如何将keras预训练模型馈送到计算图

Question

最近我一直在玩用Keras，TF编写的CNN。不幸的是，我在那里遇到了一个问题：

在这些精彩的教程中(https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/vgg16.py；代码的其余部分在这里(，我强烈推荐，maestro加载预先训练的vgg16.tfmodel的方式非常非常丑陋。

def __init__(self):
# Now load the model from file. The way TensorFlow
# does this is confusing and requires several steps.
# Create a new TensorFlow computational graph.
self.graph = tf.Graph()
# Set the new graph as the default.
with self.graph.as_default():
# TensorFlow graphs are saved to disk as so-called Protocol Buffers
# aka. proto-bufs which is a file-format that works on multiple
# platforms. In this case it is saved as a binary file.
# Open the graph-def file for binary reading.
path = os.path.join(data_dir, path_graph_def)
with tf.gfile.FastGFile(path, 'rb') as file:
# The graph-def is a saved copy of a TensorFlow graph.
# First we need to create an empty graph-def.
graph_def = tf.GraphDef()
# Then we load the proto-buf file into the graph-def.
graph_def.ParseFromString(file.read())
# Finally we import the graph-def to the default TensorFlow graph.
tf.import_graph_def(graph_def, name='')
# Now self.graph holds the VGG16 model from the proto-buf file.
# Get a reference to the tensor for inputting images to the graph.
self.input = self.graph.get_tensor_by_name(self.tensor_name_input_image)
# Get references to the tensors for the commonly used layers.
self.layer_tensors = [self.graph.get_tensor_by_name(name + ":0") for name in self.layer_names]

问题是，我希望我自己的预训练模型以相同/相似的方式加载，这样我就可以将模型放入稍后调用的类的图中，如果可能的话，让这里的最后几行代码正常工作。(意思是从图中获取所需层的张量。(

我的所有尝试都基于从keras和comp导入的load_model。图表让我失望了。而且我不想用完全不同的方式加载它，因为之后我必须更改很多代码——对于新手来说，这是一个大问题。

好的，我希望这个问题能找到合适的人，也希望它对你来说不是太琐碎：D。

BTW：我正在解决的复杂问题是，对于您来说，图片是样式的传输，也在同一个github存储库中。(https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/15_Style_Transfer.ipynb)

Answer 1

所以你想基本上将keras模型加载到tensorflow中吗？可以用以下代码轻松完成：

import keras.backend as k
from keras.models import load_model
import tensorflow as tf
model = load_model("your model.h5")  # now it's in the memory of keras
with k.get_session() as sess:
# here you have a tensorflow computational graph, view it by:
tf.summary.FileWriter("folder name", sess.graph)
# if you need a certain tensor do:
sess.graph.get_tensor_by_name("tensor name") 

要了解有关get_session函数的一些信息，请单击此处

要查看图形，您需要使用tensorboard从FileWriter加载到文件夹中，如下所示：

tensorboard --logdir path/to/folder

希望这能提供一些帮助，祝你好运！