import pandas as pd import numpy as np from pathlib import Path from os import chdir import os import csv from tensorflow import keras from sklearn.model_selection import train_test_split from sklearn.preprocessing import LabelEncoder, StandardScaler from keras import models from keras import layers from keras import optimizers import matplotlib.pyplot as plt from sklearn.metrics import classification_report import constants from pandas import datetime # splitting of dataset into train and test dataset def train_test_data_split(X, y): X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20) print (X_train[0].shape) return X_train, X_test, y_train, y_test # creating a model def create_and_compile_model(): print("Creating a Model") from keras.models import Sequential from keras.layers import Conv2D, Dense, MaxPooling2D, Dropout, Flatten, BatchNormalization model = models.Sequential() model.add(Conv2D(32, kernel_size=(3, 3), activation="relu", input_shape=(constants.N_FEATURE, constants.FEATURE_MAX_LEN, constants.CHANNELS))) model.add(Conv2D(32, kernel_size=(3, 3), activation="relu")) model.add(MaxPooling2D(pool_size=(2,2))) model.add(Conv2D(64, kernel_size=(3, 3), activation="relu")) model.add(MaxPooling2D(pool_size=(2,2))) model.add(Dropout(0.5)) model.add(Flatten()) model.add(Dense(128, activation="relu")) model.add(Dense(constants.OUTPUT_LAYER_DIMENSIONS, activation='softmax')) print("Compiling a Model") optimizer = keras.optimizers.RMSprop() model.compile(optimizer=optimizer, loss=constants.LOSS_FUNCTION_SPARSE, metrics=[constants.ACCURACY_METRICS]) print(model.summary()) return model def preprocessing_csv_data(): print("Reading Features... ") data = pd.read_csv(constants.FEATURES_CSV_NAME) data.head() # Dropping unnecessary columns (Column Filename is dropped) data = data.drop(['filename'], axis=1) data.head() return data # Extracting classes/label column as y from csv and converting string labels to numbers using LabelEncoder def encode_labels(data): list = data.iloc[:, -1] encoder = LabelEncoder() target_labels = encoder.fit_transform(list) return target_labels, encoder 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) print("Start Training...") history = model.fit(X_train, y_train, batch_size=32, 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 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() # 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("features.csv already exists, skip extraction") else: 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)