""" rmse = np.sqrt(K.mean(K.square(y_pred - y_true))) * 48 return rmse # Define the name of the weights file that will be trained weights_file_name = "test002.h5" feature = ('left_eye_center_x', 'left_eye_center_y', 'right_eye_center_x', 'right_eye_center_y', 'nose_tip_x', 'nose_tip_y', 'mouth_center_bottom_lip_x', 'mouth_center_bottom_lip_y') flip_indices = [(0, 2), (1, 3)] # Load dataset using my previous LoadData class load = LoadData() X_train, X_val, Y_train, Y_val = load.loadNSplit(feature=feature) # Define the output number output_units = Y_train.shape[1] # Define lenet5 like model lenet5 = Sequential([ Convolution2D(128, 3, 3, border_mode='valid', input_shape=(1, 96, 96)), Activation('relu'), MaxPooling2D(pool_size=(2, 2)), Dropout(0.1), Convolution2D(256, 2, 2), Activation('relu'), MaxPooling2D(pool_size=(2, 2)), Dropout(0.25), Convolution2D(512, 2, 2),