 0)" + ] + }, + { + "cell_type": "markdown", + "id": "910f40c4", + "metadata": { + "papermill": { + "duration": 0.218258, + "end_time": "2024-05-16T11:07:48.656166", + "exception": false, + "start_time": "2024-05-16T11:07:48.437908", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## 1. inputs data is the last 60 data of train data plus true test data" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "56d6d971", + "metadata": { + "_kg_hide-output": true, + "_uuid": "31aa33ed70b08e71d0e8bfe4c1abae7901ab947d", + "execution": { + "iopub.execute_input": "2024-05-16T11:07:49.094325Z", + "iopub.status.busy": "2024-05-16T11:07:49.093941Z", + "iopub.status.idle": "2024-05-16T11:07:49.099584Z", + "shell.execute_reply": "2024-05-16T11:07:49.098698Z" + }, + "papermill": { + "duration": 0.227549, + "end_time": "2024-05-16T11:07:49.101537", + "exception": false, + "start_time": "2024-05-16T11:07:48.873988", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1000 100\n", + "(160,)\n" + ] + } + ], + "source": [ + "print(len(dataset_total),len(dataset_test))\n", + "inputs = dataset_total[len(dataset_total)-len(dataset_test)-60:].values\n", + "print(inputs.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "0572d772", + "metadata": { + "papermill": { + "duration": 0.217296, + "end_time": "2024-05-16T11:07:49.540387", + "exception": false, + "start_time": "2024-05-16T11:07:49.323091", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "In the context of a LSTM model, this reshaping may be necessary if the input data has only one feature (i.e., one time series) and the LSTM model expects a 3D input shape of (n_samples, n_time_steps, n_features). In this case, since there is only one feature, the shape does not change." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "9e7ae164", + "metadata": { + "_kg_hide-output": true, + "_uuid": "7d74568c439acc8dda77dc4365d4b413095508e1", + "execution": { + "iopub.execute_input": "2024-05-16T11:07:49.973818Z", + "iopub.status.busy": "2024-05-16T11:07:49.973439Z", + "iopub.status.idle": "2024-05-16T11:07:49.979427Z", + "shell.execute_reply": "2024-05-16T11:07:49.978363Z" + }, + "papermill": { + "duration": 0.224983, + "end_time": "2024-05-16T11:07:49.981319", + "exception": false, + "start_time": "2024-05-16T11:07:49.756336", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(160, 1)\n", + "(160, 1)\n" + ] + } + ], + "source": [ + "inputs = inputs.reshape(-1,1)\n", + "print(inputs.shape)\n", + "\n", + "#sc = MinMaxScaler(feature_range = (0,1))\n", + "inputs = sc.transform(inputs)\n", + "print(inputs.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "074d397d", + "metadata": { + "_kg_hide-output": true, + "_uuid": "ef076f0fdbe6ec72d85e7326c3e8aa73fcbad667", + "execution": { + "iopub.execute_input": "2024-05-16T11:07:50.460308Z", + "iopub.status.busy": "2024-05-16T11:07:50.459602Z", + "iopub.status.idle": "2024-05-16T11:07:50.466708Z", + "shell.execute_reply": "2024-05-16T11:07:50.465692Z" + }, + "papermill": { + "duration": 0.227565, + "end_time": "2024-05-16T11:07:50.468570", + "exception": false, + "start_time": "2024-05-16T11:07:50.241005", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(100, 60)\n", + "(100, 60, 1)\n" + ] + } + ], + "source": [ + "x_test = []\n", + "for i in range(60,len(inputs)):\n", + " x_test.append(inputs[i-60:i,0])\n", + "x_test = np.array(x_test)\n", + "print(x_test.shape)\n", + "\n", + "x_test = np.reshape(x_test, (x_test.shape[0],x_test.shape[1],1))\n", + "print(x_test.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "12042728", + "metadata": { + "_kg_hide-output": true, + "_uuid": "24730042bdad3a140e033a78ae9b170304f8cefb", + "execution": { + "iopub.execute_input": "2024-05-16T11:07:50.903905Z", + "iopub.status.busy": "2024-05-16T11:07:50.903530Z", + "iopub.status.idle": "2024-05-16T11:07:51.562192Z", + "shell.execute_reply": "2024-05-16T11:07:51.561434Z" + }, + "papermill": { + "duration": 0.880258, + "end_time": "2024-05-16T11:07:51.564333", + "exception": false, + "start_time": "2024-05-16T11:07:50.684075", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 102ms/step\n" + ] + } + ], + "source": [ + "predicted_price = regressor.predict(x_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "8e252ed5", + "metadata": { + "_kg_hide-output": true, + "_uuid": "4b06462fa06e7399373d8a552cd159176fbeef33", + "execution": { + "iopub.execute_input": "2024-05-16T11:07:51.999365Z", + "iopub.status.busy": "2024-05-16T11:07:51.998597Z", + "iopub.status.idle": "2024-05-16T11:07:52.005023Z", + "shell.execute_reply": "2024-05-16T11:07:52.004100Z" + }, + "papermill": { + "duration": 0.226239, + "end_time": "2024-05-16T11:07:52.006954", + "exception": false, + "start_time": "2024-05-16T11:07:51.780715", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(100, 1)\n" + ] + } + ], + "source": [ + "predicted_price = sc.inverse_transform(predicted_price)\n", + "print(predicted_price.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "878dcbd1", + "metadata": { + "_kg_hide-output": false, + "_uuid": "e8b30d578fafb1564da4448efb1f994537f6f588", + "execution": { + "iopub.execute_input": "2024-05-16T11:07:52.443983Z", + "iopub.status.busy": "2024-05-16T11:07:52.443107Z", + "iopub.status.idle": "2024-05-16T11:07:52.755016Z", + "shell.execute_reply": "2024-05-16T11:07:52.754063Z" + }, + "papermill": { + "duration": 0.53407, + "end_time": "2024-05-16T11:07:52.757169", + "exception": false, + "start_time": "2024-05-16T11:07:52.223099", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(real_stock_price[-100:],color = 'red', label = 'Real Price')\n", + "plt.plot(predicted_price[-100:], color = 'blue', label = 'Predicted Price')\n", + "plt.title('Dell Price Prediction')\n", + "plt.xlabel('Time')\n", + "plt.ylabel('Dell Price')\n", + "plt.ylim(0,80) \n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "211ef749", + "metadata": { + "papermill": { + "duration": 0.223993, + "end_time": "2024-05-16T11:07:53.200513", + "exception": false, + "start_time": "2024-05-16T11:07:52.976520", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## 2. inputs data is the last 60 data of train data only (not use true test data)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "d9ad3491", + "metadata": { + "_kg_hide-output": true, + "execution": { + "iopub.execute_input": "2024-05-16T11:07:53.670230Z", + "iopub.status.busy": 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regressor.predict(x)\n", + " inputs = np.append(inputs, pred)\n", + " inputs = inputs[1:]\n", + " new_predicted_price+=[pred.reshape(-1)]" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "32b0ab9a", + "metadata": { + "_kg_hide-output": true, + "execution": { + "iopub.execute_input": "2024-05-16T11:07:59.850844Z", + "iopub.status.busy": "2024-05-16T11:07:59.850021Z", + "iopub.status.idle": "2024-05-16T11:07:59.856205Z", + "shell.execute_reply": "2024-05-16T11:07:59.855399Z" + }, + "papermill": { + "duration": 0.24076, + "end_time": "2024-05-16T11:07:59.858113", + "exception": false, + "start_time": "2024-05-16T11:07:59.617353", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(100, 1)\n" + ] + } + ], + "source": [ + "new_predicted_price = sc.inverse_transform(np.array(new_predicted_price))\n", + "print(new_predicted_price.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "3146f0b6", + "metadata": { + "execution": { + "iopub.execute_input": "2024-05-16T11:08:00.370563Z", + "iopub.status.busy": "2024-05-16T11:08:00.370184Z", + "iopub.status.idle": "2024-05-16T11:08:00.609578Z", + "shell.execute_reply": "2024-05-16T11:08:00.608638Z" + }, + "papermill": { + "duration": 0.476895, + "end_time": "2024-05-16T11:08:00.611882", + "exception": false, + "start_time": "2024-05-16T11:08:00.134987", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", 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"sourceType": "notebook" + }, + "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": "3.10.13" + }, + "papermill": { + "default_parameters": {}, + "duration": 167.186816, + "end_time": "2024-05-16T11:08:03.256018", + "environment_variables": {}, + "exception": null, + "input_path": "__notebook__.ipynb", + "output_path": "__notebook__.ipynb", + "parameters": {}, + "start_time": "2024-05-16T11:05:16.069202", + "version": "2.5.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 9c4d3b55ac7f280cdb05eb07ffbdbe343a4d9e19 Mon Sep 17 00:00:00 2001 From: Nidhi Shekhawat <146202184+Nidhi1314@users.noreply.github.com> Date: Tue, 9 Jul 2024 12:06:52 +0530 Subject: [PATCH 3/3] Delete DellStockPrediction directory --- .../Data Sources/DELL_stock_action.csv | 9 - .../Data Sources/DELL_stock_dividends.csv | 7 - .../Data Sources/DELL_stock_history.csv | 1100 ----------- .../Data Sources/DELL_stock_info.csv | 1000 ---------- .../Data Sources/DELL_stock_spilts.csv | 3 - .../dell-stock-prediction-lstm.ipynb | 1723 ----------------- 6 files changed, 3842 deletions(-) delete mode 100644 Dell-Stock-Prediction-lstm/Data Sources/DELL_stock_action.csv delete mode 100644 Dell-Stock-Prediction-lstm/Data Sources/DELL_stock_dividends.csv delete mode 100644 Dell-Stock-Prediction-lstm/Data Sources/DELL_stock_history.csv delete mode 100644 Dell-Stock-Prediction-lstm/Data Sources/DELL_stock_info.csv delete mode 100644 Dell-Stock-Prediction-lstm/Data Sources/DELL_stock_spilts.csv delete mode 100644 Dell-Stock-Prediction-lstm/dell-stock-prediction-lstm.ipynb diff --git a/Dell-Stock-Prediction-lstm/Data Sources/DELL_stock_action.csv b/Dell-Stock-Prediction-lstm/Data Sources/DELL_stock_action.csv deleted file mode 100644 index 7055abe5..00000000 --- a/Dell-Stock-Prediction-lstm/Data Sources/DELL_stock_action.csv +++ /dev/null @@ -1,9 +0,0 @@ -Date Dividends Stock Splits -2018-12-28 00:00:00-05:00 0 1.806 -2021-11-02 00:00:00-04:00 0 1.973 -2022-04-19 00:00:00-04:00 0.33 0 -2022-07-19 00:00:00-04:00 0.33 0 -2022-10-18 00:00:00-04:00 0.33 0 -2023-01-24 00:00:00-05:00 0.33 0 -2023-04-24 00:00:00-04:00 0.37 0 -2023-07-24 00:00:00-04:00 0.37 0 diff --git a/Dell-Stock-Prediction-lstm/Data Sources/DELL_stock_dividends.csv b/Dell-Stock-Prediction-lstm/Data Sources/DELL_stock_dividends.csv deleted file mode 100644 index a7208929..00000000 --- a/Dell-Stock-Prediction-lstm/Data Sources/DELL_stock_dividends.csv +++ /dev/null @@ -1,7 +0,0 @@ -Date Dividends -2022-04-19 00:00:00-04:00 0.33 -2022-07-19 00:00:00-04:00 0.33 -2022-10-18 00:00:00-04:00 0.33 -2023-01-24 00:00:00-05:00 0.33 -2023-04-24 00:00:00-04:00 0.37 -2023-07-24 00:00:00-04:00 0.37 diff --git a/Dell-Stock-Prediction-lstm/Data Sources/DELL_stock_history.csv b/Dell-Stock-Prediction-lstm/Data Sources/DELL_stock_history.csv deleted file mode 100644 index 9198a3ab..00000000 --- a/Dell-Stock-Prediction-lstm/Data Sources/DELL_stock_history.csv +++ /dev/null @@ -1,1100 +0,0 @@ -Date Open High Low Close Volume Dividends Stock Splits -2016-08-17 00:00:00-04:00 11.63646657 11.77021899 11.50271416 11.50271416 271519 0 0 -2016-08-18 00:00:00-04:00 11.7702178 11.7702178 11.36896059 11.43583679 1767366 0 0 -2016-08-19 00:00:00-04:00 11.42246199 11.63646603 11.40908657 11.63646603 4735900 0 0 -2016-08-22 00:00:00-04:00 11.50271414 12.19822707 11.39571166 11.67659283 2245909 0 0 -2016-08-23 00:00:00-04:00 11.70334255 12.27847848 11.63646635 12.03772449 1483020 0 0 -2016-08-24 00:00:00-04:00 13.37524663 13.37524663 12.06447263 12.19822502 2525979 0 0 -2016-08-25 00:00:00-04:00 12.37210414 12.37210414 12.06447396 12.14740086 371646 0 0 -2016-08-26 00:00:00-04:00 12.2918526 12.2918526 12.16880035 12.23835182 223415 0 0 -2016-08-29 00:00:00-04:00 12.25172654 12.5727321 12.25172654 12.38547802 1390019 0 0 -2016-08-30 00:00:00-04:00 12.37210507 12.50585749 11.91734703 12.07784939 4805026 0 0 -2016-08-31 00:00:00-04:00 11.97084678 12.06447383 11.77021817 11.95212173 9033877 0 0 -2016-09-01 00:00:00-04:00 12.71986073 12.71986073 11.63646603 11.63646603 9048130 0 0 -2016-09-02 00:00:00-04:00 11.74614291 11.74614291 11.42246204 11.49736404 8127033 0 0 -2016-09-06 00:00:00-04:00 11.5695888 12.37210319 11.24055834 12.15809917 56316264 0 0 -2016-09-07 00:00:00-04:00 12.05644916 13.20939533 12.04307374 12.84023857 147907159 0 0 -2016-09-08 00:00:00-04:00 12.96061605 13.61332834 12.94991499 13.45817471 41159318 0 0 -2016-09-09 00:00:00-04:00 13.32977233 13.42072411 12.87501341 13.12914371 26618101 0 0 -2016-09-12 00:00:00-04:00 12.85628802 13.29499624 12.76266188 13.21474552 13989272 0 0 -2016-09-13 00:00:00-04:00 12.87501282 13.27894529 12.84291235 12.97398949 18045663 0 0 -2016-09-14 00:00:00-04:00 12.94456475 13.00609042 12.84023755 12.93386459 17208658 0 0 -2016-09-15 00:00:00-04:00 12.8723394 13.11576955 12.84558855 13.07831764 17208302 0 0 -2016-09-16 00:00:00-04:00 13.00876681 13.10506821 12.87233913 12.96596527 17786615 0 0 -2016-09-19 00:00:00-04:00 12.90443897 13.07831673 12.88303866 13.04354191 16264400 0 0 -2016-09-20 00:00:00-04:00 13.02481559 13.09169178 12.88571266 13.03551483 12000986 0 0 -2016-09-21 00:00:00-04:00 12.94189083 13.10774463 12.91246562 12.9525919 14885427 0 0 -2016-09-22 00:00:00-04:00 12.97131503 13.0408665 12.81616232 12.84826279 17054370 0 0 -2016-09-23 00:00:00-04:00 12.80546163 12.96596396 12.79208621 12.84291172 7449305 0 0 -2016-09-26 00:00:00-04:00 12.7653371 12.85628889 12.75463785 12.8108139 10128504 0 0 -2016-09-27 00:00:00-04:00 12.78138612 12.8348869 12.74661039 12.78406048 9831330 0 0 -2016-09-28 00:00:00-04:00 12.8081365 12.86966307 12.71451036 12.80278778 8013722 0 0 -2016-09-29 00:00:00-04:00 12.7359113 12.82151346 12.70916045 12.72521114 5912124 0 0 -2016-09-30 00:00:00-04:00 12.78138691 12.86698816 12.7225356 12.78673744 6165827 0 0 -2016-10-03 00:00:00-04:00 12.74126064 12.85361364 12.73056139 12.82151318 5345926 0 0 -2016-10-04 00:00:00-04:00 12.79476198 12.84023787 12.73858594 12.80278778 5993010 0 0 -2016-10-05 00:00:00-04:00 12.77068719 13.07029248 12.70648625 12.96596527 6621209 0 0 -2016-10-06 00:00:00-04:00 12.96596422 12.99271507 12.83221182 12.94456482 5139258 0 0 -2016-10-07 00:00:00-04:00 12.94456613 12.96596553 12.82418823 12.85896397 3515134 0 0 -2016-10-10 00:00:00-04:00 12.85896277 12.94723929 12.66635906 12.71183586 5420398 0 0 -2016-10-11 00:00:00-04:00 12.61553425 12.7680117 12.56203256 12.61018372 6247425 0 0 -2016-10-12 00:00:00-04:00 12.56203228 12.69311033 12.50853241 12.60215855 5851549 0 0 -2016-10-13 00:00:00-04:00 12.54598303 12.69311086 12.47643156 12.60750961 4783291 0 0 -2016-10-14 00:00:00-04:00 12.63425992 12.73591187 12.57005898 12.63693428 5453179 0 0 -2016-10-17 00:00:00-04:00 12.64228344 12.74928591 12.58075777 12.67705917 4813222 0 0 -2016-10-18 00:00:00-04:00 12.71986107 12.78673819 12.57273414 12.75463772 5366949 0 0 -2016-10-19 00:00:00-04:00 12.7198604 12.85093754 12.71183642 12.76266193 4452266 0 0 -2016-10-20 00:00:00-04:00 12.69578594 12.79743788 12.68776105 12.75998688 3849010 0 0 -2016-10-21 00:00:00-04:00 12.69578651 12.73858713 12.59413365 12.63693428 3461329 0 0 -2016-10-24 00:00:00-04:00 12.73323645 12.86698886 12.69846071 12.82418823 3621319 0 0 -2016-10-25 00:00:00-04:00 12.76801218 12.82418823 12.5272582 12.57005882 5398662 0 0 -2016-10-26 00:00:00-04:00 12.53260708 12.66635948 12.47643103 12.49783134 5900722 0 0 -2016-10-27 00:00:00-04:00 13.22009584 13.30837236 12.85093816 12.92851543 25078425 0 0 -2016-10-28 00:00:00-04:00 12.86966365 13.15321916 12.83756318 13.07296753 13622259 0 0 -2016-10-31 00:00:00-04:00 13.10774224 13.23882028 13.05959109 13.13181782 12164182 0 0 -2016-11-01 00:00:00-04:00 13.14251922 13.23614536 12.90711486 12.96596527 7967400 0 0 -2016-11-02 00:00:00-04:00 12.90443845 13.13449315 12.89106302 13.10774231 6557427 0 0 -2016-11-03 00:00:00-04:00 13.09704152 13.22276994 12.96864056 13.04621601 4763693 0 0 -2016-11-04 00:00:00-04:00 13.00609112 13.16659437 12.97398974 13.00609112 6307644 0 0 -2016-11-07 00:00:00-05:00 13.07831715 13.37524807 13.07831715 13.2468462 4566646 0 0 -2016-11-08 00:00:00-05:00 13.24684648 13.35919767 13.23614541 13.2896471 3322719 0 0 -2016-11-09 00:00:00-05:00 13.00341647 13.34314803 12.96329111 13.20672035 5840503 0 0 -2016-11-10 00:00:00-05:00 13.20404467 13.3886235 12.99806552 13.17729473 5971274 0 0 -2016-11-11 00:00:00-05:00 13.16124362 13.22811983 13.06226695 13.14786911 3162017 0 0 -2016-11-14 00:00:00-05:00 13.14519357 13.25487131 12.93921533 13.10506821 3504801 0 0 -2016-11-15 00:00:00-05:00 13.11576908 13.46352461 13.05959212 13.39664841 6991429 0 0 -2016-11-16 00:00:00-05:00 13.34047311 13.59727779 13.30034775 13.54912663 4996372 0 0 -2016-11-17 00:00:00-05:00 13.52772574 13.95038325 13.50900069 13.85675621 5726123 0 0 -2016-11-18 00:00:00-05:00 13.85140628 14.00120938 13.83000688 13.90758324 4201058 0 0 -2016-11-21 00:00:00-05:00 13.88350726 14.10018565 13.88350726 14.02795982 3149190 0 0 -2016-11-22 00:00:00-05:00 14.07076052 14.13763672 13.78988029 13.93433285 2856648 0 0 -2016-11-23 00:00:00-05:00 13.85675717 14.00120973 13.74440507 13.7872057 2990982 0 0 -2016-11-25 00:00:00-05:00 13.75242897 14.02260904 13.73637919 13.96108246 905062 0 0 -2016-11-28 00:00:00-05:00 13.91025903 14.01726151 13.85675825 13.94771004 2637865 0 0 -2016-11-29 00:00:00-05:00 13.91560848 14.30616556 13.91560848 14.24998951 4771176 0 0 -2016-11-30 00:00:00-05:00 14.32489179 14.36501715 14.22591511 14.32756615 5082603 0 0 -2016-12-01 00:00:00-05:00 14.32489067 14.3516406 13.71230379 13.87815666 8015860 0 0 -2016-12-02 00:00:00-05:00 13.82733202 14.17508847 13.6481028 14.11088753 4767969 0 0 -2016-12-05 00:00:00-05:00 14.12961144 14.29278904 14.05203508 14.19381237 6389955 0 0 -2016-12-06 00:00:00-05:00 14.12961224 14.31151581 14.10018613 14.27674007 5912124 0 0 -2016-12-07 00:00:00-05:00 14.23661355 14.48806858 14.14298741 14.41851711 9804606 0 0 -2016-12-08 00:00:00-05:00 14.38374096 14.68602241 14.25801436 14.42386723 8274908 0 0 -2016-12-09 00:00:00-05:00 14.40246582 14.57901975 14.33291526 14.43724155 5978401 0 0 -2016-12-12 00:00:00-05:00 14.44526705 14.50679363 14.19916255 14.41851711 5849055 0 0 -2016-12-13 00:00:00-05:00 14.40514164 14.5790194 14.36234011 14.38374043 4376369 0 0 -2016-12-14 00:00:00-05:00 14.35431603 14.56832007 14.3195412 14.50144386 6546737 0 0 -2016-12-15 00:00:00-05:00 14.57901935 15.24778133 14.54424271 15.1835804 12845829 0 0 -2016-12-16 00:00:00-05:00 15.15148133 15.20498302 14.91875314 15.09798145 21293198 0 0 -2016-12-19 00:00:00-05:00 15.02040242 15.17020642 14.93480117 15.08460426 4347863 0 0 -2016-12-20 00:00:00-05:00 15.13810569 15.31198437 15.11403012 15.26115704 5251500 0 0 -2016-12-21 00:00:00-05:00 15.19160603 15.42166166 15.10332952 15.32535934 3799124 0 0 -2016-12-22 00:00:00-05:00 15.26115706 15.26115706 14.83849953 14.93747711 5606755 0 0 -2016-12-23 00:00:00-05:00 14.86257622 15.03377964 14.84652644 15.00435352 2123334 0 0 -2016-12-27 00:00:00-05:00 15.00435254 15.0979805 14.9562032 14.9936533 3583905 0 0 -2016-12-28 00:00:00-05:00 14.99097844 15.03645433 14.7314985 14.80372524 4705612 0 0 -2016-12-29 00:00:00-05:00 14.73149737 14.86792504 14.61647002 14.78232288 2970315 0 0 -2016-12-30 00:00:00-05:00 14.76627478 14.79302563 14.64589779 14.70474911 2866981 0 0 -2017-01-03 00:00:00-05:00 14.73417357 14.78232382 14.48271853 14.5977459 5804515 0 0 -2017-01-04 00:00:00-05:00 14.63787128 14.82512446 14.63787128 14.71277237 7486007 0 0 -2017-01-05 00:00:00-05:00 14.65927142 14.86525057 14.65927142 14.78499985 2956062 0 0 -2017-01-06 00:00:00-05:00 14.85722553 15.23708337 14.80907619 15.19160748 4745520 0 0 -2017-01-09 00:00:00-05:00 15.09262883 15.15147923 14.8866497 15.11135387 5008487 0 0 -2017-01-10 00:00:00-05:00 15.08728061 15.42701126 15.06855466 15.2451086 5933148 0 0 -2017-01-11 00:00:00-05:00 15.30930983 15.42166376 15.25580814 15.40293598 3199788 0 0 -2017-01-12 00:00:00-05:00 15.30128369 15.45108588 15.1889325 15.41898632 3034453 0 0 -2017-01-13 00:00:00-05:00 15.38420938 15.58751325 15.38153503 15.56076241 5908561 0 0 -2017-01-17 00:00:00-05:00 15.53936237 15.77744198 15.42433593 15.49923611 4932234 0 0 -2017-01-18 00:00:00-05:00 15.46446182 15.67579242 15.41898683 15.66776752 2487496 0 0 -2017-01-19 00:00:00-05:00 15.61426521 15.88711874 15.5393632 15.73196602 4574485 0 0 -2017-01-20 00:00:00-05:00 15.7266151 15.90851865 15.71859022 15.85234261 3960183 0 0 -2017-01-23 00:00:00-05:00 15.79349093 15.84699262 15.41898459 15.59018707 3911010 0 0 -2017-01-24 00:00:00-05:00 15.67579071 16.02889675 15.61693849 15.98877048 7179925 0 0 -2017-01-25 00:00:00-05:00 16.05029734 16.13857295 15.92724419 16.06634712 5854400 0 0 -2017-01-26 00:00:00-05:00 16.11449771 16.11449771 15.91654438 15.94596958 5667330 0 0 -2017-01-27 00:00:00-05:00 16.4435301 16.85281221 16.37130246 16.73243523 10710024 0 0 -2017-01-30 00:00:00-05:00 16.61206067 16.73511203 16.4970342 16.70301247 5309581 0 0 -2017-01-31 00:00:00-05:00 16.6120599 16.85281389 16.5585582 16.85013771 4879854 0 0 -2017-02-01 00:00:00-05:00 16.80466228 16.93306598 16.6388103 16.87956429 5165626 0 0 -2017-02-02 00:00:00-05:00 16.79663376 17.14706541 16.72975848 17.10961533 3918137 0 0 -2017-02-03 00:00:00-05:00 16.99993856 17.29151802 16.99993856 17.21394348 2343542 0 0 -2017-02-06 00:00:00-05:00 17.1283437 17.36642334 17.12031881 17.28884697 5024522 0 0 -2017-02-07 00:00:00-05:00 17.35037422 17.42527442 17.24069647 17.35037422 3279248 0 0 -2017-02-08 00:00:00-05:00 17.31826945 17.41457176 17.25406852 17.34502029 4484691 0 0 -2017-02-09 00:00:00-05:00 17.35572164 17.5964756 17.283494 17.3878212 3731423 0 0 -2017-02-10 00:00:00-05:00 17.43864889 17.51355089 17.28884669 17.31559753 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16.98389083 17.25139565 16.9785403 17.14171791 4997441 0 0 -2017-03-02 00:00:00-05:00 17.06681785 17.25139578 16.95981446 16.97854042 2517784 0 0 -2017-03-03 00:00:00-05:00 16.8956123 16.9705143 16.78593638 16.9705143 3103580 0 0 -2017-03-06 00:00:00-05:00 16.88758631 16.94108799 16.70835712 16.77523422 4740888 0 0 -2017-03-07 00:00:00-05:00 16.77523627 16.97586579 16.76721138 16.86618805 3097523 0 0 -2017-03-08 00:00:00-05:00 16.89026001 17.10961545 16.8688597 16.99191284 4431955 0 0 -2017-03-09 00:00:00-05:00 16.9384121 16.97318784 16.76988398 16.88758659 3310961 0 0 -2017-03-10 00:00:00-05:00 16.95981421 17.07484248 16.82873798 16.98121452 2885154 0 0 -2017-03-13 00:00:00-04:00 16.93306476 17.07751642 16.88758796 16.98656464 5339512 0 0 -2017-03-14 00:00:00-04:00 16.98924116 17.02669307 16.81001375 16.98389244 3095029 0 0 -2017-03-15 00:00:00-04:00 17.00261509 17.14974292 16.93038926 17.04809189 3156316 0 0 -2017-03-16 00:00:00-04:00 17.06681568 17.18184213 16.94911307 17.12031555 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17.11764257 17.36642142 16.99726377 17.08019066 6089574 0 0 -2017-04-04 00:00:00-04:00 17.05076395 17.28081865 17.03738853 17.16846657 5680158 0 0 -2017-04-05 00:00:00-04:00 17.23801967 17.36909771 17.07751642 17.12566757 7759307 0 0 -2017-04-06 00:00:00-04:00 17.15241637 17.25139303 17.08018874 17.17381668 6751980 0 0 -2017-04-07 00:00:00-04:00 17.20592048 17.37712482 17.17382091 17.35304832 5746434 0 0 -2017-04-10 00:00:00-04:00 17.35572366 17.4573747 17.29419708 17.36374855 7153200 0 0 -2017-04-11 00:00:00-04:00 17.36910033 17.44132617 17.21127142 17.41724968 5299604 0 0 -2017-04-12 00:00:00-04:00 17.28349343 17.36642031 17.04541383 17.09356499 4598359 0 0 -2017-04-13 00:00:00-04:00 17.07484234 17.24604484 17.04274096 17.09891701 3780952 0 0 -2017-04-17 00:00:00-04:00 17.10961837 17.21126941 17.10961837 17.18719292 2295082 0 0 -2017-04-18 00:00:00-04:00 17.12566894 17.29419709 17.05611746 17.22464561 2970672 0 0 -2017-04-19 00:00:00-04:00 17.30757139 17.30757139 17.22999503 17.26209641 2495336 0 0 -2017-04-20 00:00:00-04:00 17.27012266 17.39584837 17.15509438 17.36107445 3361915 0 0 -2017-04-21 00:00:00-04:00 17.39584744 17.39584744 17.17916814 17.23266983 5961653 0 0 -2017-04-24 00:00:00-04:00 17.38782189 17.71952764 17.36642158 17.70347786 8070021 0 0 -2017-04-25 00:00:00-04:00 17.82920741 17.90945813 17.72488019 17.74895668 7657755 0 0 -2017-04-26 00:00:00-04:00 17.74895626 18.06728662 17.74895626 17.93888474 5431444 0 0 -2017-04-27 00:00:00-04:00 18.0565831 18.08065959 17.9388823 18.03785896 3114270 0 0 -2017-04-28 00:00:00-04:00 18.04588329 18.12345964 17.82385347 17.95225716 3107144 0 0 -2017-05-01 00:00:00-04:00 18.04053402 18.11275985 17.93353246 18.07530975 6160839 0 0 -2017-05-02 00:00:00-04:00 18.07530863 18.1368352 17.83990521 17.90410614 4057459 0 0 -2017-05-03 00:00:00-04:00 17.87735679 17.87735679 17.65532696 17.70080376 3095741 0 0 -2017-05-04 00:00:00-04:00 17.70347674 17.79710469 17.62322421 17.74092865 3128167 0 0 -2017-05-05 00:00:00-04:00 17.7462806 18.02448466 17.72755282 18.00040817 5035924 0 0 -2017-05-08 00:00:00-04:00 18.02716105 18.18766433 17.93888544 18.091362 7401202 0 0 -2017-05-09 00:00:00-04:00 18.08333434 18.25721301 17.93620833 17.98703384 4447990 0 0 -2017-05-10 00:00:00-04:00 17.96295727 18.03518491 17.80780455 17.91748047 3982987 0 0 -2017-05-11 00:00:00-04:00 17.78640461 17.82118035 17.62857572 17.68207741 4992096 0 0 -2017-05-12 00:00:00-04:00 17.6820799 17.75698191 17.37444878 17.37444878 21295692 0 0 -2017-05-15 00:00:00-04:00 17.39584692 17.80513085 17.35037193 17.72487831 5009200 0 0 -2017-05-16 00:00:00-04:00 17.76500416 17.91213198 17.75697927 17.79977989 4190368 0 0 -2017-05-17 00:00:00-04:00 17.58845318 17.6927804 17.35037355 17.35839844 4093804 0 0 -2017-05-18 00:00:00-04:00 17.26209536 17.34769661 17.10961701 17.29686928 5006706 0 0 -2017-05-19 00:00:00-04:00 17.33699364 17.56169779 17.33164311 17.36641884 2515646 0 0 -2017-05-22 00:00:00-04:00 17.50017425 18.05658419 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17.21127197 16.94109276 17.01866913 23029564 0 0 -2017-06-26 00:00:00-04:00 17.06414209 17.21394611 16.93306586 16.97319031 3548985 0 0 -2017-06-27 00:00:00-04:00 16.94643752 16.95981294 16.56925681 16.57995605 5902860 0 0 -2017-06-28 00:00:00-04:00 16.66555864 16.8260619 16.56658197 16.77256203 5792756 0 0 -2017-06-29 00:00:00-04:00 16.66288612 16.70568675 16.12252589 16.26430321 6447679 0 0 -2017-06-30 00:00:00-04:00 16.26965074 16.43015398 16.26162585 16.3472271 2801061 0 0 -2017-07-03 00:00:00-04:00 16.3525809 16.57728512 16.14660263 16.14660263 2397703 0 0 -2017-07-05 00:00:00-04:00 16.12520025 16.3178049 16.0315741 16.26430321 3322363 0 0 -2017-07-06 00:00:00-04:00 16.13589859 16.29372748 16.09577233 16.27500153 4698486 0 0 -2017-07-07 00:00:00-04:00 16.30442906 16.51575785 16.28035439 16.45423126 1986862 0 0 -2017-07-10 00:00:00-04:00 16.46760503 16.5077313 16.33117827 16.45155525 2479301 0 0 -2017-07-11 00:00:00-04:00 16.43817941 16.58263288 16.34990381 16.45690536 3818010 0 0 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17.17381998 16.85548781 16.99459076 2235576 0 0 -2017-07-28 00:00:00-04:00 16.93573995 17.07751726 16.86351412 17.04541588 1592411 0 0 -2017-07-31 00:00:00-04:00 17.1203172 17.26477067 17.09356636 17.19254303 2475381 0 0 -2017-08-01 00:00:00-04:00 17.26477202 17.28884669 17.10961929 17.24604607 2052781 0 0 -2017-08-02 00:00:00-04:00 17.25139497 17.25139497 16.96784038 17.08019066 2658888 0 0 -2017-08-03 00:00:00-04:00 17.0373923 17.15242058 16.97586753 17.12299538 1309134 0 0 -2017-08-04 00:00:00-04:00 17.17649445 17.26477188 17.13636818 17.22464561 1564261 0 0 -2017-08-07 00:00:00-04:00 17.17649311 17.3048968 17.10961782 17.24069405 1465916 0 0 -2017-08-08 00:00:00-04:00 17.17916747 17.33164583 17.10159111 17.24069405 1850746 0 0 -2017-08-09 00:00:00-04:00 17.08019049 17.20859418 17.0133152 17.15241814 1875688 0 0 -2017-08-10 00:00:00-04:00 17.09356387 17.12031471 16.88223513 16.89828491 1715699 0 0 -2017-08-11 00:00:00-04:00 16.83141257 16.94109031 16.78058705 16.86351395 2807119 0 0 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19.602764 19.09718135 19.51181221 2672428 0 0 -2017-08-30 00:00:00-04:00 19.55193494 19.87561759 19.52518592 19.85689163 3611698 0 0 -2017-08-31 00:00:00-04:00 19.91841989 20.06287154 19.87294309 20.04414558 4385990 0 0 -2017-09-01 00:00:00-04:00 20.05752289 20.08159938 19.91574559 19.99867249 4858475 0 0 -2017-09-05 00:00:00-04:00 19.89434444 20.08962343 19.77129309 20.00937271 5303880 0 0 -2017-09-06 00:00:00-04:00 20.06287137 20.10032146 19.97191959 19.97727013 7478880 0 0 -2017-09-07 00:00:00-04:00 19.92911978 20.15382398 19.87026938 19.92109489 7320316 0 0 -2017-09-08 00:00:00-04:00 19.89702113 20.13242642 19.84886997 19.86492157 4231701 0 0 -2017-09-11 00:00:00-04:00 20.09497084 20.22604889 20.02542119 20.18592262 5352340 0 0 -2017-09-12 00:00:00-04:00 20.19662541 20.40260548 20.13777319 20.39725494 4059953 0 0 -2017-09-13 00:00:00-04:00 20.39992823 20.57915562 20.36782685 20.52565575 4696704 0 0 -2017-09-14 00:00:00-04:00 20.52297904 20.78246077 20.48552895 20.69418335 5563996 0 0 -2017-09-15 00:00:00-04:00 20.66743521 20.71023584 20.5550831 20.59253502 12313125 0 0 -2017-09-18 00:00:00-04:00 20.5524078 20.71290924 20.48820686 20.55775833 3515847 0 0 -2017-09-19 00:00:00-04:00 20.59253092 20.66208057 20.43470204 20.5497303 3352294 0 0 -2017-09-20 00:00:00-04:00 20.54170525 20.63800756 20.47215378 20.55240631 5695123 0 0 -2017-09-21 00:00:00-04:00 20.48553043 20.62998391 20.25012699 20.49088097 2662808 0 0 -2017-09-22 00:00:00-04:00 20.50158066 20.60055733 20.44807897 20.60055733 3148477 0 0 -2017-09-25 00:00:00-04:00 20.52297816 20.58450474 20.21802328 20.31165123 5187718 0 0 -2017-09-26 00:00:00-04:00 20.4641312 20.48553152 20.31165464 20.36248016 8079642 0 0 -2017-09-27 00:00:00-04:00 20.46680257 20.74233408 20.46680257 20.62998199 4617956 0 0 -2017-09-28 00:00:00-04:00 20.62463269 20.68883363 20.4828554 20.63800812 3929539 0 0 -2017-09-29 00:00:00-04:00 20.68080739 20.75838375 20.61393028 20.65405655 4027884 0 0 -2017-10-02 00:00:00-04:00 20.66208253 20.98041467 20.65940817 20.84933662 5107902 0 0 -2017-10-03 00:00:00-04:00 20.86271054 21.04728844 20.83863587 20.93761253 4300828 0 0 -2017-10-04 00:00:00-04:00 20.94028798 20.98308861 20.7904858 20.9242382 4130149 0 0 -2017-10-05 00:00:00-04:00 20.99913936 21.18104293 20.93226406 21.14359283 4806095 0 0 -2017-10-06 00:00:00-04:00 21.03926563 21.14894155 20.95633873 21.14359283 4832820 0 0 -2017-10-09 00:00:00-04:00 21.16766644 21.27199364 21.13289252 21.20244217 2616842 0 0 -2017-10-10 00:00:00-04:00 21.27734431 21.34957014 21.09811511 21.15429115 3383651 0 0 -2017-10-11 00:00:00-04:00 21.15696691 21.26664464 21.09811469 21.22919273 4204977 0 0 -2017-10-12 00:00:00-04:00 21.26129644 21.52345074 21.25059538 21.47797394 4101287 0 0 -2017-10-13 00:00:00-04:00 21.53949956 21.65720219 21.48064915 21.58230019 3269627 0 0 -2017-10-16 00:00:00-04:00 21.60637505 21.79897785 21.58497292 21.69465065 4104850 0 0 -2017-10-17 00:00:00-04:00 21.70802661 21.78827914 21.61707483 21.66522598 3295639 0 0 -2017-10-18 00:00:00-04:00 21.70535197 21.85515415 21.69465091 21.76152802 7860859 0 0 -2017-10-19 00:00:00-04:00 21.74012893 21.86853081 21.64650097 21.83910561 5617088 0 0 -2017-10-20 00:00:00-04:00 21.93540615 22.06915673 21.887255 21.99960709 4080620 0 0 -2017-10-23 00:00:00-04:00 22.04775933 22.24838703 22.0424088 22.12533569 4089172 0 0 -2017-10-24 00:00:00-04:00 22.11463601 22.26978874 22.07718592 22.26978874 3689020 0 0 -2017-10-25 00:00:00-04:00 22.15743579 22.29653873 21.96483297 22.09591103 4773314 0 0 -2017-10-26 00:00:00-04:00 22.13603684 22.25641384 22.1012611 22.19488907 2828142 0 0 -2017-10-27 00:00:00-04:00 22.27246437 22.46506719 22.16546099 22.43831635 3460973 0 0 -2017-10-30 00:00:00-04:00 22.41959121 22.43831534 22.17883542 22.24303818 2744762 0 0 -2017-10-31 00:00:00-04:00 22.25908876 22.28851578 22.04776179 22.14138794 7310339 0 0 -2017-11-01 00:00:00-04:00 22.28851441 22.28851441 21.94610758 22.0424099 3965171 0 0 -2017-11-02 00:00:00-04:00 21.98890864 21.99693534 21.72675434 21.89795685 3752446 0 0 -2017-11-03 00:00:00-04:00 21.84980257 21.89527755 21.52344559 21.63312149 3463467 0 0 -2017-11-06 00:00:00-05:00 21.66790389 21.9969353 21.64115304 21.80700684 4700267 0 0 -2017-11-07 00:00:00-05:00 21.87120382 22.11730831 21.86317893 22.06380844 3366904 0 0 -2017-11-08 00:00:00-05:00 21.95948113 22.15743446 21.90063072 22.01833153 3046212 0 0 -2017-11-09 00:00:00-05:00 21.78292997 21.98623386 21.57695173 21.90330696 4332897 0 0 -2017-11-10 00:00:00-05:00 21.84712887 22.09590954 21.83375345 22.09055901 2737992 0 0 -2017-11-13 00:00:00-05:00 21.90598274 22.04508387 21.78560394 21.88458061 3760998 0 0 -2017-11-14 00:00:00-05:00 21.82840401 21.91668143 21.74815329 21.79897881 4054965 0 0 -2017-11-15 00:00:00-05:00 21.64650261 21.70268048 21.31479682 21.47262573 5543329 0 0 -2017-11-16 00:00:00-05:00 21.60369765 21.93540518 21.58229734 21.75617599 4502864 0 0 -2017-11-17 00:00:00-05:00 21.71605191 21.90598219 21.63847555 21.79897881 3988332 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20.19127245 19.7284905 20.07892036 7828790 0 0 -2017-12-07 00:00:00-05:00 20.28222505 20.58985612 20.2126754 20.300951 7163534 0 0 -2017-12-08 00:00:00-05:00 20.55240659 20.78246131 20.31432698 20.66475868 10242171 0 0 -2017-12-11 00:00:00-05:00 20.72093358 21.22384362 20.62463128 21.21046638 4331828 0 0 -2017-12-12 00:00:00-05:00 21.14359202 21.40039577 20.96436281 21.19976807 7608938 0 0 -2017-12-13 00:00:00-05:00 21.28269712 21.60637801 21.1863948 21.45122528 5821618 0 0 -2017-12-14 00:00:00-05:00 21.61707329 21.71070124 21.32549384 21.33619308 3869676 0 0 -2017-12-15 00:00:00-05:00 21.45924775 21.69732736 21.23186921 21.66522598 5660560 0 0 -2017-12-18 00:00:00-05:00 21.79630461 22.67639542 21.71337772 22.67639542 7497765 0 0 -2017-12-19 00:00:00-05:00 22.59882013 22.59882013 22.15743661 22.2938652 8377173 0 0 -2017-12-20 00:00:00-05:00 22.44366714 22.50786808 22.12266063 22.29653931 5652364 0 0 -2017-12-21 00:00:00-05:00 22.29118754 22.37144008 22.04240869 22.07985878 3014143 0 0 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22.30723748 22.63359451 2417657 0 0 -2018-01-11 00:00:00-05:00 22.87434907 23.23013131 22.81817484 23.22745514 3255018 0 0 -2018-01-12 00:00:00-05:00 23.23815424 23.3612074 23.05357634 23.20605469 5480973 0 0 -2018-01-16 00:00:00-05:00 23.33445961 23.47088639 22.9893766 23.02950287 2471818 0 0 -2018-01-17 00:00:00-05:00 23.22478118 23.65813979 23.02950219 23.56986237 2734785 0 0 -2018-01-18 00:00:00-05:00 23.5591608 23.67953777 23.44145818 23.64476204 3423559 0 0 -2018-01-19 00:00:00-05:00 23.61266229 23.79456585 23.56718731 23.67151451 3065097 0 0 -2018-01-22 00:00:00-05:00 23.69024222 23.92564568 23.47088673 23.7892189 4748371 0 0 -2018-01-23 00:00:00-05:00 23.71966539 23.9416934 23.68221348 23.83736801 2962120 0 0 -2018-01-24 00:00:00-05:00 23.80794198 24.04334724 23.47623622 23.63941383 2822441 0 0 -2018-01-25 00:00:00-05:00 23.75176441 23.86144215 23.47890907 23.65813828 2761866 0 0 -2018-01-26 00:00:00-05:00 24.45797807 24.71745798 20.47215323 22.13335991 127220424 0 0 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18.39364118 17.94958331 18.27058983 6382472 0 0 -2018-02-14 00:00:00-05:00 18.16626234 18.73872208 18.12346172 18.69057274 6240299 0 0 -2018-02-15 00:00:00-05:00 18.83235143 18.85642792 18.29466557 18.4658699 4498944 0 0 -2018-02-16 00:00:00-05:00 18.45784352 18.90190137 18.39631693 18.66917229 4259495 0 0 -2018-02-20 00:00:00-05:00 18.52204379 18.7520985 18.41236605 18.6771965 5299960 0 0 -2018-02-21 00:00:00-05:00 18.72534738 19.01960121 18.6638208 18.70394707 4621876 0 0 -2018-02-22 00:00:00-05:00 18.81095164 19.04635693 18.67184868 18.87515259 5628134 0 0 -2018-02-23 00:00:00-05:00 19.02762707 19.29780807 18.93400093 19.20150757 4686371 0 0 -2018-02-26 00:00:00-05:00 19.2951335 19.49843738 19.16405545 19.36468315 2943947 0 0 -2018-02-27 00:00:00-05:00 19.43423451 19.58136232 19.308507 19.45830917 3983700 0 0 -2018-02-28 00:00:00-05:00 19.76059178 20.17522443 19.73384094 19.87294388 6955441 0 0 -2018-03-01 00:00:00-05:00 19.81676878 20.71023506 19.19615786 20.16184998 20337537 0 0 -2018-03-02 00:00:00-05:00 19.48506072 19.81944263 18.80827383 19.70441437 8609139 0 0 -2018-03-05 00:00:00-05:00 19.59741295 19.8622434 19.33258251 19.79536629 6716704 0 0 -2018-03-06 00:00:00-05:00 19.88096663 20.27687328 19.88096663 20.22337341 6174022 0 0 -2018-03-07 00:00:00-05:00 20.10032283 20.7396615 20.09229793 20.67813492 7766434 0 0 -2018-03-08 00:00:00-05:00 20.73698453 21.35759541 20.60323213 20.71023369 7081579 0 0 -2018-03-09 00:00:00-05:00 20.73966029 21.0365903 20.5363546 20.99378967 4560232 0 0 -2018-03-12 00:00:00-04:00 21.05264022 21.2024424 20.70755905 20.80653572 4801463 0 0 -2018-03-13 00:00:00-04:00 20.8413116 20.86538627 20.51495456 20.68883324 5062292 0 0 -2018-03-14 00:00:00-04:00 20.73163279 20.89213604 20.54973106 20.81723404 3280673 0 0 -2018-03-15 00:00:00-04:00 20.86538776 20.89748914 20.61660889 20.76373672 4101643 0 0 -2018-03-16 00:00:00-04:00 20.83863696 20.99913839 20.75303389 20.85468674 4376725 0 0 -2018-03-19 00:00:00-04:00 20.74768303 20.89213466 20.50425291 20.85735893 4104494 0 0 -2018-03-20 00:00:00-04:00 20.73430934 21.14894015 20.73430934 20.92958832 3084695 0 0 -2018-03-21 00:00:00-04:00 20.91621274 20.91621274 20.30362855 20.33037758 4168988 0 0 -2018-03-22 00:00:00-04:00 20.18057315 20.2420979 19.88899187 19.91841888 5233684 0 0 -2018-03-23 00:00:00-04:00 19.90771823 20.39725285 19.52518698 19.56531143 7074809 0 0 -2018-03-26 00:00:00-04:00 19.89166856 20.16720009 19.67766543 20.09764862 3974792 0 0 -2018-03-27 00:00:00-04:00 20.22872247 20.37852647 19.58671133 19.70976448 4270184 0 0 -2018-03-28 00:00:00-04:00 19.70441445 19.75523996 19.18010409 19.28710747 5426455 0 0 -2018-03-29 00:00:00-04:00 19.30583191 19.6937137 19.19615599 19.58403778 5134982 0 0 -2018-04-02 00:00:00-04:00 19.60811149 19.7552393 18.95272493 19.41015816 4099505 0 0 -2018-04-03 00:00:00-04:00 19.46098367 19.57868629 19.14532954 19.47970963 3132443 0 0 -2018-04-04 00:00:00-04:00 19.18010211 19.58938598 19.06240132 19.51983452 2429416 0 0 -2018-04-05 00:00:00-04:00 19.66964025 19.77931799 19.30048439 19.47703743 2737992 0 0 -2018-04-06 00:00:00-04:00 19.36200833 19.50378562 18.91795049 19.07042885 2893349 0 0 -2018-04-09 00:00:00-04:00 19.12660478 19.62148813 19.0945034 19.23360634 2571233 0 0 -2018-04-10 00:00:00-04:00 19.54123688 19.64556408 19.27640643 19.47168541 2600807 0 0 -2018-04-11 00:00:00-04:00 19.41016061 19.53321378 19.19080694 19.24163246 1615572 0 0 -2018-04-12 00:00:00-04:00 19.30851023 19.54926422 19.26035907 19.44226265 2214196 0 0 -2018-04-13 00:00:00-04:00 19.53321336 19.62951568 19.2683829 19.50111198 2908315 0 0 -2018-04-16 00:00:00-04:00 20.11637459 20.25012701 19.38340977 19.88899422 8523622 0 0 -2018-04-17 00:00:00-04:00 19.98262027 20.26617485 19.84084116 20.16719818 8506518 0 0 -2018-04-18 00:00:00-04:00 20.50960762 20.57113239 20.15650153 20.27420235 6295529 0 0 -2018-04-19 00:00:00-04:00 20.1350972 20.2394244 19.66963905 19.76861572 3905665 0 0 -2018-04-20 00:00:00-04:00 19.63486381 19.81409301 19.22825606 19.43958664 6829658 0 0 -2018-04-23 00:00:00-04:00 19.49041234 19.60543879 19.34863323 19.5519371 3955194 0 0 -2018-04-24 00:00:00-04:00 19.632187 19.79536459 19.27105428 19.37538147 4319357 0 0 -2018-04-25 00:00:00-04:00 19.27105722 19.45028643 18.96877667 19.20150757 2386300 0 0 -2018-04-26 00:00:00-04:00 19.27907877 19.59473469 19.21487784 19.51983452 2144000 0 0 -2018-04-27 00:00:00-04:00 19.60811205 19.75523987 19.40748436 19.4262085 2511014 0 0 -2018-04-30 00:00:00-04:00 19.43690925 19.54123644 18.99552579 19.19882965 3834757 0 0 -2018-05-01 00:00:00-04:00 19.11590504 19.28175883 18.90457626 19.10253143 3664078 0 0 -2018-05-02 00:00:00-04:00 19.06775228 19.1266045 18.82699832 18.91259956 4113758 0 0 -2018-05-03 00:00:00-04:00 18.93400038 19.07845385 18.4926169 19.04635429 5001005 0 0 -2018-05-04 00:00:00-04:00 18.95807639 19.67498956 18.88049821 19.51448631 2996327 0 0 -2018-05-07 00:00:00-04:00 19.52786047 19.8221161 19.34328256 19.39143372 2057770 0 0 -2018-05-08 00:00:00-04:00 19.3994615 19.3994615 19.00355298 19.26303291 5340225 0 0 -2018-05-09 00:00:00-04:00 19.33526015 19.70173984 19.27908229 19.45296097 5428237 0 0 -2018-05-10 00:00:00-04:00 19.50378689 20.17255078 19.49041328 19.97994614 4681738 0 0 -2018-05-11 00:00:00-04:00 19.96924552 19.97727041 19.75524058 19.88096809 3411800 0 0 -2018-05-14 00:00:00-04:00 19.97994406 20.07624637 19.5305357 19.70441437 3034810 0 0 -2018-05-15 00:00:00-04:00 19.50378486 19.86491761 19.40213384 19.77396584 3116764 0 0 -2018-05-16 00:00:00-04:00 19.79536712 20.01204642 19.55193698 19.85421753 2960694 0 0 -2018-05-17 00:00:00-04:00 19.69906453 20.18859917 19.69906453 19.88096809 3250029 0 0 -2018-05-18 00:00:00-04:00 20.36515424 21.57160218 20.30897818 21.08206749 23393726 0 0 -2018-05-21 00:00:00-04:00 21.16499398 21.64382759 20.99914018 21.52612495 13764788 0 0 -2018-05-22 00:00:00-04:00 21.46459935 21.65185163 21.04729052 21.60637665 6473691 0 0 -2018-05-23 00:00:00-04:00 21.51809747 21.93540626 21.34956934 21.90598106 8038665 0 0 -2018-05-24 00:00:00-04:00 21.92470512 22.17348396 21.74280156 22.11463356 4948981 0 0 -2018-05-25 00:00:00-04:00 22.09858282 22.18418406 21.90865438 21.94075394 3519410 0 0 -2018-05-29 00:00:00-04:00 21.88190736 21.88190736 21.51275149 21.54752541 4892326 0 0 -2018-05-30 00:00:00-04:00 21.71872658 21.82840249 21.37899414 21.67860031 6896647 0 0 -2018-05-31 00:00:00-04:00 21.68662599 21.74547821 21.40307141 21.57695007 13683190 0 0 -2018-06-01 00:00:00-04:00 22.07183445 22.97332557 22.07183445 22.69779587 13488637 0 0 -2018-06-04 00:00:00-04:00 22.89574523 23.1338248 22.18685886 22.74594307 7577938 0 0 -2018-06-05 00:00:00-04:00 22.8021215 23.21140359 22.60684253 23.17930222 7295730 0 0 -2018-06-06 00:00:00-04:00 23.20070611 23.40133381 23.13650335 23.36655807 7572593 0 0 -2018-06-07 00:00:00-04:00 23.37458257 23.56183666 23.19268082 23.55648613 5152798 0 0 -2018-06-08 00:00:00-04:00 23.54043808 23.72234166 23.25153294 23.62871552 4860613 0 0 -2018-06-11 00:00:00-04:00 23.62068496 23.62068496 23.30235466 23.44413376 2595106 0 0 -2018-06-12 00:00:00-04:00 23.44413706 23.53241267 23.06962885 23.19535637 3344455 0 0 -2018-06-13 00:00:00-04:00 23.27293101 23.6608128 23.06962714 23.51635933 6625485 0 0 -2018-06-14 00:00:00-04:00 23.58056208 23.89354187 23.31305728 23.51635933 4414496 0 0 -2018-06-15 00:00:00-04:00 23.44413435 23.76781337 23.31305631 23.64476204 5355903 0 0 -2018-06-18 00:00:00-04:00 23.40401064 23.97112167 23.27293259 23.96042061 4744095 0 0 -2018-06-19 00:00:00-04:00 23.65278682 23.83469037 23.37458096 23.80793953 3049775 0 0 -2018-06-20 00:00:00-04:00 23.83469172 24.30550041 23.83469172 23.86144257 4345725 0 0 -2018-06-21 00:00:00-04:00 23.89889128 23.98181817 23.57253609 23.83201599 6051447 0 0 -2018-06-22 00:00:00-04:00 23.74373966 24.10754678 23.22477983 23.74909019 11507477 0 0 -2018-06-25 00:00:00-04:00 23.68221419 23.74641513 22.76734763 22.99472618 3139925 0 0 -2018-06-26 00:00:00-04:00 23.04020149 23.23012994 22.89039749 23.1284771 3782377 0 0 -2018-06-27 00:00:00-04:00 23.21407909 23.25420535 22.45168916 22.47041512 2910096 0 0 -2018-06-28 00:00:00-04:00 22.45704029 22.8796978 22.33933767 22.81014633 3891056 0 0 -2018-06-29 00:00:00-04:00 22.86900237 23.17395731 22.6255722 22.6255722 2902970 0 0 -2018-07-02 00:00:00-04:00 25.33004528 25.34074453 24.09149605 24.6639576 57614352 0 0 -2018-07-03 00:00:00-04:00 24.9100632 25.46112264 24.85656332 25.36749649 16730471 0 0 -2018-07-05 00:00:00-04:00 25.41297204 25.41297204 25.00636428 25.10534096 12780266 0 0 -2018-07-06 00:00:00-04:00 25.09731582 25.1427908 24.81375941 24.96891212 4826762 0 0 -2018-07-09 00:00:00-04:00 25.25514038 25.26049091 24.824458 25.04648781 8951210 0 0 -2018-07-10 00:00:00-04:00 25.3032943 25.72060312 25.01706353 25.24444389 10233976 0 0 -2018-07-11 00:00:00-04:00 25.01171152 25.26584272 25.00903716 25.16954041 5040913 0 0 -2018-07-12 00:00:00-04:00 25.23641958 25.51194929 25.19361895 25.44507217 3680112 0 0 -2018-07-13 00:00:00-04:00 25.44507134 25.50124738 25.18559323 25.38622093 3623813 0 0 -2018-07-16 00:00:00-04:00 25.47449714 25.58952359 25.32201879 25.35411835 4394185 0 0 -2018-07-17 00:00:00-04:00 25.17221493 25.45309515 25.16419004 25.3755188 5000292 0 0 -2018-07-18 00:00:00-04:00 25.37551998 25.44774581 25.28991873 25.43169785 5152442 0 0 -2018-07-19 00:00:00-04:00 25.37284523 25.4129715 25.17221571 25.34342003 4024677 0 0 -2018-07-20 00:00:00-04:00 25.354119 25.38354602 25.22304277 25.23106766 4622232 0 0 -2018-07-23 00:00:00-04:00 25.18024037 25.20431503 24.93145971 25.0438118 6138034 0 0 -2018-07-24 00:00:00-04:00 25.11604338 25.19094539 24.77631269 24.90471458 3951987 0 0 -2018-07-25 00:00:00-04:00 24.92878712 25.09196472 24.83783534 25.09196472 1860723 0 0 -2018-07-26 00:00:00-04:00 24.86191014 25.31131851 24.85388525 25.25246811 5619226 0 0 -2018-07-27 00:00:00-04:00 25.15349226 25.23374298 24.99833954 25.05718994 4565221 0 0 -2018-07-30 00:00:00-04:00 24.97961382 25.03043934 24.62918211 24.80038643 6206804 0 0 -2018-07-31 00:00:00-04:00 24.80305727 24.96623486 24.6826803 24.74955559 5433582 0 0 -2018-08-01 00:00:00-04:00 24.74956074 24.98228984 24.7228099 24.80306244 5767813 0 0 -2018-08-02 00:00:00-04:00 24.74420878 24.95018701 24.74420878 24.88866043 3224018 0 0 -2018-08-03 00:00:00-04:00 24.92076271 24.97961494 24.71478446 24.90203857 3970160 0 0 -2018-08-06 00:00:00-04:00 24.84050997 25.07858958 24.84050997 24.99566269 3299202 0 0 -2018-08-07 00:00:00-04:00 25.15349158 25.29794323 24.94483717 25.06521416 3587112 0 0 -2018-08-08 00:00:00-04:00 25.10801448 25.23374199 24.99298803 25.18559265 2842751 0 0 -2018-08-09 00:00:00-04:00 25.27386872 25.45844664 25.17489205 25.34074402 2119058 0 0 -2018-08-10 00:00:00-04:00 25.0999907 25.43972134 25.0625406 25.2872448 2462910 0 0 -2018-08-13 00:00:00-04:00 25.2738658 25.43169468 25.07056195 25.14546394 3114983 0 0 -2018-08-14 00:00:00-04:00 25.17221493 25.404944 25.09731475 25.3755188 2663877 0 0 -2018-08-15 00:00:00-04:00 25.26316503 25.4290188 25.14546424 25.32736778 4963947 0 0 -2018-08-16 00:00:00-04:00 25.58149829 25.68849984 25.37551825 25.56812286 5335949 0 0 -2018-08-17 00:00:00-04:00 25.48519707 25.64837467 25.38354604 25.4156456 2978154 0 0 -2018-08-20 00:00:00-04:00 25.42634434 25.525321 25.36481776 25.4209938 4022896 0 0 -2018-08-21 00:00:00-04:00 25.47984642 25.58149926 25.39157082 25.43972015 4926889 0 0 -2018-08-22 00:00:00-04:00 25.46647047 25.66174944 25.43971962 25.60824776 9033521 0 0 -2018-08-23 00:00:00-04:00 25.63500215 25.69385256 25.46379964 25.4958992 7901124 0 0 -2018-08-24 00:00:00-04:00 25.44774546 25.64570062 25.30061945 25.50392151 7384454 0 0 -2018-08-27 00:00:00-04:00 25.68047483 25.70187514 25.46646991 25.52264595 4435162 0 0 -2018-08-28 00:00:00-04:00 25.56544804 25.58417399 25.26049314 25.48252296 4899096 0 0 -2018-08-29 00:00:00-04:00 25.53602397 25.80620497 25.50124823 25.69920158 5012763 0 0 -2018-08-30 00:00:00-04:00 25.59487354 25.93192982 25.57079887 25.84900475 3314168 0 0 -2018-08-31 00:00:00-04:00 25.79817415 25.97472898 25.63232221 25.72594833 3288156 0 0 -2018-09-04 00:00:00-04:00 25.69919991 25.81422817 25.44239617 25.72060204 5195557 0 0 -2018-09-05 00:00:00-04:00 25.63232517 25.8383034 25.43437183 25.59754944 3957688 0 0 -2018-09-06 00:00:00-04:00 25.59755095 25.88913044 25.53602437 25.64837646 4332185 0 0 -2018-09-07 00:00:00-04:00 25.54671913 25.8008503 25.4691446 25.7500248 3987620 0 0 -2018-09-10 00:00:00-04:00 25.84097697 25.94798035 25.71525128 25.90785408 5450685 0 0 -2018-09-11 00:00:00-04:00 25.81690186 25.96938021 25.75537528 25.86772919 3643767 0 0 -2018-09-12 00:00:00-04:00 25.81423104 26.10046183 25.70455329 26.00951004 5137833 0 0 -2018-09-13 00:00:00-04:00 25.9372797 26.12988251 25.85435463 26.0416069 11293327 0 0 -2018-09-14 00:00:00-04:00 26.06835963 26.48834283 26.05498421 26.38936615 4975349 0 0 -2018-09-17 00:00:00-04:00 26.35993984 26.42414078 25.99078397 26.03358459 4512485 0 0 -2018-09-18 00:00:00-04:00 25.98008264 26.28771374 25.77945493 25.78212929 6427725 0 0 -2018-09-19 00:00:00-04:00 25.80887945 25.80887945 25.36482159 25.53602409 5717928 0 0 -2018-09-20 00:00:00-04:00 25.63767545 25.72862723 25.46647114 25.66977501 4500370 0 0 -2018-09-21 00:00:00-04:00 25.8142289 25.8142289 25.5681244 25.73397636 9117257 0 0 -2018-09-24 00:00:00-04:00 25.65372565 25.75537668 25.20431727 25.73397636 11023589 0 0 -2018-09-25 00:00:00-04:00 25.86505508 25.94798197 25.74735245 25.93460655 6052872 0 0 -2018-09-26 00:00:00-04:00 25.98007948 26.0228801 25.73130064 25.84365273 4822130 0 0 -2018-09-27 00:00:00-04:00 25.81423053 26.02555931 25.7794548 26.01485825 4451910 0 0 -2018-09-28 00:00:00-04:00 25.95868306 26.04695867 25.82760501 25.98008156 4712739 0 0 -2018-10-01 00:00:00-04:00 25.99613463 26.17268768 25.83028083 25.8704071 2540589 0 0 -2018-10-02 00:00:00-04:00 25.87575455 26.02020619 25.76607681 25.88377762 6606243 0 0 -2018-10-03 00:00:00-04:00 25.97740512 26.08173232 25.68047513 25.84365273 5113959 0 0 -2018-10-04 00:00:00-04:00 26.00415803 26.00415803 25.65907685 25.86505508 7052717 0 0 -2018-10-05 00:00:00-04:00 25.99613097 26.18605941 25.58684706 25.93727875 9323925 0 0 -2018-10-08 00:00:00-04:00 25.7660788 25.97473321 25.38087134 25.56010056 7358443 0 0 -2018-10-09 00:00:00-04:00 25.48787332 25.70455263 24.94483878 25.30062103 14442160 0 0 -2018-10-10 00:00:00-04:00 25.33806795 25.33806795 24.64255513 24.73618126 9549122 0 0 -2018-10-11 00:00:00-04:00 24.73618592 24.92879057 24.31085398 24.54090691 7159614 0 0 -2018-10-12 00:00:00-04:00 24.95821406 25.30864578 24.76560942 25.27922058 6043964 0 0 -2018-10-15 00:00:00-04:00 25.68047631 25.93192952 25.28991834 25.4129715 9367040 0 0 -2018-10-16 00:00:00-04:00 25.52264811 25.75002848 25.35679432 25.58952522 5456743 0 0 -2018-10-17 00:00:00-04:00 25.66174998 25.68047593 25.21234161 25.43972015 4550611 0 0 -2018-10-18 00:00:00-04:00 25.40762071 25.48519707 25.15616568 25.27654266 6164758 0 0 -2018-10-19 00:00:00-04:00 25.32737039 25.47984694 24.96891198 25.08661461 6891302 0 0 -2018-10-22 00:00:00-04:00 25.20699022 25.51997182 25.04113827 25.33539391 5229408 0 0 -2018-10-23 00:00:00-04:00 25.01438984 25.10801598 24.55963091 24.84318733 4885556 0 0 -2018-10-24 00:00:00-04:00 24.76560901 24.83783665 24.03532219 24.03532219 8437035 0 0 -2018-10-25 00:00:00-04:00 24.27340073 24.51148033 24.03532112 24.30282593 4320426 0 0 -2018-10-26 00:00:00-04:00 23.8213152 24.11557084 23.54043498 23.66616249 5816630 0 0 -2018-10-29 00:00:00-04:00 24.04869524 24.34027654 22.93854971 23.2087307 4351783 0 0 -2018-10-30 00:00:00-04:00 23.27293313 23.75979163 23.21140654 23.72234154 3753159 0 0 -2018-10-31 00:00:00-04:00 24.02462061 24.39645263 24.02462061 24.17977333 4539565 0 0 -2018-11-01 00:00:00-04:00 24.31352399 24.72815661 24.2091968 24.38574982 6915532 0 0 -2018-11-02 00:00:00-04:00 24.51682935 24.57033104 23.94436964 24.39110184 2761509 0 0 -2018-11-05 00:00:00-05:00 24.51950527 24.57300514 23.96041916 24.53287888 3831193 0 0 -2018-11-06 00:00:00-05:00 24.5061289 24.80573326 24.3723765 24.63720512 1757033 0 0 -2018-11-07 00:00:00-05:00 25.41297317 26.37599091 25.26316915 26.17803574 9221304 0 0 -2018-11-08 00:00:00-05:00 26.18873448 26.58999168 26.04428283 26.46158981 3516916 0 0 -2018-11-09 00:00:00-05:00 26.30376285 26.44554015 25.88912841 26.22886086 4799682 0 0 -2018-11-12 00:00:00-05:00 26.22351183 26.48566793 25.71792739 26.34923935 4936866 0 0 -2018-11-13 00:00:00-05:00 26.42413862 27.71351333 26.25561229 27.17047882 15506143 0 0 -2018-11-14 00:00:00-05:00 27.60651249 28.36890249 27.03137658 28.27260017 21665200 0 0 -2018-11-15 00:00:00-05:00 28.52405576 28.68188284 27.95426856 28.10407257 50323254 0 0 -2018-11-16 00:00:00-05:00 28.16024439 28.20571937 27.92751534 27.94623947 15978628 0 0 -2018-11-19 00:00:00-05:00 27.94356807 28.05859452 27.47275938 27.63326263 16403010 0 0 -2018-11-20 00:00:00-05:00 27.32028223 27.6145379 27.19188035 27.45671082 10983681 0 0 -2018-11-21 00:00:00-05:00 27.58511257 27.66803947 27.23468086 27.55033684 6005838 0 0 -2018-11-23 00:00:00-05:00 27.36575754 27.97834352 27.36308137 27.79911613 6246713 0 0 -2018-11-26 00:00:00-05:00 28.06929755 28.27260143 28.06394701 28.25654984 13889502 0 0 -2018-11-27 00:00:00-05:00 28.16827281 28.2137478 27.94356862 27.99707031 6362161 0 0 -2018-11-28 00:00:00-05:00 28.18432179 28.58557901 28.00509441 28.51602936 13814674 0 0 -2018-11-29 00:00:00-05:00 28.25119686 28.58023006 28.08802107 28.35552406 11596914 0 0 -2018-11-30 00:00:00-05:00 28.62303271 28.64443121 27.97299479 28.21642494 21957029 0 0 -2018-12-03 00:00:00-05:00 28.44647675 28.54277907 28.01579433 28.22979927 12675863 0 0 -2018-12-04 00:00:00-05:00 28.08802074 28.18699741 27.66268705 27.94356728 12579655 0 0 -2018-12-06 00:00:00-05:00 27.78039094 28.27259995 27.67338756 28.25119781 17103542 0 0 -2018-12-07 00:00:00-05:00 28.18164744 28.30737496 27.68676227 27.82051468 12979807 0 0 -2018-12-10 00:00:00-05:00 27.88471699 28.0398697 27.50218392 27.84994125 12304573 0 0 -2018-12-11 00:00:00-05:00 28.16826928 28.35552154 27.80178784 28.01579094 14477080 0 0 -2018-12-12 00:00:00-05:00 28.40902667 28.41972773 28.02114486 28.09069633 14229791 0 0 -2018-12-13 00:00:00-05:00 28.14152252 28.35017693 27.99974522 28.22177505 13518212 0 0 -2018-12-14 00:00:00-05:00 28.06126922 28.33145021 28.06126922 28.29934883 13998537 0 0 -2018-12-17 00:00:00-05:00 28.2485218 28.28329753 28.03719305 28.10941887 16345285 0 0 -2018-12-18 00:00:00-05:00 28.22177468 28.48660332 28.06126961 28.31807518 20432676 0 0 -2018-12-19 00:00:00-05:00 28.35552469 28.59628048 27.98369449 28.04789543 21565429 0 0 -2018-12-20 00:00:00-05:00 28.11209567 28.25922349 27.36575729 27.6626873 21679096 0 0 -2018-12-21 00:00:00-05:00 27.681415 27.79911946 26.83342549 27.48078728 29978590 0 0 -2018-12-24 00:00:00-05:00 21.80165581 22.07985988 20.08962349 21.53414917 7411179 0 0 -2018-12-26 00:00:00-05:00 21.19441807 21.71605229 21.0954414 21.70267868 5595709 0 0 -2018-12-27 00:00:00-05:00 21.54752226 21.82840247 21.19976675 21.40039444 6841061 0 0 -2018-12-28 00:00:00-05:00 22.22324161 22.46479915 21.62901251 21.94786644 8881065 0 1.806 -2018-12-31 00:00:00-05:00 21.9816866 23.60977936 21.9816866 23.60977936 11479901 0 0 -2019-01-02 00:00:00-05:00 23.30058683 23.72089546 22.25222965 22.76433182 12052465 0 0 -2019-01-03 00:00:00-05:00 22.49378593 22.70635461 21.63384282 21.80293274 13618830 0 0 -2019-01-04 00:00:00-05:00 22.05898395 22.68703066 21.67249296 22.23290443 17573116 0 0 -2019-01-07 00:00:00-05:00 22.18942412 22.88994028 21.99617772 22.37783813 9718603 0 0 -2019-01-08 00:00:00-05:00 22.77399069 23.10250755 22.41165621 22.64354897 14375870 0 0 -2019-01-09 00:00:00-05:00 22.74017474 22.89477114 22.60007187 22.67737007 6509914 0 0 -2019-01-10 00:00:00-05:00 22.67737024 22.89477131 21.69181775 21.83192062 7030785 0 0 -2019-01-11 00:00:00-05:00 21.71597397 22.08797147 21.45026174 21.48407936 5326903 0 0 -2019-01-14 00:00:00-05:00 21.2908278 21.37778894 20.65795061 20.6772747 7232426 0 0 -2019-01-15 00:00:00-05:00 20.79805821 21.1120816 20.52268302 20.7787323 5581222 0 0 -2019-01-16 00:00:00-05:00 20.67727793 20.8753549 20.30044808 20.59031677 4849239 0 0 -2019-01-17 00:00:00-05:00 20.63379392 21.12173951 20.56615869 21.11690712 6989155 0 0 -2019-01-18 00:00:00-05:00 21.15072927 21.15555984 20.82704297 20.94298935 6425075 0 0 -2019-01-22 00:00:00-05:00 21.19903997 21.57103741 20.92366481 21.2859993 6256186 0 0 -2019-01-23 00:00:00-05:00 21.82708804 22.17009846 21.45992296 21.55654526 6218107 0 0 -2019-01-24 00:00:00-05:00 21.69664773 21.80776355 21.35846789 21.72080421 7007899 0 0 -2019-01-25 00:00:00-05:00 21.85124499 22.22324246 21.84641442 22.17976189 5165709 0 0 -2019-01-28 00:00:00-05:00 22.17492965 22.89476988 22.03965736 22.65804291 11741718 0 0 -2019-01-29 00:00:00-05:00 22.75466893 22.86578475 22.46480022 22.63389015 7021315 0 0 -2019-01-30 00:00:00-05:00 22.87544323 23.18946838 22.78365151 23.18946838 7855697 0 0 -2019-01-31 00:00:00-05:00 23.10734139 23.91414099 23.04936729 23.47450829 7381190 0 0 -2019-02-01 00:00:00-05:00 23.66291849 24.03491774 23.43102574 23.9866066 3583363 0 0 -2019-02-04 00:00:00-05:00 24.16052865 25.0301329 24.16052865 24.55668259 6294659 0 0 -2019-02-05 00:00:00-05:00 24.76925261 24.92868139 24.46489219 24.67263031 5435023 0 0 -2019-02-06 00:00:00-05:00 24.71128173 25.07844866 24.56151589 24.60982704 5595823 0 0 -2019-02-07 00:00:00-05:00 24.44556334 24.54701801 24.04941125 24.36826515 3516675 0 0 -2019-02-08 00:00:00-05:00 24.17985416 24.92385093 23.98660956 24.87553978 3539167 0 0 -2019-02-11 00:00:00-05:00 25.05912226 25.15574456 24.6629701 25.13158989 5366757 0 0 -2019-02-12 00:00:00-05:00 25.32483447 25.98669881 25.22337979 25.89490891 6666767 0 0 -2019-02-13 00:00:00-05:00 26.03984079 26.38768175 25.78862212 26.06399727 3504245 0 0 -2019-02-14 00:00:00-05:00 25.9335582 26.37319274 25.85626 26.27173805 5128024 0 0 -2019-02-15 00:00:00-05:00 26.46981141 27.00123579 26.08815103 26.57126427 4529613 0 0 -2019-02-19 00:00:00-05:00 26.614748 26.80316384 26.35386635 26.59059334 2480850 0 0 -2019-02-20 00:00:00-05:00 26.70170769 26.95775694 26.39251491 26.8321476 2726883 0 0 -2019-02-21 00:00:00-05:00 26.84181055 27.09302923 26.70653826 26.8321476 3834131 0 0 -2019-02-22 00:00:00-05:00 26.97225216 27.14134026 26.83697986 27.14134026 3644723 0 0 -2019-02-25 00:00:00-05:00 27.27661383 27.4940149 27.20414802 27.36357498 4612085 0 0 -2019-02-26 00:00:00-05:00 27.19931679 27.59063835 26.87079809 27.4070549 4619188 0 0 -2019-02-27 00:00:00-05:00 27.31526232 27.44087166 27.1461724 27.17515945 3335948 0 0 -2019-02-28 00:00:00-05:00 27.1510021 27.29593735 26.65339534 26.96741867 8152633 0 0 -2019-03-01 00:00:00-05:00 27.866013 28.49889208 27.14617276 27.36840439 6934700 0 0 -2019-03-04 00:00:00-05:00 27.57131371 27.60513315 26.32971194 26.73552704 5875989 0 0 -2019-03-05 00:00:00-05:00 26.69204671 26.97708485 26.31038809 26.62924385 5502302 0 0 -2019-03-06 00:00:00-05:00 26.48430795 26.48430795 25.53257308 25.95771408 4962687 0 0 -2019-03-07 00:00:00-05:00 25.90457186 26.2862323 25.51808084 25.66784668 3652220 0 0 -2019-03-08 00:00:00-05:00 25.40213326 25.50358613 24.91901815 25.29101563 3776717 0 0 -2019-03-11 00:00:00-04:00 25.31517018 26.19926792 25.24753312 26.16061974 3516872 0 0 -2019-03-12 00:00:00-04:00 26.20410084 26.78866879 26.17511379 26.42633247 4896000 0 0 -2019-03-13 00:00:00-04:00 26.64373572 26.8418127 26.45532169 26.54711342 4083913 0 0 -2019-03-14 00:00:00-04:00 27.0543801 28.02060842 26.90461429 27.6244545 7633537 0 0 -2019-03-15 00:00:00-04:00 27.986792 29.11727789 27.97229666 29.0979538 11238800 0 0 -2019-03-18 00:00:00-04:00 29.15592519 29.54724854 28.86122594 29.22356224 5256664 0 0 -2019-03-19 00:00:00-04:00 29.46995026 29.55690959 29.14143339 29.19940567 3981711 0 0 -2019-03-20 00:00:00-04:00 29.17041898 29.35400423 28.62450105 28.80808449 5130984 0 0 -2019-03-21 00:00:00-04:00 28.74045062 29.44096497 28.69697005 29.32984734 3257028 0 0 -2019-03-22 00:00:00-04:00 28.93852389 29.55207886 28.75010988 28.76943398 2970352 0 0 -2019-03-25 00:00:00-04:00 28.74528007 28.74528007 27.59546828 27.67759705 3709832 0 0 -2019-03-26 00:00:00-04:00 27.98196083 28.49406117 27.72107918 28.4505806 3527921 0 0 -2019-03-27 00:00:00-04:00 28.50855532 28.93369634 27.74523627 27.79837799 3628742 0 0 -2019-03-28 00:00:00-04:00 27.75972769 27.96746579 27.37323671 27.49884605 3301224 0 0 -2019-03-29 00:00:00-04:00 27.64377896 28.40226737 27.60996134 28.35395622 3746924 0 0 -2019-04-01 00:00:00-04:00 28.8805548 29.19940874 28.75494363 29.19457817 3523383 0 0 -2019-04-02 00:00:00-04:00 29.08828919 29.61971539 28.86122518 29.60038948 4647599 0 0 -2019-04-03 00:00:00-04:00 29.59556076 29.78397477 29.3008615 29.75498772 4143695 0 0 -2019-04-04 00:00:00-04:00 29.72116884 29.75498646 28.61000709 29.15109444 4261680 0 0 -2019-04-05 00:00:00-04:00 29.19940823 30.14148197 29.19940823 30.04969025 4349676 0 0 -2019-04-08 00:00:00-04:00 30.03036385 30.2719214 29.692184 30.18979263 2637506 0 0 -2019-04-09 00:00:00-04:00 30.0931689 30.14631244 29.61971857 29.82262611 2530570 0 0 -2019-04-10 00:00:00-04:00 29.84678107 30.74054368 29.82745698 30.5762825 3311089 0 0 -2019-04-11 00:00:00-04:00 30.66324204 30.85648662 30.32023163 30.85165787 3843207 0 0 -2019-04-12 00:00:00-04:00 30.93862059 31.19950042 30.51831196 30.6825695 2378452 0 0 -2019-04-15 00:00:00-04:00 30.72604811 30.90479916 30.46999704 30.86131859 2291048 0 0 -2019-04-16 00:00:00-04:00 31.09321466 31.13669524 30.28158271 30.33472443 2220809 0 0 -2019-04-17 00:00:00-04:00 30.48932348 30.67773751 30.19945477 30.31540298 2039687 0 0 -2019-04-18 00:00:00-04:00 30.31056878 30.43134755 29.91441666 30.36371231 2066915 0 0 -2019-04-22 00:00:00-04:00 30.32989323 30.55695724 30.20428208 30.55695724 2132221 0 0 -2019-04-23 00:00:00-04:00 30.54246547 31.51835317 30.47965898 31.4024086 3039407 0 0 -2019-04-24 00:00:00-04:00 31.39274129 32.10292035 31.28645786 32.00629807 3057953 0 0 -2019-04-25 00:00:00-04:00 31.98214049 32.47008794 31.70193477 32.36863327 3190736 0 0 -2019-04-26 00:00:00-04:00 31.9145062 32.19954432 31.29612064 31.57632637 3018098 0 0 -2019-04-29 00:00:00-04:00 31.59565181 31.77923524 31.41206837 31.66811943 2449085 0 0 -2019-04-30 00:00:00-04:00 31.71642574 32.61018818 31.71642574 32.56670761 2385752 0 0 -2019-05-01 00:00:00-04:00 32.80343551 33.43148219 32.56671036 33.13195419 4112718 0 0 -2019-05-02 00:00:00-04:00 33.06432308 33.3107094 32.09809282 32.91938782 3875958 0 0 -2019-05-03 00:00:00-04:00 33.20442349 33.47979684 32.97252708 33.18992996 1860144 0 0 -2019-05-06 00:00:00-04:00 32.48457813 33.09813122 32.41211051 32.94353485 1331183 0 0 -2019-05-07 00:00:00-04:00 32.63434517 32.98701852 31.99180497 32.60535812 2800279 0 0 -2019-05-08 00:00:00-04:00 32.38795936 32.5135687 32.1319083 32.41694641 2022128 0 0 -2019-05-09 00:00:00-04:00 31.90967586 32.50873547 31.71642765 32.41211319 2736354 0 0 -2019-05-10 00:00:00-04:00 32.25751847 32.55705011 31.66811996 32.4459343 1412076 0 0 -2019-05-13 00:00:00-04:00 31.44588744 31.83720897 30.77435655 30.93861771 5181295 0 0 -2019-05-14 00:00:00-04:00 31.04490387 32.23819449 30.96760748 31.95798874 1958400 0 0 -2019-05-15 00:00:00-04:00 31.72126155 32.98219097 31.64396154 32.80343628 2051328 0 0 -2019-05-16 00:00:00-04:00 32.87590209 33.92908972 32.80826686 33.72135162 2041858 0 0 -2019-05-17 00:00:00-04:00 33.33002826 34.08368421 33.09813188 33.54259872 2938389 0 0 -2019-05-20 00:00:00-04:00 33.13678775 33.31554246 32.40728629 32.85175323 2650134 0 0 -2019-05-21 00:00:00-04:00 33.29621426 33.89044519 33.05465671 33.64888763 2322024 0 0 -2019-05-22 00:00:00-04:00 33.9435862 34.00639269 33.17543661 33.33486176 3092283 0 0 -2019-05-23 00:00:00-04:00 32.67782867 32.67782867 31.1850064 31.74058914 6202520 0 0 -2019-05-24 00:00:00-04:00 31.89518928 32.46043322 31.54734461 31.94349861 3735678 0 0 -2019-05-28 00:00:00-04:00 31.97248327 32.78894587 31.88552211 32.08359909 3530881 0 0 -2019-05-29 00:00:00-04:00 31.77923767 31.99180818 31.25747437 31.9821434 4759665 0 0 -2019-05-30 00:00:00-04:00 32.13674445 32.51840125 31.65362932 32.08359909 4113113 0 0 -2019-05-31 00:00:00-04:00 29.30569076 30.03036154 27.59063699 28.76943398 15620241 0 0 -2019-06-03 00:00:00-04:00 28.69696909 28.75977194 26.78866887 26.98674583 6365687 0 0 -2019-06-04 00:00:00-04:00 27.5326652 27.90466267 27.02056486 27.86601448 5077516 0 0 -2019-06-05 00:00:00-04:00 27.91915447 28.04476381 25.97220608 26.32971001 7388293 0 0 -2019-06-06 00:00:00-04:00 26.54711208 26.58576209 25.17989919 25.71615601 7644388 0 0 -2019-06-07 00:00:00-04:00 25.79828543 26.64373317 25.6098696 26.23308754 4520538 0 0 -2019-06-10 00:00:00-04:00 26.47947374 26.82731652 26.10264573 26.18477631 3659520 0 0 -2019-06-11 00:00:00-04:00 26.31521747 26.51812501 25.89007647 26.2524128 3505429 0 0 -2019-06-12 00:00:00-04:00 26.18960602 26.5326164 25.58088163 25.61953163 2527018 0 0 -2019-06-13 00:00:00-04:00 25.6002084 26.09781704 25.37314619 26.00602531 4077796 0 0 -2019-06-14 00:00:00-04:00 25.58088488 25.8514295 24.52286655 24.79340935 3634463 0 0 -2019-06-17 00:00:00-04:00 24.87554 24.96249934 24.43107488 24.46006012 3298461 0 0 -2019-06-18 00:00:00-04:00 24.73543467 25.43595085 24.5953318 24.8852005 4490943 0 0 -2019-06-19 00:00:00-04:00 25.12675878 25.87075374 24.90452531 25.80794907 4167568 0 0 -2019-06-20 00:00:00-04:00 26.57126832 27.66310611 25.75963811 26.28623199 4939406 0 0 -2019-06-21 00:00:00-04:00 26.12197071 26.38285235 25.84659737 26.08332253 4973933 0 0 -2019-06-24 00:00:00-04:00 26.0833245 26.23309033 25.63402881 25.7499752 3584744 0 0 -2019-06-25 00:00:00-04:00 25.74997501 26.0253502 25.21855056 25.27169228 4532178 0 0 -2019-06-26 00:00:00-04:00 25.58571609 25.96737469 25.49875494 25.71615601 1905523 0 0 -2019-06-27 00:00:00-04:00 25.33932552 25.5374043 24.43107309 25.22337914 9568655 0 0 -2019-06-28 00:00:00-04:00 25.44078191 25.48909487 24.46972292 24.54219055 13893669 0 0 -2019-07-01 00:00:00-04:00 25.22338044 25.23787396 24.75476067 24.90935707 6958376 0 0 -2019-07-02 00:00:00-04:00 24.74509603 25.21371576 24.44556439 25.20888519 4973933 0 0 -2019-07-03 00:00:00-04:00 25.05911934 25.585715 24.74992657 25.56638908 5140454 0 0 -2019-07-05 00:00:00-04:00 25.18473186 25.43595055 25.02047251 25.30067825 4130870 0 0 -2019-07-08 00:00:00-04:00 25.0253016 25.1605739 24.62914768 24.7789135 5919197 0 0 -2019-07-09 00:00:00-04:00 24.61948901 24.79824008 24.46006022 24.68712425 5298492 0 0 -2019-07-10 00:00:00-04:00 24.89486094 25.60504006 24.70644692 25.26202965 9092176 0 0 -2019-07-11 00:00:00-04:00 25.24753469 25.83693504 25.20405593 25.83693504 5895324 0 0 -2019-07-12 00:00:00-04:00 26.01568627 26.24757904 25.71615464 25.98186684 7217037 0 0 -2019-07-15 00:00:00-04:00 26.08815284 26.70653841 26.06399818 26.67755318 7950993 0 0 -2019-07-16 00:00:00-04:00 26.82248658 27.53266386 26.63407256 27.05921173 7118387 0 0 -2019-07-17 00:00:00-04:00 27.05437906 27.30559772 26.75001687 27.29593658 3554359 0 0 -2019-07-18 00:00:00-04:00 27.17032935 27.91915669 26.93843656 27.72591209 4343560 0 0 -2019-07-19 00:00:00-04:00 27.99645309 28.5858516 27.3442499 27.44087219 5908543 0 0 -2019-07-22 00:00:00-04:00 27.56648075 27.72107714 27.24279445 27.25728798 3269853 0 0 -2019-07-23 00:00:00-04:00 27.39256045 27.39256045 26.39734507 26.79349899 4539084 0 0 -2019-07-24 00:00:00-04:00 26.65339765 27.21380914 26.65339765 27.16066742 2860850 0 0 -2019-07-25 00:00:00-04:00 27.20897936 27.37323872 26.46015202 26.60508728 3693851 0 0 -2019-07-26 00:00:00-04:00 26.63407525 26.80799575 26.46981588 26.70654106 3268669 0 0 -2019-07-29 00:00:00-04:00 26.96742075 27.1703283 26.3055546 27.03988838 3832947 0 0 -2019-07-30 00:00:00-04:00 26.92394218 27.72591309 26.80316522 27.64861488 4031431 0 0 -2019-07-31 00:00:00-04:00 27.67276511 28.0157755 27.32975473 27.89499855 5389447 0 0 -2019-08-01 00:00:00-04:00 27.99162458 28.57619256 27.68243177 27.87084579 4970579 0 0 -2019-08-02 00:00:00-04:00 26.49880046 26.81282381 24.85621243 25.3200016 20770166 0 0 -2019-08-05 00:00:00-04:00 24.64364265 24.68229266 23.32474271 24.35860634 11192632 0 0 -2019-08-06 00:00:00-04:00 24.59533185 24.89003112 23.8899851 24.52769661 7244461 0 0 -2019-08-07 00:00:00-04:00 24.102556 24.43590529 23.80302615 24.33928299 3653601 0 0 -2019-08-08 00:00:00-04:00 24.45522834 25.14125098 24.45522834 24.79823875 4783539 0 0 -2019-08-09 00:00:00-04:00 24.77891571 24.77891571 24.05907542 24.36343765 2521691 0 0 -2019-08-12 00:00:00-04:00 24.17985174 24.27164345 23.75954315 23.89481544 2621131 0 0 -2019-08-13 00:00:00-04:00 23.79336341 24.59533248 23.53731232 23.92380333 7019934 0 0 -2019-08-14 00:00:00-04:00 23.52281827 23.6677517 22.68702984 23.47933769 8122644 0 0 -2019-08-15 00:00:00-04:00 23.25710437 23.368222 22.33918899 22.65804291 5727422 0 0 -2019-08-16 00:00:00-04:00 22.89477177 23.76437607 22.82713471 23.39720917 4909219 0 0 -2019-08-19 00:00:00-04:00 23.82234854 23.97694493 23.56629747 23.6967392 3959219 0 0 -2019-08-20 00:00:00-04:00 23.53731138 23.81751894 23.28126212 23.41653442 3635647 0 0 -2019-08-21 00:00:00-04:00 23.51315789 24.03009064 23.45518378 23.66292191 2741484 0 0 -2019-08-22 00:00:00-04:00 23.88032573 23.94313041 23.39238002 23.6919117 3195865 0 0 -2019-08-23 00:00:00-04:00 23.068692 23.16048373 22.09280246 22.13145065 7242291 0 0 -2019-08-26 00:00:00-04:00 22.22324298 22.74500629 22.19908831 22.42131996 4026696 0 0 -2019-08-27 00:00:00-04:00 22.68220203 22.78848728 21.87056999 22.02999878 3852085 0 0 -2019-08-28 00:00:00-04:00 21.99134459 22.03965573 21.4261008 21.88505936 3722656 0 0 -2019-08-29 00:00:00-04:00 22.10246521 22.67253966 22.05415225 22.59523964 6461575 0 0 -2019-08-30 00:00:00-04:00 24.56634427 25.70166067 23.98660692 24.89485931 31586941 0 0 -2019-09-03 00:00:00-04:00 24.65330621 25.0639537 24.44556628 24.67263031 8237867 0 0 -2019-09-04 00:00:00-04:00 24.88037075 25.06395421 24.2861398 24.73543549 5511378 0 0 -2019-09-05 00:00:00-04:00 24.90935534 25.84659663 24.69195428 25.56638908 8994907 0 0 -2019-09-06 00:00:00-04:00 25.60504069 25.9866993 25.24753493 25.77896118 6831315 0 0 -2019-09-09 00:00:00-04:00 26.08815146 26.30555251 25.67267345 26.22825432 7168304 0 0 -2019-09-10 00:00:00-04:00 26.03017972 26.98191459 25.7258175 26.80316353 6298408 0 0 -2019-09-11 00:00:00-04:00 26.62924245 26.81282407 26.42633309 26.71620178 5916238 0 0 -2019-09-12 00:00:00-04:00 26.83697938 27.02056282 26.11230859 26.16061974 5659748 0 0 -2019-09-13 00:00:00-04:00 26.11230871 26.40217739 25.81760764 25.85142708 2785284 0 0 -2019-09-16 00:00:00-04:00 25.60504251 26.35386807 25.5470684 26.13646698 3021058 0 0 -2019-09-17 00:00:00-04:00 26.08815184 26.13646298 25.6581803 26.01568604 2821587 0 0 -2019-09-18 00:00:00-04:00 26.05433497 26.05433497 25.12675663 25.50841522 3759551 0 0 -2019-09-19 00:00:00-04:00 25.48909057 25.91906027 25.39246648 25.62436104 4931908 0 0 -2019-09-20 00:00:00-04:00 25.69683209 25.7692979 24.9818224 25.04945946 6002458 0 0 -2019-09-23 00:00:00-04:00 25.03013197 25.60503876 24.64847339 25.57605171 4902708 0 0 -2019-09-24 00:00:00-04:00 25.63402778 25.91906409 24.95766808 25.21854973 6605999 0 0 -2019-09-25 00:00:00-04:00 25.21371434 26.09781387 25.14607911 25.96254158 6056124 0 0 -2019-09-26 00:00:00-04:00 25.6050387 25.6050387 24.79340676 25.19439125 6061253 0 0 -2019-09-27 00:00:00-04:00 25.32483569 25.54706734 24.34411374 24.61465836 4381441 0 0 -2019-09-30 00:00:00-04:00 24.61465618 25.13641945 24.57600617 25.05428886 2619552 0 0 -2019-10-01 00:00:00-04:00 25.18473267 25.55189776 24.83205928 24.94800568 3819136 0 0 -2019-10-02 00:00:00-04:00 24.88037074 24.88520131 24.23299626 24.53252792 3143581 0 0 -2019-10-03 00:00:00-04:00 24.54702022 24.81756482 23.8609994 24.667799 2660788 0 0 -2019-10-04 00:00:00-04:00 24.74992772 24.88036945 24.25232091 24.28613853 3822885 0 0 -2019-10-07 00:00:00-04:00 24.20401003 24.67262979 24.15086649 24.40691757 3134900 0 0 -2019-10-08 00:00:00-04:00 24.24749001 24.33444935 23.57113034 23.58079147 3017112 0 0 -2019-10-09 00:00:00-04:00 23.74021906 24.02525535 23.55180506 23.89481544 2605938 0 0 -2019-10-10 00:00:00-04:00 23.87065959 23.95762074 23.61944091 23.67258263 4467661 0 0 -2019-10-11 00:00:00-04:00 24.09289436 24.82239578 24.04458321 24.30063248 3014744 0 0 -2019-10-14 00:00:00-04:00 24.27647675 24.51803431 24.20884151 24.48421669 1551173 0 0 -2019-10-15 00:00:00-04:00 24.39725219 24.99631362 24.32478456 24.70161438 2061193 0 0 -2019-10-16 00:00:00-04:00 24.60016356 24.60499413 24.23782723 24.41657829 1980103 0 0 -2019-10-17 00:00:00-04:00 24.56151379 24.75959077 24.30546453 24.33444977 1985824 0 0 -2019-10-18 00:00:00-04:00 24.32962069 24.36826888 23.91414262 24.09289551 3410133 0 0 -2019-10-21 00:00:00-04:00 24.10738688 24.40208798 23.95762287 24.15086746 2371743 0 0 -2019-10-22 00:00:00-04:00 24.30546493 24.46005951 24.04941385 24.14603615 2048763 0 0 -2019-10-23 00:00:00-04:00 24.1025555 24.35377601 23.79819327 24.30063248 2469210 0 0 -2019-10-24 00:00:00-04:00 24.47455622 24.75959255 24.12188282 24.64364433 3018493 0 0 -2019-10-25 00:00:00-04:00 24.69195378 25.27652171 24.61465559 24.96732712 3189946 0 0 -2019-10-28 00:00:00-04:00 25.08810663 25.33932531 24.86587501 24.92384911 3117143 0 0 -2019-10-29 00:00:00-04:00 24.95283658 25.73547968 24.90935601 25.72581673 5226082 0 0 -2019-10-30 00:00:00-04:00 25.70649309 26.16061928 25.47459669 25.96737289 3210860 0 0 -2019-10-31 00:00:00-04:00 25.91906395 25.99153158 25.17506901 25.55189705 3032698 0 0 -2019-11-01 00:00:00-04:00 25.69199836 25.79345304 25.50358436 25.72098541 2398971 0 0 -2019-11-04 00:00:00-05:00 26.01568902 26.15095951 25.83693613 26.01085663 1979905 0 0 -2019-11-05 00:00:00-05:00 26.04467213 26.49880016 25.86592107 26.29589081 4079177 0 0 -2019-11-06 00:00:00-05:00 26.57126781 26.57126781 25.94322107 26.24758148 2791006 0 0 -2019-11-07 00:00:00-05:00 26.46015241 26.54711357 26.1509596 26.23792076 2908202 0 0 -2019-11-08 00:00:00-05:00 26.26207543 26.37319307 25.87075385 26.23792076 1962346 0 0 -2019-11-11 00:00:00-05:00 26.0011909 26.50846064 25.91906214 26.33937073 1830944 0 0 -2019-11-12 00:00:00-05:00 26.46981228 26.94809496 26.40217704 26.77900505 5463434 0 0 -2019-11-13 00:00:00-05:00 26.57126833 26.76451475 26.34903668 26.46498489 2171878 0 0 -2019-11-14 00:00:00-05:00 26.28139939 27.14617307 26.17028176 26.59542274 3357060 0 0 -2019-11-15 00:00:00-05:00 26.88529131 26.9819136 26.64856433 26.88046074 2799292 0 0 -2019-11-18 00:00:00-05:00 26.88529164 27.00123802 26.6050859 26.84664345 1931764 0 0 -2019-11-19 00:00:00-05:00 26.89012208 27.38289831 26.70653863 27.15583611 2811525 0 0 -2019-11-20 00:00:00-05:00 26.96742005 27.1896535 26.54228088 26.70170784 2832242 0 0 -2019-11-21 00:00:00-05:00 26.68721583 26.86113633 25.9383885 25.94805145 3173373 0 0 -2019-11-22 00:00:00-05:00 25.9432208 26.08815424 25.60504095 25.89974022 2850788 0 0 -2019-11-25 00:00:00-05:00 26.08815372 26.86596809 26.08815372 26.60025406 3744557 0 0 -2019-11-26 00:00:00-05:00 26.89495367 27.02539357 25.67750484 25.69683075 8725790 0 0 -2019-11-27 00:00:00-05:00 24.66296847 25.19922347 24.03491993 24.3102951 25892863 0 0 -2019-11-29 00:00:00-05:00 24.30063386 24.37793207 23.40204248 23.42619896 6382852 0 0 -2019-12-02 00:00:00-05:00 23.35855952 23.51315592 22.8657833 23.05419731 7351990 0 0 -2019-12-03 00:00:00-05:00 22.60973418 22.94308347 22.43098129 22.89960289 7722322 0 0 -2019-12-04 00:00:00-05:00 23.1411573 23.23777958 22.967235 23.03487206 4132843 0 0 -2019-12-05 00:00:00-05:00 23.00105482 23.0590271 22.52277032 22.8706131 4072469 0 0 -2019-12-06 00:00:00-05:00 22.94791345 23.52765086 22.72085124 22.76433182 5935771 0 0 -2019-12-09 00:00:00-05:00 22.61939551 23.17497641 22.44064264 23.02038002 7451034 0 0 -2019-12-10 00:00:00-05:00 22.9044323 23.17980747 22.76916181 22.88027763 6115511 0 0 -2019-12-11 00:00:00-05:00 22.86095199 23.06869009 22.65804264 23.049366 4372957 0 0 -2019-12-12 00:00:00-05:00 22.860953 23.9914389 22.7450048 23.9141407 5211877 0 0 -2019-12-13 00:00:00-05:00 23.8803229 24.19434626 23.68707832 23.8416729 5212666 0 0 -2019-12-16 00:00:00-05:00 24.15086511 24.2281633 23.80302414 23.90930939 6638750 0 0 -2019-12-17 00:00:00-05:00 24.0204249 24.25715005 23.94312671 24.12187958 4669302 0 0 -2019-12-18 00:00:00-05:00 24.15569878 24.44073509 23.97211351 24.15569878 4543424 0 0 -2019-12-19 00:00:00-05:00 24.18468309 24.31512481 24.04941261 24.10255432 2995014 0 0 -2019-12-20 00:00:00-05:00 24.30063135 24.42141012 23.91897094 23.97211266 5335979 0 0 -2019-12-23 00:00:00-05:00 23.99144058 24.72577441 23.97211467 24.37793159 3213228 0 0 -2019-12-24 00:00:00-05:00 24.37309914 24.79823831 24.27647504 24.55668259 1891910 0 0 -2019-12-26 00:00:00-05:00 24.76442036 25.25719658 24.69195455 25.12675667 3034474 0 0 -2019-12-27 00:00:00-05:00 25.21854882 25.21854882 24.493878 24.62914848 3107672 0 0 -2019-12-30 00:00:00-05:00 24.58083576 24.90935444 24.39725233 24.59532928 2622512 0 0 -2019-12-31 00:00:00-05:00 24.54218655 25.07361091 24.42140779 24.82722282 3263145 0 0 -2020-01-02 00:00:00-05:00 25.15091201 25.42145661 24.9045239 25.26202965 4278253 0 0 -2020-01-03 00:00:00-05:00 24.83205866 25.11226441 24.35860651 24.43107414 4447142 0 0 -2020-01-06 00:00:00-05:00 24.19917701 24.7789144 24.19917701 24.67745972 4983403 0 0 -2020-01-07 00:00:00-05:00 24.65813393 24.78857565 23.94795669 24.05907249 6135833 0 0 -2020-01-08 00:00:00-05:00 23.91414122 24.17502287 23.7450513 24.15569878 6934306 0 0 -2020-01-09 00:00:00-05:00 24.25232021 24.28130726 23.68224579 23.97211266 7158833 0 0 -2020-01-10 00:00:00-05:00 23.97211182 24.12671002 23.87065896 23.91897011 4865615 0 0 -2020-01-13 00:00:00-05:00 24.0059331 24.15086654 23.51798743 24.07356834 6736414 0 0 -2020-01-14 00:00:00-05:00 24.07356791 24.89486103 24.06390677 24.57117653 6555490 0 0 -2020-01-15 00:00:00-05:00 24.4987078 24.7064459 24.34894198 24.44556427 3906145 0 0 -2020-01-16 00:00:00-05:00 24.64364508 24.99148792 24.48904867 24.80307388 4445958 0 0 -2020-01-17 00:00:00-05:00 24.88036716 24.88036716 24.17985106 24.31995392 6502416 0 0 -2020-01-21 00:00:00-05:00 24.22816506 24.23782802 23.62427299 23.81268883 5397142 0 0 -2020-01-22 00:00:00-05:00 23.9141404 24.21367204 23.9141404 24.12671089 2705180 0 0 -2020-01-23 00:00:00-05:00 24.16535985 24.38276273 23.85133648 24.33444977 2187860 0 0 -2020-01-24 00:00:00-05:00 24.42141132 24.65813649 23.92863508 24.14603615 3948170 0 0 -2020-01-27 00:00:00-05:00 23.50832236 23.61943816 23.17497314 23.26193428 6143133 0 0 -2020-01-28 00:00:00-05:00 23.43585753 23.7692068 23.35855934 23.5179863 4225377 0 0 -2020-01-29 00:00:00-05:00 23.64842796 23.76920673 23.450351 23.64842796 2788836 0 0 -2020-01-30 00:00:00-05:00 23.38271652 23.89964742 23.26676831 23.85616684 4742697 0 0 -2020-01-31 00:00:00-05:00 23.73539044 23.79336272 23.26193828 23.56146812 4627869 0 0 -2020-02-03 00:00:00-05:00 23.57113036 23.86582962 23.48416921 23.75954437 3265118 0 0 -2020-02-04 00:00:00-05:00 24.15569825 24.87070612 23.88515366 24.65330505 5845604 0 0 -2020-02-05 00:00:00-05:00 24.87070661 25.31034113 24.44556561 24.53735733 7337587 0 0 -2020-02-06 00:00:00-05:00 24.71611111 25.57122371 24.66779996 25.5325737 6581533 0 0 -2020-02-07 00:00:00-05:00 25.36348213 25.36348213 24.63881131 24.93834114 9318084 0 0 -2020-02-10 00:00:00-05:00 24.63881297 25.20405686 24.42624246 25.17506981 5327100 0 0 -2020-02-11 00:00:00-05:00 25.17023742 25.65335069 25.12192446 25.2475338 8685935 0 0 -2020-02-12 00:00:00-05:00 25.47460002 25.85626046 25.4021342 25.7499752 6874129 0 0 -2020-02-13 00:00:00-05:00 25.09776941 25.52291039 24.84654891 25.38763809 6760485 0 0 -2020-02-14 00:00:00-05:00 25.58088335 25.87075205 25.42628696 25.54706573 4158295 0 0 -2020-02-18 00:00:00-05:00 25.26685917 25.58088252 25.06395164 25.49392319 5139862 0 0 -2020-02-19 00:00:00-05:00 25.55672805 25.89007733 25.48426224 25.76929855 5512759 0 0 -2020-02-20 00:00:00-05:00 25.76929896 25.79828783 25.26203099 25.58088493 5310724 0 0 -2020-02-21 00:00:00-05:00 25.46010645 25.51324999 24.74026797 24.98665428 4403144 0 0 -2020-02-24 00:00:00-05:00 24.04458005 24.0928912 22.90926188 23.3392334 7265770 0 0 -2020-02-25 00:00:00-05:00 23.69674181 23.76920944 22.30537232 22.32952881 6799550 0 0 -2020-02-26 00:00:00-05:00 22.571083 22.87061281 21.88506041 22.18942261 6851242 0 0 -2020-02-27 00:00:00-05:00 21.5130648 22.00101047 20.72558929 21.04444504 11049984 0 0 -2020-02-28 00:00:00-05:00 19.90429685 20.8270447 18.93806842 19.54679108 16947084 0 0 -2020-03-02 00:00:00-05:00 19.67239897 20.52751151 19.03468931 20.47920036 7908770 0 0 -2020-03-03 00:00:00-05:00 20.61447127 21.11690859 20.00574688 20.18449974 9216475 0 0 -2020-03-04 00:00:00-05:00 20.52751173 20.95748329 19.8994632 20.95748329 6008377 0 0 -2020-03-05 00:00:00-05:00 20.30528106 20.56133032 19.56128611 19.7352066 7139103 0 0 -2020-03-06 00:00:00-05:00 18.84144423 19.74969664 18.65785897 19.14097404 8400837 0 0 -2020-03-09 00:00:00-04:00 17.62399796 17.77859254 17.13122173 17.51288033 10960804 0 0 -2020-03-10 00:00:00-04:00 18.21339856 19.02503063 17.15054778 18.90908241 9943920 0 0 -2020-03-11 00:00:00-04:00 18.18440939 18.62404209 17.6674785 17.84622955 9386942 0 0 -2020-03-12 00:00:00-04:00 16.41621059 16.87033859 15.60941107 15.81714916 13329588 0 0 -2020-03-13 00:00:00-04:00 16.99594917 17.36794662 16.01039672 17.36794662 9643038 0 0 -2020-03-16 00:00:00-04:00 14.09726578 16.57564147 14.09726578 15.56110191 10100379 0 0 -2020-03-17 00:00:00-04:00 15.85096763 16.16499279 14.60936505 15.13112831 9976277 0 0 -2020-03-18 00:00:00-04:00 14.0103039 14.56588476 12.32423632 13.94749832 9067711 0 0 -2020-03-19 00:00:00-04:00 14.01030529 15.80265739 13.61415135 15.44032288 8396299 0 0 -2020-03-20 00:00:00-04:00 15.86546204 17.82207349 15.46930991 15.69637394 13518799 0 0 -2020-03-23 00:00:00-04:00 15.94276252 17.26166447 15.23258511 16.39689064 7340349 0 0 -2020-03-24 00:00:00-04:00 17.6143339 18.77380678 16.68192321 18.76897621 7084254 0 0 -2020-03-25 00:00:00-04:00 19.02019459 20.48402965 18.44045725 19.00570107 7389280 0 0 -2020-03-26 00:00:00-04:00 19.29557111 20.72558865 19.08300062 20.55166817 6317941 0 0 -2020-03-27 00:00:00-04:00 19.43567478 19.80284168 18.48877229 18.72066689 6310246 0 0 -2020-03-30 00:00:00-04:00 19.18445703 19.889802 18.73516134 19.22793579 6928979 0 0 -2020-03-31 00:00:00-04:00 19.06367618 19.80767108 18.78830102 19.10715675 3495761 0 0 -2020-04-01 00:00:00-04:00 18.23755174 18.77380669 17.63848848 17.8269043 4866207 0 0 -2020-04-02 00:00:00-04:00 17.67231112 18.38248849 17.47423412 17.94285393 3894307 0 0 -2020-04-03 00:00:00-04:00 17.86072448 18.20373491 17.16503884 17.60467339 4084899 0 0 -2020-04-06 00:00:00-04:00 18.58539172 18.90907799 18.31484715 18.79796219 6457629 0 0 -2020-04-07 00:00:00-04:00 19.28590617 19.70138416 18.43079553 18.53708076 7205396 0 0 -2020-04-08 00:00:00-04:00 18.74482345 19.65791022 18.64820114 19.50814438 6720038 0 0 -2020-04-09 00:00:00-04:00 20.131362 20.65312352 19.8753109 20.07821846 8348158 0 0 -2020-04-13 00:00:00-04:00 19.64341519 19.96226914 19.28590942 19.54679108 3434006 0 0 -2020-04-14 00:00:00-04:00 19.89946383 20.45987532 19.71104981 19.97193146 5004120 0 0 -2020-04-15 00:00:00-04:00 19.33905222 19.74003674 19.13131229 19.54195976 4119427 0 0 -2020-04-16 00:00:00-04:00 19.68206135 19.8994624 19.26175276 19.85598183 3042169 0 0 -2020-04-17 00:00:00-04:00 20.36325084 20.42122493 19.80766998 20.05405807 6397453 0 0 -2020-04-20 00:00:00-04:00 19.4888156 20.17483822 19.3970257 19.78834724 4069510 0 0 -2020-04-21 00:00:00-04:00 19.29073995 19.39702519 17.97183827 18.06846237 7012437 0 0 -2020-04-22 00:00:00-04:00 18.50326581 18.93806796 18.30518702 18.72066689 7617950 0 0 -2020-04-23 00:00:00-04:00 18.94289561 19.21827074 18.69650752 18.79796219 2845855 0 0 -2020-04-24 00:00:00-04:00 19.00087275 19.31489612 18.59988822 19.17479324 3493788 0 0 -2020-04-27 00:00:00-04:00 19.40185589 19.61925694 19.32938826 19.41634941 3014349 0 0 -2020-04-28 00:00:00-04:00 19.80284101 19.98159389 19.4211806 19.73037338 5056996 0 0 -2020-04-29 00:00:00-04:00 20.06855392 21.097587 20.00091868 20.96231651 6579363 0 0 -2020-04-30 00:00:00-04:00 20.77389989 20.981638 20.45504414 20.62413406 3432231 0 0 -2020-05-01 00:00:00-04:00 20.09270966 20.28595424 19.21827301 19.32455826 3702926 0 0 -2020-05-04 00:00:00-04:00 19.16513181 19.59993395 18.73999081 19.590271 2514589 0 0 -2020-05-05 00:00:00-04:00 19.80767095 20.12652669 19.39702532 19.41634941 2149781 0 0 -2020-05-06 00:00:00-04:00 19.61442673 19.8704778 19.42601272 19.56128502 2265201 0 0 -2020-05-07 00:00:00-04:00 19.83665844 20.20865589 19.74003615 20.17000771 2209365 0 0 -2020-05-08 00:00:00-04:00 20.48403109 21.16039077 20.41639585 21.08792496 4143695 0 0 -2020-05-11 00:00:00-04:00 20.89951035 21.14589848 20.54683697 20.95265388 3142200 0 0 -2020-05-12 00:00:00-04:00 21.03477988 21.15555864 20.41156197 20.41156197 4281805 0 0 -2020-05-13 00:00:00-04:00 20.45987428 20.62896601 19.49364775 19.82216644 4291472 0 0 -2020-05-14 00:00:00-04:00 19.40185528 20.24247244 18.98637727 20.22314835 3262750 0 0 -2020-05-15 00:00:00-04:00 19.88496725 20.41639342 19.59993098 20.33426285 3069593 0 0 -2020-05-18 00:00:00-04:00 20.71109748 21.44059895 20.55650107 21.15073204 3665439 0 0 -2020-05-19 00:00:00-04:00 21.05893752 21.7111407 21.05893752 21.27633858 4253591 0 0 -2020-05-20 00:00:00-04:00 21.45992222 21.72080386 21.14106648 21.57586861 4144484 0 0 -2020-05-21 00:00:00-04:00 21.59036036 21.75945027 21.03477952 21.44542694 3477018 0 0 -2020-05-22 00:00:00-04:00 20.96714706 21.16039165 20.23764383 20.80771828 5418450 0 0 -2020-05-26 00:00:00-04:00 21.63384445 21.78844085 21.44543042 21.47924805 3831961 0 0 -2020-05-27 00:00:00-04:00 21.74012965 22.01550484 21.10241813 22.01550484 5659748 0 0 -2020-05-28 00:00:00-04:00 21.87057031 22.31503544 21.85607679 22.02033615 8541906 0 0 -2020-05-29 00:00:00-04:00 23.67258251 24.15569758 23.18946925 23.98177528 23420102 0 0 -2020-06-01 00:00:00-04:00 23.78853086 24.05907363 23.32474169 23.85133553 7528179 0 0 -2020-06-02 00:00:00-04:00 23.86583112 23.98177751 23.48900125 23.74505234 5828439 0 0 -2020-06-03 00:00:00-04:00 23.91414074 23.94312779 23.53248033 23.58079147 5198658 0 0 -2020-06-04 00:00:00-04:00 23.40204012 23.7885311 23.26193725 23.769207 5245812 0 0 -2020-06-05 00:00:00-04:00 23.88515428 24.80790209 23.88515428 24.40691757 8202353 0 0 -2020-06-08 00:00:00-04:00 24.63881067 25.14124983 24.54218838 24.9576664 7115624 0 0 -2020-06-09 00:00:00-04:00 24.78374579 24.78374579 24.10255376 24.17019081 4731057 0 0 -2020-06-10 00:00:00-04:00 24.18951696 24.26681516 23.65326013 23.86100006 2883540 0 0 -2020-06-11 00:00:00-04:00 23.18463766 23.31024882 22.44064274 22.5082798 6145698 0 0 -2020-06-12 00:00:00-04:00 23.368222 23.517986 22.26672137 22.65804291 3785003 0 0 -2020-06-15 00:00:00-04:00 22.12662126 22.90443387 22.0396601 22.73534393 4336259 0 0 -2020-06-16 00:00:00-04:00 23.44068711 23.52764825 22.76915986 23.03487206 3159957 0 0 -2020-06-17 00:00:00-04:00 23.07835362 23.48899925 22.93341836 22.95757484 4104235 0 0 -2020-06-18 00:00:00-04:00 22.82230436 22.96723781 22.57591623 22.90926552 4487391 0 0 -2020-06-19 00:00:00-04:00 23.35372459 23.92379895 23.21845413 23.63393211 6059478 0 0 -2020-06-22 00:00:00-04:00 23.48416715 23.79819048 23.11216972 23.32957077 3595003 0 0 -2020-06-23 00:00:00-04:00 23.59045469 23.92380215 23.35372771 23.67741585 10216983 0 0 -2020-06-24 00:00:00-04:00 26.49879845 26.52295311 24.98181987 25.65334892 31476453 0 0 -2020-06-25 00:00:00-04:00 25.32966493 25.38280665 24.53252648 24.92868042 7400723 0 0 -2020-06-26 00:00:00-04:00 24.74026514 25.56638878 24.54218818 25.24270248 10050659 0 0 -2020-06-29 00:00:00-04:00 25.44561415 26.04950622 25.03496666 25.97220802 7782696 0 0 -2020-06-30 00:00:00-04:00 26.00119289 26.68238498 25.8079483 26.5422821 6045272 0 0 -2020-07-01 00:00:00-04:00 26.60025293 26.84664286 26.12197024 26.22342491 5496778 0 0 -2020-07-02 00:00:00-04:00 26.6485631 26.92877064 25.95287937 25.96737289 3671556 0 0 -2020-07-06 00:00:00-04:00 26.32971031 26.41183907 25.97220638 26.11713982 3368306 0 0 -2020-07-07 00:00:00-04:00 25.8997407 26.06400005 25.54706731 25.58088493 3580008 0 0 -2020-07-08 00:00:00-04:00 25.69683057 26.16061974 25.55672589 26.16061974 4085294 0 0 -2020-07-09 00:00:00-04:00 26.28140107 26.28140107 25.60504136 25.93838882 2747008 0 0 -2020-07-10 00:00:00-04:00 25.846596 25.9383859 25.56638846 25.81277657 2285129 0 0 -2020-07-13 00:00:00-04:00 25.8465974 25.9383873 25.00114781 25.01080894 4150008 0 0 -2020-07-14 00:00:00-04:00 24.80790064 25.39246857 24.67745891 25.36348152 2630404 0 0 -2020-07-15 00:00:00-04:00 25.45527574 25.7934556 25.19922466 25.45044518 3846166 0 0 -2020-07-16 00:00:00-04:00 28.3201379 29.77431001 28.28632028 28.55203247 50015945 0 0 -2020-07-17 00:00:00-04:00 28.38777587 29.29602829 27.99162194 29.16558838 11194999 0 0 -2020-07-20 00:00:00-04:00 29.13177222 29.88059954 29.07379812 29.68735313 6124389 0 0 -2020-07-21 00:00:00-04:00 29.80813038 30.19462135 29.49410522 29.49893761 5450018 0 0 -2020-07-22 00:00:00-04:00 29.63420879 30.19945265 29.56174299 29.83711815 5695459 0 0 -2020-07-23 00:00:00-04:00 29.83711863 30.38303658 29.43130173 29.72117043 4228534 0 0 -2020-07-24 00:00:00-04:00 29.30086145 29.57140604 29.09795391 29.37332726 2538265 0 0 -2020-07-27 00:00:00-04:00 29.39748475 29.63420991 29.20423834 29.45062828 2799687 0 0 -2020-07-28 00:00:00-04:00 29.29602923 29.43613391 28.61483899 28.68247604 3281099 0 0 -2020-07-29 00:00:00-04:00 28.71146311 29.08829298 28.59551672 28.83707428 3532459 0 0 -2020-07-30 00:00:00-04:00 28.54720464 28.74528162 28.07375249 28.58102226 3671161 0 0 -2020-07-31 00:00:00-04:00 28.81291714 28.94818944 28.26216678 28.90470886 3420985 0 0 -2020-08-03 00:00:00-04:00 28.94335767 29.39265332 28.68247603 29.21873283 3327070 0 0 -2020-08-04 00:00:00-04:00 29.20423657 29.30569124 28.89504379 29.14143372 3335159 0 0 -2020-08-05 00:00:00-04:00 29.34917316 29.5520807 29.16558971 29.2815361 2275856 0 0 -2020-08-06 00:00:00-04:00 29.11244956 29.58590176 28.94336144 29.44579887 3726208 0 0 -2020-08-07 00:00:00-04:00 29.35883555 29.42647079 29.01099276 29.37332726 2072834 0 0 -2020-08-10 00:00:00-04:00 29.55208159 29.98205317 29.45062873 29.87093735 3092086 0 0 -2020-08-11 00:00:00-04:00 30.06901096 30.52796954 29.65353295 29.73083115 4107589 0 0 -2020-08-12 00:00:00-04:00 30.00137726 30.09316899 29.61488626 29.87093735 1955046 0 0 -2020-08-13 00:00:00-04:00 29.57140759 29.76948275 28.74528205 28.7742691 5350381 0 0 -2020-08-14 00:00:00-04:00 28.8805528 29.15592614 28.78392869 28.94335747 2139719 0 0 -2020-08-17 00:00:00-04:00 29.08346241 29.32018758 28.8129178 28.83707428 1871588 0 0 -2020-08-18 00:00:00-04:00 28.80325551 29.16076129 28.80325551 28.99650192 1631276 0 0 -2020-08-19 00:00:00-04:00 29.14143552 29.31535601 28.851565 28.91437149 3573103 0 0 -2020-08-20 00:00:00-04:00 28.87088766 28.96750995 28.65831719 28.82257652 1906510 0 0 -2020-08-21 00:00:00-04:00 28.67281339 29.31535545 28.6534893 29.20907021 4371182 0 0 -2020-08-24 00:00:00-04:00 29.90475312 30.04968837 29.5472492 29.74049377 2523467 0 0 -2020-08-25 00:00:00-04:00 29.74049514 29.9047545 29.20423832 29.29603004 2681110 0 0 -2020-08-26 00:00:00-04:00 29.86610522 30.08350811 29.39265487 30.01587105 4099499 0 0 -2020-08-27 00:00:00-04:00 30.24293543 30.52314118 29.69701745 30.16080666 7382966 0 0 -2020-08-28 00:00:00-04:00 31.65845965 32.66816678 31.30578628 31.98697472 17972452 0 0 -2020-08-31 00:00:00-04:00 31.67778152 32.54738577 31.61014628 31.92417145 5917619 0 0 -2020-09-01 00:00:00-04:00 32.08843232 32.29133806 31.74542006 32.06427765 4548357 0 0 -2020-09-02 00:00:00-04:00 32.33481315 33.14644688 32.04977868 33.07881165 4577755 0 0 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31.65362954 30.78402521 31.64396477 4275294 0 0 -2020-09-22 00:00:00-04:00 31.94349241 32.04494709 31.38791335 32.03528595 2148794 0 0 -2020-09-23 00:00:00-04:00 32.47491803 32.53289213 31.57149797 31.69710732 4193414 0 0 -2020-09-24 00:00:00-04:00 31.46521139 31.87102647 31.08838336 31.58115959 3206322 0 0 -2020-09-25 00:00:00-04:00 31.40240699 31.81788319 31.2284847 31.75024796 2413768 0 0 -2020-09-28 00:00:00-04:00 32.27684438 32.67782706 32.11258321 32.51839828 3783622 0 0 -2020-09-29 00:00:00-04:00 32.60535984 33.08847493 32.31549297 33.04499435 2713467 0 0 -2020-09-30 00:00:00-04:00 33.04016041 33.27688919 32.45076192 32.70198059 3286229 0 0 -2020-10-01 00:00:00-04:00 32.93871707 33.09814225 32.42661484 32.62952423 2183716 0 0 -2020-10-02 00:00:00-04:00 31.83237748 32.67299468 31.75024872 32.4459343 1975565 0 0 -2020-10-05 00:00:00-04:00 32.79860854 33.00151426 32.51357041 32.95320129 4981036 0 0 -2020-10-06 00:00:00-04:00 32.89523162 33.27689205 32.46525823 32.60052872 3792698 0 0 -2020-10-07 00:00:00-04:00 33.00150968 33.02566434 32.4024501 32.56670761 4220050 0 0 -2020-10-08 00:00:00-04:00 32.61019322 33.44114931 32.58120617 33.17060471 4895013 0 0 -2020-10-09 00:00:00-04:00 33.37834481 33.54743475 33.01601028 33.04016495 2297559 0 0 -2020-10-12 00:00:00-04:00 33.16577218 33.58125205 33.09330455 33.4556427 1562221 0 0 -2020-10-13 00:00:00-04:00 33.81797546 34.32041281 33.60057622 34.06436539 3167454 0 0 -2020-10-14 00:00:00-04:00 34.18031311 34.51849297 33.95324908 34.13200378 2077372 0 0 -2020-10-15 00:00:00-04:00 33.59091701 34.01122567 33.26239827 33.83730698 2558192 0 0 -2020-10-16 00:00:00-04:00 34.21412819 34.40254037 33.87594837 33.89044189 2992055 0 0 -2020-10-19 00:00:00-04:00 34.01122197 34.25761191 33.4459781 33.57641983 2087631 0 0 -2020-10-20 00:00:00-04:00 33.88078454 34.00639389 33.51361763 33.53293991 1474620 0 0 -2020-10-21 00:00:00-04:00 33.68270576 34.08852083 33.50878345 33.73584747 3767641 0 0 -2020-10-22 00:00:00-04:00 34.02088451 34.02088451 32.74546159 33.22857666 2001214 0 0 -2020-10-23 00:00:00-04:00 33.20925405 33.26722816 32.7744519 32.93871307 2602979 0 0 -2020-10-26 00:00:00-04:00 32.53772403 32.60535927 30.89030546 31.27679825 3910486 0 0 -2020-10-27 00:00:00-04:00 31.3057864 31.3975763 30.67290366 30.71155548 1867642 0 0 -2020-10-28 00:00:00-04:00 29.97239241 30.165637 29.31052622 29.32501793 3457485 0 0 -2020-10-29 00:00:00-04:00 29.22356266 29.48444429 28.89987635 29.23805618 3550216 0 0 -2020-10-30 00:00:00-04:00 29.19940507 29.40714498 28.44574725 29.11244392 3561265 0 0 -2020-11-02 00:00:00-05:00 29.56174188 29.69218179 29.01582214 29.12693977 2206603 0 0 -2020-11-03 00:00:00-05:00 29.50376994 30.37337423 29.47478289 29.83228683 1673893 0 0 -2020-11-04 00:00:00-05:00 30.18012858 30.46033431 29.63420882 30.06418037 3366135 0 0 -2020-11-05 00:00:00-05:00 30.53280082 31.20916051 30.43617853 31.16567993 3003301 0 0 -2020-11-06 00:00:00-05:00 31.26713521 31.34443522 30.6294237 31.20433235 1501650 0 0 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34.05953431 33.52810989 33.97740555 5787006 0 0 -2020-11-25 00:00:00-05:00 33.73584844 33.80831607 32.48941613 33.50395203 8768407 0 0 -2020-11-27 00:00:00-05:00 33.50878641 33.92426266 33.34935762 33.73101807 2779365 0 0 -2020-11-30 00:00:00-05:00 33.93392354 34.02088469 32.85657758 33.34935379 4739935 0 0 -2020-12-01 00:00:00-05:00 33.74551226 34.18997921 33.36385181 34.06919861 6792842 0 0 -2020-12-02 00:00:00-05:00 34.1078468 34.34940437 33.6585511 34.06919861 4693964 0 0 -2020-12-03 00:00:00-05:00 34.21412975 34.54747719 33.86145639 34.25761032 4426623 0 0 -2020-12-04 00:00:00-05:00 34.32041734 34.91948069 34.13200512 34.83251953 5061337 0 0 -2020-12-07 00:00:00-05:00 34.89532278 35.09339975 33.79865085 34.00156021 6222645 0 0 -2020-12-08 00:00:00-05:00 33.97257389 34.74555588 33.88561273 34.72140121 3625585 0 0 -2020-12-09 00:00:00-05:00 34.85183959 35.25282226 34.30591805 34.55714035 4662396 0 0 -2020-12-10 00:00:00-05:00 34.27693382 34.90015179 33.96774103 34.8615036 4902116 0 0 -2020-12-11 00:00:00-05:00 34.64410252 35.04508887 34.37356154 34.61028671 2887091 0 0 -2020-12-14 00:00:00-05:00 34.78420526 35.25282317 34.74555344 34.99194336 4259312 0 0 -2020-12-15 00:00:00-05:00 35.42191516 35.69245977 34.96778711 35.62482452 3963362 0 0 -2020-12-16 00:00:00-05:00 35.72627407 35.89053524 35.58134246 35.68762589 3201982 0 0 -2020-12-17 00:00:00-05:00 35.83256137 36.14658654 35.81323546 36.09344482 3632490 0 0 -2020-12-18 00:00:00-05:00 36.23355046 36.42196267 34.96778855 35.22867203 9961677 0 0 -2020-12-21 00:00:00-05:00 34.93880085 35.43158073 34.3155865 35.29630661 3446042 0 0 -2020-12-22 00:00:00-05:00 35.60549284 35.68762159 35.04991383 35.13687134 2565492 0 0 -2020-12-23 00:00:00-05:00 35.21900974 35.67796655 34.9726198 35.46056366 3252096 0 0 -2020-12-24 00:00:00-05:00 35.63448496 35.69729145 35.14654112 35.26248932 3294318 0 0 -2020-12-28 00:00:00-05:00 35.53303076 35.62965306 34.99677395 35.19485092 6292884 0 0 diff --git a/Dell-Stock-Prediction-lstm/Data Sources/DELL_stock_info.csv b/Dell-Stock-Prediction-lstm/Data Sources/DELL_stock_info.csv deleted file mode 100644 index 67531e78..00000000 --- a/Dell-Stock-Prediction-lstm/Data Sources/DELL_stock_info.csv +++ /dev/null @@ -1,1000 +0,0 @@ -address1 One Dell Way -city Round Rock -state TX -zip 78682 -country United States -phone 800 289 3355 -website https://www.delltechnologies.com -industry Computer Hardware -industryKey computer-hardware -industryDisp Computer Hardware -sector Technology -sectorKey technology -sectorDisp Technology -longBusinessSummary Dell Technologies Inc. designs, develops, manufactures, markets, sells, and supports various comprehensive and integrated solutions, products, and services in the Americas, Europe, the Middle East, Asia, and internationally. The company operates through two segments, Infrastructure Solutions Group (ISG) and Client Solutions Group (CSG). The ISG segment provides traditional and next-generation storage solutions, including all-flash arrays, scale-out file, object platforms, hyper-converged infrastructure, and software-defined storage; and rack, blade, tower, and hyperscale servers. This segment also offers networking products and services that help its business customers to transform and modernize their infrastructure, mobilize and enrich end-user experiences, and accelerate business applications and processes; attached software and peripherals; and support and deployment, configuration, and extended warranty services. The CSG segment provides desktops, workstations, and notebooks; displays, docking stations, and other electronics; and third-party software and peripherals, as well as support and deployment, configuration, and extended warranty services. The company is also involved in the provision of cybersecurity technology-driven security solutions to prevent security breaches, detect malicious activity, respond rapidly when a security breach occurs, and identify emerging threats; originating, collecting, and servicing customer financing arrangements; and infrastructure-as-a-service solutions, as well as in the resale of VMware products and services. The company was formerly known as Denali Holding Inc. and changed its name to Dell Technologies Inc. in August 2016. Dell Technologies Inc. was founded in 1984 and is headquartered in Round Rock, Texas. -fullTimeEmployees 133000 -companyOfficers [{'maxAge': 1, 'name': 'Mr. Michael Saul Dell', 'age': 57, 'title': 'Chairman & CEO', 'yearBorn': 1965, 'fiscalYear': 2023, 'totalPay': 2797308, 'exercisedValue': 0, 'unexercisedValue': 0}, {'maxAge': 1, 'name': 'Mr. Jeffrey W. Clarke', 'age': 59, 'title': 'COO & Vice Chairman', 'yearBorn': 1963, 'fiscalYear': 2023, 'totalPay': 2853441, 'exercisedValue': 0, 'unexercisedValue': 0}, {'maxAge': 1, 'name': 'Mr. William F. Scannell', 'age': 60, 'title': 'President of Global Sales & Customer Operations', 'yearBorn': 1962, 'fiscalYear': 2023, 'totalPay': 1931384, 'exercisedValue': 0, 'unexercisedValue': 0}, {'maxAge': 1, 'name': 'Ms. Yvonne McGill', 'age': 55, 'title': 'Chief Financial Officer', 'yearBorn': 1967, 'fiscalYear': 2023, 'exercisedValue': 0, 'unexercisedValue': 0}, {'maxAge': 1, 'name': 'Ms. Brunilda Rios', 'age': 56, 'title': 'Senior VP of Corporate Finance & Chief Accounting Officer', 'yearBorn': 1966, 'fiscalYear': 2023, 'exercisedValue': 0, 'unexercisedValue': 0}, {'maxAge': 1, 'name': 'Mr. Richard J. Rothberg Esq.', 'age': 58, 'title': 'General Counsel & Secretary', 'yearBorn': 1964, 'fiscalYear': 2023, 'totalPay': 1727358, 'exercisedValue': 24596768, 'unexercisedValue': 20991448}, {'maxAge': 1, 'name': 'Ms. Allison Dew', 'age': 52, 'title': 'Chief Marketing Officer', 'yearBorn': 1970, 'fiscalYear': 2023, 'totalPay': 2273277, 'exercisedValue': 0, 'unexercisedValue': 7912755}, {'maxAge': 1, 'name': 'Mr. Michael Zimmerman', 'title': 'Vice President of Corporate Development', 'fiscalYear': 2023, 'exercisedValue': 0, 'unexercisedValue': 0}, {'maxAge': 1, 'name': 'Dr. Jennifer D. Saavedra Ph.D.', 'age': 52, 'title': 'Chief Human Resources Officer', 'yearBorn': 1970, 'fiscalYear': 2023, 'exercisedValue': 0, 'unexercisedValue': 0}, {'maxAge': 1, 'name': 'Mr. Howard D. Elias', 'age': 64, 'title': 'Chief Customer Officer and President of Services & Digital', 'yearBorn': 1958, 'fiscalYear': 2023, 'totalPay': 6171429, 'exercisedValue': 4068717, 'unexercisedValue': 0}] -auditRisk 7 -boardRisk 9 -compensationRisk 3 -shareHolderRightsRisk 10 -overallRisk 9 -governanceEpochDate 1696118400 -compensationAsOfEpochDate 1703980800 -maxAge 86400 -priceHint 2 -previousClose 67.76 -open 67.26 -dayLow 66.7 -dayHigh 68.645 -regularMarketPreviousClose 67.76 -regularMarketOpen 67.26 -regularMarketDayLow 66.7 -regularMarketDayHigh 68.645 -dividendRate 1.48 -dividendYield 0.0218 -exDividendDate 1698019200 -payoutRatio 0.5385 -beta 0.899 -trailingPE 26.115387 -forwardPE 9.956012 -volume 4065220 -regularMarketVolume 4065220 -averageVolume 4792907 -averageVolume10days 5914220 -averageDailyVolume10Day 5914220 -bid 0 -ask 0 -bidSize 1000 -askSize 800 -marketCap 49120694272 -fiftyTwoWeekLow 33.9 -fiftyTwoWeekHigh 72.82 -priceToSalesTrailing12Months 0.52470404 -fiftyDayAverage 64.3104 -twoHundredDayAverage 49.78645 -trailingAnnualDividendRate 1.4 -trailingAnnualDividendYield 0.020661157 -currency USD -enterpriseValue 68861665280 -profitMargins 0.02034 -floatShares 244315646 -sharesOutstanding 254312000 -sharesShort 9529693 -sharesShortPriorMonth 6485647 -sharesShortPreviousMonthDate 1693440000 -dateShortInterest 1695945600 -sharesPercentSharesOut 0.013200001 -heldPercentInsiders 0.03323 -heldPercentInstitutions 0.77109003 -shortRatio 1.48 -shortPercentOfFloat 0.0388 -impliedSharesOutstanding 723427008 -bookValue -3.829 -lastFiscalYearEnd 1675382400 -nextFiscalYearEnd 1706918400 -mostRecentQuarter 1691107200 -earningsQuarterlyGrowth -0.096 -netIncomeToCommon 1904000000 -trailingEps 2.6 -forwardEps 6.82 -pegRatio 13.51 -lastSplitFactor 1989:40:00 -lastSplitDate 1635811200 -enterpriseToRevenue 0.736 -enterpriseToEbitda 8.404 -52WeekChange 0.9686866 -SandP52WeekChange 0.18349409 -lastDividendValue 0.37 -lastDividendDate 1690156800 -exchange NYQ -quoteType EQUITY -symbol DELL -underlyingSymbol DELL -shortName Dell Technologies Inc. -longName Dell Technologies Inc. -firstTradeDateEpochUtc 1471440600 -timeZoneFullName America/New_York -timeZoneShortName EDT -uuid 04c74ad6-2535-3f70-9335-6e441ef69610 -messageBoardId finmb_266017 -gmtOffSetMilliseconds -14400000 -currentPrice 67.9 -targetHighPrice 89 -targetLowPrice 53 -targetMeanPrice 74.07 -targetMedianPrice 79 -recommendationMean 2.1 -recommendationKey buy -numberOfAnalystOpinions 14 -totalCash 8364000256 -totalCashPerShare 11.562 -ebitda 8193999872 -totalDebt 28010000384 -quickRatio 0.47 -currentRatio 0.766 -totalRevenue 93615996928 -revenuePerShare 129.393 -returnOnAssets 0.03716 -grossProfits 22686000000 -freeCashflow 5964624896 -operatingCashflow 8101000192 -earningsGrowth -0.074 -revenueGrowth -0.132 -grossMargins 0.23358999 -ebitdaMargins 0.08753 -operatingMargins 0.0508 -financialCurrency USD -trailingPegRatio 10.0779 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 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"cells": [ - { - "cell_type": "markdown", - "id": "079620cf", - "metadata": { - "_kg_hide-output": true, - "papermill": { - "duration": 0.00762, - "end_time": "2024-05-16T11:05:18.822959", - "exception": false, - "start_time": "2024-05-16T11:05:18.815339", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "# Dell Stock prediction - LSTM\n", - "#### 1. inputs data is the last 60 data of train data plus true test data\n", - "#### 2. inputs data is the last 60 data of train data only (not use true test data)" - ] - }, - { - "cell_type": "markdown", - "id": "1545818b", - "metadata": { - "papermill": { - "duration": 0.006846, - "end_time": "2024-05-16T11:05:18.837250", - "exception": false, - "start_time": "2024-05-16T11:05:18.830404", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### RNNs\n", - "RNNs are a type of neural network that is used for sequential data, such as time series or text. They are characterized by their ability to take in a sequence of inputs and produce a sequence of outputs, while maintaining a \"memory\" of past inputs. However, traditional RNNs suffer from the vanishing gradient problem, which occurs when the gradient of the loss function with respect to the weights becomes too small, making it difficult to learn long-term dependencies in the data.\n", - "### LSTM\n", - "LSTM stands for Long Short-Term Memory, and it is a type of Recurrent Neural Network (RNN) architecture that is designed to better handle the vanishing gradient problem that arises in traditional RNNs.\n", - "### The vanishing gradient problem \n", - "The vanishing gradient problem is a phenomenon that can occur during the training of neural networks, particularly deep neural networks with many layers. During backpropagation, the gradients of the loss function with respect to the weights in the earlier layers of the network can become extremely small, which means that the weights in these layers are updated very slowly or not at all.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "3acfaf1a", - "metadata": { - "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", - "_kg_hide-output": true, - "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5", - "execution": { - "iopub.execute_input": "2024-05-16T11:05:18.852694Z", - "iopub.status.busy": "2024-05-16T11:05:18.852082Z", - "iopub.status.idle": "2024-05-16T11:05:32.024136Z", - "shell.execute_reply": "2024-05-16T11:05:32.023264Z" - }, - "papermill": { - "duration": 13.182731, - "end_time": "2024-05-16T11:05:32.026935", - "exception": false, - "start_time": "2024-05-16T11:05:18.844204", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-05-16 11:05:21.380665: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "2024-05-16 11:05:21.380761: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "2024-05-16 11:05:21.517178: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n" - ] - } - ], - "source": [ - "import os\n", - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "from keras.models import Sequential\n", - "from keras.layers import Dense\n", - "from keras.layers import LSTM\n", - "from keras.layers import Dropout\n", - "from sklearn.preprocessing import MinMaxScaler" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "da3f9b10", - "metadata": { - "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0", - "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a", - "execution": { - "iopub.execute_input": "2024-05-16T11:05:32.047259Z", - "iopub.status.busy": "2024-05-16T11:05:32.046286Z", - "iopub.status.idle": "2024-05-16T11:05:32.070364Z", - "shell.execute_reply": "2024-05-16T11:05:32.069209Z" - }, - "papermill": { - "duration": 0.034694, - "end_time": "2024-05-16T11:05:32.072277", - "exception": false, - "start_time": "2024-05-16T11:05:32.037583", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1099\n", - "['Date', 'Open', 'High', 'Low', 'Close', 'Volume', 'Dividends', 'Stock Splits']\n" - ] - } - ], - "source": [ - "dataset = pd.read_csv(\"/kaggle/input/dell-stock-data-latest-and-updated/DELL_stock_history.csv\",sep='\\t')\n", - "print(len(dataset))\n", - "print(dataset.columns.tolist())" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "c725310d", - "metadata": { - "_kg_hide-output": true, - "_uuid": "9de88d689da1a279477470f6cef999c0a45bf98a", - "execution": { - "iopub.execute_input": "2024-05-16T11:05:32.088331Z", - "iopub.status.busy": "2024-05-16T11:05:32.088004Z", - "iopub.status.idle": "2024-05-16T11:05:32.092638Z", - "shell.execute_reply": "2024-05-16T11:05:32.091804Z" - }, - "papermill": { - "duration": 0.014882, - "end_time": "2024-05-16T11:05:32.094628", - "exception": false, - "start_time": "2024-05-16T11:05:32.079746", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "dataset_train=dataset.iloc[-1000:-100]\n", - "dataset_test=dataset.iloc[-100:]" - ] - }, - { - "cell_type": "markdown", - "id": "992edcea", - "metadata": { - "papermill": { - "duration": 0.007141, - "end_time": "2024-05-16T11:05:32.109189", - "exception": false, - "start_time": "2024-05-16T11:05:32.102048", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "# dataset_train" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "c66afead", - "metadata": { - "_uuid": "99cca4c05240bd82c4492c6c3c200925c5c096e5", - "execution": { - "iopub.execute_input": "2024-05-16T11:05:32.124616Z", - "iopub.status.busy": "2024-05-16T11:05:32.124356Z", - "iopub.status.idle": "2024-05-16T11:05:32.133956Z", - "shell.execute_reply": "2024-05-16T11:05:32.133086Z" - }, - "papermill": { - "duration": 0.019685, - "end_time": "2024-05-16T11:05:32.135971", - "exception": false, - "start_time": "2024-05-16T11:05:32.116286", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[15.09262883],\n", - " [15.08728061],\n", - " [15.30930983],\n", - " [15.30128369],\n", - " [15.38420938]])" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "trainset = dataset_train.iloc[:,1:2].values #Open\n", - "display(trainset[0:5])" - ] - }, - { - "cell_type": "markdown", - "id": "8b6034ec", - "metadata": { - "papermill": { - "duration": 0.007157, - "end_time": "2024-05-16T11:05:32.150671", - "exception": false, - "start_time": "2024-05-16T11:05:32.143514", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "## MinMaxScaler\n", - "MinMaxScaler is used to scale the values of data to a range of 0 to 1." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "4d087609", - "metadata": { - "_uuid": "1abc6fe47fb5c4443767a2e2053cf9999c8a9a5c", - "execution": { - "iopub.execute_input": "2024-05-16T11:05:32.170051Z", - "iopub.status.busy": "2024-05-16T11:05:32.169499Z", - "iopub.status.idle": "2024-05-16T11:05:32.176389Z", - "shell.execute_reply": "2024-05-16T11:05:32.175537Z" - }, - "papermill": { - "duration": 0.019588, - "end_time": "2024-05-16T11:05:32.178386", - "exception": false, - "start_time": "2024-05-16T11:05:32.158798", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[0.05429738]\n", - " [0.05402907]\n", - " [0.06516769]\n", - " [0.06476504]\n", - " [0.0689252 ]]\n", - "900\n" - ] - } - ], - "source": [ - "sc = MinMaxScaler(feature_range = (0,1))\n", - "training_scaled = sc.fit_transform(trainset)\n", - "print(training_scaled[0:5])\n", - "print(len(training_scaled))" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "84cdb138", - "metadata": { - "_uuid": "d0d549e9a4af9ab516c3304fb44d650a815dcedc", - "execution": { - "iopub.execute_input": "2024-05-16T11:05:32.195115Z", - "iopub.status.busy": "2024-05-16T11:05:32.194846Z", - "iopub.status.idle": "2024-05-16T11:05:32.198483Z", - "shell.execute_reply": "2024-05-16T11:05:32.197622Z" - }, - "papermill": { - "duration": 0.014204, - "end_time": "2024-05-16T11:05:32.200337", - "exception": false, - "start_time": "2024-05-16T11:05:32.186133", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "x_train = []\n", - "y_train = []" - ] - }, - { - "cell_type": "markdown", - "id": "502c87ee", - "metadata": { - "papermill": { - "duration": 0.007351, - "end_time": "2024-05-16T11:05:32.215097", - "exception": false, - "start_time": "2024-05-16T11:05:32.207746", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "* x_train is data during the past 60 days. \n", - "* y_data is the data of the day after the last day of x_train" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "634c2706", - "metadata": { - "_uuid": "4c4f8ba0c635e7da0ba1bc52abf0a42068acdc89", - "execution": { - "iopub.execute_input": "2024-05-16T11:05:32.231029Z", - "iopub.status.busy": "2024-05-16T11:05:32.230762Z", - "iopub.status.idle": "2024-05-16T11:05:32.238556Z", - "shell.execute_reply": "2024-05-16T11:05:32.237443Z" - }, - "papermill": { - "duration": 0.017873, - "end_time": "2024-05-16T11:05:32.240376", - "exception": false, - "start_time": "2024-05-16T11:05:32.222503", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(840, 60) (840,)\n" - ] - } - ], - "source": [ - "for i in range(60,len(training_scaled)):\n", - " x_train.append(training_scaled[i-60:i, 0])\n", - " y_train.append(training_scaled[i,0])\n", - "x_train,y_train = np.array(x_train),np.array(y_train)\n", - "print(x_train.shape,y_train.shape)" - ] - }, - { - "cell_type": "markdown", - "id": "09b120f9", - "metadata": { - "papermill": { - "duration": 0.007582, - "end_time": "2024-05-16T11:05:32.255420", - "exception": false, - "start_time": "2024-05-16T11:05:32.247838", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "This code is reshaping the input data x_train into a 3D tensor with dimensions (samples, time steps, features) which is required as input for a Long Short-Term Memory (LSTM) neural network.\n", - "\n", - "The reshaping step is necessary because LSTMs require input data to be in a specific format, where:\n", - "\n", - "* samples refers to the number of input sequences (or samples) in the dataset\n", - "* time steps refers to the number of time steps in each input sequence\n", - "* features refers to the number of features (or variables) in each time step of the input sequence\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "7caeddc9", - "metadata": { - "_uuid": "316c5775b5c0a984b432f138792d2c0e9a484491", - "execution": { - "iopub.execute_input": "2024-05-16T11:05:32.271288Z", - "iopub.status.busy": "2024-05-16T11:05:32.271002Z", - "iopub.status.idle": "2024-05-16T11:05:32.275674Z", - "shell.execute_reply": "2024-05-16T11:05:32.274873Z" - }, - "papermill": { - "duration": 0.014753, - "end_time": "2024-05-16T11:05:32.277575", - "exception": false, - "start_time": "2024-05-16T11:05:32.262822", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(samples, time steps, features) = (840, 60, 1)\n" - ] - } - ], - "source": [ - "x_train = np.reshape(x_train, (x_train.shape[0],x_train.shape[1],1))\n", - "print('(samples, time steps, features) =',x_train.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "7cd71da2", - "metadata": { - "_uuid": "ed24d733423254641f134c0e84f44a8ce175589f", - "execution": { - "iopub.execute_input": "2024-05-16T11:05:32.293552Z", - "iopub.status.busy": "2024-05-16T11:05:32.293284Z", - "iopub.status.idle": "2024-05-16T11:05:33.541532Z", - "shell.execute_reply": "2024-05-16T11:05:33.540536Z" - }, - "papermill": { - "duration": 1.258825, - "end_time": "2024-05-16T11:05:33.543835", - "exception": false, - "start_time": "2024-05-16T11:05:32.285010", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/conda/lib/python3.10/site-packages/keras/src/layers/rnn/rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", - " super().__init__(**kwargs)\n" - ] - } - ], - "source": [ - "regressor = Sequential()\n", - "regressor.add(LSTM(units = 50,return_sequences = True,input_shape = (x_train.shape[1],1)))\n", - "regressor.add(Dropout(0.2))\n", - "regressor.add(LSTM(units = 50,return_sequences = True))\n", - "regressor.add(Dropout(0.2))\n", - "regressor.add(LSTM(units = 50,return_sequences = True))\n", - "regressor.add(Dropout(0.2))\n", - "regressor.add(LSTM(units = 50))\n", - "regressor.add(Dropout(0.2))\n", - "regressor.add(Dense(units = 1))" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "6c408daa", - "metadata": { - "_kg_hide-output": true, - "_uuid": "6681ff55d9a29b1ad0f37cdc82318fb2c8fbc0bd", - "execution": { - "iopub.execute_input": "2024-05-16T11:05:33.561006Z", - "iopub.status.busy": "2024-05-16T11:05:33.560706Z", - "iopub.status.idle": "2024-05-16T11:07:47.334674Z", - "shell.execute_reply": "2024-05-16T11:07:47.333741Z" - }, - "papermill": { - "duration": 133.78489, - "end_time": "2024-05-16T11:07:47.336729", - "exception": false, - "start_time": "2024-05-16T11:05:33.551839", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 17ms/step - loss: 0.0860\n", - "Epoch 2/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0174\n", - "Epoch 3/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0143\n", - "Epoch 4/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0132\n", - "Epoch 5/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0106\n", - "Epoch 6/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0099\n", - "Epoch 7/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0107\n", - "Epoch 8/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0094\n", - "Epoch 9/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0077\n", - "Epoch 10/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0078\n", - "Epoch 11/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0074\n", - "Epoch 12/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0067\n", - "Epoch 13/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0082\n", - "Epoch 14/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - loss: 0.0071\n", - "Epoch 15/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0063\n", - "Epoch 16/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0063\n", - "Epoch 17/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0067\n", - "Epoch 18/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0062\n", - "Epoch 19/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0063\n", - "Epoch 20/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0067\n", - "Epoch 21/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - loss: 0.0054\n", - "Epoch 22/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0053\n", - "Epoch 23/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0060\n", - "Epoch 24/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0055\n", - "Epoch 25/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0066\n", - "Epoch 26/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - 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loss: 0.0011\n", - "Epoch 258/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", - "Epoch 259/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", - "Epoch 260/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0011\n", - "Epoch 261/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0017\n", - "Epoch 262/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", - "Epoch 263/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", - "Epoch 264/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", - "Epoch 265/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", - "Epoch 266/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", - "Epoch 267/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0018\n", - "Epoch 268/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", - "Epoch 269/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0012\n", - "Epoch 270/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", - "Epoch 271/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", - "Epoch 272/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", - "Epoch 273/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", - "Epoch 274/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", - "Epoch 275/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", - "Epoch 276/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", - "Epoch 277/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 0.0013\n", - "Epoch 278/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0012\n", - "Epoch 279/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", - "Epoch 280/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", - "Epoch 281/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", - "Epoch 282/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", - "Epoch 283/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0016\n", - "Epoch 284/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", - "Epoch 285/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0012\n", - "Epoch 286/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", - "Epoch 287/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - loss: 0.0016\n", - "Epoch 288/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0011\n", - "Epoch 289/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", - "Epoch 290/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", - "Epoch 291/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", - "Epoch 292/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", - "Epoch 293/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", - "Epoch 294/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0012\n", - "Epoch 295/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0014\n", - "Epoch 296/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - loss: 0.0014\n", - "Epoch 297/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0015\n", - "Epoch 298/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", - "Epoch 299/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0013\n", - "Epoch 300/300\n", - "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0012\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "regressor.compile(optimizer = 'adam',loss = 'mean_squared_error')\n", - "\n", - "regressor.fit(x_train,y_train,epochs =300, batch_size = 32)" - ] - }, - { - "cell_type": "markdown", - "id": "94a9c3eb", - "metadata": { - "papermill": { - "duration": 0.215501, - "end_time": "2024-05-16T11:07:47.770140", - "exception": false, - "start_time": "2024-05-16T11:07:47.554639", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "# dataset_test" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "7ab8b6a3", - "metadata": { - "_uuid": "6acd6a66dc0f9c013ea008c0d99734697757cae6", - "execution": { - "iopub.execute_input": "2024-05-16T11:07:48.208096Z", - "iopub.status.busy": "2024-05-16T11:07:48.207240Z", - "iopub.status.idle": "2024-05-16T11:07:48.218575Z", - "shell.execute_reply": "2024-05-16T11:07:48.217682Z" - }, - "papermill": { - "duration": 0.232795, - "end_time": "2024-05-16T11:07:48.220501", - "exception": false, - "start_time": "2024-05-16T11:07:47.987706", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "real_stock_price = dataset_test.iloc[:,1:2].values\n", - "\n", - "dataset_total = pd.concat((dataset_train['Open'],dataset_test['Open']),axis = 0)" - ] - }, - { - "cell_type": "markdown", - "id": "910f40c4", - "metadata": { - "papermill": { - "duration": 0.218258, - "end_time": "2024-05-16T11:07:48.656166", - "exception": false, - "start_time": "2024-05-16T11:07:48.437908", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "## 1. inputs data is the last 60 data of train data plus true test data" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "56d6d971", - "metadata": { - "_kg_hide-output": true, - "_uuid": "31aa33ed70b08e71d0e8bfe4c1abae7901ab947d", - "execution": { - "iopub.execute_input": "2024-05-16T11:07:49.094325Z", - "iopub.status.busy": "2024-05-16T11:07:49.093941Z", - "iopub.status.idle": "2024-05-16T11:07:49.099584Z", - "shell.execute_reply": "2024-05-16T11:07:49.098698Z" - }, - "papermill": { - "duration": 0.227549, - "end_time": "2024-05-16T11:07:49.101537", - "exception": false, - "start_time": "2024-05-16T11:07:48.873988", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1000 100\n", - "(160,)\n" - ] - } - ], - "source": [ - "print(len(dataset_total),len(dataset_test))\n", - "inputs = dataset_total[len(dataset_total)-len(dataset_test)-60:].values\n", - "print(inputs.shape)" - ] - }, - { - "cell_type": "markdown", - "id": "0572d772", - "metadata": { - "papermill": { - "duration": 0.217296, - "end_time": "2024-05-16T11:07:49.540387", - "exception": false, - "start_time": "2024-05-16T11:07:49.323091", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "In the context of a LSTM model, this reshaping may be necessary if the input data has only one feature (i.e., one time series) and the LSTM model expects a 3D input shape of (n_samples, n_time_steps, n_features). In this case, since there is only one feature, the shape does not change." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "9e7ae164", - "metadata": { - "_kg_hide-output": true, - "_uuid": "7d74568c439acc8dda77dc4365d4b413095508e1", - "execution": { - "iopub.execute_input": "2024-05-16T11:07:49.973818Z", - "iopub.status.busy": "2024-05-16T11:07:49.973439Z", - "iopub.status.idle": "2024-05-16T11:07:49.979427Z", - "shell.execute_reply": "2024-05-16T11:07:49.978363Z" - }, - "papermill": { - "duration": 0.224983, - "end_time": "2024-05-16T11:07:49.981319", - "exception": false, - "start_time": "2024-05-16T11:07:49.756336", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(160, 1)\n", - "(160, 1)\n" - ] - } - ], - "source": [ - "inputs = inputs.reshape(-1,1)\n", - "print(inputs.shape)\n", - "\n", - "#sc = MinMaxScaler(feature_range = (0,1))\n", - "inputs = sc.transform(inputs)\n", - "print(inputs.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "074d397d", - "metadata": { - "_kg_hide-output": true, - "_uuid": "ef076f0fdbe6ec72d85e7326c3e8aa73fcbad667", - "execution": { - "iopub.execute_input": "2024-05-16T11:07:50.460308Z", - "iopub.status.busy": "2024-05-16T11:07:50.459602Z", - "iopub.status.idle": "2024-05-16T11:07:50.466708Z", - "shell.execute_reply": "2024-05-16T11:07:50.465692Z" - }, - "papermill": { - "duration": 0.227565, - "end_time": "2024-05-16T11:07:50.468570", - "exception": false, - "start_time": "2024-05-16T11:07:50.241005", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(100, 60)\n", - "(100, 60, 1)\n" - ] - } - ], - "source": [ - "x_test = []\n", - "for i in range(60,len(inputs)):\n", - " x_test.append(inputs[i-60:i,0])\n", - "x_test = np.array(x_test)\n", - "print(x_test.shape)\n", - "\n", - "x_test = np.reshape(x_test, (x_test.shape[0],x_test.shape[1],1))\n", - "print(x_test.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "12042728", - "metadata": { - "_kg_hide-output": true, - "_uuid": "24730042bdad3a140e033a78ae9b170304f8cefb", - "execution": { - "iopub.execute_input": "2024-05-16T11:07:50.903905Z", - "iopub.status.busy": "2024-05-16T11:07:50.903530Z", - "iopub.status.idle": "2024-05-16T11:07:51.562192Z", - "shell.execute_reply": "2024-05-16T11:07:51.561434Z" - }, - "papermill": { - "duration": 0.880258, - "end_time": "2024-05-16T11:07:51.564333", - "exception": false, - "start_time": "2024-05-16T11:07:50.684075", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 102ms/step\n" - ] - } - ], - "source": [ - "predicted_price = regressor.predict(x_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "8e252ed5", - "metadata": { - "_kg_hide-output": true, - "_uuid": "4b06462fa06e7399373d8a552cd159176fbeef33", - "execution": { - "iopub.execute_input": "2024-05-16T11:07:51.999365Z", - "iopub.status.busy": "2024-05-16T11:07:51.998597Z", - "iopub.status.idle": "2024-05-16T11:07:52.005023Z", - "shell.execute_reply": "2024-05-16T11:07:52.004100Z" - }, - "papermill": { - "duration": 0.226239, - "end_time": "2024-05-16T11:07:52.006954", - "exception": false, - "start_time": "2024-05-16T11:07:51.780715", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(100, 1)\n" - ] - } - ], - "source": [ - "predicted_price = sc.inverse_transform(predicted_price)\n", - "print(predicted_price.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "878dcbd1", - "metadata": { - "_kg_hide-output": false, - "_uuid": "e8b30d578fafb1564da4448efb1f994537f6f588", - "execution": { - "iopub.execute_input": "2024-05-16T11:07:52.443983Z", - "iopub.status.busy": "2024-05-16T11:07:52.443107Z", - "iopub.status.idle": "2024-05-16T11:07:52.755016Z", - "shell.execute_reply": "2024-05-16T11:07:52.754063Z" - }, - "papermill": { - "duration": 0.53407, - "end_time": "2024-05-16T11:07:52.757169", - "exception": false, - "start_time": "2024-05-16T11:07:52.223099", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(real_stock_price[-100:],color = 'red', label = 'Real Price')\n", - "plt.plot(predicted_price[-100:], color = 'blue', label = 'Predicted Price')\n", - "plt.title('Dell Price Prediction')\n", - "plt.xlabel('Time')\n", - "plt.ylabel('Dell Price')\n", - "plt.ylim(0,80) \n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "211ef749", - "metadata": { - "papermill": { - "duration": 0.223993, - "end_time": "2024-05-16T11:07:53.200513", - "exception": false, - "start_time": "2024-05-16T11:07:52.976520", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "## 2. inputs data is the last 60 data of train data only (not use true test data)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "d9ad3491", - "metadata": { - "_kg_hide-output": true, - "execution": { - "iopub.execute_input": "2024-05-16T11:07:53.670230Z", - "iopub.status.busy": 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regressor.predict(x)\n", - " inputs = np.append(inputs, pred)\n", - " inputs = inputs[1:]\n", - " new_predicted_price+=[pred.reshape(-1)]" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "32b0ab9a", - "metadata": { - "_kg_hide-output": true, - "execution": { - "iopub.execute_input": "2024-05-16T11:07:59.850844Z", - "iopub.status.busy": "2024-05-16T11:07:59.850021Z", - "iopub.status.idle": "2024-05-16T11:07:59.856205Z", - "shell.execute_reply": "2024-05-16T11:07:59.855399Z" - }, - "papermill": { - "duration": 0.24076, - "end_time": "2024-05-16T11:07:59.858113", - "exception": false, - "start_time": "2024-05-16T11:07:59.617353", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(100, 1)\n" - ] - } - ], - "source": [ - "new_predicted_price = sc.inverse_transform(np.array(new_predicted_price))\n", - "print(new_predicted_price.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "3146f0b6", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-16T11:08:00.370563Z", - "iopub.status.busy": "2024-05-16T11:08:00.370184Z", - "iopub.status.idle": "2024-05-16T11:08:00.609578Z", - "shell.execute_reply": "2024-05-16T11:08:00.608638Z" - }, - "papermill": { - "duration": 0.476895, - "end_time": "2024-05-16T11:08:00.611882", - "exception": false, - "start_time": "2024-05-16T11:08:00.134987", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", 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