machine learning - Using Keras ImageDataGenerator in a regression model

Question

I want to use the

flow_from_directory

method of the

ImageDataGenerator

to generate training data for a regression model, where the target value can be any float value between 1 and -1.

flow_from_directory

has a "class_mode" parameter with the descripton

class_mode: one of "categorical", "binary", "sparse" or None. Default: "categorical". Determines the type of label arrays that are returned: "categorical" will be 2D one-hot encoded labels, "binary" will be 1D binary labels, "sparse" will be 1D integer labels.

Which of these values should I take? None of them seems to really fit...

Answer 1

With Keras 2.2.4 you can use ".flow_from_dataframe" that solves what you want to do, allowing you to flow images from a directory for regression problems. You should store all your images in a folder and load a dataframe containing in one column the image IDs and in the other column the regression score (labels) and set "class_mode='other'" in ".flow_from_dataframe".

Here you can find an example where the images are in "image_dir", the dataframe with the image IDs and the regression scores is loaded with pandas from "train file"

train_label_df = pd.read_csv(train_file, delimiter=' ', header=None, names=['id', 'score'])

train_datagen = ImageDataGenerator(rescale = 1./255, horizontal_flip = True,
                                   fill_mode = "nearest", zoom_range = 0.2,
                                   width_shift_range = 0.2, height_shift_range=0.2,
                                   rotation_range=30) 

train_generator = train_datagen.flow_from_dataframe(dataframe=train_label_df, directory=image_dir, 
                                              x_col="id", y_col="score", has_ext=True, 
                                              class_mode="other", target_size=(img_width, img_height), 
                                              batch_size=bs)