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Commit b52cc91f authored by Hotwani's avatar Hotwani
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Commit for code optimization

parent 6c4a93b8
Pipeline #1990 failed with stages
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constants.py
View file @ b52cc91f
from pandas import datetime
from _datetime import datetime
MFCC_FEATURE_START = 1
MFCC_FEATURE_END = 21
MFCC_RANGE_START = 1
MFCC_RANGE_END = 21
TRAINING_DATA_DIRECTORY_NAME = 'DemoTrainingDataset'
TESTING_DATA_DIRECTORY_NAME = 'TEST'
TESTING_DATA_DIRECTORY_NAME = 'Test'
CAR = 'Car'
......@@ -36,4 +36,6 @@ ACCURACY_METRICS = 'accuracy'
LOG_DIR_PATH = "logs/scalars/" + datetime.now().strftime("%Y%m%d-%H%M%S")
TRAINED_MODEL = 'trained_model.h5'
TRAINED_MODEL = 'Trained_Model/trained_model.h5'
TEST_DATA_SPLIT = 0.20
main.py
View file @ b52cc91f
......@@ -10,36 +10,43 @@ from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder, StandardScaler
from keras import models
from keras import layers
import matplotlib.pyplot as plt
from sklearn.metrics import classification_report
import constants
import sys
def create_csv_header():
header = 'filename '
for i in range(constants.MFCC_FEATURE_START, constants.MFCC_FEATURE_END):
header += f' mfcc{i}'
header += ' label'
header = header.split()
file = open(constants.FEATURES_CSV_NAME, 'w', newline='')
with file:
writer = csv.writer(file)
writer.writerow(header)
if os.path.isfile(constants.TRAINED_MODEL):
sys.exit("Trained model file already exists, "
"remove/move trained_model.h5 to another location and start training again")
if os.path.isfile(constants.FEATURES_CSV_NAME):
sys.exit("features.csv already exist, please remove/move the file to another location and run main.py again")
else:
header = 'filename '
for i in range(constants.MFCC_RANGE_START, constants.MFCC_RANGE_END):
header += f' mfcc{i}'
header += ' label'
header = header.split()
file = open(constants.FEATURES_CSV_NAME, 'x', newline='')
with file:
writer = csv.writer(file)
writer.writerow(header)
def extract_features(trainingDataDir, trainingDataSubDirs):
create_csv_header()
# Looping over every file inside the subdirectories for feature extraction
for trainingDataSubDir in trainingDataSubDirs:
for fileName in os.listdir(trainingDataDir/f'{trainingDataSubDir}'):
if fileName.endswith(".wav"):
audioFile = trainingDataDir/f'{trainingDataSubDir}/{fileName}'
print("Extracting Features from Directory "+trainingDataSubDir+" and file "+audioFile.name)
y, sr = librosa.load(audioFile, mono=True)
mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=(constants.MFCC_FEATURE_END - constants.MFCC_FEATURE_START))
to_append = f'{audioFile.name}'
for g in mfcc:
to_append += f' {np.mean(g)}'
for audio_file_name in os.listdir(trainingDataDir/f'{trainingDataSubDir}'):
if audio_file_name.endswith(".wav"):
audio_file = trainingDataDir/f'{trainingDataSubDir}/{audio_file_name}'
print("Extracting Features from Directory "+trainingDataSubDir+" and file "+audio_file.name)
y, sr = librosa.load(audio_file, mono=True)
mfcc_features = librosa.feature.mfcc(y=y, sr=sr,
n_mfcc=(constants.MFCC_RANGE_END - constants.MFCC_RANGE_START))
to_append = f'{audio_file.name}'
for mfcc_segment in mfcc_features:
to_append += f' {np.mean(mfcc_segment)}'
if trainingDataSubDir == constants.CAR:
to_append += f' {constants.LIGHT_WEIGHT}'
elif trainingDataSubDir == constants.BUS:
......@@ -58,104 +65,84 @@ def extract_features(trainingDataDir, trainingDataSubDirs):
def preprocessing_csv_data():
print("Reading Features... ")
data = pd.read_csv(constants.FEATURES_CSV_NAME)
data.head()
features_data = pd.read_csv(constants.FEATURES_CSV_NAME)
features_data.head()
# Dropping unnecessary columns (Column Filename is dropped)
data = data.drop(['filename'], axis=1)
data.head()
return data
updated_features_data = features_data.drop(['filename'], axis=1)
updated_features_data.head()
return updated_features_data
def encode_labels(data):
def encode_labels(processedFeaturesData):
# Extracting classes/label column as y from csv and converting string labels to numbers using LabelEncoder
audio_list = data.iloc[:, -1]
encoder = LabelEncoder()
target_labels = encoder.fit_transform(audio_list)
return target_labels, encoder
audio_labels_list = processedFeaturesData.iloc[:, -1]
encode_object = LabelEncoder()
encoded_target_audio_labels = encode_object.fit_transform(audio_labels_list)
return encoded_target_audio_labels, encode_object
def normalize_data(data):
def normalize_data(processedData):
# normalizing - Extracting Remaining Columns as X and normalizing them to a common scale
scaler = StandardScaler()
X = scaler.fit_transform(np.array(data.iloc[:, :-1], dtype=float))
return X
scale_object = StandardScaler()
X_normalized_features = scale_object.fit_transform(np.array(processedData.iloc[:, :-1], dtype=float))
return X_normalized_features
def train_test_data_split(X, y):
def train_test_data_split(XInput, yLabels):
# splitting of dataset into train and test dataset
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)
return X_train, X_test, y_train, y_test
X_split_train, X_split_test, y_split_train, y_split_test = train_test_split(XInput, yLabels,
test_size=constants.TEST_DATA_SPLIT)
return X_split_train, X_split_test, y_split_train, y_split_test
def create_and_compile_model():
print("Creating a Model")
# creating a model
model = models.Sequential()
model.add(layers.Dense(constants.HIDDEN_LAYER_1_DIMENSIONS, activation=constants.ACTIVATION_RELU, input_shape=(X.shape[1],)))
model.add(layers.Dense(constants.HIDDEN_LAYER_2_DIMENSIONS, activation=constants.ACTIVATION_RELU))
model.add(layers.Dense(constants.HIDDEN_LAYER_3_DIMENSIONS, activation=constants.ACTIVATION_RELU))
model.add(layers.Dense(constants.OUTPUT_LAYER_DIMENSIONS, activation=constants.ACTIVATION_SOFTMAX))
model_instance = models.Sequential()
model_instance.add(layers.Dense(constants.HIDDEN_LAYER_1_DIMENSIONS, activation=constants.ACTIVATION_RELU,
input_shape=(X_input_features.shape[1],)))
model_instance.add(layers.Dense(constants.HIDDEN_LAYER_2_DIMENSIONS, activation=constants.ACTIVATION_RELU))
model_instance.add(layers.Dense(constants.HIDDEN_LAYER_3_DIMENSIONS, activation=constants.ACTIVATION_RELU))
model_instance.add(layers.Dense(constants.OUTPUT_LAYER_DIMENSIONS, activation=constants.ACTIVATION_SOFTMAX))
print("Compiling a Model")
model.compile(optimizer= constants.OPTIMIZER_ADAM, loss= constants.LOSS_FUNCTION_SPARSE, metrics=[constants.ACCURACY_METRICS])
return model
model_instance.compile(optimizer=constants.OPTIMIZER_ADAM,
loss=constants.LOSS_FUNCTION_SPARSE,
metrics=[constants.ACCURACY_METRICS])
return model_instance
def train_and_save_model(model, X_train, y_train, X_test, y_test):
logdir = constants.LOG_DIR_PATH
tensorboard_callback = keras.callbacks.TensorBoard(log_dir=logdir)
def train_and_save_model(compiledModel, X_train, y_train, X_test, y_test):
log_directory = constants.LOG_DIR_PATH
tensorboard_callback = keras.callbacks.TensorBoard(log_dir=log_directory)
print("Start Training...")
history = model.fit(X_train, y_train, epochs=35, validation_data=(X_test, y_test), callbacks=[tensorboard_callback])
training_history = compiledModel.fit(X_train, y_train, epochs=35,
validation_data=(X_test, y_test),
callbacks=[tensorboard_callback])
# Saving the trained model to avoid re-training
model.save(constants.TRAINED_MODEL)
return history
#print(training_history)
compiledModel.save(constants.TRAINED_MODEL)
return training_history
def predict(X_test, y_test):
print("Predictions.....")
predictions = np.argmax(model.predict(X_test), axis=-1)
target_names = [constants.LIGHT_WEIGHT, constants.MEDIUM_WEIGHT, constants.HEAVY_WEIGHT,constants.TWO_WHEELED, constants.RAIL_BOUND]
print(classification_report(y_test, predictions, target_names=target_names))
def plot_model_accuracy(history):
# Plot graph Model Accuracy
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('Model Accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
plt.show()
def plot_model_loss(history):
# Plot graph Model Loss
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper right')
plt.show()
final_predictions = np.argmax(compiled_model.predict(X_test), axis=-1)
target_names = [constants.LIGHT_WEIGHT, constants.MEDIUM_WEIGHT, constants.HEAVY_WEIGHT, constants.TWO_WHEELED,
constants.RAIL_BOUND]
print(classification_report(y_test, final_predictions, target_names=target_names))
# Changing Directory to Training Dataset Folder
chdir(constants.TRAINING_DATA_DIRECTORY_NAME)
trainingDataDir = Path.cwd()
trainingDataSubDirs = os.listdir(trainingDataDir)
extract_features(trainingDataDir, trainingDataSubDirs)
data = preprocessing_csv_data()
target_labels, encoder = encode_labels(data)
X = normalize_data(data)
X_train, X_test, y_train, y_test = train_test_data_split(X, target_labels)
model = create_and_compile_model()
history = train_and_save_model(model, X_train, y_train, X_test, y_test)
predict(X_test, y_test)
plot_model_accuracy(history)
plot_model_loss(history)
training_data_directory = Path.cwd()
training_data_sub_directories = os.listdir(training_data_directory)
extract_features(training_data_directory, training_data_sub_directories)
processed_features_data = preprocessing_csv_data()
target_audio_labels, encoder_object = encode_labels(processed_features_data)
X_input_features = normalize_data(processed_features_data)
X_train_data, X_test_data, y_train_data, y_test_data = train_test_data_split(X_input_features, target_audio_labels)
compiled_model = create_and_compile_model()
model_training_history = train_and_save_model(compiled_model, X_train_data, y_train_data, X_test_data, y_test_data)
predict(X_test_data, y_test_data)
test.py
View file @ b52cc91f
......@@ -7,31 +7,36 @@ import csv
from tensorflow import keras
from sklearn.preprocessing import LabelEncoder, StandardScaler
import constants
import sys
def create_csv_header():
header=''
for i in range(constants.MFCC_FEATURE_START, constants.MFCC_FEATURE_END):
header += f' mfcc{i}'
header = header.split()
file = open(constants.TEST_CSV_NAME, 'w', newline='')
with file:
writer = csv.writer(file)
writer.writerow(header)
if os.path.isfile(constants.TEST_CSV_NAME):
sys.exit("test.csv already exist, please remove/move the file to another location and run test.py again")
else:
header = ''
for i in range(constants.MFCC_RANGE_START, constants.MFCC_RANGE_END):
header += f' mfcc{i}'
header = header.split()
file = open(constants.TEST_CSV_NAME, 'x', newline='')
with file:
writer = csv.writer(file)
writer.writerow(header)
def extract_features(workingDir, subDirectories):
create_csv_header()
for subDirectory in subDirectories:
if subDirectory == constants.TESTING_DATA_DIRECTORY_NAME:
for fileName in os.listdir(workingDir/f'{subDirectory}'):
if fileName.endswith(".wav"):
audioFile = workingDir / f'{subDirectory}/{fileName}'
y, sr = librosa.load(audioFile, mono=True)
mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=(constants.MFCC_FEATURE_END - constants.MFCC_FEATURE_START))
for test_audio_file_name in os.listdir(workingDir/f'{subDirectory}'):
if test_audio_file_name.endswith(".wav"):
test_audio_file = workingDir / f'{subDirectory}/{test_audio_file_name}'
y, sr = librosa.load(test_audio_file, mono=True)
mfcc_features = librosa.feature.mfcc(y=y, sr=sr,
n_mfcc=(constants.MFCC_RANGE_END - constants.MFCC_RANGE_START))
to_append = ''
for g in mfcc:
to_append += f' {np.mean(g)}'
for mfcc_segment in mfcc_features:
to_append += f' {np.mean(mfcc_segment)}'
file = open(constants.TEST_CSV_NAME, 'a', newline='')
with file:
writer = csv.writer(file)
......@@ -39,30 +44,29 @@ def extract_features(workingDir, subDirectories):
def preprocessing_csv_data():
# reading dataset from csv
print("Reading Features... ")
data = pd.read_csv(constants.TEST_CSV_NAME)
data.head()
return data
test_features_data = pd.read_csv(constants.TEST_CSV_NAME)
test_features_data.head()
return test_features_data
def normalize_data(data):
def normalize_data(processedData):
# # normalizing - Extracting Remaining Columns as X and normalizing them to a common scale
scaler = StandardScaler()
X = scaler.fit_transform(np.array(data.iloc[:, :], dtype=float))
print(X)
print(X.shape)
return X
scale_object = StandardScaler()
X_test = scale_object.fit_transform(np.array(processedData.iloc[:, :], dtype=float))
return X_test
WorkingDir = Path.cwd()
subDirectories = os.listdir(WorkingDir)
extract_features(WorkingDir, subDirectories)
data = preprocessing_csv_data()
X = normalize_data(data)
model = keras.models.load_model('./DemoTrainingDataset/trained_model.h5')
model.summary()
predictions = np.argmax(model.predict(X), axis=-1)
working_directory = Path.cwd()
sub_directories = os.listdir(working_directory)
extract_features(working_directory, sub_directories)
processed_data = preprocessing_csv_data()
X_test_data = normalize_data(processed_data)
if os.path.isfile('./DemoTrainingDataset/Trained_Model/trained_model.h5'):
model = keras.models.load_model('./DemoTrainingDataset/Trained_Model/trained_model.h5')
else:
sys.exit("Trained model file does not exists")
predictions = np.argmax(model.predict(X_test_data), axis=-1)
encoder = LabelEncoder()
labels = ['Light-Weight', 'Medium-Weight', 'Heavy-Weight', 'Two-Wheeled', 'Rail-Bound']
encoder.fit_transform(labels)
......
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