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I keep getting the following error for below code when I try to train the model: TypeError: fit_generator() missing 1 required positional argument: 'generator'. For the life of me I can not figure out what is causing this error. x_train is an rgb image of shape (400, 256, 256, 3) and for y_train i have 10 output classes making it shape (400, 256, 256, 10). What is going wrong here?

If necessary the data can be downloaded with the following link:
https://www49.zippyshare.com/v/5pR3GPv3/file.html

import skimage

from skimage.io import imread, imshow, imread_collection, concatenate_images

from skimage.transform import resize

from skimage.morphology import label

import numpy as np

import matplotlib.pyplot as plt

from keras.models import Model

from keras.layers import Input, merge, Convolution2D, MaxPooling2D, UpSampling2D, Reshape, core, Dropout

from keras.optimizers import Adam

from keras.callbacks import ModelCheckpoint, LearningRateScheduler

from keras import backend as K

from sklearn.metrics import jaccard_similarity_score

from shapely.geometry import MultiPolygon, Polygon

import shapely.wkt

import shapely.affinity

from collections import defaultdict

from keras.preprocessing.image import ImageDataGenerator

from keras.utils.np_utils import to_categorical

from keras import utils as np_utils

import os

from keras.preprocessing.image import ImageDataGenerator

gen = ImageDataGenerator()

#Importing image and labels

labels = skimage.io.imread("ede_subset_293_wegen.tif")

images = skimage.io.imread("ede_subset_293_20180502_planetscope.tif")[...,:-1]





#scaling image

img_scaled = images / images.max()



#Make non-roads 0

labels[labels == 15] = 0



#Resizing image and mask and labels

img_scaled_resized = img_scaled[:6400, :6400 ]

print(img_scaled_resized.shape)

labels_resized = labels[:6400, :6400]

print(labels_resized.shape)



#splitting images

split_img = [

    np.split(array, 25, axis=0) 

    for array in np.split(img_scaled_resized, 25, axis=1)

]



split_img[-1][-1].shape



#splitting labels

split_labels = [

    np.split(array, 25, axis=0) 

    for array in np.split(labels_resized, 25, axis=1)

]



#Convert to np.array

split_labels = np.array(split_labels)

split_img = np.array(split_img)



train_images = np.reshape(split_img, (625, 256, 256, 3))

train_labels = np.reshape(split_labels, (625, 256, 256, 10))



train_labels = np_utils.to_categorical(train_labels, 10)



#Create train test and val

x_train = train_images[:400,:,:,:]

x_val = train_images[400:500,:,:,:]

x_test = train_images[500:625,:,:,:]

y_train = train_labels[:400,:,:]

y_val = train_labels[400:500,:,:]

y_test = train_labels[500:625,:,:]



# Create image generator (credit to Ioannis Nasios)

data_gen_args = dict(rotation_range=5,

                     width_shift_range=0.1,

                     height_shift_range=0.1,

                     validation_split=0.2)

image_datagen = ImageDataGenerator(**data_gen_args)



seed = 1

batch_size = 100



def XYaugmentGenerator(X1, y, seed, batch_size):

    genX1 = gen.flow(X1, y, batch_size=batch_size, seed=seed)

    genX2 = gen.flow(y, X1, batch_size=batch_size, seed=seed)

    while True:

        X1i = genX1.next()

        X2i = genX2.next()



        yield X1i[0], X2i[0]





# Train model

Model.fit_generator(XYaugmentGenerator(x_train, y_train, seed, batch_size), steps_per_epoch=np.ceil(float(len(x_train)) / float(batch_size)),

                validation_data = XYaugmentGenerator(x_val, y_val,seed, batch_size), 

                validation_steps = np.ceil(float(len(x_val)) / float(batch_size))

, shuffle=True, epochs=20)

You have a few mistakes in your code, but considering your error:

TypeError: fit_generator() missing 1 required positional argument:
'generator'

this is caused because fit_generator call XYaugmentGenerator but no augmentation generator is called inside.

gen.flow(...

won't work because gen is not declared. You should either rename image_datagen to gen as:

gen = ImageDataGenerator(**data_gen_args)

or, replace gen with image_datagen

genX1 = image_datagen.flow(X1, y, batch_size=batch_size, seed=seed)

genX2 = image_datagen.flow(y, X1, batch_size=batch_size, seed=seed)