CNN1D_keras_activity.py

import pandas as pd
import numpy as np
import keras
from matplotlib import pyplot
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Flatten
from keras.layers import Dropout
from keras.layers.convolutional import Conv1D
from keras.layers.convolutional import MaxPooling1D

# load a single file as a numpy array
def load_file(filepath):
	dataframe = pd.read_csv(filepath, header=None, delim_whitespace=True)
	return dataframe.values

# load a list of files into a 3D array of [samples, timesteps, features]
def load_group(filenames, prefix=''):
	loaded = list()
	for name in filenames:
		data = load_file(prefix + name)
		loaded.append(data)
	# stack group so that features are the 3rd dimension
	loaded = np.dstack(loaded)
	return loaded

# load a dataset group, such as train or test
def load_dataset_group(group, prefix=''):
	filepath = prefix + group + '/Inertial Signals/'
	# load all 9 files as a single array
	filenames = list()
	# total acceleration
	filenames += ['total_acc_x_'+group+'.txt', 'total_acc_y_'+group+'.txt', 'total_acc_z_'+group+'.txt']
	# body acceleration
	filenames += ['body_acc_x_'+group+'.txt', 'body_acc_y_'+group+'.txt', 'body_acc_z_'+group+'.txt']
	# body gyroscope
	filenames += ['body_gyro_x_'+group+'.txt', 'body_gyro_y_'+group+'.txt', 'body_gyro_z_'+group+'.txt']
	# load input data
	X = load_group(filenames, filepath)
	# load class output
	y = load_file(prefix + group + '/y_'+group+'.txt')
	return X, y

# load the dataset, returns train and test X and y elements
def load_dataset(prefix=''):
	# load all train
	trainX, trainy = load_dataset_group('train', prefix + 'HARDataset/')
	print(trainX.shape, trainy.shape)
	# load all test
	testX, testy = load_dataset_group('test', prefix + 'HARDataset/')
	print(testX.shape, testy.shape)
	# zero-offset class values
	trainy = trainy - 1
	testy = testy - 1
	# one hot encode y
	trainy = keras.utils.to_categorical(trainy)
	testy = keras.utils.to_categorical(testy)
	print(trainX.shape, trainy.shape, testX.shape, testy.shape)
	return trainX, trainy, testX, testy

# load data set and split into training and testing inputs (X) and outputs (y)
trainX, trainy, testX, testy = load_dataset('D:/PhD/MachineLearning/UCIHARDataset/')

# fit and evaluate a model
def evaluate_model1(trainX, trainy, testX, testy):
	verbose, epochs, batch_size = 0, 10, 32
	n_timesteps, n_features, n_outputs = trainX.shape[1], trainX.shape[2], trainy.shape[1]
	model = Sequential()
	model.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(n_timesteps,n_features)))
	model.add(Conv1D(filters=64, kernel_size=3, activation='relu'))
	model.add(Dropout(0.5))
	model.add(MaxPooling1D(pool_size=2))
	model.add(Flatten())
	model.add(Dense(100, activation='relu'))
	model.add(Dense(n_outputs, activation='softmax'))
	model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
	# fit network
	model.fit(trainX, trainy, epochs=epochs, batch_size=batch_size, verbose=verbose)
	# evaluate model
	_, accuracy = model.evaluate(testX, testy, batch_size=batch_size, verbose=0)
	return accuracy

def evaluate_model2(trainX, trainy, testX, testy):
	verbose, epochs, batch_size = 0, 10, 32
	n_timesteps, n_features, n_outputs = trainX.shape[1], trainX.shape[2], trainy.shape[1]
	model = Sequential()
	model.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(n_timesteps,n_features)))
	model.add(Conv1D(filters=64, kernel_size=3, activation='relu'))
	model.add(Dropout(0.5))
	model.add(MaxPooling1D(pool_size=10))
	model.add(Flatten())
	model.add(Dense(100, activation='relu'))
	model.add(Dense(n_outputs, activation='softmax'))
	model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
	# fit network
	model.fit(trainX, trainy, epochs=epochs, batch_size=batch_size, verbose=verbose)
	# evaluate model
	_, accuracy = model.evaluate(testX, testy, batch_size=batch_size, verbose=0)
	return accuracy

def evaluate_model3(trainX, trainy, testX, testy):
	verbose, epochs, batch_size = 0, 10, 32
	n_timesteps, n_features, n_outputs = trainX.shape[1], trainX.shape[2], trainy.shape[1]
	model = Sequential()
	model.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(n_timesteps,n_features)))
	model.add(Conv1D(filters=64, kernel_size=3, activation='relu'))
	model.add(Conv1D(filters=64, kernel_size=3, activation='relu'))
	model.add(Dropout(0.5))
	model.add(MaxPooling1D(pool_size=2))
	model.add(Flatten())
	model.add(Dense(100, activation='relu'))
	model.add(Dense(n_outputs, activation='softmax'))
	model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
	# fit network
	model.fit(trainX, trainy, epochs=epochs, batch_size=batch_size, verbose=verbose)
	# evaluate model
	_, accuracy = model.evaluate(testX, testy, batch_size=batch_size, verbose=0)
	return accuracy

def evaluate_model4(trainX, trainy, testX, testy):
	verbose, epochs, batch_size = 0, 10, 32
	n_timesteps, n_features, n_outputs = trainX.shape[1], trainX.shape[2], trainy.shape[1]
	model = Sequential()
	model.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(n_timesteps,n_features)))
	model.add(Dropout(0.5))
	model.add(MaxPooling1D(pool_size=2))
	model.add(Flatten())
	model.add(Dense(100, activation='relu'))
	model.add(Dense(n_outputs, activation='softmax'))
	model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
	# fit network
	model.fit(trainX, trainy, epochs=epochs, batch_size=batch_size, verbose=verbose)
	# evaluate model
	_, accuracy = model.evaluate(testX, testy, batch_size=batch_size, verbose=0)
	return accuracy

def evaluate_model5(trainX, trainy, testX, testy):
	verbose, epochs, batch_size = 0, 10, 32
	n_timesteps, n_features, n_outputs = trainX.shape[1], trainX.shape[2], trainy.shape[1]
	model = Sequential()
	model.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(n_timesteps,n_features)))
	model.add(Dropout(0.5))
	model.add(MaxPooling1D(pool_size=10))
	model.add(Flatten())
	model.add(Dense(100, activation='relu'))
	model.add(Dense(n_outputs, activation='softmax'))
	model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
	# fit network
	model.fit(trainX, trainy, epochs=epochs, batch_size=batch_size, verbose=verbose)
	# evaluate model
	_, accuracy = model.evaluate(testX, testy, batch_size=batch_size, verbose=0)
	return accuracy

# summarize scores
def summarize_results(scores):
	print(scores)
	m, s = np.mean(scores), np.std(scores)
	print('Accuracy: %.3f%% (+/-%.3f)' % (m, s))

scores = list()
repeats = 3
for r in range(repeats):
	score = evaluate_model5(trainX,trainy,testX,testy)*100
	print('>#%d: %.3f' % (r+1, score))
	scores.append(score)

summarize_results(scores)