量化Keras神经网络模型

Question

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，使用TF教程中的示例进行实验运行良好，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日起不再提供)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，它没有包含任何关于应该在何处使用此代码或如何将其连接到TF代码的信息(甚至没有提到使用Keras创建的高级模型)。我不知道这个量化部分与之前创建的神经网络模型有什么关系。只要在神经网络代码后面插入它，就会遇到以下错误：

Traceback (most recent call last):
  File "so.py", line 41, in <module>
    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

有没有可能用这种方式量化Keras NN模型，或者我错过了一些基本的东西？我脑海中闪过的一个可能的解决方案是使用低级TF API而不是Keras (需要做相当多的工作来构建模型)，或者尝试从Keras模型中提取一些低级方法。

Answer 1

由于你的网络看起来很简单，你可以使用Tensorflow lite。

Answer 2

Tensorflow lite可用于量化keras模型。

以下代码是为tensorflow 1.14编写的。它可能不适用于早期版本。

首先，在训练模型之后，您应该将模型保存到h5

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)
model.save("model.h5")

要加载keras模型，请使用tf.lite.TFLiteConverter.from_keras_model_file

# load the previously saved model
converter  = tf.lite.TFLiteConverter.from_keras_model_file("model.h5")
tflite_model = converter.convert()
# Save the model to file
with open("tflite_model.tflite", "wb") as output_file:
    output_file.write(tflite_model)

保存的模型可以加载到python脚本或其他平台和语言。为了使用保存的tflite模型，tensorlfow.lite提供了Interpreter。下面来自here的例子展示了如何使用python脚本从本地文件加载tflite模型。

import numpy as np
import tensorflow as tf

# Load TFLite model and allocate tensors.
interpreter = tf.lite.Interpreter(model_path="tflite_model.tflite")
interpreter.allocate_tensors()

# Get input and output tensors.
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()

# Test model on random input data.
input_shape = input_details[0]['shape']
input_data = np.array(np.random.random_sample(input_shape), dtype=np.float32)
interpreter.set_tensor(input_details[0]['index'], input_data)

interpreter.invoke()

# The function `get_tensor()` returns a copy of the tensor data.
# Use `tensor()` in order to get a pointer to the tensor.
output_data = interpreter.get_tensor(output_details[0]['index'])
print(output_data)