338 lines
14 KiB
Python
338 lines
14 KiB
Python
import json
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from python_speech_features.python_speech_features.base import mfcc
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import numpy as np
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from sklearn.model_selection import train_test_split
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import tensorflow as tf
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import tensorflow.keras as keras
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from keras import backend as K
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from keras.regularizers import l2
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from pathlib import Path
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import pandas as pd
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import matplotlib.pyplot as plt
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import statistics
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# Path to json file that stores MFCCs and subject labels for each processed sample
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DATA_PATH_MFCC = str(Path.cwd()) + "/mfcc_data.json"
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# Loads data from the json file and reshapes X_data(samples, 1, 208) and y_data(samples, 1)
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# Input: JSON path
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# Ouput: X(mfcc data), y(labels), session_lengths
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def load_data_from_json(data_path, nr_classes):
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with open(data_path, "r") as fp:
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data = json.load(fp)
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# Convert lists to numpy arrays and reshapes them
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X = np.array(data['mfcc'])
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X = X.reshape(X.shape[0], 1, X.shape[1])
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y = np.array(data["labels"])
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y = keras.utils.to_categorical(y, nr_classes)
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session_lengths = np.array(data['session_lengths'])
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print("Data succesfully loaded!")
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return X, y, session_lengths
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# Plots the training history with two subplots. First training and test accuracy, and then
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# loss with respect to epochs
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# Input: History(from model.fit(...))
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# Ouput: None -> plot
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def plot_train_history(history, val_data=False):
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fig, axs = plt.subplots(2)
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# create accuracy sublpot
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axs[0].plot(history.history["accuracy"], label="train accuracy")
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if val_data:
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axs[0].plot(history.history["val_accuracy"], label="validation accuracy")
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axs[0].set_ylabel("Accuracy")
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axs[0].legend(loc="lower right")
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axs[0].set_title("Accuracy eval")
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# create error sublpot
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axs[1].plot(history.history["loss"], label="train error")
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if val_data:
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axs[1].plot(history.history["val_loss"], label="validation error")
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axs[1].set_ylabel("Error")
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axs[1].set_xlabel("Epoch")
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axs[1].legend(loc="upper right")
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axs[1].set_title("Error eval")
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plt.show()
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# Takes in data and labels, and splits it into train, validation and test sets by percentage
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# Input: Data, labels, whether to shuffle, % validatiion, % test
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# Ouput: X_train, X_validation, X_test, y_train, y_validation, y_test
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def prepare_datasets_percentsplit(X, y, shuffle_vars, validation_size=0.2, test_size=0.25,):
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# Create train, validation and test split
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, shuffle=shuffle_vars)
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X_train, X_validation, y_train, y_validation = train_test_split(X_train, y_train, test_size=validation_size, shuffle=shuffle_vars)
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return X_train, X_validation, X_test, y_train, y_validation, y_test
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# Takes in data, labels, and session_lengths and splits it into train and test sets by session_index
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# Input: Data, labels, session_lengths, test_session_index
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# Ouput: X_train, X_test, y_train, y_test
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def prepare_datasets_sessions(X, y, session_lengths, test_session_index=4, nr_subjects=5):
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session_lengths = session_lengths.tolist()
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subject_starting_index = 0
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start_test_index = subject_starting_index + sum(session_lengths[0][:test_session_index-1])
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end_test_index = start_test_index + session_lengths[0][test_session_index-1]
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end_subject_index = subject_starting_index + sum(session_lengths[0])
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# Testing to check correctly slicing
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'''
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print(session_lengths[0], 'Sum:', sum(session_lengths[0]))
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print('Subject start:', subject_starting_index)
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print('Test start:', start_test_index)
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print('Test end:', end_test_index)
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print('Subject end:', end_subject_index, '\n -------')
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'''
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if start_test_index == subject_starting_index:
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X_test = X[start_test_index:end_test_index]
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y_test = y[start_test_index:end_test_index]
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X_train = X[end_test_index:end_subject_index]
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y_train = y[end_test_index:end_subject_index]
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elif end_test_index == end_subject_index:
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#print(X[subject_starting_index:start_test_index].shape)
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X_train = X[subject_starting_index:start_test_index]
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y_train = y[subject_starting_index:start_test_index]
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X_test = X[start_test_index:end_test_index]
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#print(X[start_test_index:end_test_index].shape, '\n ---')
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y_test = y[start_test_index:end_test_index]
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else:
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X_train = X[subject_starting_index:start_test_index]
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y_train = y[subject_starting_index:start_test_index]
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X_test = X[start_test_index:end_test_index]
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y_test = y[start_test_index:end_test_index]
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X_train = np.concatenate((X_train, X[end_test_index:end_subject_index]))
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y_train = np.concatenate((y_train, y[end_test_index:end_subject_index]))
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#print(X_train.shape, '\n -------')
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subject_starting_index = max(end_subject_index, end_test_index)
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for i in range(1, nr_subjects):
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start_test_index = subject_starting_index + sum(session_lengths[i][:test_session_index-1])
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end_test_index = start_test_index + session_lengths[i][test_session_index-1]
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end_subject_index = subject_starting_index + sum(session_lengths[i])
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# Testing to check correctly slicing
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'''
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print(session_lengths[i], 'Sum:', sum(session_lengths[i]))
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print('Subject start:', subject_starting_index)
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print('Test start:', start_test_index)
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print('Test end:', end_test_index)
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print('Subject end:', end_subject_index, '\n -------')
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'''
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if start_test_index == subject_starting_index:
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X_test = np.concatenate((X_test, X[start_test_index:end_test_index]))
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y_test = np.concatenate((y_test, y[start_test_index:end_test_index]))
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X_train = np.concatenate((X_train, X[end_test_index:end_subject_index]))
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y_train = np.concatenate((y_train, y[end_test_index:end_subject_index]))
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elif end_test_index == end_subject_index:
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#print(X[subject_starting_index:start_test_index].shape)
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X_train = np.concatenate((X_train, X[subject_starting_index:start_test_index]))
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y_train = np.concatenate((y_train, y[subject_starting_index:start_test_index]))
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#print(X[start_test_index:end_test_index].shape, '\n ---')
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X_test = np.concatenate((X_test, X[start_test_index:end_test_index]))
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y_test = np.concatenate((y_test, y[start_test_index:end_test_index]))
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else:
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X_train = np.concatenate((X_train, X[subject_starting_index:start_test_index]))
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y_train = np.concatenate((y_train, y[subject_starting_index:start_test_index]))
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X_test = np.concatenate((X_test, X[start_test_index:end_test_index]))
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y_test = np.concatenate((y_test, y[start_test_index:end_test_index]))
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X_train = np.concatenate((X_train, X[end_test_index:end_subject_index]))
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y_train = np.concatenate((y_train, y[end_test_index:end_subject_index]))
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#print(X_train.shape, '\n -------')
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subject_starting_index = max(end_subject_index, end_test_index)
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return X_train, X_test, y_train, y_test
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# Trains the model
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# Input: Keras.model, batch_size, nr epochs, training, and validation data
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# Ouput: History
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def train(model, X_train, y_train, verbose, batch_size=64, epochs=30, X_validation=None, y_validation=None):
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optimiser = keras.optimizers.Adam(learning_rate=0.0001)
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model.compile(optimizer=optimiser,
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loss='categorical_crossentropy',
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metrics=['accuracy'])
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if X_validation != None:
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history = model.fit(X_train,
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y_train,
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validation_data=(X_validation, y_validation),
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batch_size=batch_size,
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epochs=epochs,
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verbose=verbose)
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else:
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history = model.fit(X_train,
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y_train,
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batch_size=batch_size,
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epochs=epochs,
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verbose=verbose)
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return history
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# Gives nr of datapoints for chosen session
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# Input: session_lengths 2d-list, session_nr, nr of subjects
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# Ouput: int(datapoints)
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def get_nr_in_session(session_lengths:list, session_nr, nr_subjects=5):
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summ = 0
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for i in range(nr_subjects):
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summ += session_lengths[i][session_nr-1]
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return summ
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# Prints session and training data
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# Input: None
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# Ouput: None -> print
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def print_session_train_data(X_train, X_test, y_train, y_test, session_lengths, session_nr):
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print(X_train.size)
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print(X_train.shape)
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print(X_test.shape)
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print(y_train.shape)
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print(y_test.shape)
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print('Datapoints in session ' + str(session_nr) + ':', get_nr_in_session(session_lengths, session_nr))
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print('Should be remaining:', 2806 - get_nr_in_session(session_lengths, session_nr))
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# Reshapes training og test data into batches NOT RELEVANT?
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# Input: training, test data (and validation), batch_size
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# Ouput: training, test data (and validation)
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def batch_formatting(X_train, X_test, y_train, y_test, batch_size=64, nr_classes=5, X_validation=None, y_validation=None):
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train_splits = X_train.shape[0] // batch_size
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train_rest = X_train.shape[0] % batch_size
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test_splits = X_test.shape[0] // batch_size
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test_rest = X_test.shape[0] % batch_size
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X_train = X_train[:-train_rest]
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y_train = y_train[:-train_rest]
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X_test = X_test[:-test_rest]
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y_test = y_test[:-test_rest]
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X_train_batch = np.reshape(X_train, (batch_size, train_splits, 208))
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y_train_batch = np.reshape(y_train, (batch_size, train_splits, nr_classes))
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X_test_batch = np.reshape(X_test, (batch_size, test_splits, 208))
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y_test_batch = np.reshape(y_test, (batch_size, test_splits, nr_classes))
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if X_validation != None:
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val_splits = X_validation.shape[0] // batch_size
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val_rest = X_validation.shape[0] % batch_size
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X_validation = X_validation[:-val_rest]
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y_validation = y_validation[:-val_rest]
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X_val_batch = np.reshape(X_validation, (batch_size, val_splits, 208))
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y_val_batch = np.reshape(y_validation, (batch_size, val_splits))
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return X_train_batch, X_test_batch, y_train_batch, y_test_batch, X_val_batch, y_val_batch
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return X_train_batch, X_test_batch, y_train_batch, y_test_batch
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# Retrieves data sets for each session as test set and evalutes
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# the average of networks trained om them
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# Input: raw data, session_lengths list, total nr of sessions, batch_size, and nr of epochs
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# Ouput: tuple(cross validation average, list(result for each dataset(len=nr_sessions)))
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def session_cross_validation_LSTM(X, y, session_lengths, nr_sessions, batch_size=64, epochs=30):
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session_training_results = []
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for i in range(nr_sessions):
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model = LSTM(input_shape=(1, 208))
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X_train_session, X_test_session, y_train_session, y_test_session = prepare_datasets_sessions(X, y, session_lengths, i)
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train(model, X_train_session, y_train_session, verbose=0, batch_size=batch_size, epochs=epochs)
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test_loss, test_acc = model.evaluate(X_test_session, y_test_session, verbose=2)
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session_training_results.append(test_acc)
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del model
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K.clear_session()
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print('Session', i, 'as test data gives accuracy:', test_acc)
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average_result = statistics.mean((session_training_results))
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return average_result, session_training_results
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# ----- MODELS ------
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# Creates a keras.model with focus on LSTM layers
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# Input: input shape, classes of classification
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# Ouput: model:Keras.model
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def LSTM(input_shape, nr_classes=5):
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model = keras.Sequential()
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model.add(keras.layers.Bidirectional(keras.layers.LSTM(64), input_shape=input_shape, name='Bidirectional_LSTM'))
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model.add(keras.layers.Dense(64, activation='relu', activity_regularizer=l2(0.001), name='Dense_relu'))
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model.add(keras.layers.Dropout(0.3, name='Dropout'))
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# Output layer
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model.add(keras.layers.Dense(nr_classes, activation='softmax', name='Dense_relu_output'))
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return model
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# Creates a keras.model with focus on GRU layers
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# Input: input shape, classes of classification
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# Ouput: model:Keras.model
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def GRU(input_shape, nr_classes=5):
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model = keras.Sequential()
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model.add(keras.layers.Bidirectional(keras.layers.GRU(64), input_shape=input_shape, name='Bidirectional_GRU'))
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model.add(keras.layers.Dense(64, activation='relu', activity_regularizer=l2(0.001), name='Dense_relu'))
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model.add(keras.layers.Dropout(0.3, name='Dropout'))
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# Output layer:
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model.add(keras.layers.Dense(nr_classes, activation='softmax', name='Dense_relu_output'))
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return model
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if __name__ == "__main__":
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# ----- Load data ------
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# X.shape = (2806, 1, 208)
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# y.shape = (2806, nr_subjects)
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# session_lengths.shape = (nr_subjects, nr_sessions)
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X, y, session_lengths = load_data_from_json(DATA_PATH_MFCC, nr_classes=5)
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# Parameters:
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NR_SUBJECTS = 5
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NR_SESSIONS = 4
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BATCH_SIZE = 64
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EPOCHS = 30
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TEST_SESSION_NR = 4
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VERBOSE = 0
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# ----- Get prepared data: train, validation, and test ------
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# X_train.shape = (2806-X_test, 1, 208)
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# X_test.shape = (X_test(from session nr. ?), 1, 208)
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# y_train.shape = (2806-y_test, nr_subjects)
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# y_test.shape = (y_test(from session nr. ?), nr_subjects)
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X_train, X_test, y_train, y_test = prepare_datasets_sessions(X, y, session_lengths, TEST_SESSION_NR)
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#'''
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# ----- Make model ------
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model_GRU = GRU(input_shape=(1, 208)) # (timestep, 13*16 MFCC coefficients)
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model_LSTM = LSTM(input_shape=(1, 208)) # (timestep, 13*16 MFCC coefficients)
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model_GRU.summary()
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model_LSTM.summary()
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# ----- Train network ------
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history_GRU = train(model_GRU, X_train, y_train, verbose=VERBOSE, batch_size=BATCH_SIZE, epochs=EPOCHS)
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history_LSTM = train(model_LSTM, X_train, y_train, verbose=VERBOSE, batch_size=BATCH_SIZE, epochs=EPOCHS)
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#average = session_cross_validation_LSTM(X, y, session_lengths, NR_SESSIONS, BATCH_SIZE, EPOCHS)
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#print('\nCrossvalidated:', average)
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# ----- Plot train accuracy/error -----
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#plot_train_history(history)
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# ----- Evaluate model on test set ------
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test_loss, test_acc = model_GRU.evaluate(X_test, y_test, verbose=VERBOSE)
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print('\nTest accuracy GRU:', test_acc, '\n')
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test_loss, test_acc = model_LSTM.evaluate(X_test, y_test, verbose=VERBOSE)
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print('\nTest accuracy LSTM:', test_acc, '\n')
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#'''
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