datagen = ImageDataGenerator(
rotation_range=3,
# featurewise_std_normalization=True,
fill_mode='nearest',
width_shift_range=0.2,
height_shift_range=0.2,
horizontal_flip=True
)
train_generator = datagen.flow_from_directory(
path+'/train',
target_size=(224, 224),
batch_size=batch_size,)
I have a custom generator for my multi output model like:
a = np.arange(8).reshape(2, 4)
# print(a)
print(train_generator.filenames)
def generate():
while 1:
x,y = train_generator.next()
yield [x] ,[a,y]
