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feat: add basic FFN for comparison with other networks

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Skudalen 2021-07-13 15:43:21 +02:00
parent 2bca68fcae
commit 5123586fa6
1 changed files with 27 additions and 5 deletions
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32
Neural_Network_Analysis.py
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@ -250,11 +250,12 @@ def session_cross_validation(model_name:str, X, y, session_lengths, nr_sessions,
continue continue
model = CNN(input_shape=(1, 208)) model = CNN(input_shape=(1, 208))
elif model_name == 'FNN': elif model_name == 'FNN':
continue
model = FFN(input_shape=(1, 208)) model = FFN(input_shape=(1, 208))
else: else:
raise Exception('Model not found') raise Exception('Model not found')
model.summary()
X_train_session, X_test_session, y_train_session, y_test_session = prepare_datasets_sessions(X, y, session_lengths, i) 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=1, 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) test_loss, test_acc = model.evaluate(X_test_session, y_test_session, verbose=2)
@ -298,6 +299,23 @@ def GRU(input_shape, nr_classes=5):
return model return model
# Creates a keras.model with focus on GRU layers
# Input: input shape, classes of classification
# Ouput: model:Keras.model
def FFN(input_shape, nr_classes=5):
model = keras.Sequential(name='FFN_model')
model.add(keras.layers.Reshape((input_shape[-1],), input_shape=input_shape))
model.add(keras.layers.Dense(256, activation='relu', input_shape=input_shape, name='Dense_relu_1'))
model.add(keras.layers.Dense(128, activation='relu', activity_regularizer=l2(0.005), name='Dense_relu_2'))
model.add(keras.layers.Dense(64, activation='relu', activity_regularizer=l2(0.005), name='Dense_relu_3'))
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__": if __name__ == "__main__":
@ -337,10 +355,14 @@ if __name__ == "__main__":
#history_GRU = train(model_GRU, X_train, y_train, verbose=VERBOSE, batch_size=BATCH_SIZE, epochs=EPOCHS) #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) #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_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) #verage_LSTM = session_cross_validation('LSTM', X, y, session_lengths, NR_SESSIONS, BATCH_SIZE, EPOCHS)
print('\nCrossvalidated GRU:', average_GRU) average_FFN = session_cross_validation('FNN', X, y, session_lengths, NR_SESSIONS, BATCH_SIZE, EPOCHS)
print('Crossvalidated LSTM:', average_LSTM) print('\n')
#print('Crossvalidated GRU:', average_GRU)
#print('Crossvalidated LSTM:', average_LSTM)
print('Crossvalidated FFN:', average_FFN)
print('\n')
# ----- Plot train accuracy/error ----- # ----- Plot train accuracy/error -----
#plot_train_history(history) #plot_train_history(history)