resnetFitGenerator.py

def modelFitGenerator(fitModel):

    num_train_samples = sum([len(files) for r, d, files in os.walk(train_data_dir)])
    num_valid_samples = sum([len(files) for r, d, files in os.walk(validation_data_dir)])

    num_train_steps = math.floor(num_train_samples/batch_size)
    num_valid_steps = math.floor(num_valid_samples/batch_size)
    
    train_datagen = ImageDataGenerator(  
      rotation_range=90,      
      horizontal_flip=True,    
      vertical_flip=True,
      zoom_range=0.4)

    test_datagen = ImageDataGenerator()

    train_generator = train_datagen.flow_from_directory(
      train_data_dir,
      target_size=image_size ,
      batch_size=batch_size,
      class_mode='categorical', shuffle=True
    )

    validation_generator = test_datagen.flow_from_directory(
      validation_data_dir,
      target_size=image_size ,
      batch_size=batch_size,
      class_mode='categorical', shuffle=True
    )

    print("start history model")
    history = fitModel.fit_generator(
      train_generator,
      steps_per_epoch=num_train_steps,
      epochs=nb_epoch,
      validation_data=validation_generator,
      validation_steps=num_valid_steps)