求助，NotImplementedError

从GitHub上clone了一套keras实现fcn的代码，在运行train.py时出现了NotImplementedError。刚接触神经网络不久，这个是哪里出了问题呢？
代码如下：

import os
import yaml
import datetime
import numpy as np
import keras
import keras.backend as K
import tensorflow as tf
from keras_fcn import FCN
from voc_generator import PascalVocGenerator, ImageSetLoader
from keras.callbacks import (
ReduceLROnPlateau,
CSVLogger,
EarlyStopping,
ModelCheckpoint,
TerminateOnNaN)
from keras_fcn.callbacks import CheckNumericsOps

os.environ[“CUDA_VISIBLE_DEVICES”] = “-1”

global _SESSION
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.allow_growth = False
_SESSION = tf.Session(config=config)
K.set_session(_SESSION)

with open(“init_args.yml”, ‘r’) as stream:
try:
init_args = yaml.load(stream)
except yaml.YAMLError as exc:
print(exc)

checkpointer = ModelCheckpoint(
filepath="/tmp/fcn_vgg16_weights.h5",
verbose=1,
save_best_only=True)
lr_reducer = ReduceLROnPlateau(monitor=‘val_loss’,
factor=np.sqrt(0.1),
cooldown=0,
patience=10, min_lr=1e-12)
early_stopper = EarlyStopping(monitor=‘val_loss’,
min_delta=0.001,
patience=30)
nan_terminator = TerminateOnNaN()
csv_logger = CSVLogger(
#‘output/{}_fcn_vgg16.csv’.format(datetime.datetime.now().isoformat()))
‘output/tmp_fcn_vgg16.csv’)
#check_num = CheckNumericsOps(validation_data=[np.random.random((1, 224, 224, 3)), 1],

histogram_freq=100)

datagen = PascalVocGenerator(image_shape=[224, 224, 3],
image_resample=True,
pixelwise_center=True,
pixel_mean=[115.85100, 110.50989, 102.16182],
pixelwise_std_normalization=True,
pixel_std=[70.30930, 69.41244, 72.60676])

train_loader = ImageSetLoader(**init_args[‘image_set_loader’][‘train’])
val_loader = ImageSetLoader(**init_args[‘image_set_loader’][‘val’])

fcn_vgg16 = FCN(input_shape=(224, 224, 3), classes=21, weight_decay=3e-3,
weights=‘imagenet’, trainable_encoder=True)
optimizer = keras.optimizers.Adam(1e-4)

fcn_vgg16.compile(optimizer=optimizer,
loss=‘categorical_crossentropy’,
metrics=[‘accuracy’])

fcn_vgg16.fit_generator(
datagen.flow_from_imageset(
class_mode=‘categorical’,
classes=21,
batch_size=1,
shuffle=True,
image_set_loader=train_loader),
steps_per_epoch=1112,
epochs=100,
validation_data=datagen.flow_from_imageset(
class_mode=‘categorical’,
classes=21,
batch_size=1,
shuffle=True,
image_set_loader=val_loader),
validation_steps=1111,
verbose=1,
callbacks=[lr_reducer, early_stopper, csv_logger, checkpointer, nan_terminator])

fcn_vgg16.save(‘output/fcn_vgg16.h5’)