Cfrgtkky

The main task is to predict a mask for the input image. So I have the following data for training:

• lot's of 768x768 original pics like this:

• and output mask pics(also 768x768) like this:

Also I have validation original pics.

I prepare some kind of neural model that should predict the output mask. I prepared keras model configuaration that should have a topology which looks like below:

The code I prepared for training is there.

import keras

epochs=100



image_datagen = keras.preprocessing.image.ImageDataGenerator()

mask_datagen = keras.preprocessing.image.ImageDataGenerator()

seed = 1

image_generator = image_datagen.flow_from_directory(

    'H:/LABS/ship_detection/test_train/',

    color_mode='rgb',batch_size=32,target_size=(768,768),

    seed=seed)



mask_generator = mask_datagen.flow_from_directory(

    'H:/LABS/ship_detection/test_mask/',

    class_mode="categorical",batch_size=32,target_size=(768,768),

    seed=seed)



train_generator = zip(image_generator, mask_generator)



model.fit_generator(generator=train_generator,

                    epochs=epochs,

                    callbacks=callbacks,steps_per_epoch=1)

But when I try to fit generator for prediction I have an issue:

c:usersharwisterappdatalocalprogramspythonpython36libsite-packageskerasenginetraining_generator.py in fit_generator(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)

    208                     batch_size = list(x.values())[0].shape[0]

    209                 else:

--> 210                     batch_size = x.shape[0]

    211                 batch_logs['batch'] = batch_index

    212                 batch_logs['size'] = batch_size



AttributeError: 'tuple' object has no attribute 'shape'

I do something wrong for sure, but I can't understand anything from these kind of errors. The simple question I can't find a response in Google is: How can I push into Keras two images (input and output images) for training and after training get an output image providing an input image?

Since you have separate generators for the images and the labels (i.e. masks), you need to set the class_mode argument to None to prevent the generators from producing any labels arrays:

image_generator = image_datagen.flow_from_directory(class_mode=None, ...)

mask_generator = mask_datagen.flow_from_directory(class_mode=None, ...)

This way, image_generator would only generate the input images and the mask_generator would only generate the mask (i.e. true label) images.