量化Keras神经网络模型

最近,我开始使用Tensorflow + Keras创建神经网络,我想尝试Tensorflow中提供的量化功能.到目前为止,尝试TF教程的示例工作得很好,我有这个基本的工作示例(来自https://www.tensorflow.org/tutorials/keras/basic_classification):

import tensorflow as tf
from tensorflow import keras

fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()

# fashion mnist data labels (indexes related to their respective labelling in the data set)
class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']

# preprocess the train and test images
train_images = train_images / 255.0
test_images = test_images / 255.0

# settings variables
input_shape = (train_images.shape[1], train_images.shape[2])

# create the model layers
model = keras.Sequential([
keras.layers.Flatten(input_shape=input_shape),
keras.layers.Dense(128, activation=tf.nn.relu),
keras.layers.Dense(10, activation=tf.nn.softmax)
])

# compile the model with added settings
model.compile(optimizer=tf.train.AdamOptimizer(),
          loss='sparse_categorical_crossentropy',
          metrics=['accuracy'])

# train the model
epochs = 3
model.fit(train_images, train_labels, epochs=epochs)

# evaluate the accuracy of model on test data
test_loss, test_acc = model.evaluate(test_images, test_labels)
print('Test accuracy:', test_acc)

现在,我想在学习和分类过程中使用量化.量化文档(https://www.tensorflow.org/performance/quantization)(该页面自2018年9月15日cca以后不再可用)建议使用这段代码:

loss = tf.losses.get_total_loss()
tf.contrib.quantize.create_training_graph(quant_delay=2000000)
optimizer = tf.train.GradientDescentOptimizer(0.00001)
optimizer.minimize(loss)

但是,它不包含有关应该使用此代码的位置或如何将其连接到TF代码的任何信息(甚至不提及使用Keras创建的高级模型).我不知道这个量化部分如何与先前创建的神经网络模型相关.只需在神经网络代码后插入它就会遇到以下错误:

Traceback (most recent call last):
  File "so.py", line 41, in 
    loss = tf.losses.get_total_loss()
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/losses/util.py", line 112, in get_total_loss
    return math_ops.add_n(losses, name=name)
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/math_ops.py", line 2119, in add_n
    raise ValueError("inputs must be a list of at least one Tensor with the "
ValueError: inputs must be a list of at least one Tensor with the same dtype and shape

是否可以量化以这种方式量化Keras NN模型,还是我遗漏了一些基本的东西？我想到的一个可能的解决方案可能是使用低级TF API而不是Keras(需要做很多工作来构建模型),或者尝试从Keras模型中提取一些较低级别的方法.