feat: add a RNN network model with GRU layers
This commit is contained in:
Skudalen
parent
e0e88abf41
commit
ca1eeb0a83
@ -5,6 +5,7 @@ from sklearn.model_selection import train_test_split
|
||||
import tensorflow as tf
|
||||
import tensorflow.keras as keras
|
||||
from keras import backend as K
|
||||
from keras.regularizers import l2
|
||||
from pathlib import Path
|
||||
import pandas as pd
|
||||
import matplotlib.pyplot as plt
|
||||
@ -21,21 +22,15 @@ def load_data_from_json(data_path, nr_classes):
|
||||
with open(data_path, "r") as fp:
|
||||
data = json.load(fp)
|
||||
|
||||
# convert lists to numpy arraysls
|
||||
# Convert lists to numpy arrays and reshapes them
|
||||
X = np.array(data['mfcc'])
|
||||
#print(X.shape)
|
||||
X = X.reshape(X.shape[0], 1, X.shape[1])
|
||||
print(X.shape)
|
||||
|
||||
y = np.array(data["labels"])
|
||||
#print(y.shape)
|
||||
y = keras.utils.to_categorical(y, nr_classes)
|
||||
print(y.shape)
|
||||
|
||||
session_lengths = np.array(data['session_lengths'])
|
||||
#print(session_lengths.shape)
|
||||
|
||||
|
||||
print("Data succesfully loaded!")
|
||||
|
||||
return X, y, session_lengths
|
||||
@ -44,24 +39,22 @@ def load_data_from_json(data_path, nr_classes):
|
||||
# loss with respect to epochs
|
||||
# Input: History(from model.fit(...))
|
||||
# Ouput: None -> plot
|
||||
def plot_history(history):
|
||||
"""Plots accuracy/loss for training/validation set as a function of the epochs
|
||||
:param history: Training history of model
|
||||
:return:
|
||||
"""
|
||||
def plot_train_history(history, val_data=False):
|
||||
|
||||
fig, axs = plt.subplots(2)
|
||||
|
||||
# create accuracy sublpot
|
||||
axs[0].plot(history.history["accuracy"], label="train accuracy")
|
||||
axs[0].plot(history.history["val_accuracy"], label="test accuracy")
|
||||
if val_data:
|
||||
axs[0].plot(history.history["val_accuracy"], label="validation accuracy")
|
||||
axs[0].set_ylabel("Accuracy")
|
||||
axs[0].legend(loc="lower right")
|
||||
axs[0].set_title("Accuracy eval")
|
||||
|
||||
# create error sublpot
|
||||
axs[1].plot(history.history["loss"], label="train error")
|
||||
axs[1].plot(history.history["val_loss"], label="test error")
|
||||
if val_data:
|
||||
axs[1].plot(history.history["val_loss"], label="validation error")
|
||||
axs[1].set_ylabel("Error")
|
||||
axs[1].set_xlabel("Epoch")
|
||||
axs[1].legend(loc="upper right")
|
||||
@ -162,31 +155,6 @@ def prepare_datasets_sessions(X, y, session_lengths, test_session_index=4, nr_su
|
||||
|
||||
return X_train, X_test, y_train, y_test
|
||||
|
||||
# Creates a RNN_LSTM neural network model
|
||||
# Input: input shape, classes of classification
|
||||
# Ouput: model:Keras.model
|
||||
def RNN_LSTM(input_shape, nr_classes=5):
|
||||
"""Generates RNN-LSTM model
|
||||
:param input_shape (tuple): Shape of input set
|
||||
:return model: RNN-LSTM model
|
||||
"""
|
||||
|
||||
# build network topology
|
||||
model = keras.Sequential()
|
||||
|
||||
# 2 LSTM layers
|
||||
model.add(keras.layers.LSTM(64, input_shape=input_shape, return_sequences=True))
|
||||
model.add(keras.layers.LSTM(64))
|
||||
|
||||
# dense layer
|
||||
model.add(keras.layers.Dense(64, activation='relu'))
|
||||
model.add(keras.layers.Dropout(0.3))
|
||||
|
||||
# output layer
|
||||
model.add(keras.layers.Dense(nr_classes, activation='softmax'))
|
||||
|
||||
return model
|
||||
|
||||
# Trains the model
|
||||
# Input: Keras.model, batch_size, nr epochs, training, and validation data
|
||||
# Ouput: History
|
||||
@ -234,7 +202,7 @@ def print_session_train_data(X_train, X_test, y_train, y_test, session_lengths,
|
||||
print('Datapoints in session ' + str(session_nr) + ':', get_nr_in_session(session_lengths, session_nr))
|
||||
print('Should be remaining:', 2806 - get_nr_in_session(session_lengths, session_nr))
|
||||
|
||||
# Reshapes training og test data into batches
|
||||
# Reshapes training og test data into batches NOT RELEVANT?
|
||||
# Input: training, test data (and validation), batch_size
|
||||
# Ouput: training, test data (and validation)
|
||||
def batch_formatting(X_train, X_test, y_train, y_test, batch_size=64, nr_classes=5, X_validation=None, y_validation=None):
|
||||
@ -264,11 +232,15 @@ def batch_formatting(X_train, X_test, y_train, y_test, batch_size=64, nr_classes
|
||||
return X_train_batch, X_test_batch, y_train_batch, y_test_batch, X_val_batch, y_val_batch
|
||||
|
||||
return X_train_batch, X_test_batch, y_train_batch, y_test_batch
|
||||
|
||||
def session_cross_validation(X, y, session_lengths, nr_sessions, batch_size=64, epochs=30):
|
||||
|
||||
# Retrieves data sets for each session as test set and evalutes
|
||||
# the average of networks trained om them
|
||||
# Input: raw data, session_lengths list, total nr of sessions, batch_size, and nr of epochs
|
||||
# Ouput: tuple(cross validation average, list(result for each dataset(len=nr_sessions)))
|
||||
def session_cross_validation_LSTM(X, y, session_lengths, nr_sessions, batch_size=64, epochs=30):
|
||||
session_training_results = []
|
||||
for i in range(nr_sessions):
|
||||
model = RNN_LSTM(input_shape=(1, 208))
|
||||
model = LSTM(input_shape=(1, 208))
|
||||
X_train_session, X_test_session, y_train_session, y_test_session = prepare_datasets_sessions(X, y, session_lengths, i)
|
||||
train(model, X_train_session, y_train_session, verbose=0, batch_size=batch_size, epochs=epochs)
|
||||
test_loss, test_acc = model.evaluate(X_test_session, y_test_session, verbose=2)
|
||||
@ -278,14 +250,45 @@ def session_cross_validation(X, y, session_lengths, nr_sessions, batch_size=64,
|
||||
print('Session', i, 'as test data gives accuracy:', test_acc)
|
||||
average_result = statistics.mean((session_training_results))
|
||||
return average_result, session_training_results
|
||||
|
||||
|
||||
|
||||
# ----- MODELS ------
|
||||
|
||||
# Creates a keras.model with focus on LSTM layers
|
||||
# Input: input shape, classes of classification
|
||||
# Ouput: model:Keras.model
|
||||
def LSTM(input_shape, nr_classes=5):
|
||||
|
||||
model = keras.Sequential()
|
||||
model.add(keras.layers.Bidirectional(keras.layers.LSTM(64), input_shape=input_shape, name='Bidirectional_LSTM'))
|
||||
model.add(keras.layers.Dense(64, activation='relu', activity_regularizer=l2(0.001), name='Dense_relu'))
|
||||
model.add(keras.layers.Dropout(0.3, name='Dropout'))
|
||||
# Output layer
|
||||
model.add(keras.layers.Dense(nr_classes, activation='softmax', name='Dense_relu_output'))
|
||||
|
||||
return model
|
||||
|
||||
# Creates a keras.model with focus on GRU layers
|
||||
# Input: input shape, classes of classification
|
||||
# Ouput: model:Keras.model
|
||||
def GRU(input_shape, nr_classes=5):
|
||||
|
||||
model = keras.Sequential()
|
||||
model.add(keras.layers.Bidirectional(keras.layers.GRU(64), input_shape=input_shape, name='Bidirectional_GRU'))
|
||||
model.add(keras.layers.Dense(64, activation='relu', activity_regularizer=l2(0.001), name='Dense_relu'))
|
||||
model.add(keras.layers.Dropout(0.3, name='Dropout'))
|
||||
# Output layer:
|
||||
model.add(keras.layers.Dense(nr_classes, activation='softmax', name='Dense_relu_output'))
|
||||
|
||||
return model
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
# ----- Load data ------
|
||||
# X.shape = (2806, 1, 208)
|
||||
# y.shape = (2806, 5)
|
||||
# session_lengths.shape = (5, 4)
|
||||
# X.shape = (2806, 1, 208)
|
||||
# y.shape = (2806, nr_subjects)
|
||||
# session_lengths.shape = (nr_subjects, nr_sessions)
|
||||
X, y, session_lengths = load_data_from_json(DATA_PATH_MFCC, nr_classes=5)
|
||||
|
||||
# Parameters:
|
||||
@ -293,34 +296,41 @@ if __name__ == "__main__":
|
||||
NR_SESSIONS = 4
|
||||
BATCH_SIZE = 64
|
||||
EPOCHS = 30
|
||||
|
||||
TEST_SESSION_NR = 4
|
||||
VERBOSE = 0
|
||||
|
||||
# ----- Get prepared data: train, validation, and test ------
|
||||
'''
|
||||
# X_train.shape = (2806-X_test, 1, 208)
|
||||
# X_test.shape = (X_test(from session nr. ?), 1, 208)
|
||||
# y_train.shape = (2806-y_test, nr_subjects)
|
||||
# y_test.shape = (y_test(from session nr. ?), nr_subjects)
|
||||
|
||||
X_train, X_test, y_train, y_test = prepare_datasets_sessions(X, y, session_lengths, TEST_SESSION_NR)
|
||||
|
||||
X_train, X_test, y_train, y_test = prepare_datasets_sessions(X, y, session_lengths, session_nr)
|
||||
print(X_train.shape)
|
||||
print(y_train.shape)
|
||||
print(X_test.shape)
|
||||
print(y_test.shape)
|
||||
#print_session_train_data(X_train, X_test, y_train, y_test, session_lengths, session_nr)
|
||||
'''
|
||||
|
||||
#'''
|
||||
# ----- Make model ------
|
||||
#model = RNN_LSTM(input_shape=(1, 208)) # (timestep, coefficients)
|
||||
#model.summary()
|
||||
model_GRU = GRU(input_shape=(1, 208)) # (timestep, 13*16 MFCC coefficients)
|
||||
model_LSTM = LSTM(input_shape=(1, 208)) # (timestep, 13*16 MFCC coefficients)
|
||||
|
||||
model_GRU.summary()
|
||||
model_LSTM.summary()
|
||||
|
||||
# ----- Train network ------
|
||||
#history = train(model, X_train, y_train, batch_size=batch_size, epochs=30)
|
||||
average = session_cross_validation(X, y, session_lengths, NR_SESSIONS, BATCH_SIZE, EPOCHS)
|
||||
print('\nCrossvalidated:', average)
|
||||
history_GRU = train(model_GRU, X_train, y_train, verbose=VERBOSE, batch_size=BATCH_SIZE, epochs=EPOCHS)
|
||||
history_LSTM = train(model_LSTM, X_train, y_train, verbose=VERBOSE, batch_size=BATCH_SIZE, epochs=EPOCHS)
|
||||
#average = session_cross_validation_LSTM(X, y, session_lengths, NR_SESSIONS, BATCH_SIZE, EPOCHS)
|
||||
#print('\nCrossvalidated:', average)
|
||||
|
||||
# plot accuracy/error for training and validation
|
||||
#plot_history(history)
|
||||
# ----- Plot train accuracy/error -----
|
||||
#plot_train_history(history)
|
||||
|
||||
# ----- Evaluate model on test set ------
|
||||
#test_loss, test_acc = model.evaluate(X_test, y_test, verbose=1)
|
||||
#print('\nTest accuracy:', test_acc)
|
||||
test_loss, test_acc = model_GRU.evaluate(X_test, y_test, verbose=VERBOSE)
|
||||
print('\nTest accuracy GRU:', test_acc, '\n')
|
||||
test_loss, test_acc = model_LSTM.evaluate(X_test, y_test, verbose=VERBOSE)
|
||||
print('\nTest accuracy LSTM:', test_acc, '\n')
|
||||
#'''
|
||||
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user