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{
"name": "stdout",
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"text": [
"/kaggle/input/exoplanet/cumulative.csv\n",
"/kaggle/input/kepler-exoplanet-search-results/cumulative.csv\n"
]
},
{
"data": {
"text/plain": [
"<bound method NDFrame.head of rowid kepid kepoi_name kepler_name koi_disposition \\\n",
"0 1 10797460 K00752.01 Kepler-227 b CONFIRMED \n",
"1 2 10797460 K00752.02 Kepler-227 c CONFIRMED \n",
"2 3 10811496 K00753.01 NaN FALSE POSITIVE \n",
"3 4 10848459 K00754.01 NaN FALSE POSITIVE \n",
"4 5 10854555 K00755.01 Kepler-664 b CONFIRMED \n",
"... ... ... ... ... ... \n",
"9559 9560 10031643 K07984.01 NaN FALSE POSITIVE \n",
"9560 9561 10090151 K07985.01 NaN FALSE POSITIVE \n",
"9561 9562 10128825 K07986.01 NaN CANDIDATE \n",
"9562 9563 10147276 K07987.01 NaN FALSE POSITIVE \n",
"9563 9564 10156110 K07989.01 NaN FALSE POSITIVE \n",
"\n",
" koi_pdisposition koi_score koi_fpflag_nt koi_fpflag_ss koi_fpflag_co \\\n",
"0 CANDIDATE 1.000 0 0 0 \n",
"1 CANDIDATE 0.969 0 0 0 \n",
"2 FALSE POSITIVE 0.000 0 1 0 \n",
"3 FALSE POSITIVE 0.000 0 1 0 \n",
"4 CANDIDATE 1.000 0 0 0 \n",
"... ... ... ... ... ... \n",
"9559 FALSE POSITIVE 0.000 0 0 0 \n",
"9560 FALSE POSITIVE 0.000 0 1 1 \n",
"9561 CANDIDATE 0.497 0 0 0 \n",
"9562 FALSE POSITIVE 0.021 0 0 1 \n",
"9563 FALSE POSITIVE 0.000 0 0 1 \n",
"\n",
" ... koi_steff_err2 koi_slogg koi_slogg_err1 koi_slogg_err2 \\\n",
"0 ... -81.0 4.467 0.064 -0.096 \n",
"1 ... -81.0 4.467 0.064 -0.096 \n",
"2 ... -176.0 4.544 0.044 -0.176 \n",
"3 ... -174.0 4.564 0.053 -0.168 \n",
"4 ... -211.0 4.438 0.070 -0.210 \n",
"... ... ... ... ... ... \n",
"9559 ... -152.0 4.296 0.231 -0.189 \n",
"9560 ... -166.0 4.529 0.035 -0.196 \n",
"9561 ... -220.0 4.444 0.056 -0.224 \n",
"9562 ... -236.0 4.447 0.056 -0.224 \n",
"9563 ... -225.0 4.385 0.054 -0.216 \n",
"\n",
" koi_srad koi_srad_err1 koi_srad_err2 ra dec koi_kepmag \n",
"0 0.927 0.105 -0.061 291.93423 48.141651 15.347 \n",
"1 0.927 0.105 -0.061 291.93423 48.141651 15.347 \n",
"2 0.868 0.233 -0.078 297.00482 48.134129 15.436 \n",
"3 0.791 0.201 -0.067 285.53461 48.285210 15.597 \n",
"4 1.046 0.334 -0.133 288.75488 48.226200 15.509 \n",
"... ... ... ... ... ... ... \n",
"9559 1.088 0.313 -0.228 298.74921 46.973351 14.478 \n",
"9560 0.903 0.237 -0.079 297.18875 47.093819 14.082 \n",
"9561 1.031 0.341 -0.114 286.50937 47.163219 14.757 \n",
"9562 1.041 0.341 -0.114 294.16489 47.176281 15.385 \n",
"9563 1.193 0.410 -0.137 297.00977 47.121021 14.826 \n",
"\n",
"[9564 rows x 50 columns]>"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"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\n",
"\n",
"import numpy as np #linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"import torch\n",
"from tqdm import tqdm\n",
"from pylab import rcParams\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib import rc\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.metrics import confusion_matrix, classification_report\n",
"\n",
"# Input data files are available in the read-only \"../input/\" directory\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
"\n",
"import os\n",
"for dirname, _, filenames in os.walk('/kaggle/input'):\n",
" for filename in filenames:\n",
" print(os.path.join(dirname, filename))\n",
"\n",
"# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
"# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session\n",
"\n",
"df=pd.read_csv('../input/kepler-exoplanet-search-results/cumulative.csv')\n",
"df.head"
]
},
{
"cell_type": "markdown",
"id": "5624ff24",
"metadata": {
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"tags": []
},
"source": [
"It is clear that the data needs some cleaning. We shall replace the term \"Candidate\" by 0, the term \"False Positive\" by 1, the term \"Confirmed\" by 2 and the term \"Not Dispositioned\" by 3. Afterwards, we shall cut off the parts of the data that aren't necessary. For this analysis, we consider the following quantities as relevant: the koi_disposition, which is the result (y), the koi_score, the koi_fpflag_nt, the koi_fpflag_ss, the koi_period,the koi_prad and the koi_smass. These shall be the most relevant aspects of our analysis. We shall use the KNN algorithm in order to split our data in the required categories. "
]
},
{
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"id": "8dde05cf",
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},
"outputs": [],
"source": [
"df=df.replace(['FALSE POSITIVE'], 1)\n",
"df=df.replace(['CONFIRMED'], 2)\n",
"df=df.replace(['NOT DISPOSITIONED'], 3)\n",
"df=df.replace(['CANDIDATE'], 0)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "416dba21",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" koi_disposition koi_score koi_fpflag_nt koi_fpflag_ss koi_period \\\n",
"0 2 1.000 0 0 9.488036 \n",
"1 2 0.969 0 0 54.418383 \n",
"2 1 0.000 0 1 19.899140 \n",
"3 1 0.000 0 1 1.736952 \n",
"4 2 1.000 0 0 2.525592 \n",
"... ... ... ... ... ... \n",
"9559 1 0.000 0 0 8.589871 \n",
"9560 1 0.000 0 1 0.527699 \n",
"9561 0 0.497 0 0 1.739849 \n",
"9562 1 0.021 0 0 0.681402 \n",
"9563 1 0.000 0 0 4.856035 \n",
"\n",
" koi_prad \n",
"0 2.26 \n",
"1 2.83 \n",
"2 14.60 \n",
"3 33.46 \n",
"4 2.75 \n",
"... ... \n",
"9559 1.11 \n",
"9560 29.35 \n",
"9561 0.72 \n",
"9562 1.07 \n",
"9563 1.05 \n",
"\n",
"[7995 rows x 6 columns]\n"
]
}
],
"source": [
"cols=['koi_disposition','koi_score','koi_fpflag_nt','koi_fpflag_ss','koi_period','koi_prad']\n",
"data=df[cols]\n",
"data=data.dropna()\n",
"print(data)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "f7f1d6e4",
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-06T16:14:32.112476Z",
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"outputs": [],
"source": [
"from sklearn.neighbors import KNeighborsClassifier\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"#Split the data into parameters and results, and afterwards splitting it into test and training.\n",
"X=data[['koi_score','koi_fpflag_nt','koi_fpflag_ss','koi_period','koi_prad']]\n",
"y=data[['koi_disposition']]\n",
"X_train, X_test, y_train, y_test=train_test_split(X, y, test_size=0.2, random_state=21)"
]
},
{
"cell_type": "markdown",
"id": "2777baca",
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"tags": []
},
"source": [
"We shall now decide how many neighbours we need for the KNN. In order to do this, we shall plot a graph for several values of the number of neighbours, and afterwards we shall analyze the best result."
]
},
{
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"outputs": [
{
"name": "stderr",
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"/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:11: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
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"/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:11: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
" # This is added back by InteractiveShellApp.init_path()\n",
"/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:11: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
" # This is added back by InteractiveShellApp.init_path()\n",
"/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:11: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
" # This is added back by InteractiveShellApp.init_path()\n",
"/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:11: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
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"/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:11: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
" # This is added back by InteractiveShellApp.init_path()\n",
"/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:11: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
" # This is added back by InteractiveShellApp.init_path()\n",
"/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:11: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
" # This is added back by InteractiveShellApp.init_path()\n",
"/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:11: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
" # This is added back by InteractiveShellApp.init_path()\n",
"/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:11: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
" # This is added back by InteractiveShellApp.init_path()\n",
"/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:11: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
" # This is added back by InteractiveShellApp.init_path()\n",
"/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:11: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
" # This is added back by InteractiveShellApp.init_path()\n",
"/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:11: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
" # This is added back by InteractiveShellApp.init_path()\n",
"/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:11: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
" # This is added back by InteractiveShellApp.init_path()\n",
"/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:11: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
" # This is added back by InteractiveShellApp.init_path()\n",
"/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:11: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
" # This is added back by InteractiveShellApp.init_path()\n",
"/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:11: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
" # This is added back by InteractiveShellApp.init_path()\n",
"/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:11: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
" # This is added back by InteractiveShellApp.init_path()\n",
"/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:11: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
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]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"neighbors = np.arange(1, 20)\n",
"train_accuracy = np.empty(len(neighbors))\n",
"test_accuracy = np.empty(len(neighbors))\n",
"\n",
"# Loop over different values of k\n",
"for i, k in enumerate(neighbors):\n",
" # Setup a k-NN Classifier with k neighbors: knn\n",
" knn = KNeighborsClassifier(n_neighbors=k)\n",
"\n",
" # Fit the classifier to the training data\n",
" knn.fit(X_train, y_train)\n",
" \n",
" #Compute accuracy on the training set\n",
" train_accuracy[i] = knn.score(X_train, y_train)\n",
"\n",
" #Compute accuracy on the testing set\n",
" test_accuracy[i] = knn.score(X_test, y_test)\n",
"\n",
"# Generate plot\n",
"plt.title('k-NN: Varying Number of Neighbors')\n",
"plt.plot(neighbors, test_accuracy, label = 'Testing Accuracy')\n",
"plt.plot(neighbors, train_accuracy, label = 'Training Accuracy')\n",
"plt.legend()\n",
"plt.xlabel('Number of Neighbors')\n",
"plt.ylabel('Accuracy')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "59a8dab4",
"metadata": {
"papermill": {
"duration": 0.019709,
"end_time": "2021-11-06T16:14:38.660612",
"exception": false,
"start_time": "2021-11-06T16:14:38.640903",
"status": "completed"
},
"tags": []
},
"source": [
"We can see that, for roughly 13 neighbours, we get the best results. Therefore, we shall keep that as the number of neighbours. We shall proceed to analyze the KNN model with 13 neighbours."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "48cf7c82",
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-06T16:14:38.702627Z",
"iopub.status.busy": "2021-11-06T16:14:38.701623Z",
"iopub.status.idle": "2021-11-06T16:14:38.784485Z",
"shell.execute_reply": "2021-11-06T16:14:38.785033Z",
"shell.execute_reply.started": "2021-11-06T15:53:37.889550Z"
},
"papermill": {
"duration": 0.105265,
"end_time": "2021-11-06T16:14:38.785220",
"exception": false,
"start_time": "2021-11-06T16:14:38.679955",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.7861163227016885\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
" \n"
]
}
],
"source": [
"knn=KNeighborsClassifier(n_neighbors=13)\n",
"knn.fit(X,y)\n",
"print(knn.score(X_test, y_test))"
]
},
{
"cell_type": "markdown",
"id": "9e7aeb15",
"metadata": {
"papermill": {
"duration": 0.018922,
"end_time": "2021-11-06T16:14:38.823610",
"exception": false,
"start_time": "2021-11-06T16:14:38.804688",
"status": "completed"
},
"tags": []
},
"source": [
"The KNN model with 13 neighbours has 78% accuracy in deducing the status of an observed object. Below, we shall use a correlation heatmap in order to observe which terms are most linked together. We can see clearly that the best predictor remains the score; however, nothing predicts the score too well, and this means that other data, which was disconsidered when we cleaned the data, was relevant."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "e33390c1",
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-06T16:14:38.868347Z",
"iopub.status.busy": "2021-11-06T16:14:38.867474Z",
"iopub.status.idle": "2021-11-06T16:14:39.291756Z",
"shell.execute_reply": "2021-11-06T16:14:39.292503Z",
"shell.execute_reply.started": "2021-11-06T15:53:41.481234Z"
},
"papermill": {
"duration": 0.449801,
"end_time": "2021-11-06T16:14:39.292689",
"exception": false,
"start_time": "2021-11-06T16:14:38.842888",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import seaborn as sns\n",
"sns.heatmap(data.corr(), square=True, cmap='RdYlGn')"
]
},
{
"cell_type": "markdown",
"id": "25022342",
"metadata": {
"papermill": {
"duration": 0.020216,
"end_time": "2021-11-06T16:14:39.333571",
"exception": false,
"start_time": "2021-11-06T16:14:39.313355",
"status": "completed"
},
"tags": []
},
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "6774bc39",
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-06T16:13:04.834497Z",
"iopub.status.busy": "2021-11-06T16:13:04.834193Z",
"iopub.status.idle": "2021-11-06T16:13:04.996954Z",
"shell.execute_reply": "2021-11-06T16:13:04.995818Z",
"shell.execute_reply.started": "2021-11-06T16:13:04.834438Z"
},
"papermill": {
"duration": 0.020138,
"end_time": "2021-11-06T16:14:39.374197",
"exception": false,
"start_time": "2021-11-06T16:14:39.354059",
"status": "completed"
},
"tags": []
},
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"source": []
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"metadata": {
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"display_name": "Python 3",
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"language_info": {
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"name": "ipython",
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| 0078/947/78947914.ipynb | s3://data-agents/kaggle-outputs/sharded/044_00078.jsonl.gz |
{
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"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\n",
"\n",
"import numpy as np \n",
"import tensorflow as tf\n",
"from tensorflow import keras\n",
"from keras.models import Sequential\n",
"from keras.layers import Dense, Dropout, Flatten\n",
"from keras.layers import Conv2D, MaxPooling2D\n",
"# from keras.utils import to_categorical\n",
"from sklearn.preprocessing import LabelEncoder\n",
"from keras.utils import np_utils\n",
"import matplotlib.pyplot as plt\n",
"from keras.models import load_model\n",
"# Input data files are available in the read-only \"../input/\" directory\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
"\n",
"import os\n",
"for dirname, _, filenames in os.walk('/kaggle/input'):\n",
" for filename in filenames:\n",
" print(os.path.join(dirname, filename))\n",
"\n",
"# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
"# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
]
},
{
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"id": "4b6e7062",
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
"170500096/170498071 [==============================] - 3s 0us/step\n",
"170508288/170498071 [==============================] - 3s 0us/step\n"
]
}
],
"source": [
"data = tf.keras.datasets.cifar10.load_data()\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "f4d223ff",
"metadata": {
"execution": {
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"source": [
"(x_train, y_train), (x_test, y_test) = data\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "d39a8529",
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-06T16:16:24.727666Z",
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},
"outputs": [],
"source": [
"assert x_train.shape == (50000, 32, 32, 3)\n",
"assert x_test.shape == (10000, 32, 32, 3)\n",
"assert y_train.shape == (50000, 1)\n",
"assert y_test.shape == (10000, 1)"
]
},
{
"cell_type": "markdown",
"id": "d435bd67",
"metadata": {
"execution": {
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},
"tags": []
},
"source": [
"Normalization:"
]
},
{
"cell_type": "markdown",
"id": "29975ad9",
"metadata": {
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"source": [
"x_train = x_train/255\n",
"x_test = x_test/255"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "5f8c513f",
"metadata": {
"execution": {
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},
"outputs": [],
"source": [
"x_train = x_train/255\n",
"x_test = x_test/255"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "8db3a23e",
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-06T16:16:25.492278Z",
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},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(50000, 10)\n",
"(10000, 10)\n"
]
}
],
"source": [
"y_train=np_utils.to_categorical(y_train)\n",
"print(y_train.shape)\n",
"y_test=np_utils.to_categorical(y_test)\n",
"print(y_test.shape)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "b508fafa",
"metadata": {
"execution": {
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},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n",
"User settings:\n",
"\n",
" KMP_AFFINITY=granularity=fine,verbose,compact,1,0\n",
" KMP_BLOCKTIME=0\n",
" KMP_DUPLICATE_LIB_OK=True\n",
" KMP_INIT_AT_FORK=FALSE\n",
" KMP_SETTINGS=1\n",
" KMP_WARNINGS=0\n",
"\n",
"Effective settings:\n",
"\n",
" KMP_ABORT_DELAY=0\n",
" KMP_ADAPTIVE_LOCK_PROPS='1,1024'\n",
" KMP_ALIGN_ALLOC=64\n",
" KMP_ALL_THREADPRIVATE=128\n",
" KMP_ATOMIC_MODE=2\n",
" KMP_BLOCKTIME=0\n",
" KMP_CPUINFO_FILE: value is not defined\n",
" KMP_DETERMINISTIC_REDUCTION=false\n",
" KMP_DEVICE_THREAD_LIMIT=2147483647\n",
" KMP_DISP_NUM_BUFFERS=7\n",
" KMP_DUPLICATE_LIB_OK=true\n",
" KMP_ENABLE_TASK_THROTTLING=true\n",
" KMP_FORCE_REDUCTION: value is not defined\n",
" KMP_FOREIGN_THREADS_THREADPRIVATE=true\n",
" KMP_FORKJOIN_BARRIER='2,2'\n",
" KMP_FORKJOIN_BARRIER_PATTERN='hyper,hyper'\n",
" KMP_GTID_MODE=3\n",
" KMP_HANDLE_SIGNALS=false\n",
" KMP_HOT_TEAMS_MAX_LEVEL=1\n",
" KMP_HOT_TEAMS_MODE=0\n",
" KMP_INIT_AT_FORK=true\n",
" KMP_LIBRARY=throughput\n",
" KMP_LOCK_KIND=queuing\n",
" KMP_MALLOC_POOL_INCR=1M\n",
" KMP_NUM_LOCKS_IN_BLOCK=1\n",
" KMP_PLAIN_BARRIER='2,2'\n",
" KMP_PLAIN_BARRIER_PATTERN='hyper,hyper'\n",
" KMP_REDUCTION_BARRIER='1,1'\n",
" KMP_REDUCTION_BARRIER_PATTERN='hyper,hyper'\n",
" KMP_SCHEDULE='static,balanced;guided,iterative'\n",
" KMP_SETTINGS=true\n",
" KMP_SPIN_BACKOFF_PARAMS='4096,100'\n",
" KMP_STACKOFFSET=64\n",
" KMP_STACKPAD=0\n",
" KMP_STACKSIZE=8M\n",
" KMP_STORAGE_MAP=false\n",
" KMP_TASKING=2\n",
" KMP_TASKLOOP_MIN_TASKS=0\n",
" KMP_TASK_STEALING_CONSTRAINT=1\n",
" KMP_TEAMS_THREAD_LIMIT=4\n",
" KMP_TOPOLOGY_METHOD=all\n",
" KMP_USE_YIELD=1\n",
" KMP_VERSION=false\n",
" KMP_WARNINGS=false\n",
" OMP_AFFINITY_FORMAT='OMP: pid %P tid %i thread %n bound to OS proc set {%A}'\n",
" OMP_ALLOCATOR=omp_default_mem_alloc\n",
" OMP_CANCELLATION=false\n",
" OMP_DEFAULT_DEVICE=0\n",
" OMP_DISPLAY_AFFINITY=false\n",
" OMP_DISPLAY_ENV=false\n",
" OMP_DYNAMIC=false\n",
" OMP_MAX_ACTIVE_LEVELS=1\n",
" OMP_MAX_TASK_PRIORITY=0\n",
" OMP_NESTED: deprecated; max-active-levels-var=1\n",
" OMP_NUM_THREADS: value is not defined\n",
" OMP_PLACES: value is not defined\n",
" OMP_PROC_BIND='intel'\n",
" OMP_SCHEDULE='static'\n",
" OMP_STACKSIZE=8M\n",
" OMP_TARGET_OFFLOAD=DEFAULT\n",
" OMP_THREAD_LIMIT=2147483647\n",
" OMP_WAIT_POLICY=PASSIVE\n",
" KMP_AFFINITY='verbose,warnings,respect,granularity=fine,compact,1,0'\n",
"\n",
"2021-11-06 16:16:25.607154: I tensorflow/core/common_runtime/process_util.cc:146] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance.\n"
]
}
],
"source": [
"from tensorflow.compat.v1 import ConfigProto\n",
"from tensorflow.compat.v1 import InteractiveSession\n",
"config = ConfigProto()\n",
"config.gpu_options.allow_growth = True\n",
"session = InteractiveSession(config=config)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "5b2a56c5",
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-06T16:16:25.682395Z",
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"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2021-11-06 16:16:25.701813: I tensorflow/core/common_runtime/process_util.cc:146] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: \"sequential\"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"conv2d (Conv2D) (None, 30, 30, 32) 896 \n",
"_________________________________________________________________\n",
"max_pooling2d (MaxPooling2D) (None, 15, 15, 32) 0 \n",
"_________________________________________________________________\n",
"conv2d_1 (Conv2D) (None, 13, 13, 64) 18496 \n",
"_________________________________________________________________\n",
"max_pooling2d_1 (MaxPooling2 (None, 6, 6, 64) 0 \n",
"_________________________________________________________________\n",
"flatten (Flatten) (None, 2304) 0 \n",
"_________________________________________________________________\n",
"dense (Dense) (None, 64) 147520 \n",
"_________________________________________________________________\n",
"dense_1 (Dense) (None, 10) 650 \n",
"=================================================================\n",
"Total params: 167,562\n",
"Trainable params: 167,562\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"model = Sequential()\n",
"\n",
"model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(32, 32, 3)))\n",
"model.add(MaxPooling2D(pool_size=(2, 2)))\n",
"\n",
"model.add(Conv2D(64, kernel_size=(3, 3), activation='relu', input_shape=(32, 32, 3)))\n",
"model.add(MaxPooling2D(pool_size=(2, 2)))\n",
"\n",
"model.add(Flatten())\n",
"\n",
"\n",
"model.add(Dense(64, activation='relu'))\n",
"\n",
"\n",
"model.add(Dense(10, activation='softmax'))\n",
"\n",
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "df7bd247",
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-06T16:16:26.060588Z",
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"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"model.compile(loss='categorical_crossentropy',optimizer='Adam',metrics=['accuracy'])"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "7182ca57",
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-06T16:16:26.142084Z",
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"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2021-11-06 16:16:27.003406: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/30\n",
"1250/1250 [==============================] - 20s 16ms/step - loss: 1.4989 - accuracy: 0.4581 - val_loss: 1.2217 - val_accuracy: 0.5738\n",
"Epoch 2/30\n",
"1250/1250 [==============================] - 18s 15ms/step - loss: 1.1361 - accuracy: 0.5994 - val_loss: 1.1090 - val_accuracy: 0.6058\n",
"Epoch 3/30\n",
"1250/1250 [==============================] - 19s 15ms/step - loss: 1.0012 - accuracy: 0.6486 - val_loss: 1.0029 - val_accuracy: 0.6517\n",
"Epoch 4/30\n",
"1250/1250 [==============================] - 18s 15ms/step - loss: 0.9048 - accuracy: 0.6847 - val_loss: 0.9512 - val_accuracy: 0.6723\n",
"Epoch 5/30\n",
"1250/1250 [==============================] - 19s 15ms/step - loss: 0.8343 - accuracy: 0.7082 - val_loss: 1.0052 - val_accuracy: 0.6554\n",
"Epoch 6/30\n",
"1250/1250 [==============================] - 19s 15ms/step - loss: 0.7727 - accuracy: 0.7289 - val_loss: 0.9072 - val_accuracy: 0.6915\n",
"Epoch 7/30\n",
"1250/1250 [==============================] - 18s 15ms/step - loss: 0.7223 - accuracy: 0.7466 - val_loss: 0.9135 - val_accuracy: 0.6886\n",
"Epoch 8/30\n",
"1250/1250 [==============================] - 19s 15ms/step - loss: 0.6678 - accuracy: 0.7675 - val_loss: 0.9356 - val_accuracy: 0.6879\n",
"Epoch 9/30\n",
"1250/1250 [==============================] - 18s 14ms/step - loss: 0.6249 - accuracy: 0.7793 - val_loss: 0.9622 - val_accuracy: 0.6790\n",
"Epoch 10/30\n",
"1250/1250 [==============================] - 19s 15ms/step - loss: 0.5694 - accuracy: 0.8009 - val_loss: 0.9576 - val_accuracy: 0.6933\n",
"Epoch 11/30\n",
"1250/1250 [==============================] - 18s 15ms/step - loss: 0.5357 - accuracy: 0.8101 - val_loss: 0.9776 - val_accuracy: 0.6928\n",
"Epoch 12/30\n",
"1250/1250 [==============================] - 19s 16ms/step - loss: 0.4916 - accuracy: 0.8279 - val_loss: 1.0252 - val_accuracy: 0.6847\n",
"Epoch 13/30\n",
"1250/1250 [==============================] - 19s 15ms/step - loss: 0.4536 - accuracy: 0.8389 - val_loss: 1.0523 - val_accuracy: 0.6909\n",
"Epoch 14/30\n",
"1250/1250 [==============================] - 19s 15ms/step - loss: 0.4155 - accuracy: 0.8542 - val_loss: 1.1365 - val_accuracy: 0.6865\n",
"Epoch 15/30\n",
"1250/1250 [==============================] - 19s 15ms/step - loss: 0.3826 - accuracy: 0.8656 - val_loss: 1.1903 - val_accuracy: 0.6778\n",
"Epoch 16/30\n",
"1250/1250 [==============================] - 21s 17ms/step - loss: 0.3506 - accuracy: 0.8751 - val_loss: 1.2466 - val_accuracy: 0.6905\n",
"Epoch 17/30\n",
"1250/1250 [==============================] - 19s 15ms/step - loss: 0.3256 - accuracy: 0.8831 - val_loss: 1.2804 - val_accuracy: 0.6762\n",
"Epoch 18/30\n",
"1250/1250 [==============================] - 19s 15ms/step - loss: 0.2945 - accuracy: 0.8946 - val_loss: 1.3626 - val_accuracy: 0.6703\n",
"Epoch 19/30\n",
"1250/1250 [==============================] - 18s 15ms/step - loss: 0.2733 - accuracy: 0.9033 - val_loss: 1.4203 - val_accuracy: 0.6824\n",
"Epoch 20/30\n",
"1250/1250 [==============================] - 19s 15ms/step - loss: 0.2472 - accuracy: 0.9103 - val_loss: 1.4930 - val_accuracy: 0.6779\n",
"Epoch 21/30\n",
"1250/1250 [==============================] - 18s 14ms/step - loss: 0.2298 - accuracy: 0.9179 - val_loss: 1.5264 - val_accuracy: 0.6801\n",
"Epoch 22/30\n",
"1250/1250 [==============================] - 19s 15ms/step - loss: 0.2066 - accuracy: 0.9266 - val_loss: 1.6141 - val_accuracy: 0.6774\n",
"Epoch 23/30\n",
"1250/1250 [==============================] - 18s 14ms/step - loss: 0.1948 - accuracy: 0.9311 - val_loss: 1.7142 - val_accuracy: 0.6742\n",
"Epoch 24/30\n",
"1250/1250 [==============================] - 19s 15ms/step - loss: 0.1764 - accuracy: 0.9368 - val_loss: 1.8060 - val_accuracy: 0.6702\n",
"Epoch 25/30\n",
"1250/1250 [==============================] - 19s 15ms/step - loss: 0.1635 - accuracy: 0.9413 - val_loss: 1.8740 - val_accuracy: 0.6740\n",
"Epoch 26/30\n",
"1250/1250 [==============================] - 19s 15ms/step - loss: 0.1540 - accuracy: 0.9445 - val_loss: 1.9945 - val_accuracy: 0.6584\n",
"Epoch 27/30\n",
"1250/1250 [==============================] - 19s 15ms/step - loss: 0.1428 - accuracy: 0.9493 - val_loss: 2.0612 - val_accuracy: 0.6752\n",
"Epoch 28/30\n",
"1250/1250 [==============================] - 18s 14ms/step - loss: 0.1368 - accuracy: 0.9508 - val_loss: 2.1564 - val_accuracy: 0.6638\n",
"Epoch 29/30\n",
"1250/1250 [==============================] - 20s 16ms/step - loss: 0.1283 - accuracy: 0.9539 - val_loss: 2.2487 - val_accuracy: 0.6684\n",
"Epoch 30/30\n",
"1250/1250 [==============================] - 18s 14ms/step - loss: 0.1220 - accuracy: 0.9557 - val_loss: 2.3343 - val_accuracy: 0.6602\n"
]
},
{
"data": {
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},
"execution_count": 10,
"metadata": {},
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}
],
"source": [
"model.fit(x_train, y_train, epochs=30, validation_split=0.2)\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "82391545",
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-06T16:25:57.552324Z",
"iopub.status.busy": "2021-11-06T16:25:57.551320Z",
"iopub.status.idle": "2021-11-06T16:25:59.290496Z",
"shell.execute_reply": "2021-11-06T16:25:59.291011Z",
"shell.execute_reply.started": "2021-11-06T16:14:29.474124Z"
},
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"start_time": "2021-11-06T16:25:54.519108",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"313/313 [==============================] - 1s 4ms/step - loss: 2.4410 - accuracy: 0.6478\n"
]
},
{
"data": {
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"[2.440995216369629, 0.6478000283241272]"
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},
"execution_count": 11,
"metadata": {},
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}
],
"source": [
"model.evaluate(x_test,y_test)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cb43fc33",
"metadata": {
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},
"tags": []
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"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
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"name": "ipython",
"version": 3
},
"file_extension": ".py",
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"name": "python",
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"papermill": {
"default_parameters": {},
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"end_time": "2021-11-06T16:26:11.767661",
"environment_variables": {},
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"input_path": "__notebook__.ipynb",
"output_path": "__notebook__.ipynb",
"parameters": {},
"start_time": "2021-11-06T16:16:02.791881",
"version": "2.3.3"
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| 0078/948/78948021.ipynb | s3://data-agents/kaggle-outputs/sharded/044_00078.jsonl.gz |
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"{\"metadata\":{\"kernelspec\":{\"language\":\"python\",\"display_name\":\"Python 3\",\"name\":\"pyt(...TRUNCATED) | 0078/948/78948776.ipynb | s3://data-agents/kaggle-outputs/sharded/044_00078.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"bf14429f\",\n \"metadata\": (...TRUNCATED) | 0078/949/78949328.ipynb | s3://data-agents/kaggle-outputs/sharded/044_00078.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"bd9(...TRUNCATED) | 0078/949/78949587.ipynb | s3://data-agents/kaggle-outputs/sharded/044_00078.jsonl.gz |
"{\"metadata\":{\"kernelspec\":{\"language\":\"python\",\"display_name\":\"Python 3\",\"name\":\"pyt(...TRUNCATED) | 0078/949/78949593.ipynb | s3://data-agents/kaggle-outputs/sharded/044_00078.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"881e4104\",\n \"metadata\": (...TRUNCATED) | 0078/949/78949836.ipynb | s3://data-agents/kaggle-outputs/sharded/044_00078.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"cf5760b2\",\n \"metadata\": (...TRUNCATED) | 0078/950/78950388.ipynb | s3://data-agents/kaggle-outputs/sharded/044_00078.jsonl.gz |
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