Commit e90c8a50a8b54535a87a4c84b6301e9d57dd2451
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added test scripts
Showing 5 changed files with 188 additions and 145 deletions Inline Diff
python-notebook/__pycache__/tools.cpython-37.pyc
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e90c8a5
python-notebook/prepare_trteva_data.ipynb
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e90c8a5
{ | 1 | 1 | { | |
"cells": [ | 2 | 2 | "cells": [ | |
{ | 3 | 3 | { | |
"cell_type": "code", | 4 | 4 | "cell_type": "code", | |
"execution_count": 1, | 5 | 5 | "execution_count": 1, | |
"metadata": {}, | 6 | 6 | "metadata": {}, | |
"outputs": [], | 7 | 7 | "outputs": [], | |
"source": [ | 8 | 8 | "source": [ | |
"import numpy as np\n", | 9 | 9 | "import numpy as np\n", | |
"import pandas as pd\n", | 10 | 10 | "import pandas as pd\n", | |
"import os\n", | 11 | 11 | "import os\n", | |
"from tools import *\n", | 12 | 12 | "from tools import *\n", | |
"from constants import *\n", | 13 | 13 | "from constants import *\n", | |
"from tensorflow.keras.utils import to_categorical\n", | 14 | 14 | "from tensorflow.keras.utils import to_categorical\n", | |
"\n", | 15 | 15 | "\n", | |
"# %load_ext line_profiler" | 16 | 16 | "# %load_ext line_profiler" | |
] | 17 | 17 | ] | |
}, | 18 | 18 | }, | |
{ | 19 | 19 | { | |
"cell_type": "markdown", | 20 | 20 | "cell_type": "markdown", | |
"metadata": {}, | 21 | 21 | "metadata": {}, | |
"source": [ | 22 | 22 | "source": [ | |
"# Prepare Training, Testing, and Validation Data\n", | 23 | 23 | "# Prepare Training, Testing, and Validation Data\n", | |
"## Loading the preprocessed data" | 24 | 24 | "## Loading the preprocessed data" | |
] | 25 | 25 | ] | |
}, | 26 | 26 | }, | |
{ | 27 | 27 | { | |
"cell_type": "code", | 28 | 28 | "cell_type": "code", | |
"execution_count": 2, | 29 | 29 | "execution_count": 2, | |
"metadata": {}, | 30 | 30 | "metadata": {}, | |
"outputs": [], | 31 | 31 | "outputs": [], | |
"source": [ | 32 | 32 | "source": [ | |
"# to use unlimited memory for large dataframes\n", | 33 | 33 | "# to use unlimited memory for large dataframes\n", | |
"pd.options.mode.chained_assignment = None\n", | 34 | 34 | "pd.options.mode.chained_assignment = None\n", | |
"\n", | 35 | 35 | "\n", | |
"data_dir = '../data'\n", | 36 | 36 | "data_dir = '../data'\n", | |
"\n", | 37 | 37 | "\n", | |
"padded_hours = pd.read_pickle(os.path.join(data_dir, 'padded_hours.pkl'))\n", | 38 | 38 | "padded_hours = pd.read_pickle(os.path.join(data_dir, 'padded_hours.pkl'))\n", | |
"padded_threehours = pd.read_pickle(os.path.join(data_dir, 'padded_threehours.pkl'))" | 39 | 39 | "padded_threehours = pd.read_pickle(os.path.join(data_dir, 'padded_threehours.pkl'))" | |
] | 40 | 40 | ] | |
}, | 41 | 41 | }, | |
{ | 42 | 42 | { | |
"cell_type": "markdown", | 43 | 43 | "cell_type": "markdown", | |
"metadata": {}, | 44 | 44 | "metadata": {}, | |
"source": [ | 45 | 45 | "source": [ | |
"# Expanding one-hot-encoded gaits" | 46 | 46 | "# Expanding one-hot-encoded gaits" | |
] | 47 | 47 | ] | |
}, | 48 | 48 | }, | |
{ | 49 | 49 | { | |
"cell_type": "code", | 50 | 50 | "cell_type": "code", | |
"execution_count": 13, | 51 | 51 | "execution_count": 3, | |
"metadata": {}, | 52 | 52 | "metadata": {}, | |
"outputs": [ | 53 | 53 | "outputs": [ | |
{ | 54 | 54 | { | |
"name": "stdout", | 55 | 55 | "name": "stdout", | |
"output_type": "stream", | 56 | 56 | "output_type": "stream", | |
"text": [ | 57 | 57 | "text": [ | |
"(42360, 4) -> (127080, 4)\n" | 58 | 58 | "(42360, 4) -> (127080, 4)\n" | |
] | 59 | 59 | ] | |
} | 60 | 60 | } | |
], | 61 | 61 | ], | |
"source": [ | 62 | 62 | "source": [ | |
"def mass_one_hot_encoding(padded_hours, colname, n_classes):\n", | 63 | 63 | "def mass_one_hot_encoding(padded_hours, colname, n_classes):\n", | |
" def __mass_one_hot_encoding(padded_hours, colname, n_classes, n):\n", | 64 | 64 | " def __mass_one_hot_encoding(padded_hours, colname, n_classes, n):\n", | |
" temp = padded_hours[padded_hours[colname] == n]\n", | 65 | 65 | " temp = padded_hours[padded_hours[colname] == n]\n", | |
"\n", | 66 | 66 | "\n", | |
" return_df = pd.DataFrame(dtype=int)\n", | 67 | 67 | " return_df = pd.DataFrame(dtype=int)\n", | |
"\n", | 68 | 68 | "\n", | |
" for i in range(n_classes):\n", | 69 | 69 | " for i in range(n_classes):\n", | |
" temp_2 = temp.copy(deep=True)\n", | 70 | 70 | " temp_2 = temp.copy(deep=True)\n", | |
" temp_2[\"var\"] = i\n", | 71 | 71 | " temp_2[\"var\"] = i\n", | |
" temp_2[\"value\"] = (n == i) if 1 else 0\n", | 72 | 72 | " temp_2[\"value\"] = (n == i) if 1 else 0\n", | |
" return_df = pd.concat([return_df, temp_2], ignore_index=True)\n", | 73 | 73 | " return_df = pd.concat([return_df, temp_2], ignore_index=True)\n", | |
"\n", | 74 | 74 | "\n", | |
" return return_df\n", | 75 | 75 | " return return_df\n", | |
" \n", | 76 | 76 | " \n", | |
" mass_encoded = pd.DataFrame(dtype=int)\n", | 77 | 77 | " mass_encoded = pd.DataFrame(dtype=int)\n", | |
" for n in range(n_classes):\n", | 78 | 78 | " for n in range(n_classes):\n", | |
" mass_encoded = pd.concat([mass_encoded, __mass_one_hot_encoding(padded_hours, colname, n_classes, n)], ignore_index=True)\n", | 79 | 79 | " mass_encoded = pd.concat([mass_encoded, __mass_one_hot_encoding(padded_hours, colname, n_classes, n)], ignore_index=True)\n", | |
" return mass_encoded\n", | 80 | 80 | " return mass_encoded\n", | |
"\n", | 81 | 81 | "\n", | |
"padded_hours_encoded = mass_one_hot_encoding(padded_hours, 'walked', 3)\n", | 82 | 82 | "padded_hours_encoded = mass_one_hot_encoding(padded_hours, 'walked', 3)\n", | |
"padded_hours_encoded[\"local_date\"] = padded_hours_encoded[\"local_date\"].astype(str)\n", | 83 | 83 | "padded_hours_encoded[\"local_date\"] = padded_hours_encoded[\"local_date\"].astype(str)\n", | |
"padded_hours_encoded = padded_hours_encoded.set_index(['user', 'local_date']).sort_index()\n", | 84 | 84 | "padded_hours_encoded = padded_hours_encoded.set_index(['user', 'local_date']).sort_index()\n", | |
"\n", | 85 | 85 | "\n", | |
"print(\"{} -> {}\".format(padded_hours.shape, padded_hours_encoded.shape))" | 86 | 86 | "print(\"{} -> {}\".format(padded_hours.shape, padded_hours_encoded.shape))" | |
] | 87 | 87 | ] | |
}, | 88 | 88 | }, | |
{ | 89 | 89 | { | |
"cell_type": "markdown", | 90 | 90 | "cell_type": "markdown", | |
"metadata": {}, | 91 | 91 | "metadata": {}, | |
"source": [ | 92 | 92 | "source": [ | |
"## Enumerating Output Data" | 93 | 93 | "## Enumerating Output Data" | |
] | 94 | 94 | ] | |
}, | 95 | 95 | }, | |
{ | 96 | 96 | { | |
"cell_type": "code", | 97 | 97 | "cell_type": "code", | |
"execution_count": 17, | 98 | 98 | "execution_count": 4, | |
"metadata": {}, | 99 | 99 | "metadata": {}, | |
"outputs": [], | 100 | 100 | "outputs": [], | |
"source": [ | 101 | 101 | "source": [ | |
"# return output value\n", | 102 | 102 | "# return output value\n", | |
"def get_output(y):\n", | 103 | 103 | "def get_output(y):\n", | |
" return y[\"walked\"]\n", | 104 | 104 | " return y[\"walked\"]\n", | |
"\n", | 105 | 105 | "\n", | |
"# return intput value\n", | 106 | 106 | "# return intput value\n", | |
"def get_input(y, padded_hours):\n", | 107 | 107 | "def get_input(y, padded_hours):\n", | |
" # base information\n", | 108 | 108 | " # base information\n", | |
" user = y[\"user\"]\n", | 109 | 109 | " user = y[\"user\"]\n", | |
" local_date = y[\"local_date\"]\n", | 110 | 110 | " local_date = y[\"local_date\"]\n", | |
" threehour_idx = y[\"threehour\"]\n", | 111 | 111 | " threehour_idx = y[\"threehour\"]\n", | |
" \n", | 112 | 112 | " \n", | |
" # derived information\n", | 113 | 113 | " # derived information\n", | |
" hour_idx = threehour_idx * 3\n", | 114 | 114 | " hour_idx = threehour_idx * 3\n", | |
" encoded_hour_idx = to_categorical(hour_idx, num_classes=24)\n", | 115 | 115 | " encoded_hour_idx = to_categorical(hour_idx, num_classes=24)\n", | |
" end_date = local_date - timedelta(days=1)\n", | 116 | 116 | " end_date = local_date - timedelta(days=1)\n", | |
" start_date = end_date - timedelta(days=7*NUMBER_OF_WEEKS_FOR_LOOKING_BACK-1)\n", | 117 | 117 | " start_date = end_date - timedelta(days=7*NUMBER_OF_WEEKS_FOR_LOOKING_BACK-1)\n", | |
" weekday = local_date.weekday()\n", | 118 | 118 | " weekday = local_date.weekday()\n", | |
" encoded_weekday = to_categorical(weekday, num_classes=7)\n", | 119 | 119 | " encoded_weekday = to_categorical(weekday, num_classes=7)\n", | |
" encoded_month = to_categorical(local_date.month, num_classes=12)\n", | 120 | 120 | " encoded_month = to_categorical(local_date.month, num_classes=12)\n", | |
" encoded_day_of_month = to_categorical(local_date.day, num_classes=31)\n", | 121 | 121 | " encoded_day_of_month = to_categorical(local_date.day, num_classes=31)\n", | |
"\n", | 122 | 122 | "\n", | |
" gait = pd.Series([], dtype=int)\n", | 123 | 123 | " gait = pd.Series([], dtype=int)\n", | |
" # gait movement\n", | 124 | 124 | " # gait movement\n", | |
" zero_move = 0\n", | 125 | 125 | " zero_move = 0\n", | |
" for a_date in date_range(start_date, end_date):\n", | 126 | 126 | " for a_date in date_range(start_date, end_date):\n", | |
" key = (user, a_date.strftime(\"%Y-%m-%d\"))\n", | 127 | 127 | " key = (user, a_date.strftime(\"%Y-%m-%d\"))\n", | |
" if key in padded_hours_encoded.index:\n", | 128 | 128 | " if key in padded_hours_encoded.index:\n", | |
" day_df = padded_hours_encoded.loc[key, \"value\"]\n", | 129 | 129 | " day_df = padded_hours_encoded.loc[key, \"value\"]\n", | |
" gait = pd.concat([gait, day_df], ignore_index=True)\n", | 130 | 130 | " gait = pd.concat([gait, day_df], ignore_index=True)\n", | |
" else:\n", | 131 | 131 | " else:\n", | |
" gait = pd.concat([gait, pd.Series([1,0,0] * 24, dtype=int)], ignore_index=True)\n", | 132 | 132 | " gait = pd.concat([gait, pd.Series([1,0,0] * 24, dtype=int)], ignore_index=True)\n", | |
" zero_move += 1\n", | 133 | 133 | " zero_move += 1\n", | |
" if zero_move == 5 * 7:\n", | 134 | 134 | " if zero_move == 5 * 7:\n", | |
" raise Exception(\"No movement data\")\n", | 135 | 135 | " raise Exception(\"No movement data\")\n", | |
"\n", | 136 | 136 | "\n", | |
" return_series = pd.Series([], dtype=int)\n", | 137 | 137 | " return_series = pd.Series([], dtype=int)\n", | |
" return_series = pd.concat([return_series, pd.Series(encoded_hour_idx, dtype=np.int_)])\n", | 138 | 138 | " return_series = pd.concat([return_series, pd.Series(encoded_hour_idx, dtype=np.int_)])\n", | |
" return_series = pd.concat([return_series, pd.Series(encoded_weekday, dtype=np.int_)])\n", | 139 | 139 | " return_series = pd.concat([return_series, pd.Series(encoded_weekday, dtype=np.int_)])\n", | |
" return_series = pd.concat([return_series, pd.Series(encoded_month, dtype=np.int_)])\n", | 140 | 140 | " return_series = pd.concat([return_series, pd.Series(encoded_month, dtype=np.int_)])\n", | |
" return_series = pd.concat([return_series, pd.Series(encoded_day_of_month, dtype=np.int_)])\n", | 141 | 141 | " return_series = pd.concat([return_series, pd.Series(encoded_day_of_month, dtype=np.int_)])\n", | |
" return_series = pd.concat([return_series, gait])\n", | 142 | 142 | " return_series = pd.concat([return_series, gait])\n", | |
" \n", | 143 | 143 | " \n", | |
" return return_series\n", | 144 | 144 | " return return_series\n", | |
"\n", | 145 | 145 | "\n", | |
"def get_database(start_idx, end_idx):\n", | 146 | 146 | "def get_database(start_idx, end_idx):\n", | |
" database = pd.DataFrame({}, dtype=int)\n", | 147 | 147 | " database = pd.DataFrame({}, dtype=int)\n", | |
"\n", | 148 | 148 | "\n", | |
" for i in range(start_idx, end_idx):\n", | 149 | 149 | " for i in range(start_idx, end_idx):\n", | |
" try:\n", | 150 | 150 | " try:\n", | |
" y = padded_threehours.iloc[i, :]\n", | 151 | 151 | " y = padded_threehours.iloc[i, :]\n", | |
" user = y[\"user\"]\n", | 152 | 152 | " user = y[\"user\"]\n", | |
" local_date = y[\"local_date\"]\n", | 153 | 153 | " local_date = y[\"local_date\"]\n", | |
" first_day = padded_hours[padded_hours[\"user\"] == user][\"local_date\"].min()\n", | 154 | 154 | " first_day = padded_hours[padded_hours[\"user\"] == user][\"local_date\"].min()\n", | |
" date_diff = (local_date - first_day).days\n", | 155 | 155 | " date_diff = (local_date - first_day).days\n", | |
"\n", | 156 | 156 | "\n", | |
" threehour_idx = y[\"threehour\"]\n", | 157 | 157 | " threehour_idx = y[\"threehour\"]\n", | |
" hour_idx = threehour_idx * 3\n", | 158 | 158 | " hour_idx = threehour_idx * 3\n", | |
"\n", | 159 | 159 | "\n", | |
" output = get_output(y)\n", | 160 | 160 | " output = get_output(y)\n", | |
" input = get_input(y, padded_hours)\n", | 161 | 161 | " input = get_input(y, padded_hours)\n", | |
"\n", | 162 | 162 | "\n", | |
" temp_series = pd.Series([], dtype=int)\n", | 163 | 163 | " temp_series = pd.Series([], dtype=int)\n", | |
" temp_series = pd.concat([temp_series, pd.Series(user, dtype=int)])\n", | 164 | 164 | " temp_series = pd.concat([temp_series, pd.Series(user, dtype=int)])\n", | |
" temp_series = pd.concat([temp_series, pd.Series(date_diff, dtype=int)])\n", | 165 | 165 | " temp_series = pd.concat([temp_series, pd.Series(date_diff, dtype=int)])\n", | |
" temp_series = pd.concat([temp_series, pd.Series(threehour_idx, dtype=int)])\n", | 166 | 166 | " temp_series = pd.concat([temp_series, pd.Series(threehour_idx, dtype=int)])\n", | |
" temp_series = pd.concat([temp_series, pd.Series(hour_idx, dtype=int)])\n", | 167 | 167 | " temp_series = pd.concat([temp_series, pd.Series(hour_idx, dtype=int)])\n", | |
" temp_series = pd.concat([temp_series, pd.Series(output, dtype=int)])\n", | 168 | 168 | " temp_series = pd.concat([temp_series, pd.Series(output, dtype=int)])\n", | |
" temp_series = pd.concat([temp_series, pd.Series(input, dtype=int)]).reset_index(drop=True)\n", | 169 | 169 | " temp_series = pd.concat([temp_series, pd.Series(input, dtype=int)]).reset_index(drop=True)\n", | |
"\n", | 170 | 170 | "\n", | |
" database = pd.concat([database, temp_series], axis=1)\n", | 171 | 171 | " database = pd.concat([database, temp_series], axis=1)\n", | |
" # print(input)\n", | 172 | 172 | " # print(input)\n", | |
" except Exception as e:\n", | 173 | 173 | " except Exception as e:\n", | |
" # print(\"Error:\", e)\n", | 174 | 174 | " # print(\"Error:\", e)\n", | |
" pass\n", | 175 | 175 | " pass\n", | |
"\n", | 176 | 176 | "\n", | |
" return database\n", | 177 | 177 | " return database\n", | |
"\n", | 178 | 178 | "\n", | |
"database = get_database(0, padded_threehours.shape[0])\n", | 179 | 179 | "database = get_database(0, padded_threehours.shape[0])\n", | |
"\n", | 180 | 180 | "\n", | |
"database.to_pickle(os.path.join(data_dir, \"database.pkl\"))" | 181 | 181 | "database.to_pickle(os.path.join(data_dir, \"database.pkl\"))" | |
] | 182 | 182 | ] | |
}, | 183 | 183 | }, | |
{ | 184 | 184 | { | |
"cell_type": "code", | 185 | 185 | "cell_type": "code", | |
"execution_count": 24, | 186 | 186 | "execution_count": 5, | |
"metadata": {}, | 187 | 187 | "metadata": {}, | |
"outputs": [ | 188 | 188 | "outputs": [ | |
{ | 189 | 189 | { | |
"name": "stdout", | 190 | 190 | "name": "stdout", | |
"output_type": "stream", | 191 | 191 | "output_type": "stream", | |
"text": [ | 192 | 192 | "text": [ | |
"user\n", | 193 | 193 | "user\n", | |
"21 48\n", | 194 | 194 | "21 48\n", | |
"Name: walked, dtype: int64\n", | 195 | 195 | "Name: walked, dtype: int64\n", | |
" user local_date threehour walked\n", | 196 | 196 | " user local_date threehour walked\n", | |
"4888 21 2015-10-12 0 1\n", | 197 | 197 | "4888 21 2015-10-12 0 1\n", | |
"4889 21 2015-10-12 1 1\n", | 198 | 198 | "4889 21 2015-10-12 1 1\n", | |
"4890 21 2015-10-12 2 1\n", | 199 | 199 | "4890 21 2015-10-12 2 1\n", | |
"4891 21 2015-10-12 3 1\n", | 200 | 200 | "4891 21 2015-10-12 3 1\n", | |
"4892 21 2015-10-12 4 1\n", | 201 | 201 | "4892 21 2015-10-12 4 1\n", | |
"4893 21 2015-10-12 5 1\n", | 202 | 202 | "4893 21 2015-10-12 5 1\n", | |
"4894 21 2015-10-12 6 2\n", | 203 | 203 | "4894 21 2015-10-12 6 2\n", | |
"4895 21 2015-10-12 7 1\n", | 204 | 204 | "4895 21 2015-10-12 7 1\n", | |
"4896 21 2015-10-14 0 1\n", | 205 | 205 | "4896 21 2015-10-14 0 1\n", | |
"4897 21 2015-10-14 1 1\n", | 206 | 206 | "4897 21 2015-10-14 1 1\n", | |
"4898 21 2015-10-14 2 1\n", | 207 | 207 | "4898 21 2015-10-14 2 1\n", | |
"4899 21 2015-10-14 3 2\n", | 208 | 208 | "4899 21 2015-10-14 3 2\n", | |
"4900 21 2015-10-14 4 1\n", | 209 | 209 | "4900 21 2015-10-14 4 1\n", | |
"4901 21 2015-10-14 5 1\n", | 210 | 210 | "4901 21 2015-10-14 5 1\n", | |
"4902 21 2015-10-14 6 1\n", | 211 | 211 | "4902 21 2015-10-14 6 1\n", | |
"4903 21 2015-10-14 7 1\n", | 212 | 212 | "4903 21 2015-10-14 7 1\n", | |
"4904 21 2015-10-18 0 1\n", | 213 | 213 | "4904 21 2015-10-18 0 1\n", | |
"4905 21 2015-10-18 1 1\n", | 214 | 214 | "4905 21 2015-10-18 1 1\n", | |
"4906 21 2015-10-18 2 1\n", | 215 | 215 | "4906 21 2015-10-18 2 1\n", | |
"4907 21 2015-10-18 3 1\n", | 216 | 216 | "4907 21 2015-10-18 3 1\n", | |
"4908 21 2015-10-18 4 1\n", | 217 | 217 | "4908 21 2015-10-18 4 1\n", | |
"4909 21 2015-10-18 5 2\n", | 218 | 218 | "4909 21 2015-10-18 5 2\n", | |
"4910 21 2015-10-18 6 1\n", | 219 | 219 | "4910 21 2015-10-18 6 1\n", | |
"4911 21 2015-10-18 7 1\n", | 220 | 220 | "4911 21 2015-10-18 7 1\n", | |
"4912 21 2015-10-23 0 1\n", | 221 | 221 | "4912 21 2015-10-23 0 1\n", | |
"4913 21 2015-10-23 1 1\n", | 222 | 222 | "4913 21 2015-10-23 1 1\n", | |
"4914 21 2015-10-23 2 1\n", | 223 | 223 | "4914 21 2015-10-23 2 1\n", | |
"4915 21 2015-10-23 3 1\n", | 224 | 224 | "4915 21 2015-10-23 3 1\n", | |
"4916 21 2015-10-23 4 1\n", | 225 | 225 | "4916 21 2015-10-23 4 1\n", | |
"4917 21 2015-10-23 5 2\n", | 226 | 226 | "4917 21 2015-10-23 5 2\n", | |
"4918 21 2015-10-23 6 1\n", | 227 | 227 | "4918 21 2015-10-23 6 1\n", | |
"4919 21 2015-10-23 7 2\n", | 228 | 228 | "4919 21 2015-10-23 7 2\n", | |
"4920 21 2015-10-24 0 1\n", | 229 | 229 | "4920 21 2015-10-24 0 1\n", | |
"4921 21 2015-10-24 1 1\n", | 230 | 230 | "4921 21 2015-10-24 1 1\n", | |
"4922 21 2015-10-24 2 1\n", | 231 | 231 | "4922 21 2015-10-24 2 1\n", | |
"4923 21 2015-10-24 3 1\n", | 232 | 232 | "4923 21 2015-10-24 3 1\n", | |
"4924 21 2015-10-24 4 1\n", | 233 | 233 | "4924 21 2015-10-24 4 1\n", | |
"4925 21 2015-10-24 5 1\n", | 234 | 234 | "4925 21 2015-10-24 5 1\n", | |
"4926 21 2015-10-24 6 2\n", | 235 | 235 | "4926 21 2015-10-24 6 2\n", | |
"4927 21 2015-10-24 7 2\n", | 236 | 236 | "4927 21 2015-10-24 7 2\n", | |
"4928 21 2015-11-01 0 1\n", | 237 | 237 | "4928 21 2015-11-01 0 1\n", | |
"4929 21 2015-11-01 1 1\n", | 238 | 238 | "4929 21 2015-11-01 1 1\n", | |
"4930 21 2015-11-01 2 1\n", | 239 | 239 | "4930 21 2015-11-01 2 1\n", | |
"4931 21 2015-11-01 3 1\n", | 240 | 240 | "4931 21 2015-11-01 3 1\n", | |
"4932 21 2015-11-01 4 1\n", | 241 | 241 | "4932 21 2015-11-01 4 1\n", | |
"4933 21 2015-11-01 5 2\n", | 242 | 242 | "4933 21 2015-11-01 5 2\n", | |
"4934 21 2015-11-01 6 1\n", | 243 | 243 | "4934 21 2015-11-01 6 1\n", | |
"4935 21 2015-11-01 7 1\n" | 244 | 244 | "4935 21 2015-11-01 7 1\n" | |
] | 245 | 245 | ] | |
} | 246 | 246 | } | |
], | 247 | 247 | ], | |
"source": [ | 248 | 248 | "source": [ | |
"print(padded_threehours[padded_threehours[\"user\"]==21].groupby([\"user\"])[\"walked\"].count())\n", | 249 | 249 | "print(padded_threehours[padded_threehours[\"user\"]==21].groupby([\"user\"])[\"walked\"].count())\n", | |
"\n", | 250 | 250 | "\n", | |
"print(padded_threehours[padded_threehours[\"user\"]==21])" | 251 | 251 | "print(padded_threehours[padded_threehours[\"user\"]==21])" | |
] | 252 | 252 | ] | |
}, | 253 | 253 | }, | |
{ | 254 | 254 | { | |
"cell_type": "code", | 255 | 255 | "cell_type": "code", | |
"execution_count": 4, | 256 | 256 | "execution_count": 6, | |
"metadata": {}, | 257 | 257 | "metadata": {}, | |
"outputs": [], | 258 | 258 | "outputs": [], | |
"source": [ | 259 | 259 | "source": [ | |
"from tensorflow.keras.datasets import mnist\n", | 260 | 260 | "from tensorflow.keras.datasets import mnist\n", | |
"from tensorflow.keras.models import Sequential\n", | 261 | 261 | "from tensorflow.keras.models import Sequential\n", | |
"from tensorflow.keras.layers import Dense, Dropout, Flatten, Activation\n", | 262 | 262 | "from tensorflow.keras.layers import Dense, Dropout, Flatten, Activation\n", | |
"\n", | 263 | 263 | "\n", | |
"\n", | 264 | 264 | "\n", | |
"(x_train, y_train), (x_test, y_test) = mnist.load_data(path='mnist.npz')\n", | 265 | 265 | "(x_train, y_train), (x_test, y_test) = mnist.load_data(path='mnist.npz')\n", | |
"\n", | 266 | 266 | "\n", | |
"X_train = x_train.reshape(60000, 784).astype('float32') / 255\n", | 267 | 267 | "X_train = x_train.reshape(60000, 784).astype('float32') / 255\n", | |
"X_test = x_test.reshape(10000, 784).astype('float32') / 255\n", | 268 | 268 | "X_test = x_test.reshape(10000, 784).astype('float32') / 255\n", | |
"\n", | 269 | 269 | "\n", | |
"Y_train = to_categorical(y_train, 10)\n", | 270 | 270 | "Y_train = to_categorical(y_train, 10)\n", | |
"Y_test = to_categorical(y_test, 10)" | 271 | 271 | "Y_test = to_categorical(y_test, 10)" | |
] | 272 | 272 | ] | |
}, | 273 | 273 | }, | |
{ | 274 | 274 | { | |
"cell_type": "code", | 275 | 275 | "cell_type": "code", | |
"execution_count": null, | 276 | 276 | "execution_count": null, | |
"metadata": {}, | 277 | 277 | "metadata": {}, | |
"outputs": [], | 278 | 278 | "outputs": [], | |
"source": [] | 279 | 279 | "source": [] | |
} | 280 | 280 | } | |
], | 281 | 281 | ], | |
"metadata": { | 282 | 282 | "metadata": { | |
"interpreter": { | 283 | 283 | "interpreter": { | |
"hash": "80dbe1014b4652684caa329d41db00af3ae439be86b11eab7e35b518e5d8ab1a" | 284 | 284 | "hash": "80dbe1014b4652684caa329d41db00af3ae439be86b11eab7e35b518e5d8ab1a" | |
}, | 285 | 285 | }, | |
"kernelspec": { | 286 | 286 | "kernelspec": { | |
"display_name": "Python 3.7.9 64-bit ('venv': venv)", | 287 | 287 | "display_name": "Python 3.7.9 64-bit ('venv': venv)", | |
"language": "python", | 288 | 288 | "language": "python", | |
"name": "python3" | 289 | 289 | "name": "python3" | |
}, | 290 | 290 | }, | |
"language_info": { | 291 | 291 | "language_info": { | |
"codemirror_mode": { | 292 | 292 | "codemirror_mode": { | |
"name": "ipython", | 293 | 293 | "name": "ipython", | |
"version": 3 | 294 | 294 | "version": 3 | |
}, | 295 | 295 | }, | |
"file_extension": ".py", | 296 | 296 | "file_extension": ".py", | |
"mimetype": "text/x-python", | 297 | 297 | "mimetype": "text/x-python", | |
"name": "python", | 298 | 298 | "name": "python", | |
"nbconvert_exporter": "python", | 299 | 299 | "nbconvert_exporter": "python", | |
"pygments_lexer": "ipython3", | 300 | 300 | "pygments_lexer": "ipython3", | |
"version": "3.7.9" | 301 | 301 | "version": "3.7.9" | |
}, | 302 | 302 | }, | |
"orig_nbformat": 4 | 303 | 303 | "orig_nbformat": 4 | |
}, | 304 | 304 | }, | |
"nbformat": 4, | 305 | 305 | "nbformat": 4, | |
"nbformat_minor": 2 | 306 | 306 | "nbformat_minor": 2 | |
} | 307 | 307 | } | |
308 | 308 | |||
python-notebook/test_mnist.ipynb
View file @
e90c8a5
{ | 1 | 1 | { | |
"cells": [ | 2 | 2 | "cells": [ | |
{ | 3 | 3 | { | |
"cell_type": "code", | 4 | 4 | "cell_type": "code", | |
"execution_count": 1, | 5 | 5 | "execution_count": 3, | |
"metadata": {}, | 6 | 6 | "metadata": {}, | |
"outputs": [], | 7 | 7 | "outputs": [], | |
"source": [ | 8 | 8 | "source": [ | |
"from tensorflow.keras.models import Sequential\n", | 9 | 9 | "from tensorflow.keras.models import Sequential\n", | |
"from tensorflow.keras.layers import Dense, Activation\n", | 10 | 10 | "from tensorflow.keras.layers import Dense, Activation\n", | |
"from tensorflow.keras.utils import to_categorical\n", | 11 | 11 | "from tensorflow.keras.utils import to_categorical\n", | |
"from tensorflow.keras.datasets import mnist\n", | 12 | 12 | "from tensorflow.keras.datasets import mnist\n", | |
13 | "from tensorflow.keras.callbacks import EarlyStopping\n", | |||
14 | "from tensorflow import keras\n", | |||
"import numpy as np\n", | 13 | 15 | "import numpy as np\n", | |
"import matplotlib.pyplot as plt" | 14 | 16 | "import matplotlib.pyplot as plt\n", | |
17 | "from tools import *" | |||
] | 15 | 18 | ] | |
}, | 16 | 19 | }, | |
{ | 17 | 20 | { | |
"cell_type": "code", | 18 | 21 | "cell_type": "code", | |
"execution_count": 2, | 19 | 22 | "execution_count": 4, | |
"metadata": {}, | 20 | 23 | "metadata": {}, | |
"outputs": [ | 21 | 24 | "outputs": [ | |
{ | 22 | 25 | { | |
"name": "stdout", | 23 | 26 | "name": "stdout", | |
"output_type": "stream", | 24 | 27 | "output_type": "stream", | |
"text": [ | 25 | 28 | "text": [ | |
"x_train.shape: (60000, 28, 28)\n", | 26 | 29 | "x_train.shape: (60000, 28, 28)\n", | |
"y_train.shape: (60000,)\n", | 27 | 30 | "y_train.shape: (60000,)\n", | |
"x_test.shape: (10000, 28, 28)\n", | 28 | 31 | "x_test.shape: (10000, 28, 28)\n", | |
"y_test.shape: (10000,)\n" | 29 | 32 | "y_test.shape: (10000,)\n" | |
] | 30 | 33 | ] | |
} | 31 | 34 | } | |
], | 32 | 35 | ], | |
"source": [ | 33 | 36 | "source": [ | |
"(x_train, y_train), (x_test, y_test) = mnist.load_data()\n", | 34 | 37 | "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n", | |
"print(\"x_train.shape:\", x_train.shape)\n", | 35 | 38 | "print(\"x_train.shape:\", x_train.shape)\n", | |
"print(\"y_train.shape:\", y_train.shape)\n", | 36 | 39 | "print(\"y_train.shape:\", y_train.shape)\n", | |
"print(\"x_test.shape:\", x_test.shape)\n", | 37 | 40 | "print(\"x_test.shape:\", x_test.shape)\n", | |
"print(\"y_test.shape:\", y_test.shape)" | 38 | 41 | "print(\"y_test.shape:\", y_test.shape)" | |
] | 39 | 42 | ] | |
}, | 40 | 43 | }, | |
{ | 41 | 44 | { | |
"cell_type": "code", | 42 | 45 | "cell_type": "code", | |
"execution_count": 3, | 43 | 46 | "execution_count": 5, | |
"metadata": {}, | 44 | 47 | "metadata": {}, | |
"outputs": [ | 45 | 48 | "outputs": [ | |
{ | 46 | 49 | { | |
"name": "stdout", | 47 | 50 | "name": "stdout", | |
"output_type": "stream", | 48 | 51 | "output_type": "stream", | |
"text": [ | 49 | 52 | "text": [ | |
"X Training matrix shape: (60000, 784)\n", | 50 | 53 | "X Training matrix shape: (60000, 784)\n", | |
"X Testing matrix shape: (10000, 784)\n" | 51 | 54 | "X Testing matrix shape: (10000, 784)\n" | |
] | 52 | 55 | ] | |
} | 53 | 56 | } | |
], | 54 | 57 | ], | |
"source": [ | 55 | 58 | "source": [ | |
"X_train = x_train.reshape(60000, 784)\n", | 56 | 59 | "X_train = x_train.reshape(60000, 784)\n", | |
"X_test = x_test.reshape(10000, 784)\n", | 57 | 60 | "X_test = x_test.reshape(10000, 784)\n", | |
"X_train = X_train.astype('float32')\n", | 58 | 61 | "X_train = X_train.astype('float32')\n", | |
"X_test = X_test.astype('float32')\n", | 59 | 62 | "X_test = X_test.astype('float32')\n", | |
"X_train /= 255\n", | 60 | 63 | "X_train /= 255\n", | |
"X_test /= 255\n", | 61 | 64 | "X_test /= 255\n", | |
"print(\"X Training matrix shape:\", X_train.shape)\n", | 62 | 65 | "print(\"X Training matrix shape:\", X_train.shape)\n", | |
"print(\"X Testing matrix shape:\", X_test.shape)" | 63 | 66 | "print(\"X Testing matrix shape:\", X_test.shape)" | |
] | 64 | 67 | ] | |
}, | 65 | 68 | }, | |
{ | 66 | 69 | { | |
"cell_type": "code", | 67 | 70 | "cell_type": "code", | |
"execution_count": 4, | 68 | 71 | "execution_count": 6, | |
"metadata": {}, | 69 | 72 | "metadata": {}, | |
"outputs": [ | 70 | 73 | "outputs": [ | |
{ | 71 | 74 | { | |
"name": "stdout", | 72 | 75 | "name": "stdout", | |
"output_type": "stream", | 73 | 76 | "output_type": "stream", | |
"text": [ | 74 | 77 | "text": [ | |
"Y Training matrix shape: (60000, 10)\n", | 75 | 78 | "Y Training matrix shape: (60000, 10)\n", | |
"Y Testing matrix shape: (10000, 10)\n" | 76 | 79 | "Y Testing matrix shape: (10000, 10)\n" | |
] | 77 | 80 | ] | |
} | 78 | 81 | } | |
], | 79 | 82 | ], | |
"source": [ | 80 | 83 | "source": [ | |
"Y_train = to_categorical(y_train, 10)\n", | 81 | 84 | "Y_train = to_categorical(y_train, 10)\n", | |
"Y_test = to_categorical(y_test, 10)\n", | 82 | 85 | "Y_test = to_categorical(y_test, 10)\n", | |
"print(\"Y Training matrix shape:\", Y_train.shape)\n", | 83 | 86 | "print(\"Y Training matrix shape:\", Y_train.shape)\n", | |
"print(\"Y Testing matrix shape:\", Y_test.shape)" | 84 | 87 | "print(\"Y Testing matrix shape:\", Y_test.shape)" | |
] | 85 | 88 | ] | |
}, | 86 | 89 | }, | |
{ | 87 | 90 | { | |
"cell_type": "code", | 88 | 91 | "cell_type": "code", | |
"execution_count": 26, | 89 | 92 | "execution_count": 19, | |
"metadata": {}, | 90 | 93 | "metadata": {}, | |
"outputs": [ | 91 | 94 | "outputs": [ | |
{ | 92 | 95 | { | |
"name": "stdout", | 93 | 96 | "name": "stdout", | |
"output_type": "stream", | 94 | 97 | "output_type": "stream", | |
"text": [ | 95 | 98 | "text": [ | |
"Model: \"sequential_5\"\n", | 96 | 99 | "Model: \"sequential_4\"\n", | |
"_________________________________________________________________\n", | 97 | 100 | "_________________________________________________________________\n", | |
" Layer (type) Output Shape Param # \n", | 98 | 101 | " Layer (type) Output Shape Param # \n", | |
"=================================================================\n", | 99 | 102 | "=================================================================\n", | |
" dense_14 (Dense) (None, 10) 7850 \n", | 100 | 103 | " dense_9 (Dense) (None, 10) 7850 \n", | |
" \n", | 101 | 104 | " \n", | |
" dense_15 (Dense) (None, 10) 110 \n", | 102 | 105 | " dense_10 (Dense) (None, 50) 550 \n", | |
" \n", | 103 | 106 | " \n", | |
107 | " dense_11 (Dense) (None, 10) 510 \n", | |||
108 | " \n", | |||
"=================================================================\n", | 104 | 109 | "=================================================================\n", | |
"Total params: 7,960\n", | 105 | 110 | "Total params: 8,910\n", | |
"Trainable params: 7,960\n", | 106 | 111 | "Trainable params: 8,910\n", | |
"Non-trainable params: 0\n", | 107 | 112 | "Non-trainable params: 0\n", | |
"_________________________________________________________________\n", | 108 | 113 | "_________________________________________________________________\n" | |
"Epoch 1/10\n", | 109 | |||
"469/469 [==============================] - 1s 2ms/step - loss: 0.7754 - accuracy: 0.7699\n", | 110 | |||
"Epoch 2/10\n", | 111 | |||
"469/469 [==============================] - 1s 2ms/step - loss: 0.3596 - accuracy: 0.8988\n", | 112 | |||
"Epoch 3/10\n", | 113 | |||
"469/469 [==============================] - 1s 2ms/step - loss: 0.3038 - accuracy: 0.9142\n", | 114 | |||
"Epoch 4/10\n", | 115 | |||
"469/469 [==============================] - 1s 2ms/step - loss: 0.2782 - accuracy: 0.9214\n", | 116 | |||
"Epoch 5/10\n", | 117 | |||
"469/469 [==============================] - 1s 2ms/step - loss: 0.2628 - accuracy: 0.9265\n", | 118 | |||
"Epoch 6/10\n", | 119 | |||
"469/469 [==============================] - 1s 2ms/step - loss: 0.2522 - accuracy: 0.9291\n", | 120 | |||
"Epoch 7/10\n", | 121 | |||
"469/469 [==============================] - 1s 2ms/step - loss: 0.2446 - accuracy: 0.9311\n", | 122 | |||
"Epoch 8/10\n", | 123 | |||
"469/469 [==============================] - 1s 2ms/step - loss: 0.2390 - accuracy: 0.9321\n", | 124 | |||
"Epoch 9/10\n", | 125 | |||
"469/469 [==============================] - 1s 2ms/step - loss: 0.2348 - accuracy: 0.9333\n", | 126 | |||
"Epoch 10/10\n", | 127 | |||
"469/469 [==============================] - 1s 2ms/step - loss: 0.2301 - accuracy: 0.9351\n", | 128 | |||
"313/313 [==============================] - 0s 1ms/step - loss: 0.2358 - accuracy: 0.9323\n", | 129 | |||
"Test score: 0.23583918809890747\n", | 130 | |||
"Test accuracy: 0.9322999715805054\n" | 131 | |||
] | 132 | 114 | ] | |
115 | }, | |||
116 | { | |||
117 | "ename": "NameError", | |||
118 | "evalue": "name 'EarlyStopping' is not defined", | |||
119 | "output_type": "error", | |||
120 | "traceback": [ | |||
121 | "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", | |||
122 | "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", | |||
123 | "\u001b[0;32m/var/folders/m6/l3x11zj94l3dp3wnxy1vnscc0000gn/T/ipykernel_25908/4165624864.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;31m# patience ๋งค๊ฐ๋ณ์๋ ์ฑ๋ฅ ํฅ์์ ์ฒดํฌํ ์ํฌํฌ ํ์์ ๋๋ค\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mearly_stop\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mEarlyStopping\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmonitor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'val_loss'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpatience\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'categorical_crossentropy'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'adam'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'accuracy'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |||
124 | "\u001b[0;31mNameError\u001b[0m: name 'EarlyStopping' is not defined" | |||
125 | ] | |||
} | 133 | 126 | } | |
], | 134 | 127 | ], | |
"source": [ | 135 | 128 | "source": [ | |
"model = Sequential(\n", | 136 | 129 | "model = Sequential(\n", | |
" [\n", | 137 | 130 | " [\n", | |
" Dense(10, activation='relu', input_shape=(784,)),\n", | 138 | 131 | " Dense(10, activation='relu', input_shape=(784,)),\n", | |
132 | " Dense(50, activation='relu'),\n", | |||
" Dense(10, activation='softmax'),\n", | 139 | 133 | " Dense(10, activation='softmax'),\n", | |
" ]\n", | 140 | 134 | " ]\n", | |
")\n", | 141 | 135 | ")\n", | |
"model.summary()\n", | 142 | 136 | "model.summary()\n", | |
"\n", | 143 | 137 | "\n", | |
138 | "# patience ๋งค๊ฐ๋ณ์๋ ์ฑ๋ฅ ํฅ์์ ์ฒดํฌํ ์ํฌํฌ ํ์์ ๋๋ค\n", | |||
139 | "early_stop = keras.callbacks.EarlyStopping(monitor='val_loss', patience=10)\n", | |||
140 | "\n", | |||
"model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n", | 144 | 141 | "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n", | |
"model.fit(X_train, Y_train, batch_size=128, epochs=10, verbose=1)\n", | 145 | 142 | "history = model.fit(X_train, Y_train, batch_size=128, epochs=50, verbose=1, validation_split=0.2, callbacks=[early_stop])\n", | |
"\n", | 146 | 143 | "\n", | |
"score = model.evaluate(X_test, Y_test)\n", | 147 | 144 | "score = model.evaluate(X_test, Y_test)\n", | |
"print('Test score:', score[0])\n", | 148 | 145 | "print('Test score:', score[0])\n", | |
"print('Test accuracy:', score[1])" | 149 | 146 | "print('Test accuracy:', score[1])" | |
] | 150 | 147 | ] | |
}, | 151 | 148 | }, | |
{ | 152 | 149 | { | |
"cell_type": "code", | 153 | 150 | "cell_type": "code", | |
"execution_count": 24, | 154 | 151 | "execution_count": 18, | |
152 | "metadata": {}, | |||
153 | "outputs": [ | |||
154 | { | |||
155 | "data": { | |||
156 | "image/png": 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", | |||
157 | "text/plain": [ | |||
158 | "<Figure size 576x864 with 2 Axes>" | |||
159 | ] | |||
160 | }, | |||
161 | "metadata": { | |||
162 | "needs_background": "light" | |||
163 | }, | |||
164 | "output_type": "display_data" | |||
165 | } | |||
166 | ], | |||
167 | "source": [ | |||
168 | "hist = pd.DataFrame(history.history)\n", | |||
169 | "hist['epoch'] = history.epoch\n", | |||
170 | "plt.figure(figsize=(8,12))\n", | |||
171 | "plt.subplot(2,1,1)\n", | |||
172 | "plt.xlabel('Epoch')\n", | |||
173 | "plt.ylabel('Accuracy')\n", | |||
174 | "plt.plot(hist['epoch'], hist['accuracy'],\n", | |||
175 | " label='Train Accuracy')\n", | |||
176 | "plt.plot(hist['epoch'], hist['val_accuracy'],\n", | |||
177 | " label = 'Val Accuracy')\n", | |||
178 | "# plt.ylim([0,5])\n", | |||
179 | "plt.legend()\n", | |||
180 | "\n", | |||
181 | "plt.subplot(2,1,2)\n", | |||
182 | "plt.xlabel('Epoch')\n", | |||
183 | "plt.ylabel('Loss')\n", | |||
184 | "plt.plot(hist['epoch'], hist['loss'],\n", | |||
185 | " label='Train Loss')\n", | |||
186 | "plt.plot(hist['epoch'], hist['val_loss'],\n", | |||
187 | " label = 'Val Loss')\n", | |||
188 | "# plt.ylim([0,20])\n", | |||
189 | "plt.legend()\n", | |||
190 | "plt.show()\n" | |||
191 | ] | |||
192 | }, | |||
193 | { | |||
194 | "cell_type": "code", | |||
195 | "execution_count": null, | |||
"metadata": {}, | 155 | 196 | "metadata": {}, | |
"outputs": [ | 156 | 197 | "outputs": [ | |
{ | 157 | 198 | { | |
"data": { | 158 | 199 | "data": { | |
"image/png": 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", | 159 | 200 | "image/png": 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", | |
"text/plain": [ | 160 | 201 | "text/plain": [ | |
"<Figure size 432x288 with 8 Axes>" | 161 | 202 | "<Figure size 432x288 with 8 Axes>" | |
] | 162 | 203 | ] | |
}, | 163 | 204 | }, | |
"metadata": {}, | 164 | 205 | "metadata": {}, | |
"output_type": "display_data" | 165 | 206 | "output_type": "display_data" | |
}, | 166 | 207 | }, | |
{ | 167 | 208 | { | |
"data": { | 168 | 209 | "data": { | |
"image/png": 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", | 169 | 210 | "image/png": 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", | |
"text/plain": [ | 170 | 211 | "text/plain": [ | |
"<Figure size 432x288 with 8 Axes>" | 171 | 212 | "<Figure size 432x288 with 8 Axes>" | |
] | 172 | 213 | ] | |
}, | 173 | 214 | }, | |
"metadata": {}, | 174 | 215 | "metadata": {}, | |
"output_type": "display_data" | 175 | 216 | "output_type": "display_data" | |
} | 176 | 217 | } | |
], | 177 | 218 | ], | |
"source": [ | 178 | 219 | "source": [ | |
"predicted_classes = np.argmax(model.predict(X_test), axis=1)\n", | 179 | 220 | "predicted_classes = np.argmax(model.predict(X_test), axis=1)\n", | |
"correct_indices = np.nonzero(predicted_classes == y_test)[0]\n", | 180 | 221 | "correct_indices = np.nonzero(predicted_classes == y_test)[0]\n", | |
"incorrect_indices = np.nonzero(predicted_classes != y_test)[0]\n", | 181 | 222 | "incorrect_indices = np.nonzero(predicted_classes != y_test)[0]\n", | |
"\n", | 182 | 223 | "\n", | |
"ax = plt.figure()\n", | 183 | 224 | "ax = plt.figure()\n", | |
"ax.patch.set_facecolor('white')\n", | 184 | 225 | "ax.patch.set_facecolor('white')\n", | |
"for i in range(9):\n", | 185 | 226 | "for i in range(9):\n", | |
" plt.subplot(3,3,i+1)\n", | 186 | 227 | " plt.subplot(3,3,i+1)\n", | |
" correct = correct_indices[i]\n", | 187 | 228 | " correct = correct_indices[i]\n", | |
" plt.imshow(X_test[correct].reshape(28, 28), cmap='gray')\n", | 188 | 229 | " plt.imshow(X_test[correct].reshape(28, 28), cmap='gray')\n", | |
" plt.title(\"Predicted {}, Class {}\".format(predicted_classes[correct], y_test[correct]))\n", | 189 | 230 | " plt.title(\"Predicted {}, Class {}\".format(predicted_classes[correct], y_test[correct]))\n", | |
" plt.tight_layout()\n", | 190 | 231 | " plt.tight_layout()\n", | |
"\n", | 191 | 232 | "\n", | |
"ax = plt.figure()\n", | 192 | 233 | "ax = plt.figure()\n", | |
"ax.patch.set_facecolor('white')\n", | 193 | 234 | "ax.patch.set_facecolor('white')\n", | |
"for i in range(9):\n", | 194 | 235 | "for i in range(9):\n", | |
" plt.subplot(3,3,i+1)\n", | 195 | 236 | " plt.subplot(3,3,i+1)\n", | |
" incorrect = incorrect_indices[i]\n", | 196 | 237 | " incorrect = incorrect_indices[i]\n", | |
" plt.imshow(X_test[incorrect].reshape(28, 28), cmap='gray')\n", | 197 | 238 | " plt.imshow(X_test[incorrect].reshape(28, 28), cmap='gray')\n", | |
" plt.title(\"Predicted {}, Class {}\".format(predicted_classes[incorrect], y_test[incorrect]))\n", | 198 | 239 | " plt.title(\"Predicted {}, Class {}\".format(predicted_classes[incorrect], y_test[incorrect]))\n", | |
" plt.tight_layout()" | 199 | 240 | " plt.tight_layout()" | |
] | 200 | 241 | ] | |
} | 201 | 242 | } | |
], | 202 | 243 | ], | |
"metadata": { | 203 | 244 | "metadata": { | |
"interpreter": { | 204 | 245 | "interpreter": { | |
"hash": "80dbe1014b4652684caa329d41db00af3ae439be86b11eab7e35b518e5d8ab1a" | 205 | 246 | "hash": "80dbe1014b4652684caa329d41db00af3ae439be86b11eab7e35b518e5d8ab1a" | |
}, | 206 | 247 | }, | |
"kernelspec": { | 207 | 248 | "kernelspec": { | |
"display_name": "Python 3.7.9 64-bit ('venv': venv)", | 208 | 249 | "display_name": "Python 3.7.9 64-bit ('venv': venv)", | |
"language": "python", | 209 | 250 | "language": "python", | |
"name": "python3" | 210 | 251 | "name": "python3" | |
}, | 211 | 252 | }, | |
"language_info": { | 212 | 253 | "language_info": { | |
"codemirror_mode": { | 213 | 254 | "codemirror_mode": { | |
"name": "ipython", | 214 | 255 | "name": "ipython", | |
"version": 3 | 215 | 256 | "version": 3 | |
}, | 216 | 257 | }, | |
"file_extension": ".py", | 217 | 258 | "file_extension": ".py", | |
"mimetype": "text/x-python", | 218 | 259 | "mimetype": "text/x-python", | |
"name": "python", | 219 | 260 | "name": "python", | |
"nbconvert_exporter": "python", | 220 | 261 | "nbconvert_exporter": "python", | |
"pygments_lexer": "ipython3", | 221 | 262 | "pygments_lexer": "ipython3", | |
"version": "3.7.9" | 222 | 263 | "version": "3.7.9" | |
}, | 223 | 264 | }, | |
"orig_nbformat": 4 | 224 | 265 | "orig_nbformat": 4 | |
}, | 225 | 266 | }, | |
"nbformat": 4, | 226 | 267 | "nbformat": 4, | |
"nbformat_minor": 2 | 227 | 268 | "nbformat_minor": 2 | |
} | 228 | 269 | } | |
229 | 270 | |||
python-notebook/test_mpg.ipynb
View file @
e90c8a5
{ | 1 | 1 | { | |
"cells": [ | 2 | 2 | "cells": [ | |
{ | 3 | 3 | { | |
"cell_type": "code", | 4 | 4 | "cell_type": "code", | |
"execution_count": 18, | 5 | 5 | "execution_count": 19, | |
"metadata": {}, | 6 | 6 | "metadata": {}, | |
"outputs": [ | 7 | 7 | "outputs": [ | |
{ | 8 | 8 | { | |
"name": "stdout", | 9 | 9 | "name": "stdout", | |
"output_type": "stream", | 10 | 10 | "output_type": "stream", | |
"text": [ | 11 | 11 | "text": [ | |
"2.7.0\n" | 12 | 12 | "2.7.0\n" | |
] | 13 | 13 | ] | |
} | 14 | 14 | } | |
], | 15 | 15 | ], | |
"source": [ | 16 | 16 | "source": [ | |
"import pathlib\n", | 17 | 17 | "import pathlib\n", | |
"\n", | 18 | 18 | "\n", | |
"import matplotlib.pyplot as plt\n", | 19 | 19 | "import matplotlib.pyplot as plt\n", | |
"import pandas as pd\n", | 20 | 20 | "import pandas as pd\n", | |
"import seaborn as sns\n", | 21 | 21 | "import seaborn as sns\n", | |
"\n", | 22 | 22 | "\n", | |
"import tensorflow as tf\n", | 23 | 23 | "import tensorflow as tf\n", | |
"from tensorflow import keras\n", | 24 | 24 | "from tensorflow import keras\n", | |
"from tensorflow.keras import layers\n", | 25 | 25 | "from tensorflow.keras import layers\n", | |
26 | "from tools import * \n", | |||
"\n", | 26 | 27 | "\n", | |
"print(tf.__version__)" | 27 | 28 | "print(tf.__version__)" | |
] | 28 | 29 | ] | |
}, | 29 | 30 | }, | |
{ | 30 | 31 | { | |
"cell_type": "code", | 31 | 32 | "cell_type": "code", | |
"execution_count": 19, | 32 | 33 | "execution_count": 2, | |
"metadata": {}, | 33 | 34 | "metadata": {}, | |
"outputs": [ | 34 | 35 | "outputs": [ | |
{ | 35 | 36 | { | |
"data": { | 36 | 37 | "data": { | |
"text/plain": [ | 37 | 38 | "text/plain": [ | |
"'/Users/ffee21/.keras/datasets/auto-mpg.data'" | 38 | 39 | "'/Users/ffee21/.keras/datasets/auto-mpg.data'" | |
] | 39 | 40 | ] | |
}, | 40 | 41 | }, | |
"execution_count": 19, | 41 | 42 | "execution_count": 2, | |
"metadata": {}, | 42 | 43 | "metadata": {}, | |
"output_type": "execute_result" | 43 | 44 | "output_type": "execute_result" | |
} | 44 | 45 | } | |
], | 45 | 46 | ], | |
"source": [ | 46 | 47 | "source": [ | |
"dataset_path = keras.utils.get_file(\"auto-mpg.data\", \"http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data\")\n", | 47 | 48 | "dataset_path = keras.utils.get_file(\"auto-mpg.data\", \"http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data\")\n", | |
"dataset_path" | 48 | 49 | "dataset_path" | |
] | 49 | 50 | ] | |
}, | 50 | 51 | }, | |
{ | 51 | 52 | { | |
"cell_type": "code", | 52 | 53 | "cell_type": "code", | |
"execution_count": 20, | 53 | 54 | "execution_count": 3, | |
"metadata": {}, | 54 | 55 | "metadata": {}, | |
"outputs": [ | 55 | 56 | "outputs": [ | |
{ | 56 | 57 | { | |
"data": { | 57 | 58 | "data": { | |
"text/html": [ | 58 | 59 | "text/html": [ | |
"<div>\n", | 59 | 60 | "<div>\n", | |
"<style scoped>\n", | 60 | 61 | "<style scoped>\n", | |
" .dataframe tbody tr th:only-of-type {\n", | 61 | 62 | " .dataframe tbody tr th:only-of-type {\n", | |
" vertical-align: middle;\n", | 62 | 63 | " vertical-align: middle;\n", | |
" }\n", | 63 | 64 | " }\n", | |
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" <thead>\n", | 74 | 75 | " <thead>\n", | |
" <tr style=\"text-align: right;\">\n", | 75 | 76 | " <tr style=\"text-align: right;\">\n", | |
" <th></th>\n", | 76 | 77 | " <th></th>\n", | |
" <th>MPG</th>\n", | 77 | 78 | " <th>MPG</th>\n", | |
" <th>Cylinders</th>\n", | 78 | 79 | " <th>Cylinders</th>\n", | |
" <th>Displacement</th>\n", | 79 | 80 | " <th>Displacement</th>\n", | |
" <th>Horsepower</th>\n", | 80 | 81 | " <th>Horsepower</th>\n", | |
" <th>Weight</th>\n", | 81 | 82 | " <th>Weight</th>\n", | |
" <th>Acceleration</th>\n", | 82 | 83 | " <th>Acceleration</th>\n", | |
" <th>Model Year</th>\n", | 83 | 84 | " <th>Model Year</th>\n", | |
" <th>USA</th>\n", | 84 | 85 | " <th>USA</th>\n", | |
" <th>Europe</th>\n", | 85 | 86 | " <th>Europe</th>\n", | |
" <th>Japan</th>\n", | 86 | 87 | " <th>Japan</th>\n", | |
" </tr>\n", | 87 | 88 | " </tr>\n", | |
" </thead>\n", | 88 | 89 | " </thead>\n", | |
" <tbody>\n", | 89 | 90 | " <tbody>\n", | |
" <tr>\n", | 90 | 91 | " <tr>\n", | |
" <th>393</th>\n", | 91 | 92 | " <th>393</th>\n", | |
" <td>27.0</td>\n", | 92 | 93 | " <td>27.0</td>\n", | |
" <td>4</td>\n", | 93 | 94 | " <td>4</td>\n", | |
" <td>140.0</td>\n", | 94 | 95 | " <td>140.0</td>\n", | |
" <td>86.0</td>\n", | 95 | 96 | " <td>86.0</td>\n", | |
" <td>2790.0</td>\n", | 96 | 97 | " <td>2790.0</td>\n", | |
" <td>15.6</td>\n", | 97 | 98 | " <td>15.6</td>\n", | |
" <td>82</td>\n", | 98 | 99 | " <td>82</td>\n", | |
" <td>1.0</td>\n", | 99 | 100 | " <td>1.0</td>\n", | |
" <td>0.0</td>\n", | 100 | 101 | " <td>0.0</td>\n", | |
" <td>0.0</td>\n", | 101 | 102 | " <td>0.0</td>\n", | |
" </tr>\n", | 102 | 103 | " </tr>\n", | |
" <tr>\n", | 103 | 104 | " <tr>\n", | |
" <th>394</th>\n", | 104 | 105 | " <th>394</th>\n", | |
" <td>44.0</td>\n", | 105 | 106 | " <td>44.0</td>\n", | |
" <td>4</td>\n", | 106 | 107 | " <td>4</td>\n", | |
" <td>97.0</td>\n", | 107 | 108 | " <td>97.0</td>\n", | |
" <td>52.0</td>\n", | 108 | 109 | " <td>52.0</td>\n", | |
" <td>2130.0</td>\n", | 109 | 110 | " <td>2130.0</td>\n", | |
" <td>24.6</td>\n", | 110 | 111 | " <td>24.6</td>\n", | |
" <td>82</td>\n", | 111 | 112 | " <td>82</td>\n", | |
" <td>0.0</td>\n", | 112 | 113 | " <td>0.0</td>\n", | |
" <td>1.0</td>\n", | 113 | 114 | " <td>1.0</td>\n", | |
" <td>0.0</td>\n", | 114 | 115 | " <td>0.0</td>\n", | |
" </tr>\n", | 115 | 116 | " </tr>\n", | |
" <tr>\n", | 116 | 117 | " <tr>\n", | |
" <th>395</th>\n", | 117 | 118 | " <th>395</th>\n", | |
" <td>32.0</td>\n", | 118 | 119 | " <td>32.0</td>\n", | |
" <td>4</td>\n", | 119 | 120 | " <td>4</td>\n", | |
" <td>135.0</td>\n", | 120 | 121 | " <td>135.0</td>\n", | |
" <td>84.0</td>\n", | 121 | 122 | " <td>84.0</td>\n", | |
" <td>2295.0</td>\n", | 122 | 123 | " <td>2295.0</td>\n", | |
" <td>11.6</td>\n", | 123 | 124 | " <td>11.6</td>\n", | |
" <td>82</td>\n", | 124 | 125 | " <td>82</td>\n", | |
" <td>1.0</td>\n", | 125 | 126 | " <td>1.0</td>\n", | |
" <td>0.0</td>\n", | 126 | 127 | " <td>0.0</td>\n", | |
" <td>0.0</td>\n", | 127 | 128 | " <td>0.0</td>\n", | |
" </tr>\n", | 128 | 129 | " </tr>\n", | |
" <tr>\n", | 129 | 130 | " <tr>\n", | |
" <th>396</th>\n", | 130 | 131 | " <th>396</th>\n", | |
" <td>28.0</td>\n", | 131 | 132 | " <td>28.0</td>\n", | |
" <td>4</td>\n", | 132 | 133 | " <td>4</td>\n", | |
" <td>120.0</td>\n", | 133 | 134 | " <td>120.0</td>\n", | |
" <td>79.0</td>\n", | 134 | 135 | " <td>79.0</td>\n", | |
" <td>2625.0</td>\n", | 135 | 136 | " <td>2625.0</td>\n", | |
" <td>18.6</td>\n", | 136 | 137 | " <td>18.6</td>\n", | |
" <td>82</td>\n", | 137 | 138 | " <td>82</td>\n", | |
" <td>1.0</td>\n", | 138 | 139 | " <td>1.0</td>\n", | |
" <td>0.0</td>\n", | 139 | 140 | " <td>0.0</td>\n", | |
" <td>0.0</td>\n", | 140 | 141 | " <td>0.0</td>\n", | |
" </tr>\n", | 141 | 142 | " </tr>\n", | |
" <tr>\n", | 142 | 143 | " <tr>\n", | |
" <th>397</th>\n", | 143 | 144 | " <th>397</th>\n", | |
" <td>31.0</td>\n", | 144 | 145 | " <td>31.0</td>\n", | |
" <td>4</td>\n", | 145 | 146 | " <td>4</td>\n", | |
" <td>119.0</td>\n", | 146 | 147 | " <td>119.0</td>\n", | |
" <td>82.0</td>\n", | 147 | 148 | " <td>82.0</td>\n", | |
" <td>2720.0</td>\n", | 148 | 149 | " <td>2720.0</td>\n", | |
" <td>19.4</td>\n", | 149 | 150 | " <td>19.4</td>\n", | |
" <td>82</td>\n", | 150 | 151 | " <td>82</td>\n", | |
" <td>1.0</td>\n", | 151 | 152 | " <td>1.0</td>\n", | |
" <td>0.0</td>\n", | 152 | 153 | " <td>0.0</td>\n", | |
" <td>0.0</td>\n", | 153 | 154 | " <td>0.0</td>\n", | |
" </tr>\n", | 154 | 155 | " </tr>\n", | |
" </tbody>\n", | 155 | 156 | " </tbody>\n", | |
"</table>\n", | 156 | 157 | "</table>\n", | |
"</div>" | 157 | 158 | "</div>" | |
], | 158 | 159 | ], | |
"text/plain": [ | 159 | 160 | "text/plain": [ | |
" MPG Cylinders Displacement Horsepower Weight Acceleration \\\n", | 160 | 161 | " MPG Cylinders Displacement Horsepower Weight Acceleration \\\n", | |
"393 27.0 4 140.0 86.0 2790.0 15.6 \n", | 161 | 162 | "393 27.0 4 140.0 86.0 2790.0 15.6 \n", | |
"394 44.0 4 97.0 52.0 2130.0 24.6 \n", | 162 | 163 | "394 44.0 4 97.0 52.0 2130.0 24.6 \n", | |
"395 32.0 4 135.0 84.0 2295.0 11.6 \n", | 163 | 164 | "395 32.0 4 135.0 84.0 2295.0 11.6 \n", | |
"396 28.0 4 120.0 79.0 2625.0 18.6 \n", | 164 | 165 | "396 28.0 4 120.0 79.0 2625.0 18.6 \n", | |
"397 31.0 4 119.0 82.0 2720.0 19.4 \n", | 165 | 166 | "397 31.0 4 119.0 82.0 2720.0 19.4 \n", | |
"\n", | 166 | 167 | "\n", | |
" Model Year USA Europe Japan \n", | 167 | 168 | " Model Year USA Europe Japan \n", | |
"393 82 1.0 0.0 0.0 \n", | 168 | 169 | "393 82 1.0 0.0 0.0 \n", | |
"394 82 0.0 1.0 0.0 \n", | 169 | 170 | "394 82 0.0 1.0 0.0 \n", | |
"395 82 1.0 0.0 0.0 \n", | 170 | 171 | "395 82 1.0 0.0 0.0 \n", | |
"396 82 1.0 0.0 0.0 \n", | 171 | 172 | "396 82 1.0 0.0 0.0 \n", | |
"397 82 1.0 0.0 0.0 " | 172 | 173 | "397 82 1.0 0.0 0.0 " | |
] | 173 | 174 | ] | |
}, | 174 | 175 | }, | |
"execution_count": 20, | 175 | 176 | "execution_count": 3, | |
"metadata": {}, | 176 | 177 | "metadata": {}, | |
"output_type": "execute_result" | 177 | 178 | "output_type": "execute_result" | |
} | 178 | 179 | } | |
], | 179 | 180 | ], | |
"source": [ | 180 | 181 | "source": [ | |
"column_names = ['MPG','Cylinders','Displacement','Horsepower','Weight',\n", | 181 | 182 | "column_names = ['MPG','Cylinders','Displacement','Horsepower','Weight',\n", | |
" 'Acceleration', 'Model Year', 'Origin']\n", | 182 | 183 | " 'Acceleration', 'Model Year', 'Origin']\n", | |
"raw_dataset = pd.read_csv(dataset_path, names=column_names,\n", | 183 | 184 | "raw_dataset = pd.read_csv(dataset_path, names=column_names,\n", | |
" na_values = \"?\", comment='\\t',\n", | 184 | 185 | " na_values = \"?\", comment='\\t',\n", | |
" sep=\" \", skipinitialspace=True)\n", | 185 | 186 | " sep=\" \", skipinitialspace=True)\n", | |
"\n", | 186 | 187 | "\n", | |
"dataset = raw_dataset.copy()\n", | 187 | 188 | "dataset = raw_dataset.copy()\n", | |
"# dataset.tail()\n", | 188 | 189 | "# dataset.tail()\n", | |
"# dataset.isna().sum()\n", | 189 | 190 | "# dataset.isna().sum()\n", | |
"dataset = dataset.dropna()\n", | 190 | 191 | "dataset = dataset.dropna()\n", | |
"origin = dataset.pop('Origin')\n", | 191 | 192 | "origin = dataset.pop('Origin')\n", | |
"dataset['USA'] = (origin == 1)*1.0\n", | 192 | 193 | "dataset['USA'] = (origin == 1)*1.0\n", | |
"dataset['Europe'] = (origin == 2)*1.0\n", | 193 | 194 | "dataset['Europe'] = (origin == 2)*1.0\n", | |
"dataset['Japan'] = (origin == 3)*1.0\n", | 194 | 195 | "dataset['Japan'] = (origin == 3)*1.0\n", | |
"dataset.tail()" | 195 | 196 | "dataset.tail()" | |
] | 196 | 197 | ] | |
}, | 197 | 198 | }, | |
{ | 198 | 199 | { | |
"cell_type": "code", | 199 | 200 | "cell_type": "code", | |
"execution_count": 21, | 200 | 201 | "execution_count": 4, | |
"metadata": {}, | 201 | 202 | "metadata": {}, | |
"outputs": [], | 202 | 203 | "outputs": [], | |
"source": [ | 203 | 204 | "source": [ | |
"train_dataset = dataset.sample(frac=0.8,random_state=0)\n", | 204 | 205 | "train_dataset = dataset.sample(frac=0.8,random_state=0)\n", | |
"test_dataset = dataset.drop(train_dataset.index)" | 205 | 206 | "test_dataset = dataset.drop(train_dataset.index)" | |
] | 206 | 207 | ] | |
}, | 207 | 208 | }, | |
{ | 208 | 209 | { | |
"cell_type": "code", | 209 | 210 | "cell_type": "code", | |
"execution_count": 22, | 210 | 211 | "execution_count": 5, | |
"metadata": {}, | 211 | 212 | "metadata": {}, | |
"outputs": [ | 212 | 213 | "outputs": [ | |
{ | 213 | 214 | { | |
"data": { | 214 | 215 | "data": { | |
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" <thead>\n", | 231 | 232 | " <thead>\n", | |
" <tr style=\"text-align: right;\">\n", | 232 | 233 | " <tr style=\"text-align: right;\">\n", | |
" <th></th>\n", | 233 | 234 | " <th></th>\n", | |
" <th>count</th>\n", | 234 | 235 | " <th>count</th>\n", | |
" <th>mean</th>\n", | 235 | 236 | " <th>mean</th>\n", | |
" <th>std</th>\n", | 236 | 237 | " <th>std</th>\n", | |
" <th>min</th>\n", | 237 | 238 | " <th>min</th>\n", | |
" <th>25%</th>\n", | 238 | 239 | " <th>25%</th>\n", | |
" <th>50%</th>\n", | 239 | 240 | " <th>50%</th>\n", | |
" <th>75%</th>\n", | 240 | 241 | " <th>75%</th>\n", | |
" <th>max</th>\n", | 241 | 242 | " <th>max</th>\n", | |
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" </thead>\n", | 243 | 244 | " </thead>\n", | |
" <tbody>\n", | 244 | 245 | " <tbody>\n", | |
" <tr>\n", | 245 | 246 | " <tr>\n", | |
" <th>Cylinders</th>\n", | 246 | 247 | " <th>Cylinders</th>\n", | |
" <td>314.0</td>\n", | 247 | 248 | " <td>314.0</td>\n", | |
" <td>5.477707</td>\n", | 248 | 249 | " <td>5.477707</td>\n", | |
" <td>1.699788</td>\n", | 249 | 250 | " <td>1.699788</td>\n", | |
" <td>3.0</td>\n", | 250 | 251 | " <td>3.0</td>\n", | |
" <td>4.00</td>\n", | 251 | 252 | " <td>4.00</td>\n", | |
" <td>4.0</td>\n", | 252 | 253 | " <td>4.0</td>\n", | |
" <td>8.00</td>\n", | 253 | 254 | " <td>8.00</td>\n", | |
" <td>8.0</td>\n", | 254 | 255 | " <td>8.0</td>\n", | |
" </tr>\n", | 255 | 256 | " </tr>\n", | |
" <tr>\n", | 256 | 257 | " <tr>\n", | |
" <th>Displacement</th>\n", | 257 | 258 | " <th>Displacement</th>\n", | |
" <td>314.0</td>\n", | 258 | 259 | " <td>314.0</td>\n", | |
" <td>195.318471</td>\n", | 259 | 260 | " <td>195.318471</td>\n", | |
" <td>104.331589</td>\n", | 260 | 261 | " <td>104.331589</td>\n", | |
" <td>68.0</td>\n", | 261 | 262 | " <td>68.0</td>\n", | |
" <td>105.50</td>\n", | 262 | 263 | " <td>105.50</td>\n", | |
" <td>151.0</td>\n", | 263 | 264 | " <td>151.0</td>\n", | |
" <td>265.75</td>\n", | 264 | 265 | " <td>265.75</td>\n", | |
" <td>455.0</td>\n", | 265 | 266 | " <td>455.0</td>\n", | |
" </tr>\n", | 266 | 267 | " </tr>\n", | |
" <tr>\n", | 267 | 268 | " <tr>\n", | |
" <th>Horsepower</th>\n", | 268 | 269 | " <th>Horsepower</th>\n", | |
" <td>314.0</td>\n", | 269 | 270 | " <td>314.0</td>\n", | |
" <td>104.869427</td>\n", | 270 | 271 | " <td>104.869427</td>\n", | |
" <td>38.096214</td>\n", | 271 | 272 | " <td>38.096214</td>\n", | |
" <td>46.0</td>\n", | 272 | 273 | " <td>46.0</td>\n", | |
" <td>76.25</td>\n", | 273 | 274 | " <td>76.25</td>\n", | |
" <td>94.5</td>\n", | 274 | 275 | " <td>94.5</td>\n", | |
" <td>128.00</td>\n", | 275 | 276 | " <td>128.00</td>\n", | |
" <td>225.0</td>\n", | 276 | 277 | " <td>225.0</td>\n", | |
" </tr>\n", | 277 | 278 | " </tr>\n", | |
" <tr>\n", | 278 | 279 | " <tr>\n", | |
" <th>Weight</th>\n", | 279 | 280 | " <th>Weight</th>\n", | |
" <td>314.0</td>\n", | 280 | 281 | " <td>314.0</td>\n", | |
" <td>2990.251592</td>\n", | 281 | 282 | " <td>2990.251592</td>\n", | |
" <td>843.898596</td>\n", | 282 | 283 | " <td>843.898596</td>\n", | |
" <td>1649.0</td>\n", | 283 | 284 | " <td>1649.0</td>\n", | |
" <td>2256.50</td>\n", | 284 | 285 | " <td>2256.50</td>\n", | |
" <td>2822.5</td>\n", | 285 | 286 | " <td>2822.5</td>\n", | |
" <td>3608.00</td>\n", | 286 | 287 | " <td>3608.00</td>\n", | |
" <td>5140.0</td>\n", | 287 | 288 | " <td>5140.0</td>\n", | |
" </tr>\n", | 288 | 289 | " </tr>\n", | |
" <tr>\n", | 289 | 290 | " <tr>\n", | |
" <th>Acceleration</th>\n", | 290 | 291 | " <th>Acceleration</th>\n", | |
" <td>314.0</td>\n", | 291 | 292 | " <td>314.0</td>\n", | |
" <td>15.559236</td>\n", | 292 | 293 | " <td>15.559236</td>\n", | |
" <td>2.789230</td>\n", | 293 | 294 | " <td>2.789230</td>\n", | |
" <td>8.0</td>\n", | 294 | 295 | " <td>8.0</td>\n", | |
" <td>13.80</td>\n", | 295 | 296 | " <td>13.80</td>\n", | |
" <td>15.5</td>\n", | 296 | 297 | " <td>15.5</td>\n", | |
" <td>17.20</td>\n", | 297 | 298 | " <td>17.20</td>\n", | |
" <td>24.8</td>\n", | 298 | 299 | " <td>24.8</td>\n", | |
" </tr>\n", | 299 | 300 | " </tr>\n", | |
" <tr>\n", | 300 | 301 | " <tr>\n", | |
" <th>Model Year</th>\n", | 301 | 302 | " <th>Model Year</th>\n", | |
" <td>314.0</td>\n", | 302 | 303 | " <td>314.0</td>\n", | |
" <td>75.898089</td>\n", | 303 | 304 | " <td>75.898089</td>\n", | |
" <td>3.675642</td>\n", | 304 | 305 | " <td>3.675642</td>\n", | |
" <td>70.0</td>\n", | 305 | 306 | " <td>70.0</td>\n", | |
" <td>73.00</td>\n", | 306 | 307 | " <td>73.00</td>\n", | |
" <td>76.0</td>\n", | 307 | 308 | " <td>76.0</td>\n", | |
" <td>79.00</td>\n", | 308 | 309 | " <td>79.00</td>\n", | |
" <td>82.0</td>\n", | 309 | 310 | " <td>82.0</td>\n", | |
" </tr>\n", | 310 | 311 | " </tr>\n", | |
" <tr>\n", | 311 | 312 | " <tr>\n", | |
" <th>USA</th>\n", | 312 | 313 | " <th>USA</th>\n", | |
" <td>314.0</td>\n", | 313 | 314 | " <td>314.0</td>\n", | |
" <td>0.624204</td>\n", | 314 | 315 | " <td>0.624204</td>\n", | |
" <td>0.485101</td>\n", | 315 | 316 | " <td>0.485101</td>\n", | |
" <td>0.0</td>\n", | 316 | 317 | " <td>0.0</td>\n", | |
" <td>0.00</td>\n", | 317 | 318 | " <td>0.00</td>\n", | |
" <td>1.0</td>\n", | 318 | 319 | " <td>1.0</td>\n", | |
" <td>1.00</td>\n", | 319 | 320 | " <td>1.00</td>\n", | |
" <td>1.0</td>\n", | 320 | 321 | " <td>1.0</td>\n", | |
" </tr>\n", | 321 | 322 | " </tr>\n", | |
" <tr>\n", | 322 | 323 | " <tr>\n", | |
" <th>Europe</th>\n", | 323 | 324 | " <th>Europe</th>\n", | |
" <td>314.0</td>\n", | 324 | 325 | " <td>314.0</td>\n", | |
" <td>0.178344</td>\n", | 325 | 326 | " <td>0.178344</td>\n", | |
" <td>0.383413</td>\n", | 326 | 327 | " <td>0.383413</td>\n", | |
" <td>0.0</td>\n", | 327 | 328 | " <td>0.0</td>\n", | |
" <td>0.00</td>\n", | 328 | 329 | " <td>0.00</td>\n", | |
" <td>0.0</td>\n", | 329 | 330 | " <td>0.0</td>\n", | |
" <td>0.00</td>\n", | 330 | 331 | " <td>0.00</td>\n", | |
" <td>1.0</td>\n", | 331 | 332 | " <td>1.0</td>\n", | |
" </tr>\n", | 332 | 333 | " </tr>\n", | |
" <tr>\n", | 333 | 334 | " <tr>\n", | |
" <th>Japan</th>\n", | 334 | 335 | " <th>Japan</th>\n", | |
" <td>314.0</td>\n", | 335 | 336 | " <td>314.0</td>\n", | |
" <td>0.197452</td>\n", | 336 | 337 | " <td>0.197452</td>\n", | |
" <td>0.398712</td>\n", | 337 | 338 | " <td>0.398712</td>\n", | |
" <td>0.0</td>\n", | 338 | 339 | " <td>0.0</td>\n", | |
" <td>0.00</td>\n", | 339 | 340 | " <td>0.00</td>\n", | |
" <td>0.0</td>\n", | 340 | 341 | " <td>0.0</td>\n", | |
" <td>0.00</td>\n", | 341 | 342 | " <td>0.00</td>\n", | |
" <td>1.0</td>\n", | 342 | 343 | " <td>1.0</td>\n", | |
" </tr>\n", | 343 | 344 | " </tr>\n", | |
" </tbody>\n", | 344 | 345 | " </tbody>\n", | |
"</table>\n", | 345 | 346 | "</table>\n", | |
"</div>" | 346 | 347 | "</div>" | |
], | 347 | 348 | ], | |
"text/plain": [ | 348 | 349 | "text/plain": [ | |
" count mean std min 25% 50% \\\n", | 349 | 350 | " count mean std min 25% 50% \\\n", | |
"Cylinders 314.0 5.477707 1.699788 3.0 4.00 4.0 \n", | 350 | 351 | "Cylinders 314.0 5.477707 1.699788 3.0 4.00 4.0 \n", | |
"Displacement 314.0 195.318471 104.331589 68.0 105.50 151.0 \n", | 351 | 352 | "Displacement 314.0 195.318471 104.331589 68.0 105.50 151.0 \n", | |
"Horsepower 314.0 104.869427 38.096214 46.0 76.25 94.5 \n", | 352 | 353 | "Horsepower 314.0 104.869427 38.096214 46.0 76.25 94.5 \n", | |
"Weight 314.0 2990.251592 843.898596 1649.0 2256.50 2822.5 \n", | 353 | 354 | "Weight 314.0 2990.251592 843.898596 1649.0 2256.50 2822.5 \n", | |
"Acceleration 314.0 15.559236 2.789230 8.0 13.80 15.5 \n", | 354 | 355 | "Acceleration 314.0 15.559236 2.789230 8.0 13.80 15.5 \n", | |
"Model Year 314.0 75.898089 3.675642 70.0 73.00 76.0 \n", | 355 | 356 | "Model Year 314.0 75.898089 3.675642 70.0 73.00 76.0 \n", | |
"USA 314.0 0.624204 0.485101 0.0 0.00 1.0 \n", | 356 | 357 | "USA 314.0 0.624204 0.485101 0.0 0.00 1.0 \n", | |
"Europe 314.0 0.178344 0.383413 0.0 0.00 0.0 \n", | 357 | 358 | "Europe 314.0 0.178344 0.383413 0.0 0.00 0.0 \n", | |
"Japan 314.0 0.197452 0.398712 0.0 0.00 0.0 \n", | 358 | 359 | "Japan 314.0 0.197452 0.398712 0.0 0.00 0.0 \n", | |
"\n", | 359 | 360 | "\n", | |
" 75% max \n", | 360 | 361 | " 75% max \n", | |
"Cylinders 8.00 8.0 \n", | 361 | 362 | "Cylinders 8.00 8.0 \n", | |
"Displacement 265.75 455.0 \n", | 362 | 363 | "Displacement 265.75 455.0 \n", | |
"Horsepower 128.00 225.0 \n", | 363 | 364 | "Horsepower 128.00 225.0 \n", | |
"Weight 3608.00 5140.0 \n", | 364 | 365 | "Weight 3608.00 5140.0 \n", | |
"Acceleration 17.20 24.8 \n", | 365 | 366 | "Acceleration 17.20 24.8 \n", | |
"Model Year 79.00 82.0 \n", | 366 | 367 | "Model Year 79.00 82.0 \n", | |
"USA 1.00 1.0 \n", | 367 | 368 | "USA 1.00 1.0 \n", | |
"Europe 0.00 1.0 \n", | 368 | 369 | "Europe 0.00 1.0 \n", | |
"Japan 0.00 1.0 " | 369 | 370 | "Japan 0.00 1.0 " | |
] | 370 | 371 | ] | |
}, | 371 | 372 | }, | |
"execution_count": 22, | 372 | 373 | "execution_count": 5, | |
"metadata": {}, | 373 | 374 | "metadata": {}, | |
"output_type": "execute_result" | 374 | 375 | "output_type": "execute_result" | |
}, | 375 | 376 | }, | |
{ | 376 | 377 | { | |
"data": { | 377 | 378 | "data": { | |
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", | 378 | 379 | "image/png": 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", | |
"text/plain": [ | 379 | 380 | "text/plain": [ | |
"<Figure size 720x720 with 20 Axes>" | 380 | 381 | "<Figure size 720x720 with 20 Axes>" | |
] | 381 | 382 | ] | |
}, | 382 | 383 | }, | |
"metadata": { | 383 | 384 | "metadata": { | |
"needs_background": "light" | 384 | 385 | "needs_background": "light" | |
}, | 385 | 386 | }, | |
"output_type": "display_data" | 386 | 387 | "output_type": "display_data" | |
} | 387 | 388 | } | |
], | 388 | 389 | ], | |
"source": [ | 389 | 390 | "source": [ | |
"sns.pairplot(train_dataset[[\"MPG\", \"Cylinders\", \"Displacement\", \"Weight\"]], diag_kind=\"kde\")\n", | 390 | 391 | "sns.pairplot(train_dataset[[\"MPG\", \"Cylinders\", \"Displacement\", \"Weight\"]], diag_kind=\"kde\")\n", | |
"\n", | 391 | 392 | "\n", | |
"train_stats = train_dataset.describe()\n", | 392 | 393 | "train_stats = train_dataset.describe()\n", | |
"train_stats.pop(\"MPG\")\n", | 393 | 394 | "train_stats.pop(\"MPG\")\n", | |
"train_stats = train_stats.transpose()\n", | 394 | 395 | "train_stats = train_stats.transpose()\n", | |
"train_stats" | 395 | 396 | "train_stats" | |
] | 396 | 397 | ] | |
}, | 397 | 398 | }, | |
{ | 398 | 399 | { | |
"cell_type": "code", | 399 | 400 | "cell_type": "code", | |
"execution_count": 23, | 400 | 401 | "execution_count": 6, | |
"metadata": {}, | 401 | 402 | "metadata": {}, | |
"outputs": [], | 402 | 403 | "outputs": [], | |
"source": [ | 403 | 404 | "source": [ | |
"train_labels = train_dataset.pop('MPG')\n", | 404 | 405 | "train_labels = train_dataset.pop('MPG')\n", | |
"test_labels = test_dataset.pop('MPG')\n", | 405 | 406 | "test_labels = test_dataset.pop('MPG')\n", | |
"\n", | 406 | 407 | "\n", | |
"def norm(x):\n", | 407 | 408 | "def norm(x):\n", | |
" return (x - train_stats['mean']) / train_stats['std']\n", | 408 | 409 | " return (x - train_stats['mean']) / train_stats['std']\n", | |
"normed_train_data = norm(train_dataset)\n", | 409 | 410 | "normed_train_data = norm(train_dataset)\n", | |
"normed_test_data = norm(test_dataset)" | 410 | 411 | "normed_test_data = norm(test_dataset)" | |
] | 411 | 412 | ] | |
}, | 412 | 413 | }, | |
{ | 413 | 414 | { | |
"cell_type": "code", | 414 | 415 | "cell_type": "code", | |
"execution_count": 24, | 415 | 416 | "execution_count": 7, | |
"metadata": {}, | 416 | 417 | "metadata": {}, | |
"outputs": [], | 417 | 418 | "outputs": [], | |
"source": [ | 418 | 419 | "source": [ | |
"def build_model():\n", | 419 | 420 | "def build_model():\n", | |
" model = keras.Sequential([\n", | 420 | 421 | " model = keras.Sequential([\n", | |
" layers.Dense(64, activation='relu', input_shape=[len(train_dataset.keys())]),\n", | 421 | 422 | " layers.Dense(64, activation='relu', input_shape=[len(train_dataset.keys())]),\n", | |
" layers.Dense(64, activation='relu'),\n", | 422 | 423 | " layers.Dense(64, activation='relu'),\n", | |
" layers.Dense(1)\n", | 423 | 424 | " layers.Dense(1)\n", | |
" ])\n", | 424 | 425 | " ])\n", | |
"\n", | 425 | 426 | "\n", | |
" optimizer = tf.keras.optimizers.RMSprop(0.001)\n", | 426 | 427 | " optimizer = tf.keras.optimizers.RMSprop(0.001)\n", | |
"\n", | 427 | 428 | "\n", | |
" model.compile(loss='mse',\n", | 428 | 429 | " model.compile(loss='mse',\n", | |
" optimizer=optimizer,\n", | 429 | 430 | " optimizer=optimizer,\n", | |
" metrics=['mae', 'mse'])\n", | 430 | 431 | " metrics=['mae', 'mse'])\n", | |
" return model" | 431 | 432 | " return model" | |
] | 432 | 433 | ] | |
}, | 433 | 434 | }, | |
{ | 434 | 435 | { | |
"cell_type": "code", | 435 | 436 | "cell_type": "code", | |
"execution_count": 25, | 436 | 437 | "execution_count": 8, | |
"metadata": {}, | 437 | 438 | "metadata": {}, | |
"outputs": [ | 438 | 439 | "outputs": [ | |
{ | 439 | 440 | { | |
"name": "stderr", | 440 | 441 | "name": "stderr", | |
"output_type": "stream", | 441 | 442 | "output_type": "stream", | |
"text": [ | 442 | 443 | "text": [ | |
"2022-02-17 16:36:25.433421: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", | 443 | 444 | "2022-02-21 18:59:10.451428: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", | |
"To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" | 444 | 445 | "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" | |
] | 445 | 446 | ] | |
} | 446 | 447 | } | |
], | 447 | 448 | ], | |
"source": [ | 448 | 449 | "source": [ | |
"model = build_model()" | 449 | 450 | "model = build_model()" | |
] | 450 | 451 | ] | |
}, | 451 | 452 | }, | |
{ | 452 | 453 | { | |
"cell_type": "code", | 453 | 454 | "cell_type": "code", | |
"execution_count": 26, | 454 | 455 | "execution_count": 9, | |
"metadata": {}, | 455 | 456 | "metadata": {}, | |
"outputs": [ | 456 | 457 | "outputs": [ | |
{ | 457 | 458 | { | |
"name": "stdout", | 458 | 459 | "name": "stdout", | |
"output_type": "stream", | 459 | 460 | "output_type": "stream", | |
"text": [ | 460 | 461 | "text": [ | |
"Model: \"sequential\"\n", | 461 | 462 | "Model: \"sequential\"\n", | |
"_________________________________________________________________\n", | 462 | 463 | "_________________________________________________________________\n", | |
" Layer (type) Output Shape Param # \n", | 463 | 464 | " Layer (type) Output Shape Param # \n", | |
"=================================================================\n", | 464 | 465 | "=================================================================\n", | |
" dense (Dense) (None, 64) 640 \n", | 465 | 466 | " dense (Dense) (None, 64) 640 \n", | |
" \n", | 466 | 467 | " \n", | |
" dense_1 (Dense) (None, 64) 4160 \n", | 467 | 468 | " dense_1 (Dense) (None, 64) 4160 \n", | |
" \n", | 468 | 469 | " \n", | |
" dense_2 (Dense) (None, 1) 65 \n", | 469 | 470 | " dense_2 (Dense) (None, 1) 65 \n", | |
" \n", | 470 | 471 | " \n", | |
"=================================================================\n", | 471 | 472 | "=================================================================\n", | |
"Total params: 4,865\n", | 472 | 473 | "Total params: 4,865\n", | |
"Trainable params: 4,865\n", | 473 | 474 | "Trainable params: 4,865\n", | |
"Non-trainable params: 0\n", | 474 | 475 | "Non-trainable params: 0\n", | |
"_________________________________________________________________\n" | 475 | 476 | "_________________________________________________________________\n" | |
] | 476 | 477 | ] | |
} | 477 | 478 | } | |
], | 478 | 479 | ], | |
"source": [ | 479 | 480 | "source": [ | |
"model.summary()" | 480 | 481 | "model.summary()" | |
] | 481 | 482 | ] | |
}, | 482 | 483 | }, | |
{ | 483 | 484 | { | |
"cell_type": "code", | 484 | 485 | "cell_type": "code", | |
"execution_count": 27, | 485 | 486 | "execution_count": 10, | |
"metadata": {}, | 486 | 487 | "metadata": {}, | |
"outputs": [ | 487 | 488 | "outputs": [ | |
{ | 488 | 489 | { | |
"data": { | 489 | 490 | "data": { | |
"text/plain": [ | 490 | 491 | "text/plain": [ | |
"array([[-0.10623884],\n", | 491 | 492 | "array([[0.5112119 ],\n", | |
" [-0.0092403 ],\n", | 492 | 493 | " [0.26631027],\n", | |
" [-0.09777171],\n", | 493 | 494 | " [0.6957131 ],\n", | |
" [-0.0091932 ],\n", | 494 | 495 | " [0.53957933],\n", | |
" [-0.3904128 ],\n", | 495 | 496 | " [0.51326776],\n", | |
" [-0.06228563],\n", | 496 | 497 | " [0.3375991 ],\n", | |
" [-0.3566698 ],\n", | 497 | 498 | " [0.57435524],\n", | |
" [-0.5763702 ],\n", | 498 | 499 | " [0.8451005 ],\n", | |
" [-0.0279588 ],\n", | 499 | 500 | " [0.54402167],\n", | |
" [-0.40395904]], dtype=float32)" | 500 | 501 | " [0.5616411 ]], dtype=float32)" | |
] | 501 | 502 | ] | |
}, | 502 | 503 | }, | |
"execution_count": 27, | 503 | 504 | "execution_count": 10, | |
"metadata": {}, | 504 | 505 | "metadata": {}, | |
"output_type": "execute_result" | 505 | 506 | "output_type": "execute_result" | |
} | 506 | 507 | } | |
], | 507 | 508 | ], | |
"source": [ | 508 | 509 | "source": [ | |
"example_batch = normed_train_data[:10]\n", | 509 | 510 | "example_batch = normed_train_data[:10]\n", | |
"example_result = model.predict(example_batch)\n", | 510 | 511 | "example_result = model.predict(example_batch)\n", | |
"example_result" | 511 | 512 | "example_result" | |
] | 512 | 513 | ] | |
}, | 513 | 514 | }, | |
{ | 514 | 515 | { | |
"cell_type": "code", | 515 | 516 | "cell_type": "code", | |
"execution_count": 28, | 516 | 517 | "execution_count": 11, | |
"metadata": {}, | 517 | 518 | "metadata": {}, | |
"outputs": [ | 518 | 519 | "outputs": [ | |
{ | 519 | 520 | { | |
"name": "stdout", | 520 | 521 | "name": "stdout", | |
"output_type": "stream", | 521 | 522 | "output_type": "stream", | |
"text": [ | 522 | 523 | "text": [ | |
"\n", | 523 | 524 | "\n", | |
"....................................................................................................\n", | 524 | 525 | "....................................................................................................\n", | |
"....................................................................................................\n", | 525 | 526 | "....................................................................................................\n", | |
"....................................................................................................\n", | 526 | 527 | "....................................................................................................\n", | |
"....................................................................................................\n", | 527 | 528 | "....................................................................................................\n", | |
"....................................................................................................\n", | 528 | 529 | "....................................................................................................\n", | |
"....................................................................................................\n", | 529 | 530 | "....................................................................................................\n", | |
"....................................................................................................\n", | 530 | 531 | "....................................................................................................\n", | |
"....................................................................................................\n", | 531 | 532 | "....................................................................................................\n", | |
"....................................................................................................\n", | 532 | 533 | "....................................................................................................\n", | |
"...................................................................................................." | 533 | 534 | "...................................................................................................." | |
] | 534 | 535 | ] | |
} | 535 | 536 | } | |
], | 536 | 537 | ], | |
"source": [ | 537 | 538 | "source": [ | |
"# ์ํฌํฌ๊ฐ ๋๋ ๋๋ง๋ค ์ (.)์ ์ถ๋ ฅํด ํ๋ จ ์งํ ๊ณผ์ ์ ํ์ํฉ๋๋ค\n", | 538 | 539 | "# ์ํฌํฌ๊ฐ ๋๋ ๋๋ง๋ค ์ (.)์ ์ถ๋ ฅํด ํ๋ จ ์งํ ๊ณผ์ ์ ํ์ํฉ๋๋ค\n", | |
"class PrintDot(keras.callbacks.Callback):\n", | 539 | 540 | "class PrintDot(keras.callbacks.Callback):\n", | |
" def on_epoch_end(self, epoch, logs):\n", | 540 | 541 | " def on_epoch_end(self, epoch, logs):\n", | |
" if epoch % 100 == 0: print('')\n", | 541 | 542 | " if epoch % 100 == 0: print('')\n", | |
" print('.', end='')\n", | 542 | 543 | " print('.', end='')\n", | |
"\n", | 543 | 544 | "\n", | |
"EPOCHS = 1000\n", | 544 | 545 | "EPOCHS = 1000\n", | |
"\n", | 545 | 546 | "\n", | |
"history = model.fit(\n", | 546 | 547 | "history = model.fit(\n", | |
" normed_train_data, train_labels,\n", | 547 | 548 | " normed_train_data, train_labels,\n", | |
" epochs=EPOCHS, validation_split = 0.2, verbose=0,\n", | 548 | 549 | " epochs=EPOCHS, validation_split = 0.2, verbose=0,\n", | |
" callbacks=[PrintDot()])" | 549 | 550 | " callbacks=[PrintDot()])" | |
] | 550 | 551 | ] | |
}, | 551 | 552 | }, | |
{ | 552 | 553 | { | |
"cell_type": "code", | 553 | 554 | "cell_type": "code", | |
"execution_count": 29, | 554 | 555 | "execution_count": 12, | |
"metadata": {}, | 555 | 556 | "metadata": {}, | |
"outputs": [ | 556 | 557 | "outputs": [ | |
{ | 557 | 558 | { | |
"data": { | 558 | 559 | "data": { | |
"text/html": [ | 559 | 560 | "text/html": [ | |
"<div>\n", | 560 | 561 | "<div>\n", | |
"<style scoped>\n", | 561 | 562 | "<style scoped>\n", | |
" .dataframe tbody tr th:only-of-type {\n", | 562 | 563 | " .dataframe tbody tr th:only-of-type {\n", | |
" vertical-align: middle;\n", | 563 | 564 | " vertical-align: middle;\n", | |
" }\n", | 564 | 565 | " }\n", | |
"\n", | 565 | 566 | "\n", | |
" .dataframe tbody tr th {\n", | 566 | 567 | " .dataframe tbody tr th {\n", | |
" vertical-align: top;\n", | 567 | 568 | " vertical-align: top;\n", | |
" }\n", | 568 | 569 | " }\n", | |
"\n", | 569 | 570 | "\n", | |
" .dataframe thead th {\n", | 570 | 571 | " .dataframe thead th {\n", | |
" text-align: right;\n", | 571 | 572 | " text-align: right;\n", | |
" }\n", | 572 | 573 | " }\n", | |
"</style>\n", | 573 | 574 | "</style>\n", | |
"<table border=\"1\" class=\"dataframe\">\n", | 574 | 575 | "<table border=\"1\" class=\"dataframe\">\n", | |
" <thead>\n", | 575 | 576 | " <thead>\n", | |
" <tr style=\"text-align: right;\">\n", | 576 | 577 | " <tr style=\"text-align: right;\">\n", | |
" <th></th>\n", | 577 | 578 | " <th></th>\n", | |
" <th>loss</th>\n", | 578 | 579 | " <th>loss</th>\n", | |
" <th>mae</th>\n", | 579 | 580 | " <th>mae</th>\n", | |
" <th>mse</th>\n", | 580 | 581 | " <th>mse</th>\n", | |
" <th>val_loss</th>\n", | 581 | 582 | " <th>val_loss</th>\n", | |
" <th>val_mae</th>\n", | 582 | 583 | " <th>val_mae</th>\n", | |
" <th>val_mse</th>\n", | 583 | 584 | " <th>val_mse</th>\n", | |
" <th>epoch</th>\n", | 584 | 585 | " <th>epoch</th>\n", | |
" </tr>\n", | 585 | 586 | " </tr>\n", | |
" </thead>\n", | 586 | 587 | " </thead>\n", | |
" <tbody>\n", | 587 | 588 | " <tbody>\n", | |
" <tr>\n", | 588 | 589 | " <tr>\n", | |
" <th>995</th>\n", | 589 | 590 | " <th>995</th>\n", | |
" <td>2.500433</td>\n", | 590 | 591 | " <td>2.563871</td>\n", | |
" <td>1.000197</td>\n", | 591 | 592 | " <td>0.989101</td>\n", | |
" <td>2.500433</td>\n", | 592 | 593 | " <td>2.563871</td>\n", | |
" <td>11.472550</td>\n", | 593 | 594 | " <td>9.965034</td>\n", | |
" <td>2.508417</td>\n", | 594 | 595 | " <td>2.524732</td>\n", | |
" <td>11.472550</td>\n", | 595 | 596 | " <td>9.965034</td>\n", | |
" <td>995</td>\n", | 596 | 597 | " <td>995</td>\n", | |
" </tr>\n", | 597 | 598 | " </tr>\n", | |
" <tr>\n", | 598 | 599 | " <tr>\n", | |
" <th>996</th>\n", | 599 | 600 | " <th>996</th>\n", | |
" <td>2.405449</td>\n", | 600 | 601 | " <td>2.482039</td>\n", | |
" <td>0.969858</td>\n", | 601 | 602 | " <td>1.013534</td>\n", | |
" <td>2.405449</td>\n", | 602 | 603 | " <td>2.482039</td>\n", | |
" <td>11.598408</td>\n", | 603 | 604 | " <td>10.070007</td>\n", | |
" <td>2.482901</td>\n", | 604 | 605 | " <td>2.518338</td>\n", | |
" <td>11.598408</td>\n", | 605 | 606 | " <td>10.070007</td>\n", | |
" <td>996</td>\n", | 606 | 607 | " <td>996</td>\n", | |
" </tr>\n", | 607 | 608 | " </tr>\n", | |
" <tr>\n", | 608 | 609 | " <tr>\n", | |
" <th>997</th>\n", | 609 | 610 | " <th>997</th>\n", | |
" <td>2.468478</td>\n", | 610 | 611 | " <td>2.757296</td>\n", | |
" <td>1.011330</td>\n", | 611 | 612 | " <td>1.029885</td>\n", | |
" <td>2.468478</td>\n", | 612 | 613 | " <td>2.757296</td>\n", | |
" <td>11.822487</td>\n", | 613 | 614 | " <td>9.647966</td>\n", | |
" <td>2.496955</td>\n", | 614 | 615 | " <td>2.342796</td>\n", | |
" <td>11.822487</td>\n", | 615 | 616 | " <td>9.647966</td>\n", | |
" <td>997</td>\n", | 616 | 617 | " <td>997</td>\n", | |
" </tr>\n", | 617 | 618 | " </tr>\n", | |
" <tr>\n", | 618 | 619 | " <tr>\n", | |
" <th>998</th>\n", | 619 | 620 | " <th>998</th>\n", | |
" <td>2.478367</td>\n", | 620 | 621 | " <td>2.602165</td>\n", | |
" <td>0.988899</td>\n", | 621 | 622 | " <td>0.985977</td>\n", | |
" <td>2.478367</td>\n", | 622 | 623 | " <td>2.602165</td>\n", | |
" <td>11.631916</td>\n", | 623 | 624 | " <td>10.430404</td>\n", | |
" <td>2.503966</td>\n", | 624 | 625 | " <td>2.557013</td>\n", | |
" <td>11.631916</td>\n", | 625 | 626 | " <td>10.430404</td>\n", | |
" <td>998</td>\n", | 626 | 627 | " <td>998</td>\n", | |
" </tr>\n", | 627 | 628 | " </tr>\n", | |
" <tr>\n", | 628 | 629 | " <tr>\n", | |
" <th>999</th>\n", | 629 | 630 | " <th>999</th>\n", | |
" <td>2.387425</td>\n", | 630 | 631 | " <td>2.558363</td>\n", | |
" <td>0.974386</td>\n", | 631 | 632 | " <td>1.014585</td>\n", | |
" <td>2.387425</td>\n", | 632 | 633 | " <td>2.558363</td>\n", | |
" <td>11.652516</td>\n", | 633 | 634 | " <td>9.928759</td>\n", | |
" <td>2.505530</td>\n", | 634 | 635 | " <td>2.429059</td>\n", | |
" <td>11.652516</td>\n", | 635 | 636 | " <td>9.928759</td>\n", | |
" <td>999</td>\n", | 636 | 637 | " <td>999</td>\n", | |
" </tr>\n", | 637 | 638 | " </tr>\n", | |
" </tbody>\n", | 638 | 639 | " </tbody>\n", | |
"</table>\n", | 639 | 640 | "</table>\n", | |
"</div>" | 640 | 641 | "</div>" | |
], | 641 | 642 | ], | |
"text/plain": [ | 642 | 643 | "text/plain": [ | |
" loss mae mse val_loss val_mae val_mse epoch\n", | 643 | 644 | " loss mae mse val_loss val_mae val_mse epoch\n", | |
"995 2.500433 1.000197 2.500433 11.472550 2.508417 11.472550 995\n", | 644 | 645 | "995 2.563871 0.989101 2.563871 9.965034 2.524732 9.965034 995\n", | |
"996 2.405449 0.969858 2.405449 11.598408 2.482901 11.598408 996\n", | 645 | 646 | "996 2.482039 1.013534 2.482039 10.070007 2.518338 10.070007 996\n", | |
"997 2.468478 1.011330 2.468478 11.822487 2.496955 11.822487 997\n", | 646 | 647 | "997 2.757296 1.029885 2.757296 9.647966 2.342796 9.647966 997\n", | |
"998 2.478367 0.988899 2.478367 11.631916 2.503966 11.631916 998\n", | 647 | 648 | "998 2.602165 0.985977 2.602165 10.430404 2.557013 10.430404 998\n", | |
"999 2.387425 0.974386 2.387425 11.652516 2.505530 11.652516 999" | 648 | 649 | "999 2.558363 1.014585 2.558363 9.928759 2.429059 9.928759 999" | |
] | 649 | 650 | ] | |
}, | 650 | 651 | }, | |
"execution_count": 29, | 651 | 652 | "execution_count": 12, | |
"metadata": {}, | 652 | 653 | "metadata": {}, | |
"output_type": "execute_result" | 653 | 654 | "output_type": "execute_result" | |
} | 654 | 655 | } | |
], | 655 | 656 | ], | |
"source": [ | 656 | 657 | "source": [ | |
"hist = pd.DataFrame(history.history)\n", | 657 | 658 | "hist = pd.DataFrame(history.history)\n", | |
"hist['epoch'] = history.epoch\n", | 658 | 659 | "hist['epoch'] = history.epoch\n", | |
"hist.tail()" | 659 | 660 | "hist.tail()" | |
] | 660 | 661 | ] | |
}, | 661 | 662 | }, | |
{ | 662 | 663 | { | |
"cell_type": "code", | 663 | 664 | "cell_type": "code", | |
"execution_count": 30, | 664 | 665 | "execution_count": 18, | |
"metadata": {}, | 665 | 666 | "metadata": {}, | |
"outputs": [ | 666 | 667 | "outputs": [ | |
{ | 667 | 668 | { | |
"data": { | 668 | 669 | "data": { | |
"image/png": 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", | 669 | 670 | "image/png": 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", | |
"text/plain": [ | 670 | 671 | "text/plain": [ | |
"<Figure size 576x864 with 2 Axes>" | 671 | 672 | "<Figure size 576x864 with 2 Axes>" | |
] | 672 | 673 | ] | |
}, | 673 | 674 | }, | |
"metadata": { | 674 | 675 | "metadata": { | |
"needs_background": "light" | 675 | 676 | "needs_background": "light" | |
}, | 676 | 677 | }, | |
"output_type": "display_data" | 677 | 678 | "output_type": "display_data" | |
} | 678 | 679 | } | |
], | 679 | 680 | ], | |
"source": [ | 680 | 681 | "source": [ | |
"import matplotlib.pyplot as plt\n", | 681 | |||
"\n", | 682 | 682 | "\n", | |
"def plot_history(history):\n", | 683 | |||
" hist = pd.DataFrame(history.history)\n", | 684 | |||
" hist['epoch'] = history.epoch\n", | 685 | |||
"\n", | 686 | |||
" plt.figure(figsize=(8,12))\n", | 687 | |||
"\n", | 688 | |||
" plt.subplot(2,1,1)\n", | 689 | |||
" plt.xlabel('Epoch')\n", | 690 | |||
" plt.ylabel('Mean Abs Error [MPG]')\n", | 691 | |||
" plt.plot(hist['epoch'], hist['mae'],\n", | 692 | |||
" label='Train Error')\n", | 693 | |||
" plt.plot(hist['epoch'], hist['val_mae'],\n", | 694 | |||
" label = 'Val Error')\n", | 695 | |||
" plt.ylim([0,5])\n", | 696 | |||
" plt.legend()\n", | 697 | |||
"\n", | 698 | |||
" plt.subplot(2,1,2)\n", | 699 | |||
" plt.xlabel('Epoch')\n", | 700 | |||
" plt.ylabel('Mean Square Error [$MPG^2$]')\n", | 701 | |||
" plt.plot(hist['epoch'], hist['mse'],\n", | 702 | |||
" label='Train Error')\n", | 703 | |||
" plt.plot(hist['epoch'], hist['val_mse'],\n", | 704 | |||
" label = 'Val Error')\n", | 705 | |||
" plt.ylim([0,20])\n", | 706 | |||
" plt.legend()\n", | 707 | |||
" plt.show()\n", | 708 | |||
"\n", | 709 | |||
"plot_history(history)" | 710 | 683 | "plot_history(history)" | |
] | 711 | 684 | ] | |
}, | 712 | 685 | }, | |
{ | 713 | 686 | { | |
"cell_type": "code", | 714 | 687 | "cell_type": "code", | |
"execution_count": 31, | 715 | 688 | "execution_count": 14, | |
"metadata": {}, | 716 | 689 | "metadata": {}, | |
"outputs": [ | 717 | 690 | "outputs": [ | |
{ | 718 | 691 | { | |
"name": "stdout", | 719 | 692 | "name": "stdout", | |
"output_type": "stream", | 720 | 693 | "output_type": "stream", | |
"text": [ | 721 | 694 | "text": [ | |
"\n", | 722 | 695 | "\n", | |
"...................................................................." | 723 | 696 | "...................................................................." | |
] | 724 | 697 | ] | |
}, | 725 | 698 | }, | |
{ | 726 | 699 | { | |
"data": { | 727 | 700 | "data": { | |
"image/png": 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", | 728 | 701 | "image/png": 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", | |
"text/plain": [ | 729 | 702 | "text/plain": [ | |
"<Figure size 576x864 with 2 Axes>" | 730 | 703 | "<Figure size 576x864 with 2 Axes>" | |
] | 731 | 704 | ] | |
}, | 732 | 705 | }, | |
"metadata": { | 733 | 706 | "metadata": { | |
"needs_background": "light" | 734 | 707 | "needs_background": "light" | |
}, | 735 | 708 | }, | |
"output_type": "display_data" | 736 | 709 | "output_type": "display_data" | |
} | 737 | 710 | } | |
], | 738 | 711 | ], | |
"source": [ | 739 | 712 | "source": [ | |
"model = build_model()\n", | 740 | 713 | "model = build_model()\n", | |
"\n", | 741 | 714 | "\n", | |
"# patience ๋งค๊ฐ๋ณ์๋ ์ฑ๋ฅ ํฅ์์ ์ฒดํฌํ ์ํฌํฌ ํ์์ ๋๋ค\n", | 742 | 715 | "# patience ๋งค๊ฐ๋ณ์๋ ์ฑ๋ฅ ํฅ์์ ์ฒดํฌํ ์ํฌํฌ ํ์์ ๋๋ค\n", | |
"early_stop = keras.callbacks.EarlyStopping(monitor='val_loss', patience=10)\n", | 743 | 716 | "early_stop = keras.callbacks.EarlyStopping(monitor='val_loss', patience=10)\n", | |
"\n", | 744 | 717 | "\n", | |
"history = model.fit(normed_train_data, train_labels, epochs=EPOCHS,\n", | 745 | 718 | "history = model.fit(normed_train_data, train_labels, epochs=EPOCHS,\n", | |
" validation_split = 0.2, verbose=0, callbacks=[early_stop, PrintDot()])\n", | 746 | 719 | " validation_split = 0.2, verbose=0, callbacks=[early_stop, PrintDot()])\n", | |
"\n", | 747 | 720 | "\n", | |
"plot_history(history)" | 748 | 721 | "plot_history(history)" | |
] | 749 | 722 | ] | |
}, | 750 | 723 | }, | |
{ | 751 | 724 | { | |
"cell_type": "code", | 752 | 725 | "cell_type": "code", | |
"execution_count": 32, | 753 | 726 | "execution_count": 15, | |
"metadata": {}, | 754 | 727 | "metadata": {}, | |
"outputs": [ | 755 | 728 | "outputs": [ | |
{ | 756 | 729 | { | |
"name": "stdout", | 757 | 730 | "name": "stdout", | |
"output_type": "stream", | 758 | 731 | "output_type": "stream", | |
"text": [ | 759 | 732 | "text": [ | |
"3/3 - 0s - loss: 6.6843 - mae: 2.0619 - mse: 6.6843 - 17ms/epoch - 6ms/step\n", | 760 | 733 | "3/3 - 0s - loss: 6.5964 - mae: 1.9941 - mse: 6.5964 - 38ms/epoch - 13ms/step\n", | |
"ํ ์คํธ ์ธํธ์ ํ๊ท ์ ๋ ์ค์ฐจ: 2.06 MPG\n" | 761 | 734 | "ํ ์คํธ ์ธํธ์ ํ๊ท ์ ๋ ์ค์ฐจ: 1.99 MPG\n" | |
] | 762 | 735 | ] | |
} | 763 | 736 | } | |
], | 764 | 737 | ], | |
"source": [ | 765 | 738 | "source": [ | |
"loss, mae, mse = model.evaluate(normed_test_data, test_labels, verbose=2)\n", | 766 | 739 | "loss, mae, mse = model.evaluate(normed_test_data, test_labels, verbose=2)\n", | |
"\n", | 767 | 740 | "\n", | |
"print(\"ํ ์คํธ ์ธํธ์ ํ๊ท ์ ๋ ์ค์ฐจ: {:5.2f} MPG\".format(mae))" | 768 | 741 | "print(\"ํ ์คํธ ์ธํธ์ ํ๊ท ์ ๋ ์ค์ฐจ: {:5.2f} MPG\".format(mae))" | |
] | 769 | 742 | ] | |
}, | 770 | 743 | }, | |
{ | 771 | 744 | { | |
"cell_type": "code", | 772 | 745 | "cell_type": "code", | |
"execution_count": 33, | 773 | 746 | "execution_count": 16, | |
"metadata": {}, | 774 | 747 | "metadata": {}, | |
"outputs": [ | 775 | 748 | "outputs": [ | |
{ | 776 | 749 | { | |
"data": { | 777 | 750 | "data": { | |
"image/png": 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", | 778 | 751 | "image/png": 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", | |
"text/plain": [ | 779 | 752 | "text/plain": [ | |
"<Figure size 432x288 with 1 Axes>" | 780 | 753 | "<Figure size 432x288 with 1 Axes>" | |
] | 781 | 754 | ] | |
}, | 782 | 755 | }, | |
"metadata": { | 783 | 756 | "metadata": { | |
"needs_background": "light" | 784 | 757 | "needs_background": "light" | |
}, | 785 | 758 | }, | |
"output_type": "display_data" | 786 | 759 | "output_type": "display_data" | |
} | 787 | 760 | } | |
], | 788 | 761 | ], | |
"source": [ | 789 | 762 | "source": [ | |
"test_predictions = model.predict(normed_test_data).flatten()\n", | 790 | 763 | "test_predictions = model.predict(normed_test_data).flatten()\n", | |
"\n", | 791 | 764 | "\n", | |
"plt.scatter(test_labels, test_predictions)\n", | 792 | 765 | "plt.scatter(test_labels, test_predictions)\n", | |
"plt.xlabel('True Values [MPG]')\n", | 793 | 766 | "plt.xlabel('True Values [MPG]')\n", | |
"plt.ylabel('Predictions [MPG]')\n", | 794 | 767 | "plt.ylabel('Predictions [MPG]')\n", | |
"plt.axis('equal')\n", | 795 | 768 | "plt.axis('equal')\n", | |
"plt.axis('square')\n", | 796 | 769 | "plt.axis('square')\n", | |
"plt.xlim([0,plt.xlim()[1]])\n", | 797 | 770 | "plt.xlim([0,plt.xlim()[1]])\n", | |
"plt.ylim([0,plt.ylim()[1]])\n", | 798 | 771 | "plt.ylim([0,plt.ylim()[1]])\n", | |
"_ = plt.plot([-100, 100], [-100, 100])" | 799 | 772 | "_ = plt.plot([-100, 100], [-100, 100])" | |
] | 800 | 773 | ] | |
}, | 801 | 774 | }, | |
{ | 802 | 775 | { | |
"cell_type": "code", | 803 | 776 | "cell_type": "code", | |
"execution_count": 34, | 804 | 777 | "execution_count": 17, | |
"metadata": {}, | 805 | 778 | "metadata": {}, | |
"outputs": [ | 806 | 779 | "outputs": [ | |
{ | 807 | 780 | { | |
"data": { | 808 | 781 | "data": { | |
"image/png": 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", | 809 | 782 | "image/png": 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", | |
"text/plain": [ | 810 | 783 | "text/plain": [ | |
"<Figure size 432x288 with 1 Axes>" | 811 | 784 | "<Figure size 432x288 with 1 Axes>" | |
] | 812 | 785 | ] | |
}, | 813 | 786 | }, | |
"metadata": { | 814 | 787 | "metadata": { | |
"needs_background": "light" | 815 | 788 | "needs_background": "light" | |
}, | 816 | 789 | }, | |
"output_type": "display_data" | 817 | 790 | "output_type": "display_data" | |
} | 818 | 791 | } | |
], | 819 | 792 | ], | |
"source": [ | 820 | 793 | "source": [ | |
"error = test_predictions - test_labels\n", | 821 | 794 | "error = test_predictions - test_labels\n", | |
"plt.hist(error, bins = 25)\n", | 822 | 795 | "plt.hist(error, bins = 25)\n", | |
"plt.xlabel(\"Prediction Error [MPG]\")\n", | 823 | 796 | "plt.xlabel(\"Prediction Error [MPG]\")\n", | |
"_ = plt.ylabel(\"Count\")" | 824 | 797 | "_ = plt.ylabel(\"Count\")" | |
] | 825 | 798 | ] | |
} | 826 | 799 | } | |
], | 827 | 800 | ], | |
"metadata": { | 828 | 801 | "metadata": { | |
"interpreter": { | 829 | 802 | "interpreter": { | |
"hash": "80dbe1014b4652684caa329d41db00af3ae439be86b11eab7e35b518e5d8ab1a" | 830 | 803 | "hash": "80dbe1014b4652684caa329d41db00af3ae439be86b11eab7e35b518e5d8ab1a" | |
}, | 831 | 804 | }, | |
"kernelspec": { | 832 | 805 | "kernelspec": { | |
"display_name": "Python 3.7.9 64-bit ('venv': venv)", | 833 | 806 | "display_name": "Python 3.7.9 64-bit ('venv': venv)", | |
"language": "python", | 834 | 807 | "language": "python", | |
"name": "python3" | 835 | 808 | "name": "python3" | |
}, | 836 | 809 | }, | |
"language_info": { | 837 | 810 | "language_info": { | |
"codemirror_mode": { | 838 | 811 | "codemirror_mode": { | |
"name": "ipython", | 839 | 812 | "name": "ipython", | |
"version": 3 | 840 | 813 | "version": 3 | |
}, | 841 | 814 | }, | |
"file_extension": ".py", | 842 | 815 | "file_extension": ".py", | |
"mimetype": "text/x-python", | 843 | 816 | "mimetype": "text/x-python", | |
"name": "python", | 844 | 817 | "name": "python", | |
"nbconvert_exporter": "python", | 845 | 818 | "nbconvert_exporter": "python", | |
"pygments_lexer": "ipython3", | 846 | 819 | "pygments_lexer": "ipython3", | |
"version": "3.7.9" | 847 | 820 | "version": "3.7.9" | |
}, | 848 | 821 | }, | |
"orig_nbformat": 4 | 849 | 822 | "orig_nbformat": 4 | |
}, | 850 | 823 | }, | |
"nbformat": 4, | 851 | 824 | "nbformat": 4, | |
"nbformat_minor": 2 | 852 | 825 | "nbformat_minor": 2 | |
} | 853 | 826 | } | |
854 | 827 | |||
python-notebook/tools.py
View file @
e90c8a5
import pandas as pd | 1 | 1 | import pandas as pd | |
2 | 2 | |||
from itertools import product | 3 | 3 | from itertools import product | |
from datetime import date, timedelta | 4 | 4 | from datetime import date, timedelta | |
5 | import matplotlib.pyplot as plt | |||
5 | 6 | |||
6 | ||||
# convert a datetime object to a date object | 7 | 7 | # convert a datetime object to a date object | |
def get_date(x): | 8 | 8 | def get_date(x): | |
return date(x.year, x.month, x.day) | 9 | 9 | return date(x.year, x.month, x.day) | |
10 | 10 | |||
# convert a datetime object to an integer, which denotes the number of minutes since midnight | 11 | 11 | # convert a datetime object to an integer, which denotes the number of minutes since midnight | |
def get_minute_index(x): | 12 | 12 | def get_minute_index(x): | |
return (x.hour * 60) + x.minute | 13 | 13 | return (x.hour * 60) + x.minute | |
14 | 14 | |||
# return a range of dates | 15 | 15 | # return a range of dates | |
def date_range(start_date, end_date): | 16 | 16 | def date_range(start_date, end_date): | |
delta = end_date - start_date | 17 | 17 | delta = end_date - start_date | |
18 | 18 | |||
for i in range(delta.days + 1): | 19 | 19 | for i in range(delta.days + 1): | |
yield start_date + timedelta(days=i) | 20 | 20 | yield start_date + timedelta(days=i) | |
21 | 21 | |||
# define an iterative walk calculation (merging consecutive active minutes) | 22 | 22 | # define an iterative walk calculation (merging consecutive active minutes) | |
def calculate_walk(cv): | 23 | 23 | def calculate_walk(cv): | |
nv = cv.copy(deep=True) | 24 | 24 | nv = cv.copy(deep=True) | |
nv["prev_minute_index"] = nv["local_minute_index"] - 1 | 25 | 25 | nv["prev_minute_index"] = nv["local_minute_index"] - 1 | |
26 | 26 | |||
# move midnight minutes to previous day | 27 | 27 | # move midnight minutes to previous day | |
nv[nv["prev_minute_index"] < 0]["local_date"] -= timedelta(days=1) | 28 | 28 | nv[nv["prev_minute_index"] < 0]["local_date"] -= timedelta(days=1) | |
nv[nv["prev_minute_index"] < 0]["prev_minute_index"] = 1439 | 29 | 29 | nv[nv["prev_minute_index"] < 0]["prev_minute_index"] = 1439 | |
30 | 30 | |||
nv = nv[["user", "local_date", "prev_minute_index"]] | 31 | 31 | nv = nv[["user", "local_date", "prev_minute_index"]] | |
jv = cv.merge(nv, left_on=["user", "local_date", "local_minute_index"], right_on=["user", "local_date", "prev_minute_index"], how="inner") | 32 | 32 | jv = cv.merge(nv, left_on=["user", "local_date", "local_minute_index"], right_on=["user", "local_date", "prev_minute_index"], how="inner") | |
jv["add_count"] += 1 | 33 | 33 | jv["add_count"] += 1 | |
jv = jv[["user", "local_date", "local_minute_index", "add_count"]] | 34 | 34 | jv = jv[["user", "local_date", "local_minute_index", "add_count"]] | |
35 | 35 | |||
return jv | 36 | 36 | return jv | |
37 | 37 | |||
# generate complete product of vectors | 38 | 38 | # generate complete product of vectors | |
def product_df(mat1, mat2): | 39 | 39 | def product_df(mat1, mat2): | |
mat1 = mat1.drop_duplicates() | 40 | 40 | mat1 = mat1.drop_duplicates() | |
mat2 = mat2.drop_duplicates() | 41 | 41 | mat2 = mat2.drop_duplicates() | |
42 | 42 | |||
temp = pd.DataFrame(list(product(mat1.values, mat2.values))) | 43 | 43 | temp = pd.DataFrame(list(product(mat1.values, mat2.values))) | |
for i, acol in enumerate(mat1.columns): | 44 | 44 | for i, acol in enumerate(mat1.columns): | |
temp[acol] = temp[0].apply(lambda x: x[i]) | 45 | 45 | temp[acol] = temp[0].apply(lambda x: x[i]) | |
for i, acol in enumerate(mat2.columns): | 46 | 46 | for i, acol in enumerate(mat2.columns): | |
temp[acol] = temp[1].apply(lambda x: x[i]) | 47 | 47 | temp[acol] = temp[1].apply(lambda x: x[i]) | |
temp = temp.drop(columns=[0, 1]) | 48 | 48 | temp = temp.drop(columns=[0, 1]) | |
return temp | 49 | 49 | return temp | |
50 | ||||
51 | ||||
52 | ||||
53 | def plot_history(history): | |||
54 | hist = pd.DataFrame(history.history) | |||
55 | hist['epoch'] = history.epoch | |||
56 | ||||
57 | plt.figure(figsize=(8,12)) | |||
58 | ||||
59 | plt.subplot(2,1,1) | |||
60 | plt.xlabel('Epoch') | |||
61 | plt.ylabel('Mean Abs Error [MPG]') | |||
62 | plt.plot(hist['epoch'], hist['mae'], | |||
63 | label='Train Error') | |||
64 | plt.plot(hist['epoch'], hist['val_mae'], | |||
65 | label = 'Val Error') | |||
66 | plt.ylim([0,5]) |