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b/HAR_LSTM.ipynb |
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"cells": [ |
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"cell_type": "code", |
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"execution_count": 1, |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Importing Libraries" |
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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": 2, |
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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 sys" |
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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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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Activities are the class labels\n", |
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"# It is a 6 class classification\n", |
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"ACTIVITIES = {\n", |
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" 0: 'WALKING',\n", |
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" 1: 'WALKING_UPSTAIRS',\n", |
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" 2: 'WALKING_DOWNSTAIRS',\n", |
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" 3: 'SITTING',\n", |
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" 4: 'STANDING',\n", |
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" 5: 'LAYING',\n", |
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"}\n", |
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"\n", |
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"# Utility function to print the confusion matrix\n", |
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"def confusion_matrix(Y_true, Y_pred):\n", |
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" Y_true = pd.Series([ACTIVITIES[y] for y in np.argmax(Y_true, axis=1)])\n", |
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" Y_pred = pd.Series([ACTIVITIES[y] for y in np.argmax(Y_pred, axis=1)])\n", |
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"\n", |
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" return pd.crosstab(Y_true, Y_pred, rownames=['True'], colnames=['Pred'])" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"metadata": {}, |
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"source": [ |
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"### Data" |
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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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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Data directory\n", |
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"DATADIR = 'UCI_HAR_Dataset'" |
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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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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Raw data signals\n", |
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"# Signals are from Accelerometer and Gyroscope\n", |
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"# The signals are in x,y,z directions\n", |
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"# Sensor signals are filtered to have only body acceleration\n", |
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"# excluding the acceleration due to gravity\n", |
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"# Triaxial acceleration from the accelerometer is total acceleration\n", |
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"SIGNALS = [\n", |
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" \"body_acc_x\",\n", |
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" \"body_acc_y\",\n", |
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" \"body_acc_z\",\n", |
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" \"body_gyro_x\",\n", |
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" \"body_gyro_y\",\n", |
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" \"body_gyro_z\",\n", |
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" \"total_acc_x\",\n", |
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" \"total_acc_y\",\n", |
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" \"total_acc_z\"\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": 7, |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Utility function to read the data from csv file\n", |
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"def _read_csv(filename):\n", |
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" return pd.read_csv(filename, delim_whitespace=True, header=None)\n", |
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"\n", |
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"# Utility function to load the load\n", |
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"def load_signals(subset):\n", |
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" signals_data = []\n", |
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"\n", |
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" for signal in SIGNALS:\n", |
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" filename = f'UCI_HAR_Dataset/{subset}/Inertial Signals/{signal}_{subset}.txt'\n", |
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" signals_data.append(\n", |
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" _read_csv(filename).as_matrix()\n", |
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" ) \n", |
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"\n", |
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" # Transpose is used to change the dimensionality of the output,\n", |
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" # aggregating the signals by combination of sample/timestep.\n", |
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" # Resultant shape is (7352 train/2947 test samples, 128 timesteps, 9 signals)\n", |
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" return np.transpose(signals_data, (1, 2, 0))" |
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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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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"\n", |
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"def load_y(subset):\n", |
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" \"\"\"\n", |
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" The objective that we are trying to predict is a integer, from 1 to 6,\n", |
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" that represents a human activity. We return a binary representation of \n", |
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" every sample objective as a 6 bits vector using One Hot Encoding\n", |
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" (https://pandas.pydata.org/pandas-docs/stable/generated/pandas.get_dummies.html)\n", |
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" \"\"\"\n", |
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" filename = f'UCI_HAR_Dataset/{subset}/y_{subset}.txt'\n", |
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" y = _read_csv(filename)[0]\n", |
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"\n", |
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" return pd.get_dummies(y).as_matrix()" |
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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": 9, |
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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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" \"\"\"\n", |
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" Obtain the dataset from multiple files.\n", |
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" Returns: X_train, X_test, y_train, y_test\n", |
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" \"\"\"\n", |
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" X_train, X_test = load_signals('train'), load_signals('test')\n", |
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" y_train, y_test = load_y('train'), load_y('test')\n", |
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"\n", |
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" return X_train, X_test, y_train, 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": 10, |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Importing tensorflow\n", |
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"np.random.seed(42)\n", |
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"import tensorflow as tf\n", |
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"tf.set_random_seed(42)" |
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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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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Configuring a session\n", |
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"session_conf = tf.ConfigProto(\n", |
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" intra_op_parallelism_threads=1,\n", |
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" inter_op_parallelism_threads=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": 12, |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"name": "stderr", |
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"output_type": "stream", |
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"text": [ |
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"/home/prajin/Downloads/ENTER/envs/py36/lib/python3.6/site-packages/h5py/__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", |
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" from ._conv import register_converters as _register_converters\n", |
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"Using TensorFlow backend.\n" |
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] |
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} |
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], |
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"source": [ |
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"# Import Keras\n", |
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"from keras import backend as K\n", |
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"sess = tf.Session(graph=tf.get_default_graph(), config=session_conf)\n", |
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"K.set_session(sess)" |
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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": 14, |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Importing libraries\n", |
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"from keras.models import Sequential\n", |
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"from keras.layers import LSTM\n", |
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"from keras.layers.core import Dense, Dropout" |
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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": 15, |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Initializing parameters\n", |
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"epochs = 30\n", |
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"batch_size = 16\n", |
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"n_hidden = 32" |
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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": 16, |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Utility function to count the number of classes\n", |
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"def _count_classes(y):\n", |
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" return len(set([tuple(category) for category in 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": 17, |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"name": "stderr", |
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"output_type": "stream", |
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"text": [ |
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"/home/prajin/Downloads/ENTER/envs/py36/lib/python3.6/site-packages/ipykernel_launcher.py:12: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n", |
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" if sys.path[0] == '':\n" |
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] |
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} |
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], |
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"source": [ |
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"# Loading the train and test data\n", |
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"X_train, X_test, Y_train, Y_test = 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": 18, |
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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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"128\n", |
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"9\n", |
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"7352\n" |
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] |
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} |
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], |
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"source": [ |
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"timesteps = len(X_train[0])\n", |
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"input_dim = len(X_train[0][0])\n", |
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"n_classes = _count_classes(Y_train)\n", |
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"\n", |
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"print(timesteps)\n", |
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"print(input_dim)\n", |
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"print(len(X_train))" |
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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": 19, |
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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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"(7352, 128, 9)" |
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] |
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}, |
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"execution_count": 19, |
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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" |
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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": null, |
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"metadata": {}, |
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"outputs": [], |
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"source": [] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 24, |
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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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"(7352, 6)" |
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] |
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}, |
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"execution_count": 24, |
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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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"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": 34, |
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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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"(2947, 128, 9)" |
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] |
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}, |
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"execution_count": 34, |
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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_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": 35, |
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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([[[ 1.165315e-02, -2.939904e-02, 1.068262e-01, ...,\n", |
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" 1.041216e+00, -2.697959e-01, 2.377977e-02],\n", |
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" [ 1.310909e-02, -3.972867e-02, 1.524549e-01, ...,\n", |
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" 1.041803e+00, -2.800250e-01, 7.629271e-02],\n", |
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" [ 1.126885e-02, -5.240586e-02, 2.168462e-01, ...,\n", |
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" 1.039086e+00, -2.926631e-01, 1.474754e-01],\n", |
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" ...,\n", |
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" [ 1.291511e-03, 1.173502e-02, 3.665587e-03, ...,\n", |
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" 9.930164e-01, -2.599865e-01, 1.443951e-01],\n", |
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" [ 1.469997e-03, 9.517414e-03, 4.041945e-03, ...,\n", |
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" 9.932414e-01, -2.620643e-01, 1.447033e-01],\n", |
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" [ 2.573841e-03, 7.305069e-03, 4.888436e-03, ...,\n", |
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" 9.943906e-01, -2.641348e-01, 1.454939e-01]],\n", |
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"\n", |
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" [[ 9.279629e-03, 6.650520e-03, -2.631933e-02, ...,\n", |
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" 9.991921e-01, -2.649349e-01, 1.256164e-01],\n", |
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" [ 4.929711e-03, 1.864973e-02, -2.688753e-02, ...,\n", |
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" 9.946787e-01, -2.532142e-01, 1.256249e-01],\n", |
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" [ 3.953596e-03, 1.553950e-02, -3.663861e-02, ...,\n", |
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" 9.935518e-01, -2.565887e-01, 1.163814e-01],\n", |
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" ...,\n", |
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" [ 7.787600e-03, 4.730625e-03, 1.412899e-02, ...,\n", |
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" 1.001861e+00, -2.619359e-01, 1.527878e-01],\n", |
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" [ 3.433489e-03, -4.619849e-03, 1.338054e-03, ...,\n", |
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" 9.975208e-01, -2.713225e-01, 1.398428e-01],\n", |
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" [-1.238678e-03, -1.322889e-02, -1.703861e-02, ...,\n", |
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|
380 |
" 9.928615e-01, -2.799715e-01, 1.213135e-01]],\n", |
|
|
381 |
"\n", |
|
|
382 |
" [[ 5.731945e-03, 7.304842e-03, 1.021286e-02, ...,\n", |
|
|
383 |
" 9.975931e-01, -2.639912e-01, 1.507741e-01],\n", |
|
|
384 |
" [ 7.065650e-03, 7.330912e-03, 1.341419e-02, ...,\n", |
|
|
385 |
" 9.989703e-01, -2.638194e-01, 1.539427e-01],\n", |
|
|
386 |
" [ 5.109758e-03, 7.153458e-03, 3.646559e-03, ...,\n", |
|
|
387 |
" 9.970574e-01, -2.638495e-01, 1.441536e-01],\n", |
|
|
388 |
" ...,\n", |
|
|
389 |
" [-7.428461e-04, -9.629137e-03, -2.500924e-03, ...,\n", |
|
|
390 |
" 9.918802e-01, -2.836712e-01, 1.326780e-01],\n", |
|
|
391 |
" [-1.923356e-03, -6.425974e-03, -2.524952e-03, ...,\n", |
|
|
392 |
" 9.906626e-01, -2.805970e-01, 1.326941e-01],\n", |
|
|
393 |
" [-4.304617e-03, -7.932046e-03, -3.140111e-03, ...,\n", |
|
|
394 |
" 9.882446e-01, -2.822329e-01, 1.321175e-01]],\n", |
|
|
395 |
"\n", |
|
|
396 |
" ...,\n", |
|
|
397 |
"\n", |
|
|
398 |
" [[-1.476465e-01, 5.519791e-03, 1.025031e-02, ...,\n", |
|
|
399 |
" 8.213505e-01, -2.484623e-01, -2.216934e-01],\n", |
|
|
400 |
" [-1.699026e-01, 3.235187e-02, 2.632373e-02, ...,\n", |
|
|
401 |
" 7.991996e-01, -2.232599e-01, -2.045561e-01],\n", |
|
|
402 |
" [-1.686980e-01, 7.826144e-02, -2.703439e-02, ...,\n", |
|
|
403 |
" 8.004623e-01, -1.790170e-01, -2.568719e-01],\n", |
|
|
404 |
" ...,\n", |
|
|
405 |
" [ 4.978930e-01, -3.158365e-01, -2.321939e-02, ...,\n", |
|
|
406 |
" 1.463170e+00, -5.515283e-01, -2.723974e-01],\n", |
|
|
407 |
" [ 2.141275e-01, -3.121422e-01, 1.814949e-01, ...,\n", |
|
|
408 |
" 1.179223e+00, -5.472997e-01, -6.773376e-02],\n", |
|
|
409 |
" [-1.145089e-01, -2.553472e-01, 3.870347e-01, ...,\n", |
|
|
410 |
" 8.504963e-01, -4.900368e-01, 1.378256e-01]],\n", |
|
|
411 |
"\n", |
|
|
412 |
" [[ 7.122683e-02, -1.498122e-01, -1.659306e-01, ...,\n", |
|
|
413 |
" 1.037668e+00, -3.971532e-01, -3.940817e-01],\n", |
|
|
414 |
" [-8.866530e-02, -3.755543e-02, -8.708159e-02, ...,\n", |
|
|
415 |
" 8.780725e-01, -2.848634e-01, -3.151097e-01],\n", |
|
|
416 |
" [-7.067473e-02, -1.615178e-02, 1.401189e-02, ...,\n", |
|
|
417 |
" 8.963897e-01, -2.635297e-01, -2.139040e-01],\n", |
|
|
418 |
" ...,\n", |
|
|
419 |
" [ 1.859878e-01, 7.344366e-03, 2.383924e-01, ...,\n", |
|
|
420 |
" 1.156389e+00, -2.283478e-01, -3.512052e-03],\n", |
|
|
421 |
" [ 2.737114e-01, -2.279012e-02, 1.302276e-01, ...,\n", |
|
|
422 |
" 1.243857e+00, -2.583220e-01, -1.117857e-01],\n", |
|
|
423 |
" [ 3.536738e-01, -1.118625e-01, -3.402252e-02, ...,\n", |
|
|
424 |
" 1.323546e+00, -3.472416e-01, -2.760682e-01]],\n", |
|
|
425 |
"\n", |
|
|
426 |
" [[-1.936425e-01, -1.907511e-01, 1.958357e-01, ...,\n", |
|
|
427 |
" 7.713622e-01, -4.250499e-01, -5.327655e-02],\n", |
|
|
428 |
" [-6.498738e-02, -2.035990e-01, -1.531400e-01, ...,\n", |
|
|
429 |
" 9.000949e-01, -4.375916e-01, -4.020727e-01],\n", |
|
|
430 |
" [-9.712210e-02, -2.083832e-01, -2.710627e-01, ...,\n", |
|
|
431 |
" 8.681034e-01, -4.421595e-01, -5.197379e-01],\n", |
|
|
432 |
" ...,\n", |
|
|
433 |
" [-5.075521e-02, -1.047171e-01, 1.732707e-01, ...,\n", |
|
|
434 |
" 9.188616e-01, -3.516799e-01, -7.253919e-02],\n", |
|
|
435 |
" [-1.980675e-02, -2.076396e-02, 1.956384e-01, ...,\n", |
|
|
436 |
" 9.494752e-01, -2.675260e-01, -5.097549e-02],\n", |
|
|
437 |
" [-1.104015e-02, 5.243883e-02, 2.184321e-01, ...,\n", |
|
|
438 |
" 9.578348e-01, -1.941603e-01, -2.892477e-02]]])" |
|
|
439 |
] |
|
|
440 |
}, |
|
|
441 |
"execution_count": 35, |
|
|
442 |
"metadata": {}, |
|
|
443 |
"output_type": "execute_result" |
|
|
444 |
} |
|
|
445 |
], |
|
|
446 |
"source": [ |
|
|
447 |
"X_test" |
|
|
448 |
] |
|
|
449 |
}, |
|
|
450 |
{ |
|
|
451 |
"cell_type": "code", |
|
|
452 |
"execution_count": 26, |
|
|
453 |
"metadata": {}, |
|
|
454 |
"outputs": [ |
|
|
455 |
{ |
|
|
456 |
"data": { |
|
|
457 |
"text/plain": [ |
|
|
458 |
"(2947, 6)" |
|
|
459 |
] |
|
|
460 |
}, |
|
|
461 |
"execution_count": 26, |
|
|
462 |
"metadata": {}, |
|
|
463 |
"output_type": "execute_result" |
|
|
464 |
} |
|
|
465 |
], |
|
|
466 |
"source": [ |
|
|
467 |
"Y_test.shape" |
|
|
468 |
] |
|
|
469 |
}, |
|
|
470 |
{ |
|
|
471 |
"cell_type": "markdown", |
|
|
472 |
"metadata": {}, |
|
|
473 |
"source": [ |
|
|
474 |
"- Defining the Architecture of LSTM" |
|
|
475 |
] |
|
|
476 |
}, |
|
|
477 |
{ |
|
|
478 |
"cell_type": "code", |
|
|
479 |
"execution_count": 27, |
|
|
480 |
"metadata": {}, |
|
|
481 |
"outputs": [ |
|
|
482 |
{ |
|
|
483 |
"name": "stdout", |
|
|
484 |
"output_type": "stream", |
|
|
485 |
"text": [ |
|
|
486 |
"_________________________________________________________________\n", |
|
|
487 |
"Layer (type) Output Shape Param # \n", |
|
|
488 |
"=================================================================\n", |
|
|
489 |
"lstm_2 (LSTM) (None, 32) 5376 \n", |
|
|
490 |
"_________________________________________________________________\n", |
|
|
491 |
"dropout_2 (Dropout) (None, 32) 0 \n", |
|
|
492 |
"_________________________________________________________________\n", |
|
|
493 |
"dense_2 (Dense) (None, 6) 198 \n", |
|
|
494 |
"=================================================================\n", |
|
|
495 |
"Total params: 5,574\n", |
|
|
496 |
"Trainable params: 5,574\n", |
|
|
497 |
"Non-trainable params: 0\n", |
|
|
498 |
"_________________________________________________________________\n" |
|
|
499 |
] |
|
|
500 |
} |
|
|
501 |
], |
|
|
502 |
"source": [ |
|
|
503 |
"# Initiliazing the sequential model\n", |
|
|
504 |
"\n", |
|
|
505 |
"model = Sequential()\n", |
|
|
506 |
"# Configuring the parameters\n", |
|
|
507 |
"model.add(LSTM(n_hidden, input_shape=(timesteps, input_dim)))\n", |
|
|
508 |
"# Adding a dropout layer\n", |
|
|
509 |
"model.add(Dropout(0.5))\n", |
|
|
510 |
"# Adding a dense output layer with sigmoid activation\n", |
|
|
511 |
"model.add(Dense(n_classes, activation='sigmoid'))\n", |
|
|
512 |
"model.summary()" |
|
|
513 |
] |
|
|
514 |
}, |
|
|
515 |
{ |
|
|
516 |
"cell_type": "code", |
|
|
517 |
"execution_count": 28, |
|
|
518 |
"metadata": {}, |
|
|
519 |
"outputs": [], |
|
|
520 |
"source": [ |
|
|
521 |
"# Compiling the model\n", |
|
|
522 |
"model.compile(loss='categorical_crossentropy',\n", |
|
|
523 |
" optimizer='rmsprop',\n", |
|
|
524 |
" metrics=['accuracy'])" |
|
|
525 |
] |
|
|
526 |
}, |
|
|
527 |
{ |
|
|
528 |
"cell_type": "code", |
|
|
529 |
"execution_count": 29, |
|
|
530 |
"metadata": {}, |
|
|
531 |
"outputs": [ |
|
|
532 |
{ |
|
|
533 |
"name": "stdout", |
|
|
534 |
"output_type": "stream", |
|
|
535 |
"text": [ |
|
|
536 |
"Train on 7352 samples, validate on 2947 samples\n", |
|
|
537 |
"Epoch 1/30\n", |
|
|
538 |
"7352/7352 [==============================] - 30s 4ms/step - loss: 1.3992 - acc: 0.3528 - val_loss: 1.3149 - val_acc: 0.3485\n", |
|
|
539 |
"Epoch 2/30\n", |
|
|
540 |
"7352/7352 [==============================] - 29s 4ms/step - loss: 1.1923 - acc: 0.4475 - val_loss: 1.1875 - val_acc: 0.4523\n", |
|
|
541 |
"Epoch 3/30\n", |
|
|
542 |
"7352/7352 [==============================] - 27s 4ms/step - loss: 1.0586 - acc: 0.4977 - val_loss: 1.1083 - val_acc: 0.5124\n", |
|
|
543 |
"Epoch 4/30\n", |
|
|
544 |
"7352/7352 [==============================] - 27s 4ms/step - loss: 0.9001 - acc: 0.6019 - val_loss: 0.9712 - val_acc: 0.5898\n", |
|
|
545 |
"Epoch 5/30\n", |
|
|
546 |
"7352/7352 [==============================] - 27s 4ms/step - loss: 0.8077 - acc: 0.6205 - val_loss: 0.8670 - val_acc: 0.5769\n", |
|
|
547 |
"Epoch 6/30\n", |
|
|
548 |
"7352/7352 [==============================] - 29s 4ms/step - loss: 0.7221 - acc: 0.6443 - val_loss: 0.7999 - val_acc: 0.6108\n", |
|
|
549 |
"Epoch 7/30\n", |
|
|
550 |
"7352/7352 [==============================] - 34s 5ms/step - loss: 0.7032 - acc: 0.6481 - val_loss: 0.8130 - val_acc: 0.6067\n", |
|
|
551 |
"Epoch 8/30\n", |
|
|
552 |
"7352/7352 [==============================] - 37s 5ms/step - loss: 0.6789 - acc: 0.6590 - val_loss: 0.7781 - val_acc: 0.6118\n", |
|
|
553 |
"Epoch 9/30\n", |
|
|
554 |
"7352/7352 [==============================] - 38s 5ms/step - loss: 0.6733 - acc: 0.6549 - val_loss: 0.8595 - val_acc: 0.6033\n", |
|
|
555 |
"Epoch 10/30\n", |
|
|
556 |
"7352/7352 [==============================] - 38s 5ms/step - loss: 0.6385 - acc: 0.6714 - val_loss: 0.8202 - val_acc: 0.6043\n", |
|
|
557 |
"Epoch 11/30\n", |
|
|
558 |
"7352/7352 [==============================] - 37s 5ms/step - loss: 0.5983 - acc: 0.6918 - val_loss: 0.7822 - val_acc: 0.6586\n", |
|
|
559 |
"Epoch 12/30\n", |
|
|
560 |
"7352/7352 [==============================] - 28s 4ms/step - loss: 0.5781 - acc: 0.7304 - val_loss: 0.7093 - val_acc: 0.7503\n", |
|
|
561 |
"Epoch 13/30\n", |
|
|
562 |
"7352/7352 [==============================] - 23s 3ms/step - loss: 0.5395 - acc: 0.7752 - val_loss: 0.6877 - val_acc: 0.7503\n", |
|
|
563 |
"Epoch 14/30\n", |
|
|
564 |
"7352/7352 [==============================] - 22s 3ms/step - loss: 0.5074 - acc: 0.7888 - val_loss: 0.5969 - val_acc: 0.7621\n", |
|
|
565 |
"Epoch 15/30\n", |
|
|
566 |
"7352/7352 [==============================] - 24s 3ms/step - loss: 0.4639 - acc: 0.7983 - val_loss: 0.6399 - val_acc: 0.7574\n", |
|
|
567 |
"Epoch 16/30\n", |
|
|
568 |
"7352/7352 [==============================] - 27s 4ms/step - loss: 0.4533 - acc: 0.8041 - val_loss: 0.5525 - val_acc: 0.7625\n", |
|
|
569 |
"Epoch 17/30\n", |
|
|
570 |
"7352/7352 [==============================] - 25s 3ms/step - loss: 0.4612 - acc: 0.8166 - val_loss: 0.5325 - val_acc: 0.7679\n", |
|
|
571 |
"Epoch 18/30\n", |
|
|
572 |
"7352/7352 [==============================] - 27s 4ms/step - loss: 0.3810 - acc: 0.8595 - val_loss: 0.5302 - val_acc: 0.8385\n", |
|
|
573 |
"Epoch 19/30\n", |
|
|
574 |
"7352/7352 [==============================] - 24s 3ms/step - loss: 0.3549 - acc: 0.8924 - val_loss: 0.7042 - val_acc: 0.8246\n", |
|
|
575 |
"Epoch 20/30\n", |
|
|
576 |
"7352/7352 [==============================] - 25s 3ms/step - loss: 0.3123 - acc: 0.9124 - val_loss: 0.5711 - val_acc: 0.8456\n", |
|
|
577 |
"Epoch 21/30\n", |
|
|
578 |
"7352/7352 [==============================] - 35s 5ms/step - loss: 0.2819 - acc: 0.9136 - val_loss: 0.5149 - val_acc: 0.8636\n", |
|
|
579 |
"Epoch 22/30\n", |
|
|
580 |
"7352/7352 [==============================] - 55s 7ms/step - loss: 0.2355 - acc: 0.9249 - val_loss: 0.5110 - val_acc: 0.8646\n", |
|
|
581 |
"Epoch 23/30\n", |
|
|
582 |
"7352/7352 [==============================] - 43s 6ms/step - loss: 0.2248 - acc: 0.9290 - val_loss: 0.6960 - val_acc: 0.8524\n", |
|
|
583 |
"Epoch 24/30\n", |
|
|
584 |
"7352/7352 [==============================] - 32s 4ms/step - loss: 0.2245 - acc: 0.9314 - val_loss: 0.6003 - val_acc: 0.8687\n", |
|
|
585 |
"Epoch 25/30\n", |
|
|
586 |
"7352/7352 [==============================] - 31s 4ms/step - loss: 0.2142 - acc: 0.9312 - val_loss: 0.4520 - val_acc: 0.8809\n", |
|
|
587 |
"Epoch 26/30\n", |
|
|
588 |
"7352/7352 [==============================] - 63s 9ms/step - loss: 0.2139 - acc: 0.9340 - val_loss: 0.4768 - val_acc: 0.8643\n", |
|
|
589 |
"Epoch 27/30\n", |
|
|
590 |
"7352/7352 [==============================] - 38s 5ms/step - loss: 0.2048 - acc: 0.9316 - val_loss: 0.4726 - val_acc: 0.8795\n", |
|
|
591 |
"Epoch 28/30\n", |
|
|
592 |
"7352/7352 [==============================] - 34s 5ms/step - loss: 0.1946 - acc: 0.9369 - val_loss: 0.4605 - val_acc: 0.8765\n", |
|
|
593 |
"Epoch 29/30\n", |
|
|
594 |
"7352/7352 [==============================] - 63s 9ms/step - loss: 0.2185 - acc: 0.9327 - val_loss: 0.4615 - val_acc: 0.8768\n", |
|
|
595 |
"Epoch 30/30\n", |
|
|
596 |
"7352/7352 [==============================] - 76s 10ms/step - loss: 0.1809 - acc: 0.9374 - val_loss: 0.4475 - val_acc: 0.8843\n" |
|
|
597 |
] |
|
|
598 |
}, |
|
|
599 |
{ |
|
|
600 |
"data": { |
|
|
601 |
"text/plain": [ |
|
|
602 |
"<keras.callbacks.History at 0x7fd101658ef0>" |
|
|
603 |
] |
|
|
604 |
}, |
|
|
605 |
"execution_count": 29, |
|
|
606 |
"metadata": {}, |
|
|
607 |
"output_type": "execute_result" |
|
|
608 |
} |
|
|
609 |
], |
|
|
610 |
"source": [ |
|
|
611 |
"# Training the model\n", |
|
|
612 |
"model.fit(X_train,\n", |
|
|
613 |
" Y_train,\n", |
|
|
614 |
" batch_size=batch_size,\n", |
|
|
615 |
" validation_data=(X_test, Y_test),\n", |
|
|
616 |
" epochs=epochs)" |
|
|
617 |
] |
|
|
618 |
}, |
|
|
619 |
{ |
|
|
620 |
"cell_type": "code", |
|
|
621 |
"execution_count": 30, |
|
|
622 |
"metadata": {}, |
|
|
623 |
"outputs": [ |
|
|
624 |
{ |
|
|
625 |
"name": "stdout", |
|
|
626 |
"output_type": "stream", |
|
|
627 |
"text": [ |
|
|
628 |
"2947/2947 [==============================] - 2s 821us/step\n" |
|
|
629 |
] |
|
|
630 |
} |
|
|
631 |
], |
|
|
632 |
"source": [ |
|
|
633 |
"score = model.evaluate(X_test, Y_test)" |
|
|
634 |
] |
|
|
635 |
}, |
|
|
636 |
{ |
|
|
637 |
"cell_type": "code", |
|
|
638 |
"execution_count": 33, |
|
|
639 |
"metadata": {}, |
|
|
640 |
"outputs": [ |
|
|
641 |
{ |
|
|
642 |
"data": { |
|
|
643 |
"text/plain": [ |
|
|
644 |
"array([[6.73119284e-05, 1.16691635e-05, 6.29200213e-06, 3.27186123e-03,\n", |
|
|
645 |
" 3.09266567e-01, 6.14459668e-06],\n", |
|
|
646 |
" [6.55044132e-05, 2.05861852e-05, 1.43757034e-05, 6.77454285e-03,\n", |
|
|
647 |
" 4.01470065e-01, 6.11893711e-06],\n", |
|
|
648 |
" [6.84985353e-05, 1.96996807e-05, 1.35612627e-05, 6.61419239e-03,\n", |
|
|
649 |
" 4.27904457e-01, 6.24169252e-06],\n", |
|
|
650 |
" ...,\n", |
|
|
651 |
" [1.55011995e-03, 7.93270528e-01, 3.01599008e-04, 2.30963960e-05,\n", |
|
|
652 |
" 5.54955914e-05, 1.02767759e-08],\n", |
|
|
653 |
" [4.90763341e-04, 3.85723859e-01, 1.03853172e-05, 6.35696642e-06,\n", |
|
|
654 |
" 2.14066167e-05, 1.14835030e-08],\n", |
|
|
655 |
" [7.23787583e-04, 6.95120990e-01, 2.18840923e-05, 8.08145796e-06,\n", |
|
|
656 |
" 6.29514252e-05, 5.91321019e-08]], dtype=float32)" |
|
|
657 |
] |
|
|
658 |
}, |
|
|
659 |
"execution_count": 33, |
|
|
660 |
"metadata": {}, |
|
|
661 |
"output_type": "execute_result" |
|
|
662 |
} |
|
|
663 |
], |
|
|
664 |
"source": [ |
|
|
665 |
" model.predict(X_test)" |
|
|
666 |
] |
|
|
667 |
}, |
|
|
668 |
{ |
|
|
669 |
"cell_type": "code", |
|
|
670 |
"execution_count": 31, |
|
|
671 |
"metadata": {}, |
|
|
672 |
"outputs": [ |
|
|
673 |
{ |
|
|
674 |
"name": "stdout", |
|
|
675 |
"output_type": "stream", |
|
|
676 |
"text": [ |
|
|
677 |
"Pred LAYING SITTING STANDING WALKING WALKING_DOWNSTAIRS \\\n", |
|
|
678 |
"True \n", |
|
|
679 |
"LAYING 510 0 27 0 0 \n", |
|
|
680 |
"SITTING 0 375 110 3 0 \n", |
|
|
681 |
"STANDING 0 80 446 2 0 \n", |
|
|
682 |
"WALKING 0 0 0 410 27 \n", |
|
|
683 |
"WALKING_DOWNSTAIRS 0 0 0 2 407 \n", |
|
|
684 |
"WALKING_UPSTAIRS 0 0 0 3 10 \n", |
|
|
685 |
"\n", |
|
|
686 |
"Pred WALKING_UPSTAIRS \n", |
|
|
687 |
"True \n", |
|
|
688 |
"LAYING 0 \n", |
|
|
689 |
"SITTING 3 \n", |
|
|
690 |
"STANDING 4 \n", |
|
|
691 |
"WALKING 59 \n", |
|
|
692 |
"WALKING_DOWNSTAIRS 11 \n", |
|
|
693 |
"WALKING_UPSTAIRS 458 \n" |
|
|
694 |
] |
|
|
695 |
} |
|
|
696 |
], |
|
|
697 |
"source": [ |
|
|
698 |
"# Confusion Matrix\n", |
|
|
699 |
"print(confusion_matrix(Y_test, model.predict(X_test)))" |
|
|
700 |
] |
|
|
701 |
}, |
|
|
702 |
{ |
|
|
703 |
"cell_type": "code", |
|
|
704 |
"execution_count": 32, |
|
|
705 |
"metadata": {}, |
|
|
706 |
"outputs": [ |
|
|
707 |
{ |
|
|
708 |
"data": { |
|
|
709 |
"text/plain": [ |
|
|
710 |
"[0.44746464555687265, 0.8842891075670173]" |
|
|
711 |
] |
|
|
712 |
}, |
|
|
713 |
"execution_count": 32, |
|
|
714 |
"metadata": {}, |
|
|
715 |
"output_type": "execute_result" |
|
|
716 |
} |
|
|
717 |
], |
|
|
718 |
"source": [ |
|
|
719 |
"score" |
|
|
720 |
] |
|
|
721 |
}, |
|
|
722 |
{ |
|
|
723 |
"cell_type": "code", |
|
|
724 |
"execution_count": null, |
|
|
725 |
"metadata": {}, |
|
|
726 |
"outputs": [], |
|
|
727 |
"source": [] |
|
|
728 |
}, |
|
|
729 |
{ |
|
|
730 |
"cell_type": "markdown", |
|
|
731 |
"metadata": {}, |
|
|
732 |
"source": [ |
|
|
733 |
"- With a simple 2 layer architecture we got 90.09% accuracy and a loss of 0.30\n", |
|
|
734 |
"- We can further imporve the performace with Hyperparameter tuning" |
|
|
735 |
] |
|
|
736 |
} |
|
|
737 |
], |
|
|
738 |
"metadata": { |
|
|
739 |
"kernelspec": { |
|
|
740 |
"display_name": "Python 3", |
|
|
741 |
"language": "python", |
|
|
742 |
"name": "python3" |
|
|
743 |
}, |
|
|
744 |
"language_info": { |
|
|
745 |
"codemirror_mode": { |
|
|
746 |
"name": "ipython", |
|
|
747 |
"version": 3 |
|
|
748 |
}, |
|
|
749 |
"file_extension": ".py", |
|
|
750 |
"mimetype": "text/x-python", |
|
|
751 |
"name": "python", |
|
|
752 |
"nbconvert_exporter": "python", |
|
|
753 |
"pygments_lexer": "ipython3", |
|
|
754 |
"version": "3.6.7" |
|
|
755 |
} |
|
|
756 |
}, |
|
|
757 |
"nbformat": 4, |
|
|
758 |
"nbformat_minor": 2 |
|
|
759 |
} |