Load data from multiple dataframes containing both path and labels in keras
I believe the training will continue if you don't recompile and we put dataset only in the last step.
So, something similar to this snippet should work
train_datagen = ImageDataGenerator(rescale = 1./255, shear_range = 0.2, horizontal_flip = True)

training_set_1 = train_datagen.flow_from_dataframe(dataframe=df, color_mode='grayscale',target_size = (28, 28,1), batch_size = 2)
training_set_2 = train_datagen.flow_from_dataframe(dataframe=df, color_mode='grayscale',target_size = (28, 28,1), batch_size = 2)

model = keras.models.Sequential([ ... ])

optimizer = keras.optimizers.SGD(lr=0.2, momentum=0.9, decay=0.01)
model.compile(loss=&quot;binary_crossentropy&quot;, optimizer=optimizer,
              metrics=[&quot;accuracy&quot;])

for i in list(range(10)):
    history = model.fit(training_set_1,epochs=1)
    history = model.fit(training_set_2,epochs=1)

Definitely, learning can be very noisy based on the difference in variance in datagens.
 Also,history will be reset everytime.