ImageDataGenerator从DataFrame中产生图片

from keras.applications.inception_v3 import preprocess_input
from keras.preprocessing.image import ImageDataGenerator

# image_size = 224
image_size = 299
datagen = ImageDataGenerator(preprocessing_function=preprocess_input, 
                             rescale=1./255.,
                             horizontal_flip=True,
                             width_shift_range = 0.2,
                             height_shift_range = 0.2,
                             validation_split=0.2)

train_generator=datagen.flow_from_dataframe(
                        dataframe=traindf,
                        directory="train/",
                        x_col="id",
                        y_col="breed",
                        has_ext=False,
                        subset="training",
                        batch_size=32,
                        seed=42,
                        shuffle=True,
                        class_mode="categorical",
                        target_size=(image_size, image_size))

valid_generator=datagen.flow_from_dataframe(
                        dataframe=traindf,
                        directory="train/",
                        x_col="id",
                        y_col="breed",
                        has_ext=False,
                        subset="validation",
                        batch_size=1,
                        seed=42,
                        shuffle=True,
                        class_mode="categorical",
                        target_size=(image_size, image_size))

test_datagen=ImageDataGenerator(preprocessing_function=preprocess_input,rescale=1./255.)

test_generator=test_datagen.flow_from_dataframe(
                            dataframe=testdf,
                            directory="test/",
                            x_col="id",
                            y_col=None,
                            has_ext=False,
                            batch_size=1,
                            seed=42,
                            shuffle=False,
                            class_mode=None,
                            target_size=(image_size, image_size))

STEP_SIZE_TRAIN=train_generator.n
STEP_SIZE_VALID=valid_generator.n

print(STEP_SIZE_TRAIN)
print(STEP_SIZE_VALID)

my_new_model.fit_generator(generator=train_generator,
                    steps_per_epoch=STEP_SIZE_TRAIN,
                    validation_data=valid_generator,
                    validation_steps=STEP_SIZE_VALID,
                    epochs=20,
                    verbose=1,
                    callbacks=callbacks_list
)