feat: add a RNN network model with GRU layers
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Skudalen
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@ -5,6 +5,7 @@ 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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@ -21,20 +22,14 @@ 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 arraysls
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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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#print(X.shape)
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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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#print(y.shape)
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y = keras.utils.to_categorical(y, nr_classes)
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print(y.shape)
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session_lengths = np.array(data['session_lengths'])
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#print(session_lengths.shape)
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print("Data succesfully loaded!")
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@ -44,24 +39,22 @@ def load_data_from_json(data_path, nr_classes):
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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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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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axs[0].plot(history.history["val_accuracy"], label="test 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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axs[1].plot(history.history["val_loss"], label="test 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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@ -162,31 +155,6 @@ def prepare_datasets_sessions(X, y, session_lengths, test_session_index=4, nr_su
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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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@ -234,7 +202,7 @@ def print_session_train_data(X_train, X_test, y_train, y_test, session_lengths,
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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
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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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@ -265,10 +233,14 @@ def batch_formatting(X_train, X_test, y_train, y_test, batch_size=64, nr_classes
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return X_train_batch, X_test_batch, y_train_batch, y_test_batch
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def session_cross_validation(X, y, session_lengths, nr_sessions, batch_size=64, epochs=30):
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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 = RNN_LSTM(input_shape=(1, 208))
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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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@ -280,12 +252,43 @@ def session_cross_validation(X, y, session_lengths, nr_sessions, batch_size=64,
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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, 5)
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# session_lengths.shape = (5, 4)
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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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@ -294,33 +297,40 @@ if __name__ == "__main__":
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BATCH_SIZE = 64
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EPOCHS = 30
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# ----- Get prepared data: train, validation, and test ------
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'''
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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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X_train, X_test, y_train, y_test = prepare_datasets_sessions(X, y, session_lengths, session_nr)
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print(X_train.shape)
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print(y_train.shape)
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print(X_test.shape)
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print(y_test.shape)
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#print_session_train_data(X_train, X_test, y_train, y_test, session_lengths, session_nr)
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'''
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#'''
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# ----- Make model ------
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#model = RNN_LSTM(input_shape=(1, 208)) # (timestep, coefficients)
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#model.summary()
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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 = train(model, X_train, y_train, batch_size=batch_size, epochs=30)
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average = session_cross_validation(X, y, session_lengths, NR_SESSIONS, BATCH_SIZE, EPOCHS)
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print('\nCrossvalidated:', average)
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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 accuracy/error for training and validation
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#plot_history(history)
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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.evaluate(X_test, y_test, verbose=1)
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#print('\nTest accuracy:', test_acc)
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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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