diff --git a/main.py b/main.py
index 124ce5c14c81b23452dcd521fd3dd8d30735b34e..a0b6b2b772f7e447cfa7f9040dc5980917c0ca2f 100644
--- a/main.py
+++ b/main.py
@@ -13,6 +13,9 @@ from keras import layers
import matplotlib.pyplot as plt
from sklearn.metrics import classification_report
import constants
+import json
+from openpyxl import Workbook
+import time
def create_csv_header():
@@ -88,13 +91,16 @@ def train_test_data_split(X, y):
return X_train, X_test, y_train, y_test
-def create_and_compile_model():
+def create_and_compile_model(X, hidden_layer_dimensions):
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))
+
+ for i, layer_dimension in enumerate(hidden_layer_dimensions):
+ if i == 0:
+ model.add(layers.Dense(layer_dimension, activation=constants.ACTIVATION_RELU, input_shape=(X.shape[1],)))
+ else:
+ model.add(layers.Dense(layer_dimension, activation=constants.ACTIVATION_RELU))
model.add(layers.Dense(constants.OUTPUT_LAYER_DIMENSIONS, activation=constants.ACTIVATION_SOFTMAX))
print("Compiling a Model")
@@ -111,8 +117,17 @@ def train_and_save_model(model, X_train, y_train, X_test, y_test):
model.save(constants.TRAINED_MODEL)
return history
+def model_predict(model, X_test, y_test):
+ test_loss, test_acc = model.evaluate(X_test, y_test)
+ print('test_acc: ', test_acc)
+ y_predicted = np.argmax(model.predict(X_test), axis=-1)
+ accuracy = np.mean(y_test == y_predicted)
+ print(accuracy)
+ return accuracy
+
+
-def predict(X_test, y_test):
+def predict(model, 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]
@@ -140,22 +155,125 @@ def plot_model_loss(history):
plt.legend(['Train', 'Test'], loc='upper right')
plt.show()
+def construct_and_apply_network(hidden_layer_dimensions):
+ 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(X, hidden_layer_dimensions)
+ history = train_and_save_model(model, X_train, y_train, X_test, y_test)
+ history
+ predict(model, X_test, y_test)
+ accuracy = model_predict(model, X_test, y_test)
+ #plot_model_accuracy(history)
+ #plot_model_loss(history)
+ return accuracy
+
+def save_mfcc(trainingDataDir, trainingDataSubDirs, dataset_path, json_path, n_mfcc=13, n_fft=2048, hop_length=512):
+ data = {
+ "mapping": [],
+ "mfcc": []
+ }
+
+ # 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_fft=n_fft, n_mfcc=n_mfcc, hop_length=hop_length)
+ data["mfcc"].append(mfcc.tolist())
+
+ to_append = f'{audioFile.name}'
+ for g in mfcc:
+ to_append += f' {np.mean(g)}'
+ if trainingDataSubDir == constants.CAR:
+ data["mapping"].append(constants.CAR)
+ to_append += f' {constants.LIGHT_WEIGHT}'
+ elif trainingDataSubDir == constants.BUS:
+ data["mapping"].append(constants.BUS)
+ to_append += f' {constants.MEDIUM_WEIGHT}'
+ elif trainingDataSubDir == constants.TRUCK:
+ data["mapping"].append(constants.TRUCK)
+ to_append += f' {constants.HEAVY_WEIGHT}'
+ elif trainingDataSubDir == constants.MOTORCYCLE:
+ data["mapping"].append(constants.MOTORCYCLE)
+ to_append += f' {constants.TWO_WHEELED}'
+ elif trainingDataSubDir == constants.TRAM:
+ data["mapping"].append(constants.TRAM)
+ to_append += f' {constants.RAIL_BOUND}'
+
+ file = open(constants.FEATURES_CSV_NAME, 'a', newline='')
+ with file:
+ writer = csv.writer(file)
+ writer.writerow(to_append.split())
+
+ with open(json_path, "w") as fp:
+ json.dump(data, fp, indent=4)
+
+
+if __name__ == "__main__":
+ # Changing Directory to Training Dataset Folder
+ chdir(constants.TRAINING_DATA_DIRECTORY_NAME)
+ trainingDataDir = Path.cwd()
+ trainingDataSubDirs = os.listdir(trainingDataDir)
+ chdir("..")
+ if os.path.isfile(constants.FEATURES_CSV_NAME):
+ print("already exists")
+ else:
+ extract_features(trainingDataDir, trainingDataSubDirs)
+
+ max_accuracy = 0
+ neurons_increment_by = 8
+ start_neuron_value = 8
+ max_neuron_value = 128
+ hidden_layers = 5
+ hidden_layer_dimensions = []
+
+ book = Workbook()
+ sheet = book.active
+
+ # loop_count = int((max_neuron_value / neurons_increment_by) * 4)
+ row_counter = 0
+ for i in range(hidden_layers):
+ hidden_layer_dimensions.append(0)
+ for j in range(start_neuron_value, (max_neuron_value + 1), neurons_increment_by):
+ row_counter += 1
+ hidden_layer_dimensions[i] = j
+ start = time.time()
+ new_accuracy = construct_and_apply_network(hidden_layer_dimensions)
+ end = time.time()
+ elapsed_time = end - start
+ sheet.cell(row=(row_counter), column=1).value = hidden_layer_dimensions.__str__()
+ sheet.cell(row=(row_counter), column=2).value = new_accuracy
+ sheet.cell(row=(row_counter), column=3).value = elapsed_time
-# Changing Directory to Training Dataset Folder
-chdir(constants.TRAINING_DATA_DIRECTORY_NAME)
-trainingDataDir = Path.cwd()
-trainingDataSubDirs = os.listdir(trainingDataDir)
+ '''
+ for i in range (loop_count):
+ start = time.time()
+ new_accuracy = construct_and_apply_network(hidden_layer_dimensions)
+ end = time.time()
+ if max_accuracy < new_accuracy:
+ max_accuracy = new_accuracy
+ elapsed_time = end - start
+ print("durchlauf: ", (i+1))
+ print("\nmax accuracy: ", max_accuracy)
+ print("\nnew accuracy: ", new_accuracy)
+ print("\nlist: ", hidden_layer_dimensions)
+ sheet.cell(row=(i+1), column=1).value = hidden_layer_dimensions.__str__()
+ sheet.cell(row=(i + 1), column=2).value = new_accuracy
+ sheet.cell(row=(i + 1), column=3).value = elapsed_time
-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)
+ if neurons_count == max_neuron_value:
+ neurons_count = start_neuron_value
+ hidden_layer_dimensions.append(start_neuron_value)
+ pointer += 1
+ else:
+ neurons_count += neurons_increment_by
+ hidden_layer_dimensions[pointer] = neurons_count
+ '''
+ book.save("sample.xlsx")