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chore: add ground work for a CNN model

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Skudalen 2021-07-13 16:22:58 +02:00
parent 5123586fa6
commit 7ad034fa95
1 changed files with 51 additions and 14 deletions
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Neural_Network_Analysis.py
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@ -247,8 +247,7 @@ def session_cross_validation(model_name:str, X, y, session_lengths, nr_sessions,
elif model_name == 'GRU':
model = GRU(input_shape=(1, 208))
elif model_name == 'CNN':
continue
model = CNN(input_shape=(1, 208))
model = CNN(input_shape=(52, 52, 104))
elif model_name == 'FNN':
model = FFN(input_shape=(1, 208))
else:
@ -257,6 +256,12 @@ def session_cross_validation(model_name:str, X, y, session_lengths, nr_sessions,
model.summary()
X_train_session, X_test_session, y_train_session, y_test_session = prepare_datasets_sessions(X, y, session_lengths, i)
if model_name == 'CNN':
X_train_session = X_train_session[..., np.newaxis]
X_test_session = X_test_session[..., np.newaxis]
X_train_session = np.reshape(X_train_session, (X_train_session.shape[0], 52, 52, 104))
X_test_session = np.reshape(X_test_session, (X_test_session.shape[0], 52, 52, 104))
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)
@ -295,11 +300,11 @@ def GRU(input_shape, nr_classes=5):
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'))
model.add(keras.layers.Dense(nr_classes, activation='softmax', name='Softmax'))
return model
# Creates a keras.model with focus on GRU layers
# Creates a keras.model with a basic feed-forward-network
# Input: input shape, classes of classification
# Ouput: model:Keras.model
def FFN(input_shape, nr_classes=5):
@ -311,10 +316,36 @@ def FFN(input_shape, nr_classes=5):
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'))
model.add(keras.layers.Dense(nr_classes, activation='softmax', name='Softmax'))
return model
# Creates a keras.model with focus on Convulotion layers
# Input: input shape, classes of classification
# Ouput: model:Keras.model
def CNN(input_shape, nr_classes=5):
model = keras.Sequential(name='CNN_model')
model.add(keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=input_shape))
model.add(keras.layers.MaxPooling2D((3, 3), strides=(2, 2), padding='same'))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.Conv2D(32, (3, 3), activation='relu'))
model.add(keras.layers.MaxPooling2D((3, 3), strides=(2, 2), padding='same'))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.Conv2D(32, (2, 2), activation='relu'))
model.add(keras.layers.MaxPooling2D((2, 2), strides=(2, 2), padding='same'))
model.add(keras.layers.BatchNormalization())
# flatten output and feed it into dense layer
model.add(keras.layers.Flatten())
model.add(keras.layers.Dense(64, activation='relu'))
model.add(keras.layers.Dropout(0.3))
# Ouput layer
model.add(keras.layers.Dense(nr_classes, activation='softmax', name='Softmax'))
return model
if __name__ == "__main__":
@ -340,7 +371,7 @@ if __name__ == "__main__":
# 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, TEST_SESSION_NR)
#'''
@ -351,22 +382,16 @@ if __name__ == "__main__":
#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_GRU = session_cross_validation('GRU', X, y, session_lengths, NR_SESSIONS, BATCH_SIZE, EPOCHS)
#verage_LSTM = session_cross_validation('LSTM', X, y, session_lengths, NR_SESSIONS, BATCH_SIZE, EPOCHS)
average_FFN = session_cross_validation('FNN', X, y, session_lengths, NR_SESSIONS, BATCH_SIZE, EPOCHS)
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_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')
@ -375,4 +400,16 @@ if __name__ == "__main__":
#'''
# ----- Cross validation ------
#average_GRU = session_cross_validation('GRU', X, y, session_lengths, NR_SESSIONS, BATCH_SIZE, EPOCHS)
#verage_LSTM = session_cross_validation('LSTM', X, y, session_lengths, NR_SESSIONS, BATCH_SIZE, EPOCHS)
#average_FFN = session_cross_validation('FNN', X, y, session_lengths, NR_SESSIONS, BATCH_SIZE, EPOCHS)
average_CNN = session_cross_validation('CNN', X, y, session_lengths, NR_SESSIONS, BATCH_SIZE, EPOCHS)
print('\n')
#print('Crossvalidated GRU:', average_GRU)
#print('Crossvalidated LSTM:', average_LSTM)
#print('Crossvalidated FFN:', average_FFN)
print('Crossvalidated CNN:', average_CNN)
print('\n')