{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\nimport os\nprint(os.listdir(\"../input\"))\n\n# Any results you write to the current directory are saved as output.","execution_count":50,"outputs":[{"output_type":"stream","text":"['test', 'train.csv', 'sample_submission.csv']\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"**Importing the libraries**"},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true},"cell_type":"code","source":"from fastai import *\nfrom fastai.tabular import *","execution_count":51,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from scipy import stats\nfrom scipy.signal import hann\nimport matplotlib.pyplot as plt\nfrom scipy.signal import hilbert\nfrom scipy.signal import convolve\nfrom sklearn.svm import NuSVR, SVR\nfrom sklearn.kernel_ridge import KernelRidge\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.metrics import mean_absolute_error\nfrom sklearn.preprocessing import StandardScaler\nfrom tqdm import tqdm\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.model_selection import KFold,StratifiedKFold, RepeatedKFold\nwarnings.filterwarnings(\"ignore\")","execution_count":70,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"path = Path(\"../input\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print('There are {} files in test folder '.format(len(os.listdir(os.path.join(\"../input\", 'test' )))))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"%%time\ndf_train = pd.read_csv(path/'train.csv',dtype={'acoustic_data':np.int16,'time_to_failure':np.float32})","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_train.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_train.head(2)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**Data Exploration**"},{"metadata":{"trusted":true},"cell_type":"code","source":"train_acoustic_df = df_train['acoustic_data'][:10]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_acoustic_df =df_train['acoustic_data'].values[::100]\ntrain_acoustic_df.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_time_to_failure_df = df_train['time_to_failure'].values[::100]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"%matplotlib inline\nimport matplotlib","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def plot_acc_ttf_data(train_acoustic_df,train_time_to_failure_df,title='Acoustic data and time to failure: sampled 1%'):\n fig ,ax1 = plt.subplots(figsize=(12,9))\n plt.title(title)\n plt.plot(train_acoustic_df,color='r')\n ax1.set_ylabel('train_acoustic_df',color='r')\n plt.legend(['acoustic-data'],loc=(0.01,0.95))\n ax2 = ax1.twinx()\n plt.plot(train_time_to_failure_df,color='b')\n ax2.set_ylabel('time to failure',color='b')\n plt.legend(['time_to_failure'],loc=(0.01,0.9))\n plt.grid(True)\n \n ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"plot_acc_ttf_data(train_acoustic_df,train_time_to_failure_df)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"del train_time_to_failure_df\ndel train_acoustic_df","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_acoustic_df = df_train['acoustic_data'].values[:6291455]\ntrain_time_to_failure_df = df_train['time_to_failure'].values[:6291455]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"plot_acc_ttf_data(train_acoustic_df,train_time_to_failure_df)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"rows=150000\n\ndf_train.shape\n\nsegments = int(np.floor(df_train.shape[0]/rows))\nprint(\"number of segments \",segments)","execution_count":52,"outputs":[{"output_type":"stream","text":"number of segments 4194\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_X = pd.DataFrame(index=range(segments),dtype=np.float64)\ntrain_y= pd.DataFrame(index=range(segments),dtype=np.float64,columns=['time_to_failure'])","execution_count":53,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"total_mean= df_train['acoustic_data'].mean()\ntotal_std= df_train['acoustic_data'].std()\ntotal_max= df_train['acoustic_data'].max()\ntotal_min= df_train['acoustic_data'].min()\ntotal_sum= df_train['acoustic_data'].sum()\ntotal_abs_sum= np.abs(df_train['acoustic_data']).sum()","execution_count":59,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def add_trend_feature(arr, abs_values=False):\n idx = np.array(range(len(arr)))\n if abs_values:\n arr = np.abs(arr)\n lr = LinearRegression()\n lr.fit(idx.reshape(-1, 1), arr)\n return lr.coef_[0]","execution_count":63,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def classic_sta_lta(x, length_sta, length_lta):\n sta = np.cumsum(x ** 2)\n # Convert to float\n sta = np.require(sta, dtype=np.float)\n # Copy for LTA\n lta = sta.copy()\n # Compute the STA and the LTA\n sta[length_sta:] = sta[length_sta:] - sta[:-length_sta]\n sta /= length_sta\n lta[length_lta:] = lta[length_lta:] - lta[:-length_lta]\n lta /= length_lta\n # Pad zeros\n sta[:length_lta - 1] = 0\n # Avoid division by zero by setting zero values to tiny float\n dtiny = np.finfo(0.0).tiny\n idx = lta < dtiny\n lta[idx] = dtiny\n return sta / lta","execution_count":64,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def create_features(seg_id, seg, X):\n xc = pd.Series(seg['acoustic_data'].values)\n zc = np.fft.fft(xc)\n \n X.loc[seg_id, 'mean'] = xc.mean()\n X.loc[seg_id, 'std'] = xc.std()\n X.loc[seg_id, 'max'] = xc.max()\n X.loc[seg_id, 'min'] = xc.min()\n \n #FFT transform values\n realFFT = np.real(zc)\n imagFFT = np.imag(zc)\n X.loc[seg_id, 'Rmean'] = realFFT.mean()\n X.loc[seg_id, 'Rstd'] = realFFT.std()\n X.loc[seg_id, 'Rmax'] = realFFT.max()\n X.loc[seg_id, 'Rmin'] = realFFT.min()\n X.loc[seg_id, 'Imean'] = imagFFT.mean()\n X.loc[seg_id, 'Istd'] = imagFFT.std()\n X.loc[seg_id, 'Imax'] = imagFFT.max()\n X.loc[seg_id, 'Imin'] = imagFFT.min()\n X.loc[seg_id, 'Rmean_last_5000'] = realFFT[-5000:].mean()\n X.loc[seg_id, 'Rstd__last_5000'] = realFFT[-5000:].std()\n X.loc[seg_id, 'Rmax_last_5000'] = realFFT[-5000:].max()\n X.loc[seg_id, 'Rmin_last_5000'] = realFFT[-5000:].min()\n X.loc[seg_id, 'Rmean_last_15000'] = realFFT[-15000:].mean()\n X.loc[seg_id, 'Rstd_last_15000'] = realFFT[-15000:].std()\n X.loc[seg_id, 'Rmax_last_15000'] = realFFT[-15000:].max()\n X.loc[seg_id, 'Rmin_last_15000'] = realFFT[-15000:].min()\n \n X.loc[seg_id, 'mean_change_abs'] = np.mean(np.diff(xc))\n X.loc[seg_id, 'mean_change_rate'] = np.mean(np.nonzero((np.diff(xc) / xc[:-1]))[0])\n X.loc[seg_id, 'abs_max'] = np.abs(xc).max()\n \n X.loc[seg_id, 'std_first_50000'] = xc[:50000].std()\n X.loc[seg_id, 'std_last_50000'] = xc[-50000:].std()\n X.loc[seg_id, 'std_first_10000'] = xc[:10000].std()\n X.loc[seg_id, 'std_last_10000'] = xc[-10000:].std()\n \n X.loc[seg_id, 'avg_first_50000'] = xc[:50000].mean()\n X.loc[seg_id, 'avg_last_50000'] = xc[-50000:].mean()\n X.loc[seg_id, 'avg_first_10000'] = xc[:10000].mean()\n X.loc[seg_id, 'avg_last_10000'] = xc[-10000:].mean()\n \n X.loc[seg_id, 'min_first_50000'] = xc[:50000].min()\n X.loc[seg_id, 'min_last_50000'] = xc[-50000:].min()\n X.loc[seg_id, 'min_first_10000'] = xc[:10000].min()\n X.loc[seg_id, 'min_last_10000'] = xc[-10000:].min()\n \n X.loc[seg_id, 'max_first_50000'] = xc[:50000].max()\n X.loc[seg_id, 'max_last_50000'] = xc[-50000:].max()\n X.loc[seg_id, 'max_first_10000'] = xc[:10000].max()\n X.loc[seg_id, 'max_last_10000'] = xc[-10000:].max()\n \n X.loc[seg_id, 'max_to_min'] = xc.max() / np.abs(xc.min())\n X.loc[seg_id, 'max_to_min_diff'] = xc.max() - np.abs(xc.min())\n X.loc[seg_id, 'sum'] = xc.sum()\n \n X.loc[seg_id, 'mean_change_rate_first_50000'] = np.mean(np.nonzero((np.diff(xc[:50000]) / xc[:50000][:-1]))[0])\n X.loc[seg_id, 'mean_change_rate_last_50000'] = np.mean(np.nonzero((np.diff(xc[-50000:]) / xc[-50000:][:-1]))[0])\n X.loc[seg_id, 'mean_change_rate_first_10000'] = np.mean(np.nonzero((np.diff(xc[:10000]) / xc[:10000][:-1]))[0])\n X.loc[seg_id, 'mean_change_rate_last_10000'] = np.mean(np.nonzero((np.diff(xc[-10000:]) / xc[-10000:][:-1]))[0])\n \n X.loc[seg_id, 'q95'] = np.quantile(xc, 0.95)\n X.loc[seg_id, 'q99'] = np.quantile(xc, 0.99)\n X.loc[seg_id, 'q05'] = np.quantile(xc, 0.05)\n X.loc[seg_id, 'q01'] = np.quantile(xc, 0.01)\n \n X.loc[seg_id, 'abs_q95'] = np.quantile(np.abs(xc), 0.95)\n X.loc[seg_id, 'abs_q99'] = np.quantile(np.abs(xc), 0.99)\n X.loc[seg_id, 'abs_q05'] = np.quantile(np.abs(xc), 0.05)\n \n X.loc[seg_id, 'trend'] = add_trend_feature(xc)\n X.loc[seg_id, 'abs_trend'] = add_trend_feature(xc, abs_values=True)\n X.loc[seg_id, 'abs_mean'] = np.abs(xc).mean()\n X.loc[seg_id, 'abs_std'] = np.abs(xc).std()\n \n X.loc[seg_id, 'mad'] = xc.mad()\n X.loc[seg_id, 'kurt'] = xc.kurtosis()\n X.loc[seg_id, 'skew'] = xc.skew()\n \n X.loc[seg_id, 'Hilbert_mean'] = np.abs(hilbert(xc)).mean()\n X.loc[seg_id, 'Hann_window_mean'] = (convolve(xc, hann(150), mode='same') / sum(hann(150))).mean()\n X.loc[seg_id, 'classic_sta_lta1_mean'] = classic_sta_lta(xc, 500, 10000).mean()\n X.loc[seg_id, 'classic_sta_lta2_mean'] = classic_sta_lta(xc, 5000, 100000).mean()\n X.loc[seg_id, 'classic_sta_lta3_mean'] = classic_sta_lta(xc, 3333, 6666).mean()\n X.loc[seg_id, 'classic_sta_lta4_mean'] = classic_sta_lta(xc, 10000, 25000).mean()\n X.loc[seg_id, 'Moving_average_700_mean'] = xc.rolling(window=700).mean().mean(skipna=True)\n X.loc[seg_id, 'Moving_average_1500_mean'] = xc.rolling(window=1500).mean().mean(skipna=True)\n X.loc[seg_id, 'Moving_average_3000_mean'] = xc.rolling(window=3000).mean().mean(skipna=True)\n X.loc[seg_id, 'Moving_average_6000_mean'] = xc.rolling(window=6000).mean().mean(skipna=True)\n ewma = pd.Series.ewm\n X.loc[seg_id, 'exp_Moving_average_300_mean'] = (ewma(xc, span=300).mean()).mean(skipna=True)\n X.loc[seg_id, 'exp_Moving_average_3000_mean'] = ewma(xc, span=3000).mean().mean(skipna=True)\n X.loc[seg_id, 'exp_Moving_average_30000_mean'] = ewma(xc, span=6000).mean().mean(skipna=True)\n no_of_std = 2\n X.loc[seg_id, 'MA_700MA_std_mean'] = xc.rolling(window=700).std().mean()\n X.loc[seg_id,'MA_700MA_BB_high_mean'] = (X.loc[seg_id, 'Moving_average_700_mean'] + no_of_std * X.loc[seg_id, 'MA_700MA_std_mean']).mean()\n X.loc[seg_id,'MA_700MA_BB_low_mean'] = (X.loc[seg_id, 'Moving_average_700_mean'] - no_of_std * X.loc[seg_id, 'MA_700MA_std_mean']).mean()\n X.loc[seg_id, 'MA_400MA_std_mean'] = xc.rolling(window=400).std().mean()\n X.loc[seg_id,'MA_400MA_BB_high_mean'] = (X.loc[seg_id, 'Moving_average_700_mean'] + no_of_std * X.loc[seg_id, 'MA_400MA_std_mean']).mean()\n X.loc[seg_id,'MA_400MA_BB_low_mean'] = (X.loc[seg_id, 'Moving_average_700_mean'] - no_of_std * X.loc[seg_id, 'MA_400MA_std_mean']).mean()\n X.loc[seg_id, 'MA_1000MA_std_mean'] = xc.rolling(window=1000).std().mean()\n \n X.loc[seg_id, 'iqr'] = np.subtract(*np.percentile(xc, [75, 25]))\n X.loc[seg_id, 'q999'] = np.quantile(xc,0.999)\n X.loc[seg_id, 'q001'] = np.quantile(xc,0.001)\n X.loc[seg_id, 'ave10'] = stats.trim_mean(xc, 0.1)\n \n for windows in [10, 100, 1000]:\n x_roll_std = xc.rolling(windows).std().dropna().values\n x_roll_mean = xc.rolling(windows).mean().dropna().values\n \n X.loc[seg_id, 'ave_roll_std_' + str(windows)] = x_roll_std.mean()\n X.loc[seg_id, 'std_roll_std_' + str(windows)] = x_roll_std.std()\n X.loc[seg_id, 'max_roll_std_' + str(windows)] = x_roll_std.max()\n X.loc[seg_id, 'min_roll_std_' + str(windows)] = x_roll_std.min()\n X.loc[seg_id, 'q01_roll_std_' + str(windows)] = np.quantile(x_roll_std, 0.01)\n X.loc[seg_id, 'q05_roll_std_' + str(windows)] = np.quantile(x_roll_std, 0.05)\n X.loc[seg_id, 'q95_roll_std_' + str(windows)] = np.quantile(x_roll_std, 0.95)\n X.loc[seg_id, 'q99_roll_std_' + str(windows)] = np.quantile(x_roll_std, 0.99)\n X.loc[seg_id, 'av_change_abs_roll_std_' + str(windows)] = np.mean(np.diff(x_roll_std))\n X.loc[seg_id, 'av_change_rate_roll_std_' + str(windows)] = np.mean(np.nonzero((np.diff(x_roll_std) / x_roll_std[:-1]))[0])\n X.loc[seg_id, 'abs_max_roll_std_' + str(windows)] = np.abs(x_roll_std).max()\n \n X.loc[seg_id, 'std_roll_mean_' + str(windows)] = x_roll_mean.std()\n X.loc[seg_id, 'max_roll_mean_' + str(windows)] = x_roll_mean.max()\n X.loc[seg_id, 'min_roll_mean_' + str(windows)] = x_roll_mean.min()\n X.loc[seg_id, 'q01_roll_mean_' + str(windows)] = np.quantile(x_roll_mean, 0.01)\n X.loc[seg_id, 'q05_roll_mean_' + str(windows)] = np.quantile(x_roll_mean, 0.05)\n X.loc[seg_id, 'q95_roll_mean_' + str(windows)] = np.quantile(x_roll_mean, 0.95)\n X.loc[seg_id, 'q99_roll_mean_' + str(windows)] = np.quantile(x_roll_mean, 0.99)\n X.loc[seg_id, 'av_change_abs_roll_mean_' + str(windows)] = np.mean(np.diff(x_roll_mean))\n X.loc[seg_id, 'av_change_rate_roll_mean_' + str(windows)] = np.mean(np.nonzero((np.diff(x_roll_mean) / x_roll_mean[:-1]))[0])\n X.loc[seg_id, 'abs_max_roll_mean_' + str(windows)] = np.abs(x_roll_mean).max()","execution_count":68,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"for seg_id in tqdm(range(segments)):\n seg = df_train.iloc[seg_id*rows:seg_id*rows+rows]\n create_features(seg_id, seg, train_X)\n train_y.loc[seg_id, 'time_to_failure'] = seg['time_to_failure'].values[-1]\n","execution_count":71,"outputs":[{"output_type":"stream","text":"100%|██████████| 4194/4194 [19:45<00:00, 3.58it/s]\n","name":"stderr"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"df = pd.concat([train_X, train_y], axis=1)","execution_count":72,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"dep_var = 'time_to_failure'\ncont_names = None\ncat_names = None\n\nprocs = [FillMissing, Categorify, Normalize]\n","execution_count":73,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission = pd.read_csv(path/'sample_submission.csv', index_col='seg_id')\ntest_X = pd.DataFrame(columns=train_X.columns, dtype=np.float64, index=submission.index)","execution_count":75,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"for seg_id in tqdm(test_X.index):\n seg = pd.read_csv(path/f'test/{seg_id}.csv')\n create_features(seg_id, seg, test_X)","execution_count":76,"outputs":[{"output_type":"stream","text":"100%|██████████| 2624/2624 [13:27<00:00, 3.32it/s]\n","name":"stderr"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"test_X.reset_index(inplace=True)","execution_count":77,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df = pd.concat([train_X, train_y], axis=1)","execution_count":78,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df.to_feather(\"df\")\ntest_X.to_hdf('df_test.h5', key=\"df_test\")\ndf = pd.read_feather(\"df\")\ntest_df = pd.read_hdf('df_test.h5', key=\"df_test\")","execution_count":79,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"dep_var = 'time_to_failure'\ncont_names = None\ncat_names = None\n\nprocs = [FillMissing, Categorify, Normalize]","execution_count":80,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"data = TabularDataBunch.from_df(path, df, dep_var, valid_idx=range(0,800), procs=procs, 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"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"learn = tabular_learner(data, layers=[200,100], model_dir=Path(\"/\"))","execution_count":83,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"learn.lr_find(); learn.recorder.plot()\n","execution_count":84,"outputs":[{"output_type":"display_data","data":{"text/plain":"","text/html":""},"metadata":{}},{"output_type":"stream","text":"LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"
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2Gdm3L/qM6V3jhmzKka1D6J24a254UZWTSKjeSi3i3ommwzpJnTF1JnBI98vprXv9+IK0w4v2szfnlma85Ka3LMkMwLNu7lqlfnMaBtY/517QBWbD/A5S/PZUgHN6/8Kr3K4ZtVlde8N4wNqiRZGOMLpWXl3PbuEqav2g1A84Rozu3s5obBabSzQQXNcerMncW+4ItE8ENWHr94dT6X9kvlnhGdaJZQ9S38Hy7ext3/Xc4lfVKYl70Hl0uYfPvgKsf9N8bfcg4VMnNtLjMyc5i1Lpf46Ag+u30QTav5XJvQY4mgguLSckZNmEVJmfLl74dUOT1jRU9+kck/Z24gMjyMj24ZSPeUhqd8fGOctHrHQS596Qc6NI3jg5vOqtXn24SGOjHERF3x6pxsNuQW8PBF3Wr9Jbn7/E7cdm47nr+ijyUBU6d1bZHAhCv6kLH9AHf/d7ndeGZOWtAngm37DvOPb7IY0a0Z53ZuWuvtwsKEe0Z0tj7+pl4Y3rUZ947ozOSMnTz/TVagwzH1TND3Gnrk89UA/PnCbgGOxBhn3XxOGutzDvHs1+vo2CyOUT2SAx2SqSeC+ozg28zdfLl6N789rz0piTGBDscYR4kIj1/Sgz6tErnnwww25hUEOiRTTwR1Inhx5gbauRtww9lpgQ7FGL+ICnfxwi/6Eu4SbntnCYUlZYEOydQDQZ0IXr+mPy9f3c/mgjUhJSUxhqfH92L1zoM8Onl1oMMx9UBQtxHER0cQb8M7mBB0Xpdm3DQkjZdnZTOgbWPG9k6psuzqHQdZtHkvK7YdYOWOg2Tn5tOnVSJjerZgVPfmNImzeTKCXUjcR2BMKCopK+eKifPI3HmQ/948kK4tEo55XVV57uv1TPhmPQCNG0TSPaUhrRvH8v2GPLJzC3CFCWe3T+KpS3vazWr1kN1QZoxhx/4jXPTC9xwsLOEPwztyw+A0XGFCUWkZf5y0go+Xbmdc31T+cH5HkhtG/zhukaqSuesQUzJ2MnF2Nhf2bMHTl9lAd/WNDTpnjKFFYgzT7hzMA5+s4PFpmUxftYs/XdCVv0/LZMGmvdwzohO3Dm13wsB1IkKX5AS6JCdQUlbOxNnZ3DC4LV2SE6o4kqnPrBXVmCDnjo/ipV/2Y8IVvdmQW8C4F39g2bb9PH9lH247t32No5feOrQ9CdERPDHtxMmXTHCwMwJjQoCIMLZ3CmelNeHF7zYwpmeLEyZMqkrD2AhuP7c9f5u6hu+z8mx03SBkZwTGhJCmCdE8dGG3WieBo64+qzUpiTE8Pm0N5eV1v13RnBxLBMaYGkVHuLh7REdWbj/I5xk7Ah2O8TFLBMaYWhnbK4WuyQk8NX0tW/ceDnQ4xocsERhjaiUsTHhgTBd2Hihk8JMzGPvCHCbO2sD2/UcCHZo5TY4nAhFxichSEZnsfd5WROaLSJaIfCAikU7HYIzxjYHtkph591DuH9UZBR6bmsmQJ2fwzvzNgQ7NnAZ/nBHcCayp8PzvwLOq2h7YB1zvhxiMMT7SsnEsN53Tjs9uP5tZ95zLkA5J/OnjlUz4er1NilNPOZoIRCQVuAB41ftcgGHAh94ibwI/dzIGY4xzWjWJZeKv0hnXN5Vnv17HA5+spMx6FdU7Tt9H8BxwLxDvfd4E2K+qpd7n24BKR8MSkRuBGwFatWrlcJjGmFMV4Qrjf8f39Ny49t0G9uQX88Iv+hDusibI+sKxd0pExgA5qrr4VLZX1Ymqmq6q6W6328fRGWN8SUT446jO/Gl0F75YtYtXZm8MdEjmJDiZsgcBF4nIJuB9PJeEJgCJInL0TCQV2O5gDMYYP/rNkDRGdmvOs1+vIysnP9DhmFpyLBGo6v2qmqqqbYArgG9V9SpgBnCpt9ivgU+disEY43+P/LwbMREu7puUYe0F9UQgLuLdB9wlIll42gxeC0AMxhiHNI2P5qELu7J48z7+PXdToMMxteCXRKCqM1V1jPdxtqoOUNX2qjpeVYv8EYMxxn8u7pPC0E5unvxiLVv22F3IdZ016xtjfE5EeOziHrjChD9+lGED1dVxlgiMMY5okRjDAxd04YcNe3hy+tpAh2OqYfMRGGMcc3n/lqzYfoCXvttAmyaxXDHA7gmqiywRGGMcIyL85aJubNl7mAc+WUnLxrE2sU0dZJeGjDGOinCF8X9X9SXN3YCb317M+t2HAh2SOY4lAmOM4xKiI3j9mv5Ehbu4/s1FFJWWBTokU4ElAmOMX6Q2iuXv43qwZe9hZmTmBDocU4ElAmOM35zT0Y07PopJS2xkmbrEEoExxm/CXWH8vHcLZq7NYW9BcaDDMV6WCIwxfnVJ31RKypTJGTsCHYrxskRgjPGrLskJdG4eb5eH6hBLBMYYvxvXN5XlW/ezIdeGqq4LLBEYY/xubO8WhAl8bGcFdYIlAmOM3zVNiObsDm4+XrrdBqSrAywRGGMCYlzfFLbvP8KCTXsDHUrIs0RgjAmI87s2p0Gki4+WbAt0KCHPEoExJiBiIl2M6pHM1BW7OFJsQ04EkiUCY0zAjOubSn5RKV+u3hXoUEKaJQJjTMCc0bYxqY1i+HCxXR4KJEsExpiACQsTxvVNZU5WHjv2Hwl0OCHLsUQgItEiskBElovIKhF52Lv+PBFZIiLLRGSOiLR3KgZjTN03rm8qqvDxUrunIFCcPCMoAoapai+gNzBSRM4EXgSuUtXewLvAAw7GYIyp41o1ieWMto35cPE2VO2egkBwLBGox9H7xyO8i3qXBO/6hoCNPGVMiLu0Xyob8wpYsmVfoEMJSY62EYiIS0SWATnAV6o6H7gBmCoi24CrgSeq2PZGEVkkIotyc3OdDNMYE2CjeyQTG+myRuMAcTQRqGqZ9xJQKjBARLoDvwdGq2oq8AbwTBXbTlTVdFVNd7vdToZpjAmwBlHhjOqezOTlO+2eggDwS68hVd0PzABGAb28ZwYAHwAD/RGDMaZuu7RfKofsnoKAcLLXkFtEEr2PY4DhwBqgoYh09BY7us4YE+LsnoLAqVUiEJF2IhLlfTxURO44+ke+GsnADBHJABbiaSOYDPwGmCQiy/G0Edxz6uEbY4JFWJhwSZ8U5mTlkXOoMNDhhJTanhFMAsq8ff4nAi3xdP2skqpmqGofVe2pqt1V9RHv+o9VtYeq9lLVoaqafVo1MMYEjQt6tkAVvly1O9ChhJTaJoJyVS0FLgb+oar34PnFb4wxPtOxWRxp7gZMW7kz0KGElNomghIRuRL4NTDZuy7CmZCMMaFKRBjdPZl52XvZk18U6HBCRm0TwbXAWcDfVHWjiLQF3nIuLGNMqBrVozll5cpXq+3ykL/UKhGo6mpVvUNV3xORRkC8qv7d4diMMSGoa3ICrZvEMnWldSP1l9r2GpopIgki0hhYArwiIpXeCGaMMadDRBjVPZkfsvLYf7g40OGEhNpeGmqoqgeBS4B/q+oZwM+cC8sYE8pG92hOqV0e8pvaJoJwEUkGLuOnxmJjjHFEj5SGpCTGMM0uD/lFbRPBI8B0YIOqLhSRNGC9c2EZY0KZiDC6R3Nmr8/lYGFJoMMJerVtLP6v98awW7zPs1V1nLOhGWNC2egeyZSUKV/b5SHH1baxOFVEPhaRHO8ySURSnQ7OGBO6erdMpEXDaKausMtDTqvtpaE3gM+AFt7lc+86Y4xxhIgwqkcys9blsq/Aeg85qbaJwK2qb6hqqXf5F2CTBBhjHDU+PZXisnKbz9hhtU0Ee0Tkl94Zx1wi8ktgj5OBGWNM5+YJ9GqZyAcLt9p8xg6qbSK4Dk/X0V3ATuBS4BqHYjLGmB9dnt6StbsPsXzbgUCHErRq22tos6pepKpuVW2qqj8HrNeQMcZxF/ZKJibCxQcLtwQ6lKB1OjOU3eWzKIwxpgrx0RFc0DOZz5btoKCoNNDhBKXTSQTisyiMMaYal/dvSUFxGVNW2DwFTjidRGAtN8YYv0hv3Yg0dwP+s3BroEMJStUmAhE5JCIHK1kO4bmfwBhjHCciXJ7ekkWb95GVkx/ocIJOtYlAVeNVNaGSJV5Vw6vbVkSiRWSBiCwXkVUi8rB3vYjI30RknYisEZE7fFkhY0xwuqRvKuFhwn8W2VmBr1X7x/w0FQHDVDVfRCKAOSIyDegCtAQ6q2q5iDR1MAZjTJBwx0cxvGsz3l+whd8MTsMdHxXokILG6bQRVEs9jp7DRXgXBW4BHlHVcm+5HKdiMMYEl7tHdKKwpJxHJ68OdChBxbFEAOC9C3kZkAN8parzgXbA5SKySESmiUgHJ2MwxgSPdu44bj23HZ8t38F363IDHU7QcDQRqGqZqvYGUoEBItIdiAIKVTUdeAV4vbJtReRGb7JYlJtrb7gxxuOWoe1IczfggU9WcKS4LNDhBAVHE8FRqrofmAGMBLYBH3lf+hjoWcU2E1U1XVXT3W4b384Y4xEV7uKxi3uwde8RJnxj82P5gmOJQETcIpLofRwDDAcygU+Ac73FzgHWORWDMSY4nZnWhMvSU3lldjZrdh4MdDj1npNnBMnADBHJABbiaSOYDDwBjBORFcDjwA0OxmCMCVL3j+pCw5gIbn57MbOsveC0SH0Y2jU9PV0XLVoU6DCMMXXM/Ow93Dspg817DvOzLs14cEwXWjdpEOiw6gwRWextj62WX9oIjDHGCWekNeHL3w/h3pGd+GFDHsOfmcW/vt8Y6LDqHUsExph6LSrcxa1D2zPj7qGc1a4Jf52yhk15BYEOq16xRGCMCQrNEqJ5anxPIsPDeHJ6ZqDDqVcsERhjgkbT+GhuGtKOqSt2sXjzvkCHU29YIjDGBJXfDGlL0/go/jZltc1zXEuWCIwxQSU2Mpy7hndkyZb9fLFyV6DDqRcsERhjgs749JZ0ahbPE19kUlxaHuhw6jxLBMaYoOMKE/44ujOb9xzm7XmbAx1OnWeJwBgTlIZ2dHNG28a8Nmcj5eXWVlAdSwTGmKAkIvzijFZs33+E+Rv3BjqcOs0SgTEmaJ3ftTnxUeF8uHhboEOp0ywRGGOCVkykizG9kpm2cicFRaWBDqfOskRgjAlql/ZL5XBxGVNX7Ax0KHWWJQJjTFDr26oRbZMa2OWhalgiMMYENRFhXN8U5m/cy9a9hwMdTp1kicAYE/Qu7puKCExaYmcFlbFEYIwJeimJMQxql8SkJdvsnoJKWCIwxoSES/ulsnXvERZssnsKjmeJwBgTEkZ0a05cVDhvzbUhJ45nicAYExJiIl1cd3ZbpqzYyXc22f0xHEsEIhItIgtEZLmIrBKRh497/XkRyXfq+MYYc7xbh7Yjzd2AP328gsPFdoPZUU6eERQBw1S1F9AbGCkiZwKISDrQyMFjG2PMCaIjXDxxSU+27TvCs1+tC3Q4dYZjiUA9jv7ij/AuKiIu4CngXqeObYwxVRnQtjFXDmjFa3M2smLbgUCHUyc42kYgIi4RWQbkAF+p6nzgduAzVbX7vY0xAfHHUZ1JiovivkkZlJTZxDWOJgJVLVPV3kAqMEBEhgDjgX/UtK2I3Cgii0RkUW6uNewYY3ynYUwED1/UjdU7D/LK7OxAhxNwfuk1pKr7gRnAuUB7IEtENgGxIpJVxTYTVTVdVdPdbrc/wjTGhJCR3ZszukdznvlyHcu37g90OAHlZK8ht4gkeh/HAMOBxaraXFXbqGob4LCqtncqBmOMqYqI8NjFPWgaH8Ud7y/lUGFJoEMKGCfPCJKBGSKSASzE00Yw2cHjGWPMSUmMjWTClX3Yuvcwf/50VaDDCZhwp3asqhlAnxrKxDl1fGOMqY3+bRpz53kdefbrdZzdPolx/VIDHZLf2Z3FxpiQd/uw9pzRtjEPfrqS7NzQu8/VEoExJuS5woTnruhNhCuMBz9dGehw/M4SgTHGAMkNY7jzvA58n7WH2etDq8u6JQJjjPG66sxWpDaK4YlpmSE1b4ElAmOM8YoKd/GH8zuyasdBJofQZPeWCIwxpoKxvVLokpzA/05fS3FpaAw/YYnAGGMqCAsT7hvZiS17D/Pegi2BDscvLBEYY8xxzuno5qy0Jjz/zXryi4J/3gJLBMYYcxwR4b5RndlTUMwL31Y6HFpQsURgjDGV6N0ykcvSU5k4awOLNwf3hPeWCIwxpgoPjulKi8QYfv/BcgqC+BKRJQJX/5PjAAAOoUlEQVRjjKlCfHQEz1zWm637DvPXKasDHY5jLBEYY0w1BrRtzI1D0nhvwVa+WbM70OE4whKBMcbU4K7hHencPJ77JmWwJ78o0OH4nCUCY4ypQVS4i+eu6M2BIyX8Iwh7EVkiMMaYWujcPIExPVvw4eJtQXdvgSUCY4yppV8PbEN+USmTFm8LdCg+ZYnAGGNqqXfLRHq1TOTNuZuCanRSSwTGGHMSrhnYmuzcAuZk5QU6FJ+xRGCMMSdhdI9kkuIiefOHTYEOxWccSwQiEi0iC0RkuYisEpGHvevfEZG1IrJSRF4XkQinYjDGGF+LCnfxiwGt+HZtDlv2HA50OD7h5BlBETBMVXsBvYGRInIm8A7QGegBxAA3OBiDMcb43FVntsYlwr/nbnLsGNm5+bz83Qby/HDfgmOJQD3yvU8jvIuq6lTvawosAFKdisEYY5zQLCGakd2b859FWzlc7ExX0gUb9/L4tEwOF5U5sv+KHG0jEBGXiCwDcoCvVHV+hdcigKuBL5yMwRhjnHDNwDYcLCzlg4VbHdn/htx8osLDSGkU48j+K3I0Eahqmar2xvOrf4CIdK/w8j+BWao6u7JtReRGEVkkIotyc3OdDNMYY05av9aNfpy85sDhEp/vPysnnzR3HK4w8fm+j+eXXkOquh+YAYwEEJGHADdwVzXbTFTVdFVNd7vd/gjTGGNqTUR4YEwX9h8p4R/frvf5/rNy82nfNM7n+62Mk72G3CKS6H0cAwwHMkXkBmAEcKWqhsbM0MaYoNStRUMuT2/Jm3M3sTGvwGf7LSwpY9u+I7RzN/DZPqvj5BlBMjBDRDKAhXjaCCYDLwHNgLkiskxE/uxgDMYY46i7zu9IpCuMx6au8dk+N+Tmo4rfzgjCndqxqmYAfSpZ79gxjTHG35rGR3PbsPY8+cVafsjKY2D7pNPeZ1aOp8Nlvb80ZIwxoeK6QW1JbRTDI5NXU+aDMYg25OQTJtA2qf5fGjLGmJAQHeHi/lFdyNx1iA8Xn3530g25BbRqHEtUuMsH0dXMEoExxvjA6B7N6d0ykee+Xk9hyendBJaV478eQ2CJwBhjfEJEuG9kZ3YeKOStuZtPeT+lZeVszCugnSUCY4ypf85q14TBHZL458wsDhae2k1mW/cdobisnHZuSwTGGFMv3TuiM/sOl/DqrOxT2t7fPYbAEoExxvhUj9SGXNAzmVfnbCT30MmPHGqJwBhjgsAfhnekqLSc/5uRddLbZuXk0zQ+ioRo/03VYonAGGN8LM0dx2XpqbwzfzNb957c5DUb/DjG0FGWCIwxxgF3nNcBEWHCN7UfkE5V2eDnrqNgicAYYxyR3DCGq89szUdLtrEhN7/mDYCcQ0UcKiq1RGCMMcHilqHtiAp3MeHr2p0VHG0o9mfXUbBEYIwxjkmKi+LaQW34PGMHmbsO1lg+ED2GwBKBMcY46sYhacRFhvPsV+tqLJuVk098VDhN46P8ENlPLBEYY4yDEmMjuWFwGtNX7WbFtgPVls3Kyadd0zhEnJ+esiJLBMYY47Drzm5DYmwEz3y1ttpygeg6CpYIjDHGcfHREdw0pB0z1uby3oItlZY5WFhCzqEiSwTGGBOsrhnYhsEdkrj/oxXc/1HGMUNVl5Urny7dDkB7P/cYAgenqjTGGPOTmEgX/7p2AE9/uZZ/ztzAqh0HmXBFH+Zn7+HlWdlszCugY7M4+rdt7PfYRPX0p1VzWnp6ui5atCjQYRhjjE9MX7WLu/+znENFpQB0T0ng1qHtGdGtOa4w3zUUi8hiVU2vqZxjZwQiEg3MAqK8x/lQVR8SkbbA+0ATYDFwtaoWOxWHMcbUNSO6NafD7XG8OmcjI7s1Z3CHJL/3FKrIyTaCImCYqvYCegMjReRM4O/As6raHtgHXO9gDMYYUyelueN47OIeDOnoDmgSAAcTgXocHWAjwrsoMAz40Lv+TeDnTsVgjDGmZo72GhIRl4gsA3KAr4ANwH5VLfUW2QakOBmDMcaY6jmaCFS1TFV7A6nAAKBzbbcVkRtFZJGILMrNzXUsRmOMCXV+uY9AVfcDM4CzgEQROdpInQpsr2KbiaqarqrpbrfbH2EaY0xIciwRiIhbRBK9j2OA4cAaPAnhUm+xXwOfOhWDMcaYmjl5Q1ky8KaIuPAknP+o6mQRWQ28LyJ/BZYCrzkYgzHGmBo4lghUNQPoU8n6bDztBcYYY+oAG2vIGGNCXL0YYkJEcoHNx61uCBw/uPfx6yo+r+lxEpB3iiFWFkttX68u5pqeH33sjzpUV8YX70XFdYF4L062DhWf+/vzVF2ZuvJeBLoOFR/X1ffCH9/t1qpac28bVa2XCzCxpnUVn9f0GFjky1hq+3p1Mde2Tv6oQ3VlfPFeHLfO7+/FydahmtjtvagDdagP74W/vtu1WerzpaHPa7Hu85N87MtYavt6dTHX9PzzKsqcitrso6oyvngvfFGH2uzHV3Wo+Nzfn6fqytSV9yLQdahtDDVxsh7++m7XqF5cGvIHEVmktRilry4LhjpAcNQjGOoAwVEPq0PN6vMZga9NDHQAPhAMdYDgqEcw1AGCox5WhxrYGYExxoQ4OyMwxpgQF5SJQEReF5EcEVl5Ctv2E5EVIpIlIs9LhYHCReS3IpIpIqtE5EnfRn1CHD6vg4j8RUS2i8gy7zLa95GfEIsj74X39T+IiIpIku8irjQOJ96LR0Ukw/s+fCkiLXwf+TFxOFGHp7zfhwwR+fjokDJOcqge473f6XIRcew6/OnEXsX+fi0i673Lryusr/Z7UyknuyQFagGGAH2Blaew7QLgTECAacAo7/pzga+BKO/zpvWwDn8B7q7v74X3tZbAdDz3lyTVtzoACRXK3AG8VA/rcD4Q7n38d+Dv9fHzBHQBOgEzgfS6Frs3rjbHrWsMZHv/beR93Ki6ela3BOUZgarOAvZWXCci7UTkCxFZLCKzReSEIbFFJBnPF3Seev5H/81PE+fcAjyhqkXeY+TUwzr4nYP1eBa4F89kR45yog6qerBC0QY4XA+H6vCl/jS3yDw8owk7yqF6rFHVtXU19iqMAL5S1b2qug/PfC8jT/X7H5SJoAoTgd+qaj/gbuCflZRJwTNZzlEVJ87pCAwWkfki8p2I9Hc02sqdbh0Abveeyr8uIo2cC7Vap1UPERkLbFfV5U4HWo3Tfi9E5G8ishW4Cvizg7FWxRefp6Ouw/PrMxB8WQ9/q03slUkBtlZ4frQ+p1RPJ0cfrTNEJA4YCPy3wuWyqJPcTTie07Azgf7Af0QkzZt1HeejOrwIPIrn1+ejwNN4vsB+c7r1EJFY4H/wXJYICB+9F6jqn4A/icj9wO3AQz4Lsga+qoN3X38CSoF3fBPdSR3bZ/Xwt+piF5FrgTu969oDU0WkGNioqhf7OpaQSAR4znz2q2e2tB+JZ4jsxd6nn+H5Q1nx9LbixDnbgI+8f/gXiEg5nvE//DV92mnXQVV3V9juFWCykwFX4XTr0Q5oCyz3fnlSgSUiMkBVdzkc+1G++DxV9A4wFT8mAnxUBxG5BhgDnOevH0XH8fV74U+Vxg6gqm8AbwCIyEzgGlXdVKHIdmBoheepeNoStnMq9XSqYSTQC9CGCo0ywA/AeO9jAXpVsd3xDS2jvetvBh7xPu6I57RM6lkdkiuU+T3wfn18L44rswmHG4sdei86VCjzW+DDeliHkcBqwO2Pz5HTnyccbiw+1dipurF4I56G4kbex41rU89K4/LnG+jHD8p7wE6gBM8v+evx/Ir8Alju/fD+uYpt04GVwAbgBX666S4SeNv72hJgWD2sw1vACiADz6+kZCfr4FQ9jiuzCed7DTnxXkzyrs/AM55MSj2sQxaeH0TLvIujPZ8crMfF3n0VAbuB6XUpdipJBN7113nfgyzg2pP53hy/2J3FxhgT4kKp15AxxphKWCIwxpgQZ4nAGGNCnCUCY4wJcZYIjDEmxFkiMPWSiOT7+XivikhXH+2rTDyjjq4Ukc9rGrVTRBJF5FZfHNuYylj3UVMviUi+qsb5cH/h+tMAao6qGLuIvAmsU9W/VVO+DTBZVbv7Iz4TeuyMwAQNEXGLyCQRWehdBnnXDxCRuSKyVER+EJFO3vXXiMhnIvIt8I2IDBWRmSLyoXjG2X/n6Fju3vXp3sf53gHjlovIPBFp5l3fzvt8hYj8tZZnLXP5aTC9OBH5RkSWePcx1lvmCaCd9yziKW/Ze7x1zBCRh33432hCkCUCE0wmAM+qan9gHPCqd30mMFhV++AZ5fOxCtv0BS5V1XO8z/sAvwO6AmnAoEqO0wCYp6q9gFnAbyocf4Kq9uDYESAr5R0P5zw8d3kDFAIXq2pfPPNfPO1NRH8ENqhqb1W9R0TOBzoAA4DeQD8RGVLT8YypSqgMOmdCw8+ArhVGckzwjvDYEHhTRDrgGXk1osI2X6lqxTHiF6jqNgARWYZnbJg5xx2nmJ8G7FsMDPc+Poufxn5/F/jfKuKM8e47BViDZyx58IwN85j3j3q59/VmlWx/vndZ6n0ehycxzKrieMZUyxKBCSZhwJmqWlhxpYi8AMxQ1Yu919tnVni54Lh9FFV4XEbl35ES/alxraoy1Tmiqr29Q2pPB24DnsczL4Eb6KeqJSKyCYiuZHsBHlfVl0/yuMZUyi4NmWDyJZ6RPAEQkaPD+zbkp6F4r3Hw+PPwXJICuKKmwqp6GM80lX8QkXA8ceZ4k8C5QGtv0UNAfIVNpwPXec92EJEUEWnqozqYEGSJwNRXsSKyrcJyF54/quneBtTVeIYOB3gSeFxEluLsWfDvgLtEJAPPZCIHatpAVZfiGYH0SjzzEqSLyArgV3jaNlDVPcD33u6mT6nql3guPc31lv2QYxOFMSfFuo8a4yPeSz1HVFVF5ArgSlUdW9N2xgSatREY4zv9gBe8PX324+dpQI05VXZGYIwxIc7aCIwxJsRZIjDGmBBnicAYY0KcJQJjjAlxlgiMMSbEWSIwxpgQ9/85Vo7e39q2pwAAAABJRU5ErkJggg==\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"lr = 1e-02","execution_count":85,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"learn.fit_one_cycle(5, slice(lr))","execution_count":86,"outputs":[{"output_type":"display_data","data":{"text/plain":"","text/html":"Total time: 00:04

\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
epochtrain_lossvalid_losstime
031.32347718.69159500:00
114.0065368.58327700:00
29.3314566.90596900:00
37.8489677.34144000:00
47.1823897.25259500:00
"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"learn.save('stage-1')","execution_count":87,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"learn.load('stage-1');","execution_count":88,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"learn.unfreeze()","execution_count":89,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"learn.lr_find(); learn.recorder.plot()","execution_count":90,"outputs":[{"output_type":"display_data","data":{"text/plain":"","text/html":""},"metadata":{}},{"output_type":"stream","text":"LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"

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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"learn.fit_one_cycle(10, slice(2e-05))","execution_count":91,"outputs":[{"output_type":"display_data","data":{"text/plain":"","text/html":"Total time: 00:09

\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
epochtrain_lossvalid_losstime
06.7892417.41908700:00
16.8512087.22662800:00
26.8359637.10987800:00
36.8537657.27030100:00
46.8562217.27164100:00
56.7869307.15648000:00
66.7721547.08164300:00
76.6733217.12284700:00
86.8305267.13262100:00
96.7826477.03835600:00
"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"preds, _ = learn.get_preds(ds_type=DatasetType.Test)","execution_count":92,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"subm_df = pd.DataFrame(test_df[\"seg_id\"])","execution_count":93,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"subm_df[\"time_to_failure\"] = preds.numpy()","execution_count":94,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"subm_df.head()","execution_count":95,"outputs":[{"output_type":"execute_result","execution_count":95,"data":{"text/plain":" seg_id time_to_failure\n0 seg_00030f 2.923770\n1 seg_0012b5 5.931650\n2 seg_00184e 4.903156\n3 seg_003339 8.073957\n4 seg_0042cc 7.393991","text/html":"

\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
seg_idtime_to_failure
0seg_00030f2.923770
1seg_0012b55.931650
2seg_00184e4.903156
3seg_0033398.073957
4seg_0042cc7.393991
\n
"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"subm_df.to_csv('submission.csv', index=False)","execution_count":96,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.4","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}