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conv1d_model.py
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conv1d_model.py
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# cnn model vary kernel size
from numpy import mean
from numpy import std
from numpy import dstack
from pandas import read_csv
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
from keras.utils import to_categorical
# load a single file as a numpy array
def load_file(filepath):
dataframe = read_csv(filepath, header=None, delim_whitespace=True)
return dataframe.values
# load a list of files and return as a 3d numpy array
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 = 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 = to_categorical(trainy)
testy = to_categorical(testy)
print(trainX.shape, trainy.shape, testX.shape, testy.shape)
return trainX, trainy, testX, testy
# fit and evaluate a model
def evaluate_model(trainX, trainy, testX, testy, n_kernel):
verbose, epochs, batch_size = 0, 15, 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=n_kernel, activation='relu', input_shape=(n_timesteps,n_features)))
model.add(Conv1D(filters=64, kernel_size=n_kernel, 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
# summarize scores
def summarize_results(scores, params):
print(scores, params)
# summarize mean and standard deviation
for i in range(len(scores)):
m, s = mean(scores[i]), std(scores[i])
print('Param=%d: %.3f%% (+/-%.3f)' % (params[i], m, s))
# boxplot of scores
pyplot.boxplot(scores, labels=params)
pyplot.savefig('exp_cnn_kernel.png')
# run an experiment
def run_experiment(params, repeats=10):
# load data
trainX, trainy, testX, testy = load_dataset()
# test each parameter
all_scores = list()
for p in params:
# repeat experiment
scores = list()
for r in range(repeats):
score = evaluate_model(trainX, trainy, testX, testy, p)
score = score * 100.0
print('>p=%d #%d: %.3f' % (p, r+1, score))
scores.append(score)
all_scores.append(scores)
# summarize results
summarize_results(all_scores, params)
# run the experiment
n_params = [2, 3, 5, 7, 11]
run_experiment(n_params)