Stock_Price_Prediction_Models_Comparison.ipynb

{"cells":[{"cell_type":"markdown","metadata":{},"source":["# Stock Price Prediction using HMM, LSTM, ARIMA, and RNN\n","\n","In this notebook, we will predict stock prices using various methods including Hidden Markov Model (HMM), Long Short-Term Memory (LSTM), AutoRegressive Integrated Moving Average (ARIMA), and Recurrent Neural Network (RNN). We will compare the performance of these models using various error metrics.\n"]},{"cell_type":"code","execution_count":1,"metadata":{"executionInfo":{"elapsed":7,"status":"ok","timestamp":1688657774569,"user":{"displayName":"Nikhil Kumar Patel","userId":"10043475971450413836"},"user_tz":-330},"id":"qmz8neaC_4Zm"},"outputs":[{"name":"stderr","output_type":"stream","text":["/Users/nikhilkumarpatel/Library/Python/3.9/lib/python/site-packages/urllib3/__init__.py:35: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020\n","  warnings.warn(\n"]}],"source":["# Import necessary libraries for data manipulation, model building, and evaluation\n","%matplotlib inline\n","from hmmlearn import hmm\n","import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","import yfinance as yf\n","import math\n","import scipy.stats as stats\n","import statsmodels.api as sm\n","import gdown\n","from tensorflow.keras.models import Sequential\n","from tensorflow.keras.layers import SimpleRNN, LSTM, Dense\n","from sklearn.preprocessing import MinMaxScaler\n","from statsmodels.tsa.arima.model import ARIMA\n","from sklearn.metrics import mean_squared_error\n"]},{"cell_type":"markdown","metadata":{},"source":["## Load and Prepare Data\n","\n","In this section, we load the NIFTY 50 data and preprocess it for modeling.\n"]},{"cell_type":"code","execution_count":2,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":965,"status":"ok","timestamp":1688659946948,"user":{"displayName":"Nikhil Kumar Patel","userId":"10043475971450413836"},"user_tz":-330},"id":"YNVeoqtLlULH","outputId":"8a3ce376-c1ee-4f4f-b6ca-e2a5e54773fb"},"outputs":[{"name":"stdout","output_type":"stream","text":["          Date      Open      High       Low     Close\n","0   2000-01-31   1482.15   1671.15   1482.15   1546.20\n","1   2000-02-29   1546.20   1818.15   1521.40   1654.80\n","2   2000-03-31   1661.50   1773.85   1489.10   1528.45\n","3   2000-04-30   1528.70   1636.95   1311.30   1406.55\n","4   2000-05-31   1410.00   1436.60   1201.50   1380.45\n","..         ...       ...       ...       ...       ...\n","253 2021-02-28  13758.60  15431.75  13661.75  14529.15\n","254 2021-03-31  14702.50  15336.30  14264.40  14690.70\n","255 2021-04-30  14798.40  15044.35  14151.40  14631.10\n","256 2021-05-31  14481.05  15606.35  14416.25  15582.80\n","257 2021-06-30  15629.65  15915.65  15450.90  15721.50\n","\n","[258 rows x 5 columns]\n","          Date     Open     High      Low    Close\n","0   2000-01-31  1482.15  1671.15  1482.15  1546.20\n","1   2000-02-29  1546.20  1818.15  1521.40  1654.80\n","2   2000-03-31  1661.50  1773.85  1489.10  1528.45\n","3   2000-04-30  1528.70  1636.95  1311.30  1406.55\n","4   2000-05-31  1410.00  1436.60  1201.50  1380.45\n","..         ...      ...      ...      ...      ...\n","157 2013-02-28  6040.95  6052.95  5671.90  5693.05\n","158 2013-03-31  5702.45  5971.20  5604.85  5682.55\n","159 2013-04-30  5697.35  5962.30  5477.20  5930.20\n","160 2013-05-31  5911.40  6229.45  5910.95  5985.95\n","161 2013-06-30  5997.35  6011.00  5566.25  5842.20\n","\n","[162 rows x 5 columns]\n"]},{"name":"stderr","output_type":"stream","text":["/var/folders/k5/fy34_4zs2rn2gwm5d84vh2gm0000gn/T/ipykernel_2015/963645270.py:13: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n","  obs = data.resample('M').agg({'Open': 'first','High': 'max','Low': 'min','Close': 'last'})\n"]}],"source":["data_csv = pd.read_csv(r\"/Users/nikhilkumarpatel/Downloads/NIFTY 50.csv\")\n","\n","data = data_csv[data_csv.columns[0:5]]\n","data = data[:5348]\n","\n","# Convert 'Date' column to datetime type\n","data['Date'] = pd.to_datetime(data['Date'])\n","\n","# Set the 'Date' column as the index\n","data.set_index('Date', inplace=True)\n","\n","# Resample the data to monthly frequency\n","obs = data.resample('M').agg({'Open': 'first','High': 'max','Low': 'min','Close': 'last'})\n","\n","# Reset the index to have 'Date' as a column again\n","obs = obs.reset_index()\n","\n","# Print the monthly data\n","print(obs)\n","\n","data = obs[:162]\n","print(data)"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"b58dPUyeJDaP"},"source":["## Hidden Markov Model (HMM)\n","\n","We will implement the HMM to predict future stock prices."]},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":502},"executionInfo":{"elapsed":18131,"status":"ok","timestamp":1688660021605,"user":{"displayName":"Nikhil Kumar Patel","userId":"10043475971450413836"},"user_tz":-330},"id":"ffWXywmdNDuL","outputId":"acb151c0-4021-4d08-c76c-c04165c60f7e"},"outputs":[{"name":"stdout","output_type":"stream","text":["T=  162\n","HMM Prices: \n","[5742.0, 5471.8, 5735.3, 5171.450000000001, 6176.1, 6304.0, 6089.5, 6276.95, 6704.2, 6696.4, 6508.95, 6802.7, 7603.55, 7908.75, 7943.900000000001, 7431.25, 8855.75, 8893.8, 7756.500000000002, 9074.949999999999, 8596.300000000001, 8583.95, 8433.65, 8368.5, 8532.85, 8697.2, 7409.75, 8065.8, 7935.25, 7946.35, 7957.450000000001, 7433.0, 7738.4, 7855.3, 7961.200000000001, 8137.7, 8222.6, 8076.95, 8375.35, 8992.55, 8735.650000000001, 8411.95, 8147.1, 8879.6, 9255.099999999999, 9165.95, 9556.199999999999, 9446.199999999999, 9498.5, 9515.55, 10228.2, 9900.0, 10881.999999999998, 10977.899999999998, 10658.35, 11331.850000000002, 10989.85, 10610.7, 10867.0, 11361.8, 10711.099999999999, 11680.5, 11297.7, 10547.650000000001, 10876.75, 10493.95, 10873.65, 10080.900000000001, 10248.65, 11635.0, 11759.25, 11788.85, 11785.65, 10583.15, 10644.1, 11784.75, 11342.6, 11947.3, 12794.1, 12286.1, 8597.75, 8594.55, 10262.9, 10205.949999999999, 11564.25, 11397.45, 11253.550000000001, 12078.949999999999, 11766.65, 13981.75, 14471.9, 13809.25, 14690.7, 15703.5, 15525.65, 15744.35]\n"]}],"source":["data = data[data.columns[1:5]]\n","obs = obs[obs.columns[1:5]]\n","# Calculate number of rows and set training window\n","T = data.shape[0]\n","print(\"T= \", T)\n","\n","# Define the size of the training window\n","d = 96\n","D = 96\n","hmm_price = []\n","\n","temp_T = T\n","first_time = True\n","\n","# Sliding window approach to predict future prices\n","while T < temp_T + d:\n","\n","    # Train HMM on data from T-D+1 to T\n","    train_data = obs.iloc[T-D:T]\n","    train_data = train_data.dropna()\n","\n","    # Set the random seed\n","    np.random.seed(123)\n","\n","    if(first_time):\n","      first_time = False\n","      model = hmm.GaussianHMM(n_components=5)\n","    else:\n","      old_model= model\n","      model = hmm.GaussianHMM(n_components=5, init_params=\"c\")\n","      model.startprob_ = old_model.startprob_\n","      model.transmat_ = old_model.transmat_\n","      model.means_ = old_model.means_\n","\n","    model.fit(train_data)\n","\n","    # Calculate original likelihood\n","    original_likelihood = model.score(train_data)\n","\n","    # Loop to find new likelihood\n","    t=T\n","    min_diff = float('inf')\n","    min_t = T\n","    min_likelihood = original_likelihood\n","    while t-D>  0:\n","        t = t-1\n","\n","        train_data = obs.iloc[t-D:t]\n","        new_likelihood = model.score(train_data)\n","        if (abs(new_likelihood - original_likelihood))< min_diff:  # Threshold for comparison by choosing that new_likelihood which is minimum\n","            min_diff = abs(new_likelihood - original_likelihood)\n","            min_t = t\n","            min_likelihood = new_likelihood\n","\n","    # Calculate the predicted close price\n","    close_price = obs['Close'][T-1] + ((obs['Close'][min_t + 1] - obs['Close'][min_t]) * np.sign(original_likelihood - min_likelihood))\n","\n","    hmm_price.append(close_price)\n","    T=T+1\n","\n","# Print the calculated prices\n","print(\"HMM Prices: \")\n","print(hmm_price)\n","\n","# Plot the predicted and observed prices\n","close = []\n","truncated_obs = obs.iloc[T-d:T]\n","\n","for i in truncated_obs['Close']:\n","  close.append(i)\n","\n","# plt.plot(hmm_price,marker='.', label = \"HMM Predicted Price\")\n","# plt.plot(close,marker = '.', label= \"Observed Price\")\n","# plt.ylabel(\"Close Price\")\n","# plt.legend()\n","# plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["## LSTM Model\n","\n","In this section, we train an LSTM model to predict future stock prices.\n"]},{"cell_type":"code","execution_count":4,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch 1/5\n"]},{"name":"stderr","output_type":"stream","text":["/Users/nikhilkumarpatel/Library/Python/3.9/lib/python/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"]},{"name":"stdout","output_type":"stream","text":["\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 37ms/step - loss: 0.2408\n","Epoch 2/5\n","\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.0173\n","Epoch 3/5\n","\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.0185\n","Epoch 4/5\n","\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.0109\n","Epoch 5/5\n","\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.0083\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m 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60ms/step - loss: 0.0047\n","Epoch 2/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0033\n","Epoch 3/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0020\n","Epoch 4/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0011\n","Epoch 5/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 4.6492e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n","Epoch 1/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 1.0387e-04\n","Epoch 2/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 1.0645e-04\n","Epoch 3/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 2.9017e-04\n","Epoch 4/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 5.7340e-04\n","Epoch 5/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 8.8321e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n","Epoch 1/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0023\n","Epoch 2/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0025\n","Epoch 3/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m 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\u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n","Epoch 1/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 7.2408e-04\n","Epoch 2/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 6.5662e-04\n","Epoch 3/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 5.7137e-04\n","Epoch 4/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 4.8453e-04\n","Epoch 5/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 4.0969e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n","Epoch 1/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 4.2932e-04\n","Epoch 2/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 4.2834e-04\n","Epoch 3/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 4.4445e-04\n","Epoch 4/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 4.6981e-04\n","Epoch 5/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 4.9668e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n","Epoch 1/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 6.2635e-04\n","Epoch 2/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 6.3935e-04\n","Epoch 3/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 6.3472e-04\n","Epoch 4/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 6.1483e-04\n","Epoch 5/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 5.8451e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n","Epoch 1/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 9.3146e-04\n","Epoch 2/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 8.6850e-04\n","Epoch 3/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 8.0778e-04\n","Epoch 4/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 7.5853e-04\n","Epoch 5/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 7.2662e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n","Epoch 1/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0011\n","Epoch 2/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0011\n","Epoch 3/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0011\n","Epoch 4/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0011\n","Epoch 5/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0011\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n","Epoch 1/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0013\n","Epoch 2/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0013\n","Epoch 3/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.0014\n","Epoch 4/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0014\n","Epoch 5/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0014\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n","Epoch 1/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0016\n","Epoch 2/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0016\n","Epoch 3/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0016\n","Epoch 4/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0016\n","Epoch 5/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0016\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n","Epoch 1/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0017\n","Epoch 2/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0017\n","Epoch 3/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0017\n","Epoch 4/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0017\n","Epoch 5/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0017\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n","Epoch 1/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0019\n","Epoch 2/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0019\n","Epoch 3/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0019\n","Epoch 4/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0019\n","Epoch 5/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0019\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n","Epoch 1/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0020\n","Epoch 2/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0020\n","Epoch 3/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0020\n","Epoch 4/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0020\n","Epoch 5/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0020\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n","Epoch 1/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0020\n","Epoch 2/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0020\n","Epoch 3/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0020\n","Epoch 4/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0020\n","Epoch 5/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0020\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n","Epoch 1/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0020\n","Epoch 2/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0020\n","Epoch 3/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0020\n","Epoch 4/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0020\n","Epoch 5/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0020\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n","Epoch 1/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0020\n","Epoch 2/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0020\n","Epoch 3/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.0020\n","Epoch 4/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0020\n","Epoch 5/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0020\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n","Epoch 1/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0019\n","Epoch 2/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0019\n","Epoch 3/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0019\n","Epoch 4/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0019\n","Epoch 5/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0019\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n","Epoch 1/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0018\n","Epoch 2/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0018\n","Epoch 3/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0018\n","Epoch 4/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0018\n","Epoch 5/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0018\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n","Epoch 1/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0017\n","Epoch 2/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0017\n","Epoch 3/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0017\n","Epoch 4/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0017\n","Epoch 5/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0017\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n","Epoch 1/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0016\n","Epoch 2/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0016\n","Epoch 3/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0016\n","Epoch 4/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0016\n","Epoch 5/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0016\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n","Epoch 1/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0016\n","Epoch 2/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0016\n","Epoch 3/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0016\n","Epoch 4/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0016\n","Epoch 5/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0016\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n","Epoch 1/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0017\n","Epoch 2/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0017\n","Epoch 3/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0017\n","Epoch 4/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0017\n","Epoch 5/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0017\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n","Epoch 1/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.0018\n","Epoch 2/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.0018\n","Epoch 3/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.0018\n","Epoch 4/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0018\n","Epoch 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0.0018\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n","Epoch 1/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0019\n","Epoch 2/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0019\n","Epoch 3/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0019\n","Epoch 4/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0019\n","Epoch 5/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0019\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n","Epoch 1/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0020\n","Epoch 2/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0020\n","Epoch 3/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.0020\n","Epoch 4/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.0020\n","Epoch 5/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0020\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n","Epoch 1/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0021\n","Epoch 2/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0021\n","Epoch 3/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.0021\n","Epoch 4/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.0021\n","Epoch 5/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.0021\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n","Epoch 1/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0024\n","Epoch 2/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0024\n","Epoch 3/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 0.0024\n","Epoch 4/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0024\n","Epoch 5/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0024\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.0024\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0025\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.0031\n","Epoch 4/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.0033\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.0028\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0023\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0023\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0023\n","Epoch 4/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0023\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0023\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0022\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0021\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0022\n","Epoch 4/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0022\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0023\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0022\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0020\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0020\n","Epoch 4/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0020\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0019\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0018\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0022\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0026\n","Epoch 4/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0025\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0019\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0022\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0028\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0026\n","Epoch 4/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0020\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0019\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0021\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0022\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0021\n","Epoch 4/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0018\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0018\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0020\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0017\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0017\n","Epoch 4/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0017\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0017\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.0017\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0017\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0017\n","Epoch 4/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.0016\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0015\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.0016\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.0015\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0017\n","Epoch 4/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0016\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0016\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0015\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.0015\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0015\n","Epoch 4/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0015\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0015\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n","Epoch 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2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0016\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0014\n","Epoch 4/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0014\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0014\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0014\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0014\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0015\n","Epoch 4/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0014\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0015\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0014\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0015\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0015\n","Epoch 4/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0016\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0015\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0014\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0015\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0015\n","Epoch 4/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0015\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0015\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0015\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0015\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0014\n","Epoch 4/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0015\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0016\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0015\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0016\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0015\n","Epoch 4/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0016\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0015\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0015\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0014\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0015\n","Epoch 4/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0014\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0015\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0014\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0015\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0015\n","Epoch 4/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0014\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0013\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0015\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0014\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0014\n","Epoch 4/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0014\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0014\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0013\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0014\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0012\n","Epoch 4/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0013\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0012  \n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0014\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0013\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0014\n","Epoch 4/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0013\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0013\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0012\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0011\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0012\n","Epoch 4/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0012\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0012\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0011\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0010   \n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 9.6233e-04\n","Epoch 4/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 9.9054e-04\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0011\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 9.9527e-04\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 9.0154e-04\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0010\n","Epoch 4/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 9.6794e-04\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 8.6775e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 8.7145e-04\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 8.9389e-04\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 9.1960e-04\n","Epoch 4/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 8.4680e-04\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 8.5064e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 7.6757e-04\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 8.5456e-04\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 9.2451e-04\n","Epoch 4/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 7.9131e-04\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 7.6643e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 7.9860e-04\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 7.9207e-04\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m 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\u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 8.5780e-04\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0010\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 7.5711e-04\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 8.8706e-04\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 8.6811e-04\n","Epoch 4/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 7.9182e-04\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 7.3678e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 8.5348e-04\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 8.1437e-04\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 8.3531e-04\n","Epoch 4/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 9.2474e-04\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 7.5763e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 8.9265e-04\n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0011\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 9.8963e-04\n","Epoch 4/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0045\n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0010\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0011\n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 9.2708e-04\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 9.1884e-04\n","Epoch 4/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 8.2595e-04\n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 9.4178e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 9.2971e-04\n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 9.3456e-04\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 8.8464e-04\n","Epoch 4/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 8.9373e-04\n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 8.4818e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 8.0184e-04\n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 7.8507e-04\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 245ms/step - loss: 8.0022e-04\n","Epoch 4/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 9.2859e-04\n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 8.9707e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0010\n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 9.1528e-04\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m 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\u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 9.0184e-04\n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 7.6195e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 8.2745e-04\n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 9.2076e-04\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 8.6374e-04\n","Epoch 4/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 7.9938e-04\n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 8.5482e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 8.5359e-04\n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 8.6482e-04\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0010  \n","Epoch 4/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 8.8739e-04\n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 8.0879e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0011\n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 8.5757e-04\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 177ms/step - loss: 8.8020e-04\n","Epoch 4/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 8.9792e-04\n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 7.8192e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 9.6479e-04\n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 8.6923e-04\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 9.3887e-04\n","Epoch 4/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 9.3695e-04\n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 8.5782e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 9.1771e-04\n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 8.7661e-04\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 9.9705e-04\n","Epoch 4/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 8.1135e-04\n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 9.4080e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 9.7082e-04\n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 8.4342e-04\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 9.0967e-04\n","Epoch 4/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0010\n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 9.9456e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 9.2771e-04\n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 9.3529e-04\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m 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5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 7.8675e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 9.3576e-04\n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 8.5932e-04\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 7.7612e-04\n","Epoch 4/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 7.8660e-04\n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 9.3881e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0012   \n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0015\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0015\n","Epoch 4/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0018\n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0014\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0018\n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0014\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.0013  \n","Epoch 4/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0015\n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0015\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0013\n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0016\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0015\n","Epoch 4/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0018\n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0019\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0016 \n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0022\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0018\n","Epoch 4/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0017\n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0021\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0018\n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0016\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0018\n","Epoch 4/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0014\n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0014\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0013\n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0017\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0016\n","Epoch 4/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0019\n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0019\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0016\n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0016\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0015\n","Epoch 4/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0016\n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0018\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0014\n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0018\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0014 \n","Epoch 4/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0018\n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0016\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0016\n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0022\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0014  \n","Epoch 4/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0020\n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0017\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0021\n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0019\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0014  \n","Epoch 4/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0022\n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0017\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0018\n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0021\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0025\n","Epoch 4/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0016\n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0017\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0022\n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0015\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0018\n","Epoch 4/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0016\n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0017\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0016\n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0017\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0020\n","Epoch 4/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0014\n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0021\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0016\n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0013\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0020\n","Epoch 4/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0013  \n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0015\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0015 \n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0020\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0018\n","Epoch 4/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0016\n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0015\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0019\n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0018\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0024\n","Epoch 4/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0017\n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0022\n","(96, 1)\n"]}],"source":["\n","# Scaling the data to the range (0, 1)\n","scaler = MinMaxScaler(feature_range=(0, 1))\n","scaled_data = scaler.fit_transform(obs['Close'].values.reshape(-1, 1))\n","\n","# Prepare data for LSTM model (using the first D months)\n","def prepare_data(data, time_step):\n","    X, y = [], []\n","    for i in range(len(data) - time_step):\n","        X.append(data[i:(i + time_step), 0])\n","        y.append(data[i + time_step, 0])\n","    return np.array(X), np.array(y)\n","\n","time_step = obs.shape[0] - D\n","\n","# Initialize an empty list to store the predictions\n","lstm_predictions = []\n","\n","# Initial training on the first 162 months\n","X_train, y_train = prepare_data(scaled_data, D)\n","\n","# Reshape input to be [samples, time steps, features] for LSTM\n","X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)\n","\n","# Train the LSTM model\n","model_lstm = Sequential()\n","model_lstm.add(LSTM(units=50, return_sequences=True, input_shape=(time_step, 1)))\n","model_lstm.add(LSTM(units=50))\n","model_lstm.add(Dense(1))\n","model_lstm.compile(optimizer='adam', loss='mean_squared_error')\n","model_lstm.fit(X_train, y_train, epochs=5, batch_size=32)\n","\n","# Iteratively predict the next 96 months and retrain the model\n","for i in range(96):  # Predict the next 96 months\n","    X_input = scaled_data[-time_step:]  # Use the last 'time_step' months for prediction\n","    X_input = X_input.reshape(1, time_step, 1)\n","    pred = model_lstm.predict(X_input)\n","    lstm_predictions.append(pred[0, 0])\n","    \n","    # Add the predicted data to the training set\n","    new_data = np.append(scaled_data[:time_step + i + 1], pred)\n","    scaled_data = np.append(scaled_data, pred).reshape(-1, 1)\n","    \n","    # Re-prepare the training data including the new data\n","    X_train, y_train = prepare_data(scaled_data[:time_step + i + 1], time_step)\n","    X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)\n","    \n","    # Retrain the LSTM model with the updated data\n","    model_lstm.fit(X_train, y_train, epochs=5, batch_size=32)\n","\n","# Inverse transform the predictions to the original scale\n","lstm_predictions = scaler.inverse_transform(np.array(lstm_predictions).reshape(-1, 1))\n","\n","# LSTM predictions for the next 96 months\n","print(lstm_predictions.shape)\n"]},{"cell_type":"markdown","metadata":{},"source":["## ARIMA Model\n","\n","In this section, we use the ARIMA model to predict future stock prices.\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# # AutoRegressive Integrated Moving Average (ARIMA) Model\n","\n","# # Use the 'Close' prices for ARIMA model\n","# arima_data = obs['Close']\n","# arima_predictions = []\n","# D = 96\n","# time_step = obs.shape[0] - D\n","\n","# for i in range(D):\n","#     # Define the ARIMA model (order can be adjusted based on ACF/PACF plots or a grid search)\n","#     print(obs.iloc[time_step+i-1]['Close'])\n","#     model_arima = ARIMA(arima_data[:time_step + i - 1], order=(3, 2, 0))  \n","#     model_arima_fit = model_arima.fit()\n","\n","#     # Forecast the next 96 months\n","#     arima_prediction = model_arima_fit.forecast(steps=1)\n","#     arima_predictions.append(arima_prediction)\n","\n","\n","# # Convert predictions to a numpy array\n","# arima_predictions = np.array(arima_predictions).reshape(-1, 1)\n","\n","# print(arima_predictions.shape)"]},{"cell_type":"code","execution_count":49,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["5742.0\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.15 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2249.106, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2250.948, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2250.985, Time=0.01 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=2248.792, Time=0.00 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=2252.944, Time=0.05 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0]          \n","Total fit time: 0.236 seconds\n","5471.8\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.12 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2262.539, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2264.376, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2264.411, Time=0.01 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=2262.114, Time=0.00 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=2266.408, Time=0.03 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0]          \n","Total fit time: 0.179 seconds\n","5735.3\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.12 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2275.787, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2277.641, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2277.676, Time=0.01 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=2275.287, Time=0.00 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=2279.635, Time=0.04 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0]          \n","Total fit time: 0.196 seconds\n","6299.15\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.15 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2290.019, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2291.903, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2291.939, Time=0.01 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=2289.324, Time=0.00 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=2293.890, Time=0.06 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0]          \n","Total fit time: 0.247 seconds\n","6176.1\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.14 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2303.833, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2305.660, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2305.695, Time=0.01 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=2303.311, Time=0.00 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=2307.693, Time=0.03 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0]          \n","Total fit time: 0.205 seconds\n","6304.0\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.15 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2320.825, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2322.751, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2322.785, Time=0.01 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=2320.680, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=inf, Time=0.04 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0]          \n","Total fit time: 0.218 seconds\n","6089.5\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.17 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2334.177, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2336.050, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2336.081, Time=0.01 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=2333.935, Time=0.00 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=2338.005, Time=0.07 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0]          \n","Total fit time: 0.276 seconds\n","6276.95\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.15 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2347.348, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2349.209, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2349.199, Time=0.03 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=2347.203, Time=0.00 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=2351.197, Time=0.05 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0]          \n","Total fit time: 0.248 seconds\n","6704.2\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.13 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2361.188, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2363.028, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2363.018, Time=0.03 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=2360.871, Time=0.00 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=2364.991, Time=0.07 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0]          \n","Total fit time: 0.251 seconds\n","6696.4\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.16 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2374.569, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2376.375, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2376.407, Time=0.01 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=2374.390, Time=0.00 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=2378.365, Time=0.04 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0]          \n","Total fit time: 0.241 seconds\n","7229.95\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.14 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2389.783, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2391.645, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2391.676, Time=0.01 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=2389.918, Time=0.00 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=2393.631, Time=0.04 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.217 seconds\n","7611.35\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.14 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2402.834, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2404.685, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2404.716, Time=0.01 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=2402.962, Time=0.00 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=2406.635, Time=0.07 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.238 seconds\n","7721.3\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.14 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2419.323, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2421.161, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2421.198, Time=0.01 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=2419.861, Time=0.00 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=2423.160, Time=0.04 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.219 seconds\n","7954.35\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.13 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2434.007, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2435.960, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2435.996, Time=0.01 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=2434.866, Time=0.00 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=2437.979, Time=0.03 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.189 seconds\n","7964.8\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.15 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2447.126, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2449.091, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2449.126, Time=0.01 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=2448.088, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=2451.118, Time=0.02 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.215 seconds\n","8322.2\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.16 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2460.695, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2462.666, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2462.701, Time=0.01 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=2461.874, Time=0.00 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=2464.684, Time=0.02 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.218 seconds\n","8588.25\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.14 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2473.740, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2475.709, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2475.744, Time=0.01 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=2474.929, Time=0.00 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=2477.727, Time=0.02 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.205 seconds\n","8282.7\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.14 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2488.180, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2490.145, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2490.145, Time=0.03 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=2489.703, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=2492.161, Time=0.02 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.222 seconds\n","8808.9\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.17 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2501.922, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2503.911, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2503.910, Time=0.03 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=2503.709, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=2505.929, Time=0.02 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.246 seconds\n","8901.85\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.13 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2516.582, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2518.545, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2518.580, Time=0.01 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=2518.019, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=2520.564, Time=0.03 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.193 seconds\n","8491.0\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.14 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2532.854, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2534.723, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2534.764, Time=0.01 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=2534.773, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=2536.733, Time=0.02 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.200 seconds\n","8181.5\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.16 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2545.938, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2547.826, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2547.866, Time=0.01 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=2547.956, Time=0.00 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=2549.834, Time=0.03 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.222 seconds\n","8433.65\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.18 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2561.729, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2563.602, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2563.637, Time=0.01 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=2563.262, Time=0.00 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=2565.600, Time=0.05 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.265 seconds\n","8368.5\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.16 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2576.399, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2578.362, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2578.395, Time=0.01 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=2577.600, Time=0.00 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=2580.379, Time=0.02 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.214 seconds\n","8532.85\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.15 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2590.078, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2592.005, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2592.038, Time=0.01 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=2591.512, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=2594.005, Time=0.03 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.207 seconds\n","7971.3\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.17 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2603.271, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2605.186, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2605.181, Time=0.02 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=2604.639, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=2607.188, Time=0.03 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.246 seconds\n","7948.9\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.16 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2616.538, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2618.445, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2618.439, Time=0.03 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=2618.065, Time=0.00 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=2620.453, Time=0.03 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.239 seconds\n","8065.8\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.14 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2634.388, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2636.245, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2636.279, Time=0.01 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=2635.300, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=2638.242, Time=0.05 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.230 seconds\n","7935.25\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.16 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2647.494, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2649.378, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2649.411, Time=0.01 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=2648.385, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=2651.361, Time=0.08 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.277 seconds\n","7946.35\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.15 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2660.644, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2662.525, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2662.518, Time=0.03 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=2661.640, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=2664.543, Time=0.03 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.231 seconds\n","7563.55\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.17 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2674.063, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2675.934, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2675.927, Time=0.03 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=2674.936, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=2677.952, Time=0.03 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.250 seconds\n","6987.05\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.18 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2687.121, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2688.993, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2688.987, Time=0.03 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=2688.003, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=2691.012, Time=0.03 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.266 seconds\n","7738.4\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.16 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2702.498, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2704.378, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2704.410, Time=0.01 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=2703.018, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=inf, Time=0.05 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.247 seconds\n","7849.8\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.17 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2720.455, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2722.447, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2722.478, Time=0.01 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=2720.466, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=2724.446, Time=0.05 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.255 seconds\n","8160.1\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.17 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2740.299, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2742.044, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2742.024, Time=0.03 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=2740.811, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=2743.987, Time=0.08 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.310 seconds\n","8287.75\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.17 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2753.482, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2755.284, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2755.267, Time=0.03 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=2754.084, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=2756.287, Time=0.05 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.280 seconds\n","8638.5\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.16 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2767.570, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2769.390, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2769.419, Time=0.01 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=2768.418, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=2771.363, Time=0.08 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.275 seconds\n","8786.2\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.16 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2780.781, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2782.620, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2782.650, Time=0.01 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=2781.737, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=2784.640, Time=0.03 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.224 seconds\n","8611.15\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.14 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2795.163, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2797.024, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2797.056, Time=0.01 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=2796.412, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=2799.020, Time=0.05 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.225 seconds\n","8625.7\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.18 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2808.418, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2810.303, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2810.335, Time=0.01 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=2809.800, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=2812.328, Time=0.03 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.241 seconds\n","8224.5\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.18 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2822.086, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2823.955, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2823.949, Time=0.03 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=2823.299, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=2825.975, Time=0.03 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.276 seconds\n","8185.8\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.16 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2835.180, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2837.051, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2837.045, Time=0.03 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=2836.405, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=2839.071, Time=0.03 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.252 seconds\n","8561.3\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.18 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2850.731, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2852.610, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2852.602, Time=0.04 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=2851.569, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=2854.608, Time=0.05 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.292 seconds\n","8879.6\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.19 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2863.888, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2865.786, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2865.817, Time=0.01 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=2864.693, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=2867.813, Time=0.04 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.269 seconds\n","9173.75\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.22 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2878.496, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2880.380, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2880.409, Time=0.04 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=2879.602, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=2882.394, Time=0.03 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.324 seconds\n","9304.05\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.19 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2892.627, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2894.564, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2894.593, Time=0.01 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=2894.002, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=2896.576, Time=0.03 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.258 seconds\n","9621.25\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.17 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2906.579, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2908.546, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2908.576, Time=0.01 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=2908.213, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=2910.563, Time=0.03 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.235 seconds\n","9520.9\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.17 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2919.780, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2921.755, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2921.784, Time=0.01 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=2921.536, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=2923.777, Time=0.03 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.233 seconds\n","10077.1\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.18 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2933.881, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2935.863, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2935.892, Time=0.01 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=2935.927, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=2937.887, Time=0.03 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.245 seconds\n","9917.9\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.19 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2947.217, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2949.187, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2949.217, Time=0.01 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=2949.160, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=2951.190, Time=0.07 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.297 seconds\n","9788.6\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.19 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2963.778, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2965.722, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2965.754, Time=0.01 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=2966.211, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=2967.735, Time=0.03 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.252 seconds\n","10335.3\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.19 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2977.385, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2979.277, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2979.312, Time=0.01 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=2979.646, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=2981.274, Time=0.05 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.280 seconds\n","10226.55\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.18 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2990.844, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2992.755, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2992.787, Time=0.01 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=2992.968, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=2994.767, Time=0.03 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.243 seconds\n","10530.7\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.17 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=3007.255, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=3009.117, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=3009.150, Time=0.01 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=3009.867, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=3011.136, Time=0.03 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.236 seconds\n","11027.7\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.16 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=3020.642, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=3022.451, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=3022.442, Time=0.04 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=3023.136, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=3024.474, Time=0.03 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.255 seconds\n","10492.85\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.17 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=3034.630, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=3036.407, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=3036.446, Time=0.01 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=3037.421, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=3038.421, Time=0.03 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.238 seconds\n","10113.7\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.18 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=3050.381, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=3052.248, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=3052.284, Time=0.01 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=3053.646, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=3054.249, Time=0.03 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.250 seconds\n","10739.35\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.19 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=3067.729, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=3069.385, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=3069.374, Time=0.03 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=3070.334, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=3071.418, Time=0.03 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.273 seconds\n","10736.15\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=3083.448, Time=0.16 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=3083.063, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=3084.925, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=3084.952, Time=0.01 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=3085.247, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=3086.919, Time=0.03 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.227 seconds\n","10714.3\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.18 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=3100.456, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=3102.126, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=3102.092, Time=0.04 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=3103.175, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=3104.085, Time=0.03 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.273 seconds\n","11356.5\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.20 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=3113.618, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=3115.267, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=3115.233, Time=0.03 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=3116.333, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=3117.227, Time=0.03 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.286 seconds\n","11680.5\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.16 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=3126.801, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=3128.451, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=3128.415, Time=0.03 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=3129.493, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=3130.407, Time=0.03 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.244 seconds\n","10930.45\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.19 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=3144.383, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=3146.000, Time=0.17 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=3145.960, Time=0.03 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=3147.642, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=3147.941, Time=0.03 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.440 seconds\n","10386.6\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=3164.303, Time=0.18 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=3158.485, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=3160.260, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=3160.233, Time=0.04 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=3162.069, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=3162.199, Time=0.03 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.274 seconds\n","10876.75\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.17 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=3179.266, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=3180.849, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=3180.778, Time=0.03 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=3181.904, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=3182.696, Time=0.03 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.252 seconds\n","10862.55\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.19 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=3196.491, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=3198.423, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=3198.408, Time=0.03 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=3198.539, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=3200.338, Time=0.08 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.326 seconds\n","10830.95\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.19 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=3212.056, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=3213.839, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=3213.786, Time=0.03 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=3214.514, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=3214.479, Time=0.09 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.337 seconds\n","10792.5\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=3223.202, Time=0.17 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=3225.284, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=3227.051, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=3226.995, Time=0.03 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=3227.729, Time=0.01 sec\n"," ARIMA(1,1,2)(0,0,0)[0] intercept   : AIC=3228.198, Time=0.06 sec\n"," ARIMA(2,1,1)(0,0,0)[0] intercept   : AIC=3227.881, Time=0.08 sec\n"," ARIMA(3,1,2)(0,0,0)[0] intercept   : AIC=3230.505, Time=0.12 sec\n"," ARIMA(2,1,3)(0,0,0)[0] intercept   : AIC=3230.467, Time=0.14 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=3227.731, Time=0.06 sec\n"," ARIMA(1,1,3)(0,0,0)[0] intercept   : AIC=3228.831, Time=0.10 sec\n"," ARIMA(3,1,1)(0,0,0)[0] intercept   : AIC=3228.541, Time=0.14 sec\n"," ARIMA(3,1,3)(0,0,0)[0] intercept   : AIC=3230.599, Time=0.26 sec\n"," ARIMA(2,1,2)(0,0,0)[0]             : AIC=inf, Time=0.15 sec\n","\n","Best model:  ARIMA(2,1,2)(0,0,0)[0] intercept\n","Total fit time: 1.338 seconds\n","11623.9\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=3235.718, Time=0.19 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=3238.535, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=3240.303, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=3240.247, Time=0.03 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=3240.948, Time=0.01 sec\n"," ARIMA(1,1,2)(0,0,0)[0] intercept   : AIC=3241.287, Time=0.11 sec\n"," ARIMA(2,1,1)(0,0,0)[0] intercept   : AIC=3241.000, Time=0.12 sec\n"," ARIMA(3,1,2)(0,0,0)[0] intercept   : AIC=3243.713, Time=0.13 sec\n"," ARIMA(2,1,3)(0,0,0)[0] intercept   : AIC=3243.685, Time=0.16 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=3240.945, Time=0.09 sec\n"," ARIMA(1,1,3)(0,0,0)[0] intercept   : AIC=3241.845, Time=0.15 sec\n"," ARIMA(3,1,1)(0,0,0)[0] intercept   : AIC=3242.062, Time=0.04 sec\n"," ARIMA(3,1,3)(0,0,0)[0] intercept   : AIC=3243.967, Time=0.25 sec\n"," ARIMA(2,1,2)(0,0,0)[0]             : AIC=inf, Time=0.14 sec\n","\n","Best model:  ARIMA(2,1,2)(0,0,0)[0] intercept\n","Total fit time: 1.460 seconds\n","11748.15\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.16 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=3251.793, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=3253.565, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=3253.509, Time=0.03 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=3254.169, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=3254.229, Time=0.07 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.281 seconds\n","11922.8\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=3275.189, Time=0.16 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=3272.261, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=3273.991, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=3273.924, Time=0.03 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=3275.290, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=3275.036, Time=0.08 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.304 seconds\n","11788.85\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=3288.497, Time=0.15 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=3285.543, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=3287.328, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=3287.276, Time=0.03 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=3288.694, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=3288.256, Time=0.07 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.274 seconds\n","11118.0\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=3302.204, Time=0.15 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=3298.943, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=3300.735, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=3300.688, Time=0.06 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=3302.266, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=3302.396, Time=0.08 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.324 seconds\n","11023.25\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=3316.045, Time=0.16 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=3312.513, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=3314.288, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=3314.237, Time=0.03 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=3315.694, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=3315.494, Time=0.06 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.284 seconds\n","11474.45\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.19 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=3331.580, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=3333.441, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=3333.406, Time=0.03 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=3333.999, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=3333.936, Time=0.08 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.324 seconds\n","11877.45\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.17 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=3345.008, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=3346.920, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=3346.899, Time=0.03 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=3347.337, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=3347.402, Time=0.10 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.325 seconds\n","12056.05\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=3361.738, Time=0.18 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=3360.145, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=3362.031, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=3361.999, Time=0.03 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=3362.854, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=3362.700, Time=0.08 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.309 seconds\n","12168.45\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=3375.766, Time=0.18 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=3374.842, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=3376.792, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=3376.776, Time=0.03 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=3377.906, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=3377.156, Time=0.10 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.339 seconds\n","11962.1\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.19 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=3388.270, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=3390.235, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=3390.224, Time=0.03 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=3391.504, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=3390.625, Time=0.07 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.316 seconds\n","11201.75\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.18 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=3401.541, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=3403.508, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=3403.498, Time=0.03 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=3404.885, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=3403.864, Time=0.08 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.314 seconds\n","8597.75\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=3417.480, Time=0.17 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=3415.473, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=3417.435, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=3417.424, Time=0.03 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=3418.600, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=3417.712, Time=0.06 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.290 seconds\n","9859.9\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=3438.072, Time=0.10 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=3435.978, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=3437.977, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=3437.975, Time=0.03 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=3438.260, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=3438.292, Time=0.08 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.238 seconds\n","9580.3\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=3521.294, Time=0.21 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=3516.871, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=3517.225, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=3516.924, Time=0.03 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=3516.607, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=3517.866, Time=0.10 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0]          \n","Total fit time: 0.360 seconds\n","10302.1\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=3542.676, Time=0.16 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=3542.943, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=3544.541, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=3544.456, Time=0.03 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=3543.232, Time=0.01 sec\n"," ARIMA(1,1,2)(0,0,0)[0] intercept   : AIC=3546.084, Time=0.03 sec\n"," ARIMA(2,1,1)(0,0,0)[0] intercept   : AIC=3545.992, Time=0.03 sec\n"," ARIMA(3,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.21 sec\n"," ARIMA(2,1,3)(0,0,0)[0] intercept   : AIC=3544.892, Time=0.19 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=3546.364, Time=0.03 sec\n"," ARIMA(1,1,3)(0,0,0)[0] intercept   : AIC=3547.976, Time=0.10 sec\n"," ARIMA(3,1,1)(0,0,0)[0] intercept   : AIC=3547.957, Time=0.04 sec\n"," ARIMA(3,1,3)(0,0,0)[0] intercept   : AIC=3546.065, Time=0.23 sec\n"," ARIMA(2,1,2)(0,0,0)[0]             : AIC=3542.603, Time=0.11 sec\n"," ARIMA(1,1,2)(0,0,0)[0]             : AIC=3547.047, Time=0.05 sec\n"," ARIMA(2,1,1)(0,0,0)[0]             : AIC=3546.936, Time=0.02 sec\n"," ARIMA(3,1,2)(0,0,0)[0]             : AIC=3544.544, Time=0.18 sec\n"," ARIMA(2,1,3)(0,0,0)[0]             : AIC=3544.680, Time=0.18 sec\n"," ARIMA(1,1,1)(0,0,0)[0]             : AIC=3546.460, Time=0.04 sec\n"," ARIMA(1,1,3)(0,0,0)[0]             : AIC=3548.974, Time=0.09 sec\n"," ARIMA(3,1,1)(0,0,0)[0]             : AIC=3548.838, Time=0.03 sec\n"," ARIMA(3,1,3)(0,0,0)[0]             : AIC=3546.364, Time=0.22 sec\n","\n","Best model:  ARIMA(2,1,2)(0,0,0)[0]          \n","Total fit time: 2.008 seconds\n","11073.45\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=3556.637, Time=0.17 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=3557.299, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=3558.630, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=3558.538, Time=0.03 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=3557.430, Time=0.01 sec\n"," ARIMA(1,1,2)(0,0,0)[0] intercept   : AIC=3561.540, Time=0.11 sec\n"," ARIMA(2,1,1)(0,0,0)[0] intercept   : AIC=3561.557, Time=0.05 sec\n"," ARIMA(3,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.25 sec\n"," ARIMA(2,1,3)(0,0,0)[0] intercept   : AIC=inf, Time=0.25 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=inf, Time=0.13 sec\n"," ARIMA(1,1,3)(0,0,0)[0] intercept   : AIC=3563.478, Time=0.12 sec\n"," ARIMA(3,1,1)(0,0,0)[0] intercept   : AIC=3563.400, Time=0.04 sec\n"," ARIMA(3,1,3)(0,0,0)[0] intercept   : AIC=3560.543, Time=0.30 sec\n"," ARIMA(2,1,2)(0,0,0)[0]             : AIC=3556.830, Time=0.10 sec\n","\n","Best model:  ARIMA(2,1,2)(0,0,0)[0] intercept\n","Total fit time: 1.587 seconds\n","11387.5\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.18 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=3574.669, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=3575.818, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=3575.798, Time=0.01 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=3575.160, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=inf, Time=0.13 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.347 seconds\n","11247.55\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.19 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=3592.532, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=3594.105, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=3594.106, Time=0.02 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=3593.427, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=inf, Time=0.13 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.359 seconds\n","11642.4\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.19 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=3606.715, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=3608.416, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=3608.394, Time=0.03 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=3607.795, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=inf, Time=0.15 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.394 seconds\n","12968.95\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.19 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=3620.554, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=3622.228, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=3622.237, Time=0.01 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=3621.545, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=3624.261, Time=0.03 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.263 seconds\n","13981.75\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.18 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=3635.136, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=3636.772, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=3636.783, Time=0.01 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=3636.362, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=3632.621, Time=0.14 sec\n"," ARIMA(2,1,1)(0,0,0)[0] intercept   : AIC=3640.475, Time=0.04 sec\n"," ARIMA(1,1,2)(0,0,0)[0] intercept   : AIC=3640.433, Time=0.07 sec\n"," ARIMA(0,1,2)(0,0,0)[0] intercept   : AIC=3638.394, Time=0.04 sec\n"," ARIMA(2,1,0)(0,0,0)[0] intercept   : AIC=3638.454, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0]             : AIC=3639.647, Time=0.04 sec\n","\n","Best model:  ARIMA(1,1,1)(0,0,0)[0] intercept\n","Total fit time: 0.566 seconds\n","13634.6\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.16 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=3661.498, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=3663.365, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=3663.386, Time=0.01 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=3663.415, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=3659.867, Time=0.13 sec\n"," ARIMA(2,1,1)(0,0,0)[0] intercept   : AIC=3666.948, Time=0.04 sec\n"," ARIMA(1,1,2)(0,0,0)[0] intercept   : AIC=3666.904, Time=0.06 sec\n"," ARIMA(0,1,2)(0,0,0)[0] intercept   : AIC=3664.901, Time=0.08 sec\n"," ARIMA(2,1,0)(0,0,0)[0] intercept   : AIC=3664.933, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0]             : AIC=3667.430, Time=0.01 sec\n","\n","Best model:  ARIMA(1,1,1)(0,0,0)[0] intercept\n","Total fit time: 0.531 seconds\n","14529.15\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=3689.907, Time=0.07 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=3682.102, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=3684.051, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=3684.070, Time=0.01 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=3684.612, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=inf, Time=0.07 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.185 seconds\n","14690.7\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=3704.416, Time=0.12 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=3696.914, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=3698.913, Time=0.02 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=3698.937, Time=0.01 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=3699.158, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=3700.907, Time=0.06 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.222 seconds\n","14631.1\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=3723.722, Time=0.08 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=3715.802, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=3717.793, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=3717.792, Time=0.03 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=3718.593, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=3719.817, Time=0.03 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.172 seconds\n","15582.8\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=3737.470, Time=0.08 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=3729.559, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=3731.558, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=3731.556, Time=0.03 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=3732.467, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=3733.579, Time=0.03 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.169 seconds\n","15721.5\n","Performing stepwise search to minimize aic\n"," ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=3751.185, Time=0.08 sec\n"," ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=3743.315, Time=0.01 sec\n"," ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=3745.313, Time=0.01 sec\n"," ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=3745.311, Time=0.03 sec\n"," ARIMA(0,1,0)(0,0,0)[0]             : AIC=3746.178, Time=0.01 sec\n"," ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=3747.334, Time=0.03 sec\n","\n","Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n","Total fit time: 0.169 seconds\n","(96, 1)\n"]}],"source":["# Use auto_arima to find the best ARIMA model parameters\n","\n","from pmdarima import auto_arima\n","\n","arima_data = obs['Close']\n","arima_predictions = []\n","D = 96\n","time_step = obs.shape[0] - D\n","\n","for i in range(D):\n","    \n","    # Fit auto_arima model\n","    model_auto_arima = auto_arima(\n","        arima_data[:time_step + i - 1],  # Data up to current index\n","        seasonal=True,              # Set to True if your data is seasonal\n","        trace=True,                  # Display the search progress\n","        error_action='ignore',       # Ignore errors to continue with the next iteration\n","        suppress_warnings=True,      # Suppress warnings during the model fitting\n","        stepwise=True                # Use stepwise approach to find the best model\n","    )\n","    \n","    # Forecast the next step\n","    arima_prediction = model_auto_arima.predict(n_periods=1)\n","    arima_predictions.append(arima_prediction)\n","\n","# Convert predictions to a numpy array\n","arima_predictions = np.array(arima_predictions).reshape(-1, 1)\n","\n","print(arima_predictions.shape)\n","\n"]},{"cell_type":"markdown","metadata":{},"source":["## RNN Model\n","\n","In this section, we train an RNN model to predict future stock prices.\n"]},{"cell_type":"code","execution_count":6,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["(96, 162)\n","Epoch 1/5\n"]},{"name":"stderr","output_type":"stream","text":["/Users/nikhilkumarpatel/Library/Python/3.9/lib/python/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"]},{"name":"stdout","output_type":"stream","text":["\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 26ms/step - loss: 0.0227\n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.0164\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.0158\n","Epoch 4/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.0125\n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.0047\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m 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36ms/step - loss: 0.0125\n","Epoch 2/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.0107\n","Epoch 3/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 0.0043\n","Epoch 4/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 1.3279e-04\n","Epoch 5/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 0.0018\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n","Epoch 1/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 0.0041\n","Epoch 2/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.0058\n","Epoch 3/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.0044\n","Epoch 4/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.0018\n","Epoch 5/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 8.3905e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n","Epoch 1/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 0.0037\n","Epoch 2/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.0050\n","Epoch 3/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.0042\n","Epoch 4/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.0023\n","Epoch 5/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.0012\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n","Epoch 1/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.0015\n","Epoch 2/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.0026\n","Epoch 3/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.0028\n","Epoch 4/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.0019\n","Epoch 5/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 9.1674e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n","Epoch 1/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 7.0856e-04\n","Epoch 2/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0013\n","Epoch 3/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0017\n","Epoch 4/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0015\n","Epoch 5/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 8.6062e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n","Epoch 1/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 4.7428e-04\n","Epoch 2/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 7.2658e-04\n","Epoch 3/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0011\n","Epoch 4/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.0011\n","Epoch 5/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 7.1570e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n","Epoch 1/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 3.9257e-04\n","Epoch 2/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 4.6978e-04\n","Epoch 3/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 6.8679e-04\n","Epoch 4/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 7.4072e-04\n","Epoch 5/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 5.7026e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n","Epoch 1/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 4.1205e-04\n","Epoch 2/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 3.6465e-04\n","Epoch 3/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 4.9709e-04\n","Epoch 4/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 5.9822e-04\n","Epoch 5/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 5.3114e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n","Epoch 1/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 3.4874e-04\n","Epoch 2/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 3.0694e-04\n","Epoch 3/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 3.7769e-04\n","Epoch 4/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 4.4004e-04\n","Epoch 5/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 4.0577e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n","Epoch 1/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 4.2564e-04\n","Epoch 2/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 3.5151e-04\n","Epoch 3/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m 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\u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 4.3378e-04\n","Epoch 5/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 4.1564e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n","Epoch 1/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 4.6787e-04\n","Epoch 2/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 4.3661e-04\n","Epoch 3/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 4.5355e-04\n","Epoch 4/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 4.7896e-04\n","Epoch 5/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 4.6858e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n","Epoch 1/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 4.0421e-04\n","Epoch 2/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 3.9250e-04\n","Epoch 3/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 4.0477e-04\n","Epoch 4/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 4.1210e-04\n","Epoch 5/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 3.9761e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n","Epoch 1/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 3.5267e-04\n","Epoch 2/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 3.4891e-04\n","Epoch 3/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 3.5653e-04\n","Epoch 4/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 3.5565e-04\n","Epoch 5/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 3.4206e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n","Epoch 1/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 3.6667e-04\n","Epoch 2/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 3.6871e-04\n","Epoch 3/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 3.6363e-04\n","Epoch 4/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 3.5187e-04\n","Epoch 5/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 3.4348e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n","Epoch 1/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 3.4232e-04\n","Epoch 2/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 3.3958e-04\n","Epoch 3/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 3.3458e-04\n","Epoch 4/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 3.2549e-04\n","Epoch 5/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 3.1683e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n","Epoch 1/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 3.0449e-04\n","Epoch 2/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 3.0100e-04\n","Epoch 3/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 2.9699e-04\n","Epoch 4/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 2.9100e-04\n","Epoch 5/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 2.8516e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n","Epoch 1/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 2.7534e-04\n","Epoch 2/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 2.7143e-04\n","Epoch 3/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m 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\u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 2.3729e-04\n","Epoch 5/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 2.3418e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n","Epoch 1/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 2.5003e-04\n","Epoch 2/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 2.4425e-04\n","Epoch 3/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 2.4021e-04\n","Epoch 4/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 2.4033e-04\n","Epoch 5/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 2.3984e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n","Epoch 1/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 3.8011e-04\n","Epoch 2/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 3.7876e-04\n","Epoch 3/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 3.7625e-04\n","Epoch 4/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 3.7518e-04\n","Epoch 5/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 3.7463e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n","Epoch 1/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 3.5700e-04\n","Epoch 2/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 3.5412e-04\n","Epoch 3/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 3.5167e-04\n","Epoch 4/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 3.4938e-04\n","Epoch 5/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 3.4633e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n","Epoch 1/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 3.2855e-04\n","Epoch 2/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 3.2541e-04\n","Epoch 3/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 3.2256e-04\n","Epoch 4/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 3.1929e-04\n","Epoch 5/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 3.1570e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n","Epoch 1/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 3.0040e-04\n","Epoch 2/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 2.9741e-04\n","Epoch 3/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 2.9413e-04\n","Epoch 4/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 2.9082e-04\n","Epoch 5/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 2.8784e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n","Epoch 1/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 3.4804e-04\n","Epoch 2/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 3.4211e-04\n","Epoch 3/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 3.3802e-04\n","Epoch 4/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 3.3473e-04\n","Epoch 5/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 3.2987e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n","Epoch 1/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 3.1727e-04\n","Epoch 2/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 3.1334e-04\n","Epoch 3/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m 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\u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 2.7312e-04\n","Epoch 5/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 2.6963e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n","Epoch 1/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 2.5835e-04\n","Epoch 2/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 2.5434e-04\n","Epoch 3/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 2.5083e-04\n","Epoch 4/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 2.4774e-04\n","Epoch 5/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 2.4436e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n","Epoch 1/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 2.5722e-04\n","Epoch 2/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 2.5351e-04\n","Epoch 3/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 2.4958e-04\n","Epoch 4/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 2.4615e-04\n","Epoch 5/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 2.4290e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n","Epoch 1/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 2.6896e-04\n","Epoch 2/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 2.6476e-04\n","Epoch 3/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 2.6072e-04\n","Epoch 4/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 2.5706e-04\n","Epoch 5/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 2.5321e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n","Epoch 1/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 3.2369e-04\n","Epoch 2/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 3.1808e-04\n","Epoch 3/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 3.1318e-04\n","Epoch 4/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 3.0987e-04\n","Epoch 5/5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 3.0569e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - loss: 3.0543e-04\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - loss: 3.0675e-04\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - loss: 3.1293e-04\n","Epoch 4/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - loss: 6.9302e-04\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - loss: 3.7439e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - loss: 7.3744e-04\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - loss: 0.0011\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - loss: 6.0361e-04\n","Epoch 4/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - loss: 0.0012\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - loss: 4.0559e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - loss: 6.5001e-04\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 4.4547e-04\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m 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\u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - loss: 2.4219e-04\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - loss: 7.4325e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 3.4218e-04\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - loss: 6.8617e-04\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 3.2767e-04\n","Epoch 4/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 3.9458e-04\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 3.4960e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 4.0230e-04\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 3.0885e-04\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 3.7540e-04\n","Epoch 4/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 3.8424e-04\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 6.1488e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 4.1577e-04\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 5.9949e-04\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 3.4169e-04\n","Epoch 4/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 3.1674e-04\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 3.1256e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - loss: 2.6237e-04\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - loss: 4.2131e-04\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 2.1414e-04\n","Epoch 4/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 4.0529e-04\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 2.3687e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 2.9788e-04\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 4.1522e-04\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 3.2654e-04\n","Epoch 4/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 3.0114e-04\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 3.2323e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 3.0979e-04\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 3.4587e-04\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 2.7149e-04\n","Epoch 4/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 2.6481e-04\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 2.9414e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 2.2480e-04\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 2.6653e-04\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m 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\u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 2.4015e-04\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 2.3095e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 2.5492e-04\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 2.4137e-04\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 2.6839e-04\n","Epoch 4/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 2.4880e-04\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 3.4294e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 2.7127e-04\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 2.6504e-04\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 2.4566e-04\n","Epoch 4/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 2.7479e-04\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 2.2536e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - loss: 2.5223e-04\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 1.9455e-04\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 2.1934e-04\n","Epoch 4/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - loss: 2.3865e-04\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 2.3218e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 2.0119e-04\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 2.1081e-04\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 1.8630e-04\n","Epoch 4/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 1.5641e-04\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 1.6609e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - loss: 1.8112e-04\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 1.5377e-04\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 1.5062e-04\n","Epoch 4/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 1.4404e-04\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 1.4710e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - loss: 1.6594e-04\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - loss: 1.4233e-04\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - loss: 1.4116e-04\n","Epoch 4/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 1.2644e-04\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 1.3677e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - loss: 1.2959e-04\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - loss: 1.1887e-04\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m 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\u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 1.5242e-04\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - loss: 1.6680e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - loss: 1.8277e-04\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - loss: 1.4473e-04\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - loss: 1.5260e-04\n","Epoch 4/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - loss: 1.5042e-04\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - loss: 1.5099e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - loss: 1.5984e-04\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - loss: 1.1489e-04\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - loss: 1.2717e-04\n","Epoch 4/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - loss: 1.3284e-04\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - loss: 1.4623e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - loss: 1.3494e-04\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - loss: 1.5639e-04\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - loss: 1.2701e-04\n","Epoch 4/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - loss: 1.4735e-04\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - loss: 1.1131e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - loss: 1.3019e-04\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - loss: 1.2998e-04\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - loss: 1.1424e-04\n","Epoch 4/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - loss: 1.3289e-04\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - loss: 1.3715e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - loss: 1.7956e-04\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - loss: 1.4359e-04\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - loss: 1.8933e-04\n","Epoch 4/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - loss: 1.8206e-04\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - loss: 1.8199e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 1.5738e-04\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - loss: 1.6769e-04\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - loss: 1.6726e-04\n","Epoch 4/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - loss: 1.3877e-04\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - loss: 1.4773e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - loss: 1.5127e-04\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - loss: 1.2748e-04\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m 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\u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - loss: 1.2101e-04\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 1.7945e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - loss: 1.8240e-04\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - loss: 1.9162e-04\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - loss: 1.9557e-04\n","Epoch 4/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - loss: 2.0056e-04\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - loss: 1.6729e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - loss: 2.2399e-04\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - loss: 2.4201e-04\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 2.2647e-04\n","Epoch 4/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - loss: 2.3340e-04\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - loss: 2.2494e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - loss: 2.4786e-04\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 2.0670e-04\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - loss: 2.3843e-04\n","Epoch 4/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - loss: 2.9409e-04\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - loss: 2.6654e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n","Epoch 1/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 3.4087e-04\n","Epoch 2/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - loss: 3.7653e-04\n","Epoch 3/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 3.0683e-04\n","Epoch 4/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 2.8888e-04\n","Epoch 5/5\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - loss: 3.5171e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 2.7387e-04\n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 4.6386e-04\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.0014\n","Epoch 4/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 8.7985e-04\n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.0013\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.0014\n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 6.4066e-04\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - loss: 0.0019\n","Epoch 4/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.0018\n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 0.0014\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 7.7719e-04\n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - loss: 0.0029\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 0.0020\n","Epoch 4/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 0.0018\n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.0015\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 0.0010  \n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - loss: 0.0013\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 4.2867e-04\n","Epoch 4/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 4.7135e-04\n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 9.8744e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - loss: 4.3160e-04\n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 4.3823e-04\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 4.1680e-04\n","Epoch 4/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 4.6058e-04\n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 3.7838e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 3.8821e-04\n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 4.0259e-04\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 3.7760e-04\n","Epoch 4/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 4.6822e-04\n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - loss: 4.5534e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 5.0392e-04\n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 6.5846e-04\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 5.1928e-04\n","Epoch 4/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 7.7595e-04\n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - loss: 3.9012e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 3.7503e-04\n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 4.6573e-04\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - loss: 6.2382e-04\n","Epoch 4/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - loss: 3.9487e-04\n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 4.4978e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 3.6547e-04\n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 4.4025e-04\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 4.1699e-04\n","Epoch 4/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 3.7755e-04\n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - loss: 4.0097e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - loss: 3.1618e-04\n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - loss: 2.6819e-04\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m 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5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - loss: 2.8821e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 2.4671e-04\n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - loss: 3.1303e-04\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - loss: 2.4390e-04\n","Epoch 4/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - loss: 2.6536e-04\n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - loss: 2.6223e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - loss: 2.6521e-04\n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - loss: 2.7750e-04\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - loss: 2.1033e-04\n","Epoch 4/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 2.0705e-04\n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 2.3632e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - loss: 2.4446e-04\n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - loss: 2.2109e-04\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - loss: 2.2422e-04\n","Epoch 4/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - loss: 2.0392e-04\n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - loss: 2.2292e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - loss: 2.6527e-04\n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - loss: 2.6191e-04\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - loss: 2.7815e-04\n","Epoch 4/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - loss: 2.4163e-04\n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - loss: 2.7924e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - loss: 9.2565e-04\n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - loss: 5.4689e-04\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - loss: 8.0577e-04\n","Epoch 4/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - loss: 7.9184e-04\n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - loss: 4.6286e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 6.8620e-04\n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 5.3791e-04\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m 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\u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 0.0011   \n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 9.4013e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 0.0011  \n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 8.7196e-04\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 7.7221e-04\n","Epoch 4/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 6.1896e-04\n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 6.1005e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 5.4620e-04\n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 5.6692e-04\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 7.4236e-04\n","Epoch 4/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 7.1363e-04\n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 4.9717e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 4.7463e-04\n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 4.2010e-04\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 6.5790e-04\n","Epoch 4/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 6.1536e-04\n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 5.5281e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 4.3958e-04\n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 5.4965e-04\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 3.8264e-04\n","Epoch 4/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 3.8262e-04\n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 4.1849e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 4.5204e-04\n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 4.0810e-04\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 3.3349e-04\n","Epoch 4/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 3.7715e-04\n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 4.8679e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 8.9316e-04\n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 7.1933e-04\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.0011\n","Epoch 4/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 5.9258e-04\n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 7.7081e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 0.0012\n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 9.9466e-04\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 5.8945e-04\n","Epoch 4/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 9.1069e-04\n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 8.5556e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 7.0312e-04\n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 7.0789e-04\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 6.7801e-04\n","Epoch 4/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - loss: 8.8864e-04\n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 5.1214e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 6.8036e-04\n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 6.1733e-04\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - loss: 7.2576e-04\n","Epoch 4/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - loss: 6.9857e-04\n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - loss: 5.5054e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - loss: 6.3779e-04\n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - loss: 4.4088e-04\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 6.2636e-04\n","Epoch 4/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 5.8852e-04\n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - loss: 4.4033e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - loss: 4.7440e-04\n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - loss: 6.1440e-04\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - loss: 5.3815e-04\n","Epoch 4/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - loss: 6.4335e-04\n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - loss: 5.0937e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - loss: 5.8427e-04\n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - loss: 6.7592e-04\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - loss: 7.1263e-04\n","Epoch 4/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - loss: 7.2797e-04\n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - loss: 8.7271e-04\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step\n","Epoch 1/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - loss: 0.0013\n","Epoch 2/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - loss: 9.5328e-04\n","Epoch 3/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - loss: 0.0011\n","Epoch 4/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 8.8896e-04\n","Epoch 5/5\n","\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - loss: 7.1416e-04\n","(96, 1)\n"]}],"source":["\n","\n","# Scaling the data\n","scaler = MinMaxScaler(feature_range=(0, 1))\n","scaled_data = scaler.fit_transform(obs['Close'].values.reshape(-1, 1))\n","\n","# Prepare data function for RNN model\n","def prepare_data(data, time_step):\n","    X, y = [], []\n","    for i in range(len(data) - time_step):\n","        X.append(data[i:(i + time_step), 0])\n","        y.append(data[i + time_step, 0])\n","    return np.array(X), np.array(y)\n","\n","time_step = obs.shape[0] - D  # D is the training window size\n","\n","# Initialize an empty list to store the predictions\n","rnn_predictions = []\n","\n","# Initial training on the first 162 months\n","X_train, y_train = prepare_data(scaled_data, time_step)\n","\n","print(X_train.shape)\n","X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)\n","\n","# Train the initial RNN model\n","model_rnn = Sequential()\n","model_rnn.add(SimpleRNN(units=50, return_sequences=True, input_shape=(time_step, 1)))\n","model_rnn.add(SimpleRNN(units=50))\n","model_rnn.add(Dense(1))\n","model_rnn.compile(optimizer='adam', loss='mean_squared_error')\n","model_rnn.fit(X_train, y_train, epochs=5, batch_size=32)\n","\n","# Iteratively predict the next month and retrain the model\n","for i in range(96):  # Predict the next 96 months iteratively\n","    # Predict the next month\n","    X_input = scaled_data[-time_step:]  # Use the last 'time_step' months for prediction\n","    X_input = X_input.reshape(1, time_step, 1)\n","    pred = model_rnn.predict(X_input)\n","    rnn_predictions.append(pred[0, 0])\n","    \n","    # Add the real data for the next month to the training set\n","    new_data = np.append(scaled_data[:time_step + i + 1], pred)\n","    scaled_data = np.append(scaled_data, pred).reshape(-1, 1)\n","    \n","    # Re-prepare the training data including the new data\n","    X_train, y_train = prepare_data(scaled_data[:time_step + i + 1], time_step)\n","    X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)\n","    \n","    # Retrain the RNN model with the updated data\n","    model_rnn.fit(X_train, y_train, epochs=5, batch_size=32)\n","\n","# Inverse transform the predictions to the original scale\n","rnn_predictions = scaler.inverse_transform(np.array(rnn_predictions).reshape(-1, 1))\n","\n","# rnn_predictions now contains the predicted closing prices for the next 96 months\n","print(rnn_predictions.shape)"]},{"cell_type":"markdown","metadata":{},"source":["## Plot Predictions\n","\n","Now, we plot the predictions from all models alongside the actual observed prices.\n"]},{"cell_type":"code","execution_count":50,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1400x700 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plt.figure(figsize=(14, 7))\n","\n","# Plot actual prices for the corresponding period\n","plt.plot(close, marker='.', label='Observed Price')\n","\n","# Plot HMM predicted prices\n","plt.plot(hmm_price, marker='.', label='HMM Predicted Price')\n","\n","# Plot ARIMA predicted prices\n","plt.plot(arima_predictions, marker='.', label='ARIMA Predicted Price')\n","\n","# Plot RNN predicted prices\n","plt.plot(rnn_predictions, marker='.', label='RNN Predicted Price')\n","\n","# Plot LSTM predicted prices\n","plt.plot(lstm_predictions, marker='.', label='LSTM Predicted Price')\n","\n","plt.ylabel(\"Close Price\")\n","plt.legend()\n","plt.show()\n"]},{"cell_type":"markdown","metadata":{},"source":["## Model Comparison and Efficiency Calculation\n","\n","In this section, we calculate various error metrics for each model and determine the efficiency of HMM compared to LSTM, ARIMA, and RNN.\n"]},{"cell_type":"code","execution_count":52,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["96\n","HMM vs LSTM Efficiency: {'APE': array([0.87800217], dtype=float32), 'AAE': array([0.87800217], dtype=float32), 'ARPE': array([0.87800217], dtype=float32), 'RMSE': array([0.84900576], dtype=float32), 'CSPE': 0.9999980661590363}\n","HMM vs ARIMA Efficiency: {'APE': array([0.32848031]), 'AAE': array([0.32848031]), 'ARPE': array([0.32848031]), 'RMSE': array([0.30762654]), 'CSPE': 0.9999976697372494}\n","HMM vs RNN Efficiency: {'APE': array([0.83743715], dtype=float32), 'AAE': array([0.83743715], dtype=float32), 'ARPE': array([0.83743715], dtype=float32), 'RMSE': array([0.8293565], dtype=float32), 'CSPE': 0.9999975365697749}\n"]}],"source":["# Convert lists to numpy arrays\n","close = np.array(close)\n","hmm_price = np.array(hmm_price)\n","lstm_predictions = np.array(lstm_predictions)\n","arima_predictions = np.array(arima_predictions)\n","rnn_predictions = np.array(rnn_predictions)\n","\n","# Ensure all are trimmed to the same length\n","min_length = min(len(close), len(hmm_price), len(lstm_predictions), len(arima_predictions), len(rnn_predictions))\n","print(min_length)\n","close = close[-min_length:]\n","hmm_price = hmm_price[-min_length:]\n","lstm_predictions = lstm_predictions[-min_length:]\n","arima_predictions = arima_predictions[-min_length:]\n","rnn_predictions = rnn_predictions[-min_length:]\n","\n","# 1. Absolute Percentage Error (APE)\n","def ape(real_, pred_):\n","    APE = 0\n","    sum = 0\n","    N = len(real_)\n","    # Calculate the sum of absolute differences between real and predicted values\n","    for i in range(1, N):\n","        sum += (np.abs(real_[i] - pred_[i])) / N\n","\n","    # Calculate APE as a ratio of the sum to the mean of real values\n","    APE = sum / (np.mean(real_))\n","\n","    return APE\n","\n","ape_hmm = ape(close, hmm_price)\n","ape_lstm = ape(close, lstm_predictions)\n","ape_arima = ape(close, arima_predictions)\n","ape_rnn = ape(close, rnn_predictions)\n","\n","# 2. Average Absolute Error (AAE)\n","def aae(real_, pred_):\n","  AAE = 0\n","  sum = 0\n","  N = len(real_)\n","  for i in range(1,N):\n","    sum += (np.abs(real_[i] - pred_[i]))/N\n","  AAE = sum\n","  return AAE\n","\n","aae_hmm = aae(close, hmm_price)\n","aae_lstm = aae(close, lstm_predictions)\n","aae_arima = aae(close, arima_predictions)\n","aae_rnn = aae(close, rnn_predictions)\n","\n","# 3. Average Relative Percentage Error (ARPE)\n","def arpe(real_, pred_):\n","\n","  sum = 0\n","  N = len(real_)\n","  for i in range(1,N):\n","    sum += (np.abs(real_[i] - pred_[i]))/N\n","  ARPE = sum/N\n","  return ARPE\n","\n","arpe_hmm = arpe(close, hmm_price)\n","arpe_lstm = arpe(close, lstm_predictions)\n","arpe_arima = arpe(close, arima_predictions)\n","arpe_rnn = arpe(close, rnn_predictions)\n","\n","# 4. Root Mean Squared Error (RMSE)\n","def rmse(real_, pred_):\n","  sum = 0\n","  N = len(real_)\n","  for i in range(1,N):\n","    sum += (np.square(real_[i] - pred_[i]))/N\n","  RMSE = np.sqrt(sum)\n","  return RMSE\n","\n","rmse_hmm = rmse(close, hmm_price)\n","rmse_lstm = rmse(close, lstm_predictions)\n","rmse_arima = rmse(close, arima_predictions)\n","rmse_rnn = rmse(close, rnn_predictions)\n","\n","\n","# 6. Efficiency Calculation\n","def efficiency(hmm_metric, other_metric):\n","    return 1 - (hmm_metric / other_metric)\n","\n","eff_lstm = {\n","    'APE': efficiency(ape_hmm, ape_lstm),\n","    'AAE': efficiency(aae_hmm, aae_lstm),\n","    'ARPE': efficiency(arpe_hmm, arpe_lstm),\n","    'RMSE': efficiency(rmse_hmm, rmse_lstm),\n","}\n","\n","eff_arima = {\n","    'APE': efficiency(ape_hmm, ape_arima),\n","    'AAE': efficiency(aae_hmm, aae_arima),\n","    'ARPE': efficiency(arpe_hmm, arpe_arima),\n","    'RMSE': efficiency(rmse_hmm, rmse_arima),\n","}\n","\n","eff_rnn = {\n","    'APE': efficiency(ape_hmm, ape_rnn),\n","    'AAE': efficiency(aae_hmm, aae_rnn),\n","    'ARPE': efficiency(arpe_hmm, arpe_rnn),\n","    'RMSE': efficiency(rmse_hmm, rmse_rnn),\n","}\n","\n","# Print the results\n","print(\"HMM vs LSTM Efficiency:\", eff_lstm)\n","print(\"HMM vs ARIMA Efficiency:\", eff_arima)\n","print(\"HMM vs RNN Efficiency:\", eff_rnn)\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]}],"metadata":{"colab":{"authorship_tag":"ABX9TyNcRhb8+5lQAltW2YDGwPTf","mount_file_id":"1VDHPeAZWidZgPIAkUhqGMxeyurfZYJOa","provenance":[]},"kernelspec":{"display_name":"Python 3","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":"3.9.6"}},"nbformat":4,"nbformat_minor":0}