422 lines
11 KiB
Plaintext
422 lines
11 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "53f57e68",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"import os\n",
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"import pickle\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "b240abfb",
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"metadata": {},
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"outputs": [],
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"source": [
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"delim = ';'\n",
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"\n",
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"user_count = 100\n",
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"\n",
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"base_path = '/opt/iui-datarelease1-sose2021/'\n",
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"\n",
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"Xpickle_file = './X.pickle'\n",
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"\n",
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"ypickle_file = './y.pickle'"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "f5464e9e",
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"metadata": {},
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"outputs": [],
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"source": [
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"def load_pickles():\n",
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" _p = open(Xpickle_file, 'rb')\n",
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" X = pickle.load(_p)\n",
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" _p.close()\n",
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" \n",
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" _p = open(ypickle_file, 'rb')\n",
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" y = pickle.load(_p)\n",
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" _p.close()\n",
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" \n",
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" return (np.asarray(X, dtype=pd.DataFrame), np.asarray(y, dtype=str))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "3c51ef28",
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"metadata": {},
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"outputs": [],
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"source": [
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"def load_data():\n",
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" if os.path.isfile(Xpickle_file) and os.path.isfile(ypickle_file):\n",
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" return load_pickles()\n",
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" data = []\n",
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" label = []\n",
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" for user in range(0, user_count):\n",
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" user_path = base_path + str(user) + '/split_letters_csv/'\n",
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" for file in os.listdir(user_path):\n",
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" file_name = user_path + file\n",
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" letter = ''.join(filter(lambda x: x.isalpha(), file))[0]\n",
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" data.append(pd.read_csv(file_name, delim))\n",
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" label.append(letter)\n",
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" return (np.asarray(data, dtype=pd.DataFrame), np.asarray(label, dtype=str))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "03ce941d",
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"metadata": {},
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"outputs": [],
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"source": [
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"X, y = load_data()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "7ea0c433",
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"metadata": {},
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"outputs": [],
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"source": [
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"def save_pickle():\n",
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"# _p = open(np.asarray(data, dtype=pd.DataFrame), 'wb')\n",
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" _p = open(Xpickle_file, 'wb')\n",
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" pickle.dump(X, _p)\n",
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" _p.close()\n",
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"\n",
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"# _p = open(np.asarray(label, dtype=str), 'wb')\n",
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" _p = open(ypickle_file, 'wb')\n",
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" pickle.dump(y, _p)\n",
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" _p.close()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "210a3d37",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(13102, 13102)"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"len(X), len(y)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 51,
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"id": "eadec7ee",
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"metadata": {},
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"outputs": [],
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"source": [
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"def plot_data(data):\n",
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" fig, axs = plt.subplots(4, 3, figsize=(3*3, 3*4))\n",
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" t = data['Millis']\n",
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" axs[0][0].plot(t, data['Acc1 X'])\n",
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" axs[0][1].plot(t, data['Acc1 Y'])\n",
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" axs[0][2].plot(t, data['Acc1 Z'])\n",
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" axs[1][0].plot(t, data['Acc2 X'])\n",
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" axs[1][1].plot(t, data['Acc2 Y'])\n",
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" axs[1][2].plot(t, data['Acc2 Z'])\n",
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" axs[2][0].plot(t, data['Gyro X'])\n",
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" axs[2][1].plot(t, data['Gyro Y'])\n",
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" axs[2][2].plot(t, data['Gyro Z'])\n",
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" axs[3][0].plot(t, data['Mag X'])\n",
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" axs[3][1].plot(t, data['Mag Y'])\n",
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" axs[3][2].plot(t, data['Mag Z'])\n",
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"\n",
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" for a in axs:\n",
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" for b in a:\n",
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" b.plot(t, data['Force'])\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "ade23f06",
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"metadata": {},
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"outputs": [],
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"source": [
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"# FIRST CELL: set these variables to limit GPU usage.\n",
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"import os\n",
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"os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true' # this is required\n",
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"os.environ['CUDA_VISIBLE_DEVICES'] = '2' # set to '0' for GPU0, '1' for GPU1 or '2' for GPU2. Check \"gpustat\" in a terminal."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 57,
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"id": "d5db75fc",
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.preprocessing import LabelEncoder\n",
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"\n",
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"le = LabelEncoder()\n",
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"yyt_filtered = le.fit_transform(yy_filtered) # Lables in Zahlenwerte transformiert\n",
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"XX_filtered = np.asarray(XX_filtered).astype('float64')\n",
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"XXX_filtered = np.delete(np.delete(XX_filtered, 0, 2), 13,2) # Drops time col and Millis col"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 59,
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"id": "290be797",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(10271, 456, 13)\n",
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"(2568, 456, 13)\n",
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"(10271,)\n",
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"(2568,)\n"
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]
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}
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],
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"source": [
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"X_train, X_test, y_train, y_test = train_test_split(XXX_filtered, yyt_filtered, test_size=0.2, random_state=177013)\n",
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"\n",
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"print(X_train.shape)\n",
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"print(X_test.shape)\n",
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"print(y_train.shape)\n",
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"print(y_test.shape)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 77,
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"id": "cf763407",
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"metadata": {},
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"outputs": [],
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"source": [
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"import tensorflow as tf\n",
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"from tensorflow.keras.models import Sequential\n",
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"from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, Conv1D, MaxPooling1D\n",
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"\n",
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"model = Sequential()\n",
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"\n",
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"# model.add(Conv1D(32, 3, input_shape = X_train.shape[1:]))\n",
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"# model.add(Activation('relu'))\n",
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"# model.add(MaxPooling1D(pool_size=3))\n",
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"\n",
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"# model.add(Conv1D(32, 3))\n",
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"# model.add(Activation('relu'))\n",
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"# model.add(MaxPooling1D(pool_size=3))\n",
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"\n",
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"model.add(Flatten(input_shape = (456,13)))\n",
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"model.add(Dense(456, activation = 'relu'))\n",
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"\n",
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"model.add(Dense(104))\n",
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"\n",
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"model.add(Dense(26))\n",
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"model.add(Activation('sigmoid'))\n",
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"\n",
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"model.compile(\n",
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" optimizer = tf.keras.optimizers.Adam(0.001),\n",
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" loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits = True),\n",
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" metrics = [tf.keras.metrics.SparseCategoricalAccuracy()],\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 78,
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"id": "ed97582c",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array(['K', 'T', 'U', ..., 'F', 'H', 'G'], dtype='<U1')"
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]
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},
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"execution_count": 78,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"le.inverse_transform(y_test)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 79,
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"id": "3f42617d",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"((10271, 456, 13), (10271,))"
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]
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},
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"execution_count": 79,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"X_train.shape, y_train.shape"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 80,
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"id": "95526298",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch 1/10\n",
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"321/321 [==============================] - 1s 2ms/step - loss: 3635.9205 - sparse_categorical_accuracy: 0.0375\n",
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"Epoch 2/10\n",
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"321/321 [==============================] - 1s 2ms/step - loss: 13.2679 - sparse_categorical_accuracy: 0.0344\n",
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"Epoch 3/10\n",
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"321/321 [==============================] - 1s 2ms/step - loss: 5.5680 - sparse_categorical_accuracy: 0.0360\n",
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"Epoch 4/10\n",
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"321/321 [==============================] - 1s 2ms/step - loss: 58.0553 - sparse_categorical_accuracy: 0.0411\n",
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"Epoch 5/10\n",
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"321/321 [==============================] - 1s 2ms/step - loss: 4.0946 - sparse_categorical_accuracy: 0.0382\n",
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"Epoch 6/10\n",
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"321/321 [==============================] - 1s 2ms/step - loss: 3.2512 - sparse_categorical_accuracy: 0.0421\n",
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"Epoch 7/10\n",
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"321/321 [==============================] - 1s 2ms/step - loss: 3.2490 - sparse_categorical_accuracy: 0.0432\n",
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"Epoch 8/10\n",
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"321/321 [==============================] - 1s 2ms/step - loss: 3.2503 - sparse_categorical_accuracy: 0.0411\n",
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"Epoch 9/10\n",
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"321/321 [==============================] - 1s 2ms/step - loss: 3.2525 - sparse_categorical_accuracy: 0.0390\n",
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"Epoch 10/10\n",
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"321/321 [==============================] - 1s 2ms/step - loss: 3.2529 - sparse_categorical_accuracy: 0.0426\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"<tensorflow.python.keras.callbacks.History at 0x7fac487cd2e0>"
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]
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},
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"execution_count": 80,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"model.fit(X_train, y_train, \n",
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" epochs=10,\n",
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" batch_size=32,\n",
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" verbose=1\n",
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" )"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 81,
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"id": "8f8fedfd",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Evaluate on test data\n",
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"81/81 [==============================] - 0s 1ms/step - loss: 11.4346 - sparse_categorical_accuracy: 0.0312\n",
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"test loss, test acc: [11.434555053710938, 0.031152648851275444]\n",
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"Generate predictions for 3 samples\n",
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"predictions shape: (3, 26)\n"
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]
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},
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{
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|
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"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"(array(['K', 'T', 'U'], dtype='<U1'), array(['R', 'R', 'R'], dtype='<U1'))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 81,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# Evaluate the model on the test data using `evaluate`\n",
|
||
|
|
"print(\"Evaluate on test data\")\n",
|
||
|
|
"results = model.evaluate(X_test, y_test, batch_size=32)\n",
|
||
|
|
"print(\"test loss, test acc:\", results)\n",
|
||
|
|
"\n",
|
||
|
|
"# Generate predictions (probabilities -- the output of the last layer)\n",
|
||
|
|
"# on new data using `predict`\n",
|
||
|
|
"print(\"Generate predictions for 3 samples\")\n",
|
||
|
|
"predictions = model.predict(X_test[:3])\n",
|
||
|
|
"print(\"predictions shape:\", predictions.shape)\n",
|
||
|
|
"fff= [np.argmax(i) for i in predictions]\n",
|
||
|
|
"\n",
|
||
|
|
"le.inverse_transform(y_test[:3]), le.inverse_transform(fff)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 82,
|
||
|
|
"id": "fbfbaf01",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"exit()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"id": "07d40334",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3",
|
||
|
|
"language": "python",
|
||
|
|
"name": "python3"
|
||
|
|
},
|
||
|
|
"language_info": {
|
||
|
|
"codemirror_mode": {
|
||
|
|
"name": "ipython",
|
||
|
|
"version": 3
|
||
|
|
},
|
||
|
|
"file_extension": ".py",
|
||
|
|
"mimetype": "text/x-python",
|
||
|
|
"name": "python",
|
||
|
|
"nbconvert_exporter": "python",
|
||
|
|
"pygments_lexer": "ipython3",
|
||
|
|
"version": "3.8.5"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 5
|
||
|
|
}
|