Multi output network generator

model.fit support multi input/output network, but if data-set is large enough and one have to use model.fit_generator, its complicated to generate tuple for such case, is there any plan to make it more simpler like model.fit.

I have network that take one input and produce two outputs, i created separate generator for each, but i am not able to run network on this. my input should be of form x, [y1, y2].

i think i need to extend generator for such case ?

image_datagen = ImageDataGenerator(
          rotation_range=15.,
          width_shift_range=0.2,
          height_shift_range=0.2,
          rescale=1./255,
          shear_range=0.2,
          zoom_range=0.2,
          horizontal_flip=True,
          vertical_flip = True,
          fill_mode='nearest')
  
  mask_datagen = ImageDataGenerator(
          rotation_range=15.,
          width_shift_range=0.2,
          height_shift_range=0.2,
          rescale=1./255,
          shear_range=0.2,
          zoom_range=0.2,
          horizontal_flip=True,
          vertical_flip = True,
          fill_mode='nearest')
  shading_datagen = ImageDataGenerator(
          rotation_range=15.,
          width_shift_range=0.2,
          height_shift_range=0.2,
          rescale=1./255,
          shear_range=0.2,
          zoom_range=0.2,
          horizontal_flip=True,
          vertical_flip = True,
          fill_mode='nearest')
  test_datagen = ImageDataGenerator(rescale=1./255)
  seed_validation = 1
  validation_image_generator = test_datagen.flow_from_directory(
          os.path.join(gen_path,'clean/test'),
          target_size=(128, 256),class_mode=None,classes=None,
          batch_size=20,seed=seed_validation)
  validation_mask_generator = test_datagen.flow_from_directory(
          os.path.join(gen_path,'albedo/test'),
          target_size=(128, 256),class_mode=None,classes=None,
          batch_size=20,seed=seed_validation)
  validation_shading_generator = test_datagen.flow_from_directory(
          os.path.join(gen_path,'gray_shading/test'),
          target_size=(128, 256),class_mode=None,classes=None,
          batch_size=20,seed=seed_validation)
  #testDataGen = ImageDataGenerator(rescale=1./255)
  seed = 5
  #image_datagen.fit(image_datagen, augment=True, seed=seed)
  #mask_datagen.fit(mask_datagen, augment=True, seed=seed)
  
  image_generator = image_datagen.flow_from_directory(
      os.path.join(gen_path,'clean/train'),
      class_mode=None,target_size=(128,256),batch_size=20,classes=None,
      seed=seed)
  
  mask_generator = mask_datagen.flow_from_directory(
      os.path.join(gen_path,'albedo/train'),
      class_mode=None,target_size=(128,256),batch_size=20,classes = None,
      seed=seed)
  shading_generator = shading_datagen.flow_from_directory(
      os.path.join(gen_path,'gray_shading/train'),
      class_mode=None,target_size=(128,256),batch_size=20,classes = None,
      seed=seed)
  
  train_generator = itertools.izip(image_generator, mask_generator, shading_generator)

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[zafarali]

Sorry if question sound silly I am not able to understand input for this join_generator ? its single zipped three generators ??

Don’t worry, I didn’t include a doc string. It’s just a list of the generators. Each generator must return (x, y). Therefore if you have:

g1 --> (x1, y1)
g2 --> (x2, y2)
join_generators(g1, g2) --> ([x1, x2], [y1, y2])

and with that code y ll still lead to one input, should i write separate y1 and y2 and yield x, [y1,y2]

This was just a skeleton of a possible implementation. For example, if your generators only return one element you could just:

def join_generators(xgenerators, ygenerator):
    while True: # keras requires all generators to be infinite
        data = [next(g) for g in xgenerators]

        yield x, next(ygenerator)

Or vary the code accordingly.

[0x00b1]

I like that trick. In my experience, writing a custom generator that yields both inputs is easier to manage than zipping two generators. It is especially evident when managing complex augmentation and sampling methods.