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feat: add functionality for multiple layer

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input in cross-val func
This commit is contained in:
Skudalen 2021-07-13 15:23:51 +02:00
parent ca1eeb0a83
commit 2bca68fcae
1 changed files with 41 additions and 22 deletions
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63
Neural_Network_Analysis.py
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@ -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')
#'''