188 lines
6.6 KiB
Python
188 lines
6.6 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 pathlib import Path
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import pandas as pd
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import matplotlib.pyplot as plt
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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):
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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 arraysls
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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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#print(X.shape)
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y = np.array(data["labels"])
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y = y.reshape(y.shape[0], 1)
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#print(y.shape)
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session_lengths = 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_history(history):
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"""Plots accuracy/loss for training/validation set as a function of the epochs
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:param history: Training history of model
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:return:
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"""
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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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axs[0].plot(history.history["val_accuracy"], label="test 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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axs[1].plot(history.history["val_loss"], label="test 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:bool, 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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subject_starting_index = 0
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X_train = np.empty((1, 1, 208))
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y_train = np.empty((1, 208))
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X_test = np.empty((1, 1, 208))
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y_test = np.empty((1, 208))
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for i in range(nr_subjects):
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start_test_index = sum(session_lengths[i][:test_session_index])
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end_test_index = start_test_index + session_lengths[i][test_session_index-1]
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end_subject_index = sum(session_lengths[i])
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if start_test_index == subject_starting_index:
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X_test.append(X[start_test_index:end_test_index])
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y_test.append(y[start_test_index:end_test_index])
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X_train.append(X[end_test_index:end_subject_index])
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y_train.append(y[end_test_index:end_subject_index])
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elif end_test_index == end_subject_index:
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X_train.append(X[subject_starting_index:start_test_index])
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y_train.append(y[subject_starting_index:start_test_index])
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X_test.append(X[start_test_index:end_test_index])
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y_test.append(y[start_test_index:end_test_index])
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else:
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X_train.append(X[subject_starting_index:start_test_index])
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y_train.append(y[subject_starting_index:start_test_index])
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X_test.append(X[start_test_index:end_test_index])
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y_test.append(y[start_test_index:end_test_index])
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X_train.append(X[end_test_index:end_subject_index])
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y_train.append(y[end_test_index:end_subject_index])
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subject_starting_index = end_subject_index
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return X_train, X_test, y_train, y_test
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# Creates a RNN_LSTM neural network model
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# Input: input shape, classes of classification
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# Ouput: model:Keras.model
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def RNN_LSTM(input_shape, nr_classes=5):
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"""Generates RNN-LSTM model
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:param input_shape (tuple): Shape of input set
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:return model: RNN-LSTM model
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"""
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# build network topology
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model = keras.Sequential()
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# 2 LSTM layers
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model.add(keras.layers.LSTM(64, input_shape=input_shape, return_sequences=True))
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model.add(keras.layers.LSTM(64))
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# dense layer
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model.add(keras.layers.Dense(64, activation='relu'))
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model.add(keras.layers.Dropout(0.3))
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# output layer
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model.add(keras.layers.Dense(nr_classes, activation='softmax'))
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return model
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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, batch_size, epochs, X_train, X_validation, y_train, y_validation):
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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='sparse_categorical_crossentropy',
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metrics=['accuracy'])
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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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return history
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if __name__ == "__main__":
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# Load data
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X, y, session_lengths = load_data_from_json(DATA_PATH_MFCC)
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# Get prepared data: train, validation, and test
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(X_train, X_validation,
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X_test, y_train,
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y_validation,
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y_test) = prepare_datasets_percentsplit(X, y, validation_size=0.2, test_size=0.25, shuffle_vars=True)
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#print(X_train.shape)
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# Make model
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model = RNN_LSTM(input_shape=(1, 208))
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model.summary()
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# Train network
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history = train(model, X_train, X_validation, y_train, y_validation, batch_size=64, epochs=30)
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# plot accuracy/error for training and validation
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plot_history(history)
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# evaluate model on test set
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test_loss, test_acc = model.evaluate(X_test, y_test, verbose=2)
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print('\nTest accuracy:', test_acc)
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