From 2bca68fcae4f3a7c8eda3c865cb91ded08def296 Mon Sep 17 00:00:00 2001 From: Skudalen Date: Tue, 13 Jul 2021 15:23:51 +0200 Subject: [PATCH] feat: add functionality for multiple layer input in cross-val func --- Neural_Network_Analysis.py | 63 +++++++++++++++++++++++++------------- 1 file changed, 41 insertions(+), 22 deletions(-) diff --git a/Neural_Network_Analysis.py b/Neural_Network_Analysis.py index 85c142d..0d4dffe 100644 --- a/Neural_Network_Analysis.py +++ b/Neural_Network_Analysis.py @@ -237,18 +237,34 @@ def batch_formatting(X_train, X_test, y_train, y_test, batch_size=64, nr_classes # 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): +def session_cross_validation(model_name:str, X, y, session_lengths, nr_sessions, batch_size=64, epochs=30): session_training_results = [] for i in range(nr_sessions): - model = LSTM(input_shape=(1, 208)) + + # Model: + if model_name == 'LSTM': + model = LSTM(input_shape=(1, 208)) + elif model_name == 'GRU': + model = GRU(input_shape=(1, 208)) + elif model_name == 'CNN': + continue + model = CNN(input_shape=(1, 208)) + elif model_name == 'FNN': + continue + model = FFN(input_shape=(1, 208)) + else: + raise Exception('Model not found') + 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) + train(model, X_train_session, y_train_session, verbose=1, batch_size=batch_size, epochs=epochs) test_loss, test_acc = model.evaluate(X_test_session, y_test_session, verbose=2) session_training_results.append(test_acc) del model K.clear_session() - print('Session', i, 'as test data gives accuracy:', test_acc) + #print('Session', i, 'as test data gives accuracy:', test_acc) + average_result = statistics.mean((session_training_results)) + return average_result, session_training_results @@ -259,9 +275,9 @@ def session_cross_validation_LSTM(X, y, session_lengths, nr_sessions, batch_size # 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 = keras.Sequential(name='LSTM_model') + model.add(keras.layers.Bidirectional(keras.layers.LSTM(128), input_shape=input_shape, name='Bidirectional_LSTM')) + model.add(keras.layers.Dense(128, activation='relu', activity_regularizer=l2(0.005), 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')) @@ -273,9 +289,9 @@ def LSTM(input_shape, nr_classes=5): # 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 = keras.Sequential(name='GRU_model') + model.add(keras.layers.Bidirectional(keras.layers.GRU(128), input_shape=input_shape, name='Bidirectional_GRU')) + model.add(keras.layers.Dense(128, activation='relu', activity_regularizer=l2(0.005), 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')) @@ -311,26 +327,29 @@ if __name__ == "__main__": #''' # ----- Make model ------ - 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 = 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() + #model_GRU.summary() + #model_LSTM.summary() # ----- Train network ------ - 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) + #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_GRU = session_cross_validation('GRU', X, y, session_lengths, NR_SESSIONS, BATCH_SIZE, EPOCHS) + average_LSTM = session_cross_validation('LSTM', X, y, session_lengths, NR_SESSIONS, BATCH_SIZE, EPOCHS) + print('\nCrossvalidated GRU:', average_GRU) + print('Crossvalidated LSTM:', average_LSTM) # ----- Plot train accuracy/error ----- #plot_train_history(history) # ----- Evaluate model on test set ------ - 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') + #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') #'''