Question: Say you have a dataset that has images and some data in a .csv for each image. Your goal is to create a NN that has a convolution branch and another one (in my case an MLP). Now, there are plenty of guides (one here, another one) on how to create the network, that's not the problem. The issue here is how do I create an iterator in the form of [[convolution_input, other_features], target] when the convolution_input is from a Keras ImageDataGenerator flow that adds augmented images. More specifically, when the nth image (that may be an augmented one or not) is fed to the NN, I want its original features inside other_features. I found a few attempts (here and here, the second one looked promising but I wasn't able to figure out how to handle augmented images) in doing exactly that but they do not seem to take into account the possible dataset manipulation that the Keras generator does.

Answer: Let's say, you have a CSV, such that your images and the other features are in the file. Where id represents the image name, and followed by the features, and followed by your target, (class for classification, number for regression) | id | feat1 | feat2 | feat3 | class | |---------------------|-------|-------|-------|-------| | 1_face_IMG_NAME.jpg | 1 | 0 | 1 | A | | 3_face_IMG_NAME.jpg | 1 | 0 | 1 | B | | 2_face_IMG_NAME.jpg | 1 | 0 | 1 | A | | ... | ... | ... | ... | ... |  First, let us define a data generator, and later we can override it. Let us read the data from the CSV in a pandas data frame and use keras's flow_from_dataframe to read from the data frame. df = pandas.read_csv("dummycsv.csv") datagen = ImageDataGenerator(rescale=1/255.) generator = datagen.flow_from_dataframe( df, directory="out/", x_col="id", y_col=df.columns[1:], class_mode="raw", batch_size=1)  You can always add your augmentation in ImageDataGenerator. Things to note in the above code in flow_from_dataframe is x_col = the image name y_col = typically columns with the class name, but let us override it later by first providing all the other columns in the CSV. i.e. feat_1, feat_2.... till class_label class_mode = raw, suggests the generator to return all the values in y as is. Now let us override/inherit the above generator and create a new one, such that it returns [img, otherfeatures], [target] Here is the code with comments as explanations: def my_custom_generator(): # to keep track of complete epoch count = 0 while True: if count == len(df.index): # if the count is matching with the length of df, # the one pass is completed, so reset the generator generator.reset() break count += 1 # get the data from the generator data = generator.next() # the data looks like this [[img,img] , [other_cols,other_cols]] based on the batch size imgs = [] cols = [] targets = [] # iterate the data and append the necessary columns in the corresponding arrays for k in range(batch_size): # the first array contains all images imgs.append(data[0][k]) # the second array contains all features with last column as class, so [:-1] cols.append(data[1][k][:-1]) # the last column in the second array from data is the class targets.append(data[1][k][-1]) # this will yield the result as you expect. yield [imgs,cols], targets  Create a similar function for your validation generator. Use train_test_split to split your data frame if you need it and create 2 generators and override them. Pass the function in model.fit_generator like this model.fit_generator(my_custom_generator(),.....other params)