![]() We can use it to adjust the brightness_range of any image for Data Augmentation. Note, my batch size is 5.Brightness_range Keras is an argument in ImageDataGenerator class of keras. In the following pycharm debug view you can see the images and labels as they exist at the point that the generator "yields". New Stack Trace During First Training Epoch I get further, so perhaps my generator is ok now, but I do see the following trace now (when I start to train the model): Yield images, (, labels, labels, labels], labels) Labels = ĭf = df.str.get_dummies().values.tolist() However I dont think this is right yet (stack trace below code snippet): def generate_image_generator(generator, data_directory, data_items, target_size, classes, batch_size, shuffle, class_mode): I have now tried to do the following (notice I now yield a 2-tuple for the label - this is the latest state of my effort), and I think I got further. How can I configure flow_from_dataframe with a "multi_output" regression label for one head, and a classification label for the second head? It thinks the first tuple in the list is actually a single key into one column rather than 4 keys into 4 columns. The error I receive is a "key error" into the pandas dataframe.Ĭlearly tensorflow keras doesnt like the list of tuples I pass for the "multi_output" labels. I am using tensorflow's keras implementation. The augmented pandas dataframe that includes my one-hot columns These are the columns referenced in the second tuple that is in my "y_col" array. Notice that in the code above, I add additional "one-hot" columns (cls_motorcycle, cls_face, cls_airplane) to the pandas dataframe. ![]() Notice in particular the following two lines: y_col=, Y_col=,Ĭlasses=classes, batch_size=batch_size, shuffle=shuffle, seed=2) GenImages = generator.flow_from_dataframe(dataframe=df, directory=data_directory, target_size=target_size, This is what I first had (see Additional Details below for what i currently have): def generate_image_generator(generator, data_directory, data_items, target_size, classes, batch_size, shuffle, class_mode):ĭf = pd.read_csv(data_directory+di) I think my code is close, but the variety of examples I've found are not quite what I need. The only difference is that I dont want to load all the images into memory at once, hence my desire to use a generator. I have a structure that is very similar to the one found here. The first network head will perform regression on the 4-vector bounding boxĪnd the second network head will perform classification on the "one-hot" 3-vector. 3 classes) as the label to the other head. 4 vector representing a bounding box) as the label to one of the 2 network heads, and a one-hot encoded vector (e.g. I've struggled to find an example of a "multi_output" custom generator that passes a vector of floats (e.g. I am using the ImageDataGenerator from import ImageDataGenerator
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